From bdbe0150cdb30a15662627fd3a2619f5092f9506 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Sat, 3 Sep 2022 09:42:04 +0000 Subject: [PATCH 01/19] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20LANENET=5FID1251=5Ff?= =?UTF-8?q?or=5FACL?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ACL_TensorFlow/contrib/cv/LANENET_ID1251_for_ACL/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/LANENET_ID1251_for_ACL/.keep diff --git a/ACL_TensorFlow/contrib/cv/LANENET_ID1251_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/LANENET_ID1251_for_ACL/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 13dd3165d31c4311c7e76e7987375628de06e5f4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Sat, 3 Sep 2022 09:43:18 +0000 Subject: [PATCH 02/19] =?UTF-8?q?=E9=87=8D=E5=91=BD=E5=90=8D=20ACL=5FTenso?= =?UTF-8?q?rFlow/contrib/cv/LANENET=5FID1251=5Ffor=5FACL=20=E4=B8=BA=20ACL?= =?UTF-8?q?=5FTensorFlow/contrib/cv/MNN-LANENET=5FID1251=5Ffor=5FACL?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../{LANENET_ID1251_for_ACL => MNN-LANENET_ID1251_for_ACL}/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename ACL_TensorFlow/contrib/cv/{LANENET_ID1251_for_ACL => MNN-LANENET_ID1251_for_ACL}/.keep (100%) diff --git a/ACL_TensorFlow/contrib/cv/LANENET_ID1251_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/.keep similarity index 100% rename from ACL_TensorFlow/contrib/cv/LANENET_ID1251_for_ACL/.keep rename to ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/.keep -- Gitee From 7dd5c1a127293b142080b5b8244627cc7901410c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Sat, 3 Sep 2022 09:53:06 +0000 Subject: [PATCH 03/19] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20=5F=5Fpycache=5F=5F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/.keep diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/.keep b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 401e5d5812865701aafb7ac7700460c49dfb018f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Sat, 3 Sep 2022 09:53:32 +0000 Subject: [PATCH 04/19] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 溺水小航母 <1396755411@qq.com> --- .../lanenet_postprocess.cpython-37.pyc | Bin 0 -> 10205 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/lanenet_postprocess.cpython-37.pyc diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/lanenet_postprocess.cpython-37.pyc b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/lanenet_postprocess.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6a76b7b5d8e296bdcaaac49170e062ad752e031b GIT binary patch literal 10205 zcmb_iU2GdycAkF@hd-iZNtPwsVr(Zi6GwKOG)=w1Cbpww4LGzRxp6X^V0Oeiq9~CZ z`p%UtvcsZSr`Q%<0|r{4=prbj1^UqE0)1)Gr=sX%-`Y;!x=$+l(5F7+W&52w6iNL! 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All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +AUG: + RESIZE_METHOD: 'stepscaling' # choice unpadding rangescaling and stepscaling + FIX_RESIZE_SIZE: [720, 720] # (width, height), for unpadding + INF_RESIZE_VALUE: 500 # for rangescaling + MAX_RESIZE_VALUE: 600 # for rangescaling + MIN_RESIZE_VALUE: 400 # for rangescaling + MAX_SCALE_FACTOR: 2.0 # for stepscaling + MIN_SCALE_FACTOR: 0.75 # for stepscaling + SCALE_STEP_SIZE: 0.25 # for stepscaling + TRAIN_CROP_SIZE: [512, 256] # crop size for training + EVAL_CROP_SIZE: [512, 256] # crop size for evaluating + CROP_PAD_SIZE: 32 + MIRROR: True + FLIP: False + FLIP_RATIO: 0.5 + RICH_CROP: + ENABLE: False + BLUR: True + BLUR_RATIO: 0.2 + MAX_ROTATION: 15 + MIN_AREA_RATIO: 0.5 + ASPECT_RATIO: 0.5 + BRIGHTNESS_JITTER_RATIO: 0.5 + CONTRAST_JITTER_RATIO: 0.5 + SATURATION_JITTER_RATIO: 0.5 +DATASET: + DATA_DIR: 'REPO_ROOT_PATH/data/training_data_example/' + IMAGE_TYPE: 'rgb' # choice rgb or rgba + NUM_CLASSES: 2 + TEST_FILE_LIST: 'REPO_ROOT_PATH/data/training_data_example/test.txt' + TRAIN_FILE_LIST: 'REPO_ROOT_PATH/data/training_data_example/train.txt' + VAL_FILE_LIST: 'REPO_ROOT_PATH/data/training_data_example/val.txt' + IGNORE_INDEX: 255 + PADDING_VALUE: [127.5, 127.5, 127.5] + MEAN_VALUE: [0.5, 0.5, 0.5] + STD_VALUE: [0.5, 0.5, 0.5] + CPU_MULTI_PROCESS_NUMS: 8 +FREEZE: + MODEL_FILENAME: 'model' + PARAMS_FILENAME: 'params' +MODEL: + MODEL_NAME: 'lanenet' + FRONT_END: 'bisenetv2' +# FRONT_END: 'vgg' + EMBEDDING_FEATS_DIMS: 4 + BISENETV2: + GE_EXPAND_RATIO: 6 + SEMANTIC_CHANNEL_LAMBDA: 0.25 + SEGHEAD_CHANNEL_EXPAND_RATIO: 2 +TEST: + TEST_MODEL: 'model/cityscapes/final' +TRAIN: + MODEL_SAVE_DIR: 'model/tusimple/' + TBOARD_SAVE_DIR: 'tboard/tusimple/' + MODEL_PARAMS_CONFIG_FILE_NAME: "model_train_config.json" + RESTORE_FROM_SNAPSHOT: + ENABLE: False + SNAPSHOT_PATH: '' + SNAPSHOT_EPOCH: 8 + BATCH_SIZE: 32 + VAL_BATCH_SIZE: 4 + EPOCH_NUMS: 905 + WARM_UP: + ENABLE: True + EPOCH_NUMS: 8 + FREEZE_BN: + ENABLE: False + COMPUTE_MIOU: + ENABLE: True + EPOCH: 1 + MULTI_GPU: + ENABLE: True + GPU_DEVICES: ['0', '1'] + CHIEF_DEVICE_INDEX: 0 +SOLVER: + LR: 0.001 + LR_POLICY: 'poly' + LR_POLYNOMIAL_POWER: 0.9 + OPTIMIZER: 'sgd' + MOMENTUM: 0.9 + WEIGHT_DECAY: 0.0005 + MOVING_AVE_DECAY: 0.9995 + LOSS_TYPE: 'cross_entropy' + OHEM: + ENABLE: False + SCORE_THRESH: 0.65 + MIN_SAMPLE_NUMS: 65536 +GPU: + GPU_MEMORY_FRACTION: 0.9 + TF_ALLOW_GROWTH: True +POSTPROCESS: + MIN_AREA_THRESHOLD: 100 + DBSCAN_EPS: 0.35 + DBSCAN_MIN_SAMPLES: 1000 +LOG: + SAVE_DIR: './log' + LEVEL: INFO -- Gitee From a5b79d9d8362937888d00fc03533f5261462dedc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Sat, 3 Sep 2022 09:56:24 +0000 Subject: [PATCH 06/19] 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All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Implement LaneNet Model +""" +import tensorflow as tf + +from lanenet_model import lanenet_back_end +from lanenet_model import lanenet_front_end +from semantic_segmentation_zoo import cnn_basenet + + +class LaneNet(cnn_basenet.CNNBaseModel): + """ + + """ + def __init__(self, phase, cfg): + """ + + """ + super(LaneNet, self).__init__() + self._cfg = cfg + self._net_flag = self._cfg.MODEL.FRONT_END + + self._frontend = lanenet_front_end.LaneNetFrondEnd( + phase=phase, net_flag=self._net_flag, cfg=self._cfg + ) + self._backend = lanenet_back_end.LaneNetBackEnd( + phase=phase, cfg=self._cfg + ) + + def inference(self, input_tensor, name, reuse=False): + """ + + :param input_tensor: + :param name: + :param reuse + :return: + """ + with tf.variable_scope(name_or_scope=name, reuse=reuse): + # first extract image features + extract_feats_result = self._frontend.build_model( + input_tensor=input_tensor, + name='{:s}_frontend'.format(self._net_flag), + reuse=reuse + ) + + # second apply backend process + binary_seg_prediction, instance_seg_prediction = self._backend.inference( + binary_seg_logits=extract_feats_result['binary_segment_logits']['data'], + instance_seg_logits=extract_feats_result['instance_segment_logits']['data'], + name='{:s}_backend'.format(self._net_flag), + reuse=reuse + ) + + return binary_seg_prediction, instance_seg_prediction + + def compute_loss(self, input_tensor, binary_label, instance_label, name, reuse=False): + """ + calculate lanenet loss for training + :param input_tensor: + :param binary_label: + :param instance_label: + :param name: + :param reuse: + :return: + """ + with tf.variable_scope(name_or_scope=name, reuse=reuse): + # first extract image features + extract_feats_result = self._frontend.build_model( + input_tensor=input_tensor, + name='{:s}_frontend'.format(self._net_flag), + reuse=reuse + ) + + # second apply backend process + calculated_losses = self._backend.compute_loss( + binary_seg_logits=extract_feats_result['binary_segment_logits']['data'], + binary_label=binary_label, + instance_seg_logits=extract_feats_result['instance_segment_logits']['data'], + instance_label=instance_label, + name='{:s}_backend'.format(self._net_flag), + reuse=reuse + ) + + return calculated_losses diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_back_end.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_back_end.py new file mode 100644 index 000000000..da31ea69b --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_back_end.py @@ -0,0 +1,231 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +LaneNet backend branch which is mainly used for binary and instance segmentation loss calculation +""" +import tensorflow as tf + +from lanenet_model import lanenet_discriminative_loss +from semantic_segmentation_zoo import cnn_basenet + + +class LaneNetBackEnd(cnn_basenet.CNNBaseModel): + """ + LaneNet backend branch which is mainly used for binary and instance segmentation loss calculation + """ + def __init__(self, phase, cfg): + """ + init lanenet backend + :param phase: train or test + """ + super(LaneNetBackEnd, self).__init__() + self._cfg = cfg + self._phase = phase + self._is_training = self._is_net_for_training() + + self._class_nums = self._cfg.DATASET.NUM_CLASSES + self._embedding_dims = self._cfg.MODEL.EMBEDDING_FEATS_DIMS + self._binary_loss_type = self._cfg.SOLVER.LOSS_TYPE + + def _is_net_for_training(self): + """ + if the net is used for training or not + :return: + """ + if isinstance(self._phase, tf.Tensor): + phase = self._phase + else: + phase = tf.constant(self._phase, dtype=tf.string) + + return tf.equal(phase, tf.constant('train', dtype=tf.string)) + + @classmethod + def _compute_class_weighted_cross_entropy_loss(cls, onehot_labels, logits, classes_weights): + """ + + :param onehot_labels: + :param logits: + :param classes_weights: + :return: + """ + loss_weights = tf.reduce_sum(tf.multiply(onehot_labels, classes_weights), axis=3) + + loss = tf.losses.softmax_cross_entropy( + onehot_labels=onehot_labels, + logits=logits, + weights=loss_weights + ) + + return loss + + @classmethod + def _multi_category_focal_loss(cls, onehot_labels, logits, classes_weights, gamma=2.0): + """ + + :param onehot_labels: + :param logits: + :param classes_weights: + :param gamma: + :return: + """ + epsilon = 1.e-7 + alpha = tf.multiply(onehot_labels, classes_weights) + alpha = tf.cast(alpha, tf.float32) + gamma = float(gamma) + y_true = tf.cast(onehot_labels, tf.float32) + y_pred = tf.nn.softmax(logits, dim=-1) + y_pred = tf.clip_by_value(y_pred, epsilon, 1. - epsilon) + y_t = tf.multiply(y_true, y_pred) + tf.multiply(1-y_true, 1-y_pred) + ce = -tf.log(y_t) + weight = tf.pow(tf.subtract(1., y_t), gamma) + fl = tf.multiply(tf.multiply(weight, ce), alpha) + loss = tf.reduce_mean(fl) + + return loss + + def compute_loss(self, binary_seg_logits, binary_label, + instance_seg_logits, instance_label, + name, reuse): + """ + compute lanenet loss + :param binary_seg_logits: + :param binary_label: + :param instance_seg_logits: + :param instance_label: + :param name: + :param reuse: + :return: + """ + with tf.variable_scope(name_or_scope=name, reuse=reuse): + # calculate class weighted binary seg loss + with tf.variable_scope(name_or_scope='binary_seg'): + binary_label_onehot = tf.one_hot( + tf.reshape( + tf.cast(binary_label, tf.int32), + shape=[binary_label.get_shape().as_list()[0], + binary_label.get_shape().as_list()[1], + binary_label.get_shape().as_list()[2]]), + depth=self._class_nums, + axis=-1 + ) + + binary_label_plain = tf.reshape( + binary_label, + shape=[binary_label.get_shape().as_list()[0] * + binary_label.get_shape().as_list()[1] * + binary_label.get_shape().as_list()[2] * + binary_label.get_shape().as_list()[3]]) + unique_labels, unique_id, counts = tf.unique_with_counts(binary_label_plain) + counts = tf.cast(counts, tf.float32) + inverse_weights = tf.divide( + 1.0, + tf.log(tf.add(tf.divide(counts, tf.reduce_sum(counts)), tf.constant(1.02))) + ) + if self._binary_loss_type == 'cross_entropy': + binary_segmenatation_loss = self._compute_class_weighted_cross_entropy_loss( + onehot_labels=binary_label_onehot, + logits=binary_seg_logits, + classes_weights=inverse_weights + ) + elif self._binary_loss_type == 'focal': + binary_segmenatation_loss = self._multi_category_focal_loss( + onehot_labels=binary_label_onehot, + logits=binary_seg_logits, + classes_weights=inverse_weights + ) + else: + raise NotImplementedError + + # calculate class weighted instance seg loss + with tf.variable_scope(name_or_scope='instance_seg'): + + pix_bn = self.layerbn( + inputdata=instance_seg_logits, is_training=self._is_training, name='pix_bn') + pix_relu = self.relu(inputdata=pix_bn, name='pix_relu') + pix_embedding = self.conv2d( + inputdata=pix_relu, + out_channel=self._embedding_dims, + kernel_size=1, + use_bias=False, + name='pix_embedding_conv' + ) + pix_image_shape = (pix_embedding.get_shape().as_list()[1], pix_embedding.get_shape().as_list()[2]) + instance_segmentation_loss, l_var, l_dist, l_reg = \ + lanenet_discriminative_loss.discriminative_loss( + pix_embedding, instance_label, self._embedding_dims, + pix_image_shape, 0.5, 3.0, 1.0, 1.0, 0.001 + ) + + l2_reg_loss = tf.constant(0.0, tf.float32) + for vv in tf.trainable_variables(): + if 'bn' in vv.name or 'gn' in vv.name: + continue + else: + l2_reg_loss = tf.add(l2_reg_loss, tf.nn.l2_loss(vv)) + l2_reg_loss *= 0.001 + total_loss = binary_segmenatation_loss + instance_segmentation_loss + l2_reg_loss + + ret = { + 'total_loss': total_loss, + 'binary_seg_logits': binary_seg_logits, + 'instance_seg_logits': pix_embedding, + 'binary_seg_loss': binary_segmenatation_loss, + 'discriminative_loss': instance_segmentation_loss + } + + return ret + + def inference(self, binary_seg_logits, instance_seg_logits, name, reuse): + """ + + :param binary_seg_logits: + :param instance_seg_logits: + :param name: + :param reuse: + :return: + """ + with tf.variable_scope(name_or_scope=name, reuse=reuse): + + with tf.variable_scope(name_or_scope='binary_seg'): + binary_seg_score = tf.nn.softmax(logits=binary_seg_logits) + binary_seg_prediction = tf.argmax(binary_seg_score, axis=-1) + + with tf.variable_scope(name_or_scope='instance_seg'): + + pix_bn = self.layerbn( + inputdata=instance_seg_logits, is_training=self._is_training, name='pix_bn') + pix_relu = self.relu(inputdata=pix_bn, name='pix_relu') + instance_seg_prediction = self.conv2d( + inputdata=pix_relu, + out_channel=self._embedding_dims, + kernel_size=1, + use_bias=False, + name='pix_embedding_conv' + ) + + return binary_seg_prediction, instance_seg_prediction diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_discriminative_loss.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_discriminative_loss.py new file mode 100644 index 000000000..f6c12b3e5 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_discriminative_loss.py @@ -0,0 +1,162 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Discriminative Loss for instance segmentation +""" +import tensorflow as tf + + +def discriminative_loss_single( + prediction, + correct_label, + feature_dim, + label_shape, + delta_v, + delta_d, + param_var, + param_dist, + param_reg): + """ + discriminative loss + :param prediction: inference of network + :param correct_label: instance label + :param feature_dim: feature dimension of prediction + :param label_shape: shape of label + :param delta_v: cut off variance distance + :param delta_d: cut off cluster distance + :param param_var: weight for intra cluster variance + :param param_dist: weight for inter cluster distances + :param param_reg: weight regularization + """ + correct_label = tf.reshape( + correct_label, [label_shape[1] * label_shape[0]] + ) + reshaped_pred = tf.reshape( + prediction, [label_shape[1] * label_shape[0], feature_dim] + ) + + # calculate instance nums + unique_labels, unique_id, counts = tf.unique_with_counts(correct_label) + counts = tf.cast(counts, tf.float32) + num_instances = tf.size(unique_labels) + + # calculate instance pixel embedding mean vec + segmented_sum = tf.unsorted_segment_sum( + reshaped_pred, unique_id, num_instances) + mu = tf.div(segmented_sum, tf.reshape(counts, (-1, 1))) + mu_expand = tf.gather(mu, unique_id) + + distance = tf.norm(tf.subtract(mu_expand, reshaped_pred), axis=1, ord=1) + distance = tf.subtract(distance, delta_v) + distance = tf.clip_by_value(distance, 0., distance) + distance = tf.square(distance) + + l_var = tf.unsorted_segment_sum(distance, unique_id, num_instances) + l_var = tf.div(l_var, counts) + l_var = tf.reduce_sum(l_var) + l_var = tf.divide(l_var, tf.cast(num_instances, tf.float32)) + + mu_interleaved_rep = tf.tile(mu, [num_instances, 1]) + mu_band_rep = tf.tile(mu, [1, num_instances]) + mu_band_rep = tf.reshape( + mu_band_rep, + (num_instances * + num_instances, + feature_dim)) + + mu_diff = tf.subtract(mu_band_rep, mu_interleaved_rep) + + intermediate_tensor = tf.reduce_sum(tf.abs(mu_diff), axis=1) + zero_vector = tf.zeros(1, dtype=tf.float32) + bool_mask = tf.not_equal(intermediate_tensor, zero_vector) + mu_diff_bool = tf.boolean_mask(mu_diff, bool_mask) + + mu_norm = tf.norm(mu_diff_bool, axis=1, ord=1) + mu_norm = tf.subtract(2. * delta_d, mu_norm) + mu_norm = tf.clip_by_value(mu_norm, 0., mu_norm) + mu_norm = tf.square(mu_norm) + + l_dist = tf.reduce_mean(mu_norm) + + l_reg = tf.reduce_mean(tf.norm(mu, axis=1, ord=1)) + + param_scale = 1. + l_var = param_var * l_var + l_dist = param_dist * l_dist + l_reg = param_reg * l_reg + + loss = param_scale * (l_var + l_dist + l_reg) + + return loss, l_var, l_dist, l_reg + + +def discriminative_loss(prediction, correct_label, feature_dim, image_shape, + delta_v, delta_d, param_var, param_dist, param_reg): + """ + + :return: discriminative loss and its three components + """ + + def cond(label, batch, out_loss, out_var, out_dist, out_reg, i): + return tf.less(i, tf.shape(batch)[0]) + + def body(label, batch, out_loss, out_var, out_dist, out_reg, i): + disc_loss, l_var, l_dist, l_reg = discriminative_loss_single( + prediction[i], correct_label[i], feature_dim, image_shape, delta_v, delta_d, param_var, param_dist, param_reg) + + out_loss = out_loss.write(i, disc_loss) + out_var = out_var.write(i, l_var) + out_dist = out_dist.write(i, l_dist) + out_reg = out_reg.write(i, l_reg) + + return label, batch, out_loss, out_var, out_dist, out_reg, i + 1 + + # TensorArray is a data structure that support dynamic writing + output_ta_loss = tf.TensorArray( + dtype=tf.float32, size=0, dynamic_size=True) + output_ta_var = tf.TensorArray( + dtype=tf.float32, size=0, dynamic_size=True) + output_ta_dist = tf.TensorArray( + dtype=tf.float32, size=0, dynamic_size=True) + output_ta_reg = tf.TensorArray( + dtype=tf.float32, size=0, dynamic_size=True) + + _, _, out_loss_op, out_var_op, out_dist_op, out_reg_op, _ = tf.while_loop( + cond, body, [ + correct_label, prediction, output_ta_loss, output_ta_var, output_ta_dist, output_ta_reg, 0]) + out_loss_op = out_loss_op.stack() + out_var_op = out_var_op.stack() + out_dist_op = out_dist_op.stack() + out_reg_op = out_reg_op.stack() + + disc_loss = tf.reduce_mean(out_loss_op) + l_var = tf.reduce_mean(out_var_op) + l_dist = tf.reduce_mean(out_dist_op) + l_reg = tf.reduce_mean(out_reg_op) + + return disc_loss, l_var, l_dist, l_reg diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_front_end.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_front_end.py new file mode 100644 index 000000000..a0112f878 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_front_end.py @@ -0,0 +1,67 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +LaneNet frontend branch which is mainly used for feature extraction +""" +from semantic_segmentation_zoo import cnn_basenet +from semantic_segmentation_zoo import vgg16_based_fcn +from semantic_segmentation_zoo import bisenet_v2 + + +class LaneNetFrondEnd(cnn_basenet.CNNBaseModel): + """ + LaneNet frontend which is used to extract image features for following process + """ + def __init__(self, phase, net_flag, cfg): + """ + + """ + super(LaneNetFrondEnd, self).__init__() + self._cfg = cfg + + self._frontend_net_map = { + 'vgg': vgg16_based_fcn.VGG16FCN(phase=phase, cfg=self._cfg), + 'bisenetv2': bisenet_v2.BiseNetV2(phase=phase, cfg=self._cfg), + } + + self._net = self._frontend_net_map[net_flag] + + def build_model(self, input_tensor, name, reuse): + """ + + :param input_tensor: + :param name: + :param reuse: + :return: + """ + + return self._net.build_model( + input_tensor=input_tensor, + name=name, + reuse=reuse + ) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_postprocess.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_postprocess.py new file mode 100644 index 000000000..2fe33f9ad --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_postprocess.py @@ -0,0 +1,463 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +LaneNet model post process +""" +import os.path as ops +import math + +import cv2 +import numpy as np +import loguru +from sklearn.cluster import DBSCAN +from sklearn.preprocessing import StandardScaler + +LOG = loguru.logger + + +def _morphological_process(image, kernel_size=5): + """ + morphological process to fill the hole in the binary segmentation result + :param image: + :param kernel_size: + :return: + """ + if len(image.shape) == 3: + raise ValueError('Binary segmentation result image should be a single channel image') + + if image.dtype is not np.uint8: + image = np.array(image, np.uint8) + + kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size)) + + # close operation fille hole + closing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1) + + return closing + + +def _connect_components_analysis(image): + """ + connect components analysis to remove the small components + :param image: + :return: + """ + if len(image.shape) == 3: + gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + else: + gray_image = image + + return cv2.connectedComponentsWithStats(gray_image, connectivity=8, ltype=cv2.CV_32S) + + +class _LaneFeat(object): + """ + + """ + def __init__(self, feat, coord, class_id=-1): + """ + lane feat object + :param feat: lane embeddng feats [feature_1, feature_2, ...] + :param coord: lane coordinates [x, y] + :param class_id: lane class id + """ + self._feat = feat + self._coord = coord + self._class_id = class_id + + @property + def feat(self): + """ + + :return: + """ + return self._feat + + @feat.setter + def feat(self, value): + """ + + :param value: + :return: + """ + if not isinstance(value, np.ndarray): + value = np.array(value, dtype=np.float64) + + if value.dtype != np.float32: + value = np.array(value, dtype=np.float64) + + self._feat = value + + @property + def coord(self): + """ + + :return: + """ + return self._coord + + @coord.setter + def coord(self, value): + """ + + :param value: + :return: + """ + if not isinstance(value, np.ndarray): + value = np.array(value) + + if value.dtype != np.int32: + value = np.array(value, dtype=np.int32) + + self._coord = value + + @property + def class_id(self): + """ + + :return: + """ + return self._class_id + + @class_id.setter + def class_id(self, value): + """ + + :param value: + :return: + """ + if not isinstance(value, np.int64): + raise ValueError('Class id must be integer') + + self._class_id = value + + +class _LaneNetCluster(object): + """ + Instance segmentation result cluster + """ + + def __init__(self, cfg): + """ + + """ + self._color_map = [np.array([255, 0, 0]), + np.array([0, 255, 0]), + np.array([0, 0, 255]), + np.array([125, 125, 0]), + np.array([0, 125, 125]), + np.array([125, 0, 125]), + np.array([50, 100, 50]), + np.array([100, 50, 100])] + self._cfg = cfg + + def _embedding_feats_dbscan_cluster(self, embedding_image_feats): + """ + dbscan cluster + :param embedding_image_feats: + :return: + """ + db = DBSCAN(eps=self._cfg.POSTPROCESS.DBSCAN_EPS, min_samples=self._cfg.POSTPROCESS.DBSCAN_MIN_SAMPLES) + try: + features = StandardScaler().fit_transform(embedding_image_feats) + db.fit(features) + except Exception as err: + LOG.error(err) + ret = { + 'origin_features': None, + 'cluster_nums': 0, + 'db_labels': None, + 'unique_labels': None, + 'cluster_center': None + } + return ret + db_labels = db.labels_ + unique_labels = np.unique(db_labels) + + num_clusters = len(unique_labels) + cluster_centers = db.components_ + + ret = { + 'origin_features': features, + 'cluster_nums': num_clusters, + 'db_labels': db_labels, + 'unique_labels': unique_labels, + 'cluster_center': cluster_centers + } + + return ret + + @staticmethod + def _get_lane_embedding_feats(binary_seg_ret, instance_seg_ret): + """ + get lane embedding features according the binary seg result + :param binary_seg_ret: + :param instance_seg_ret: + :return: + """ + idx = np.where(binary_seg_ret == 255) + lane_embedding_feats = instance_seg_ret[idx] + lane_coordinate = np.vstack((idx[1], idx[0])).transpose() + + assert lane_embedding_feats.shape[0] == lane_coordinate.shape[0] + + ret = { + 'lane_embedding_feats': lane_embedding_feats, + 'lane_coordinates': lane_coordinate + } + + return ret + + def apply_lane_feats_cluster(self, binary_seg_result, instance_seg_result): + """ + + :param binary_seg_result: + :param instance_seg_result: + :return: + """ + # get embedding feats and coords + get_lane_embedding_feats_result = self._get_lane_embedding_feats( + binary_seg_ret=binary_seg_result, + instance_seg_ret=instance_seg_result + ) + + # dbscan cluster + dbscan_cluster_result = self._embedding_feats_dbscan_cluster( + embedding_image_feats=get_lane_embedding_feats_result['lane_embedding_feats'] + ) + + mask = np.zeros(shape=[binary_seg_result.shape[0], binary_seg_result.shape[1], 3], dtype=np.uint8) + db_labels = dbscan_cluster_result['db_labels'] + unique_labels = dbscan_cluster_result['unique_labels'] + coord = get_lane_embedding_feats_result['lane_coordinates'] + + if db_labels is None: + return None, None + + lane_coords = [] + for index, label in enumerate(unique_labels.tolist()): + if label == -1: + continue + idx = np.where(db_labels == label) + pix_coord_idx = tuple((coord[idx][:, 1], coord[idx][:, 0])) + mask[pix_coord_idx] = self._color_map[index] + lane_coords.append(coord[idx]) + + return mask, lane_coords + + +class LaneNetPostProcessor(object): + """ + lanenet post process for lane generation + """ + def __init__(self, cfg, ipm_remap_file_path='./eval_data/tusimple_ipm_remap.yml'): + """ + + :param ipm_remap_file_path: ipm generate file path + """ + assert ops.exists(ipm_remap_file_path), '{:s} not exist'.format(ipm_remap_file_path) + + self._cfg = cfg + self._cluster = _LaneNetCluster(cfg=cfg) + self._ipm_remap_file_path = ipm_remap_file_path + + remap_file_load_ret = self._load_remap_matrix() + self._remap_to_ipm_x = remap_file_load_ret['remap_to_ipm_x'] + self._remap_to_ipm_y = remap_file_load_ret['remap_to_ipm_y'] + + self._color_map = [np.array([255, 0, 0]), + np.array([0, 255, 0]), + np.array([0, 0, 255]), + np.array([125, 125, 0]), + np.array([0, 125, 125]), + np.array([125, 0, 125]), + np.array([50, 100, 50]), + np.array([100, 50, 100])] + + def _load_remap_matrix(self): + """ + + :return: + """ + fs = cv2.FileStorage(self._ipm_remap_file_path, cv2.FILE_STORAGE_READ) + + remap_to_ipm_x = fs.getNode('remap_ipm_x').mat() + remap_to_ipm_y = fs.getNode('remap_ipm_y').mat() + + ret = { + 'remap_to_ipm_x': remap_to_ipm_x, + 'remap_to_ipm_y': remap_to_ipm_y, + } + + fs.release() + + return ret + + def postprocess(self, binary_seg_result, instance_seg_result=None, + min_area_threshold=100, source_image=None, + with_lane_fit=True, data_source='tusimple'): + """ + + :param binary_seg_result: + :param instance_seg_result: + :param min_area_threshold: + :param source_image: + :param with_lane_fit: + :param data_source: + :return: + """ + # convert binary_seg_result + binary_seg_result = np.array(binary_seg_result * 255, dtype=np.uint8) + + # apply image morphology operation to fill in the hold and reduce the small area + morphological_ret = _morphological_process(binary_seg_result, kernel_size=5) + + connect_components_analysis_ret = _connect_components_analysis(image=morphological_ret) + + labels = connect_components_analysis_ret[1] + stats = connect_components_analysis_ret[2] + for index, stat in enumerate(stats): + if stat[4] <= min_area_threshold: + idx = np.where(labels == index) + morphological_ret[idx] = 0 + + # apply embedding features cluster + mask_image, lane_coords = self._cluster.apply_lane_feats_cluster( + binary_seg_result=morphological_ret, + instance_seg_result=instance_seg_result + ) + + if mask_image is None: + return { + 'mask_image': None, + 'fit_params': None, + 'source_image': None, + } + if not with_lane_fit: + tmp_mask = cv2.resize( + mask_image, + dsize=(source_image.shape[1], source_image.shape[0]), + interpolation=cv2.INTER_NEAREST + ) + source_image = cv2.addWeighted(source_image, 0.6, tmp_mask, 0.4, 0.0, dst=source_image) + return { + 'mask_image': mask_image, + 'fit_params': None, + 'source_image': source_image, + } + + # lane line fit + fit_params = [] + src_lane_pts = [] # lane pts every single lane + for lane_index, coords in enumerate(lane_coords): + if data_source == 'tusimple': + tmp_mask = np.zeros(shape=(720, 1280), dtype=np.uint8) + tmp_mask[tuple((np.int_(coords[:, 1] * 720 / 256), np.int_(coords[:, 0] * 1280 / 512)))] = 255 + else: + raise ValueError('Wrong data source now only support tusimple') + tmp_ipm_mask = cv2.remap( + tmp_mask, + self._remap_to_ipm_x, + self._remap_to_ipm_y, + interpolation=cv2.INTER_NEAREST + ) + nonzero_y = np.array(tmp_ipm_mask.nonzero()[0]) + nonzero_x = np.array(tmp_ipm_mask.nonzero()[1]) + + fit_param = np.polyfit(nonzero_y, nonzero_x, 2) + fit_params.append(fit_param) + + [ipm_image_height, ipm_image_width] = tmp_ipm_mask.shape + plot_y = np.linspace(10, ipm_image_height, ipm_image_height - 10) + fit_x = fit_param[0] * plot_y ** 2 + fit_param[1] * plot_y + fit_param[2] + # fit_x = fit_param[0] * plot_y ** 3 + fit_param[1] * plot_y ** 2 + fit_param[2] * plot_y + fit_param[3] + + lane_pts = [] + for index in range(0, plot_y.shape[0], 5): + src_x = self._remap_to_ipm_x[ + int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))] + if src_x <= 0: + continue + src_y = self._remap_to_ipm_y[ + int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))] + src_y = src_y if src_y > 0 else 0 + + lane_pts.append([src_x, src_y]) + + src_lane_pts.append(lane_pts) + + # tusimple test data sample point along y axis every 10 pixels + source_image_width = source_image.shape[1] + for index, single_lane_pts in enumerate(src_lane_pts): + single_lane_pt_x = np.array(single_lane_pts, dtype=np.float32)[:, 0] + single_lane_pt_y = np.array(single_lane_pts, dtype=np.float32)[:, 1] + if data_source == 'tusimple': + start_plot_y = 240 + end_plot_y = 720 + else: + raise ValueError('Wrong data source now only support tusimple') + step = int(math.floor((end_plot_y - start_plot_y) / 10)) + for plot_y in np.linspace(start_plot_y, end_plot_y, step): + diff = single_lane_pt_y - plot_y + fake_diff_bigger_than_zero = diff.copy() + fake_diff_smaller_than_zero = diff.copy() + fake_diff_bigger_than_zero[np.where(diff <= 0)] = float('inf') + fake_diff_smaller_than_zero[np.where(diff > 0)] = float('-inf') + idx_low = np.argmax(fake_diff_smaller_than_zero) + idx_high = np.argmin(fake_diff_bigger_than_zero) + + previous_src_pt_x = single_lane_pt_x[idx_low] + previous_src_pt_y = single_lane_pt_y[idx_low] + last_src_pt_x = single_lane_pt_x[idx_high] + last_src_pt_y = single_lane_pt_y[idx_high] + + if previous_src_pt_y < start_plot_y or last_src_pt_y < start_plot_y or \ + fake_diff_smaller_than_zero[idx_low] == float('-inf') or \ + fake_diff_bigger_than_zero[idx_high] == float('inf'): + continue + + interpolation_src_pt_x = (abs(previous_src_pt_y - plot_y) * previous_src_pt_x + + abs(last_src_pt_y - plot_y) * last_src_pt_x) / \ + (abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y)) + interpolation_src_pt_y = (abs(previous_src_pt_y - plot_y) * previous_src_pt_y + + abs(last_src_pt_y - plot_y) * last_src_pt_y) / \ + (abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y)) + + if interpolation_src_pt_x > source_image_width or interpolation_src_pt_x < 10: + continue + + lane_color = self._color_map[index].tolist() + cv2.circle(source_image, (int(interpolation_src_pt_x), + int(interpolation_src_pt_y)), 5, lane_color, -1) + ret = { + 'mask_image': mask_image, + 'fit_params': fit_params, + 'source_image': source_image, + } + + return ret -- Gitee From b51aa82c9a9acf7241c404b03c71e542a8cf2110 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Sat, 3 Sep 2022 09:58:14 +0000 Subject: [PATCH 09/19] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 溺水小航母 <1396755411@qq.com> --- .../local_utils/config_utils/__init__.py | 7 + .../__pycache__/__init__.cpython-36.pyc | Bin 0 -> 139 bytes .../__pycache__/__init__.cpython-37.pyc | Bin 0 -> 143 bytes .../parse_config_utils.cpython-36.pyc | Bin 0 -> 5949 bytes .../parse_config_utils.cpython-37.pyc | Bin 0 -> 5912 bytes .../config_utils/parse_config_utils.py | 245 ++++++++++++++++++ .../local_utils/log_util/__init__.py | 10 + .../__pycache__/__init__.cpython-37.pyc | Bin 0 -> 171 bytes .../__pycache__/init_logger.cpython-37.pyc | Bin 0 -> 979 bytes .../local_utils/log_util/init_logger.py | 68 +++++ 10 files changed, 330 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__init__.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__pycache__/__init__.cpython-36.pyc create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__pycache__/__init__.cpython-37.pyc create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__pycache__/parse_config_utils.cpython-36.pyc create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__pycache__/parse_config_utils.cpython-37.pyc create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/parse_config_utils.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__init__.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__pycache__/__init__.cpython-37.pyc create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__pycache__/init_logger.cpython-37.pyc create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/init_logger.py diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__init__.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__init__.py new file mode 100644 index 000000000..e076ab98d --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__init__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# @Time : 2020/6/11 下午5:46 +# @Author : MaybeShewill-CV +# @Site : https://github.com/MaybeShewill-CV/lanenet-lane-detection 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All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Parse config utils +""" +import os +import yaml +import json +import codecs +from ast import literal_eval + + +class Config(dict): + """ + Config class + """ + def __init__(self, *args, **kwargs): + """ + init class + :param args: + :param kwargs: + """ + if 'config_path' in kwargs: + config_content = self._load_config_file(kwargs['config_path']) + super(Config, self).__init__(config_content) + else: + super(Config, self).__init__(*args, **kwargs) + self.immutable = False + + def __setattr__(self, key, value, create_if_not_exist=True): + """ + + :param key: + :param value: + :param create_if_not_exist: + :return: + """ + if key in ["immutable"]: + self.__dict__[key] = value + return + + t = self + keylist = key.split(".") + for k in keylist[:-1]: + t = t.__getattr__(k, create_if_not_exist) + + t.__getattr__(keylist[-1], create_if_not_exist) + t[keylist[-1]] = value + + def __getattr__(self, key, create_if_not_exist=True): + """ + + :param key: + :param create_if_not_exist: + :return: + """ + if key in ["immutable"]: + return self.__dict__[key] + + if key not in self: + if not create_if_not_exist: + raise KeyError + self[key] = Config() + if isinstance(self[key], dict): + self[key] = Config(self[key]) + return self[key] + + def __setitem__(self, key, value): + """ + + :param key: + :param value: + :return: + """ + if self.immutable: + raise AttributeError( + 'Attempted to set "{}" to "{}", but SegConfig is immutable'. + format(key, value)) + # + if isinstance(value, str): + try: + value = literal_eval(value) + except ValueError: + pass + except SyntaxError: + pass + super(Config, self).__setitem__(key, value) + + @staticmethod + def _load_config_file(config_file_path): + """ + + :param config_file_path + :return: + """ + if not os.access(config_file_path, os.R_OK): + raise OSError('Config file: {:s}, can not be read'.format(config_file_path)) + with open(config_file_path, 'r') as f: + config_content = yaml.safe_load(f) + + return config_content + + def update_from_config(self, other): + """ + + :param other: + :return: + """ + if isinstance(other, dict): + other = Config(other) + assert isinstance(other, Config) + diclist = [("", other)] + while len(diclist): + prefix, tdic = diclist[0] + diclist = diclist[1:] + for key, value in tdic.items(): + key = "{}.{}".format(prefix, key) if prefix else key + if isinstance(value, dict): + diclist.append((key, value)) + continue + try: + self.__setattr__(key, value, create_if_not_exist=False) + except KeyError: + raise KeyError('Non-existent config key: {}'.format(key)) + + def check_and_infer(self): + """ + + :return: + """ + if self.DATASET.IMAGE_TYPE in ['rgb', 'gray']: + self.DATASET.DATA_DIM = 3 + elif self.DATASET.IMAGE_TYPE in ['rgba']: + self.DATASET.DATA_DIM = 4 + else: + raise KeyError( + 'DATASET.IMAGE_TYPE config error, only support `rgb`, `gray` and `rgba`' + ) + if self.MEAN is not None: + self.DATASET.PADDING_VALUE = [x * 255.0 for x in self.MEAN] + + if not self.TRAIN_CROP_SIZE: + raise ValueError( + 'TRAIN_CROP_SIZE is empty! Please set a pair of values in format (width, height)' + ) + + if not self.EVAL_CROP_SIZE: + raise ValueError( + 'EVAL_CROP_SIZE is empty! Please set a pair of values in format (width, height)' + ) + + # Ensure file list is use UTF-8 encoding + train_sets = codecs.open(self.DATASET.TRAIN_FILE_LIST, 'r', 'utf-8').readlines() + val_sets = codecs.open(self.DATASET.VAL_FILE_LIST, 'r', 'utf-8').readlines() + test_sets = codecs.open(self.DATASET.TEST_FILE_LIST, 'r', 'utf-8').readlines() + self.DATASET.TRAIN_TOTAL_IMAGES = len(train_sets) + self.DATASET.VAL_TOTAL_IMAGES = len(val_sets) + self.DATASET.TEST_TOTAL_IMAGES = len(test_sets) + + if self.MODEL.MODEL_NAME == 'icnet' and \ + len(self.MODEL.MULTI_LOSS_WEIGHT) != 3: + self.MODEL.MULTI_LOSS_WEIGHT = [1.0, 0.4, 0.16] + + def update_from_list(self, config_list): + if len(config_list) % 2 != 0: + raise ValueError( + "Command line options config format error! Please check it: {}". + format(config_list)) + for key, value in zip(config_list[0::2], config_list[1::2]): + try: + self.__setattr__(key, value, create_if_not_exist=False) + except KeyError: + raise KeyError('Non-existent config key: {}'.format(key)) + + def update_from_file(self, config_file): + """ + + :param config_file: + :return: + """ + with codecs.open(config_file, 'r', 'utf-8') as f: + dic = yaml.safe_load(f) + self.update_from_config(dic) + + def set_immutable(self, immutable): + """ + + :param immutable: + :return: + """ + self.immutable = immutable + for value in self.values(): + if isinstance(value, Config): + value.set_immutable(immutable) + + def is_immutable(self): + """ + + :return: + """ + return self.immutable + + def dump_to_json_file(self, f_obj): + """ + + :param f_obj: + :return: + """ + origin_dict = dict() + for key, val in self.items(): + if isinstance(val, Config): + origin_dict.update({key: dict(val)}) + elif isinstance(val, dict): + origin_dict.update({key: val}) + else: + raise TypeError('Not supported type {}'.format(type(val))) + return json.dump(origin_dict, f_obj) + + +lanenet_cfg = Config(config_path='E:/evaluate/config/tusimple_lanenet.yaml') diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__init__.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__init__.py new file mode 100644 index 000000000..506b5b98e --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__init__.py @@ -0,0 +1,10 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# @Time : 2019/11/14 下午8:18 +# @Author : MaybeShewill-CV +# @Site : https://github.com/MaybeShewill-CV/bisenetv2-tensorflow +# @File : __init__.py.py +# @IDE: PyCharm +""" +日志配置类 +""" \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__pycache__/__init__.cpython-37.pyc 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All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +BiseNet V2 Model +""" +import collections + +import tensorflow as tf + +from semantic_segmentation_zoo import cnn_basenet +from local_utils.config_utils import parse_config_utils + + +class _StemBlock(cnn_basenet.CNNBaseModel): + """ + implementation of stem block module + """ + def __init__(self, phase): + """ + + :param phase: + """ + super(_StemBlock, self).__init__() + self._phase = phase + self._is_training = self._is_net_for_training() + self._padding = 'SAME' + + def _is_net_for_training(self): + """ + if the net is used for training or not + :return: + """ + if isinstance(self._phase, tf.Tensor): + phase = self._phase + else: + phase = tf.constant(self._phase, dtype=tf.string) + return tf.equal(phase, tf.constant('train', dtype=tf.string)) + + def _conv_block(self, input_tensor, k_size, output_channels, stride, + name, padding='SAME', use_bias=False, need_activate=False): + """ + conv block in attention refine + :param input_tensor: + :param k_size: + :param output_channels: + :param stride: + :param name: + :param padding: + :param use_bias: + :return: + """ + with tf.variable_scope(name_or_scope=name): + result = self.conv2d( + inputdata=input_tensor, + out_channel=output_channels, + kernel_size=k_size, + padding=padding, + stride=stride, + use_bias=use_bias, + name='conv' + ) + if need_activate: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + result = self.relu(inputdata=result, name='relu') + else: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + return result + + def __call__(self, *args, **kwargs): + """ + + :param args: + :param kwargs: + :return: + """ + input_tensor = kwargs['input_tensor'] + name_scope = kwargs['name'] + output_channels = kwargs['output_channels'] + if 'padding' in kwargs: + self._padding = kwargs['padding'] + with tf.variable_scope(name_or_scope=name_scope): + input_tensor = self._conv_block( + input_tensor=input_tensor, + k_size=3, + output_channels=output_channels, + stride=2, + name='conv_block_1', + padding=self._padding, + use_bias=False, + need_activate=True + ) + with tf.variable_scope(name_or_scope='downsample_branch_left'): + branch_left_output = self._conv_block( + input_tensor=input_tensor, + k_size=1, + output_channels=int(output_channels / 2), + stride=1, + name='1x1_conv_block', + padding=self._padding, + use_bias=False, + need_activate=True + ) + branch_left_output = self._conv_block( + input_tensor=branch_left_output, + k_size=3, + output_channels=output_channels, + stride=2, + name='3x3_conv_block', + padding=self._padding, + use_bias=False, + need_activate=True + ) + with tf.variable_scope(name_or_scope='downsample_branch_right'): + branch_right_output = self.maxpooling( + inputdata=input_tensor, + kernel_size=3, + stride=2, + padding=self._padding, + name='maxpooling_block' + ) + result = tf.concat([branch_left_output, branch_right_output], axis=-1, name='concate_features') + result = self._conv_block( + input_tensor=result, + k_size=3, + output_channels=output_channels, + stride=1, + name='final_conv_block', + padding=self._padding, + use_bias=False, + need_activate=True + ) + return result + + +class _ContextEmbedding(cnn_basenet.CNNBaseModel): + """ + implementation of context embedding module in bisenetv2 + """ + def __init__(self, phase): + """ + + :param phase: + """ + super(_ContextEmbedding, self).__init__() + self._phase = phase + self._is_training = self._is_net_for_training() + self._padding = 'SAME' + + def _is_net_for_training(self): + """ + if the net is used for training or not + :return: + """ + if isinstance(self._phase, tf.Tensor): + phase = self._phase + else: + phase = tf.constant(self._phase, dtype=tf.string) + return tf.equal(phase, tf.constant('train', dtype=tf.string)) + + def _conv_block(self, input_tensor, k_size, output_channels, stride, + name, padding='SAME', use_bias=False, need_activate=False): + """ + conv block in attention refine + :param input_tensor: + :param k_size: + :param output_channels: + :param stride: + :param name: + :param padding: + :param use_bias: + :return: + """ + with tf.variable_scope(name_or_scope=name): + result = self.conv2d( + inputdata=input_tensor, + out_channel=output_channels, + kernel_size=k_size, + padding=padding, + stride=stride, + use_bias=use_bias, + name='conv' + ) + if need_activate: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + result = self.relu(inputdata=result, name='relu') + else: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + return result + + def __call__(self, *args, **kwargs): + """ + + :param args: + :param kwargs: + :return: + """ + input_tensor = kwargs['input_tensor'] + name_scope = kwargs['name'] + output_channels = input_tensor.get_shape().as_list()[-1] + if 'padding' in kwargs: + self._padding = kwargs['padding'] + with tf.variable_scope(name_or_scope=name_scope): + result = tf.reduce_mean(input_tensor, axis=[1, 2], keepdims=True, name='global_avg_pooling') + result = self.layerbn(result, self._is_training, 'bn') + result = self._conv_block( + input_tensor=result, + k_size=1, + output_channels=output_channels, + stride=1, + name='conv_block_1', + padding=self._padding, + use_bias=False, + need_activate=True + ) + result = tf.add(result, input_tensor, name='fused_features') + result = self.conv2d( + inputdata=result, + out_channel=output_channels, + kernel_size=3, + padding=self._padding, + stride=1, + use_bias=False, + name='final_conv_block' + ) + return result + + +class _GatherExpansion(cnn_basenet.CNNBaseModel): + """ + implementation of gather and expansion module in bisenetv2 + """ + def __init__(self, phase): + """ + + :param phase: + """ + super(_GatherExpansion, self).__init__() + self._phase = phase + self._is_training = self._is_net_for_training() + self._padding = 'SAME' + self._stride = 1 + self._expansion_factor = 6 + + def _is_net_for_training(self): + """ + if the net is used for training or not + :return: + """ + if isinstance(self._phase, tf.Tensor): + phase = self._phase + else: + phase = tf.constant(self._phase, dtype=tf.string) + return tf.equal(phase, tf.constant('train', dtype=tf.string)) + + def _conv_block(self, input_tensor, k_size, output_channels, stride, + name, padding='SAME', use_bias=False, need_activate=False): + """ + conv block in attention refine + :param input_tensor: + :param k_size: + :param output_channels: + :param stride: + :param name: + :param padding: + :param use_bias: + :return: + """ + with tf.variable_scope(name_or_scope=name): + result = self.conv2d( + inputdata=input_tensor, + out_channel=output_channels, + kernel_size=k_size, + padding=padding, + stride=stride, + use_bias=use_bias, + name='conv' + ) + if need_activate: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + result = self.relu(inputdata=result, name='relu') + else: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + return result + + def _apply_ge_when_stride_equal_one(self, input_tensor, e, name): + """ + + :param input_tensor: + :param e: + :param name + :return: + """ + input_tensor_channels = input_tensor.get_shape().as_list()[-1] + with tf.variable_scope(name_or_scope=name): + result = self._conv_block( + input_tensor=input_tensor, + k_size=3, + output_channels=input_tensor_channels, + stride=1, + name='3x3_conv_block', + padding=self._padding, + use_bias=False, + need_activate=True + ) + result = self.depthwise_conv( + input_tensor=result, + kernel_size=3, + depth_multiplier=e, + padding=self._padding, + stride=1, + name='depthwise_conv_block' + ) + result = self.layerbn(result, self._is_training, name='dw_bn') + result = self._conv_block( + input_tensor=result, + k_size=1, + output_channels=input_tensor_channels, + stride=1, + name='1x1_conv_block', + padding=self._padding, + use_bias=False, + need_activate=False + ) + result = tf.add(input_tensor, result, name='fused_features') + result = self.relu(result, name='ge_output') + return result + + def _apply_ge_when_stride_equal_two(self, input_tensor, output_channels, e, name): + """ + + :param input_tensor: + :param output_channels: + :param e: + :param name + :return: + """ + input_tensor_channels = input_tensor.get_shape().as_list()[-1] + with tf.variable_scope(name_or_scope=name): + input_proj = self.depthwise_conv( + input_tensor=input_tensor, + kernel_size=3, + name='input_project_dw_conv_block', + depth_multiplier=1, + padding=self._padding, + stride=self._stride + ) + input_proj = self.layerbn(input_proj, self._is_training, name='input_project_bn') + input_proj = self._conv_block( + input_tensor=input_proj, + k_size=1, + output_channels=output_channels, + stride=1, + name='input_project_1x1_conv_block', + padding=self._padding, + use_bias=False, + need_activate=False + ) + + result = self._conv_block( + input_tensor=input_tensor, + k_size=3, + output_channels=input_tensor_channels, + stride=1, + name='3x3_conv_block', + padding=self._padding, + use_bias=False, + need_activate=True + ) + result = self.depthwise_conv( + input_tensor=result, + kernel_size=3, + depth_multiplier=e, + padding=self._padding, + stride=2, + name='depthwise_conv_block_1' + ) + result = self.layerbn(result, self._is_training, name='dw_bn_1') + result = self.depthwise_conv( + input_tensor=result, + kernel_size=3, + depth_multiplier=1, + padding=self._padding, + stride=1, + name='depthwise_conv_block_2' + ) + result = self.layerbn(result, self._is_training, name='dw_bn_2') + result = self._conv_block( + input_tensor=result, + k_size=1, + output_channels=output_channels, + stride=1, + name='1x1_conv_block', + padding=self._padding, + use_bias=False, + need_activate=False + ) + result = tf.add(input_proj, result, name='fused_features') + result = self.relu(result, name='ge_output') + return result + + def __call__(self, *args, **kwargs): + """ + + :param args: + :param kwargs: + :return: + """ + input_tensor = kwargs['input_tensor'] + name_scope = kwargs['name'] + output_channels = input_tensor.get_shape().as_list()[-1] + if 'output_channels' in kwargs: + output_channels = kwargs['output_channels'] + if 'padding' in kwargs: + self._padding = kwargs['padding'] + if 'stride' in kwargs: + self._stride = kwargs['stride'] + if 'e' in kwargs: + self._expansion_factor = kwargs['e'] + + with tf.variable_scope(name_or_scope=name_scope): + if self._stride == 1: + result = self._apply_ge_when_stride_equal_one( + input_tensor=input_tensor, + e=self._expansion_factor, + name='stride_equal_one_module' + ) + elif self._stride == 2: + result = self._apply_ge_when_stride_equal_two( + input_tensor=input_tensor, + output_channels=output_channels, + e=self._expansion_factor, + name='stride_equal_two_module' + ) + else: + raise NotImplementedError('No function matched with stride of {}'.format(self._stride)) + return result + + +class _GuidedAggregation(cnn_basenet.CNNBaseModel): + """ + implementation of guided aggregation module in bisenetv2 + """ + + def __init__(self, phase): + """ + + :param phase: + """ + super(_GuidedAggregation, self).__init__() + self._phase = phase + self._is_training = self._is_net_for_training() + self._padding = 'SAME' + + def _is_net_for_training(self): + """ + if the net is used for training or not + :return: + """ + if isinstance(self._phase, tf.Tensor): + phase = self._phase + else: + phase = tf.constant(self._phase, dtype=tf.string) + return tf.equal(phase, tf.constant('train', dtype=tf.string)) + + def _conv_block(self, input_tensor, k_size, output_channels, stride, + name, padding='SAME', use_bias=False, need_activate=False): + """ + conv block in attention refine + :param input_tensor: + :param k_size: + :param output_channels: + :param stride: + :param name: + :param padding: + :param use_bias: + :return: + """ + with tf.variable_scope(name_or_scope=name): + result = self.conv2d( + inputdata=input_tensor, + out_channel=output_channels, + kernel_size=k_size, + padding=padding, + stride=stride, + use_bias=use_bias, + name='conv' + ) + if need_activate: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + result = self.relu(inputdata=result, name='relu') + else: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + return result + + def __call__(self, *args, **kwargs): + """ + + :param args: + :param kwargs: + :return: + """ + detail_input_tensor = kwargs['detail_input_tensor'] + semantic_input_tensor = kwargs['semantic_input_tensor'] + name_scope = kwargs['name'] + output_channels = detail_input_tensor.get_shape().as_list()[-1] + if 'padding' in kwargs: + self._padding = kwargs['padding'] + + with tf.variable_scope(name_or_scope=name_scope): + with tf.variable_scope(name_or_scope='detail_branch'): + detail_branch_remain = self.depthwise_conv( + input_tensor=detail_input_tensor, + kernel_size=3, + name='3x3_dw_conv_block', + depth_multiplier=1, + padding=self._padding, + stride=1 + ) + detail_branch_remain = self.layerbn(detail_branch_remain, self._is_training, name='bn_1') + detail_branch_remain = self.conv2d( + inputdata=detail_branch_remain, + out_channel=output_channels, + kernel_size=1, + padding=self._padding, + stride=1, + use_bias=False, + name='1x1_conv_block' + ) + + detail_branch_downsample = self._conv_block( + input_tensor=detail_input_tensor, + k_size=3, + output_channels=output_channels, + stride=2, + name='3x3_conv_block', + padding=self._padding, + use_bias=False, + need_activate=False + ) + detail_branch_downsample = self.avgpooling( + inputdata=detail_branch_downsample, + kernel_size=3, + stride=2, + padding=self._padding, + name='avg_pooling_block' + ) + + with tf.variable_scope(name_or_scope='semantic_branch'): + semantic_branch_remain = self.depthwise_conv( + input_tensor=semantic_input_tensor, + kernel_size=3, + name='3x3_dw_conv_block', + depth_multiplier=1, + padding=self._padding, + stride=1 + ) + semantic_branch_remain = self.layerbn(semantic_branch_remain, self._is_training, name='bn_1') + semantic_branch_remain = self.conv2d( + inputdata=semantic_branch_remain, + out_channel=output_channels, + kernel_size=1, + padding=self._padding, + stride=1, + use_bias=False, + name='1x1_conv_block' + ) + semantic_branch_remain = self.sigmoid(semantic_branch_remain, name='semantic_remain_sigmoid') + + semantic_branch_upsample = self._conv_block( + input_tensor=semantic_input_tensor, + k_size=3, + output_channels=output_channels, + stride=1, + name='3x3_conv_block', + padding=self._padding, + use_bias=False, + need_activate=False + ) + semantic_branch_upsample = tf.image.resize_bilinear( + semantic_branch_upsample, + detail_input_tensor.shape[1:3], + name='semantic_upsample_features' + ) + semantic_branch_upsample = self.sigmoid(semantic_branch_upsample, name='semantic_upsample_sigmoid') + + with tf.variable_scope(name_or_scope='aggregation_features'): + guided_features_remain = tf.multiply( + detail_branch_remain, + semantic_branch_upsample, + name='guided_detail_features' + ) + guided_features_downsample = tf.multiply( + detail_branch_downsample, + semantic_branch_remain, + name='guided_semantic_features' + ) + guided_features_upsample = tf.image.resize_bilinear( + guided_features_downsample, + detail_input_tensor.shape[1:3], + name='guided_upsample_features' + ) + guided_features = tf.add(guided_features_remain, guided_features_upsample, name='fused_features') + guided_features = self._conv_block( + input_tensor=guided_features, + k_size=3, + output_channels=output_channels, + stride=1, + name='aggregation_feature_output', + padding=self._padding, + use_bias=False, + need_activate=True + ) + return guided_features + + +class _SegmentationHead(cnn_basenet.CNNBaseModel): + """ + implementation of segmentation head in bisenet v2 + """ + def __init__(self, phase): + """ + + """ + super(_SegmentationHead, self).__init__() + self._phase = phase + self._is_training = self._is_net_for_training() + self._padding = 'SAME' + + def _is_net_for_training(self): + """ + if the net is used for training or not + :return: + """ + if isinstance(self._phase, tf.Tensor): + phase = self._phase + else: + phase = tf.constant(self._phase, dtype=tf.string) + return tf.equal(phase, tf.constant('train', dtype=tf.string)) + + def _conv_block(self, input_tensor, k_size, output_channels, stride, + name, padding='SAME', use_bias=False, need_activate=False): + """ + conv block in attention refine + :param input_tensor: + :param k_size: + :param output_channels: + :param stride: + :param name: + :param padding: + :param use_bias: + :return: + """ + with tf.variable_scope(name_or_scope=name): + result = self.conv2d( + inputdata=input_tensor, + out_channel=output_channels, + kernel_size=k_size, + padding=padding, + stride=stride, + use_bias=use_bias, + name='conv' + ) + if need_activate: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + result = self.relu(inputdata=result, name='relu') + else: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + return result + + def __call__(self, *args, **kwargs): + """ + + :param args: + :param kwargs: + :return: + """ + input_tensor = kwargs['input_tensor'] + name_scope = kwargs['name'] + ratio = kwargs['upsample_ratio'] + input_tensor_size = input_tensor.get_shape().as_list()[1:3] + output_tensor_size = [int(tmp * ratio) for tmp in input_tensor_size] + feature_dims = kwargs['feature_dims'] + classes_nums = kwargs['classes_nums'] + if 'padding' in kwargs: + self._padding = kwargs['padding'] + + with tf.variable_scope(name_or_scope=name_scope): + result = self._conv_block( + input_tensor=input_tensor, + k_size=3, + output_channels=feature_dims, + stride=1, + name='3x3_conv_block', + padding=self._padding, + use_bias=False, + need_activate=True + ) + result = self.conv2d( + inputdata=result, + out_channel=classes_nums, + kernel_size=1, + padding=self._padding, + stride=1, + use_bias=False, + name='1x1_conv_block' + ) + result = tf.image.resize_bilinear( + result, + output_tensor_size, + name='segmentation_head_logits' + ) + return result + + +class BiseNetV2(cnn_basenet.CNNBaseModel): + """ + implementation of bisenet v2 + """ + def __init__(self, phase, cfg): + """ + + """ + super(BiseNetV2, self).__init__() + self._cfg = cfg + self._phase = phase + self._is_training = self._is_net_for_training() + + # set model hyper params + self._class_nums = self._cfg.DATASET.NUM_CLASSES + self._weights_decay = self._cfg.SOLVER.WEIGHT_DECAY + self._loss_type = self._cfg.SOLVER.LOSS_TYPE + self._enable_ohem = self._cfg.SOLVER.OHEM.ENABLE + if self._enable_ohem: + self._ohem_score_thresh = self._cfg.SOLVER.OHEM.SCORE_THRESH + self._ohem_min_sample_nums = self._cfg.SOLVER.OHEM.MIN_SAMPLE_NUMS + self._ge_expand_ratio = self._cfg.MODEL.BISENETV2.GE_EXPAND_RATIO + self._semantic_channel_ratio = self._cfg.MODEL.BISENETV2.SEMANTIC_CHANNEL_LAMBDA + self._seg_head_ratio = self._cfg.MODEL.BISENETV2.SEGHEAD_CHANNEL_EXPAND_RATIO + + # set module used in bisenetv2 + self._se_block = _StemBlock(phase=phase) + self._context_embedding_block = _ContextEmbedding(phase=phase) + self._ge_block = _GatherExpansion(phase=phase) + self._guided_aggregation_block = _GuidedAggregation(phase=phase) + self._seg_head_block = _SegmentationHead(phase=phase) + + # set detail branch channels + self._detail_branch_channels = self._build_detail_branch_hyper_params() + # set semantic branch channels + self._semantic_branch_channels = self._build_semantic_branch_hyper_params() + + # set op block params + self._block_maps = { + 'conv_block': self._conv_block, + 'se': self._se_block, + 'ge': self._ge_block, + 'ce': self._context_embedding_block, + } + + self._net_intermediate_results = collections.OrderedDict() + + def _is_net_for_training(self): + """ + if the net is used for training or not + :return: + """ + if isinstance(self._phase, tf.Tensor): + phase = self._phase + else: + phase = tf.constant(self._phase, dtype=tf.string) + return tf.equal(phase, tf.constant('train', dtype=tf.string)) + + @classmethod + def _build_detail_branch_hyper_params(cls): + """ + + :return: + """ + params = [ + ('stage_1', [('conv_block', 3, 64, 2, 1), ('conv_block', 3, 64, 1, 1)]), + ('stage_2', [('conv_block', 3, 64, 2, 1), ('conv_block', 3, 64, 1, 2)]), + ('stage_3', [('conv_block', 3, 128, 2, 1), ('conv_block', 3, 128, 1, 2)]), + ] + return collections.OrderedDict(params) + + def _build_semantic_branch_hyper_params(self): + """ + + :return: + """ + stage_1_channels = int(self._detail_branch_channels['stage_1'][0][2] * self._semantic_channel_ratio) + stage_3_channels = int(self._detail_branch_channels['stage_3'][0][2] * self._semantic_channel_ratio) + params = [ + ('stage_1', [('se', 3, stage_1_channels, 1, 4, 1)]), + ('stage_3', [('ge', 3, stage_3_channels, self._ge_expand_ratio, 2, 1), + ('ge', 3, stage_3_channels, self._ge_expand_ratio, 1, 1)]), + ('stage_4', [('ge', 3, stage_3_channels * 2, self._ge_expand_ratio, 2, 1), + ('ge', 3, stage_3_channels * 2, self._ge_expand_ratio, 1, 1)]), + ('stage_5', [('ge', 3, stage_3_channels * 4, self._ge_expand_ratio, 2, 1), + ('ge', 3, stage_3_channels * 4, self._ge_expand_ratio, 1, 3), + ('ce', 3, stage_3_channels * 4, self._ge_expand_ratio, 1, 1)]) + ] + return collections.OrderedDict(params) + + def _conv_block(self, input_tensor, k_size, output_channels, stride, + name, padding='SAME', use_bias=False, need_activate=False): + """ + conv block in attention refine + :param input_tensor: + :param k_size: + :param output_channels: + :param stride: + :param name: + :param padding: + :param use_bias: + :return: + """ + with tf.variable_scope(name_or_scope=name): + result = self.conv2d( + inputdata=input_tensor, + out_channel=output_channels, + kernel_size=k_size, + padding=padding, + stride=stride, + use_bias=use_bias, + name='conv' + ) + if need_activate: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + result = self.relu(inputdata=result, name='relu') + else: + result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) + return result + + def build_detail_branch(self, input_tensor, name): + """ + + :param input_tensor: + :param name: + :return: + """ + result = input_tensor + with tf.variable_scope(name_or_scope=name): + for stage_name, stage_params in self._detail_branch_channels.items(): + with tf.variable_scope(stage_name): + for block_index, param in enumerate(stage_params): + block_op = self._block_maps[param[0]] + k_size = param[1] + output_channels = param[2] + stride = param[3] + repeat_times = param[4] + for repeat_index in range(repeat_times): + with tf.variable_scope(name_or_scope='conv_block_{:d}_repeat_{:d}'.format( + block_index + 1, repeat_index + 1)): + if stage_name == 'stage_3' and block_index == 1 and repeat_index == 1: + result = block_op( + input_tensor=result, + k_size=k_size, + output_channels=output_channels, + stride=stride, + name='3x3_conv', + padding='SAME', + use_bias=False, + need_activate=False + ) + else: + result = block_op( + input_tensor=result, + k_size=k_size, + output_channels=output_channels, + stride=stride, + name='3x3_conv', + padding='SAME', + use_bias=False, + need_activate=True + ) + return result + + def build_semantic_branch(self, input_tensor, name, prepare_data_for_booster=False): + """ + + :param input_tensor: + :param name: + :param prepare_data_for_booster: + :return: + """ + seg_head_inputs = collections.OrderedDict() + result = input_tensor + source_input_tensor_size = input_tensor.get_shape().as_list()[1:3] + with tf.variable_scope(name_or_scope=name): + for stage_name, stage_params in self._semantic_branch_channels.items(): + seg_head_input = input_tensor + with tf.variable_scope(stage_name): + for block_index, param in enumerate(stage_params): + block_op_name = param[0] + block_op = self._block_maps[block_op_name] + output_channels = param[2] + expand_ratio = param[3] + stride = param[4] + repeat_times = param[5] + for repeat_index in range(repeat_times): + with tf.variable_scope(name_or_scope='{:s}_block_{:d}_repeat_{:d}'.format( + block_op_name, block_index + 1, repeat_index + 1)): + if block_op_name == 'ge': + result = block_op( + input_tensor=result, + name='gather_expansion_block', + stride=stride, + e=expand_ratio, + output_channels=output_channels + ) + seg_head_input = result + elif block_op_name == 'ce': + result = block_op( + input_tensor=result, + name='context_embedding_block' + ) + elif block_op_name == 'se': + result = block_op( + input_tensor=result, + output_channels=output_channels, + name='stem_block' + ) + seg_head_input = result + else: + raise NotImplementedError('Not support block type: {:s}'.format(block_op_name)) + if prepare_data_for_booster: + result_tensor_size = result.get_shape().as_list()[1:3] + result_tensor_dims = result.get_shape().as_list()[-1] + upsample_ratio = int(source_input_tensor_size[0] / result_tensor_size[0]) + feature_dims = result_tensor_dims * self._seg_head_ratio + seg_head_inputs[stage_name] = self._seg_head_block( + input_tensor=seg_head_input, + name='block_{:d}_seg_head_block'.format(block_index + 1), + upsample_ratio=upsample_ratio, + feature_dims=feature_dims, + classes_nums=self._class_nums + ) + return result, seg_head_inputs + + def build_aggregation_branch(self, detail_output, semantic_output, name): + """ + + :param detail_output: + :param semantic_output: + :param name: + :return: + """ + with tf.variable_scope(name_or_scope=name): + result = self._guided_aggregation_block( + detail_input_tensor=detail_output, + semantic_input_tensor=semantic_output, + name='guided_aggregation_block' + ) + return result + + def build_instance_segmentation_branch(self, input_tensor, name): + """ + + :param input_tensor: + :param name: + :return: + """ + input_tensor_size = input_tensor.get_shape().as_list()[1:3] + output_tensor_size = [int(tmp * 8) for tmp in input_tensor_size] + + with tf.variable_scope(name_or_scope=name): + output_tensor = self._conv_block( + input_tensor=input_tensor, + k_size=3, + output_channels=64, + stride=1, + name='conv_3x3', + use_bias=False, + need_activate=True + ) + output_tensor = self._conv_block( + input_tensor=output_tensor, + k_size=1, + output_channels=128, + stride=1, + name='conv_1x1', + use_bias=False, + need_activate=False + ) + output_tensor = tf.image.resize_bilinear( + output_tensor, + output_tensor_size, + name='instance_logits' + ) + return output_tensor + + def build_binary_segmentation_branch(self, input_tensor, name): + """ + + :param input_tensor: + :param name: + :return: + """ + input_tensor_size = input_tensor.get_shape().as_list()[1:3] + output_tensor_size = [int(tmp * 8) for tmp in input_tensor_size] + + with tf.variable_scope(name_or_scope=name): + output_tensor = self._conv_block( + input_tensor=input_tensor, + k_size=3, + output_channels=64, + stride=1, + name='conv_3x3', + use_bias=False, + need_activate=True + ) + output_tensor = self._conv_block( + input_tensor=output_tensor, + k_size=1, + output_channels=128, + stride=1, + name='conv_1x1', + use_bias=False, + need_activate=True + ) + output_tensor = self._conv_block( + input_tensor=output_tensor, + k_size=1, + output_channels=self._class_nums, + stride=1, + name='final_conv', + use_bias=False, + need_activate=False + ) + output_tensor = tf.image.resize_bilinear( + output_tensor, + output_tensor_size, + name='binary_logits' + ) + return output_tensor + + def build_model(self, input_tensor, name, reuse=False): + """ + + :param input_tensor: + :param name: + :param reuse: + :return: + """ + with tf.variable_scope(name_or_scope=name, reuse=reuse): + # build detail branch + detail_branch_output = self.build_detail_branch( + input_tensor=input_tensor, + name='detail_branch' + ) + # build semantic branch + semantic_branch_output, _ = self.build_semantic_branch( + input_tensor=input_tensor, + name='semantic_branch', + prepare_data_for_booster=False + ) + # build aggregation branch + aggregation_branch_output = self.build_aggregation_branch( + detail_output=detail_branch_output, + semantic_output=semantic_branch_output, + name='aggregation_branch' + ) + # build binary and instance segmentation branch + binary_seg_branch_output = self.build_binary_segmentation_branch( + input_tensor=aggregation_branch_output, + name='binary_segmentation_branch' + ) + instance_seg_branch_output = self.build_instance_segmentation_branch( + input_tensor=aggregation_branch_output, + name='instance_segmentation_branch' + ) + # gather frontend output result + self._net_intermediate_results['binary_segment_logits'] = { + 'data': binary_seg_branch_output, + 'shape': binary_seg_branch_output.get_shape().as_list() + } + self._net_intermediate_results['instance_segment_logits'] = { + 'data': instance_seg_branch_output, + 'shape': instance_seg_branch_output.get_shape().as_list() + } + return self._net_intermediate_results + + +if __name__ == '__main__': + """ + test code + """ + test_in_tensor = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input') + model = BiseNetV2(phase='train', cfg=parse_config_utils.lanenet_cfg) + ret = model.build_model(test_in_tensor, name='bisenetv2') + for layer_name, layer_info in ret.items(): + print('layer name: {:s} shape: {}'.format(layer_name, layer_info['shape'])) + diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/cnn_basenet.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/cnn_basenet.py new file mode 100644 index 000000000..fefb814d2 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/cnn_basenet.py @@ -0,0 +1,549 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +The base convolution neural networks mainly implement some useful cnn functions +""" +import tensorflow as tf +import numpy as np + + +class CNNBaseModel(object): + """ + Base model for other specific cnn ctpn_models + """ + + def __init__(self): + pass + + @staticmethod + def conv2d(inputdata, out_channel, kernel_size, padding='SAME', + stride=1, w_init=None, b_init=None, + split=1, use_bias=True, data_format='NHWC', name=None): + """ + Packing the tensorflow conv2d function. + :param name: op name + :param inputdata: A 4D tensorflow tensor which ust have known number of channels, but can have other + unknown dimensions. + :param out_channel: number of output channel. + :param kernel_size: int so only support square kernel convolution + :param padding: 'VALID' or 'SAME' + :param stride: int so only support square stride + :param w_init: initializer for convolution weights + :param b_init: initializer for bias + :param split: split channels as used in Alexnet mainly group for GPU memory save. + :param use_bias: whether to use bias. + :param data_format: default set to NHWC according tensorflow + :return: tf.Tensor named ``output`` + """ + with tf.variable_scope(name): + in_shape = inputdata.get_shape().as_list() + channel_axis = 3 if data_format == 'NHWC' else 1 + in_channel = in_shape[channel_axis] + assert in_channel is not None, "[Conv2D] Input cannot have unknown channel!" + assert in_channel % split == 0 + assert out_channel % split == 0 + + padding = padding.upper() + + if isinstance(kernel_size, list): + filter_shape = [kernel_size[0], kernel_size[1]] + [in_channel / split, out_channel] + else: + filter_shape = [kernel_size, kernel_size] + [in_channel / split, out_channel] + + if isinstance(stride, list): + strides = [1, stride[0], stride[1], 1] if data_format == 'NHWC' \ + else [1, 1, stride[0], stride[1]] + else: + strides = [1, stride, stride, 1] if data_format == 'NHWC' \ + else [1, 1, stride, stride] + + if w_init is None: + w_init = tf.contrib.layers.variance_scaling_initializer() + if b_init is None: + b_init = tf.constant_initializer() + + w = tf.get_variable('W', filter_shape, initializer=w_init) + b = None + + if use_bias: + b = tf.get_variable('b', [out_channel], initializer=b_init) + + if split == 1: + conv = tf.nn.conv2d(inputdata, w, strides, padding, data_format=data_format) + else: + inputs = tf.split(inputdata, split, channel_axis) + kernels = tf.split(w, split, 3) + outputs = [tf.nn.conv2d(i, k, strides, padding, data_format=data_format) + for i, k in zip(inputs, kernels)] + conv = tf.concat(outputs, channel_axis) + + ret = tf.identity(tf.nn.bias_add(conv, b, data_format=data_format) + if use_bias else conv, name=name) + + return ret + + @staticmethod + def depthwise_conv(input_tensor, kernel_size, name, depth_multiplier=1, + padding='SAME', stride=1): + """ + + :param input_tensor: + :param kernel_size: + :param name: + :param depth_multiplier: + :param padding: + :param stride: + :return: + """ + with tf.variable_scope(name_or_scope=name): + in_shape = input_tensor.get_shape().as_list() + in_channel = in_shape[3] + padding = padding.upper() + + depthwise_filter_shape = [kernel_size, kernel_size] + [in_channel, depth_multiplier] + w_init = tf.contrib.layers.variance_scaling_initializer() + + depthwise_filter = tf.get_variable( + name='depthwise_filter_w', shape=depthwise_filter_shape, + initializer=w_init + ) + + result = tf.nn.depthwise_conv2d( + input=input_tensor, + filter=depthwise_filter, + strides=[1, stride, stride, 1], + padding=padding, + name='depthwise_conv_output' + ) + return result + + @staticmethod + def relu(inputdata, name=None): + """ + + :param name: + :param inputdata: + :return: + """ + return tf.nn.relu(features=inputdata, name=name) + + @staticmethod + def sigmoid(inputdata, name=None): + """ + + :param name: + :param inputdata: + :return: + """ + return tf.nn.sigmoid(x=inputdata, name=name) + + @staticmethod + def maxpooling(inputdata, kernel_size, stride=None, padding='VALID', + data_format='NHWC', name=None): + """ + + :param name: + :param inputdata: + :param kernel_size: + :param stride: + :param padding: + :param data_format: + :return: + """ + padding = padding.upper() + + if stride is None: + stride = kernel_size + + if isinstance(kernel_size, list): + kernel = [1, kernel_size[0], kernel_size[1], 1] if data_format == 'NHWC' else \ + [1, 1, kernel_size[0], kernel_size[1]] + else: + kernel = [1, kernel_size, kernel_size, 1] if data_format == 'NHWC' \ + else [1, 1, kernel_size, kernel_size] + + if isinstance(stride, list): + strides = [1, stride[0], stride[1], 1] if data_format == 'NHWC' \ + else [1, 1, stride[0], stride[1]] + else: + strides = [1, stride, stride, 1] if data_format == 'NHWC' \ + else [1, 1, stride, stride] + + return tf.nn.max_pool(value=inputdata, ksize=kernel, strides=strides, padding=padding, + data_format=data_format, name=name) + + @staticmethod + def avgpooling(inputdata, kernel_size, stride=None, padding='VALID', + data_format='NHWC', name=None): + """ + + :param name: + :param inputdata: + :param kernel_size: + :param stride: + :param padding: + :param data_format: + :return: + """ + if stride is None: + stride = kernel_size + + kernel = [1, kernel_size, kernel_size, 1] if data_format == 'NHWC' \ + else [1, 1, kernel_size, kernel_size] + + strides = [1, stride, stride, 1] if data_format == 'NHWC' else [1, 1, stride, stride] + + return tf.nn.avg_pool(value=inputdata, ksize=kernel, strides=strides, padding=padding, + data_format=data_format, name=name) + + @staticmethod + def globalavgpooling(inputdata, data_format='NHWC', name=None): + """ + + :param name: + :param inputdata: + :param data_format: + :return: + """ + assert inputdata.shape.ndims == 4 + assert data_format in ['NHWC', 'NCHW'] + + axis = [1, 2] if data_format == 'NHWC' else [2, 3] + + return tf.reduce_mean(input_tensor=inputdata, axis=axis, name=name) + + @staticmethod + def layernorm(inputdata, epsilon=1e-5, use_bias=True, use_scale=True, + data_format='NHWC', name=None): + """ + :param name: + :param inputdata: + :param epsilon: epsilon to avoid divide-by-zero. + :param use_bias: whether to use the extra affine transformation or not. + :param use_scale: whether to use the extra affine transformation or not. + :param data_format: + :return: + """ + shape = inputdata.get_shape().as_list() + ndims = len(shape) + assert ndims in [2, 4] + + mean, var = tf.nn.moments(inputdata, list(range(1, len(shape))), keep_dims=True) + + if data_format == 'NCHW': + channnel = shape[1] + new_shape = [1, channnel, 1, 1] + else: + channnel = shape[-1] + new_shape = [1, 1, 1, channnel] + if ndims == 2: + new_shape = [1, channnel] + + if use_bias: + beta = tf.get_variable('beta', [channnel], initializer=tf.constant_initializer()) + beta = tf.reshape(beta, new_shape) + else: + beta = tf.zeros([1] * ndims, name='beta') + if use_scale: + gamma = tf.get_variable('gamma', [channnel], initializer=tf.constant_initializer(1.0)) + gamma = tf.reshape(gamma, new_shape) + else: + gamma = tf.ones([1] * ndims, name='gamma') + + return tf.nn.batch_normalization(inputdata, mean, var, beta, gamma, epsilon, name=name) + + @staticmethod + def instancenorm(inputdata, epsilon=1e-5, data_format='NHWC', use_affine=True, name=None): + """ + + :param name: + :param inputdata: + :param epsilon: + :param data_format: + :param use_affine: + :return: + """ + shape = inputdata.get_shape().as_list() + if len(shape) != 4: + raise ValueError("Input data of instancebn layer has to be 4D tensor") + + if data_format == 'NHWC': + axis = [1, 2] + ch = shape[3] + new_shape = [1, 1, 1, ch] + else: + axis = [2, 3] + ch = shape[1] + new_shape = [1, ch, 1, 1] + if ch is None: + raise ValueError("Input of instancebn require known channel!") + + mean, var = tf.nn.moments(inputdata, axis, keep_dims=True) + + if not use_affine: + return tf.divide(inputdata - mean, tf.sqrt(var + epsilon), name='output') + + beta = tf.get_variable('beta', [ch], initializer=tf.constant_initializer()) + beta = tf.reshape(beta, new_shape) + gamma = tf.get_variable('gamma', [ch], initializer=tf.constant_initializer(1.0)) + gamma = tf.reshape(gamma, new_shape) + return tf.nn.batch_normalization(inputdata, mean, var, beta, gamma, epsilon, name=name) + + @staticmethod + def dropout(inputdata, keep_prob, noise_shape=None, name=None): + """ + + :param name: + :param inputdata: + :param keep_prob: + :param noise_shape: + :return: + """ + return tf.nn.dropout(inputdata, keep_prob=keep_prob, noise_shape=noise_shape, name=name) + + @staticmethod + def fullyconnect(inputdata, out_dim, w_init=None, b_init=None, + use_bias=True, name=None): + """ + Fully-Connected layer, takes a N>1D tensor and returns a 2D tensor. + It is an equivalent of `tf.layers.dense` except for naming conventions. + + :param inputdata: a tensor to be flattened except for the first dimension. + :param out_dim: output dimension + :param w_init: initializer for w. Defaults to `variance_scaling_initializer`. + :param b_init: initializer for b. Defaults to zero + :param use_bias: whether to use bias. + :param name: + :return: tf.Tensor: a NC tensor named ``output`` with attribute `variables`. + """ + shape = inputdata.get_shape().as_list()[1:] + if None not in shape: + inputdata = tf.reshape(inputdata, [-1, int(np.prod(shape))]) + else: + inputdata = tf.reshape(inputdata, tf.stack([tf.shape(inputdata)[0], -1])) + + if w_init is None: + w_init = tf.contrib.layers.variance_scaling_initializer() + if b_init is None: + b_init = tf.constant_initializer() + + ret = tf.layers.dense(inputs=inputdata, activation=lambda x: tf.identity(x, name='output'), + use_bias=use_bias, name=name, + kernel_initializer=w_init, bias_initializer=b_init, + trainable=True, units=out_dim) + return ret + + @staticmethod + def layerbn(inputdata, is_training, name, scale=True): + """ + + :param inputdata: + :param is_training: + :param name: + :param scale: + :return: + """ + + # a = tf.layers.batch_normalization(inputs=inputdata, training=False, name=name, scale=scale, fused=False) + + return tf.layers.batch_normalization(inputs=inputdata, training=False, name=name, scale=scale) + # return inputdata + + + @staticmethod + def layergn(inputdata, name, group_size=32, esp=1e-5): + """ + + :param inputdata: + :param name: + :param group_size: + :param esp: + :return: + """ + with tf.variable_scope(name): + inputdata = tf.transpose(inputdata, [0, 3, 1, 2]) + n, c, h, w = inputdata.get_shape().as_list() + group_size = min(group_size, c) + inputdata = tf.reshape(inputdata, [-1, group_size, c // group_size, h, w]) + mean, var = tf.nn.moments(inputdata, [2, 3, 4], keep_dims=True) + inputdata = (inputdata - mean) / tf.sqrt(var + esp) + + # 每个通道的gamma和beta + gamma = tf.Variable(tf.constant(1.0, shape=[c]), dtype=tf.float32, name='gamma') + beta = tf.Variable(tf.constant(0.0, shape=[c]), dtype=tf.float32, name='beta') + gamma = tf.reshape(gamma, [1, c, 1, 1]) + beta = tf.reshape(beta, [1, c, 1, 1]) + + # 根据论文进行转换 [n, c, h, w, c] 到 [n, h, w, c] + output = tf.reshape(inputdata, [-1, c, h, w]) + output = output * gamma + beta + output = tf.transpose(output, [0, 2, 3, 1]) + + return output + + @staticmethod + def squeeze(inputdata, axis=None, name=None): + """ + + :param inputdata: + :param axis: + :param name: + :return: + """ + return tf.squeeze(input=inputdata, axis=axis, name=name) + + @staticmethod + def deconv2d(inputdata, out_channel, kernel_size, padding='SAME', + stride=1, w_init=None, b_init=None, + use_bias=True, activation=None, data_format='channels_last', + trainable=True, name=None): + """ + Packing the tensorflow conv2d function. + :param name: op name + :param inputdata: A 4D tensorflow tensor which ust have known number of channels, but can have other + unknown dimensions. + :param out_channel: number of output channel. + :param kernel_size: int so only support square kernel convolution + :param padding: 'VALID' or 'SAME' + :param stride: int so only support square stride + :param w_init: initializer for convolution weights + :param b_init: initializer for bias + :param activation: whether to apply a activation func to deconv result + :param use_bias: whether to use bias. + :param data_format: default set to NHWC according tensorflow + :return: tf.Tensor named ``output`` + """ + with tf.variable_scope(name): + in_shape = inputdata.get_shape().as_list() + channel_axis = 3 if data_format == 'channels_last' else 1 + in_channel = in_shape[channel_axis] + assert in_channel is not None, "[Deconv2D] Input cannot have unknown channel!" + + padding = padding.upper() + + if w_init is None: + w_init = tf.contrib.layers.variance_scaling_initializer() + if b_init is None: + b_init = tf.constant_initializer() + + ret = tf.layers.conv2d_transpose(inputs=inputdata, filters=out_channel, + kernel_size=kernel_size, + strides=stride, padding=padding, + data_format=data_format, + activation=activation, use_bias=use_bias, + kernel_initializer=w_init, + bias_initializer=b_init, trainable=trainable, + name=name) + return ret + + @staticmethod + def dilation_conv(input_tensor, k_size, out_dims, rate, padding='SAME', + w_init=None, b_init=None, use_bias=False, name=None): + """ + + :param input_tensor: + :param k_size: + :param out_dims: + :param rate: + :param padding: + :param w_init: + :param b_init: + :param use_bias: + :param name: + :return: + """ + with tf.variable_scope(name): + in_shape = input_tensor.get_shape().as_list() + in_channel = in_shape[3] + assert in_channel is not None, "[Conv2D] Input cannot have unknown channel!" + + padding = padding.upper() + + if isinstance(k_size, list): + filter_shape = [k_size[0], k_size[1]] + [in_channel, out_dims] + else: + filter_shape = [k_size, k_size] + [in_channel, out_dims] + + if w_init is None: + w_init = tf.contrib.layers.variance_scaling_initializer() + if b_init is None: + b_init = tf.constant_initializer() + + w = tf.get_variable('W', filter_shape, initializer=w_init) + b = None + + if use_bias: + b = tf.get_variable('b', [out_dims], initializer=b_init) + + conv = tf.nn.atrous_conv2d(value=input_tensor, filters=w, rate=rate, + padding=padding, name='dilation_conv') + + if use_bias: + ret = tf.add(conv, b) + else: + ret = conv + + return ret + + @staticmethod + def spatial_dropout(input_tensor, keep_prob, is_training, name, seed=1234): + """ + 空间dropout实现 + :param input_tensor: + :param keep_prob: + :param is_training: + :param name: + :param seed: + :return: + """ + + def f1(): + input_shape = input_tensor.get_shape().as_list() + noise_shape = tf.constant(value=[input_shape[0], 1, 1, input_shape[3]]) + return tf.nn.dropout(input_tensor, keep_prob, noise_shape, seed=seed, name="spatial_dropout") + + def f2(): + return input_tensor + + with tf.variable_scope(name_or_scope=name): + + output = tf.cond(is_training, f1, f2) + + return output + + @staticmethod + def lrelu(inputdata, name, alpha=0.2): + """ + + :param inputdata: + :param alpha: + :param name: + :return: + """ + with tf.variable_scope(name): + return tf.nn.relu(inputdata) - alpha * tf.nn.relu(-inputdata) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/vgg16_based_fcn.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/vgg16_based_fcn.py new file mode 100644 index 000000000..01ffb1f23 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/vgg16_based_fcn.py @@ -0,0 +1,394 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Implement VGG16 based fcn net for semantic segmentation +""" +import collections + +import tensorflow as tf + +from semantic_segmentation_zoo import cnn_basenet +from local_utils.config_utils import parse_config_utils + + +class VGG16FCN(cnn_basenet.CNNBaseModel): + """ + VGG 16 based fcn net for semantic segmentation + """ + def __init__(self, phase, cfg): + """ + + """ + super(VGG16FCN, self).__init__() + self._cfg = cfg + self._phase = phase + self._is_training = self._is_net_for_training() + self._net_intermediate_results = collections.OrderedDict() + self._class_nums = self._cfg.DATASET.NUM_CLASSES + + def _is_net_for_training(self): + """ + if the net is used for training or not + :return: + """ + if isinstance(self._phase, tf.Tensor): + phase = self._phase + else: + phase = tf.constant(self._phase, dtype=tf.string) + + return tf.equal(phase, tf.constant('train', dtype=tf.string)) + + def _vgg16_conv_stage(self, input_tensor, k_size, out_dims, name, + stride=1, pad='SAME', need_layer_norm=True): + """ + stack conv and activation in vgg16 + :param input_tensor: + :param k_size: + :param out_dims: + :param name: + :param stride: + :param pad: + :param need_layer_norm: + :return: + """ + with tf.variable_scope(name): + conv = self.conv2d( + inputdata=input_tensor, out_channel=out_dims, + kernel_size=k_size, stride=stride, + use_bias=False, padding=pad, name='conv' + ) + + if need_layer_norm: + bn = self.layerbn(inputdata=conv, is_training=self._is_training, name='bn') + + relu = self.relu(inputdata=bn, name='relu') + else: + relu = self.relu(inputdata=conv, name='relu') + + return relu + + def _decode_block(self, input_tensor, previous_feats_tensor, + out_channels_nums, name, kernel_size=4, + stride=2, use_bias=False, + previous_kernel_size=4, need_activate=True): + """ + + :param input_tensor: + :param previous_feats_tensor: + :param out_channels_nums: + :param kernel_size: + :param previous_kernel_size: + :param use_bias: + :param stride: + :param name: + :return: + """ + with tf.variable_scope(name_or_scope=name): + + deconv_weights_stddev = tf.sqrt( + tf.divide(tf.constant(2.0, tf.float32), + tf.multiply(tf.cast(previous_kernel_size * previous_kernel_size, tf.float32), + tf.cast(tf.shape(input_tensor)[3], tf.float32))) + ) + deconv_weights_init = tf.truncated_normal_initializer( + mean=0.0, stddev=deconv_weights_stddev) + + deconv = self.deconv2d( + inputdata=input_tensor, out_channel=out_channels_nums, kernel_size=kernel_size, + stride=stride, use_bias=use_bias, w_init=deconv_weights_init, + name='deconv' + ) + + deconv = self.layerbn(inputdata=deconv, is_training=self._is_training, name='deconv_bn') + + deconv = self.relu(inputdata=deconv, name='deconv_relu') + + fuse_feats = tf.add( + previous_feats_tensor, deconv, name='fuse_feats' + ) + + if need_activate: + + fuse_feats = self.layerbn( + inputdata=fuse_feats, is_training=self._is_training, name='fuse_gn' + ) + + fuse_feats = self.relu(inputdata=fuse_feats, name='fuse_relu') + + return fuse_feats + + def _vgg16_fcn_encode(self, input_tensor, name): + """ + + :param input_tensor: + :param name: + :return: + """ + with tf.variable_scope(name_or_scope=name): + # encode stage 1 + conv_1_1 = self._vgg16_conv_stage( + input_tensor=input_tensor, k_size=3, + out_dims=64, name='conv1_1', + need_layer_norm=True + ) + conv_1_2 = self._vgg16_conv_stage( + input_tensor=conv_1_1, k_size=3, + out_dims=64, name='conv1_2', + need_layer_norm=True + ) + self._net_intermediate_results['encode_stage_1_share'] = { + 'data': conv_1_2, + 'shape': conv_1_2.get_shape().as_list() + } + + # encode stage 2 + pool1 = self.maxpooling( + inputdata=conv_1_2, kernel_size=2, + stride=2, name='pool1' + ) + conv_2_1 = self._vgg16_conv_stage( + input_tensor=pool1, k_size=3, + out_dims=128, name='conv2_1', + need_layer_norm=True + ) + conv_2_2 = self._vgg16_conv_stage( + input_tensor=conv_2_1, k_size=3, + out_dims=128, name='conv2_2', + need_layer_norm=True + ) + self._net_intermediate_results['encode_stage_2_share'] = { + 'data': conv_2_2, + 'shape': conv_2_2.get_shape().as_list() + } + + # encode stage 3 + pool2 = self.maxpooling( + inputdata=conv_2_2, kernel_size=2, + stride=2, name='pool2' + ) + conv_3_1 = self._vgg16_conv_stage( + input_tensor=pool2, k_size=3, + out_dims=256, name='conv3_1', + need_layer_norm=True + ) + conv_3_2 = self._vgg16_conv_stage( + input_tensor=conv_3_1, k_size=3, + out_dims=256, name='conv3_2', + need_layer_norm=True + ) + conv_3_3 = self._vgg16_conv_stage( + input_tensor=conv_3_2, k_size=3, + out_dims=256, name='conv3_3', + need_layer_norm=True + ) + self._net_intermediate_results['encode_stage_3_share'] = { + 'data': conv_3_3, + 'shape': conv_3_3.get_shape().as_list() + } + + # encode stage 4 + pool3 = self.maxpooling( + inputdata=conv_3_3, kernel_size=2, + stride=2, name='pool3' + ) + conv_4_1 = self._vgg16_conv_stage( + input_tensor=pool3, k_size=3, + out_dims=512, name='conv4_1', + need_layer_norm=True + ) + conv_4_2 = self._vgg16_conv_stage( + input_tensor=conv_4_1, k_size=3, + out_dims=512, name='conv4_2', + need_layer_norm=True + ) + conv_4_3 = self._vgg16_conv_stage( + input_tensor=conv_4_2, k_size=3, + out_dims=512, name='conv4_3', + need_layer_norm=True + ) + self._net_intermediate_results['encode_stage_4_share'] = { + 'data': conv_4_3, + 'shape': conv_4_3.get_shape().as_list() + } + + # encode stage 5 for binary segmentation + pool4 = self.maxpooling( + inputdata=conv_4_3, kernel_size=2, + stride=2, name='pool4' + ) + conv_5_1_binary = self._vgg16_conv_stage( + input_tensor=pool4, k_size=3, + out_dims=512, name='conv5_1_binary', + need_layer_norm=True + ) + conv_5_2_binary = self._vgg16_conv_stage( + input_tensor=conv_5_1_binary, k_size=3, + out_dims=512, name='conv5_2_binary', + need_layer_norm=True + ) + conv_5_3_binary = self._vgg16_conv_stage( + input_tensor=conv_5_2_binary, k_size=3, + out_dims=512, name='conv5_3_binary', + need_layer_norm=True + ) + self._net_intermediate_results['encode_stage_5_binary'] = { + 'data': conv_5_3_binary, + 'shape': conv_5_3_binary.get_shape().as_list() + } + + # encode stage 5 for instance segmentation + conv_5_1_instance = self._vgg16_conv_stage( + input_tensor=pool4, k_size=3, + out_dims=512, name='conv5_1_instance', + need_layer_norm=True + ) + conv_5_2_instance = self._vgg16_conv_stage( + input_tensor=conv_5_1_instance, k_size=3, + out_dims=512, name='conv5_2_instance', + need_layer_norm=True + ) + conv_5_3_instance = self._vgg16_conv_stage( + input_tensor=conv_5_2_instance, k_size=3, + out_dims=512, name='conv5_3_instance', + need_layer_norm=True + ) + self._net_intermediate_results['encode_stage_5_instance'] = { + 'data': conv_5_3_instance, + 'shape': conv_5_3_instance.get_shape().as_list() + } + + return + + def _vgg16_fcn_decode(self, name): + """ + + :return: + """ + with tf.variable_scope(name): + + # decode part for binary segmentation + with tf.variable_scope(name_or_scope='binary_seg_decode'): + + decode_stage_5_binary = self._net_intermediate_results['encode_stage_5_binary']['data'] + + decode_stage_4_fuse = self._decode_block( + input_tensor=decode_stage_5_binary, + previous_feats_tensor=self._net_intermediate_results['encode_stage_4_share']['data'], + name='decode_stage_4_fuse', out_channels_nums=512, previous_kernel_size=3 + ) + decode_stage_3_fuse = self._decode_block( + input_tensor=decode_stage_4_fuse, + previous_feats_tensor=self._net_intermediate_results['encode_stage_3_share']['data'], + name='decode_stage_3_fuse', out_channels_nums=256 + ) + decode_stage_2_fuse = self._decode_block( + input_tensor=decode_stage_3_fuse, + previous_feats_tensor=self._net_intermediate_results['encode_stage_2_share']['data'], + name='decode_stage_2_fuse', out_channels_nums=128 + ) + decode_stage_1_fuse = self._decode_block( + input_tensor=decode_stage_2_fuse, + previous_feats_tensor=self._net_intermediate_results['encode_stage_1_share']['data'], + name='decode_stage_1_fuse', out_channels_nums=64 + ) + binary_final_logits_conv_weights_stddev = tf.sqrt( + tf.divide(tf.constant(2.0, tf.float32), + tf.multiply(4.0 * 4.0, + tf.cast(tf.shape(decode_stage_1_fuse)[3], tf.float32))) + ) + binary_final_logits_conv_weights_init = tf.truncated_normal_initializer( + mean=0.0, stddev=binary_final_logits_conv_weights_stddev) + + binary_final_logits = self.conv2d( + inputdata=decode_stage_1_fuse, + out_channel=self._class_nums, + kernel_size=1, use_bias=False, + w_init=binary_final_logits_conv_weights_init, + name='binary_final_logits' + ) + + self._net_intermediate_results['binary_segment_logits'] = { + 'data': binary_final_logits, + 'shape': binary_final_logits.get_shape().as_list() + } + + with tf.variable_scope(name_or_scope='instance_seg_decode'): + + decode_stage_5_instance = self._net_intermediate_results['encode_stage_5_instance']['data'] + + decode_stage_4_fuse = self._decode_block( + input_tensor=decode_stage_5_instance, + previous_feats_tensor=self._net_intermediate_results['encode_stage_4_share']['data'], + name='decode_stage_4_fuse', out_channels_nums=512, previous_kernel_size=3) + + decode_stage_3_fuse = self._decode_block( + input_tensor=decode_stage_4_fuse, + previous_feats_tensor=self._net_intermediate_results['encode_stage_3_share']['data'], + name='decode_stage_3_fuse', out_channels_nums=256) + + decode_stage_2_fuse = self._decode_block( + input_tensor=decode_stage_3_fuse, + previous_feats_tensor=self._net_intermediate_results['encode_stage_2_share']['data'], + name='decode_stage_2_fuse', out_channels_nums=128) + + decode_stage_1_fuse = self._decode_block( + input_tensor=decode_stage_2_fuse, + previous_feats_tensor=self._net_intermediate_results['encode_stage_1_share']['data'], + name='decode_stage_1_fuse', out_channels_nums=64, need_activate=False) + + self._net_intermediate_results['instance_segment_logits'] = { + 'data': decode_stage_1_fuse, + 'shape': decode_stage_1_fuse.get_shape().as_list() + } + + def build_model(self, input_tensor, name, reuse=False): + """ + + :param input_tensor: + :param name: + :param reuse: + :return: + """ + with tf.variable_scope(name_or_scope=name, reuse=reuse): + # vgg16 fcn encode part + self._vgg16_fcn_encode(input_tensor=input_tensor, name='vgg16_encode_module') + # vgg16 fcn decode part + self._vgg16_fcn_decode(name='vgg16_decode_module') + + return self._net_intermediate_results + + +if __name__ == '__main__': + """ + test code + """ + test_in_tensor = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input') + model = VGG16FCN(phase='train', cfg=parse_config_utils.lanenet_cfg) + ret = model.build_model(test_in_tensor, name='vgg16fcn') + for layer_name, layer_info in ret.items(): + print('layer name: {:s} shape: {}'.format(layer_name, layer_info['shape'])) -- Gitee From 7b683e406bc7538dd19c09b063b6f767b9fe1f0a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Sat, 3 Sep 2022 10:05:13 +0000 Subject: [PATCH 13/19] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 溺水小航母 <1396755411@qq.com> --- .../cv/MNN-LANENET_ID1251_for_ACL/README.md | 138 ++++++ .../cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py | 78 +++ .../MNN-LANENET_ID1251_for_ACL/eval_ckpt.py | 190 +++++++ .../cv/MNN-LANENET_ID1251_for_ACL/eval_om.py | 79 +++ .../cv/MNN-LANENET_ID1251_for_ACL/eval_pb.py | 169 +++++++ .../inference time.jpg.jpg | Bin 0 -> 23050 bytes .../lanenet_postprocess.py | 463 ++++++++++++++++++ .../metric_ckpt.jpg | Bin 0 -> 2247 bytes .../MNN-LANENET_ID1251_for_ACL/metric_om.jpg | Bin 0 -> 2978 bytes .../MNN-LANENET_ID1251_for_ACL/metric_pb.jpg | Bin 0 -> 2135 bytes 10 files changed, 1117 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_pb.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/inference time.jpg.jpg create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_postprocess.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/metric_ckpt.jpg create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/metric_om.jpg create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/metric_pb.jpg diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md new file mode 100644 index 000000000..285a1b468 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md @@ -0,0 +1,138 @@ +## 模型功能 + + 车道线检测 + + + + +## pb模型 + +``` + +python3.7 ckpt2pb.py +``` + +convert_ckpt_into_pb_file（）函数，pb模型 PATH=./pretrained_model/eval.pb + +ckpt：链接：https://pan.baidu.com/s/1E6W7OHgIvi-B3TPRDl6OhA?pwd=6666 +提取码：6666 + + +## om模型 + + +使用ATC模型转换工具进行模型转换时可以参考如下指令: + + +``` + +atc --model=/usr/pb2om/eval.pb + + --framework=3 + + --output=/usr/pb2om/frozen + + --soc_version=Ascend910 + + out_nodes="lanenet/binary_seg_out:0;lanenet/instance_seg_out:0" + + --input_shape="input_tensor:1,256,512,3" + + --input_format=NHWC +``` + + + +## 使用msame工具推理 + + +参考 https://gitee.com/ascend/tools/tree/master/msame, 获取msame推理工具及使用方法。 + +获取到msame可执行文件之后，进行推理测试。 + + + +## 数据集转换bin + +``` + +python3.7 eval_pb.py +``` + +freeze_graph_test（）函数 img_feed转test_img.bin + +## 推理测试 + + +使用msame推理工具，参考如下命令，发起推理测试： + + +``` + +./msame --model "/home/test_user05/pb2om/frozen.om" + + --input "/home/test_user05/pb2om/test_img.bin" + + --output "/home/test_user05/pb2om/" + + --outfmt BIN + + --loop 1 +``` + + +## 脚本和示例代码 + +输入数据：链接：https://pan.baidu.com/s/1bwZR3FfhP18mLMa44781Gw?pwd=0000 +提取码：0000 +├── eval_data + +1.pb预测 + +├── eval_pb.py //主代码 + +├── lanenet_model + + ├── lanenet.py //LANENET模型 + +├── README.md //代码说明文档 + + + +2.om预测 + +├── eval_om.py //主代码 + +├── lanenet_model + + ├── lanenet.py //LANENET模型 + +├── README.md //代码说明文档 + + + +3.ckpt转pb + +├── ckpt2pb.py + + + +##推理输出计算精度 + +``` + +python3.7 eval_om.py +``` + +## 推理精度 +定量指标采用准确率，精度为0.965，精度达标。 +定性指标采用论文中可视化binary segmentation和instance segmentation，输出在eval_output下 + +| gpu | npu |原论文 |推理 | +|-------|------|-------|-------| +| 96.5 | 96.5 | 96.4 |96.5 | + + + +## 推理性能 +inference time.jpg，性能达标 \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py new file mode 100644 index 000000000..8710bca83 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py @@ -0,0 +1,78 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Freeze Lanenet model into frozen pb file +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import os + +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + + +def convert_ckpt_into_pb_file(ckpt_file_path, pb_file_path): + """ + + :param ckpt_file_path: + :param pb_file_path: + :return: + """ + with tf.Session() as sess: + + saver = tf.train.import_meta_graph(ckpt_file_path + '.meta', clear_devices=True) + input_graph_def = tf.get_default_graph().as_graph_def() + + binary_seg_node = 'lanenet/binary_seg_out' + instance_seg_node = 'lanenet/instance_seg_out' + + saver.restore(sess, ckpt_file_path) # 恢复图并得到数据 + output_graph_def = tf.graph_util.convert_variables_to_constants( # 模型持久化，将变量值固定 + sess=sess, + input_graph_def=input_graph_def, + output_node_names=['input_tensor', binary_seg_node, instance_seg_node]) + + with tf.gfile.GFile(pb_file_path, "wb") as f: # 保存模型 + f.write(output_graph_def.SerializeToString()) # 序列化输出 + print("%d ops in the final graph." % len(output_graph_def.node)) + + +if __name__ == '__main__': + """ + test code + """ + + ckpt_path = './eval_ckpt/eval.ckpt' + pb_save_path = './pretrained_model/eval.pb' + + convert_ckpt_into_pb_file( + ckpt_file_path=ckpt_path, + pb_file_path=pb_save_path + ) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py new file mode 100644 index 000000000..993c5719c --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py @@ -0,0 +1,190 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +test LaneNet model on single image +""" +import argparse +import os.path as ops +import time + +import cv2 +import numpy as np +import tensorflow as tf + +from lanenet_model import lanenet +from lanenet_model import lanenet_postprocess +from local_utils.config_utils import parse_config_utils +from local_utils.log_util import init_logger + +CFG = parse_config_utils.lanenet_cfg +LOG = init_logger.get_logger(log_file_name_prefix='lanenet_test') + +gt_path = './eval_data/gt.png' + + +def init_args(): + """ + + :return: + """ + parser = argparse.ArgumentParser() + parser.add_argument('--image_path', default='./eval_data/test_img.jpg', + type=str, help='The image path or the src image save dir') + parser.add_argument('--weights_path', default='./pretrained_model/ckpt/tusimple_lanenet.ckpt', + type=str, help='The model weights path') + parser.add_argument('--with_lane_fit', type=args_str2bool, help='If need to do lane fit', default=False) + + return parser.parse_args() + + +def args_str2bool(arg_value): + """ + + :param arg_value: + :return: + """ + if arg_value.lower() in ('yes', 'true', 't', 'y', '1'): + return True + + elif arg_value.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Unsupported value encountered.') + + +def minmax_scale(input_arr): + """ + + :param input_arr: + :return: + """ + min_val = np.min(input_arr) + max_val = np.max(input_arr) + + output_arr = (input_arr - min_val) * 255.0 / (max_val - min_val) + + return output_arr + + +def test_lanenet(image_path, gt_path, weights_path, with_lane_fit=False): + """ + + :param image_path: + :param weights_path: + :param with_lane_fit: + :return: + """ + assert ops.exists(image_path), '{:s} not exist'.format(image_path) + + LOG.info('Start reading image and preprocessing') + t_start = time.time() + image = cv2.imread(image_path, cv2.IMREAD_COLOR) + image_vis = image + image = cv2.resize(image, (512, 256), interpolation=cv2.INTER_LINEAR) + image = image / 127.5 - 1.0 + LOG.info('Image load complete, cost time: {:.5f}s'.format(time.time() - t_start)) + + input_tensor_0 = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input_tensor') + input_tensor = tf.identity(input_tensor_0, name='input_tensor') + net = lanenet.LaneNet(phase='test', cfg=CFG) + binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='LaneNet') + + with tf.variable_scope('lanenet/'): + binary_seg_ret = tf.identity(binary_seg_ret, name='binary_seg_out') + instance_seg_ret = tf.identity(instance_seg_ret, name='instance_seg_out') + + postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG) + + sess = tf.Session() + + # define moving average version of the learned variables for eval + with tf.variable_scope(name_or_scope='moving_avg'): + variable_averages = tf.train.ExponentialMovingAverage( + CFG.SOLVER.MOVING_AVE_DECAY) + variables_to_restore = variable_averages.variables_to_restore() + + # define saver + saver = tf.train.Saver(variables_to_restore) + + with sess.as_default(): + saver.restore(sess=sess, save_path=weights_path) + image = np.expand_dims(image, axis=0) + binary_seg_image, instance_seg_image = sess.run( + [binary_seg_ret, instance_seg_ret], + feed_dict={input_tensor: image} + ) + + postprocess_result = postprocessor.postprocess( + binary_seg_result=binary_seg_image[0], + instance_seg_result=instance_seg_image[0], + source_image=image_vis, + with_lane_fit=True, + data_source='tusimple' + ) + mask_image = postprocess_result['mask_image'] + src_image = postprocess_result['source_image'] + + # -------------- 计算准确率 ------------------ # + gt = cv2.imread(gt_path, cv2.IMREAD_COLOR) + gt_trans = cv2.resize(gt, (512, 256), interpolation=cv2.INTER_LINEAR) + + gt_gray = cv2.cvtColor(gt_trans, cv2.COLOR_BGR2GRAY) + mask_image_gray = cv2.cvtColor(mask_image, cv2.COLOR_BGR2GRAY) + WIDTH = mask_image_gray.shape[0] + HIGTH = mask_image_gray.shape[1] + tp_count = 0 + tn_count = 0 + for i in range(WIDTH): + for j in range(HIGTH): + if mask_image_gray[i, j] != 0 and gt_gray[i, j] != 0: + tp_count = tp_count + 1 + if mask_image_gray[i, j] == 0 and gt_gray[i, j] == 0: + tn_count = tn_count + 1 + Accuracy = (int(tp_count) + int(tn_count)) / (int(WIDTH) * int(HIGTH)) + + print("\n# Metric_ckpt " + "\n Accuracy：{:.3f}".format(Accuracy)) + + cv2.imwrite('./eval_output/mask_ckpt.jpg', mask_image) + cv2.imwrite('./eval_output/src_ckpt.jpg', src_image) + + saver.save(sess, './eval_ckpt/eval.ckpt') + + sess.close() + + return + + +if __name__ == '__main__': + """ + test code + """ + # init args + args = init_args() + + test_lanenet(args.image_path, gt_path, args.weights_path, with_lane_fit=args.with_lane_fit) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py new file mode 100644 index 000000000..98a8e5373 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py @@ -0,0 +1,79 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import lanenet_postprocess +from local_utils.config_utils import parse_config_utils +import cv2 + +CFG = parse_config_utils.lanenet_cfg + +image_path = './eval_data/test_img.jpg' +image_vis = cv2.imread(image_path, cv2.IMREAD_COLOR) + +b_out_path = './eval_data/frozen_output_0.bin' +i_out_path = './eval_data/frozen_output_1.bin' +b_out = np.fromfile(b_out_path, dtype=np.int64) +i_out = np.fromfile(i_out_path, dtype=np.float32) +b_out = np.reshape(b_out, (1, 256, 512)) +i_out = np.reshape(i_out, (1, 256, 512, 4)) + +postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG) +postprocess_result = postprocessor.postprocess( + binary_seg_result=b_out[0], + instance_seg_result=i_out[0], + source_image=image_vis, + with_lane_fit=True, + data_source='tusimple' + ) +mask_image = postprocess_result['mask_image'] +src_image = postprocess_result['source_image'] +gt_path = './eval_data/gt.png' +gt = cv2.imread(gt_path, cv2.IMREAD_COLOR) +gt_trans = cv2.resize(gt, (512, 256), interpolation=cv2.INTER_LINEAR) + +# -------------- 计算准确率 ------------------ # +gt_gray = cv2.cvtColor(gt_trans, cv2.COLOR_BGR2GRAY) +mask_image_gray = cv2.cvtColor(mask_image, cv2.COLOR_BGR2GRAY) +WIDTH = mask_image_gray.shape[0] +HIGTH = mask_image_gray.shape[1] +tp_count = 0 +tn_count = 0 +for i in range(WIDTH): + for j in range(HIGTH): + if mask_image_gray[i, j] != 0 and gt_gray[i, j] != 0: + tp_count = tp_count + 1 + if mask_image_gray[i, j] == 0 and gt_gray[i, j] == 0: + tn_count = tn_count + 1 +Accuracy = (int(tp_count) + int(tn_count)) / (int(WIDTH) * int(HIGTH)) + +print("\n# Metric_om " + "\n Accuracy：{:.3f}".format(Accuracy)) + +cv2.imwrite('./eval_output/mask_om.jpg', mask_image) +cv2.imwrite('./eval_output/src_om.jpg', src_image) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_pb.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_pb.py new file mode 100644 index 000000000..5584b4ad7 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_pb.py @@ -0,0 +1,169 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import os +import tensorflow as tf +from tensorflow.python.platform import gfile +import lanenet_postprocess +from local_utils.config_utils import parse_config_utils +import cv2 + + +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +image_path = './eval_data/test_img.jpg' + +test_img = cv2.imread(image_path, cv2.IMREAD_COLOR) +image_vis = test_img +test_img_trans = cv2.resize(test_img, (512, 256), interpolation=cv2.INTER_LINEAR) +test_img_trans = test_img_trans / 127.5 - 1.0 + +gt_path = './eval_data/gt.png' +gt = cv2.imread(gt_path, cv2.IMREAD_COLOR) +gt_trans = cv2.resize(gt, (512, 256), interpolation=cv2.INTER_LINEAR) + +CFG = parse_config_utils.lanenet_cfg + +pb_path = './pretrained_model/eval.pb' + +# ----------- 查看 pbnode ----------- +# read graph definition +f = gfile.FastGFile(pb_path, "rb") +gd = graph_def = tf.GraphDef() +graph_def.ParseFromString(f.read()) +tf.import_graph_def(graph_def, name='') +for i, n in enumerate(graph_def.node): + print("=====node====") + print("Name of the node - %s" % n.name) +# -------------------------------- + + +def minmax_scale(input_arr): + """ + + :param input_arr: + :return: + """ + min_val = np.min(input_arr) + max_val = np.max(input_arr) + + output_arr = (input_arr - min_val) * 255.0 / (max_val - min_val) + + return output_arr + + +def freeze_graph_test(pb_path, img_path): + # :param pb_path:pb文件的路径 + # :param test_img:测试图片的路径 + # :return: + + f = gfile.FastGFile(pb_path, "rb") + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + + with tf.Graph().as_default(): + output_graph_def = tf.GraphDef() + with open(pb_path, "rb") as f: + output_graph_def.ParseFromString(f.read()) + tf.import_graph_def(graph_def, name="") + + with tf.Session() as sess: + + sess.run(tf.global_variables_initializer()) + + # 定义输入输出节点名称 + input_node = sess.graph.get_tensor_by_name("input_tensor:0") + binary_output_node = sess.graph.get_tensor_by_name("lanenet/binary_seg_out:0") + pixel_embedding_output_node = sess.graph.get_tensor_by_name("lanenet/instance_seg_out:0") + img_feed = test_img_trans.astype(np.float32) + img_feed = np.expand_dims(img_feed, axis=0) + + # img_bin = img_feed + # img_bin.tofile("./eval_data/test_img.bin") + + binary_output, pixel_embedding_output = sess.run([binary_output_node, pixel_embedding_output_node], + feed_dict={input_node: img_feed}) + + postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG) + postprocess_result = postprocessor.postprocess( + binary_seg_result=binary_output[0], + instance_seg_result=pixel_embedding_output[0], + source_image=image_vis, + with_lane_fit=True, + data_source='tusimple' + ) + mask_image = postprocess_result['mask_image'] + src_image = postprocess_result['source_image'] + # if with_lane_fit: + # lane_params = postprocess_result['fit_params'] + # LOG.info('Model have fitted {:d} lanes'.format(len(lane_params))) + # for i in range(len(lane_params)): + # LOG.info('Fitted 2-order lane {:d} curve param: {}'.format(i + 1, lane_params[i])) + + for i in range(CFG.MODEL.EMBEDDING_FEATS_DIMS): + pixel_embedding_output[:, :, i] = minmax_scale(pixel_embedding_output[:, :, i]) + embedding_image = np.array(pixel_embedding_output, np.uint8) + + # plt.figure('mask_image') + # plt.imshow(mask_image[:, :, (2, 1, 0)]) + # plt.show() + # plt.figure('src_image') + # plt.imshow(image_vis[:, :, (2, 1, 0)]) + # plt.figure('instance_image') + # plt.imshow(embedding_image[:, :, (2, 1, 0)]) + # plt.figure('binary_image') + # plt.imshow(binary_output * 255, cmap='gray') + # plt.show() + + # -------------- 计算准确率 ------------------ # + gt_gray = cv2.cvtColor(gt_trans, cv2.COLOR_BGR2GRAY) + mask_image_gray = cv2.cvtColor(mask_image, cv2.COLOR_BGR2GRAY) + WIDTH = mask_image_gray.shape[0] + HIGTH = mask_image_gray.shape[1] + tp_count = 0 + tn_count = 0 + for i in range(WIDTH): + for j in range(HIGTH): + if mask_image_gray[i, j] != 0 and gt_gray[i, j] != 0: + tp_count = tp_count + 1 + if mask_image_gray[i, j] == 0 and gt_gray[i, j] == 0: + tn_count = tn_count + 1 + Accuracy = (int(tp_count) + int(tn_count)) / (int(WIDTH) * int(HIGTH)) + + print("\n# Metric_pb " + "\n Accuracy：{:.3f}".format(Accuracy)) + + cv2.imwrite('./eval_output/mask_pb.jpg', mask_image) + cv2.imwrite('./eval_output/src_pb.jpg', src_image) + + +if __name__ == '__main__': + # 测试pb模型 + img_path = './eval_data/test_img.npy' + freeze_graph_test(pb_path=pb_path, img_path=img_path) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/inference time.jpg.jpg b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/inference time.jpg.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1dbe0e6e74cfff7198205dd82800bf688d4e010b 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zwWb|Bd%pll-B~dk^F8~5xmM@})XW_XVFr@f-wqRYP2H~6L=6S@S;5RP>pJZ4lD%A3 SnW9sd33yW$Stjh)kN*JaXvo6= literal 0 HcmV?d00001 diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_postprocess.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_postprocess.py new file mode 100644 index 000000000..2fe33f9ad --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_postprocess.py @@ -0,0 +1,463 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +LaneNet model post process +""" +import os.path as ops +import math + +import cv2 +import numpy as np +import loguru +from sklearn.cluster import DBSCAN +from sklearn.preprocessing import StandardScaler + +LOG = loguru.logger + + +def _morphological_process(image, kernel_size=5): + """ + morphological process to fill the hole in the binary segmentation result + :param image: + :param kernel_size: + :return: + """ + if len(image.shape) == 3: + raise ValueError('Binary segmentation result image should be a single channel image') + + if image.dtype is not np.uint8: + image = np.array(image, np.uint8) + + kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size)) + + # close operation fille hole + closing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1) + + return closing + + +def _connect_components_analysis(image): + """ + connect components analysis to remove the small components + :param image: + :return: + """ + if len(image.shape) == 3: + gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + else: + gray_image = image + + return cv2.connectedComponentsWithStats(gray_image, connectivity=8, ltype=cv2.CV_32S) + + +class _LaneFeat(object): + """ + + """ + def __init__(self, feat, coord, class_id=-1): + """ + lane feat object + :param feat: lane embeddng feats [feature_1, feature_2, ...] + :param coord: lane coordinates [x, y] + :param class_id: lane class id + """ + self._feat = feat + self._coord = coord + self._class_id = class_id + + @property + def feat(self): + """ + + :return: + """ + return self._feat + + @feat.setter + def feat(self, value): + """ + + :param value: + :return: + """ + if not isinstance(value, np.ndarray): + value = np.array(value, dtype=np.float64) + + if value.dtype != np.float32: + value = np.array(value, dtype=np.float64) + + self._feat = value + + @property + def coord(self): + """ + + :return: + """ + return self._coord + + @coord.setter + def coord(self, value): + """ + + :param value: + :return: + """ + if not isinstance(value, np.ndarray): + value = np.array(value) + + if value.dtype != np.int32: + value = np.array(value, dtype=np.int32) + + self._coord = value + + @property + def class_id(self): + """ + + :return: + """ + return self._class_id + + @class_id.setter + def class_id(self, value): + """ + + :param value: + :return: + """ + if not isinstance(value, np.int64): + raise ValueError('Class id must be integer') + + self._class_id = value + + +class _LaneNetCluster(object): + """ + Instance segmentation result cluster + """ + + def __init__(self, cfg): + """ + + """ + self._color_map = [np.array([255, 0, 0]), + np.array([0, 255, 0]), + np.array([0, 0, 255]), + np.array([125, 125, 0]), + np.array([0, 125, 125]), + np.array([125, 0, 125]), + np.array([50, 100, 50]), + np.array([100, 50, 100])] + self._cfg = cfg + + def _embedding_feats_dbscan_cluster(self, embedding_image_feats): + """ + dbscan cluster + :param embedding_image_feats: + :return: + """ + db = DBSCAN(eps=self._cfg.POSTPROCESS.DBSCAN_EPS, min_samples=self._cfg.POSTPROCESS.DBSCAN_MIN_SAMPLES) + try: + features = StandardScaler().fit_transform(embedding_image_feats) + db.fit(features) + except Exception as err: + LOG.error(err) + ret = { + 'origin_features': None, + 'cluster_nums': 0, + 'db_labels': None, + 'unique_labels': None, + 'cluster_center': None + } + return ret + db_labels = db.labels_ + unique_labels = np.unique(db_labels) + + num_clusters = len(unique_labels) + cluster_centers = db.components_ + + ret = { + 'origin_features': features, + 'cluster_nums': num_clusters, + 'db_labels': db_labels, + 'unique_labels': unique_labels, + 'cluster_center': cluster_centers + } + + return ret + + @staticmethod + def _get_lane_embedding_feats(binary_seg_ret, instance_seg_ret): + """ + get lane embedding features according the binary seg result + :param binary_seg_ret: + :param instance_seg_ret: + :return: + """ + idx = np.where(binary_seg_ret == 255) + lane_embedding_feats = instance_seg_ret[idx] + lane_coordinate = np.vstack((idx[1], idx[0])).transpose() + + assert lane_embedding_feats.shape[0] == lane_coordinate.shape[0] + + ret = { + 'lane_embedding_feats': lane_embedding_feats, + 'lane_coordinates': lane_coordinate + } + + return ret + + def apply_lane_feats_cluster(self, binary_seg_result, instance_seg_result): + """ + + :param binary_seg_result: + :param instance_seg_result: + :return: + """ + # get embedding feats and coords + get_lane_embedding_feats_result = self._get_lane_embedding_feats( + binary_seg_ret=binary_seg_result, + instance_seg_ret=instance_seg_result + ) + + # dbscan cluster + dbscan_cluster_result = self._embedding_feats_dbscan_cluster( + embedding_image_feats=get_lane_embedding_feats_result['lane_embedding_feats'] + ) + + mask = np.zeros(shape=[binary_seg_result.shape[0], binary_seg_result.shape[1], 3], dtype=np.uint8) + db_labels = dbscan_cluster_result['db_labels'] + unique_labels = dbscan_cluster_result['unique_labels'] + coord = get_lane_embedding_feats_result['lane_coordinates'] + + if db_labels is None: + return None, None + + lane_coords = [] + for index, label in enumerate(unique_labels.tolist()): + if label == -1: + continue + idx = np.where(db_labels == label) + pix_coord_idx = tuple((coord[idx][:, 1], coord[idx][:, 0])) + mask[pix_coord_idx] = self._color_map[index] + lane_coords.append(coord[idx]) + + return mask, lane_coords + + +class LaneNetPostProcessor(object): + """ + lanenet post process for lane generation + """ + def __init__(self, cfg, ipm_remap_file_path='./eval_data/tusimple_ipm_remap.yml'): + """ + + :param ipm_remap_file_path: ipm generate file path + """ + assert ops.exists(ipm_remap_file_path), '{:s} not exist'.format(ipm_remap_file_path) + + self._cfg = cfg + self._cluster = _LaneNetCluster(cfg=cfg) + self._ipm_remap_file_path = ipm_remap_file_path + + remap_file_load_ret = self._load_remap_matrix() + self._remap_to_ipm_x = remap_file_load_ret['remap_to_ipm_x'] + self._remap_to_ipm_y = remap_file_load_ret['remap_to_ipm_y'] + + self._color_map = [np.array([255, 0, 0]), + np.array([0, 255, 0]), + np.array([0, 0, 255]), + np.array([125, 125, 0]), + np.array([0, 125, 125]), + np.array([125, 0, 125]), + np.array([50, 100, 50]), + np.array([100, 50, 100])] + + def _load_remap_matrix(self): + """ + + :return: + """ + fs = cv2.FileStorage(self._ipm_remap_file_path, cv2.FILE_STORAGE_READ) + + remap_to_ipm_x = fs.getNode('remap_ipm_x').mat() + remap_to_ipm_y = fs.getNode('remap_ipm_y').mat() + + ret = { + 'remap_to_ipm_x': remap_to_ipm_x, + 'remap_to_ipm_y': remap_to_ipm_y, + } + + fs.release() + + return ret + + def postprocess(self, binary_seg_result, instance_seg_result=None, + min_area_threshold=100, source_image=None, + with_lane_fit=True, data_source='tusimple'): + """ + + :param binary_seg_result: + :param instance_seg_result: + :param min_area_threshold: + :param source_image: + :param with_lane_fit: + :param data_source: + :return: + """ + # convert binary_seg_result + binary_seg_result = np.array(binary_seg_result * 255, dtype=np.uint8) + + # apply image morphology operation to fill in the hold and reduce the small area + morphological_ret = _morphological_process(binary_seg_result, kernel_size=5) + + connect_components_analysis_ret = _connect_components_analysis(image=morphological_ret) + + labels = connect_components_analysis_ret[1] + stats = connect_components_analysis_ret[2] + for index, stat in enumerate(stats): + if stat[4] <= min_area_threshold: + idx = np.where(labels == index) + morphological_ret[idx] = 0 + + # apply embedding features cluster + mask_image, lane_coords = self._cluster.apply_lane_feats_cluster( + binary_seg_result=morphological_ret, + instance_seg_result=instance_seg_result + ) + + if mask_image is None: + return { + 'mask_image': None, + 'fit_params': None, + 'source_image': None, + } + if not with_lane_fit: + tmp_mask = cv2.resize( + mask_image, + dsize=(source_image.shape[1], source_image.shape[0]), + interpolation=cv2.INTER_NEAREST + ) + source_image = cv2.addWeighted(source_image, 0.6, tmp_mask, 0.4, 0.0, dst=source_image) + return { + 'mask_image': mask_image, + 'fit_params': None, + 'source_image': source_image, + } + + # lane line fit + fit_params = [] + src_lane_pts = [] # lane pts every single lane + for lane_index, coords in enumerate(lane_coords): + if data_source == 'tusimple': + tmp_mask = np.zeros(shape=(720, 1280), dtype=np.uint8) + tmp_mask[tuple((np.int_(coords[:, 1] * 720 / 256), np.int_(coords[:, 0] * 1280 / 512)))] = 255 + else: + raise ValueError('Wrong data source now only support tusimple') + tmp_ipm_mask = cv2.remap( + tmp_mask, + self._remap_to_ipm_x, + self._remap_to_ipm_y, + interpolation=cv2.INTER_NEAREST + ) + nonzero_y = np.array(tmp_ipm_mask.nonzero()[0]) + nonzero_x = np.array(tmp_ipm_mask.nonzero()[1]) + + fit_param = np.polyfit(nonzero_y, nonzero_x, 2) + fit_params.append(fit_param) + + [ipm_image_height, ipm_image_width] = tmp_ipm_mask.shape + plot_y = np.linspace(10, ipm_image_height, ipm_image_height - 10) + fit_x = fit_param[0] * plot_y ** 2 + fit_param[1] * plot_y + fit_param[2] + # fit_x = fit_param[0] * plot_y ** 3 + fit_param[1] * plot_y ** 2 + fit_param[2] * plot_y + fit_param[3] + + lane_pts = [] + for index in range(0, plot_y.shape[0], 5): + src_x = self._remap_to_ipm_x[ + int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))] + if src_x <= 0: + continue + src_y = self._remap_to_ipm_y[ + int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))] + src_y = src_y if src_y > 0 else 0 + + lane_pts.append([src_x, src_y]) + + src_lane_pts.append(lane_pts) + + # tusimple test data sample point along y axis every 10 pixels + source_image_width = source_image.shape[1] + for index, single_lane_pts in enumerate(src_lane_pts): + single_lane_pt_x = np.array(single_lane_pts, dtype=np.float32)[:, 0] + single_lane_pt_y = np.array(single_lane_pts, dtype=np.float32)[:, 1] + if data_source == 'tusimple': + start_plot_y = 240 + end_plot_y = 720 + else: + raise ValueError('Wrong data source now only support tusimple') + step = int(math.floor((end_plot_y - start_plot_y) / 10)) + for plot_y in np.linspace(start_plot_y, end_plot_y, step): + diff = single_lane_pt_y - plot_y + fake_diff_bigger_than_zero = diff.copy() + fake_diff_smaller_than_zero = diff.copy() + fake_diff_bigger_than_zero[np.where(diff <= 0)] = float('inf') + fake_diff_smaller_than_zero[np.where(diff > 0)] = float('-inf') + idx_low = np.argmax(fake_diff_smaller_than_zero) + idx_high = np.argmin(fake_diff_bigger_than_zero) + + previous_src_pt_x = single_lane_pt_x[idx_low] + previous_src_pt_y = single_lane_pt_y[idx_low] + last_src_pt_x = single_lane_pt_x[idx_high] + last_src_pt_y = single_lane_pt_y[idx_high] + + if previous_src_pt_y < start_plot_y or last_src_pt_y < start_plot_y or \ + fake_diff_smaller_than_zero[idx_low] == float('-inf') or \ + fake_diff_bigger_than_zero[idx_high] == float('inf'): + continue + + interpolation_src_pt_x = (abs(previous_src_pt_y - plot_y) * previous_src_pt_x + + abs(last_src_pt_y - plot_y) * last_src_pt_x) / \ + (abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y)) + interpolation_src_pt_y = (abs(previous_src_pt_y - plot_y) * previous_src_pt_y + + abs(last_src_pt_y - plot_y) * last_src_pt_y) / \ + (abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y)) + + if interpolation_src_pt_x > source_image_width or interpolation_src_pt_x < 10: + continue + + lane_color = self._color_map[index].tolist() + cv2.circle(source_image, (int(interpolation_src_pt_x), + int(interpolation_src_pt_y)), 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=?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Sat, 3 Sep 2022 10:06:32 +0000 Subject: [PATCH 14/19] update ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 溺水小航母 <1396755411@qq.com> --- .../cv/MNN-LANENET_ID1251_for_ACL/README.md | 189 +++++++++--------- 1 file changed, 95 insertions(+), 94 deletions(-) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md index 285a1b468..f7bbc58d2 100644 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md @@ -1,138 +1,139 @@ -## 模型功能 - +## 模型功能 + 车道线检测 - - - -## pb模型 -``` - -python3.7 ckpt2pb.py -``` -convert_ckpt_into_pb_file（）函数，pb模型 PATH=./pretrained_model/eval.pb - -ckpt：链接：https://pan.baidu.com/s/1E6W7OHgIvi-B3TPRDl6OhA?pwd=6666 -提取码：6666 + +## pb模型 + +``` + +python3.7 ckpt2pb.py +``` + +convert_ckpt_into_pb_file（）函数，pb模型 PATH=./pretrained_model/eval.pb + +ckpt：链接：https://pan.baidu.com/s/1E6W7OHgIvi-B3TPRDl6OhA?pwd=6666 +提取码：6666 -## om模型 - +## om模型 + 使用ATC模型转换工具进行模型转换时可以参考如下指令: - - -``` - + + +``` + atc --model=/usr/pb2om/eval.pb - - --framework=3 - - --output=/usr/pb2om/frozen - - --soc_version=Ascend910 - - out_nodes="lanenet/binary_seg_out:0;lanenet/instance_seg_out:0" - - --input_shape="input_tensor:1,256,512,3" - - --input_format=NHWC + + --framework=3 + + --output=/usr/pb2om/frozen + + --soc_version=Ascend910 + + out_nodes="lanenet/binary_seg_out:0;lanenet/instance_seg_out:0" + + --input_shape="input_tensor:1,256,512,3" + + --input_format=NHWC ``` - - + + ## 使用msame工具推理 - - -参考 https://gitee.com/ascend/tools/tree/master/msame, 获取msame推理工具及使用方法。 - + + +参考 https://gitee.com/ascend/tools/tree/master/msame, 获取msame推理工具及使用方法。 + 获取到msame可执行文件之后，进行推理测试。 - - -## 数据集转换bin -``` - -python3.7 eval_pb.py -``` - + +## 数据集转换bin + +``` + +python3.7 eval_pb.py +``` + freeze_graph_test（）函数 img_feed转test_img.bin ## 推理测试 - - + + 使用msame推理工具，参考如下命令，发起推理测试： - - -``` - -./msame --model "/home/test_user05/pb2om/frozen.om" - - --input "/home/test_user05/pb2om/test_img.bin" - - --output "/home/test_user05/pb2om/" - - --outfmt BIN - - --loop 1 -``` - - -## 脚本和示例代码 - -输入数据：链接：https://pan.baidu.com/s/1bwZR3FfhP18mLMa44781Gw?pwd=0000 -提取码：0000 -├── eval_data + + +``` + +./msame --model "/home/test_user05/pb2om/frozen.om" + + --input "/home/test_user05/pb2om/test_img.bin" + + --output "/home/test_user05/pb2om/" + + --outfmt BIN + + --loop 1 +``` + + +## 脚本和示例代码 + +输入数据：链接：https://pan.baidu.com/s/1bwZR3FfhP18mLMa44781Gw?pwd=0000 +提取码：0000 + +├── eval_data 1.pb预测 -├── eval_pb.py //主代码 - -├── lanenet_model - +├── eval_pb.py //主代码 + +├── lanenet_model + ├── lanenet.py //LANENET模型 ├── README.md //代码说明文档 -2.om预测 +2.om预测 + +├── eval_om.py //主代码 + +├── lanenet_model + + ├── lanenet.py //LANENET模型 + +├── README.md //代码说明文档 -├── eval_om.py //主代码 - -├── lanenet_model - - ├── lanenet.py //LANENET模型 - -├── README.md //代码说明文档 - 3.ckpt转pb -├── ckpt2pb.py - +├── ckpt2pb.py + ##推理输出计算精度 - -``` - -python3.7 eval_om.py + +``` + +python3.7 eval_om.py ``` ## 推理精度 -定量指标采用准确率，精度为0.965，精度达标。 +定量指标采用准确率，精度为0.965，精度达标。 定性指标采用论文中可视化binary segmentation和instance segmentation，输出在eval_output下 | gpu | npu |原论文 |推理 | |-------|------|-------|-------| -| 96.5 | 96.5 | 96.4 |96.5 | - - - -## 推理性能 +| 96.5 | 96.5 | 96.4 |96.5 | + + + +## 推理性能 inference time.jpg，性能达标 \ No newline at end of file -- Gitee From 48076cca1606292c235ebf04cab5de8115ea8e2d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Mon, 5 Sep 2022 12:51:48 +0000 Subject: [PATCH 15/19] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20AC?= =?UTF-8?q?L=5FTensorFlow/contrib/cv/MNN-LANENET=5FID1251=5Ffor=5FACL/=5F?= =?UTF-8?q?=5Fpycache=5F=5F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../__pycache__/.keep | 0 .../lanenet_postprocess.cpython-37.pyc | Bin 10205 -> 0 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/.keep delete mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/lanenet_postprocess.cpython-37.pyc diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/.keep b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/.keep deleted file mode 100644 index e69de29bb..000000000 diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/lanenet_postprocess.cpython-37.pyc b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/__pycache__/lanenet_postprocess.cpython-37.pyc deleted file mode 100644 index 6a76b7b5d8e296bdcaaac49170e062ad752e031b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10205 zcmb_iU2GdycAkF@hd-iZNtPwsVr(Zi6GwKOG)=w1Cbpww4LGzRxp6X^V0Oeiq9~CZ z`p%UtvcsZSr`Q%<0|r{4=prbj1^UqE0)1)Gr=sX%-`Y;!x=$+l(5F7+W&52w6iNL! 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All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -AUG: - RESIZE_METHOD: 'stepscaling' # choice unpadding rangescaling and stepscaling - FIX_RESIZE_SIZE: [720, 720] # (width, height), for unpadding - INF_RESIZE_VALUE: 500 # for rangescaling - MAX_RESIZE_VALUE: 600 # for rangescaling - MIN_RESIZE_VALUE: 400 # for rangescaling - MAX_SCALE_FACTOR: 2.0 # for stepscaling - MIN_SCALE_FACTOR: 0.75 # for stepscaling - SCALE_STEP_SIZE: 0.25 # for stepscaling - TRAIN_CROP_SIZE: [512, 256] # crop size for training - EVAL_CROP_SIZE: [512, 256] # crop size for evaluating - CROP_PAD_SIZE: 32 - MIRROR: True - FLIP: False - FLIP_RATIO: 0.5 - RICH_CROP: - ENABLE: False - BLUR: True - BLUR_RATIO: 0.2 - MAX_ROTATION: 15 - MIN_AREA_RATIO: 0.5 - ASPECT_RATIO: 0.5 - BRIGHTNESS_JITTER_RATIO: 0.5 - CONTRAST_JITTER_RATIO: 0.5 - SATURATION_JITTER_RATIO: 0.5 -DATASET: - DATA_DIR: 'REPO_ROOT_PATH/data/training_data_example/' - IMAGE_TYPE: 'rgb' # choice rgb or rgba - NUM_CLASSES: 2 - TEST_FILE_LIST: 'REPO_ROOT_PATH/data/training_data_example/test.txt' - TRAIN_FILE_LIST: 'REPO_ROOT_PATH/data/training_data_example/train.txt' - VAL_FILE_LIST: 'REPO_ROOT_PATH/data/training_data_example/val.txt' - IGNORE_INDEX: 255 - PADDING_VALUE: [127.5, 127.5, 127.5] - MEAN_VALUE: [0.5, 0.5, 0.5] - STD_VALUE: [0.5, 0.5, 0.5] - CPU_MULTI_PROCESS_NUMS: 8 -FREEZE: - MODEL_FILENAME: 'model' - PARAMS_FILENAME: 'params' -MODEL: - MODEL_NAME: 'lanenet' - FRONT_END: 'bisenetv2' -# FRONT_END: 'vgg' - EMBEDDING_FEATS_DIMS: 4 - BISENETV2: - GE_EXPAND_RATIO: 6 - SEMANTIC_CHANNEL_LAMBDA: 0.25 - SEGHEAD_CHANNEL_EXPAND_RATIO: 2 -TEST: - TEST_MODEL: 'model/cityscapes/final' -TRAIN: - MODEL_SAVE_DIR: 'model/tusimple/' - TBOARD_SAVE_DIR: 'tboard/tusimple/' - MODEL_PARAMS_CONFIG_FILE_NAME: "model_train_config.json" - RESTORE_FROM_SNAPSHOT: - ENABLE: False - SNAPSHOT_PATH: '' - SNAPSHOT_EPOCH: 8 - BATCH_SIZE: 32 - VAL_BATCH_SIZE: 4 - EPOCH_NUMS: 905 - WARM_UP: - ENABLE: True - EPOCH_NUMS: 8 - FREEZE_BN: - ENABLE: False - COMPUTE_MIOU: - ENABLE: True - EPOCH: 1 - MULTI_GPU: - ENABLE: True - GPU_DEVICES: ['0', '1'] - CHIEF_DEVICE_INDEX: 0 -SOLVER: - LR: 0.001 - LR_POLICY: 'poly' - LR_POLYNOMIAL_POWER: 0.9 - OPTIMIZER: 'sgd' - MOMENTUM: 0.9 - WEIGHT_DECAY: 0.0005 - MOVING_AVE_DECAY: 0.9995 - LOSS_TYPE: 'cross_entropy' - OHEM: - ENABLE: False - SCORE_THRESH: 0.65 - MIN_SAMPLE_NUMS: 65536 -GPU: - GPU_MEMORY_FRACTION: 0.9 - TF_ALLOW_GROWTH: True -POSTPROCESS: - MIN_AREA_THRESHOLD: 100 - DBSCAN_EPS: 0.35 - DBSCAN_MIN_SAMPLES: 1000 -LOG: - SAVE_DIR: './log' - LEVEL: INFO -- Gitee From 84ef12506974e637f54ac916bba8cc25f42f3b98 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Mon, 5 Sep 2022 12:52:32 +0000 Subject: [PATCH 17/19] 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ckpt2pb.py -``` - -convert_ckpt_into_pb_file（）函数，pb模型 PATH=./pretrained_model/eval.pb - -ckpt：链接：https://pan.baidu.com/s/1E6W7OHgIvi-B3TPRDl6OhA?pwd=6666 -提取码：6666 - - -## om模型 - - -使用ATC模型转换工具进行模型转换时可以参考如下指令: - - -``` - -atc --model=/usr/pb2om/eval.pb - - --framework=3 - - --output=/usr/pb2om/frozen - - --soc_version=Ascend910 - - out_nodes="lanenet/binary_seg_out:0;lanenet/instance_seg_out:0" - - --input_shape="input_tensor:1,256,512,3" - - --input_format=NHWC -``` - - - -## 使用msame工具推理 - - -参考 https://gitee.com/ascend/tools/tree/master/msame, 获取msame推理工具及使用方法。 - -获取到msame可执行文件之后，进行推理测试。 - - - -## 数据集转换bin - -``` - -python3.7 eval_pb.py -``` - -freeze_graph_test（）函数 img_feed转test_img.bin - -## 推理测试 - - -使用msame推理工具，参考如下命令，发起推理测试： - - -``` - -./msame --model "/home/test_user05/pb2om/frozen.om" - - --input "/home/test_user05/pb2om/test_img.bin" - - --output "/home/test_user05/pb2om/" - - --outfmt BIN - - --loop 1 -``` - - -## 脚本和示例代码 - -输入数据：链接：https://pan.baidu.com/s/1bwZR3FfhP18mLMa44781Gw?pwd=0000 -提取码：0000 - -├── eval_data - -1.pb预测 - -├── eval_pb.py //主代码 - -├── lanenet_model - - ├── lanenet.py //LANENET模型 - -├── README.md //代码说明文档 - - - -2.om预测 - -├── eval_om.py //主代码 - -├── lanenet_model - - ├── lanenet.py //LANENET模型 - -├── README.md //代码说明文档 - - - -3.ckpt转pb - -├── ckpt2pb.py - - - -##推理输出计算精度 - -``` - -python3.7 eval_om.py -``` - -## 推理精度 -定量指标采用准确率，精度为0.965，精度达标。 -定性指标采用论文中可视化binary segmentation和instance segmentation，输出在eval_output下 - -| gpu | npu |原论文 |推理 | -|-------|------|-------|-------| -| 96.5 | 96.5 | 96.4 |96.5 | - - - -## 推理性能 -inference time.jpg，性能达标 \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py deleted file mode 100644 index 8710bca83..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Freeze Lanenet model into frozen pb file -""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf -import os - -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' - - -def convert_ckpt_into_pb_file(ckpt_file_path, pb_file_path): - """ - - :param ckpt_file_path: - :param pb_file_path: - :return: - """ - with tf.Session() as sess: - - saver = tf.train.import_meta_graph(ckpt_file_path + '.meta', clear_devices=True) - input_graph_def = tf.get_default_graph().as_graph_def() - - binary_seg_node = 'lanenet/binary_seg_out' - instance_seg_node = 'lanenet/instance_seg_out' - - saver.restore(sess, ckpt_file_path) # 恢复图并得到数据 - output_graph_def = tf.graph_util.convert_variables_to_constants( # 模型持久化，将变量值固定 - sess=sess, - input_graph_def=input_graph_def, - output_node_names=['input_tensor', binary_seg_node, instance_seg_node]) - - with tf.gfile.GFile(pb_file_path, "wb") as f: # 保存模型 - f.write(output_graph_def.SerializeToString()) # 序列化输出 - print("%d ops in the final graph." % len(output_graph_def.node)) - - -if __name__ == '__main__': - """ - test code - """ - - ckpt_path = './eval_ckpt/eval.ckpt' - pb_save_path = './pretrained_model/eval.pb' - - convert_ckpt_into_pb_file( - ckpt_file_path=ckpt_path, - pb_file_path=pb_save_path - ) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py deleted file mode 100644 index 993c5719c..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py +++ /dev/null @@ -1,190 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -test LaneNet model on single image -""" -import argparse -import os.path as ops -import time - -import cv2 -import numpy as np -import tensorflow as tf - -from lanenet_model import lanenet -from lanenet_model import lanenet_postprocess -from local_utils.config_utils import parse_config_utils -from local_utils.log_util import init_logger - -CFG = parse_config_utils.lanenet_cfg -LOG = init_logger.get_logger(log_file_name_prefix='lanenet_test') - -gt_path = './eval_data/gt.png' - - -def init_args(): - """ - - :return: - """ - parser = argparse.ArgumentParser() - parser.add_argument('--image_path', default='./eval_data/test_img.jpg', - type=str, help='The image path or the src image save dir') - parser.add_argument('--weights_path', default='./pretrained_model/ckpt/tusimple_lanenet.ckpt', - type=str, help='The model weights path') - parser.add_argument('--with_lane_fit', type=args_str2bool, help='If need to do lane fit', default=False) - - return parser.parse_args() - - -def args_str2bool(arg_value): - """ - - :param arg_value: - :return: - """ - if arg_value.lower() in ('yes', 'true', 't', 'y', '1'): - return True - - elif arg_value.lower() in ('no', 'false', 'f', 'n', '0'): - return False - else: - raise argparse.ArgumentTypeError('Unsupported value encountered.') - - -def minmax_scale(input_arr): - """ - - :param input_arr: - :return: - """ - min_val = np.min(input_arr) - max_val = np.max(input_arr) - - output_arr = (input_arr - min_val) * 255.0 / (max_val - min_val) - - return output_arr - - -def test_lanenet(image_path, gt_path, weights_path, with_lane_fit=False): - """ - - :param image_path: - :param weights_path: - :param with_lane_fit: - :return: - """ - assert ops.exists(image_path), '{:s} not exist'.format(image_path) - - LOG.info('Start reading image and preprocessing') - t_start = time.time() - image = cv2.imread(image_path, cv2.IMREAD_COLOR) - image_vis = image - image = cv2.resize(image, (512, 256), interpolation=cv2.INTER_LINEAR) - image = image / 127.5 - 1.0 - LOG.info('Image load complete, cost time: {:.5f}s'.format(time.time() - t_start)) - - input_tensor_0 = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input_tensor') - input_tensor = tf.identity(input_tensor_0, name='input_tensor') - net = lanenet.LaneNet(phase='test', cfg=CFG) - binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='LaneNet') - - with tf.variable_scope('lanenet/'): - binary_seg_ret = tf.identity(binary_seg_ret, name='binary_seg_out') - instance_seg_ret = tf.identity(instance_seg_ret, name='instance_seg_out') - - postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG) - - sess = tf.Session() - - # define moving average version of the learned variables for eval - with tf.variable_scope(name_or_scope='moving_avg'): - variable_averages = tf.train.ExponentialMovingAverage( - CFG.SOLVER.MOVING_AVE_DECAY) - variables_to_restore = variable_averages.variables_to_restore() - - # define saver - saver = tf.train.Saver(variables_to_restore) - - with sess.as_default(): - saver.restore(sess=sess, save_path=weights_path) - image = np.expand_dims(image, axis=0) - binary_seg_image, instance_seg_image = sess.run( - [binary_seg_ret, instance_seg_ret], - feed_dict={input_tensor: image} - ) - - postprocess_result = postprocessor.postprocess( - binary_seg_result=binary_seg_image[0], - instance_seg_result=instance_seg_image[0], - source_image=image_vis, - with_lane_fit=True, - data_source='tusimple' - ) - mask_image = postprocess_result['mask_image'] - src_image = postprocess_result['source_image'] - - # -------------- 计算准确率 ------------------ # - gt = cv2.imread(gt_path, cv2.IMREAD_COLOR) - gt_trans = cv2.resize(gt, (512, 256), interpolation=cv2.INTER_LINEAR) - - gt_gray = cv2.cvtColor(gt_trans, cv2.COLOR_BGR2GRAY) - mask_image_gray = cv2.cvtColor(mask_image, cv2.COLOR_BGR2GRAY) - WIDTH = mask_image_gray.shape[0] - HIGTH = mask_image_gray.shape[1] - tp_count = 0 - tn_count = 0 - for i in range(WIDTH): - for j in range(HIGTH): - if mask_image_gray[i, j] != 0 and gt_gray[i, j] != 0: - tp_count = tp_count + 1 - if mask_image_gray[i, j] == 0 and gt_gray[i, j] == 0: - tn_count = tn_count + 1 - Accuracy = (int(tp_count) + int(tn_count)) / (int(WIDTH) * int(HIGTH)) - - print("\n# Metric_ckpt " - "\n Accuracy：{:.3f}".format(Accuracy)) - - cv2.imwrite('./eval_output/mask_ckpt.jpg', mask_image) - cv2.imwrite('./eval_output/src_ckpt.jpg', src_image) - - saver.save(sess, './eval_ckpt/eval.ckpt') - - sess.close() - - return - - -if __name__ == '__main__': - """ - test code - """ - # init args - args = init_args() - - test_lanenet(args.image_path, gt_path, args.weights_path, with_lane_fit=args.with_lane_fit) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt/.keep b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt/.keep deleted file mode 100644 index e69de29bb..000000000 diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py deleted file mode 100644 index 98a8e5373..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np -import lanenet_postprocess -from local_utils.config_utils import parse_config_utils -import cv2 - -CFG = parse_config_utils.lanenet_cfg - -image_path = './eval_data/test_img.jpg' -image_vis = cv2.imread(image_path, cv2.IMREAD_COLOR) - -b_out_path = './eval_data/frozen_output_0.bin' -i_out_path = './eval_data/frozen_output_1.bin' -b_out = np.fromfile(b_out_path, dtype=np.int64) -i_out = np.fromfile(i_out_path, dtype=np.float32) -b_out = np.reshape(b_out, (1, 256, 512)) -i_out = np.reshape(i_out, (1, 256, 512, 4)) - -postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG) -postprocess_result = postprocessor.postprocess( - binary_seg_result=b_out[0], - instance_seg_result=i_out[0], - source_image=image_vis, - with_lane_fit=True, - data_source='tusimple' - ) -mask_image = postprocess_result['mask_image'] -src_image = postprocess_result['source_image'] -gt_path = './eval_data/gt.png' -gt = cv2.imread(gt_path, cv2.IMREAD_COLOR) -gt_trans = cv2.resize(gt, (512, 256), interpolation=cv2.INTER_LINEAR) - -# -------------- 计算准确率 ------------------ # -gt_gray = cv2.cvtColor(gt_trans, cv2.COLOR_BGR2GRAY) -mask_image_gray = cv2.cvtColor(mask_image, cv2.COLOR_BGR2GRAY) -WIDTH = mask_image_gray.shape[0] -HIGTH = mask_image_gray.shape[1] -tp_count = 0 -tn_count = 0 -for i in range(WIDTH): - for j in range(HIGTH): - if mask_image_gray[i, j] != 0 and gt_gray[i, j] != 0: - tp_count = tp_count + 1 - if mask_image_gray[i, j] == 0 and gt_gray[i, j] == 0: - tn_count = tn_count + 1 -Accuracy = (int(tp_count) + int(tn_count)) / (int(WIDTH) * int(HIGTH)) - -print("\n# Metric_om " - "\n Accuracy：{:.3f}".format(Accuracy)) - -cv2.imwrite('./eval_output/mask_om.jpg', mask_image) 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a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet.py +++ /dev/null @@ -1,111 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Implement LaneNet Model -""" -import tensorflow as tf - -from lanenet_model import lanenet_back_end -from lanenet_model import lanenet_front_end -from semantic_segmentation_zoo import cnn_basenet - - -class LaneNet(cnn_basenet.CNNBaseModel): - """ - - """ - def __init__(self, phase, cfg): - """ - - """ - super(LaneNet, self).__init__() - self._cfg = cfg - self._net_flag = self._cfg.MODEL.FRONT_END - - self._frontend = lanenet_front_end.LaneNetFrondEnd( - phase=phase, net_flag=self._net_flag, cfg=self._cfg - ) - self._backend = lanenet_back_end.LaneNetBackEnd( - phase=phase, cfg=self._cfg - ) - - def inference(self, input_tensor, name, reuse=False): - """ - - :param input_tensor: - :param name: - :param reuse - :return: - """ - with tf.variable_scope(name_or_scope=name, reuse=reuse): - # first extract image features - extract_feats_result = self._frontend.build_model( - input_tensor=input_tensor, - name='{:s}_frontend'.format(self._net_flag), - reuse=reuse - ) - - # second apply backend process - binary_seg_prediction, instance_seg_prediction = self._backend.inference( - binary_seg_logits=extract_feats_result['binary_segment_logits']['data'], - instance_seg_logits=extract_feats_result['instance_segment_logits']['data'], - name='{:s}_backend'.format(self._net_flag), - reuse=reuse - ) - - return binary_seg_prediction, instance_seg_prediction - - def compute_loss(self, input_tensor, binary_label, instance_label, name, reuse=False): - """ - calculate lanenet loss for training - :param input_tensor: - :param binary_label: - :param instance_label: - :param name: - :param reuse: - :return: - """ - with tf.variable_scope(name_or_scope=name, reuse=reuse): - # first extract image features - extract_feats_result = self._frontend.build_model( - input_tensor=input_tensor, - name='{:s}_frontend'.format(self._net_flag), - reuse=reuse - ) - - # second apply backend process - calculated_losses = self._backend.compute_loss( - binary_seg_logits=extract_feats_result['binary_segment_logits']['data'], - binary_label=binary_label, - instance_seg_logits=extract_feats_result['instance_segment_logits']['data'], - instance_label=instance_label, - name='{:s}_backend'.format(self._net_flag), - reuse=reuse - ) - - return calculated_losses diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_back_end.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_back_end.py deleted file mode 100644 index da31ea69b..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_back_end.py +++ /dev/null @@ -1,231 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -LaneNet backend branch which is mainly used for binary and instance segmentation loss calculation -""" -import tensorflow as tf - -from lanenet_model import lanenet_discriminative_loss -from semantic_segmentation_zoo import cnn_basenet - - -class LaneNetBackEnd(cnn_basenet.CNNBaseModel): - """ - LaneNet backend branch which is mainly used for binary and instance segmentation loss calculation - """ - def __init__(self, phase, cfg): - """ - init lanenet backend - :param phase: train or test - """ - super(LaneNetBackEnd, self).__init__() - self._cfg = cfg - self._phase = phase - self._is_training = self._is_net_for_training() - - self._class_nums = self._cfg.DATASET.NUM_CLASSES - self._embedding_dims = self._cfg.MODEL.EMBEDDING_FEATS_DIMS - self._binary_loss_type = self._cfg.SOLVER.LOSS_TYPE - - def _is_net_for_training(self): - """ - if the net is used for training or not - :return: - """ - if isinstance(self._phase, tf.Tensor): - phase = self._phase - else: - phase = tf.constant(self._phase, dtype=tf.string) - - return tf.equal(phase, tf.constant('train', dtype=tf.string)) - - @classmethod - def _compute_class_weighted_cross_entropy_loss(cls, onehot_labels, logits, classes_weights): - """ - - :param onehot_labels: - :param logits: - :param classes_weights: - :return: - """ - loss_weights = tf.reduce_sum(tf.multiply(onehot_labels, classes_weights), axis=3) - - loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, - logits=logits, - weights=loss_weights - ) - - return loss - - @classmethod - def _multi_category_focal_loss(cls, onehot_labels, logits, classes_weights, gamma=2.0): - """ - - :param onehot_labels: - :param logits: - :param classes_weights: - :param gamma: - :return: - """ - epsilon = 1.e-7 - alpha = tf.multiply(onehot_labels, classes_weights) - alpha = tf.cast(alpha, tf.float32) - gamma = float(gamma) - y_true = tf.cast(onehot_labels, tf.float32) - y_pred = tf.nn.softmax(logits, dim=-1) - y_pred = tf.clip_by_value(y_pred, epsilon, 1. - epsilon) - y_t = tf.multiply(y_true, y_pred) + tf.multiply(1-y_true, 1-y_pred) - ce = -tf.log(y_t) - weight = tf.pow(tf.subtract(1., y_t), gamma) - fl = tf.multiply(tf.multiply(weight, ce), alpha) - loss = tf.reduce_mean(fl) - - return loss - - def compute_loss(self, binary_seg_logits, binary_label, - instance_seg_logits, instance_label, - name, reuse): - """ - compute lanenet loss - :param binary_seg_logits: - :param binary_label: - :param instance_seg_logits: - :param instance_label: - :param name: - :param reuse: - :return: - """ - with tf.variable_scope(name_or_scope=name, reuse=reuse): - # calculate class weighted binary seg loss - with tf.variable_scope(name_or_scope='binary_seg'): - binary_label_onehot = tf.one_hot( - tf.reshape( - tf.cast(binary_label, tf.int32), - shape=[binary_label.get_shape().as_list()[0], - binary_label.get_shape().as_list()[1], - binary_label.get_shape().as_list()[2]]), - depth=self._class_nums, - axis=-1 - ) - - binary_label_plain = tf.reshape( - binary_label, - shape=[binary_label.get_shape().as_list()[0] * - binary_label.get_shape().as_list()[1] * - binary_label.get_shape().as_list()[2] * - binary_label.get_shape().as_list()[3]]) - unique_labels, unique_id, counts = tf.unique_with_counts(binary_label_plain) - counts = tf.cast(counts, tf.float32) - inverse_weights = tf.divide( - 1.0, - tf.log(tf.add(tf.divide(counts, tf.reduce_sum(counts)), tf.constant(1.02))) - ) - if self._binary_loss_type == 'cross_entropy': - binary_segmenatation_loss = self._compute_class_weighted_cross_entropy_loss( - onehot_labels=binary_label_onehot, - logits=binary_seg_logits, - classes_weights=inverse_weights - ) - elif self._binary_loss_type == 'focal': - binary_segmenatation_loss = self._multi_category_focal_loss( - onehot_labels=binary_label_onehot, - logits=binary_seg_logits, - classes_weights=inverse_weights - ) - else: - raise NotImplementedError - - # calculate class weighted instance seg loss - with tf.variable_scope(name_or_scope='instance_seg'): - - pix_bn = self.layerbn( - inputdata=instance_seg_logits, is_training=self._is_training, name='pix_bn') - pix_relu = self.relu(inputdata=pix_bn, name='pix_relu') - pix_embedding = self.conv2d( - inputdata=pix_relu, - out_channel=self._embedding_dims, - kernel_size=1, - use_bias=False, - name='pix_embedding_conv' - ) - pix_image_shape = (pix_embedding.get_shape().as_list()[1], pix_embedding.get_shape().as_list()[2]) - instance_segmentation_loss, l_var, l_dist, l_reg = \ - lanenet_discriminative_loss.discriminative_loss( - pix_embedding, instance_label, self._embedding_dims, - pix_image_shape, 0.5, 3.0, 1.0, 1.0, 0.001 - ) - - l2_reg_loss = tf.constant(0.0, tf.float32) - for vv in tf.trainable_variables(): - if 'bn' in vv.name or 'gn' in vv.name: - continue - else: - l2_reg_loss = tf.add(l2_reg_loss, tf.nn.l2_loss(vv)) - l2_reg_loss *= 0.001 - total_loss = binary_segmenatation_loss + instance_segmentation_loss + l2_reg_loss - - ret = { - 'total_loss': total_loss, - 'binary_seg_logits': binary_seg_logits, - 'instance_seg_logits': pix_embedding, - 'binary_seg_loss': binary_segmenatation_loss, - 'discriminative_loss': instance_segmentation_loss - } - - return ret - - def inference(self, binary_seg_logits, instance_seg_logits, name, reuse): - """ - - :param binary_seg_logits: - :param instance_seg_logits: - :param name: - :param reuse: - :return: - """ - with tf.variable_scope(name_or_scope=name, reuse=reuse): - - with tf.variable_scope(name_or_scope='binary_seg'): - binary_seg_score = tf.nn.softmax(logits=binary_seg_logits) - binary_seg_prediction = tf.argmax(binary_seg_score, axis=-1) - - with tf.variable_scope(name_or_scope='instance_seg'): - - pix_bn = self.layerbn( - inputdata=instance_seg_logits, is_training=self._is_training, name='pix_bn') - pix_relu = self.relu(inputdata=pix_bn, name='pix_relu') - instance_seg_prediction = self.conv2d( - inputdata=pix_relu, - out_channel=self._embedding_dims, - kernel_size=1, - use_bias=False, - name='pix_embedding_conv' - ) - - return binary_seg_prediction, instance_seg_prediction diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_discriminative_loss.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_discriminative_loss.py deleted file mode 100644 index f6c12b3e5..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_discriminative_loss.py +++ /dev/null @@ -1,162 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Discriminative Loss for instance segmentation -""" -import tensorflow as tf - - -def discriminative_loss_single( - prediction, - correct_label, - feature_dim, - label_shape, - delta_v, - delta_d, - param_var, - param_dist, - param_reg): - """ - discriminative loss - :param prediction: inference of network - :param correct_label: instance label - :param feature_dim: feature dimension of prediction - :param label_shape: shape of label - :param delta_v: cut off variance distance - :param delta_d: cut off cluster distance - :param param_var: weight for intra cluster variance - :param param_dist: weight for inter cluster distances - :param param_reg: weight regularization - """ - correct_label = tf.reshape( - correct_label, [label_shape[1] * label_shape[0]] - ) - reshaped_pred = tf.reshape( - prediction, [label_shape[1] * label_shape[0], feature_dim] - ) - - # calculate instance nums - unique_labels, unique_id, counts = tf.unique_with_counts(correct_label) - counts = tf.cast(counts, tf.float32) - num_instances = tf.size(unique_labels) - - # calculate instance pixel embedding mean vec - segmented_sum = tf.unsorted_segment_sum( - reshaped_pred, unique_id, num_instances) - mu = tf.div(segmented_sum, tf.reshape(counts, (-1, 1))) - mu_expand = tf.gather(mu, unique_id) - - distance = tf.norm(tf.subtract(mu_expand, reshaped_pred), axis=1, ord=1) - distance = tf.subtract(distance, delta_v) - distance = tf.clip_by_value(distance, 0., distance) - distance = tf.square(distance) - - l_var = tf.unsorted_segment_sum(distance, unique_id, num_instances) - l_var = tf.div(l_var, counts) - l_var = tf.reduce_sum(l_var) - l_var = tf.divide(l_var, tf.cast(num_instances, tf.float32)) - - mu_interleaved_rep = tf.tile(mu, [num_instances, 1]) - mu_band_rep = tf.tile(mu, [1, num_instances]) - mu_band_rep = tf.reshape( - mu_band_rep, - (num_instances * - num_instances, - feature_dim)) - - mu_diff = tf.subtract(mu_band_rep, mu_interleaved_rep) - - intermediate_tensor = tf.reduce_sum(tf.abs(mu_diff), axis=1) - zero_vector = tf.zeros(1, dtype=tf.float32) - bool_mask = tf.not_equal(intermediate_tensor, zero_vector) - mu_diff_bool = tf.boolean_mask(mu_diff, bool_mask) - - mu_norm = tf.norm(mu_diff_bool, axis=1, ord=1) - mu_norm = tf.subtract(2. * delta_d, mu_norm) - mu_norm = tf.clip_by_value(mu_norm, 0., mu_norm) - mu_norm = tf.square(mu_norm) - - l_dist = tf.reduce_mean(mu_norm) - - l_reg = tf.reduce_mean(tf.norm(mu, axis=1, ord=1)) - - param_scale = 1. - l_var = param_var * l_var - l_dist = param_dist * l_dist - l_reg = param_reg * l_reg - - loss = param_scale * (l_var + l_dist + l_reg) - - return loss, l_var, l_dist, l_reg - - -def discriminative_loss(prediction, correct_label, feature_dim, image_shape, - delta_v, delta_d, param_var, param_dist, param_reg): - """ - - :return: discriminative loss and its three components - """ - - def cond(label, batch, out_loss, out_var, out_dist, out_reg, i): - return tf.less(i, tf.shape(batch)[0]) - - def body(label, batch, out_loss, out_var, out_dist, out_reg, i): - disc_loss, l_var, l_dist, l_reg = discriminative_loss_single( - prediction[i], correct_label[i], feature_dim, image_shape, delta_v, delta_d, param_var, param_dist, param_reg) - - out_loss = out_loss.write(i, disc_loss) - out_var = out_var.write(i, l_var) - out_dist = out_dist.write(i, l_dist) - out_reg = out_reg.write(i, l_reg) - - return label, batch, out_loss, out_var, out_dist, out_reg, i + 1 - - # TensorArray is a data structure that support dynamic writing - output_ta_loss = tf.TensorArray( - dtype=tf.float32, size=0, dynamic_size=True) - output_ta_var = tf.TensorArray( - dtype=tf.float32, size=0, dynamic_size=True) - output_ta_dist = tf.TensorArray( - dtype=tf.float32, size=0, dynamic_size=True) - output_ta_reg = tf.TensorArray( - dtype=tf.float32, size=0, dynamic_size=True) - - _, _, out_loss_op, out_var_op, out_dist_op, out_reg_op, _ = tf.while_loop( - cond, body, [ - correct_label, prediction, output_ta_loss, output_ta_var, output_ta_dist, output_ta_reg, 0]) - out_loss_op = out_loss_op.stack() - out_var_op = out_var_op.stack() - out_dist_op = out_dist_op.stack() - out_reg_op = out_reg_op.stack() - - disc_loss = tf.reduce_mean(out_loss_op) - l_var = tf.reduce_mean(out_var_op) - l_dist = tf.reduce_mean(out_dist_op) - l_reg = tf.reduce_mean(out_reg_op) - - return disc_loss, l_var, l_dist, l_reg diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_front_end.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_front_end.py deleted file mode 100644 index a0112f878..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_front_end.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -LaneNet frontend branch which is mainly used for feature extraction -""" -from semantic_segmentation_zoo import cnn_basenet -from semantic_segmentation_zoo import vgg16_based_fcn -from semantic_segmentation_zoo import bisenet_v2 - - -class LaneNetFrondEnd(cnn_basenet.CNNBaseModel): - """ - LaneNet frontend which is used to extract image features for following process - """ - def __init__(self, phase, net_flag, cfg): - """ - - """ - super(LaneNetFrondEnd, self).__init__() - self._cfg = cfg - - self._frontend_net_map = { - 'vgg': vgg16_based_fcn.VGG16FCN(phase=phase, cfg=self._cfg), - 'bisenetv2': bisenet_v2.BiseNetV2(phase=phase, cfg=self._cfg), - } - - self._net = self._frontend_net_map[net_flag] - - def build_model(self, input_tensor, name, reuse): - """ - - :param input_tensor: - :param name: - :param reuse: - :return: - """ - - return self._net.build_model( - input_tensor=input_tensor, - name=name, - reuse=reuse - ) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_postprocess.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_postprocess.py deleted file mode 100644 index 2fe33f9ad..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_model/lanenet_postprocess.py +++ /dev/null @@ -1,463 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -LaneNet model post process -""" -import os.path as ops -import math - -import cv2 -import numpy as np -import loguru -from sklearn.cluster import DBSCAN -from sklearn.preprocessing import StandardScaler - -LOG = loguru.logger - - -def _morphological_process(image, kernel_size=5): - """ - morphological process to fill the hole in the binary segmentation result - :param image: - :param kernel_size: - :return: - """ - if len(image.shape) == 3: - raise ValueError('Binary segmentation result image should be a single channel image') - - if image.dtype is not np.uint8: - image = np.array(image, np.uint8) - - kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size)) - - # close operation fille hole - closing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1) - - return closing - - -def _connect_components_analysis(image): - """ - connect components analysis to remove the small components - :param image: - :return: - """ - if len(image.shape) == 3: - gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - else: - gray_image = image - - return cv2.connectedComponentsWithStats(gray_image, connectivity=8, ltype=cv2.CV_32S) - - -class _LaneFeat(object): - """ - - """ - def __init__(self, feat, coord, class_id=-1): - """ - lane feat object - :param feat: lane embeddng feats [feature_1, feature_2, ...] - :param coord: lane coordinates [x, y] - :param class_id: lane class id - """ - self._feat = feat - self._coord = coord - self._class_id = class_id - - @property - def feat(self): - """ - - :return: - """ - return self._feat - - @feat.setter - def feat(self, value): - """ - - :param value: - :return: - """ - if not isinstance(value, np.ndarray): - value = np.array(value, dtype=np.float64) - - if value.dtype != np.float32: - value = np.array(value, dtype=np.float64) - - self._feat = value - - @property - def coord(self): - """ - - :return: - """ - return self._coord - - @coord.setter - def coord(self, value): - """ - - :param value: - :return: - """ - if not isinstance(value, np.ndarray): - value = np.array(value) - - if value.dtype != np.int32: - value = np.array(value, dtype=np.int32) - - self._coord = value - - @property - def class_id(self): - """ - - :return: - """ - return self._class_id - - @class_id.setter - def class_id(self, value): - """ - - :param value: - :return: - """ - if not isinstance(value, np.int64): - raise ValueError('Class id must be integer') - - self._class_id = value - - -class _LaneNetCluster(object): - """ - Instance segmentation result cluster - """ - - def __init__(self, cfg): - """ - - """ - self._color_map = [np.array([255, 0, 0]), - np.array([0, 255, 0]), - np.array([0, 0, 255]), - np.array([125, 125, 0]), - np.array([0, 125, 125]), - np.array([125, 0, 125]), - np.array([50, 100, 50]), - np.array([100, 50, 100])] - self._cfg = cfg - - def _embedding_feats_dbscan_cluster(self, embedding_image_feats): - """ - dbscan cluster - :param embedding_image_feats: - :return: - """ - db = DBSCAN(eps=self._cfg.POSTPROCESS.DBSCAN_EPS, min_samples=self._cfg.POSTPROCESS.DBSCAN_MIN_SAMPLES) - try: - features = StandardScaler().fit_transform(embedding_image_feats) - db.fit(features) - except Exception as err: - LOG.error(err) - ret = { - 'origin_features': None, - 'cluster_nums': 0, - 'db_labels': None, - 'unique_labels': None, - 'cluster_center': None - } - return ret - db_labels = db.labels_ - unique_labels = np.unique(db_labels) - - num_clusters = len(unique_labels) - cluster_centers = db.components_ - - ret = { - 'origin_features': features, - 'cluster_nums': num_clusters, - 'db_labels': db_labels, - 'unique_labels': unique_labels, - 'cluster_center': cluster_centers - } - - return ret - - @staticmethod - def _get_lane_embedding_feats(binary_seg_ret, instance_seg_ret): - """ - get lane embedding features according the binary seg result - :param binary_seg_ret: - :param instance_seg_ret: - :return: - """ - idx = np.where(binary_seg_ret == 255) - lane_embedding_feats = instance_seg_ret[idx] - lane_coordinate = np.vstack((idx[1], idx[0])).transpose() - - assert lane_embedding_feats.shape[0] == lane_coordinate.shape[0] - - ret = { - 'lane_embedding_feats': lane_embedding_feats, - 'lane_coordinates': lane_coordinate - } - - return ret - - def apply_lane_feats_cluster(self, binary_seg_result, instance_seg_result): - """ - - :param binary_seg_result: - :param instance_seg_result: - :return: - """ - # get embedding feats and coords - get_lane_embedding_feats_result = self._get_lane_embedding_feats( - binary_seg_ret=binary_seg_result, - instance_seg_ret=instance_seg_result - ) - - # dbscan cluster - dbscan_cluster_result = self._embedding_feats_dbscan_cluster( - embedding_image_feats=get_lane_embedding_feats_result['lane_embedding_feats'] - ) - - mask = np.zeros(shape=[binary_seg_result.shape[0], binary_seg_result.shape[1], 3], dtype=np.uint8) - db_labels = dbscan_cluster_result['db_labels'] - unique_labels = dbscan_cluster_result['unique_labels'] - coord = get_lane_embedding_feats_result['lane_coordinates'] - - if db_labels is None: - return None, None - - lane_coords = [] - for index, label in enumerate(unique_labels.tolist()): - if label == -1: - continue - idx = np.where(db_labels == label) - pix_coord_idx = tuple((coord[idx][:, 1], coord[idx][:, 0])) - mask[pix_coord_idx] = self._color_map[index] - lane_coords.append(coord[idx]) - - return mask, lane_coords - - -class LaneNetPostProcessor(object): - """ - lanenet post process for lane generation - """ - def __init__(self, cfg, ipm_remap_file_path='./eval_data/tusimple_ipm_remap.yml'): - """ - - :param ipm_remap_file_path: ipm generate file path - """ - assert ops.exists(ipm_remap_file_path), '{:s} not exist'.format(ipm_remap_file_path) - - self._cfg = cfg - self._cluster = _LaneNetCluster(cfg=cfg) - self._ipm_remap_file_path = ipm_remap_file_path - - remap_file_load_ret = self._load_remap_matrix() - self._remap_to_ipm_x = remap_file_load_ret['remap_to_ipm_x'] - self._remap_to_ipm_y = remap_file_load_ret['remap_to_ipm_y'] - - self._color_map = [np.array([255, 0, 0]), - np.array([0, 255, 0]), - np.array([0, 0, 255]), - np.array([125, 125, 0]), - np.array([0, 125, 125]), - np.array([125, 0, 125]), - np.array([50, 100, 50]), - np.array([100, 50, 100])] - - def _load_remap_matrix(self): - """ - - :return: - """ - fs = cv2.FileStorage(self._ipm_remap_file_path, cv2.FILE_STORAGE_READ) - - remap_to_ipm_x = fs.getNode('remap_ipm_x').mat() - remap_to_ipm_y = fs.getNode('remap_ipm_y').mat() - - ret = { - 'remap_to_ipm_x': remap_to_ipm_x, - 'remap_to_ipm_y': remap_to_ipm_y, - } - - fs.release() - - return ret - - def postprocess(self, binary_seg_result, instance_seg_result=None, - min_area_threshold=100, source_image=None, - with_lane_fit=True, data_source='tusimple'): - """ - - :param binary_seg_result: - :param instance_seg_result: - :param min_area_threshold: - :param source_image: - :param with_lane_fit: - :param data_source: - :return: - """ - # convert binary_seg_result - binary_seg_result = np.array(binary_seg_result * 255, dtype=np.uint8) - - # apply image morphology operation to fill in the hold and reduce the small area - morphological_ret = _morphological_process(binary_seg_result, kernel_size=5) - - connect_components_analysis_ret = _connect_components_analysis(image=morphological_ret) - - labels = connect_components_analysis_ret[1] - stats = connect_components_analysis_ret[2] - for index, stat in enumerate(stats): - if stat[4] <= min_area_threshold: - idx = np.where(labels == index) - morphological_ret[idx] = 0 - - # apply embedding features cluster - mask_image, lane_coords = self._cluster.apply_lane_feats_cluster( - binary_seg_result=morphological_ret, - instance_seg_result=instance_seg_result - ) - - if mask_image is None: - return { - 'mask_image': None, - 'fit_params': None, - 'source_image': None, - } - if not with_lane_fit: - tmp_mask = cv2.resize( - mask_image, - dsize=(source_image.shape[1], source_image.shape[0]), - interpolation=cv2.INTER_NEAREST - ) - source_image = cv2.addWeighted(source_image, 0.6, tmp_mask, 0.4, 0.0, dst=source_image) - return { - 'mask_image': mask_image, - 'fit_params': None, - 'source_image': source_image, - } - - # lane line fit - fit_params = [] - src_lane_pts = [] # lane pts every single lane - for lane_index, coords in enumerate(lane_coords): - if data_source == 'tusimple': - tmp_mask = np.zeros(shape=(720, 1280), dtype=np.uint8) - tmp_mask[tuple((np.int_(coords[:, 1] * 720 / 256), np.int_(coords[:, 0] * 1280 / 512)))] = 255 - else: - raise ValueError('Wrong data source now only support tusimple') - tmp_ipm_mask = cv2.remap( - tmp_mask, - self._remap_to_ipm_x, - self._remap_to_ipm_y, - interpolation=cv2.INTER_NEAREST - ) - nonzero_y = np.array(tmp_ipm_mask.nonzero()[0]) - nonzero_x = np.array(tmp_ipm_mask.nonzero()[1]) - - fit_param = np.polyfit(nonzero_y, nonzero_x, 2) - fit_params.append(fit_param) - - [ipm_image_height, ipm_image_width] = tmp_ipm_mask.shape - plot_y = np.linspace(10, ipm_image_height, ipm_image_height - 10) - fit_x = fit_param[0] * plot_y ** 2 + fit_param[1] * plot_y + fit_param[2] - # fit_x = fit_param[0] * plot_y ** 3 + fit_param[1] * plot_y ** 2 + fit_param[2] * plot_y + fit_param[3] - - lane_pts = [] - for index in range(0, plot_y.shape[0], 5): - src_x = self._remap_to_ipm_x[ - int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))] - if src_x <= 0: - continue - src_y = self._remap_to_ipm_y[ - int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))] - src_y = src_y if src_y > 0 else 0 - - lane_pts.append([src_x, src_y]) - - src_lane_pts.append(lane_pts) - - # tusimple test data sample point along y axis every 10 pixels - source_image_width = source_image.shape[1] - for index, single_lane_pts in enumerate(src_lane_pts): - single_lane_pt_x = np.array(single_lane_pts, dtype=np.float32)[:, 0] - single_lane_pt_y = np.array(single_lane_pts, dtype=np.float32)[:, 1] - if data_source == 'tusimple': - start_plot_y = 240 - end_plot_y = 720 - else: - raise ValueError('Wrong data source now only support tusimple') - step = int(math.floor((end_plot_y - start_plot_y) / 10)) - for plot_y in np.linspace(start_plot_y, end_plot_y, step): - diff = single_lane_pt_y - plot_y - fake_diff_bigger_than_zero = diff.copy() - fake_diff_smaller_than_zero = diff.copy() - fake_diff_bigger_than_zero[np.where(diff <= 0)] = float('inf') - fake_diff_smaller_than_zero[np.where(diff > 0)] = float('-inf') - idx_low = np.argmax(fake_diff_smaller_than_zero) - idx_high = np.argmin(fake_diff_bigger_than_zero) - - previous_src_pt_x = single_lane_pt_x[idx_low] - previous_src_pt_y = single_lane_pt_y[idx_low] - last_src_pt_x = single_lane_pt_x[idx_high] - last_src_pt_y = single_lane_pt_y[idx_high] - - if previous_src_pt_y < start_plot_y or last_src_pt_y < start_plot_y or \ - fake_diff_smaller_than_zero[idx_low] == float('-inf') or \ - fake_diff_bigger_than_zero[idx_high] == float('inf'): - continue - - interpolation_src_pt_x = (abs(previous_src_pt_y - plot_y) * previous_src_pt_x + - abs(last_src_pt_y - plot_y) * last_src_pt_x) / \ - (abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y)) - interpolation_src_pt_y = (abs(previous_src_pt_y - plot_y) * previous_src_pt_y + - abs(last_src_pt_y - plot_y) * last_src_pt_y) / \ - (abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y)) - - if interpolation_src_pt_x > source_image_width or interpolation_src_pt_x < 10: - continue - - lane_color = self._color_map[index].tolist() - cv2.circle(source_image, (int(interpolation_src_pt_x), - int(interpolation_src_pt_y)), 5, lane_color, -1) - ret = { - 'mask_image': mask_image, - 'fit_params': fit_params, - 'source_image': source_image, - } - - return ret diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_postprocess.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_postprocess.py deleted file mode 100644 index 2fe33f9ad..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/lanenet_postprocess.py +++ /dev/null @@ -1,463 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -LaneNet model post process -""" -import os.path as ops -import math - -import cv2 -import numpy as np -import loguru -from sklearn.cluster import DBSCAN -from sklearn.preprocessing import StandardScaler - -LOG = loguru.logger - - -def _morphological_process(image, kernel_size=5): - """ - morphological process to fill the hole in the binary segmentation result - :param image: - :param kernel_size: - :return: - """ - if len(image.shape) == 3: - raise ValueError('Binary segmentation result image should be a single channel image') - - if image.dtype is not np.uint8: - image = np.array(image, np.uint8) - - kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size)) - - # close operation fille hole - closing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1) - - return closing - - -def _connect_components_analysis(image): - """ - connect components analysis to remove the small components - :param image: - :return: - """ - if len(image.shape) == 3: - gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - else: - gray_image = image - - return cv2.connectedComponentsWithStats(gray_image, connectivity=8, ltype=cv2.CV_32S) - - -class _LaneFeat(object): - """ - - """ - def __init__(self, feat, coord, class_id=-1): - """ - lane feat object - :param feat: lane embeddng feats [feature_1, feature_2, ...] - :param coord: lane coordinates [x, y] - :param class_id: lane class id - """ - self._feat = feat - self._coord = coord - self._class_id = class_id - - @property - def feat(self): - """ - - :return: - """ - return self._feat - - @feat.setter - def feat(self, value): - """ - - :param value: - :return: - """ - if not isinstance(value, np.ndarray): - value = np.array(value, dtype=np.float64) - - if value.dtype != np.float32: - value = np.array(value, dtype=np.float64) - - self._feat = value - - @property - def coord(self): - """ - - :return: - """ - return self._coord - - @coord.setter - def coord(self, value): - """ - - :param value: - :return: - """ - if not isinstance(value, np.ndarray): - value = np.array(value) - - if value.dtype != np.int32: - value = np.array(value, dtype=np.int32) - - self._coord = value - - @property - def class_id(self): - """ - - :return: - """ - return self._class_id - - @class_id.setter - def class_id(self, value): - """ - - :param value: - :return: - """ - if not isinstance(value, np.int64): - raise ValueError('Class id must be integer') - - self._class_id = value - - -class _LaneNetCluster(object): - """ - Instance segmentation result cluster - """ - - def __init__(self, cfg): - """ - - """ - self._color_map = [np.array([255, 0, 0]), - np.array([0, 255, 0]), - np.array([0, 0, 255]), - np.array([125, 125, 0]), - np.array([0, 125, 125]), - np.array([125, 0, 125]), - np.array([50, 100, 50]), - np.array([100, 50, 100])] - self._cfg = cfg - - def _embedding_feats_dbscan_cluster(self, embedding_image_feats): - """ - dbscan cluster - :param embedding_image_feats: - :return: - """ - db = DBSCAN(eps=self._cfg.POSTPROCESS.DBSCAN_EPS, min_samples=self._cfg.POSTPROCESS.DBSCAN_MIN_SAMPLES) - try: - features = StandardScaler().fit_transform(embedding_image_feats) - db.fit(features) - except Exception as err: - LOG.error(err) - ret = { - 'origin_features': None, - 'cluster_nums': 0, - 'db_labels': None, - 'unique_labels': None, - 'cluster_center': None - } - return ret - db_labels = db.labels_ - unique_labels = np.unique(db_labels) - - num_clusters = len(unique_labels) - cluster_centers = db.components_ - - ret = { - 'origin_features': features, - 'cluster_nums': num_clusters, - 'db_labels': db_labels, - 'unique_labels': unique_labels, - 'cluster_center': cluster_centers - } - - return ret - - @staticmethod - def _get_lane_embedding_feats(binary_seg_ret, instance_seg_ret): - """ - get lane embedding features according the binary seg result - :param binary_seg_ret: - :param instance_seg_ret: - :return: - """ - idx = np.where(binary_seg_ret == 255) - lane_embedding_feats = instance_seg_ret[idx] - lane_coordinate = np.vstack((idx[1], idx[0])).transpose() - - assert lane_embedding_feats.shape[0] == lane_coordinate.shape[0] - - ret = { - 'lane_embedding_feats': lane_embedding_feats, - 'lane_coordinates': lane_coordinate - } - - return ret - - def apply_lane_feats_cluster(self, binary_seg_result, instance_seg_result): - """ - - :param binary_seg_result: - :param instance_seg_result: - :return: - """ - # get embedding feats and coords - get_lane_embedding_feats_result = self._get_lane_embedding_feats( - binary_seg_ret=binary_seg_result, - instance_seg_ret=instance_seg_result - ) - - # dbscan cluster - dbscan_cluster_result = self._embedding_feats_dbscan_cluster( - embedding_image_feats=get_lane_embedding_feats_result['lane_embedding_feats'] - ) - - mask = np.zeros(shape=[binary_seg_result.shape[0], binary_seg_result.shape[1], 3], dtype=np.uint8) - db_labels = dbscan_cluster_result['db_labels'] - unique_labels = dbscan_cluster_result['unique_labels'] - coord = get_lane_embedding_feats_result['lane_coordinates'] - - if db_labels is None: - return None, None - - lane_coords = [] - for index, label in enumerate(unique_labels.tolist()): - if label == -1: - continue - idx = np.where(db_labels == label) - pix_coord_idx = tuple((coord[idx][:, 1], coord[idx][:, 0])) - mask[pix_coord_idx] = self._color_map[index] - lane_coords.append(coord[idx]) - - return mask, lane_coords - - -class LaneNetPostProcessor(object): - """ - lanenet post process for lane generation - """ - def __init__(self, cfg, ipm_remap_file_path='./eval_data/tusimple_ipm_remap.yml'): - """ - - :param ipm_remap_file_path: ipm generate file path - """ - assert ops.exists(ipm_remap_file_path), '{:s} not exist'.format(ipm_remap_file_path) - - self._cfg = cfg - self._cluster = _LaneNetCluster(cfg=cfg) - self._ipm_remap_file_path = ipm_remap_file_path - - remap_file_load_ret = self._load_remap_matrix() - self._remap_to_ipm_x = remap_file_load_ret['remap_to_ipm_x'] - self._remap_to_ipm_y = remap_file_load_ret['remap_to_ipm_y'] - - self._color_map = [np.array([255, 0, 0]), - np.array([0, 255, 0]), - np.array([0, 0, 255]), - np.array([125, 125, 0]), - np.array([0, 125, 125]), - np.array([125, 0, 125]), - np.array([50, 100, 50]), - np.array([100, 50, 100])] - - def _load_remap_matrix(self): - """ - - :return: - """ - fs = cv2.FileStorage(self._ipm_remap_file_path, cv2.FILE_STORAGE_READ) - - remap_to_ipm_x = fs.getNode('remap_ipm_x').mat() - remap_to_ipm_y = fs.getNode('remap_ipm_y').mat() - - ret = { - 'remap_to_ipm_x': remap_to_ipm_x, - 'remap_to_ipm_y': remap_to_ipm_y, - } - - fs.release() - - return ret - - def postprocess(self, binary_seg_result, instance_seg_result=None, - min_area_threshold=100, source_image=None, - with_lane_fit=True, data_source='tusimple'): - """ - - :param binary_seg_result: - :param instance_seg_result: - :param min_area_threshold: - :param source_image: - :param with_lane_fit: - :param data_source: - :return: - """ - # convert binary_seg_result - binary_seg_result = np.array(binary_seg_result * 255, dtype=np.uint8) - - # apply image morphology operation to fill in the hold and reduce the small area - morphological_ret = _morphological_process(binary_seg_result, kernel_size=5) - - connect_components_analysis_ret = _connect_components_analysis(image=morphological_ret) - - labels = connect_components_analysis_ret[1] - stats = connect_components_analysis_ret[2] - for index, stat in enumerate(stats): - if stat[4] <= min_area_threshold: - idx = np.where(labels == index) - morphological_ret[idx] = 0 - - # apply embedding features cluster - mask_image, lane_coords = self._cluster.apply_lane_feats_cluster( - binary_seg_result=morphological_ret, - instance_seg_result=instance_seg_result - ) - - if mask_image is None: - return { - 'mask_image': None, - 'fit_params': None, - 'source_image': None, - } - if not with_lane_fit: - tmp_mask = cv2.resize( - mask_image, - dsize=(source_image.shape[1], source_image.shape[0]), - interpolation=cv2.INTER_NEAREST - ) - source_image = cv2.addWeighted(source_image, 0.6, tmp_mask, 0.4, 0.0, dst=source_image) - return { - 'mask_image': mask_image, - 'fit_params': None, - 'source_image': source_image, - } - - # lane line fit - fit_params = [] - src_lane_pts = [] # lane pts every single lane - for lane_index, coords in enumerate(lane_coords): - if data_source == 'tusimple': - tmp_mask = np.zeros(shape=(720, 1280), dtype=np.uint8) - tmp_mask[tuple((np.int_(coords[:, 1] * 720 / 256), np.int_(coords[:, 0] * 1280 / 512)))] = 255 - else: - raise ValueError('Wrong data source now only support tusimple') - tmp_ipm_mask = cv2.remap( - tmp_mask, - self._remap_to_ipm_x, - self._remap_to_ipm_y, - interpolation=cv2.INTER_NEAREST - ) - nonzero_y = np.array(tmp_ipm_mask.nonzero()[0]) - nonzero_x = np.array(tmp_ipm_mask.nonzero()[1]) - - fit_param = np.polyfit(nonzero_y, nonzero_x, 2) - fit_params.append(fit_param) - - [ipm_image_height, ipm_image_width] = tmp_ipm_mask.shape - plot_y = np.linspace(10, ipm_image_height, ipm_image_height - 10) - fit_x = fit_param[0] * plot_y ** 2 + fit_param[1] * plot_y + fit_param[2] - # fit_x = fit_param[0] * plot_y ** 3 + fit_param[1] * plot_y ** 2 + fit_param[2] * plot_y + fit_param[3] - - lane_pts = [] - for index in range(0, plot_y.shape[0], 5): - src_x = self._remap_to_ipm_x[ - int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))] - if src_x <= 0: - continue - src_y = self._remap_to_ipm_y[ - int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))] - src_y = src_y if src_y > 0 else 0 - - lane_pts.append([src_x, src_y]) - - src_lane_pts.append(lane_pts) - - # tusimple test data sample point along y axis every 10 pixels - source_image_width = source_image.shape[1] - for index, single_lane_pts in enumerate(src_lane_pts): - single_lane_pt_x = np.array(single_lane_pts, dtype=np.float32)[:, 0] - single_lane_pt_y = np.array(single_lane_pts, dtype=np.float32)[:, 1] - if data_source == 'tusimple': - start_plot_y = 240 - end_plot_y = 720 - else: - raise ValueError('Wrong data source now only support tusimple') - step = int(math.floor((end_plot_y - start_plot_y) / 10)) - for plot_y in np.linspace(start_plot_y, end_plot_y, step): - diff = single_lane_pt_y - plot_y - fake_diff_bigger_than_zero = diff.copy() - fake_diff_smaller_than_zero = diff.copy() - fake_diff_bigger_than_zero[np.where(diff <= 0)] = float('inf') - fake_diff_smaller_than_zero[np.where(diff > 0)] = float('-inf') - idx_low = np.argmax(fake_diff_smaller_than_zero) - idx_high = np.argmin(fake_diff_bigger_than_zero) - - previous_src_pt_x = single_lane_pt_x[idx_low] - previous_src_pt_y = single_lane_pt_y[idx_low] - last_src_pt_x = single_lane_pt_x[idx_high] - last_src_pt_y = single_lane_pt_y[idx_high] - - if previous_src_pt_y < start_plot_y or last_src_pt_y < start_plot_y or \ - fake_diff_smaller_than_zero[idx_low] == float('-inf') or \ - fake_diff_bigger_than_zero[idx_high] == float('inf'): - continue - - interpolation_src_pt_x = (abs(previous_src_pt_y - plot_y) * previous_src_pt_x + - abs(last_src_pt_y - plot_y) * last_src_pt_x) / \ - (abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y)) - interpolation_src_pt_y = (abs(previous_src_pt_y - plot_y) * previous_src_pt_y + - abs(last_src_pt_y - plot_y) * last_src_pt_y) / \ - (abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y)) - - if interpolation_src_pt_x > source_image_width or interpolation_src_pt_x < 10: - continue - - lane_color = self._color_map[index].tolist() - cv2.circle(source_image, (int(interpolation_src_pt_x), - int(interpolation_src_pt_y)), 5, lane_color, -1) - ret = { - 'mask_image': mask_image, - 'fit_params': fit_params, - 'source_image': source_image, - } - - return ret diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__init__.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__init__.py deleted file mode 100644 index e076ab98d..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# @Time : 2020/6/11 下午5:46 -# @Author : MaybeShewill-CV -# @Site : https://github.com/MaybeShewill-CV/lanenet-lane-detection -# @File : __init__.py -# @IDE: PyCharm diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__pycache__/__init__.cpython-36.pyc b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/config_utils/__pycache__/__init__.cpython-36.pyc deleted file mode 100644 index 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All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Parse config utils -""" -import os -import yaml -import json -import codecs -from ast import literal_eval - - -class Config(dict): - """ - Config class - """ - def __init__(self, *args, **kwargs): - """ - init class - :param args: - :param kwargs: - """ - if 'config_path' in kwargs: - config_content = self._load_config_file(kwargs['config_path']) - super(Config, self).__init__(config_content) - else: - super(Config, self).__init__(*args, **kwargs) - self.immutable = False - - def __setattr__(self, key, value, create_if_not_exist=True): - """ - - :param key: - :param value: - :param create_if_not_exist: - :return: - """ - if key in ["immutable"]: - self.__dict__[key] = value - return - - t = self - keylist = key.split(".") - for k in keylist[:-1]: - t = t.__getattr__(k, create_if_not_exist) - - t.__getattr__(keylist[-1], create_if_not_exist) - t[keylist[-1]] = value - - def __getattr__(self, key, create_if_not_exist=True): - """ - - :param key: - :param create_if_not_exist: - :return: - """ - if key in ["immutable"]: - return self.__dict__[key] - - if key not in self: - if not create_if_not_exist: - raise KeyError - self[key] = Config() - if isinstance(self[key], dict): - self[key] = Config(self[key]) - return self[key] - - def __setitem__(self, key, value): - """ - - :param key: - :param value: - :return: - """ - if self.immutable: - raise AttributeError( - 'Attempted to set "{}" to "{}", but SegConfig is immutable'. - format(key, value)) - # - if isinstance(value, str): - try: - value = literal_eval(value) - except ValueError: - pass - except SyntaxError: - pass - super(Config, self).__setitem__(key, value) - - @staticmethod - def _load_config_file(config_file_path): - """ - - :param config_file_path - :return: - """ - if not os.access(config_file_path, os.R_OK): - raise OSError('Config file: {:s}, can not be read'.format(config_file_path)) - with open(config_file_path, 'r') as f: - config_content = yaml.safe_load(f) - - return config_content - - def update_from_config(self, other): - """ - - :param other: - :return: - """ - if isinstance(other, dict): - other = Config(other) - assert isinstance(other, Config) - diclist = [("", other)] - while len(diclist): - prefix, tdic = diclist[0] - diclist = diclist[1:] - for key, value in tdic.items(): - key = "{}.{}".format(prefix, key) if prefix else key - if isinstance(value, dict): - diclist.append((key, value)) - continue - try: - self.__setattr__(key, value, create_if_not_exist=False) - except KeyError: - raise KeyError('Non-existent config key: {}'.format(key)) - - def check_and_infer(self): - """ - - :return: - """ - if self.DATASET.IMAGE_TYPE in ['rgb', 'gray']: - self.DATASET.DATA_DIM = 3 - elif self.DATASET.IMAGE_TYPE in ['rgba']: - self.DATASET.DATA_DIM = 4 - else: - raise KeyError( - 'DATASET.IMAGE_TYPE config error, only support `rgb`, `gray` and `rgba`' - ) - if self.MEAN is not None: - self.DATASET.PADDING_VALUE = [x * 255.0 for x in self.MEAN] - - if not self.TRAIN_CROP_SIZE: - raise ValueError( - 'TRAIN_CROP_SIZE is empty! Please set a pair of values in format (width, height)' - ) - - if not self.EVAL_CROP_SIZE: - raise ValueError( - 'EVAL_CROP_SIZE is empty! Please set a pair of values in format (width, height)' - ) - - # Ensure file list is use UTF-8 encoding - train_sets = codecs.open(self.DATASET.TRAIN_FILE_LIST, 'r', 'utf-8').readlines() - val_sets = codecs.open(self.DATASET.VAL_FILE_LIST, 'r', 'utf-8').readlines() - test_sets = codecs.open(self.DATASET.TEST_FILE_LIST, 'r', 'utf-8').readlines() - self.DATASET.TRAIN_TOTAL_IMAGES = len(train_sets) - self.DATASET.VAL_TOTAL_IMAGES = len(val_sets) - self.DATASET.TEST_TOTAL_IMAGES = len(test_sets) - - if self.MODEL.MODEL_NAME == 'icnet' and \ - len(self.MODEL.MULTI_LOSS_WEIGHT) != 3: - self.MODEL.MULTI_LOSS_WEIGHT = [1.0, 0.4, 0.16] - - def update_from_list(self, config_list): - if len(config_list) % 2 != 0: - raise ValueError( - "Command line options config format error! Please check it: {}". - format(config_list)) - for key, value in zip(config_list[0::2], config_list[1::2]): - try: - self.__setattr__(key, value, create_if_not_exist=False) - except KeyError: - raise KeyError('Non-existent config key: {}'.format(key)) - - def update_from_file(self, config_file): - """ - - :param config_file: - :return: - """ - with codecs.open(config_file, 'r', 'utf-8') as f: - dic = yaml.safe_load(f) - self.update_from_config(dic) - - def set_immutable(self, immutable): - """ - - :param immutable: - :return: - """ - self.immutable = immutable - for value in self.values(): - if isinstance(value, Config): - value.set_immutable(immutable) - - def is_immutable(self): - """ - - :return: - """ - return self.immutable - - def dump_to_json_file(self, f_obj): - """ - - :param f_obj: - :return: - """ - origin_dict = dict() - for key, val in self.items(): - if isinstance(val, Config): - origin_dict.update({key: dict(val)}) - elif isinstance(val, dict): - origin_dict.update({key: val}) - else: - raise TypeError('Not supported type {}'.format(type(val))) - return json.dump(origin_dict, f_obj) - - -lanenet_cfg = Config(config_path='E:/evaluate/config/tusimple_lanenet.yaml') diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__init__.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__init__.py deleted file mode 100644 index 506b5b98e..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# @Time : 2019/11/14 下午8:18 -# @Author : MaybeShewill-CV -# @Site : https://github.com/MaybeShewill-CV/bisenetv2-tensorflow -# @File : __init__.py.py -# @IDE: PyCharm -""" -日志配置类 -""" \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/local_utils/log_util/__pycache__/__init__.cpython-37.pyc 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All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -BiseNet V2 Model -""" -import collections - -import tensorflow as tf - -from semantic_segmentation_zoo import cnn_basenet -from local_utils.config_utils import parse_config_utils - - -class _StemBlock(cnn_basenet.CNNBaseModel): - """ - implementation of stem block module - """ - def __init__(self, phase): - """ - - :param phase: - """ - super(_StemBlock, self).__init__() - self._phase = phase - self._is_training = self._is_net_for_training() - self._padding = 'SAME' - - def _is_net_for_training(self): - """ - if the net is used for training or not - :return: - """ - if isinstance(self._phase, tf.Tensor): - phase = self._phase - else: - phase = tf.constant(self._phase, dtype=tf.string) - return tf.equal(phase, tf.constant('train', dtype=tf.string)) - - def _conv_block(self, input_tensor, k_size, output_channels, stride, - name, padding='SAME', use_bias=False, need_activate=False): - """ - conv block in attention refine - :param input_tensor: - :param k_size: - :param output_channels: - :param stride: - :param name: - :param padding: - :param use_bias: - :return: - """ - with tf.variable_scope(name_or_scope=name): - result = self.conv2d( - inputdata=input_tensor, - out_channel=output_channels, - kernel_size=k_size, - padding=padding, - stride=stride, - use_bias=use_bias, - name='conv' - ) - if need_activate: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - result = self.relu(inputdata=result, name='relu') - else: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - return result - - def __call__(self, *args, **kwargs): - """ - - :param args: - :param kwargs: - :return: - """ - input_tensor = kwargs['input_tensor'] - name_scope = kwargs['name'] - output_channels = kwargs['output_channels'] - if 'padding' in kwargs: - self._padding = kwargs['padding'] - with tf.variable_scope(name_or_scope=name_scope): - input_tensor = self._conv_block( - input_tensor=input_tensor, - k_size=3, - output_channels=output_channels, - stride=2, - name='conv_block_1', - padding=self._padding, - use_bias=False, - need_activate=True - ) - with tf.variable_scope(name_or_scope='downsample_branch_left'): - branch_left_output = self._conv_block( - input_tensor=input_tensor, - k_size=1, - output_channels=int(output_channels / 2), - stride=1, - name='1x1_conv_block', - padding=self._padding, - use_bias=False, - need_activate=True - ) - branch_left_output = self._conv_block( - input_tensor=branch_left_output, - k_size=3, - output_channels=output_channels, - stride=2, - name='3x3_conv_block', - padding=self._padding, - use_bias=False, - need_activate=True - ) - with tf.variable_scope(name_or_scope='downsample_branch_right'): - branch_right_output = self.maxpooling( - inputdata=input_tensor, - kernel_size=3, - stride=2, - padding=self._padding, - name='maxpooling_block' - ) - result = tf.concat([branch_left_output, branch_right_output], axis=-1, name='concate_features') - result = self._conv_block( - input_tensor=result, - k_size=3, - output_channels=output_channels, - stride=1, - name='final_conv_block', - padding=self._padding, - use_bias=False, - need_activate=True - ) - return result - - -class _ContextEmbedding(cnn_basenet.CNNBaseModel): - """ - implementation of context embedding module in bisenetv2 - """ - def __init__(self, phase): - """ - - :param phase: - """ - super(_ContextEmbedding, self).__init__() - self._phase = phase - self._is_training = self._is_net_for_training() - self._padding = 'SAME' - - def _is_net_for_training(self): - """ - if the net is used for training or not - :return: - """ - if isinstance(self._phase, tf.Tensor): - phase = self._phase - else: - phase = tf.constant(self._phase, dtype=tf.string) - return tf.equal(phase, tf.constant('train', dtype=tf.string)) - - def _conv_block(self, input_tensor, k_size, output_channels, stride, - name, padding='SAME', use_bias=False, need_activate=False): - """ - conv block in attention refine - :param input_tensor: - :param k_size: - :param output_channels: - :param stride: - :param name: - :param padding: - :param use_bias: - :return: - """ - with tf.variable_scope(name_or_scope=name): - result = self.conv2d( - inputdata=input_tensor, - out_channel=output_channels, - kernel_size=k_size, - padding=padding, - stride=stride, - use_bias=use_bias, - name='conv' - ) - if need_activate: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - result = self.relu(inputdata=result, name='relu') - else: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - return result - - def __call__(self, *args, **kwargs): - """ - - :param args: - :param kwargs: - :return: - """ - input_tensor = kwargs['input_tensor'] - name_scope = kwargs['name'] - output_channels = input_tensor.get_shape().as_list()[-1] - if 'padding' in kwargs: - self._padding = kwargs['padding'] - with tf.variable_scope(name_or_scope=name_scope): - result = tf.reduce_mean(input_tensor, axis=[1, 2], keepdims=True, name='global_avg_pooling') - result = self.layerbn(result, self._is_training, 'bn') - result = self._conv_block( - input_tensor=result, - k_size=1, - output_channels=output_channels, - stride=1, - name='conv_block_1', - padding=self._padding, - use_bias=False, - need_activate=True - ) - result = tf.add(result, input_tensor, name='fused_features') - result = self.conv2d( - inputdata=result, - out_channel=output_channels, - kernel_size=3, - padding=self._padding, - stride=1, - use_bias=False, - name='final_conv_block' - ) - return result - - -class _GatherExpansion(cnn_basenet.CNNBaseModel): - """ - implementation of gather and expansion module in bisenetv2 - """ - def __init__(self, phase): - """ - - :param phase: - """ - super(_GatherExpansion, self).__init__() - self._phase = phase - self._is_training = self._is_net_for_training() - self._padding = 'SAME' - self._stride = 1 - self._expansion_factor = 6 - - def _is_net_for_training(self): - """ - if the net is used for training or not - :return: - """ - if isinstance(self._phase, tf.Tensor): - phase = self._phase - else: - phase = tf.constant(self._phase, dtype=tf.string) - return tf.equal(phase, tf.constant('train', dtype=tf.string)) - - def _conv_block(self, input_tensor, k_size, output_channels, stride, - name, padding='SAME', use_bias=False, need_activate=False): - """ - conv block in attention refine - :param input_tensor: - :param k_size: - :param output_channels: - :param stride: - :param name: - :param padding: - :param use_bias: - :return: - """ - with tf.variable_scope(name_or_scope=name): - result = self.conv2d( - inputdata=input_tensor, - out_channel=output_channels, - kernel_size=k_size, - padding=padding, - stride=stride, - use_bias=use_bias, - name='conv' - ) - if need_activate: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - result = self.relu(inputdata=result, name='relu') - else: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - return result - - def _apply_ge_when_stride_equal_one(self, input_tensor, e, name): - """ - - :param input_tensor: - :param e: - :param name - :return: - """ - input_tensor_channels = input_tensor.get_shape().as_list()[-1] - with tf.variable_scope(name_or_scope=name): - result = self._conv_block( - input_tensor=input_tensor, - k_size=3, - output_channels=input_tensor_channels, - stride=1, - name='3x3_conv_block', - padding=self._padding, - use_bias=False, - need_activate=True - ) - result = self.depthwise_conv( - input_tensor=result, - kernel_size=3, - depth_multiplier=e, - padding=self._padding, - stride=1, - name='depthwise_conv_block' - ) - result = self.layerbn(result, self._is_training, name='dw_bn') - result = self._conv_block( - input_tensor=result, - k_size=1, - output_channels=input_tensor_channels, - stride=1, - name='1x1_conv_block', - padding=self._padding, - use_bias=False, - need_activate=False - ) - result = tf.add(input_tensor, result, name='fused_features') - result = self.relu(result, name='ge_output') - return result - - def _apply_ge_when_stride_equal_two(self, input_tensor, output_channels, e, name): - """ - - :param input_tensor: - :param output_channels: - :param e: - :param name - :return: - """ - input_tensor_channels = input_tensor.get_shape().as_list()[-1] - with tf.variable_scope(name_or_scope=name): - input_proj = self.depthwise_conv( - input_tensor=input_tensor, - kernel_size=3, - name='input_project_dw_conv_block', - depth_multiplier=1, - padding=self._padding, - stride=self._stride - ) - input_proj = self.layerbn(input_proj, self._is_training, name='input_project_bn') - input_proj = self._conv_block( - input_tensor=input_proj, - k_size=1, - output_channels=output_channels, - stride=1, - name='input_project_1x1_conv_block', - padding=self._padding, - use_bias=False, - need_activate=False - ) - - result = self._conv_block( - input_tensor=input_tensor, - k_size=3, - output_channels=input_tensor_channels, - stride=1, - name='3x3_conv_block', - padding=self._padding, - use_bias=False, - need_activate=True - ) - result = self.depthwise_conv( - input_tensor=result, - kernel_size=3, - depth_multiplier=e, - padding=self._padding, - stride=2, - name='depthwise_conv_block_1' - ) - result = self.layerbn(result, self._is_training, name='dw_bn_1') - result = self.depthwise_conv( - input_tensor=result, - kernel_size=3, - depth_multiplier=1, - padding=self._padding, - stride=1, - name='depthwise_conv_block_2' - ) - result = self.layerbn(result, self._is_training, name='dw_bn_2') - result = self._conv_block( - input_tensor=result, - k_size=1, - output_channels=output_channels, - stride=1, - name='1x1_conv_block', - padding=self._padding, - use_bias=False, - need_activate=False - ) - result = tf.add(input_proj, result, name='fused_features') - result = self.relu(result, name='ge_output') - return result - - def __call__(self, *args, **kwargs): - """ - - :param args: - :param kwargs: - :return: - """ - input_tensor = kwargs['input_tensor'] - name_scope = kwargs['name'] - output_channels = input_tensor.get_shape().as_list()[-1] - if 'output_channels' in kwargs: - output_channels = kwargs['output_channels'] - if 'padding' in kwargs: - self._padding = kwargs['padding'] - if 'stride' in kwargs: - self._stride = kwargs['stride'] - if 'e' in kwargs: - self._expansion_factor = kwargs['e'] - - with tf.variable_scope(name_or_scope=name_scope): - if self._stride == 1: - result = self._apply_ge_when_stride_equal_one( - input_tensor=input_tensor, - e=self._expansion_factor, - name='stride_equal_one_module' - ) - elif self._stride == 2: - result = self._apply_ge_when_stride_equal_two( - input_tensor=input_tensor, - output_channels=output_channels, - e=self._expansion_factor, - name='stride_equal_two_module' - ) - else: - raise NotImplementedError('No function matched with stride of {}'.format(self._stride)) - return result - - -class _GuidedAggregation(cnn_basenet.CNNBaseModel): - """ - implementation of guided aggregation module in bisenetv2 - """ - - def __init__(self, phase): - """ - - :param phase: - """ - super(_GuidedAggregation, self).__init__() - self._phase = phase - self._is_training = self._is_net_for_training() - self._padding = 'SAME' - - def _is_net_for_training(self): - """ - if the net is used for training or not - :return: - """ - if isinstance(self._phase, tf.Tensor): - phase = self._phase - else: - phase = tf.constant(self._phase, dtype=tf.string) - return tf.equal(phase, tf.constant('train', dtype=tf.string)) - - def _conv_block(self, input_tensor, k_size, output_channels, stride, - name, padding='SAME', use_bias=False, need_activate=False): - """ - conv block in attention refine - :param input_tensor: - :param k_size: - :param output_channels: - :param stride: - :param name: - :param padding: - :param use_bias: - :return: - """ - with tf.variable_scope(name_or_scope=name): - result = self.conv2d( - inputdata=input_tensor, - out_channel=output_channels, - kernel_size=k_size, - padding=padding, - stride=stride, - use_bias=use_bias, - name='conv' - ) - if need_activate: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - result = self.relu(inputdata=result, name='relu') - else: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - return result - - def __call__(self, *args, **kwargs): - """ - - :param args: - :param kwargs: - :return: - """ - detail_input_tensor = kwargs['detail_input_tensor'] - semantic_input_tensor = kwargs['semantic_input_tensor'] - name_scope = kwargs['name'] - output_channels = detail_input_tensor.get_shape().as_list()[-1] - if 'padding' in kwargs: - self._padding = kwargs['padding'] - - with tf.variable_scope(name_or_scope=name_scope): - with tf.variable_scope(name_or_scope='detail_branch'): - detail_branch_remain = self.depthwise_conv( - input_tensor=detail_input_tensor, - kernel_size=3, - name='3x3_dw_conv_block', - depth_multiplier=1, - padding=self._padding, - stride=1 - ) - detail_branch_remain = self.layerbn(detail_branch_remain, self._is_training, name='bn_1') - detail_branch_remain = self.conv2d( - inputdata=detail_branch_remain, - out_channel=output_channels, - kernel_size=1, - padding=self._padding, - stride=1, - use_bias=False, - name='1x1_conv_block' - ) - - detail_branch_downsample = self._conv_block( - input_tensor=detail_input_tensor, - k_size=3, - output_channels=output_channels, - stride=2, - name='3x3_conv_block', - padding=self._padding, - use_bias=False, - need_activate=False - ) - detail_branch_downsample = self.avgpooling( - inputdata=detail_branch_downsample, - kernel_size=3, - stride=2, - padding=self._padding, - name='avg_pooling_block' - ) - - with tf.variable_scope(name_or_scope='semantic_branch'): - semantic_branch_remain = self.depthwise_conv( - input_tensor=semantic_input_tensor, - kernel_size=3, - name='3x3_dw_conv_block', - depth_multiplier=1, - padding=self._padding, - stride=1 - ) - semantic_branch_remain = self.layerbn(semantic_branch_remain, self._is_training, name='bn_1') - semantic_branch_remain = self.conv2d( - inputdata=semantic_branch_remain, - out_channel=output_channels, - kernel_size=1, - padding=self._padding, - stride=1, - use_bias=False, - name='1x1_conv_block' - ) - semantic_branch_remain = self.sigmoid(semantic_branch_remain, name='semantic_remain_sigmoid') - - semantic_branch_upsample = self._conv_block( - input_tensor=semantic_input_tensor, - k_size=3, - output_channels=output_channels, - stride=1, - name='3x3_conv_block', - padding=self._padding, - use_bias=False, - need_activate=False - ) - semantic_branch_upsample = tf.image.resize_bilinear( - semantic_branch_upsample, - detail_input_tensor.shape[1:3], - name='semantic_upsample_features' - ) - semantic_branch_upsample = self.sigmoid(semantic_branch_upsample, name='semantic_upsample_sigmoid') - - with tf.variable_scope(name_or_scope='aggregation_features'): - guided_features_remain = tf.multiply( - detail_branch_remain, - semantic_branch_upsample, - name='guided_detail_features' - ) - guided_features_downsample = tf.multiply( - detail_branch_downsample, - semantic_branch_remain, - name='guided_semantic_features' - ) - guided_features_upsample = tf.image.resize_bilinear( - guided_features_downsample, - detail_input_tensor.shape[1:3], - name='guided_upsample_features' - ) - guided_features = tf.add(guided_features_remain, guided_features_upsample, name='fused_features') - guided_features = self._conv_block( - input_tensor=guided_features, - k_size=3, - output_channels=output_channels, - stride=1, - name='aggregation_feature_output', - padding=self._padding, - use_bias=False, - need_activate=True - ) - return guided_features - - -class _SegmentationHead(cnn_basenet.CNNBaseModel): - """ - implementation of segmentation head in bisenet v2 - """ - def __init__(self, phase): - """ - - """ - super(_SegmentationHead, self).__init__() - self._phase = phase - self._is_training = self._is_net_for_training() - self._padding = 'SAME' - - def _is_net_for_training(self): - """ - if the net is used for training or not - :return: - """ - if isinstance(self._phase, tf.Tensor): - phase = self._phase - else: - phase = tf.constant(self._phase, dtype=tf.string) - return tf.equal(phase, tf.constant('train', dtype=tf.string)) - - def _conv_block(self, input_tensor, k_size, output_channels, stride, - name, padding='SAME', use_bias=False, need_activate=False): - """ - conv block in attention refine - :param input_tensor: - :param k_size: - :param output_channels: - :param stride: - :param name: - :param padding: - :param use_bias: - :return: - """ - with tf.variable_scope(name_or_scope=name): - result = self.conv2d( - inputdata=input_tensor, - out_channel=output_channels, - kernel_size=k_size, - padding=padding, - stride=stride, - use_bias=use_bias, - name='conv' - ) - if need_activate: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - result = self.relu(inputdata=result, name='relu') - else: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - return result - - def __call__(self, *args, **kwargs): - """ - - :param args: - :param kwargs: - :return: - """ - input_tensor = kwargs['input_tensor'] - name_scope = kwargs['name'] - ratio = kwargs['upsample_ratio'] - input_tensor_size = input_tensor.get_shape().as_list()[1:3] - output_tensor_size = [int(tmp * ratio) for tmp in input_tensor_size] - feature_dims = kwargs['feature_dims'] - classes_nums = kwargs['classes_nums'] - if 'padding' in kwargs: - self._padding = kwargs['padding'] - - with tf.variable_scope(name_or_scope=name_scope): - result = self._conv_block( - input_tensor=input_tensor, - k_size=3, - output_channels=feature_dims, - stride=1, - name='3x3_conv_block', - padding=self._padding, - use_bias=False, - need_activate=True - ) - result = self.conv2d( - inputdata=result, - out_channel=classes_nums, - kernel_size=1, - padding=self._padding, - stride=1, - use_bias=False, - name='1x1_conv_block' - ) - result = tf.image.resize_bilinear( - result, - output_tensor_size, - name='segmentation_head_logits' - ) - return result - - -class BiseNetV2(cnn_basenet.CNNBaseModel): - """ - implementation of bisenet v2 - """ - def __init__(self, phase, cfg): - """ - - """ - super(BiseNetV2, self).__init__() - self._cfg = cfg - self._phase = phase - self._is_training = self._is_net_for_training() - - # set model hyper params - self._class_nums = self._cfg.DATASET.NUM_CLASSES - self._weights_decay = self._cfg.SOLVER.WEIGHT_DECAY - self._loss_type = self._cfg.SOLVER.LOSS_TYPE - self._enable_ohem = self._cfg.SOLVER.OHEM.ENABLE - if self._enable_ohem: - self._ohem_score_thresh = self._cfg.SOLVER.OHEM.SCORE_THRESH - self._ohem_min_sample_nums = self._cfg.SOLVER.OHEM.MIN_SAMPLE_NUMS - self._ge_expand_ratio = self._cfg.MODEL.BISENETV2.GE_EXPAND_RATIO - self._semantic_channel_ratio = self._cfg.MODEL.BISENETV2.SEMANTIC_CHANNEL_LAMBDA - self._seg_head_ratio = self._cfg.MODEL.BISENETV2.SEGHEAD_CHANNEL_EXPAND_RATIO - - # set module used in bisenetv2 - self._se_block = _StemBlock(phase=phase) - self._context_embedding_block = _ContextEmbedding(phase=phase) - self._ge_block = _GatherExpansion(phase=phase) - self._guided_aggregation_block = _GuidedAggregation(phase=phase) - self._seg_head_block = _SegmentationHead(phase=phase) - - # set detail branch channels - self._detail_branch_channels = self._build_detail_branch_hyper_params() - # set semantic branch channels - self._semantic_branch_channels = self._build_semantic_branch_hyper_params() - - # set op block params - self._block_maps = { - 'conv_block': self._conv_block, - 'se': self._se_block, - 'ge': self._ge_block, - 'ce': self._context_embedding_block, - } - - self._net_intermediate_results = collections.OrderedDict() - - def _is_net_for_training(self): - """ - if the net is used for training or not - :return: - """ - if isinstance(self._phase, tf.Tensor): - phase = self._phase - else: - phase = tf.constant(self._phase, dtype=tf.string) - return tf.equal(phase, tf.constant('train', dtype=tf.string)) - - @classmethod - def _build_detail_branch_hyper_params(cls): - """ - - :return: - """ - params = [ - ('stage_1', [('conv_block', 3, 64, 2, 1), ('conv_block', 3, 64, 1, 1)]), - ('stage_2', [('conv_block', 3, 64, 2, 1), ('conv_block', 3, 64, 1, 2)]), - ('stage_3', [('conv_block', 3, 128, 2, 1), ('conv_block', 3, 128, 1, 2)]), - ] - return collections.OrderedDict(params) - - def _build_semantic_branch_hyper_params(self): - """ - - :return: - """ - stage_1_channels = int(self._detail_branch_channels['stage_1'][0][2] * self._semantic_channel_ratio) - stage_3_channels = int(self._detail_branch_channels['stage_3'][0][2] * self._semantic_channel_ratio) - params = [ - ('stage_1', [('se', 3, stage_1_channels, 1, 4, 1)]), - ('stage_3', [('ge', 3, stage_3_channels, self._ge_expand_ratio, 2, 1), - ('ge', 3, stage_3_channels, self._ge_expand_ratio, 1, 1)]), - ('stage_4', [('ge', 3, stage_3_channels * 2, self._ge_expand_ratio, 2, 1), - ('ge', 3, stage_3_channels * 2, self._ge_expand_ratio, 1, 1)]), - ('stage_5', [('ge', 3, stage_3_channels * 4, self._ge_expand_ratio, 2, 1), - ('ge', 3, stage_3_channels * 4, self._ge_expand_ratio, 1, 3), - ('ce', 3, stage_3_channels * 4, self._ge_expand_ratio, 1, 1)]) - ] - return collections.OrderedDict(params) - - def _conv_block(self, input_tensor, k_size, output_channels, stride, - name, padding='SAME', use_bias=False, need_activate=False): - """ - conv block in attention refine - :param input_tensor: - :param k_size: - :param output_channels: - :param stride: - :param name: - :param padding: - :param use_bias: - :return: - """ - with tf.variable_scope(name_or_scope=name): - result = self.conv2d( - inputdata=input_tensor, - out_channel=output_channels, - kernel_size=k_size, - padding=padding, - stride=stride, - use_bias=use_bias, - name='conv' - ) - if need_activate: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - result = self.relu(inputdata=result, name='relu') - else: - result = self.layerbn(inputdata=result, is_training=self._is_training, name='bn', scale=True) - return result - - def build_detail_branch(self, input_tensor, name): - """ - - :param input_tensor: - :param name: - :return: - """ - result = input_tensor - with tf.variable_scope(name_or_scope=name): - for stage_name, stage_params in self._detail_branch_channels.items(): - with tf.variable_scope(stage_name): - for block_index, param in enumerate(stage_params): - block_op = self._block_maps[param[0]] - k_size = param[1] - output_channels = param[2] - stride = param[3] - repeat_times = param[4] - for repeat_index in range(repeat_times): - with tf.variable_scope(name_or_scope='conv_block_{:d}_repeat_{:d}'.format( - block_index + 1, repeat_index + 1)): - if stage_name == 'stage_3' and block_index == 1 and repeat_index == 1: - result = block_op( - input_tensor=result, - k_size=k_size, - output_channels=output_channels, - stride=stride, - name='3x3_conv', - padding='SAME', - use_bias=False, - need_activate=False - ) - else: - result = block_op( - input_tensor=result, - k_size=k_size, - output_channels=output_channels, - stride=stride, - name='3x3_conv', - padding='SAME', - use_bias=False, - need_activate=True - ) - return result - - def build_semantic_branch(self, input_tensor, name, prepare_data_for_booster=False): - """ - - :param input_tensor: - :param name: - :param prepare_data_for_booster: - :return: - """ - seg_head_inputs = collections.OrderedDict() - result = input_tensor - source_input_tensor_size = input_tensor.get_shape().as_list()[1:3] - with tf.variable_scope(name_or_scope=name): - for stage_name, stage_params in self._semantic_branch_channels.items(): - seg_head_input = input_tensor - with tf.variable_scope(stage_name): - for block_index, param in enumerate(stage_params): - block_op_name = param[0] - block_op = self._block_maps[block_op_name] - output_channels = param[2] - expand_ratio = param[3] - stride = param[4] - repeat_times = param[5] - for repeat_index in range(repeat_times): - with tf.variable_scope(name_or_scope='{:s}_block_{:d}_repeat_{:d}'.format( - block_op_name, block_index + 1, repeat_index + 1)): - if block_op_name == 'ge': - result = block_op( - input_tensor=result, - name='gather_expansion_block', - stride=stride, - e=expand_ratio, - output_channels=output_channels - ) - seg_head_input = result - elif block_op_name == 'ce': - result = block_op( - input_tensor=result, - name='context_embedding_block' - ) - elif block_op_name == 'se': - result = block_op( - input_tensor=result, - output_channels=output_channels, - name='stem_block' - ) - seg_head_input = result - else: - raise NotImplementedError('Not support block type: {:s}'.format(block_op_name)) - if prepare_data_for_booster: - result_tensor_size = result.get_shape().as_list()[1:3] - result_tensor_dims = result.get_shape().as_list()[-1] - upsample_ratio = int(source_input_tensor_size[0] / result_tensor_size[0]) - feature_dims = result_tensor_dims * self._seg_head_ratio - seg_head_inputs[stage_name] = self._seg_head_block( - input_tensor=seg_head_input, - name='block_{:d}_seg_head_block'.format(block_index + 1), - upsample_ratio=upsample_ratio, - feature_dims=feature_dims, - classes_nums=self._class_nums - ) - return result, seg_head_inputs - - def build_aggregation_branch(self, detail_output, semantic_output, name): - """ - - :param detail_output: - :param semantic_output: - :param name: - :return: - """ - with tf.variable_scope(name_or_scope=name): - result = self._guided_aggregation_block( - detail_input_tensor=detail_output, - semantic_input_tensor=semantic_output, - name='guided_aggregation_block' - ) - return result - - def build_instance_segmentation_branch(self, input_tensor, name): - """ - - :param input_tensor: - :param name: - :return: - """ - input_tensor_size = input_tensor.get_shape().as_list()[1:3] - output_tensor_size = [int(tmp * 8) for tmp in input_tensor_size] - - with tf.variable_scope(name_or_scope=name): - output_tensor = self._conv_block( - input_tensor=input_tensor, - k_size=3, - output_channels=64, - stride=1, - name='conv_3x3', - use_bias=False, - need_activate=True - ) - output_tensor = self._conv_block( - input_tensor=output_tensor, - k_size=1, - output_channels=128, - stride=1, - name='conv_1x1', - use_bias=False, - need_activate=False - ) - output_tensor = tf.image.resize_bilinear( - output_tensor, - output_tensor_size, - name='instance_logits' - ) - return output_tensor - - def build_binary_segmentation_branch(self, input_tensor, name): - """ - - :param input_tensor: - :param name: - :return: - """ - input_tensor_size = input_tensor.get_shape().as_list()[1:3] - output_tensor_size = [int(tmp * 8) for tmp in input_tensor_size] - - with tf.variable_scope(name_or_scope=name): - output_tensor = self._conv_block( - input_tensor=input_tensor, - k_size=3, - output_channels=64, - stride=1, - name='conv_3x3', - use_bias=False, - need_activate=True - ) - output_tensor = self._conv_block( - input_tensor=output_tensor, - k_size=1, - output_channels=128, - stride=1, - name='conv_1x1', - use_bias=False, - need_activate=True - ) - output_tensor = self._conv_block( - input_tensor=output_tensor, - k_size=1, - output_channels=self._class_nums, - stride=1, - name='final_conv', - use_bias=False, - need_activate=False - ) - output_tensor = tf.image.resize_bilinear( - output_tensor, - output_tensor_size, - name='binary_logits' - ) - return output_tensor - - def build_model(self, input_tensor, name, reuse=False): - """ - - :param input_tensor: - :param name: - :param reuse: - :return: - """ - with tf.variable_scope(name_or_scope=name, reuse=reuse): - # build detail branch - detail_branch_output = self.build_detail_branch( - input_tensor=input_tensor, - name='detail_branch' - ) - # build semantic branch - semantic_branch_output, _ = self.build_semantic_branch( - input_tensor=input_tensor, - name='semantic_branch', - prepare_data_for_booster=False - ) - # build aggregation branch - aggregation_branch_output = self.build_aggregation_branch( - detail_output=detail_branch_output, - semantic_output=semantic_branch_output, - name='aggregation_branch' - ) - # build binary and instance segmentation branch - binary_seg_branch_output = self.build_binary_segmentation_branch( - input_tensor=aggregation_branch_output, - name='binary_segmentation_branch' - ) - instance_seg_branch_output = self.build_instance_segmentation_branch( - input_tensor=aggregation_branch_output, - name='instance_segmentation_branch' - ) - # gather frontend output result - self._net_intermediate_results['binary_segment_logits'] = { - 'data': binary_seg_branch_output, - 'shape': binary_seg_branch_output.get_shape().as_list() - } - self._net_intermediate_results['instance_segment_logits'] = { - 'data': instance_seg_branch_output, - 'shape': instance_seg_branch_output.get_shape().as_list() - } - return self._net_intermediate_results - - -if __name__ == '__main__': - """ - test code - """ - test_in_tensor = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input') - model = BiseNetV2(phase='train', cfg=parse_config_utils.lanenet_cfg) - ret = model.build_model(test_in_tensor, name='bisenetv2') - for layer_name, layer_info in ret.items(): - print('layer name: {:s} shape: {}'.format(layer_name, layer_info['shape'])) - diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/cnn_basenet.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/cnn_basenet.py deleted file mode 100644 index fefb814d2..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/cnn_basenet.py +++ /dev/null @@ -1,549 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -The base convolution neural networks mainly implement some useful cnn functions -""" -import tensorflow as tf -import numpy as np - - -class CNNBaseModel(object): - """ - Base model for other specific cnn ctpn_models - """ - - def __init__(self): - pass - - @staticmethod - def conv2d(inputdata, out_channel, kernel_size, padding='SAME', - stride=1, w_init=None, b_init=None, - split=1, use_bias=True, data_format='NHWC', name=None): - """ - Packing the tensorflow conv2d function. - :param name: op name - :param inputdata: A 4D tensorflow tensor which ust have known number of channels, but can have other - unknown dimensions. - :param out_channel: number of output channel. - :param kernel_size: int so only support square kernel convolution - :param padding: 'VALID' or 'SAME' - :param stride: int so only support square stride - :param w_init: initializer for convolution weights - :param b_init: initializer for bias - :param split: split channels as used in Alexnet mainly group for GPU memory save. - :param use_bias: whether to use bias. - :param data_format: default set to NHWC according tensorflow - :return: tf.Tensor named ``output`` - """ - with tf.variable_scope(name): - in_shape = inputdata.get_shape().as_list() - channel_axis = 3 if data_format == 'NHWC' else 1 - in_channel = in_shape[channel_axis] - assert in_channel is not None, "[Conv2D] Input cannot have unknown channel!" - assert in_channel % split == 0 - assert out_channel % split == 0 - - padding = padding.upper() - - if isinstance(kernel_size, list): - filter_shape = [kernel_size[0], kernel_size[1]] + [in_channel / split, out_channel] - else: - filter_shape = [kernel_size, kernel_size] + [in_channel / split, out_channel] - - if isinstance(stride, list): - strides = [1, stride[0], stride[1], 1] if data_format == 'NHWC' \ - else [1, 1, stride[0], stride[1]] - else: - strides = [1, stride, stride, 1] if data_format == 'NHWC' \ - else [1, 1, stride, stride] - - if w_init is None: - w_init = tf.contrib.layers.variance_scaling_initializer() - if b_init is None: - b_init = tf.constant_initializer() - - w = tf.get_variable('W', filter_shape, initializer=w_init) - b = None - - if use_bias: - b = tf.get_variable('b', [out_channel], initializer=b_init) - - if split == 1: - conv = tf.nn.conv2d(inputdata, w, strides, padding, data_format=data_format) - else: - inputs = tf.split(inputdata, split, channel_axis) - kernels = tf.split(w, split, 3) - outputs = [tf.nn.conv2d(i, k, strides, padding, data_format=data_format) - for i, k in zip(inputs, kernels)] - conv = tf.concat(outputs, channel_axis) - - ret = tf.identity(tf.nn.bias_add(conv, b, data_format=data_format) - if use_bias else conv, name=name) - - return ret - - @staticmethod - def depthwise_conv(input_tensor, kernel_size, name, depth_multiplier=1, - padding='SAME', stride=1): - """ - - :param input_tensor: - :param kernel_size: - :param name: - :param depth_multiplier: - :param padding: - :param stride: - :return: - """ - with tf.variable_scope(name_or_scope=name): - in_shape = input_tensor.get_shape().as_list() - in_channel = in_shape[3] - padding = padding.upper() - - depthwise_filter_shape = [kernel_size, kernel_size] + [in_channel, depth_multiplier] - w_init = tf.contrib.layers.variance_scaling_initializer() - - depthwise_filter = tf.get_variable( - name='depthwise_filter_w', shape=depthwise_filter_shape, - initializer=w_init - ) - - result = tf.nn.depthwise_conv2d( - input=input_tensor, - filter=depthwise_filter, - strides=[1, stride, stride, 1], - padding=padding, - name='depthwise_conv_output' - ) - return result - - @staticmethod - def relu(inputdata, name=None): - """ - - :param name: - :param inputdata: - :return: - """ - return tf.nn.relu(features=inputdata, name=name) - - @staticmethod - def sigmoid(inputdata, name=None): - """ - - :param name: - :param inputdata: - :return: - """ - return tf.nn.sigmoid(x=inputdata, name=name) - - @staticmethod - def maxpooling(inputdata, kernel_size, stride=None, padding='VALID', - data_format='NHWC', name=None): - """ - - :param name: - :param inputdata: - :param kernel_size: - :param stride: - :param padding: - :param data_format: - :return: - """ - padding = padding.upper() - - if stride is None: - stride = kernel_size - - if isinstance(kernel_size, list): - kernel = [1, kernel_size[0], kernel_size[1], 1] if data_format == 'NHWC' else \ - [1, 1, kernel_size[0], kernel_size[1]] - else: - kernel = [1, kernel_size, kernel_size, 1] if data_format == 'NHWC' \ - else [1, 1, kernel_size, kernel_size] - - if isinstance(stride, list): - strides = [1, stride[0], stride[1], 1] if data_format == 'NHWC' \ - else [1, 1, stride[0], stride[1]] - else: - strides = [1, stride, stride, 1] if data_format == 'NHWC' \ - else [1, 1, stride, stride] - - return tf.nn.max_pool(value=inputdata, ksize=kernel, strides=strides, padding=padding, - data_format=data_format, name=name) - - @staticmethod - def avgpooling(inputdata, kernel_size, stride=None, padding='VALID', - data_format='NHWC', name=None): - """ - - :param name: - :param inputdata: - :param kernel_size: - :param stride: - :param padding: - :param data_format: - :return: - """ - if stride is None: - stride = kernel_size - - kernel = [1, kernel_size, kernel_size, 1] if data_format == 'NHWC' \ - else [1, 1, kernel_size, kernel_size] - - strides = [1, stride, stride, 1] if data_format == 'NHWC' else [1, 1, stride, stride] - - return tf.nn.avg_pool(value=inputdata, ksize=kernel, strides=strides, padding=padding, - data_format=data_format, name=name) - - @staticmethod - def globalavgpooling(inputdata, data_format='NHWC', name=None): - """ - - :param name: - :param inputdata: - :param data_format: - :return: - """ - assert inputdata.shape.ndims == 4 - assert data_format in ['NHWC', 'NCHW'] - - axis = [1, 2] if data_format == 'NHWC' else [2, 3] - - return tf.reduce_mean(input_tensor=inputdata, axis=axis, name=name) - - @staticmethod - def layernorm(inputdata, epsilon=1e-5, use_bias=True, use_scale=True, - data_format='NHWC', name=None): - """ - :param name: - :param inputdata: - :param epsilon: epsilon to avoid divide-by-zero. - :param use_bias: whether to use the extra affine transformation or not. - :param use_scale: whether to use the extra affine transformation or not. - :param data_format: - :return: - """ - shape = inputdata.get_shape().as_list() - ndims = len(shape) - assert ndims in [2, 4] - - mean, var = tf.nn.moments(inputdata, list(range(1, len(shape))), keep_dims=True) - - if data_format == 'NCHW': - channnel = shape[1] - new_shape = [1, channnel, 1, 1] - else: - channnel = shape[-1] - new_shape = [1, 1, 1, channnel] - if ndims == 2: - new_shape = [1, channnel] - - if use_bias: - beta = tf.get_variable('beta', [channnel], initializer=tf.constant_initializer()) - beta = tf.reshape(beta, new_shape) - else: - beta = tf.zeros([1] * ndims, name='beta') - if use_scale: - gamma = tf.get_variable('gamma', [channnel], initializer=tf.constant_initializer(1.0)) - gamma = tf.reshape(gamma, new_shape) - else: - gamma = tf.ones([1] * ndims, name='gamma') - - return tf.nn.batch_normalization(inputdata, mean, var, beta, gamma, epsilon, name=name) - - @staticmethod - def instancenorm(inputdata, epsilon=1e-5, data_format='NHWC', use_affine=True, name=None): - """ - - :param name: - :param inputdata: - :param epsilon: - :param data_format: - :param use_affine: - :return: - """ - shape = inputdata.get_shape().as_list() - if len(shape) != 4: - raise ValueError("Input data of instancebn layer has to be 4D tensor") - - if data_format == 'NHWC': - axis = [1, 2] - ch = shape[3] - new_shape = [1, 1, 1, ch] - else: - axis = [2, 3] - ch = shape[1] - new_shape = [1, ch, 1, 1] - if ch is None: - raise ValueError("Input of instancebn require known channel!") - - mean, var = tf.nn.moments(inputdata, axis, keep_dims=True) - - if not use_affine: - return tf.divide(inputdata - mean, tf.sqrt(var + epsilon), name='output') - - beta = tf.get_variable('beta', [ch], initializer=tf.constant_initializer()) - beta = tf.reshape(beta, new_shape) - gamma = tf.get_variable('gamma', [ch], initializer=tf.constant_initializer(1.0)) - gamma = tf.reshape(gamma, new_shape) - return tf.nn.batch_normalization(inputdata, mean, var, beta, gamma, epsilon, name=name) - - @staticmethod - def dropout(inputdata, keep_prob, noise_shape=None, name=None): - """ - - :param name: - :param inputdata: - :param keep_prob: - :param noise_shape: - :return: - """ - return tf.nn.dropout(inputdata, keep_prob=keep_prob, noise_shape=noise_shape, name=name) - - @staticmethod - def fullyconnect(inputdata, out_dim, w_init=None, b_init=None, - use_bias=True, name=None): - """ - Fully-Connected layer, takes a N>1D tensor and returns a 2D tensor. - It is an equivalent of `tf.layers.dense` except for naming conventions. - - :param inputdata: a tensor to be flattened except for the first dimension. - :param out_dim: output dimension - :param w_init: initializer for w. Defaults to `variance_scaling_initializer`. - :param b_init: initializer for b. Defaults to zero - :param use_bias: whether to use bias. - :param name: - :return: tf.Tensor: a NC tensor named ``output`` with attribute `variables`. - """ - shape = inputdata.get_shape().as_list()[1:] - if None not in shape: - inputdata = tf.reshape(inputdata, [-1, int(np.prod(shape))]) - else: - inputdata = tf.reshape(inputdata, tf.stack([tf.shape(inputdata)[0], -1])) - - if w_init is None: - w_init = tf.contrib.layers.variance_scaling_initializer() - if b_init is None: - b_init = tf.constant_initializer() - - ret = tf.layers.dense(inputs=inputdata, activation=lambda x: tf.identity(x, name='output'), - use_bias=use_bias, name=name, - kernel_initializer=w_init, bias_initializer=b_init, - trainable=True, units=out_dim) - return ret - - @staticmethod - def layerbn(inputdata, is_training, name, scale=True): - """ - - :param inputdata: - :param is_training: - :param name: - :param scale: - :return: - """ - - # a = tf.layers.batch_normalization(inputs=inputdata, training=False, name=name, scale=scale, fused=False) - - return tf.layers.batch_normalization(inputs=inputdata, training=False, name=name, scale=scale) - # return inputdata - - - @staticmethod - def layergn(inputdata, name, group_size=32, esp=1e-5): - """ - - :param inputdata: - :param name: - :param group_size: - :param esp: - :return: - """ - with tf.variable_scope(name): - inputdata = tf.transpose(inputdata, [0, 3, 1, 2]) - n, c, h, w = inputdata.get_shape().as_list() - group_size = min(group_size, c) - inputdata = tf.reshape(inputdata, [-1, group_size, c // group_size, h, w]) - mean, var = tf.nn.moments(inputdata, [2, 3, 4], keep_dims=True) - inputdata = (inputdata - mean) / tf.sqrt(var + esp) - - # 每个通道的gamma和beta - gamma = tf.Variable(tf.constant(1.0, shape=[c]), dtype=tf.float32, name='gamma') - beta = tf.Variable(tf.constant(0.0, shape=[c]), dtype=tf.float32, name='beta') - gamma = tf.reshape(gamma, [1, c, 1, 1]) - beta = tf.reshape(beta, [1, c, 1, 1]) - - # 根据论文进行转换 [n, c, h, w, c] 到 [n, h, w, c] - output = tf.reshape(inputdata, [-1, c, h, w]) - output = output * gamma + beta - output = tf.transpose(output, [0, 2, 3, 1]) - - return output - - @staticmethod - def squeeze(inputdata, axis=None, name=None): - """ - - :param inputdata: - :param axis: - :param name: - :return: - """ - return tf.squeeze(input=inputdata, axis=axis, name=name) - - @staticmethod - def deconv2d(inputdata, out_channel, kernel_size, padding='SAME', - stride=1, w_init=None, b_init=None, - use_bias=True, activation=None, data_format='channels_last', - trainable=True, name=None): - """ - Packing the tensorflow conv2d function. - :param name: op name - :param inputdata: A 4D tensorflow tensor which ust have known number of channels, but can have other - unknown dimensions. - :param out_channel: number of output channel. - :param kernel_size: int so only support square kernel convolution - :param padding: 'VALID' or 'SAME' - :param stride: int so only support square stride - :param w_init: initializer for convolution weights - :param b_init: initializer for bias - :param activation: whether to apply a activation func to deconv result - :param use_bias: whether to use bias. - :param data_format: default set to NHWC according tensorflow - :return: tf.Tensor named ``output`` - """ - with tf.variable_scope(name): - in_shape = inputdata.get_shape().as_list() - channel_axis = 3 if data_format == 'channels_last' else 1 - in_channel = in_shape[channel_axis] - assert in_channel is not None, "[Deconv2D] Input cannot have unknown channel!" - - padding = padding.upper() - - if w_init is None: - w_init = tf.contrib.layers.variance_scaling_initializer() - if b_init is None: - b_init = tf.constant_initializer() - - ret = tf.layers.conv2d_transpose(inputs=inputdata, filters=out_channel, - kernel_size=kernel_size, - strides=stride, padding=padding, - data_format=data_format, - activation=activation, use_bias=use_bias, - kernel_initializer=w_init, - bias_initializer=b_init, trainable=trainable, - name=name) - return ret - - @staticmethod - def dilation_conv(input_tensor, k_size, out_dims, rate, padding='SAME', - w_init=None, b_init=None, use_bias=False, name=None): - """ - - :param input_tensor: - :param k_size: - :param out_dims: - :param rate: - :param padding: - :param w_init: - :param b_init: - :param use_bias: - :param name: - :return: - """ - with tf.variable_scope(name): - in_shape = input_tensor.get_shape().as_list() - in_channel = in_shape[3] - assert in_channel is not None, "[Conv2D] Input cannot have unknown channel!" - - padding = padding.upper() - - if isinstance(k_size, list): - filter_shape = [k_size[0], k_size[1]] + [in_channel, out_dims] - else: - filter_shape = [k_size, k_size] + [in_channel, out_dims] - - if w_init is None: - w_init = tf.contrib.layers.variance_scaling_initializer() - if b_init is None: - b_init = tf.constant_initializer() - - w = tf.get_variable('W', filter_shape, initializer=w_init) - b = None - - if use_bias: - b = tf.get_variable('b', [out_dims], initializer=b_init) - - conv = tf.nn.atrous_conv2d(value=input_tensor, filters=w, rate=rate, - padding=padding, name='dilation_conv') - - if use_bias: - ret = tf.add(conv, b) - else: - ret = conv - - return ret - - @staticmethod - def spatial_dropout(input_tensor, keep_prob, is_training, name, seed=1234): - """ - 空间dropout实现 - :param input_tensor: - :param keep_prob: - :param is_training: - :param name: - :param seed: - :return: - """ - - def f1(): - input_shape = input_tensor.get_shape().as_list() - noise_shape = tf.constant(value=[input_shape[0], 1, 1, input_shape[3]]) - return tf.nn.dropout(input_tensor, keep_prob, noise_shape, seed=seed, name="spatial_dropout") - - def f2(): - return input_tensor - - with tf.variable_scope(name_or_scope=name): - - output = tf.cond(is_training, f1, f2) - - return output - - @staticmethod - def lrelu(inputdata, name, alpha=0.2): - """ - - :param inputdata: - :param alpha: - :param name: - :return: - """ - with tf.variable_scope(name): - return tf.nn.relu(inputdata) - alpha * tf.nn.relu(-inputdata) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/vgg16_based_fcn.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/vgg16_based_fcn.py deleted file mode 100644 index 01ffb1f23..000000000 --- a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/semantic_segmentation_zoo/vgg16_based_fcn.py +++ /dev/null @@ -1,394 +0,0 @@ -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Implement VGG16 based fcn net for semantic segmentation -""" -import collections - -import tensorflow as tf - -from semantic_segmentation_zoo import cnn_basenet -from local_utils.config_utils import parse_config_utils - - -class VGG16FCN(cnn_basenet.CNNBaseModel): - """ - VGG 16 based fcn net for semantic segmentation - """ - def __init__(self, phase, cfg): - """ - - """ - super(VGG16FCN, self).__init__() - self._cfg = cfg - self._phase = phase - self._is_training = self._is_net_for_training() - self._net_intermediate_results = collections.OrderedDict() - self._class_nums = self._cfg.DATASET.NUM_CLASSES - - def _is_net_for_training(self): - """ - if the net is used for training or not - :return: - """ - if isinstance(self._phase, tf.Tensor): - phase = self._phase - else: - phase = tf.constant(self._phase, dtype=tf.string) - - return tf.equal(phase, tf.constant('train', dtype=tf.string)) - - def _vgg16_conv_stage(self, input_tensor, k_size, out_dims, name, - stride=1, pad='SAME', need_layer_norm=True): - """ - stack conv and activation in vgg16 - :param input_tensor: - :param k_size: - :param out_dims: - :param name: - :param stride: - :param pad: - :param need_layer_norm: - :return: - """ - with tf.variable_scope(name): - conv = self.conv2d( - inputdata=input_tensor, out_channel=out_dims, - kernel_size=k_size, stride=stride, - use_bias=False, padding=pad, name='conv' - ) - - if need_layer_norm: - bn = self.layerbn(inputdata=conv, is_training=self._is_training, name='bn') - - relu = self.relu(inputdata=bn, name='relu') - else: - relu = self.relu(inputdata=conv, name='relu') - - return relu - - def _decode_block(self, input_tensor, previous_feats_tensor, - out_channels_nums, name, kernel_size=4, - stride=2, use_bias=False, - previous_kernel_size=4, need_activate=True): - """ - - :param input_tensor: - :param previous_feats_tensor: - :param out_channels_nums: - :param kernel_size: - :param previous_kernel_size: - :param use_bias: - :param stride: - :param name: - :return: - """ - with tf.variable_scope(name_or_scope=name): - - deconv_weights_stddev = tf.sqrt( - tf.divide(tf.constant(2.0, tf.float32), - tf.multiply(tf.cast(previous_kernel_size * previous_kernel_size, tf.float32), - tf.cast(tf.shape(input_tensor)[3], tf.float32))) - ) - deconv_weights_init = tf.truncated_normal_initializer( - mean=0.0, stddev=deconv_weights_stddev) - - deconv = self.deconv2d( - inputdata=input_tensor, out_channel=out_channels_nums, kernel_size=kernel_size, - stride=stride, use_bias=use_bias, w_init=deconv_weights_init, - name='deconv' - ) - - deconv = self.layerbn(inputdata=deconv, is_training=self._is_training, name='deconv_bn') - - deconv = self.relu(inputdata=deconv, name='deconv_relu') - - fuse_feats = tf.add( - previous_feats_tensor, deconv, name='fuse_feats' - ) - - if need_activate: - - fuse_feats = self.layerbn( - inputdata=fuse_feats, is_training=self._is_training, name='fuse_gn' - ) - - fuse_feats = self.relu(inputdata=fuse_feats, name='fuse_relu') - - return fuse_feats - - def _vgg16_fcn_encode(self, input_tensor, name): - """ - - :param input_tensor: - :param name: - :return: - """ - with tf.variable_scope(name_or_scope=name): - # encode stage 1 - conv_1_1 = self._vgg16_conv_stage( - input_tensor=input_tensor, k_size=3, - out_dims=64, name='conv1_1', - need_layer_norm=True - ) - conv_1_2 = self._vgg16_conv_stage( - input_tensor=conv_1_1, k_size=3, - out_dims=64, name='conv1_2', - need_layer_norm=True - ) - self._net_intermediate_results['encode_stage_1_share'] = { - 'data': conv_1_2, - 'shape': conv_1_2.get_shape().as_list() - } - - # encode stage 2 - pool1 = self.maxpooling( - inputdata=conv_1_2, kernel_size=2, - stride=2, name='pool1' - ) - conv_2_1 = self._vgg16_conv_stage( - input_tensor=pool1, k_size=3, - out_dims=128, name='conv2_1', - need_layer_norm=True - ) - conv_2_2 = self._vgg16_conv_stage( - input_tensor=conv_2_1, k_size=3, - out_dims=128, name='conv2_2', - need_layer_norm=True - ) - self._net_intermediate_results['encode_stage_2_share'] = { - 'data': conv_2_2, - 'shape': conv_2_2.get_shape().as_list() - } - - # encode stage 3 - pool2 = self.maxpooling( - inputdata=conv_2_2, kernel_size=2, - stride=2, name='pool2' - ) - conv_3_1 = self._vgg16_conv_stage( - input_tensor=pool2, k_size=3, - out_dims=256, name='conv3_1', - need_layer_norm=True - ) - conv_3_2 = self._vgg16_conv_stage( - input_tensor=conv_3_1, k_size=3, - out_dims=256, name='conv3_2', - need_layer_norm=True - ) - conv_3_3 = self._vgg16_conv_stage( - input_tensor=conv_3_2, k_size=3, - out_dims=256, name='conv3_3', - need_layer_norm=True - ) - self._net_intermediate_results['encode_stage_3_share'] = { - 'data': conv_3_3, - 'shape': conv_3_3.get_shape().as_list() - } - - # encode stage 4 - pool3 = self.maxpooling( - inputdata=conv_3_3, kernel_size=2, - stride=2, name='pool3' - ) - conv_4_1 = self._vgg16_conv_stage( - input_tensor=pool3, k_size=3, - out_dims=512, name='conv4_1', - need_layer_norm=True - ) - conv_4_2 = self._vgg16_conv_stage( - input_tensor=conv_4_1, k_size=3, - out_dims=512, name='conv4_2', - need_layer_norm=True - ) - conv_4_3 = self._vgg16_conv_stage( - input_tensor=conv_4_2, k_size=3, - out_dims=512, name='conv4_3', - need_layer_norm=True - ) - self._net_intermediate_results['encode_stage_4_share'] = { - 'data': conv_4_3, - 'shape': conv_4_3.get_shape().as_list() - } - - # encode stage 5 for binary segmentation - pool4 = self.maxpooling( - inputdata=conv_4_3, kernel_size=2, - stride=2, name='pool4' - ) - conv_5_1_binary = self._vgg16_conv_stage( - input_tensor=pool4, k_size=3, - out_dims=512, name='conv5_1_binary', - need_layer_norm=True - ) - conv_5_2_binary = self._vgg16_conv_stage( - input_tensor=conv_5_1_binary, k_size=3, - out_dims=512, name='conv5_2_binary', - need_layer_norm=True - ) - conv_5_3_binary = self._vgg16_conv_stage( - input_tensor=conv_5_2_binary, k_size=3, - out_dims=512, name='conv5_3_binary', - need_layer_norm=True - ) - self._net_intermediate_results['encode_stage_5_binary'] = { - 'data': conv_5_3_binary, - 'shape': conv_5_3_binary.get_shape().as_list() - } - - # encode stage 5 for instance segmentation - conv_5_1_instance = self._vgg16_conv_stage( - input_tensor=pool4, k_size=3, - out_dims=512, name='conv5_1_instance', - need_layer_norm=True - ) - conv_5_2_instance = self._vgg16_conv_stage( - input_tensor=conv_5_1_instance, k_size=3, - out_dims=512, name='conv5_2_instance', - need_layer_norm=True - ) - conv_5_3_instance = self._vgg16_conv_stage( - input_tensor=conv_5_2_instance, k_size=3, - out_dims=512, name='conv5_3_instance', - need_layer_norm=True - ) - self._net_intermediate_results['encode_stage_5_instance'] = { - 'data': conv_5_3_instance, - 'shape': conv_5_3_instance.get_shape().as_list() - } - - return - - def _vgg16_fcn_decode(self, name): - """ - - :return: - """ - with tf.variable_scope(name): - - # decode part for binary segmentation - with tf.variable_scope(name_or_scope='binary_seg_decode'): - - decode_stage_5_binary = self._net_intermediate_results['encode_stage_5_binary']['data'] - - decode_stage_4_fuse = self._decode_block( - input_tensor=decode_stage_5_binary, - previous_feats_tensor=self._net_intermediate_results['encode_stage_4_share']['data'], - name='decode_stage_4_fuse', out_channels_nums=512, previous_kernel_size=3 - ) - decode_stage_3_fuse = self._decode_block( - input_tensor=decode_stage_4_fuse, - previous_feats_tensor=self._net_intermediate_results['encode_stage_3_share']['data'], - name='decode_stage_3_fuse', out_channels_nums=256 - ) - decode_stage_2_fuse = self._decode_block( - input_tensor=decode_stage_3_fuse, - previous_feats_tensor=self._net_intermediate_results['encode_stage_2_share']['data'], - name='decode_stage_2_fuse', out_channels_nums=128 - ) - decode_stage_1_fuse = self._decode_block( - input_tensor=decode_stage_2_fuse, - previous_feats_tensor=self._net_intermediate_results['encode_stage_1_share']['data'], - name='decode_stage_1_fuse', out_channels_nums=64 - ) - binary_final_logits_conv_weights_stddev = tf.sqrt( - tf.divide(tf.constant(2.0, tf.float32), - tf.multiply(4.0 * 4.0, - tf.cast(tf.shape(decode_stage_1_fuse)[3], tf.float32))) - ) - binary_final_logits_conv_weights_init = tf.truncated_normal_initializer( - mean=0.0, stddev=binary_final_logits_conv_weights_stddev) - - binary_final_logits = self.conv2d( - inputdata=decode_stage_1_fuse, - out_channel=self._class_nums, - kernel_size=1, use_bias=False, - w_init=binary_final_logits_conv_weights_init, - name='binary_final_logits' - ) - - self._net_intermediate_results['binary_segment_logits'] = { - 'data': binary_final_logits, - 'shape': binary_final_logits.get_shape().as_list() - } - - with tf.variable_scope(name_or_scope='instance_seg_decode'): - - decode_stage_5_instance = self._net_intermediate_results['encode_stage_5_instance']['data'] - - decode_stage_4_fuse = self._decode_block( - input_tensor=decode_stage_5_instance, - previous_feats_tensor=self._net_intermediate_results['encode_stage_4_share']['data'], - name='decode_stage_4_fuse', out_channels_nums=512, previous_kernel_size=3) - - decode_stage_3_fuse = self._decode_block( - input_tensor=decode_stage_4_fuse, - previous_feats_tensor=self._net_intermediate_results['encode_stage_3_share']['data'], - name='decode_stage_3_fuse', out_channels_nums=256) - - decode_stage_2_fuse = self._decode_block( - input_tensor=decode_stage_3_fuse, - previous_feats_tensor=self._net_intermediate_results['encode_stage_2_share']['data'], - name='decode_stage_2_fuse', out_channels_nums=128) - - decode_stage_1_fuse = self._decode_block( - input_tensor=decode_stage_2_fuse, - previous_feats_tensor=self._net_intermediate_results['encode_stage_1_share']['data'], - name='decode_stage_1_fuse', out_channels_nums=64, need_activate=False) - - self._net_intermediate_results['instance_segment_logits'] = { - 'data': decode_stage_1_fuse, - 'shape': decode_stage_1_fuse.get_shape().as_list() - } - - def build_model(self, input_tensor, name, reuse=False): - """ - - :param input_tensor: - :param name: - :param reuse: - :return: - """ - with tf.variable_scope(name_or_scope=name, reuse=reuse): - # vgg16 fcn encode part - self._vgg16_fcn_encode(input_tensor=input_tensor, name='vgg16_encode_module') - # vgg16 fcn decode part - self._vgg16_fcn_decode(name='vgg16_decode_module') - - return self._net_intermediate_results - - -if __name__ == '__main__': - """ - test code - """ - test_in_tensor = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input') - model = VGG16FCN(phase='train', cfg=parse_config_utils.lanenet_cfg) - ret = model.build_model(test_in_tensor, name='vgg16fcn') - for layer_name, layer_info in ret.items(): - print('layer name: {:s} shape: {}'.format(layer_name, layer_info['shape'])) -- Gitee From ed23b89b4fd06ab0542d94c5e24301b4f2944402 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Mon, 5 Sep 2022 12:52:49 +0000 Subject: [PATCH 18/19] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20MNN-LANENET=5FID1251?= =?UTF-8?q?=5Ffor=5FACL?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/.keep diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 1c558251c348c192bb6e9cb64ef5a195114f15ac Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=BA=BA=E6=B0=B4=E5=B0=8F=E8=88=AA=E6=AF=8D?= <1396755411@qq.com> Date: Mon, 5 Sep 2022 12:57:20 +0000 Subject: [PATCH 19/19] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 溺水小航母 <1396755411@qq.com> --- .../cv/MNN-LANENET_ID1251_for_ACL/README.md | 139 +++++++++++++ .../cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py | 78 +++++++ .../MNN-LANENET_ID1251_for_ACL/eval_ckpt.py | 190 ++++++++++++++++++ .../cv/MNN-LANENET_ID1251_for_ACL/eval_om.py | 79 ++++++++ .../cv/MNN-LANENET_ID1251_for_ACL/eval_pb.py | 169 ++++++++++++++++ .../inference time.jpg.jpg | Bin 0 -> 23050 bytes .../metric_ckpt.jpg | Bin 0 -> 2247 bytes .../MNN-LANENET_ID1251_for_ACL/metric_om.jpg | Bin 0 -> 2978 bytes .../MNN-LANENET_ID1251_for_ACL/metric_pb.jpg | Bin 0 -> 2135 bytes 9 files changed, 655 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_pb.py create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/inference time.jpg.jpg create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/metric_ckpt.jpg create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/metric_om.jpg create mode 100644 ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/metric_pb.jpg diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md new file mode 100644 index 000000000..6a5ec5e7e --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/README.md @@ -0,0 +1,139 @@ +## 模型功能 + + 车道线检测 + + + + +## pb模型 + +``` + +python3.7 ckpt2pb.py +``` + +convert_ckpt_into_pb_file（）函数，pb模型 PATH=./pretrained_model/eval.pb + +## om模型 + + +使用ATC模型转换工具进行模型转换时可以参考如下指令: + + +``` + +atc --model=/usr/pb2om/eval.pb + + --framework=3 + + --output=/usr/pb2om/frozen + + --soc_version=Ascend910 + + out_nodes="lanenet/binary_seg_out:0;lanenet/instance_seg_out:0" + + --input_shape="input_tensor:1,256,512,3" + + --input_format=NHWC +``` + + + +## 使用msame工具推理 + + +参考 https://gitee.com/ascend/tools/tree/master/msame, 获取msame推理工具及使用方法。 + +获取到msame可执行文件之后，进行推理测试。 + + + +## 数据集转换bin + +``` + +python3.7 eval_pb.py +``` + +freeze_graph_test（）函数 img_feed转test_img.bin + +## 推理测试 + + +使用msame推理工具，参考如下命令，发起推理测试： + + +``` + +./msame --model "/home/test_user05/pb2om/frozen.om" + + --input "/home/test_user05/pb2om/test_img.bin" + + --output "/home/test_user05/pb2om/" + + --outfmt BIN + + --loop 1 +``` + + +## 脚本和示例代码 + +代码：链接：链接：https://pan.baidu.com/s/1oqdEQGH8ixPv4tNMBRExfQ?pwd=6666 +提取码：6666 + + +输入数据：链接：https://pan.baidu.com/s/1bwZR3FfhP18mLMa44781Gw?pwd=0000 提取码：0000 + +├── eval_data + + +1.pb预测 + +├── eval_pb.py //主代码 + +├── lanenet_model + + ├── lanenet.py //LANENET模型 + +├── README.md //代码说明文档 + + + +2.om预测 + +├── eval_om.py //主代码 + +├── lanenet_model + + ├── lanenet.py //LANENET模型 + +├── README.md //代码说明文档 + + + +3.ckpt转pb + +├── ckpt2pb.py + + + +##推理输出计算精度 + +``` + +python3.7 eval_om.py +``` + +## 推理精度 +定量指标采用准确率，精度为0.965，精度达标。 +定性指标采用论文中可视化binary segmentation和instance segmentation，输出在eval_output下 + +| gpu | npu |原论文 |推理 | +|-------|------|-------|-------| +| 96.5 | 96.5 | 96.4 |96.5 | + + + +## 推理性能 +inference time.jpg，性能达标 \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py new file mode 100644 index 000000000..8710bca83 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/ckpt2pb.py @@ -0,0 +1,78 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Freeze Lanenet model into frozen pb file +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import os + +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + + +def convert_ckpt_into_pb_file(ckpt_file_path, pb_file_path): + """ + + :param ckpt_file_path: + :param pb_file_path: + :return: + """ + with tf.Session() as sess: + + saver = tf.train.import_meta_graph(ckpt_file_path + '.meta', clear_devices=True) + input_graph_def = tf.get_default_graph().as_graph_def() + + binary_seg_node = 'lanenet/binary_seg_out' + instance_seg_node = 'lanenet/instance_seg_out' + + saver.restore(sess, ckpt_file_path) # 恢复图并得到数据 + output_graph_def = tf.graph_util.convert_variables_to_constants( # 模型持久化，将变量值固定 + sess=sess, + input_graph_def=input_graph_def, + output_node_names=['input_tensor', binary_seg_node, instance_seg_node]) + + with tf.gfile.GFile(pb_file_path, "wb") as f: # 保存模型 + f.write(output_graph_def.SerializeToString()) # 序列化输出 + print("%d ops in the final graph." % len(output_graph_def.node)) + + +if __name__ == '__main__': + """ + test code + """ + + ckpt_path = './eval_ckpt/eval.ckpt' + pb_save_path = './pretrained_model/eval.pb' + + convert_ckpt_into_pb_file( + ckpt_file_path=ckpt_path, + pb_file_path=pb_save_path + ) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py new file mode 100644 index 000000000..993c5719c --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_ckpt.py @@ -0,0 +1,190 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +test LaneNet model on single image +""" +import argparse +import os.path as ops +import time + +import cv2 +import numpy as np +import tensorflow as tf + +from lanenet_model import lanenet +from lanenet_model import lanenet_postprocess +from local_utils.config_utils import parse_config_utils +from local_utils.log_util import init_logger + +CFG = parse_config_utils.lanenet_cfg +LOG = init_logger.get_logger(log_file_name_prefix='lanenet_test') + +gt_path = './eval_data/gt.png' + + +def init_args(): + """ + + :return: + """ + parser = argparse.ArgumentParser() + parser.add_argument('--image_path', default='./eval_data/test_img.jpg', + type=str, help='The image path or the src image save dir') + parser.add_argument('--weights_path', default='./pretrained_model/ckpt/tusimple_lanenet.ckpt', + type=str, help='The model weights path') + parser.add_argument('--with_lane_fit', type=args_str2bool, help='If need to do lane fit', default=False) + + return parser.parse_args() + + +def args_str2bool(arg_value): + """ + + :param arg_value: + :return: + """ + if arg_value.lower() in ('yes', 'true', 't', 'y', '1'): + return True + + elif arg_value.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Unsupported value encountered.') + + +def minmax_scale(input_arr): + """ + + :param input_arr: + :return: + """ + min_val = np.min(input_arr) + max_val = np.max(input_arr) + + output_arr = (input_arr - min_val) * 255.0 / (max_val - min_val) + + return output_arr + + +def test_lanenet(image_path, gt_path, weights_path, with_lane_fit=False): + """ + + :param image_path: + :param weights_path: + :param with_lane_fit: + :return: + """ + assert ops.exists(image_path), '{:s} not exist'.format(image_path) + + LOG.info('Start reading image and preprocessing') + t_start = time.time() + image = cv2.imread(image_path, cv2.IMREAD_COLOR) + image_vis = image + image = cv2.resize(image, (512, 256), interpolation=cv2.INTER_LINEAR) + image = image / 127.5 - 1.0 + LOG.info('Image load complete, cost time: {:.5f}s'.format(time.time() - t_start)) + + input_tensor_0 = tf.placeholder(dtype=tf.float32, shape=[1, 256, 512, 3], name='input_tensor') + input_tensor = tf.identity(input_tensor_0, name='input_tensor') + net = lanenet.LaneNet(phase='test', cfg=CFG) + binary_seg_ret, instance_seg_ret = net.inference(input_tensor=input_tensor, name='LaneNet') + + with tf.variable_scope('lanenet/'): + binary_seg_ret = tf.identity(binary_seg_ret, name='binary_seg_out') + instance_seg_ret = tf.identity(instance_seg_ret, name='instance_seg_out') + + postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG) + + sess = tf.Session() + + # define moving average version of the learned variables for eval + with tf.variable_scope(name_or_scope='moving_avg'): + variable_averages = tf.train.ExponentialMovingAverage( + CFG.SOLVER.MOVING_AVE_DECAY) + variables_to_restore = variable_averages.variables_to_restore() + + # define saver + saver = tf.train.Saver(variables_to_restore) + + with sess.as_default(): + saver.restore(sess=sess, save_path=weights_path) + image = np.expand_dims(image, axis=0) + binary_seg_image, instance_seg_image = sess.run( + [binary_seg_ret, instance_seg_ret], + feed_dict={input_tensor: image} + ) + + postprocess_result = postprocessor.postprocess( + binary_seg_result=binary_seg_image[0], + instance_seg_result=instance_seg_image[0], + source_image=image_vis, + with_lane_fit=True, + data_source='tusimple' + ) + mask_image = postprocess_result['mask_image'] + src_image = postprocess_result['source_image'] + + # -------------- 计算准确率 ------------------ # + gt = cv2.imread(gt_path, cv2.IMREAD_COLOR) + gt_trans = cv2.resize(gt, (512, 256), interpolation=cv2.INTER_LINEAR) + + gt_gray = cv2.cvtColor(gt_trans, cv2.COLOR_BGR2GRAY) + mask_image_gray = cv2.cvtColor(mask_image, cv2.COLOR_BGR2GRAY) + WIDTH = mask_image_gray.shape[0] + HIGTH = mask_image_gray.shape[1] + tp_count = 0 + tn_count = 0 + for i in range(WIDTH): + for j in range(HIGTH): + if mask_image_gray[i, j] != 0 and gt_gray[i, j] != 0: + tp_count = tp_count + 1 + if mask_image_gray[i, j] == 0 and gt_gray[i, j] == 0: + tn_count = tn_count + 1 + Accuracy = (int(tp_count) + int(tn_count)) / (int(WIDTH) * int(HIGTH)) + + print("\n# Metric_ckpt " + "\n Accuracy：{:.3f}".format(Accuracy)) + + cv2.imwrite('./eval_output/mask_ckpt.jpg', mask_image) + cv2.imwrite('./eval_output/src_ckpt.jpg', src_image) + + saver.save(sess, './eval_ckpt/eval.ckpt') + + sess.close() + + return + + +if __name__ == '__main__': + """ + test code + """ + # init args + args = init_args() + + test_lanenet(args.image_path, gt_path, args.weights_path, with_lane_fit=args.with_lane_fit) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py new file mode 100644 index 000000000..98a8e5373 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_om.py @@ -0,0 +1,79 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import lanenet_postprocess +from local_utils.config_utils import parse_config_utils +import cv2 + +CFG = parse_config_utils.lanenet_cfg + +image_path = './eval_data/test_img.jpg' +image_vis = cv2.imread(image_path, cv2.IMREAD_COLOR) + +b_out_path = './eval_data/frozen_output_0.bin' +i_out_path = './eval_data/frozen_output_1.bin' +b_out = np.fromfile(b_out_path, dtype=np.int64) +i_out = np.fromfile(i_out_path, dtype=np.float32) +b_out = np.reshape(b_out, (1, 256, 512)) +i_out = np.reshape(i_out, (1, 256, 512, 4)) + +postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG) +postprocess_result = postprocessor.postprocess( + binary_seg_result=b_out[0], + instance_seg_result=i_out[0], + source_image=image_vis, + with_lane_fit=True, + data_source='tusimple' + ) +mask_image = postprocess_result['mask_image'] +src_image = postprocess_result['source_image'] +gt_path = './eval_data/gt.png' +gt = cv2.imread(gt_path, cv2.IMREAD_COLOR) +gt_trans = cv2.resize(gt, (512, 256), interpolation=cv2.INTER_LINEAR) + +# -------------- 计算准确率 ------------------ # +gt_gray = cv2.cvtColor(gt_trans, cv2.COLOR_BGR2GRAY) +mask_image_gray = cv2.cvtColor(mask_image, cv2.COLOR_BGR2GRAY) +WIDTH = mask_image_gray.shape[0] +HIGTH = mask_image_gray.shape[1] +tp_count = 0 +tn_count = 0 +for i in range(WIDTH): + for j in range(HIGTH): + if mask_image_gray[i, j] != 0 and gt_gray[i, j] != 0: + tp_count = tp_count + 1 + if mask_image_gray[i, j] == 0 and gt_gray[i, j] == 0: + tn_count = tn_count + 1 +Accuracy = (int(tp_count) + int(tn_count)) / (int(WIDTH) * int(HIGTH)) + +print("\n# Metric_om " + "\n Accuracy：{:.3f}".format(Accuracy)) + +cv2.imwrite('./eval_output/mask_om.jpg', mask_image) +cv2.imwrite('./eval_output/src_om.jpg', src_image) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_pb.py b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_pb.py new file mode 100644 index 000000000..5584b4ad7 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/eval_pb.py @@ -0,0 +1,169 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import os +import tensorflow as tf +from tensorflow.python.platform import gfile +import lanenet_postprocess +from local_utils.config_utils import parse_config_utils +import cv2 + + +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +image_path = './eval_data/test_img.jpg' + +test_img = cv2.imread(image_path, cv2.IMREAD_COLOR) +image_vis = test_img +test_img_trans = cv2.resize(test_img, (512, 256), interpolation=cv2.INTER_LINEAR) +test_img_trans = test_img_trans / 127.5 - 1.0 + +gt_path = './eval_data/gt.png' +gt = cv2.imread(gt_path, cv2.IMREAD_COLOR) +gt_trans = cv2.resize(gt, (512, 256), interpolation=cv2.INTER_LINEAR) + +CFG = parse_config_utils.lanenet_cfg + +pb_path = './pretrained_model/eval.pb' + +# ----------- 查看 pbnode ----------- +# read graph definition +f = gfile.FastGFile(pb_path, "rb") +gd = graph_def = tf.GraphDef() +graph_def.ParseFromString(f.read()) +tf.import_graph_def(graph_def, name='') +for i, n in enumerate(graph_def.node): + print("=====node====") + print("Name of the node - %s" % n.name) +# -------------------------------- + + +def minmax_scale(input_arr): + """ + + :param input_arr: + :return: + """ + min_val = np.min(input_arr) + max_val = np.max(input_arr) + + output_arr = (input_arr - min_val) * 255.0 / (max_val - min_val) + + return output_arr + + +def freeze_graph_test(pb_path, img_path): + # :param pb_path:pb文件的路径 + # :param test_img:测试图片的路径 + # :return: + + f = gfile.FastGFile(pb_path, "rb") + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + + with tf.Graph().as_default(): + output_graph_def = tf.GraphDef() + with open(pb_path, "rb") as f: + output_graph_def.ParseFromString(f.read()) + tf.import_graph_def(graph_def, name="") + + with tf.Session() as sess: + + sess.run(tf.global_variables_initializer()) + + # 定义输入输出节点名称 + input_node = sess.graph.get_tensor_by_name("input_tensor:0") + binary_output_node = sess.graph.get_tensor_by_name("lanenet/binary_seg_out:0") + pixel_embedding_output_node = sess.graph.get_tensor_by_name("lanenet/instance_seg_out:0") + img_feed = test_img_trans.astype(np.float32) + img_feed = np.expand_dims(img_feed, axis=0) + + # img_bin = img_feed + # img_bin.tofile("./eval_data/test_img.bin") + + binary_output, pixel_embedding_output = sess.run([binary_output_node, pixel_embedding_output_node], + feed_dict={input_node: img_feed}) + + postprocessor = lanenet_postprocess.LaneNetPostProcessor(cfg=CFG) + postprocess_result = postprocessor.postprocess( + binary_seg_result=binary_output[0], + instance_seg_result=pixel_embedding_output[0], + source_image=image_vis, + with_lane_fit=True, + data_source='tusimple' + ) + mask_image = postprocess_result['mask_image'] + src_image = postprocess_result['source_image'] + # if with_lane_fit: + # lane_params = postprocess_result['fit_params'] + # LOG.info('Model have fitted {:d} lanes'.format(len(lane_params))) + # for i in range(len(lane_params)): + # LOG.info('Fitted 2-order lane {:d} curve param: {}'.format(i + 1, lane_params[i])) + + for i in range(CFG.MODEL.EMBEDDING_FEATS_DIMS): + pixel_embedding_output[:, :, i] = minmax_scale(pixel_embedding_output[:, :, i]) + embedding_image = np.array(pixel_embedding_output, np.uint8) + + # plt.figure('mask_image') + # plt.imshow(mask_image[:, :, (2, 1, 0)]) + # plt.show() + # plt.figure('src_image') + # plt.imshow(image_vis[:, :, (2, 1, 0)]) + # plt.figure('instance_image') + # plt.imshow(embedding_image[:, :, (2, 1, 0)]) + # plt.figure('binary_image') + # plt.imshow(binary_output * 255, cmap='gray') + # plt.show() + + # -------------- 计算准确率 ------------------ # + gt_gray = cv2.cvtColor(gt_trans, cv2.COLOR_BGR2GRAY) + mask_image_gray = cv2.cvtColor(mask_image, cv2.COLOR_BGR2GRAY) + WIDTH = mask_image_gray.shape[0] + HIGTH = mask_image_gray.shape[1] + tp_count = 0 + tn_count = 0 + for i in range(WIDTH): + for j in range(HIGTH): + if mask_image_gray[i, j] != 0 and gt_gray[i, j] != 0: + tp_count = tp_count + 1 + if mask_image_gray[i, j] == 0 and gt_gray[i, j] == 0: + tn_count = tn_count + 1 + Accuracy = (int(tp_count) + int(tn_count)) / (int(WIDTH) * int(HIGTH)) + + print("\n# Metric_pb " + "\n Accuracy：{:.3f}".format(Accuracy)) + + cv2.imwrite('./eval_output/mask_pb.jpg', mask_image) + cv2.imwrite('./eval_output/src_pb.jpg', src_image) + + +if __name__ == '__main__': + # 测试pb模型 + img_path = './eval_data/test_img.npy' + freeze_graph_test(pb_path=pb_path, img_path=img_path) diff --git a/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/inference time.jpg.jpg b/ACL_TensorFlow/contrib/cv/MNN-LANENET_ID1251_for_ACL/inference time.jpg.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1dbe0e6e74cfff7198205dd82800bf688d4e010b 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