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基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：Attribute-Object Compositions** + +**版本（Version）：1.1** + +**修改时间（Modified） ：2022.03.28** + +**大小（Size）：74M** + +**框架（Framework）：TensorFlow 1.15.0** + +**模型格式（Model Format）：ckpt** + +**精度（Precision）：Mixed** + +**处理器（Processor）：昇腾910** + +**应用级别（Categories）：Official** + +**描述（Description）：基于TensorFlow框架的Symnet网络训练代码** + +

概述

+ +Symnet结合属性-对象转换的对称性原理和群论公理，由耦合网络和解耦网络两个模块组成，提出了基于Relative Moving Distance(RMD)的识别方法，利用属性的变化而非属性本身去分类属性。在Attribute-Object Composition零样本学习任务上取得了重大改进。 + +- 参考论文： + + [Li, Yong-Lu, et al. "Symmetry and group in attribute-object compositions." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.](https://arxiv.org/abs/2004.00587) + +- 参考实现： + + [SymNet](https://github.com/DirtyHarryLYL/SymNet) + +- 适配昇腾 AI 处理器的实现： + + [https://gitee.com/ascend/modelzoo/tree/master/built-in/TensorFlow/Official/cv/image_classification/DenseNet121_ID0067_for_TensorFlow](https://gitee.com/ascend/modelzoo/tree/master/built-in/TensorFlow/Official/cv/image_classification/DenseNet121_ID0067_for_TensorFlow) + + +## 默认配置 + +- 数据集预处理： + - 在本地运行`download_data.sh`下载数据集并进行预处理 +- 训练超参 + - Batch size: 256 + - obj_pred: UT_obj_lr1e-3_test_ep260.pkl + - data: UT + - batchnorm: True + - wordvec: onehot + - lr: 1e-4 + - lambda_cls_attr: 1 + - lambda_cls_obj: 0.5 + - lambda_trip: 0.5 + - lambda_sym: 0.01 + - lambda_axiom: 0.03 + - Train epoch: 700 + +## 支持特性 + +| 特性列表 | 是否支持 | +|-------|------| +| 分布式训练 | 是 | +| 混合精度 | 是 | +| 并行数据 | 否 | + +

训练环境准备

+1. NPU环境&硬件环境： + +``` +NPU: 1*Ascend 910 +CPU: 24*vCPUs 96GB +Image: ascend-share/5.1.rc1.alpha003_tensorflow-ascend910-cp37-euleros2.8-aarch64-training:1.15.0-21.0.2_0317 +``` + +2. 第三方依赖： + +``` +pip-requirements.txt +``` + + +

快速上手

+ +### 数据集准备 + +用户在本地运行`download_data.sh`下载数据集并进行预处理，预处理后的数据集已上传至obs中。obs路径：obs://cann-id1292-symnet/data/。 + + +### 模型训练 + +- 单击“立即下载”，并选择合适的下载方式下载源码包。 + +- 启动训练之前，首先要配置程序运行相关环境变量。环境变量配置信息参见：[Ascend 910训练平台环境变量设置](https://gitee.com/ascend/modelzoo/wikis/Ascend%20910%E8%AE%AD%E7%BB%83%E5%B9%B3%E5%8F%B0%E7%8E%AF%E5%A2%83%E5%8F%98%E9%87%8F%E8%AE%BE%E7%BD%AE?sort_id=3148819) + +- 单卡训练 + +1. 配置训练参数。 + +首先在脚本test/train_full_1p.sh中，配置训练数据集路径及参数，请用户根据实际路径配置，参数如下所示： +``` +--data_dir=/opt/npu/slimImagenet +--name UT_best +--data UT +--epoch=${epochs} +--obj_pred UT_obj_lr1e-3_test_ep260.pkl +--batchnorm +--wordvec onehot +--lr 1e-4 +--bz=${batch_size} +--lambda_cls_attr 1 +--lambda_cls_obj 0.5 +--lambda_trip 0.5 +--lambda_sym 0.01 +--lambda_axiom 0.03 +--data_url=${data_path} +--train_url=${output_path} +``` +2. 启动训练。 + +启动单卡训练 （脚本为SYMNET_ID1292_for_TensorFlow/test/train_full_1p.sh） + + ``` + bash ./test/train_full_1p.sh --data_path=%s --output_path=%s + ``` +### 验证 +在train时已经执行模型验证测试，如果需要单独测试指定模型（指定epoch），按如下步骤操作。 + +1. 测试的时候，需要修改脚本启动参数（SYMNET_ID1292_for_TensorFlow/test/train_full_1p.sh），修改train_full_1p.sh运行test_symnet.py即可测试指定模型，请用户根据实际路径进行修改。 + +``` +python3.7 ./test_symnet.py --name UT_best --data UT --epoch=${epochs} --obj_pred UT_obj_lr1e-3_test_ep260.pkl --wordvec onehot --batchnorm --data_url=${data_path} --train_url=${output_path} +``` +2. 测试指令（脚本位于SYMNET_ID1292_for_TensorFlow/test/train_full_1p.sh） +``` +bash ./test/train_full_1p.sh --data_path=%s --output_path=%s +``` + +## 训练过程 + +1. 执行`modelarts_entry_acc.py`拉起训练。 +2. 保存checkpoint文件。 +3. GPU复现和NPU复现精度如下 + +| | 数据集 | EPOCH| 精度 | +|-------|------|------|------| +| 原文 | UT | <700 | T1:52.1   T2:67.8   T3:76.0 | +| GPU | UT | 574 | T1:0.5116   T2:0.6719   T3:0.7616 | +| NPU | UT | 636 | T1:0.5007   T2:0.6684   T3:0.7571 | + + +[comment]: <> (## 推理/验证过程) + +[comment]: <> (```) + +[comment]: <> (待补充) + +[comment]: <> (```) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/download_data.sh b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/download_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..57bc362acd52039bd3ac34567b68f58b3c4433fd --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/download_data.sh @@ -0,0 +1,73 @@ +# 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. + +mkdir tmp + +# Download everything +mwget --show-progress -O tmp/attr-ops-data.tar.gz https://www.cs.utexas.edu/~tushar/attribute-ops/attr-ops-data.tar.gz +mwget --show-progress -O tmp/mitstates.zip http://wednesday.csail.mit.edu/joseph_result/state_and_transformation/release_dataset.zip +mwget --show-progress -O tmp/utzap.zip http://vision.cs.utexas.edu/projects/finegrained/utzap50k/ut-zap50k-images.zip +mwget --show-progress -O tmp/natural.tar.gz http://www.cs.cmu.edu/~spurushw/publication/compositional/compositional_split_natural.tar.gz +echo "Data downloaded. Extracting files..." + + +# Dataset metadata and features +tar -zxvf tmp/attr-ops-data.tar.gz --strip 1 +mv data/mit-states mit-states-original +mv data/ut-zap50k ut-zap50k-original +rm -r cv tensor-completion data + + + +# MIT-States +unzip tmp/mitstates.zip 'release_dataset/images/*' -d mit-states-original/ +mv mit-states-original/release_dataset/images mit-states-original/images/ +rm -r mit-states-original/release_dataset +rename "s/ /_/g" mit-states-original/images/* + +# UT-Zappos50k +unzip tmp/utzap.zip -d ut-zap50k-original/ +mv ut-zap50k-original/ut-zap50k-images ut-zap50k-original/_images/ +python reorganize_utzap.py +rm -r ut-zap50k-original/_images + + +# Natural split +tar -zxvf tmp/natural.tar.gz +mv mit-states/metadata_compositional-split-natural.t7 mit-states/metadata.t7 +mv ut-zap50k/metadata_compositional-split-natural.t7 ut-zap50k/metadata.t7 +mv mit-states/compositional-split-natural mit-states/compositional-split +mv ut-zap50k/compositional-split-natural ut-zap50k/compositional-split +mv mit-states mit-states-natural +mv ut-zap50k ut-zap50k-natural +ln -s ../mit-states-original/images mit-states-natural/images +ln -s ../ut-zap50k-original/images ut-zap50k-natural/images + + +# remove all zip files and temporary files +rm -r tmp diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/modelarts_entry_acc.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/modelarts_entry_acc.py new file mode 100644 index 0000000000000000000000000000000000000000..1cffe6691487ace03be9475755922b0b26d65104 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/modelarts_entry_acc.py @@ -0,0 +1,63 @@ +# 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 os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash ./test/train_full_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/modelarts_entry_perf.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/modelarts_entry_perf.py new file mode 100644 index 0000000000000000000000000000000000000000..fc298b47ca5232979f1f06c10b1144217c8cc530 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/modelarts_entry_perf.py @@ -0,0 +1,63 @@ +# 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 os +import argparse +import sys + +# 解析输入参数data_url +parser = argparse.ArgumentParser() +parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0") +parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/") +config = parser.parse_args() + +print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0])) +code_dir = sys.path[0] +os.chdir(code_dir) +print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd())) + +print("[CANN-Modelzoo] before train - list my run files:") +os.system("ls -al /usr/local/Ascend/ascend-toolkit/") + +print("[CANN-Modelzoo] before train - list my dataset files:") +os.system("ls -al %s" % config.data_url) + +print("[CANN-Modelzoo] start run train shell") +# 设置sh文件格式为linux可执行 +os.system("dos2unix ./test/*") + +# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定 +# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS +os.system("bash ./test/train_performance_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url)) + +print("[CANN-Modelzoo] finish run train shell") + +# 将当前执行目录所有文件拷贝到obs的output进行备份 +print("[CANN-Modelzoo] after train - list my output files:") +os.system("cp -r %s %s " % (code_dir, config.train_url)) +os.system("ls -al %s" % config.train_url) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/modelzoo_level.txt b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/modelzoo_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..2667a7c30ba0228b3c3a484d1bab16523c43f295 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/modelzoo_level.txt @@ -0,0 +1,3 @@ +FuncStatus:OK +NPUMigrationStatus:OK +PrecisionStatus:OK \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__init__.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/__init__.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fe5a40efc67af9c97bef30a5f2b89999b203e004 Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/__init__.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/base_network.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/base_network.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..65537defd6c1f0c7147a006e6be0faf6cf962207 Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/base_network.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/fc_obj.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/fc_obj.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..165d5b190c31ddb0682253e63c86c652092dffdc Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/fc_obj.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/symnet.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/symnet.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7cc64cc2b1aea7b7ff6907c16dd5b928ae374676 Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/__pycache__/symnet.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/base_network.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/base_network.py new file mode 100644 index 0000000000000000000000000000000000000000..b45890f16b0cd3c4b2350ed6aad3457a846d6ea1 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/base_network.py @@ -0,0 +1,210 @@ +# 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 tensorflow as tf +import tensorflow.contrib.slim as slim +import torch +from tensorflow.python.ops import array_ops +from npu_bridge.estimator import npu_ops + +class BaseNetwork(object): + def logger(self, suffix): + return self.root_logger.getChild(suffix) + + def __init__(self, dataloader, args, feat_dim=None): + self.dataloader = dataloader + self.num_attr = len(dataloader.dataset.attrs) + self.num_obj = len(dataloader.dataset.objs) + if feat_dim is not None: + self.feat_dim = feat_dim + else: + self.feat_dim = dataloader.dataset.feat_dim + + self.args = args + self.dropout = args.dropout + + if self.args.loss_class_weight: + from utils.aux_data import load_loss_weight + + self.num_pair = len(dataloader.dataset.pairs) + attr_weight, obj_weight, pair_weight = load_loss_weight(self.args.data) + self.attr_weight = np.array(attr_weight, dtype=np.float32) + self.obj_weight = np.array(obj_weight, dtype=np.float32) + self.pair_weight = np.array(pair_weight, dtype=np.float32) + + else: + self.attr_weight = None + self.obj_weight = None + self.pair_weight = None + + def basic_argscope(self, is_training): + activation_list = { + 'relu': tf.nn.relu, + 'elu': tf.nn.elu, + } + if hasattr(tf.nn, "leaky_relu"): + activation_list['leaky_relu'] = tf.nn.leaky_relu + if hasattr(tf.nn, "relu6"): + activation_list['relu6'] = tf.nn.relu6 + + if self.args.initializer is None: + initializer = tf.contrib.layers.xavier_initializer() + else: + initializer = tf.random_normal_initializer(0, self.args.initializer) + + return slim.arg_scope( + [slim.fully_connected], + activation_fn=activation_list[self.args.activation], + normalizer_fn=slim.batch_norm if self.args.batchnorm else None, + normalizer_params={ + 'is_training': is_training, + 'decay': 0.95, + 'fused': False + }, + ) + + def MLP(self, input_feat, output_dim, is_training, name, hidden_layers=[]): + """multi-layer perceptron, 1 layers as default""" + + with self.basic_argscope(is_training): + with tf.variable_scope(name) as scope: + for i, size in enumerate(hidden_layers): + input_feat = slim.fully_connected(input_feat, size, + trainable=is_training, reuse=tf.AUTO_REUSE, scope="fc_%d" % i) + + if self.dropout is not None: + input_feat = slim.dropout(input_feat, keep_prob=self.dropout, is_training=is_training, + scope='dropout_%d' % i) + + output_feat = slim.fully_connected(input_feat, output_dim, + trainable=is_training, activation_fn=None, reuse=tf.AUTO_REUSE, + scope="fc_out") + + return output_feat + + def cross_entropy(self, prob, labels, depth, target=1, weight=None, sample_weight=None, neg_loss=0): + """cross entropy with GT label id (int)""" + onehot_label = tf.one_hot(labels, depth=depth, axis=1) # (bz, depth) + return self.cross_entropy_with_labelvec(prob, onehot_label, target=target, + weight=weight, sample_weight=sample_weight, neg_loss=neg_loss) + + def cross_entropy_with_labelvec(self, prob, label_vecs, target=1, weight=None, sample_weight=None, neg_loss=0, + mask=None): + """cross entropy with GT onehot vector""" + assert target in [0, 1] + epsilon = 1e-8 + gamma = self.args.focal_loss + alpha = neg_loss + + zeros = array_ops.zeros_like(label_vecs, dtype=prob.dtype) # (bz, depth) + ones = array_ops.ones_like(label_vecs, dtype=prob.dtype) # (bz, depth) + + if target == 0: + prob = 1 - prob + + pos_position = label_vecs > array_ops.zeros_like(label_vecs, dtype=label_vecs.dtype) + pos_prob = array_ops.where(pos_position, prob, ones) # pos -> p, neg -> 1 (no loss) + neg_prob = array_ops.where(pos_position, zeros, prob) # pos -> 0(no loss), neg ->p + + pos_xent = - tf.log(tf.clip_by_value(pos_prob, epsilon, 1.0)) + if gamma is not None: + pos_xent = ((1 - pos_prob) ** gamma) * pos_xent + + if alpha is None or alpha == 0: + neg_xent = 0 + else: + neg_xent = - alpha * tf.log(tf.clip_by_value(1.0 - neg_prob, epsilon, 1.0)) + + if gamma is not None: + neg_xent = (neg_prob ** gamma) * neg_xent + + xent = pos_xent + neg_xent # (bz, depth) + + if mask is not None: + xent = xent * mask + + if sample_weight is not None: + xent = xent * tf.expand_dims(sample_weight, axis=1) + xent = tf.reduce_mean(xent, axis=0) # (depth,) + + if weight is not None: + xent = xent * weight + + return tf.reduce_sum(xent) + + def distance_metric(self, a, b): + if (self.args.distance_metric == 'L2'): + return tf.norm(a - b, axis=-1) + elif (self.args.distance_metric == 'L1'): + return tf.norm(a - b, axis=-1, ord=1) + elif (self.args.distance_metric == 'cos'): + return tf.reduce_sum( + tf.multiply( + tf.nn.l2_normalize(a, axis=-1), + tf.nn.l2_normalize(b, axis=-1) + ), axis=-1) + else: + raise NotImplementedError("Unsupported distance metric: %s" + \ + self.args.distance_metric) + + def MSELoss(self, a, b): + return tf.reduce_mean(self.distance_metric(a, b)) + + # def L2distance(self, a, b): + # return self.distance_metric(a, b) + + def triplet_margin_loss(self, anchor, positive, negative): + dist = self.distance_metric(anchor, positive) - self.distance_metric(anchor, + negative) + self.args.triplet_margin + return tf.maximum(dist, 0) + + def test_step(self, sess, blobs, score_op): + dset = self.dataloader.dataset + test_att = np.array([dset.attr2idx[attr] for attr, _ in dset.pairs]) + test_obj = np.array([dset.obj2idx[obj] for _, obj in dset.pairs]) + + feed_dict = { + self.pos_image_feat: blobs[4], + self.test_attr_id: test_att, + self.test_obj_id: test_obj, + } + if self.args.obj_pred is not None: + feed_dict[self.pos_obj_prediction] = blobs[-1] + + score = sess.run(score_op, feed_dict=feed_dict) + + for key in score_op.keys(): + score[key][0] = { + ################################################## + (a, o): torch.from_numpy(score[key][0][:, i]) + # (a, o): tf.convert_to_tensor(score[key][0][:, i]) + ################################################## + for i, (a, o) in enumerate(zip(test_att, test_obj)) + } + + return score diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/fc_obj.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/fc_obj.py new file mode 100644 index 0000000000000000000000000000000000000000..e9b8db017f3bf5a2ff0b650b0025430a8be435e7 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/fc_obj.py @@ -0,0 +1,124 @@ +# 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. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import numpy as np +import os, logging, torch +from collections import OrderedDict + +from network.base_network import * +from utils.utils import Embedder +from utils import config as cfg + + + +class Network(BaseNetwork): + root_logger = logging.getLogger("network %s"%__file__) + + def __init__(self, dataloader, args, feat_dim=None): + super(Network, self).__init__(dataloader, args, feat_dim) + + self.pos_obj_id = tf.placeholder(tf.int32, shape=[None]) + self.pos_image_feat = tf.placeholder(tf.float32, shape=[None, self.feat_dim]) + self.lr = tf.placeholder(tf.float32) + + self.test_attr_id = tf.placeholder(tf.int32, shape=[None], name='test_attr_id') + self.test_obj_id = tf.placeholder(tf.int32, shape=[None], name='test_obj_id') + + + def build_network(self, test_only=False): + logger = self.logger('create_train_arch') + + + ########################## classifier losses ########################## + score_pos_O = self.MLP(self.pos_image_feat, self.num_obj, + is_training=True, name='obj_cls', + hidden_layers=self.args.fc_cls) + prob_pos_O = tf.nn.softmax(score_pos_O, 1) + + loss = self.cross_entropy(prob_pos_O, self.pos_obj_id, + depth=self.num_obj, weight=self.obj_weight) + + + ################################ test ################################# + + score_pos_O = self.MLP(self.pos_image_feat, self.num_obj, + is_training=False, name='obj_cls', + hidden_layers=self.args.fc_cls) + prob_pos_O = tf.nn.softmax(score_pos_O, 1) + + batchsize = tf.shape(self.pos_image_feat)[0] + prob_pos_A = tf.zeros([batchsize, self.num_attr], + dtype=self.pos_image_feat.dtype) + score_original = tf.zeros([batchsize, self.num_pair], + dtype=self.pos_image_feat.dtype) + + score_res = OrderedDict([ + ("score_fc", [score_original, prob_pos_A, prob_pos_O]), + ]) + + + # summary + + with tf.device("/cpu:0"): + tf.summary.scalar('loss_total', loss) + tf.summary.scalar('lr', self.lr) + + + train_summary_op = tf.summary.merge_all() + + + if test_only: + return prob_pos_O + else: + return loss, score_res, train_summary_op + + + + def train_step(self, sess, blobs, lr, train_op, train_summary_op): + summary, _ = sess.run( + [train_summary_op, train_op], + feed_dict={ + self.pos_obj_id : blobs[2], + self.pos_image_feat : blobs[9], + self.lr: lr, + }) + + return summary + + def test_step_no_postprocess(self, sess, blobs, score_op): + score = sess.run( + score_op, + feed_dict={ + self.pos_image_feat: blobs[4], + }) + + return score \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/symnet.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/symnet.py new file mode 100644 index 0000000000000000000000000000000000000000..f61340fdf165d60294958b01027368de953a338a --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/network/symnet.py @@ -0,0 +1,404 @@ +# 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. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import os, logging, torch, json +import os.path as osp +from collections import OrderedDict + +from network.base_network import * +from utils import utils +from utils import config as cfg + + + + +class Network(BaseNetwork): + root_logger = logging.getLogger("network %s"%__file__.split('/')[-1]) + + + def __init__(self, dataloader, args, feat_dim=None): + super(Network, self).__init__(dataloader, args, feat_dim) + + self.dset = self.dataloader.dataset + self.attr_embedder = utils.Embedder(args.wordvec, self.dset.attrs, args.data) + self.emb_dim = self.attr_embedder.emb_dim # dim of wordvec (attr or obj) + + self.pos_attr_id = tf.placeholder(tf.int32, shape=[None]) + self.pos_obj_id = tf.placeholder(tf.int32, shape=[None]) + self.pos_image_feat = tf.placeholder(tf.float32, shape=[None, self.feat_dim]) + + self.neg_attr_id = tf.placeholder(tf.int32, shape=[None]) + self.neg_obj_id = tf.placeholder(tf.int32, shape=[None]) + self.neg_image_feat = tf.placeholder(tf.float32, shape=[None, self.feat_dim]) + + self.test_attr_id = tf.placeholder(tf.int32, shape=[None], name='test_attr_id') + self.test_obj_id = tf.placeholder(tf.int32, shape=[None], name='test_obj_id') + + self.pos_obj_prediction = tf.placeholder(tf.float32, shape=[None, self.num_obj]) + + self.lr = tf.placeholder(tf.float32) + + + def rep_embedder(self, img, is_training, name): + """transform ResNet feature to image representation""" + img = self.MLP(img, self.args.rep_dim, is_training, name, hidden_layers=[]) + return img + + + + def transformer(self, rep, v_attr, is_training, name): + """CoN or DecoN in our paper""" + + with tf.variable_scope(name) as scope: + in_dim = out_dim = self.args.rep_dim + + + if not self.args.no_attention: + attention = self.MLP(v_attr, in_dim, is_training, + name='fc_attention', hidden_layers=self.args.fc_att) + attention = tf.sigmoid(attention) + + rep = attention*rep + rep # short connection + + hidden = tf.concat([rep, v_attr], axis=1) # 512+300 + + + output = self.MLP(hidden, out_dim, is_training, + name='fc_out', hidden_layers=self.args.fc_compress) + + + return output + + + def attr_classification(self, emb, is_training, name='classifier'): + with tf.variable_scope(name) as scope: + score_A = self.MLP(emb, self.num_attr, is_training, "attr", + hidden_layers=self.args.fc_cls) + prob_A = tf.nn.softmax(score_A, 1) + return score_A, prob_A + + + def obj_classification(self, emb, is_training, name='classifier'): + with tf.variable_scope(name) as scope: + score_O = self.MLP(emb, self.num_obj, is_training, "obj", + hidden_layers=self.args.fc_cls) + prob_O = tf.nn.softmax(score_O, 1) + return score_O, prob_O + + + + + def RMD_prob(self, feat_plus, feat_minus, repeat_img_feat, is_training, metric): + """return attribute classification probability with our RMD""" + # feat_plus, feat_minus: shape=(bz, #attr, dim_emb) + # d_plus: distance between feature before&after CoN + # d_minus: distance between feature before&after DecoN + + d_plus = self.distance_metric(feat_plus, repeat_img_feat) + d_minus = self.distance_metric(feat_minus, repeat_img_feat) + d_plus = tf.reshape(d_plus, [-1, self.num_attr]) # bz, #attr + d_minus = tf.reshape(d_minus, [-1, self.num_attr]) # bz, #attr + + + if metric == 'softmax': + p_plus = tf.nn.softmax(-d_plus, 1) # (bz, #attr), smaller = better + p_minus = tf.nn.softmax(d_minus, 1) # (bz, #attr), larger = better + return p_plus, p_minus + + + elif metric == 'rmd': + d_plus_comp = self.dset.comp_gamma['b']*d_plus + d_minus_comp = self.dset.comp_gamma['a']*d_minus + d_plus_attr = self.dset.attr_gamma['b']*d_plus + d_minus_attr = self.dset.attr_gamma['a']*d_minus + + p_comp = tf.nn.softmax(d_minus_comp - d_plus_comp, axis=1) + p_attr = tf.nn.softmax(d_minus_attr - d_plus_attr, axis=1) + + return p_comp, p_attr + + + + + def build_network(self, test_only=False): + logger = self.logger('create_train_arch') + + total_losses = [] + + batchsize = tf.shape(self.pos_image_feat)[0] + + pos_attr_emb = self.attr_embedder.get_embedding(self.pos_attr_id) + neg_attr_emb = self.attr_embedder.get_embedding(self.neg_attr_id) + + pos_img = self.rep_embedder(self.pos_image_feat, + is_training=True, name='embedder') # (bz,dim) + neg_img = self.rep_embedder(self.neg_image_feat, + is_training=True, name='embedder') # (bz,dim) + + # rA = remove positive attribute A + # aA = add positive attribute A + # rB = remove negative attribute B + # aB = add negative attribute B + pos_aA = self.transformer(pos_img, pos_attr_emb, True, name='CoN') + pos_aB = self.transformer(pos_img, neg_attr_emb, True, name='CoN') + pos_rA = self.transformer(pos_img, pos_attr_emb, True, name='DeCoN') + pos_rB = self.transformer(pos_img, neg_attr_emb, True, name='DeCoN') + + attr_emb = self.attr_embedder.get_embedding(np.arange(self.num_attr)) + # (#attr, dim_emb), wordvec of all attributes + tile_attr_emb = utils.tile_tensor(attr_emb, 0, batchsize) + # (bz*#attr, dim_emb) + + + ########################## classification losses ###################### + # unnecessary to compute cls loss for neg_img + + if self.args.lambda_cls_attr > 0: + + # original image + _, prob_pos_A = self.attr_classification(pos_img, is_training=True) + loss_cls_pos_a = self.cross_entropy( + prob_pos_A, self.pos_attr_id, self.num_attr, + weight=self.attr_weight) + + # after removing pos_attr + _, prob_pos_rA_A = self.attr_classification(pos_rA, is_training=True) + loss_cls_pos_rA_a = self.cross_entropy( + prob_pos_rA_A, self.pos_attr_id, self.num_attr, + target=0, weight=self.attr_weight) + + + # rmd + repeat_img_feat = utils.repeat_tensor(pos_img, 0, self.num_attr) + # (bz*#attr, dim_rep) + feat_plus = self.transformer(repeat_img_feat, tile_attr_emb, + is_training=True, name='CoN') + feat_minus = self.transformer(repeat_img_feat, tile_attr_emb, + is_training=True, name='DeCoN') + + prob_RMD_plus, prob_RMD_minus = self.RMD_prob(feat_plus, feat_minus, + repeat_img_feat, is_training=True, metric=self.args.rmd_metric) + + loss_cls_rmd_plus = self.cross_entropy( + prob_RMD_plus, self.pos_attr_id, self.num_attr, + weight=self.attr_weight) + loss_cls_rmd_minus = self.cross_entropy( + prob_RMD_minus, self.pos_attr_id, self.num_attr, + weight=self.attr_weight) + + + + loss_cls_attr = self.args.lambda_cls_attr * sum([ + loss_cls_pos_a, loss_cls_pos_rA_a, + loss_cls_rmd_plus, loss_cls_rmd_minus + ]) + + total_losses.append(loss_cls_attr) + + # with tf.device("/cpu:0"): + tf.summary.scalar('loss/loss_cls_attr', loss_cls_attr) + + + + if self.args.lambda_cls_obj > 0: + # original image + _, prob_pos_O = self.obj_classification(pos_img, is_training=True) + loss_cls_pos_o = self.cross_entropy( + prob_pos_O, self.pos_obj_id, self.num_obj, + weight=self.obj_weight) + + # after removing pos_attr + _, prob_pos_rA_O = self.obj_classification(pos_rA, is_training=True) + loss_cls_pos_rA_o = self.cross_entropy( + prob_pos_rA_O, self.pos_obj_id, self.num_obj, + weight=self.obj_weight) + + # after adding neg_attr + _, prob_pos_aB_O = self.obj_classification(pos_aB, is_training=True) + loss_cls_pos_aB_o = self.cross_entropy( + prob_pos_aB_O, self.pos_obj_id, self.num_obj, + weight=self.obj_weight) + + + loss_cls_obj = self.args.lambda_cls_obj * sum([ + loss_cls_pos_o, + loss_cls_pos_rA_o, + loss_cls_pos_aB_o + ]) + + total_losses.append(loss_cls_obj) + + # with tf.device("/cpu:0"): + tf.summary.scalar('loss/loss_cls_obj', loss_cls_obj) + + + + ############################# symmetry loss ########################### + + if self.args.lambda_sym > 0: + + loss_sym_pos = self.MSELoss(pos_aA, pos_img) + loss_sym_neg = self.MSELoss(pos_rB, pos_img) + + loss_sym = self.args.lambda_sym * (loss_sym_pos + loss_sym_neg) + total_losses.append(loss_sym) + + # with tf.device("/cpu:0"): + tf.summary.scalar('loss/loss_sym', loss_sym) + + + ############################## axiom losses ########################### + if self.args.lambda_axiom > 0: + loss_clo = loss_inv = loss_com = 0 + + # closure + if not self.args.remove_clo: + pos_aA_rA = self.transformer(pos_aA, pos_attr_emb, + is_training=True, name='DeCoN') + pos_rB_aB = self.transformer(pos_rB, neg_attr_emb, + is_training=True, name='CoN') + loss_clo = self.MSELoss(pos_aA_rA, pos_rA) + \ + self.MSELoss(pos_rB_aB, pos_aB) + + # invertibility + if not self.args.remove_inv: + pos_rA_aA = self.transformer(pos_rA, pos_attr_emb, + is_training=True, name='CoN') + pos_aB_rB = self.transformer(pos_aB, neg_attr_emb, + is_training=True, name='DeCoN') + loss_inv = self.MSELoss(pos_rA_aA, pos_img) + \ + self.MSELoss(pos_aB_rB, pos_img) + + # Commutativity + if not self.args.remove_com: + pos_aA_rB = self.transformer(pos_aA, neg_attr_emb, + is_training=True, name='DeCoN') + pos_rB_aA = self.transformer(pos_rB, pos_attr_emb, + is_training=True, name='CoN') + loss_com = self.MSELoss(pos_aA_rB, pos_rB_aA) + + + loss_axiom = self.args.lambda_axiom * ( + loss_clo + loss_inv + loss_com) + total_losses.append(loss_axiom) + + # with tf.device("/cpu:0"): + with tf.name_scope('loss'): + tf.summary.scalar('loss_axiom', loss_axiom) + tf.summary.scalar('loss_clo', loss_clo) + tf.summary.scalar('loss_inv', loss_inv) + tf.summary.scalar('loss_com', loss_com) + + + ############################# triplet loss ############################ + + if self.args.lambda_trip > 0: + pos_triplet = tf.reduce_mean( + self.triplet_margin_loss(pos_img, pos_aA, pos_rA)) + neg_triplet = tf.reduce_mean( + self.triplet_margin_loss(pos_img, pos_rB, pos_aB)) + + loss_triplet = self.args.lambda_trip * (pos_triplet + neg_triplet) + total_losses.append(loss_triplet) + + # with tf.device("/cpu:0"): + tf.summary.scalar('loss/loss_triplet', loss_triplet) + + + ################################ testing ############################## + pos_img = self.rep_embedder(self.pos_image_feat, False, "embedder") + repeat_img_feat = utils.repeat_tensor(pos_img, 0, self.num_attr) + # (bz*#attr, dim_rep) + + feat_plus = self.transformer(repeat_img_feat, tile_attr_emb, + is_training=False, name='CoN') + feat_minus = self.transformer(repeat_img_feat, tile_attr_emb, + is_training=False, name='DeCoN') + + prob_A_rmd, prob_A_attr = self.RMD_prob(feat_plus, feat_minus, + repeat_img_feat, is_training=False, metric='rmd') + + _, prob_A_fc = self.attr_classification(pos_img, is_training=False) + _, prob_O_fc = self.obj_classification(pos_img, is_training=False) + + + + + if self.args.obj_pred is None: + prob_O = prob_O_fc + else: + prob_O = self.pos_obj_prediction + + test_a_onehot = tf.one_hot(self.test_attr_id, depth=self.num_attr, axis=1) + test_o_onehot = tf.one_hot(self.test_obj_id, depth=self.num_obj, axis=1) + # (n_p, n_o) + prob_P_rmd = tf.multiply( + tf.matmul(prob_A_rmd, tf.transpose(test_a_onehot)), + tf.matmul(prob_O, tf.transpose(test_o_onehot)) + ) + + score_res = dict([ + ("score_rmd", [prob_P_rmd, prob_A_attr, prob_O]), + ]) + + ############################### summary ############################### + + loss = sum(total_losses) + + # with tf.device("/cpu:0"): + tf.summary.scalar('loss_total', loss) + tf.summary.scalar('lr', self.lr) + + + train_summary_op = tf.summary.merge_all() + + + return loss, score_res, train_summary_op + + + + + def train_step(self, sess, blobs, lr, train_op, train_summary_op): + + summary, _ = sess.run( + [train_summary_op, train_op], + feed_dict={ + self.pos_attr_id : blobs[1], + self.pos_obj_id : blobs[2], + self.pos_image_feat : blobs[4], + self.neg_attr_id : blobs[6], + self.neg_image_feat : blobs[9], + self.lr: lr, + }) + + return summary + diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/pip-requirements.txt b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/pip-requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a90c02fe57c8fd8b78226eabfbc114d1f35278a1 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/pip-requirements.txt @@ -0,0 +1,3 @@ +torch==1.10.0 +torchvision==0.11.3 +numpy==1.21.2 \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/run_symnet.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/run_symnet.py new file mode 100644 index 0000000000000000000000000000000000000000..7556ce8d15a588fefa194326e25ff517bb47be45 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/run_symnet.py @@ -0,0 +1,423 @@ +# 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. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from utils.base_solver import BaseSolver + +import os, logging, importlib, re, copy, random, tqdm, argparse +import os.path as osp +import time +from collections import defaultdict + +import tensorflow as tf +import tensorflow.contrib.slim as slim +import torch +import sys + +from utils import config as cfg +from utils import dataset, utils +from utils.evaluator import CZSL_Evaluator +from npu_bridge.npu_init import * +from npu_bridge.estimator.npu.npu_loss_scale_manager import ExponentialUpdateLossScaleManager +from npu_bridge.estimator.npu.npu_loss_scale_optimizer import NPULossScaleOptimizer + + +# os.environ['ASCEND_GLOBAL_LOG_LEVEL'] = '1' +# os.environ['ASCEND_SLOG_PRINT_TO_STDOUT'] = '1' +# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0' + +def make_parser(): + parser = argparse.ArgumentParser() + + ############################## + parser.add_argument("--train_url", type=str, default="./output", required=True, + help="output path") + parser.add_argument("--data_url", type=str, default="./data", required=True, + help="output path") + ############################## + # basic training settings + parser.add_argument("--name", type=str, required=True, + help="Experiment name") + parser.add_argument("--data", type=str, default='UT', + choices=['MIT', 'UT', 'MITg', 'UTg'], + help="Dataset name") + parser.add_argument("--network", type=str, default='symnet', + help="Network name (the file name in `network` folder, but without suffix `.py`)") + + parser.add_argument("--epoch", type=int, default=500, + help="Maximum epoch during training phase, or epoch to be tested during testing") + parser.add_argument("--bz", type=int, default=512, + help="Train batch size") + parser.add_argument("--test_bz", type=int, default=1024, + help="Test batch size") + + parser.add_argument("--trained_weight", type=str, default=None, + help="Restore from a certain trained weight (relative path to './weights')") + parser.add_argument("--weight_type", type=int, default=1, + help="Type of the trained weight: 1-previous checkpoint(default), 2-pretrained classifier, 3-pretrained transformer") + + parser.add_argument("--test_freq", type=int, default=1, + help="Frequency of testing (#epoch). Set to 0 to skip test phase") + parser.add_argument("--snapshot_freq", type=int, default=10, + help="Frequency of saving snapshots (#epoch)") + + parser.add_argument("--force", default=False, action='store_true', + help="WARINING: clear experiment with duplicated name") + + # model settings + + parser.add_argument("--rmd_metric", type=str, default='softmax', + help="Similarity metric in RMD classification") + parser.add_argument("--distance_metric", type=str, default='L2', + help="Distance form") + + parser.add_argument("--obj_pred", type=str, default=None, + help="Object prediction from pretrained model") + + parser.add_argument("--wordvec", type=str, default='onehot', + help="Pre-extracted word vector type") + + # important hyper-parameters + + parser.add_argument("--rep_dim", type=int, default=300, + help="Dimentionality of attribute/object representation") + + parser.add_argument("--lr", type=float, default=1e-3, + help="Learning rate") + + parser.add_argument("--dropout", type=float, + help="Keep probability of dropout") + parser.add_argument("--batchnorm", default=True, action='store_true', + help="Use batch normalization") + parser.add_argument("--loss_class_weight", default=True, action='store_true', + help="Add weight between classes (default=true)") + + parser.add_argument("--fc_att", type=int, default=[512], nargs='*', + help="#fc layers after word vector") + parser.add_argument("--fc_compress", type=int, default=[768], nargs='*', + help="#fc layers after hidden layer") + parser.add_argument("--fc_cls", type=int, default=[512], nargs='*', + help="#fc layers in classifiers") + + parser.add_argument("--lambda_cls_attr", type=float, default=1) + parser.add_argument("--lambda_cls_obj", type=float, default=0.5) + + parser.add_argument("--lambda_trip", type=float, default=0.5) + parser.add_argument("--triplet_margin", type=float, default=0.5, + help="Triplet loss margin") + + parser.add_argument("--lambda_sym", type=float, default=0.01) + + parser.add_argument("--lambda_axiom", type=float, default=0.03) + parser.add_argument("--remove_inv", default=False, action="store_true") + parser.add_argument("--remove_com", default=False, action="store_true") + parser.add_argument("--remove_clo", default=False, action="store_true") + + parser.add_argument("--no_attention", default=False, action="store_true") + + # not so important + parser.add_argument("--activation", type=str, default='relu', + help="Activation function (relu, elu, leaky_relu, relu6)") + parser.add_argument("--initializer", type=float, default=None, + help="Weight initializer for fc (default=xavier init, set a float number to use Gaussian init)") + parser.add_argument("--optimizer", type=str, default='sgd', + choices=['sgd', 'adam', 'adamw', 'momentum', 'rmsprop'], + help="Type of optimizer (sgd, adam, momentum)") + + parser.add_argument("--lr_decay_type", type=str, default='no', + help="Type of Learning rate decay: no/exp/cos") + parser.add_argument("--lr_decay_step", type=int, default=100, + help="The first learning rate decay step (only for cos/exp)") + parser.add_argument("--lr_decay_rate", type=float, default=0.9, + help="Decay rate of cos/exp") + + parser.add_argument("--focal_loss", type=float, + help="`gamma` in focal loss. default=0 (=CE)") + + parser.add_argument("--clip_grad", default=False, action='store_true', + help="Use gradient clipping") + + parser.add_argument("--ifNPU", default=True, action='store_true', + help="Use gradient clipping") + return parser + + +############################ add ######################### +parser = make_parser() +args = parser.parse_args() + +if args.ifNPU == True: + print("Use NPU!!") + from npu_bridge.npu_init import * + +profiling_dir = "/cache/profiling" +os.makedirs(profiling_dir) + +import moxing as mox + + +############################################################ + +def main(): + logger = logging.getLogger('MAIN') + + utils.duplication_check(args) + utils.display_args(args, logger) + + logger.info("Loading dataset") + + train_dataloader = dataset.get_dataloader(args.data_url, args.data, 'train', + batchsize=args.bz) + test_dataloader = dataset.get_dataloader(args.data_url, args.data, 'test', + batchsize=args.test_bz, obj_pred=args.obj_pred) + + logger.info("Loading network and solver") + network = importlib.import_module('network.' + args.network) + net = network.Network(train_dataloader, args) + + with utils.create_session() as sess: + sw = SolverWrapper(net, train_dataloader, test_dataloader, args) + sw.trainval_model(sess, args.epoch) + + mox.file.copy_parallel(profiling_dir, args.train_url) + + +################################################################################ + + +class SolverWrapper(BaseSolver): + def __init__(self, network, train_dataloader, test_dataloader, args): + logger = self.logger("init") + self.network = network + + if args.network == 'fc_obj': + self.criterion = 'real_obj_acc' + self.key_score_name = "score_fc" + self.save_epoch = 100 + else: + self.criterion = 'top1_acc' + self.key_score_name = "score_rmd" + self.save_epoch = 500 + + self.train_dataloader = train_dataloader + self.test_dataloader = test_dataloader + + self.weight_dir1 = osp.join(cfg.WEIGHT_ROOT_DIR, args.name) + self.log_dir1 = osp.join(cfg.LOG_ROOT_DIR, args.name) + + self.weight_dir = osp.join(args.train_url, self.weight_dir1) + self.log_dir = osp.join(args.train_url, self.log_dir1) + + self.args = args + + logger.info("training weight => " + self.weight_dir) + logger.info("training log => " + self.log_dir) + + if not os.path.exists(self.weight_dir): + os.makedirs(self.weight_dir) + if not os.path.exists(self.log_dir): + os.makedirs(self.log_dir) + + if args.trained_weight is not None: + # self.trained_weight = os.path.join(cfg.WEIGHT_ROOT_DIR, args.trained_weight) + + weight_dir = os.path.join(self.args.data_url, './weights') + logger.info("weight_dir => " + weight_dir) + self.trained_weight = os.path.join(weight_dir, args.trained_weight) + logger.info("trained_weight => " + self.trained_weight) + self.logger("init").info("pretrained model <= " + self.trained_weight) + if args.weight_type != 1: + self.clear_folder() + else: + self.trained_weight = None + self.clear_folder() + + def construct_graph(self, sess): + logger = self.logger('construct_graph') + + with sess.graph.as_default(): + if cfg.RANDOM_SEED is not None: + tf.set_random_seed(cfg.RANDOM_SEED) + + loss_op, score_op, train_summary_op = self.network.build_network() + + self.epoch_num = 0 + if self.trained_weight is not None and self.args.weight_type == 1: + try: + self.epoch_num = int(self.trained_weight.split('.ckpt')[0].split('_')[-1]) + except: + pass + ########################################################################################################## + loss_scale_manager = ExponentialUpdateLossScaleManager(init_loss_scale=2 ** 32, incr_every_n_steps=1000, + decr_every_n_nan_or_inf=2, decr_ratio=0.5) + ################################################################################################################ + global_step = tf.Variable(self.epoch_num, trainable=False) + + lr = self.set_lr_decay(global_step) + optimizer = self.set_optimizer(lr) + + ######################################################################## + optimizer = NPULossScaleOptimizer(optimizer, loss_scale_manager) + ######################################################################## + + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + with tf.control_dependencies([tf.group(*update_ops)]): + grad = optimizer.compute_gradients(loss_op, tf.trainable_variables()) + if self.args.clip_grad: + grad = [(tf.clip_by_norm(g, cfg.GRADIENT_CLIPPING), var) for g, var in grad] + + train_op = optimizer.apply_gradients(grad, global_step=global_step) + + self.saver = tf.train.Saver(max_to_keep=None) + + return lr, score_op, train_op, train_summary_op + + def trainval_model(self, sess, max_epoch): + logger = self.logger('train_model') + logger.info('Begin training') + + lr, score_op, train_op, train_summary_op = self.construct_graph(sess) + # for x in tf.global_variables(): + # print(x.name) + + self.initialize(sess) + sess.graph.finalize() + + writer = tf.summary.FileWriter(self.log_dir, sess.graph) + evaluator = CZSL_Evaluator(self.test_dataloader.dataset, self.network) + best_report = defaultdict(dict) + + for epoch in range(self.epoch_num + 1, max_epoch + 1): + ############################# train ############################### + summary = None + for batch_ind, batch in tqdm.tqdm( + enumerate(self.train_dataloader), + total=len(self.train_dataloader), + postfix='epoch %d/%d' % (epoch, max_epoch)): + start_time = time.time() + if isinstance(lr, float): + eval_lr = lr + else: + eval_lr = lr.eval() + + summary = self.network.train_step( + sess, batch, eval_lr, + train_op, train_summary_op) + cost_time = time.time() - start_time + print("sec/step : {}".format(cost_time)) + + writer.add_summary(summary, float(epoch)) + + if self.args.test_freq > 0 and epoch % self.args.test_freq == 0: + ########################## test czsl ########################## + + accuracies_pair = defaultdict(list) + accuracies_attr = defaultdict(list) + accuracies_obj = defaultdict(list) + + for image_ind, batch in tqdm.tqdm(enumerate(self.test_dataloader), total=len(self.test_dataloader), + postfix='test'): + + predictions = self.network.test_step(sess, batch, score_op) + ################################################## + attr_truth = torch.from_numpy(batch[1]) + obj_truth = torch.from_numpy(batch[2]) + + # attr_truth = tf.convert_to_tensor(batch[1]) + # obj_truth = tf.convert_to_tensor(batch[2]) + ################################################## + for key in score_op.keys(): + p_pair, p_a, p_o = predictions[key] + pair_results = evaluator.score_model(p_pair, obj_truth) + match_stats = evaluator.evaluate_predictions( + pair_results, attr_truth, obj_truth) + accuracies_pair[key].append(match_stats) + # 0/1 sequence of t/f + + a_match, o_match = evaluator.evaluate_only_attr_obj( + p_a, attr_truth, p_o, obj_truth) + + accuracies_attr[key].append(a_match) + accuracies_obj[key].append(o_match) + + all_report = {'Current': {}, 'Best': {}} + + for name in accuracies_pair.keys(): + accuracies = accuracies_pair[name] + accuracies = zip(*accuracies) + + ########################################################## + accuracies = map(torch.mean, map(torch.cat, accuracies)) + + ########################################################## + attr_acc, obj_acc, closed_1_acc, closed_2_acc, closed_3_acc, _, objoracle_acc = map( + lambda x: x.item(), accuracies) + + real_attr_acc = torch.mean(torch.cat(accuracies_attr[name])).item() + real_obj_acc = torch.mean(torch.cat(accuracies_obj[name])).item() + + report_dict = { + 'real_attr_acc': real_attr_acc, + 'real_obj_acc': real_obj_acc, + 'top1_acc': closed_1_acc, + 'top2_acc': closed_2_acc, + 'top3_acc': closed_3_acc, + 'name': self.args.name, + 'epoch': epoch, + } + + if self.criterion not in best_report[name] or report_dict[self.criterion] > best_report[name][ + self.criterion]: + best_report[name] = report_dict + if epoch > self.save_epoch: + self.snapshot(sess, epoch) + + # print test results + all_report['Current'][name] = report_dict + all_report['Best'][name] = best_report[name] + for key in all_report.keys(): + print("%s %s: " % (key, name) + utils.formated_czsl_result(all_report[key][name])) + + # save to tensorboard + summary = tf.Summary() + for key, value in report_dict.items(): + if key not in ['name', 'epoch']: + summary.value.add(tag="%s/%s" % (name, key), simple_value=value) + writer.add_summary(summary, epoch) + + if epoch > 0 & (epoch % self.args.snapshot_freq == 0): + self.snapshot(sess, epoch) + + writer.close() + logger.info('Finished.') + + +if __name__ == "__main__": + main() diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test/train_full_1p.sh b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test/train_full_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..c663be49d974421d9f32e639709d71a18b35156a --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test/train_full_1p.sh @@ -0,0 +1,187 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +modelarts_flag=${MODELARTS_MODEL_PATH} +if [ x"${modelarts_flag}" != x ]; +then + echo "running without etp..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo "### get your log here : ${print_log}" + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 基础参数，需要模型审视修改 +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +batch_size=256 +epochs=700 + +if [ x"${modelarts_flag}" != x ]; +then + python3.7 ./run_symnet.py --name UT_best --data UT --epoch=${epochs} --obj_pred UT_obj_lr1e-3_test_ep260.pkl --trained_weight snapshot_epoch_586.ckpt --batchnorm --wordvec onehot --lr 1e-4 --bz=${batch_size} --lambda_cls_attr 1 --lambda_cls_obj 0.5 --lambda_trip 0.5 --lambda_sym 0.01 --lambda_axiom 0.03 --data_url=${data_path} --train_url=${output_path} +# python3.7 ./test_symnet.py --name UT_best --data UT --epoch=${epochs} --obj_pred UT_obj_lr1e-3_test_ep260.pkl --wordvec onehot --batchnorm --data_url=${data_path} --train_url=${output_path} +else + python3.7 ./run_symnet.py --name UT_best --data UT --epoch${epochs} --obj_pred UT_obj_lr1e-3_test_ep260.pkl --trained_weight snapshot_epoch_586.ckpt --batchnorm --wordvec onehot --lr 1e-4 --bz=${batch_size} --lambda_cls_attr 1 --lambda_cls_obj 0.5 --lambda_trip 0.5 --lambda_sym 0.01 --lambda_axiom 0.03 --data_url=${data_path} --train_url=${output_path} 1>${print_log} 2>&1 +# python3.7 ./test_symnet.py --name UT_best --data UT --epoch=${epochs} --obj_pred UT_obj_lr1e-3_test_ep260.pkl --wordvec onehot --batchnorm --data_url=${data_path} --train_url=${output_path} +fi + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 20 | awk -F ':' '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Best score_rmd" ${print_log} | tail -n 1 | awk -F / '{print $NF}'` +# 提取所有loss打印信息 +grep "Current score_rmd" ${print_log} | awk -F "/" '{print NF}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainAccuracy = ${train_accuracy}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test/train_performance_1p.sh new file mode 100644 index 0000000000000000000000000000000000000000..385d47da4a9564040f228f7e5b5b178299324071 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test/train_performance_1p.sh @@ -0,0 +1,187 @@ +#!/bin/bash + +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## +# shell脚本所在路径 +cur_path=`echo $(cd $(dirname $0);pwd)` + +# 判断当前shell是否是performance +perf_flag=`echo $0 | grep performance | wc -l` + +# 当前执行网络的名称 +Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'` + +export RANK_SIZE=1 +export RANK_ID=0 +export JOB_ID=10087 + +# 路径参数初始化 +data_path="" +output_path="" + +# 帮助信息，不需要修改 +if [[ $1 == --help || $1 == -h ]];then + echo"usage:./train_performance_1P.sh " + echo " " + echo "parameter explain: + --data_path # dataset of training + --output_path # output of training + --train_steps # max_step for training + --train_epochs # max_epoch for training + --batch_size # batch size + -h/--help show help message + " + exit 1 +fi + +# 参数校验，不需要修改 +for para in $* +do + if [[ $para == --data_path* ]];then + data_path=`echo ${para#*=}` + elif [[ $para == --output_path* ]];then + output_path=`echo ${para#*=}` + elif [[ $para == --train_steps* ]];then + train_steps=`echo ${para#*=}` + elif [[ $para == --train_epochs* ]];then + train_epochs=`echo ${para#*=}` + elif [[ $para == --batch_size* ]];then + batch_size=`echo ${para#*=}` + fi +done + +# 校验是否传入data_path,不需要修改 +if [[ $data_path == "" ]];then + echo "[Error] para \"data_path\" must be config" + exit 1 +fi + +# 校验是否传入output_path,不需要修改 +if [[ $output_path == "" ]];then + output_path="./test/output/${ASCEND_DEVICE_ID}" +fi + +# 设置打屏日志文件名，请保留，文件名为${print_log} +print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log" +modelarts_flag=${MODELARTS_MODEL_PATH} +if [ x"${modelarts_flag}" != x ]; +then + echo "running with modelarts..." + print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank` + print_log="/home/ma-user/modelarts/log/${print_log_name}" +fi +echo "### get your log here : ${print_log}" + +CaseName="" +function get_casename() +{ + if [ x"${perf_flag}" = x1 ]; + then + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf' + else + CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc' + fi +} + +# 跳转到code目录 +cd ${cur_path}/../ +rm -rf ./test/output/${ASCEND_DEVICE_ID} +mkdir -p ./test/output/${ASCEND_DEVICE_ID} + +# 训练开始时间记录，不需要修改 +start_time=$(date +%s) +########################################################## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +#########第3行 至 100行，请一定不要、不要、不要修改########## +########################################################## + +#========================================================= +#========================================================= +#========训练执行命令，需要根据您的网络进行修改============== +#========================================================= +#========================================================= +# 基础参数，需要模型审视修改 +# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取 +# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取 +# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值 +train_epochs=2 +train_steps=100 +batch_size=256 + +if [ x"${modelarts_flag}" != x ]; +then + # python3.7 ./LeNet.py --data_path=${data_path} --output_path=${output_path} --steps=${train_steps} + python3.7 ${code_dir}/run_symnet.py --name UT_best --data UT --epoch train_epochs --obj_pred UT_obj_lr1e-3_test_ep260.pkl --trained_weight snapshot_epoch_599.ckpt --batchnorm --wordvec onehot --lr 1e-4 --bz batch_size --lambda_cls_attr 1 --lambda_cls_obj 0.5 --lambda_trip 0.5 --lambda_sym 0.01 --lambda_axiom 0.03 --data_url=${data_path} --train_url=${output_path} +else + # python3.7 ./LeNet.py --data_path=${data_path} --output_path=${output_path} --steps=${train_steps} 1>${print_log} 2>&1 + python3.7 ${code_dir}/run_symnet.py --name UT_best --data UT --epoch train_epochs --obj_pred UT_obj_lr1e-3_test_ep260.pkl --trained_weight snapshot_epoch_599.ckpt --batchnorm --wordvec onehot --lr 1e-4 --bz batch_size --lambda_cls_attr 1 --lambda_cls_obj 0.5 --lambda_trip 0.5 --lambda_sym 0.01 --lambda_axiom 0.03 --data_url=${data_path} --train_url=${output_path} 1>${print_log} 2>&1 +fi + +# 性能相关数据计算 +StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'` +FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'` + +# 精度相关数据计算 +train_accuracy=`grep "Final Accuracy accuracy" ${print_log} | awk '{print $NF}'` +# 提取所有loss打印信息 +grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt + + +########################################################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +#########后面的所有内容请不要修改########################### +########################################################### + +# 判断本次执行是否正确使用Ascend NPU +use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l` +if [ x"${use_npu_flag}" == x0 ]; +then + echo "------------------ ERROR NOTICE START ------------------" + echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration." + echo "------------------ ERROR NOTICE END------------------" +else + echo "------------------ INFO NOTICE START------------------" + echo "INFO, your task have used Ascend NPU, please check your result." + echo "------------------ INFO NOTICE END------------------" +fi + +# 获取最终的casename，请保留，case文件名为${CaseName} +get_casename + +# 重命名loss文件 +if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ]; +then + mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt +fi + +# 训练端到端耗时 +end_time=$(date +%s) +e2e_time=$(( $end_time - $start_time )) + +echo "------------------ Final result ------------------" +# 输出性能FPS/单step耗时/端到端耗时 +echo "Final Performance images/sec : $FPS" +echo "Final Performance sec/step : $StepTime" +echo "E2E Training Duration sec : $e2e_time" + +# 输出训练精度 +echo "Final Train Accuracy : ${train_accuracy}" + +# 最后一个迭代loss值，不需要修改 +ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`) + +#关键信息打印到${CaseName}.log中，不需要修改 +echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log +echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test_obj.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test_obj.py new file mode 100644 index 0000000000000000000000000000000000000000..14b3552ab73d80375d4f0b732d11cb2ff34913c1 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test_obj.py @@ -0,0 +1,167 @@ +# 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. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os, logging, pickle, importlib, re, copy, random, tqdm +import os.path as osp +import numpy as np + +import tensorflow as tf +import tensorflow.contrib.slim as slim +import torch + +from utils.base_solver import BaseSolver +from utils import config as cfg +from utils import dataset, utils + +from run_symnet import make_parser + + +def main(): + logger = logging.getLogger('MAIN') + + parser = make_parser() + parser.add_argument("--test_set", type=str, default='test', + choices=['test','val']) + args = parser.parse_args() + + + logger.info("Loading dataset") + test_dataloader = dataset.get_dataloader(args.data, args.test_set, + batchsize=args.test_bz) + + + logger.info("Loading network and solver") + network = importlib.import_module('network.'+args.network) + net = network.Network(test_dataloader, args) + + + with utils.create_session() as sess: + sw = SolverWrapper(net, test_dataloader, args) + sw.trainval_model(sess) + + + + +################################################################################ + + + +class SolverWrapper(BaseSolver): + def __init__(self, network, test_dataloader, args): + logger = self.logger("init") + self.network = network + + self.test_dataloader = test_dataloader + + + self.args = args + self.trained_weight = os.path.join( + cfg.WEIGHT_ROOT_DIR, args.name, + 'snapshot_epoch_{}.ckpt'.format(args.epoch)) + logger.info("trained weight <= "+self.trained_weight) + + self.filename = "%s_%s_ep%d.pkl"%(args.name, args.test_set, args.epoch) + self.filepath = osp.join(cfg.ROOT_DIR, "data/obj_scores", self.filename) + assert args.force or not os.path.exists(self.filepath), self.filepath + logger.info("obj prediction => "+self.filepath) + + + + + def initialize(self, sess): + """weight initialization""" + logger = self.logger('initialize') + logger.info('Restoring whole snapshots from {:s}'.format( + self.trained_weight)) + saver_restore = tf.train.Saver() + saver_restore.restore(sess, self.trained_weight) + + + + + def construct_graph(self, sess): + logger = self.logger('construct_graph') + + with sess.graph.as_default(): + if cfg.RANDOM_SEED is not None: + tf.set_random_seed(cfg.RANDOM_SEED) + + prob_op = self.network.build_network(test_only=True) + + return prob_op + + + + def trainval_model(self, sess): + logger = self.logger('train_model') + logger.info('Begin training') + + prob_op = self.construct_graph(sess) + + self.initialize(sess) + sess.graph.finalize() + + + ##################################### test czsl ####################################### + + predictions = [] + obj_truth = [] + + for image_ind, batch in tqdm.tqdm(enumerate(self.test_dataloader), total=len(self.test_dataloader), postfix='test'): + pred = self.network.test_step_no_postprocess(sess, batch, prob_op) + predictions.append(pred) + obj_truth.append(batch[2]) + + + predictions = np.concatenate(predictions, axis=0) + + print("Saving %s tensor to %s"%(str(predictions.shape),self.filename)) + with open(self.filepath, 'wb') as fp: + pickle.dump(predictions.tolist(), fp) + + + obj_truth = np.concatenate(obj_truth, axis=0) + obj_pred = predictions.argmax(axis=1) + print(obj_pred.shape, obj_truth.shape) + + match = (obj_pred==obj_truth).astype(float) + acc = np.sum(match)/match.shape[0] + + print("Obj acc = %.3f"%(acc*100)) + + + logger.info('Finished.') + + + + +if __name__=="__main__": + main() diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test_symnet.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test_symnet.py new file mode 100644 index 0000000000000000000000000000000000000000..372392dc5d7e416756001d248af4c7576f2dc908 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/test_symnet.py @@ -0,0 +1,142 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from utils.base_solver import BaseSolver + +import os, logging, importlib, re, copy, random, tqdm, argparse +import os.path as osp +# import cPickle as pickle +# from pprint import pprint +from datetime import datetime +import numpy as np +from collections import defaultdict + +import tensorflow as tf +import tensorflow.contrib.slim as slim +import torch + +from utils import config as cfg +from utils import dataset, utils +from utils.evaluator import CZSL_Evaluator + +from run_symnet import make_parser +from npu_bridge.npu_init import * +import moxing as mox + + +def main(): + logger = logging.getLogger('MAIN') + + parser = make_parser() + args = parser.parse_args() + utils.display_args(args, logger) + + logger.info("Loading dataset") + test_dataloader = dataset.get_dataloader(args.data_url, args.data, 'test', batchsize=args.test_bz, + obj_pred=args.obj_pred) + + logger.info("Loading network and solver") + network = importlib.import_module('network.' + args.network) + net = network.Network(test_dataloader, args) + + with utils.create_session() as sess: + sw = SolverWrapper(net, test_dataloader, args) + sw.trainval_model(sess, args.epoch) + + +################################################################################ + + +class SolverWrapper(BaseSolver): + def __init__(self, network, test_dataloader, args): + logger = self.logger("init") + self.network = network + + self.test_dataloader = test_dataloader + self.weight_dir = osp.join(cfg.WEIGHT_ROOT_DIR, args.name) + self.args = args + + weight_dir = os.path.join(self.args.data_url, './weights') + self.trained_weight = os.path.join(weight_dir, "snapshot_epoch_%d.ckpt" % args.epoch) + self.logger("init").info("pretrained model <= " + self.trained_weight) + + def construct_graph(self, sess): + logger = self.logger('construct_graph') + + with sess.graph.as_default(): + if cfg.RANDOM_SEED is not None: + tf.set_random_seed(cfg.RANDOM_SEED) + + loss_op, score_op, train_summary_op = self.network.build_network() + + global_step = tf.Variable(self.args.epoch, trainable=False) + + return score_op, train_summary_op + + def trainval_model(self, sess, max_epoch): + logger = self.logger('train_model') + logger.info('Begin training') + + score_op, train_summary_op = self.construct_graph(sess) + # for x in tf.global_variables(): + # print(x.name) + + self.initialize(sess) + sess.graph.finalize() + + evaluator = CZSL_Evaluator(self.test_dataloader.dataset, self.network) + + ############################## test czsl ################################ + + accuracies_pair = defaultdict(list) + accuracies_attr = defaultdict(list) + accuracies_obj = defaultdict(list) + + for image_ind, batch in tqdm.tqdm(enumerate(self.test_dataloader), total=len(self.test_dataloader), + postfix='test'): + + predictions = self.network.test_step(sess, batch, score_op) # ordereddict of [score_pair, score_a, score_o] + + attr_truth, obj_truth = batch[1], batch[2] + attr_truth, obj_truth = torch.from_numpy(attr_truth), torch.from_numpy(obj_truth) + + match_stats = [] + for key in score_op.keys(): + p_pair, p_a, p_o = predictions[key] + pair_results = evaluator.score_model(p_pair, obj_truth) + match_stats = evaluator.evaluate_predictions(pair_results, attr_truth, obj_truth) + accuracies_pair[key].append(match_stats) # 0/1 sequence of t/f + + a_match, o_match = evaluator.evaluate_only_attr_obj(p_a, attr_truth, p_o, obj_truth) + + accuracies_attr[key].append(a_match) + accuracies_obj[key].append(o_match) + + for name in accuracies_pair.keys(): + accuracies = accuracies_pair[name] + accuracies = zip(*accuracies) + accuracies = map(torch.mean, map(torch.cat, accuracies)) + attr_acc, obj_acc, closed_1_acc, closed_2_acc, closed_3_acc, _, objoracle_acc = map(lambda x: x.item(), + accuracies) + + real_attr_acc = torch.mean(torch.cat(accuracies_attr[name])).item() + real_obj_acc = torch.mean(torch.cat(accuracies_obj[name])).item() + + report_dict = { + 'real_attr_acc': real_attr_acc, + 'real_obj_acc': real_obj_acc, + 'top1_acc': closed_1_acc, + 'top2_acc': closed_2_acc, + 'top3_acc': closed_3_acc, + 'name': self.args.name, + 'epoch': self.args.epoch, + } + + print(name + ": " + utils.formated_czsl_result(report_dict)) + + logger.info('Finished.') + + +if __name__ == "__main__": + main() diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/__init__.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/__pycache__/__init__.cpython-36.pyc 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0000000000000000000000000000000000000000..c25a453353dec1fd265e136ebd46adcf259716e6 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/MIT_objs.json @@ -0,0 +1 @@ +{"lightbulb": "lightbulb", "shoes": "shoes", "deck": "deck", "laptop": "laptop", "ceramic": "ceramic", "paper": "paper", "vacuum": "vacuum", "keyboard": "keyboard", "chair": "chair", "milk": "milk", "roots": "roots", "carpet": "carpet", "sauce": "sauce", "tire": "tire", "mud": "mud", "sky": "sky", "lake": "lake", "sugar": "sugar", "bush": "bush", "bike": "bike", "fig": "fig", "garage": "garage", "orange": "orange", "furniture": "furniture", "hat": "hat", "persimmon": "persimmon", "boulder": "boulder", "plate": "plate", "coffee": "coffee", "handle": "handle", "branch": "branch", "wire": "wire", "bear": "bear", "coast": "coast", "vegetable": "vegetable", "bean": "bean", "tulip": "tulip", "fan": "fan", "snake": "snake", "desk": "desk", "silk": "silk", "bread": "bread", "aluminum": "aluminum", 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"rock": "rock", "horse": "horse", "water": "water", "newspaper": "newspaper", "cookie": "cookie", "key": "key", "pasta": "pasta", "paste": "paste", "trail": "trail", "card": "card", "kitchen": "kitchen", "box": "box", "stone": "stone", "drum": "drum", "thread": "thread", "column": "column", "island": "island", "tie": "tie", "berry": "berry", "smoke": "smoke", "castle": "castle", "glasses": "glasses", "road": "road", "cheese": "cheese", "apple": "apple", "wall": "wall", "pot": "pot", "canyon": "canyon", "tomato": "tomato", "frame": "frame", "church": "church", "table": "table", "ring": "ring", "brass": "brass", "boat": "boat", "belt": "belt", "city": "city", "bathroom": "bathroom", "toy": "toy", "fabric": "fabric", "beef": "beef", "window": "window", "tree": "tree", "plastic": "plastic", "paint": "paint", "camera": "camera", "bronze": "bronze", "tea": "tea", "valley": "valley", "bubble": "bubble", "banana": "banana", "building": "building", "ceiling": "ceiling", "diamond": "diamond", 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"beach": "beach", "pizza": "pizza", "wheel": "wheel", "wave": "wave", "plant": "plant", "ball": "ball", "mat": "mat", "screw": "screw", "farm": "farm", "eggs": "eggs", "foam": "foam", "pear": "pear", "necklace": "necklace", "fruit": "fruit", "garlic": "garlic", "log": "log", "moss": "moss", "dust": "dust", "velvet": "velvet", "basement": "basement", "coin": "coin", "desert": "desert", "pool": "pool", "cliff": "cliff", "butter": "butter", "phone": "phone", "coat": "coat", "seafood": "seafood", "floor": "floor", "metal": "metal", "dog": "dog", "meat": "meat", "jacket": "jacket", "coal": "coal", "shore": "shore", "truck": "truck", "jungle": "jungle", "bottle": "bottle", "basket": "basket"} \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/MIT_weight.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/MIT_weight.py new file mode 100644 index 0000000000000000000000000000000000000000..dfa77c11445fbbc99fd3bbedc43d90221fc8d06e --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/MIT_weight.py @@ -0,0 +1,30 @@ +# 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. +attr_weight = [4.561632133281056, 4.325826700719732, 5.5088676690046325, 7.118305581438733, 4.974046539682426, 3.6046302163498876, 4.8447080253179395, 4.474159182316003, 3.8754342510143167, 4.419824831352674, 4.471624326712815, 5.098651958137871, 4.88216558785284, 6.260855349587511, 7.192413553592455, 5.638325736241519, 3.8465662670134644, 5.560160963392184, 5.537855205877885, 4.041981300554439, 5.137304112572149, 4.417423869815136, 4.382084503369827, 4.048592894886752, 4.794518280794085, 4.454058002994916, 5.4070849746946905, 5.487665461354029, 5.606323005155346, 5.070612738073478, 4.474159182316003, 4.937081345448955, 4.85952311110308, 4.129741797363355, 7.231634266745736, 4.266361200676454, 3.769655412823722, 4.542427153445006, 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0000000000000000000000000000000000000000..6ad23d6ba6b5e8e14abe2d04c150b20ecbe95c10 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/UT_attrs.json @@ -0,0 +1 @@ +{"Synthetic": "synthetic", "Canvas": "canvas", "Sheepskin": "sheepskin", "Patent.Leather": "leather", "Wool": "wool", "Leather": "leather", "Satin": "satin", "Hair.Calf": "hair", "Full.grain.leather": "leather", "Rubber": "rubber", "Faux.Leather": "leather", "Suede": "suede", "Nylon": "nylon", "Nubuck": "leather", "Faux.Fur": "fur", "Cotton": "cotton"} \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/UT_gamma.json b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/UT_gamma.json new file mode 100644 index 0000000000000000000000000000000000000000..e7630ac336f349ede2925aeb3a7dd4fd772c661b --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/UT_gamma.json @@ -0,0 +1 @@ +{"attr_b": [1.0, 1.0, 0.8, 1.0, 1.2, 1.0, 1.0, 1.0, 1.2, 0.8, 1.0, 0.8, 1.2, 1.0, 0.8, 1.0], "attr_a": [1.0, 1.0, 0.8, 0.8, 1.2, 1.0, 0.8, 1.0, 1.0, 0.8, 0.8, 0.8, 1.2, 1.0, 0.8, 0.8], "comp_b": [0.84, 0.1, 1.0, 0.46, 0.5, 0.74, 0.62, 2.3000000000000003, 0.55, 0.56, 0.36, 0.6000000000000001, 0.68, 0.78, 0.54, 0.5], "comp_a": [0.8400000000000001, 0.0, 1.0, 0.44000000000000017, 0.48000000000000015, 0.7800000000000002, 0.6000000000000001, 2.3000000000000003, 0.45, 0.5600000000000003, 0.3199999999999999, 0.6000000000000001, 0.6599999999999997, 0.7800000000000002, 0.5400000000000003, 0.48000000000000015]} \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/UT_objs.json b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/UT_objs.json new file mode 100644 index 0000000000000000000000000000000000000000..13ff1ede97754a862c1a95e629bca8386ba08e1a --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/UT_objs.json @@ -0,0 +1 @@ +{"Shoes.Clogs.and.Mules": "clogs", "Shoes.Heels": "heels", "Boots.Mid-Calf": "midcalf", "Shoes.Flats": "flats", "Boots.Knee.High": "knee-high", "Shoes.Sneakers.and.Athletic.Shoes": "sneakers", "Shoes.Boat.Shoes": "shoes", "Shoes.Oxfords": "oxfords", "Boots.Ankle": "boots", "Sandals": "sandals", "Slippers": "slippers", "Shoes.Loafers": "loafers"} \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/UT_weight.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/UT_weight.py new file mode 100644 index 0000000000000000000000000000000000000000..d3315e704f21f9af935dc4cf260fb388db34902a --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/UT_weight.py @@ -0,0 +1,30 @@ +# 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. +attr_weight = [3.3346977756321516, 6.045005314036012, 6.315880268171411, 4.128581330635162, 3.4429435720973482, 5.932888015915306, 0.5385720013774027, 4.785004678240413, 4.751904729814068, 3.435434150075217, 3.7892631298020407, 4.174507768761085, 5.468582407784208, 2.0151239462217565, 2.3481072476387737, 5.180900335332427] +obj_weight = [2.0469601212700113, 2.7404183922042185, 2.442367117505141, 2.230464336693612, 5.948155488046094, 3.5078171577379704, 2.256203834895188, 1.7291003739616688, 2.5755686404252045, 2.947818448105355, 1.840059754211169, 4.629481314601183] +pair_weight = [6.190717125217406, 7.637636108153731, 6.7903382477665275, -0.0, 7.078020320218308, -0.0, -0.0, 7.483485428326473, 3.4879094000800452, 7.414492556839521, -0.0, -0.0, 6.9444889275937856, 7.819957664947686, 7.819957664947686, 7.557593400480195, -0.0, 6.656806855142005, 6.01166889376842, -0.0, -0.0, 5.29422902063943, 6.539023819485621, 5.468582407784208, 7.232171000045566, 6.097191067206582, -0.0, 5.765833931252139, 4.8861007951117825, 5.449713923479825, 7.557593400480195, -0.0, 5.859862880900415, 6.152250844389609, -0.0, 5.173782867563563, 5.145809015521157, 7.289329413885515, 7.637636108153731, 7.078020320218308, -0.0, 7.34995403570195, 2.445142327426924, 3.2258484263190192, 2.9401906460564686, 2.412785893487567, -0.0, 3.8254334380077957, 2.78041102735701, 2.6231194673495026, 2.928105906841397, 3.1359762985353044, 2.7908277882152652, -0.0, 6.230722459831105, 7.724647485143361, -0.0, -0.0, 7.557593400480195, 6.864446219920249, 6.721345376279576, 7.232171000045566, -0.0, 6.01166889376842, -0.0, 7.232171000045566, 5.111907463845475, 6.987048542012581, 6.9444889275937856, -0.0, 7.178103778775291, 7.178103778775291, -0.0, 6.33835312402347, -0.0, 3.678411501241291, 6.045005314036012, 6.656806855142005, -0.0, 5.679891501451415, 4.16411806491195, -0.0, 7.34995403570195, 5.805054644405421, -0.0, 7.414492556839521, 4.2144598197728005, -0.0, 5.917850138550765, 6.484956598215345, 3.382023398335508, 4.5695831730201135, -0.0, 5.105262921126807, 6.171299039360304, -0.0, 4.258911582343634, 3.4181284029776244, 4.270340278167257, -0.0, 3.915966831216804, -0.0, -0.0, 4.935156952100976, -0.0, -0.0, 5.404043886646637, 3.855342209400369, 3.7492229683647187, 5.579247975671727, 6.511624845297507, 3.440434160491922, 6.293901361452637, 7.557593400480195, -0.0, 5.27835567148314] diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__init__.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dc82d5136ef0402074d79102c4ee033e0e2a7b09 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__init__.py @@ -0,0 +1,58 @@ +# 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 importlib + +def load_loss_weight(dataset_name): + """Loss weight to balance the categories + weight = -log(frequency)""" + + if dataset_name[-1]=='g': + dataset_name = dataset_name[:-1] + + try: + Weight = importlib.import_module('utils.aux_data.%s_weight'%dataset_name) + + if 'pair_weight' in Weight.__dict__: + return Weight.attr_weight, Weight.obj_weight, Weight.pair_weight + else: + return Weight.attr_weight, Weight.obj_weight, None + + except ImportError: + raise NotImplementedError("Loss weight for %s is not implemented yet"%dataset_name) + + +def load_wordvec_dict(dataset_name, vec_type): + if dataset_name[-1]=='g': + dataset_name = dataset_name[:-1] + + try: + Wordvec = importlib.import_module('utils.aux_data.%s_%s'%(vec_type, dataset_name)) + return Wordvec.attrs_dict, Wordvec.objs_dict + + except ImportError: + raise NotImplementedError("%s vector for %s is not ready yet"%(vec_type, dataset_name)) diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/MIT_weight.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/MIT_weight.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e1535769c6a41679c86ef4f8c73781b2a2b12cad Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/MIT_weight.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/UT_weight.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/UT_weight.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3703d240d57f6e90ecf75d6569b7db046fb3d84b Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/UT_weight.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/__init__.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..61d4a4fbb2f0f24c309e56e8b9976f0c8109c9be Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/__init__.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/glove_MIT.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/glove_MIT.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..85a48aaef3a6ede5bed7b5618a745164315c4103 Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/__pycache__/glove_MIT.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/glove_MIT.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/glove_MIT.py new file mode 100644 index 0000000000000000000000000000000000000000..b7835eb22f93959f8ac23611745032e9f38b26e6 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/aux_data/glove_MIT.py @@ -0,0 +1,29 @@ +# 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. +attrs_dict = {u'crumpled': [-0.34428998827934265, -0.041088998317718506, -0.2932800054550171, -0.4592599868774414, 0.11011999845504761, -0.04722899943590164, -0.19939999282360077, -0.6640099883079529, -0.5397099852561951, 0.3364099860191345, -0.33511999249458313, 0.2157599925994873, -0.007037200033664703, -0.18472999334335327, -0.07743799686431885, 0.45440998673439026, -0.1596899926662445, 0.13877999782562256, 0.07464399933815002, -0.42572999000549316, 0.5098099708557129, 0.1652500033378601, 0.19673000276088715, -0.053564999252557755, -0.6513400077819824, -0.5163699984550476, -0.027967000380158424, 0.33294999599456787, 0.39096999168395996, -0.5551900267601013, -0.3602299988269806, -0.08196599781513214, 0.24785000085830688, 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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. +attrs_dict = {u'Synthetic': [-0.04577599838376045, 0.3800700008869171, 0.06601399928331375, 0.30629000067710876, -0.3903000056743622, -0.4559600055217743, -0.24815000593662262, -0.15424999594688416, 0.251910001039505, -1.542199969291687, 0.296889990568161, 0.04115299880504608, -0.10824000090360641, -0.5511000156402588, 0.15072999894618988, -0.6427199840545654, -0.6002699732780457, 0.6632300019264221, 0.01593099907040596, 0.25126999616622925, -0.7840200066566467, -0.06940600275993347, -0.7046599984169006, 1.0583000183105469, -0.7482500076293945, -0.047265999019145966, 0.12145999819040298, -0.19754000008106232, -0.39076000452041626, 0.8264899849891663, -0.23531000316143036, 0.12178999930620193, -0.4373599886894226, 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-0.4771699905395508, 0.20774999260902405, 0.4093799889087677, -0.10367000102996826, -0.33474001288414, -0.8932700157165527, -0.18310999870300293, 0.6604300141334534, -0.2998200058937073, 0.9802299737930298, -0.6742600202560425, -0.4010699987411499, -0.046254999935626984, -0.12870000302791595, 0.3562000095844269, 0.13203999400138855]} diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/base_solver.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/base_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..c66e0c343bdbb307c0554c42ed5ef07e319e854a --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/base_solver.py @@ -0,0 +1,134 @@ +# 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. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import logging +import os + +from utils import config as cfg + +if cfg.ROOT_DIR.startswith('/home'): + pass +# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # set tensorflow logger to WARNING level +import tensorflow as tf + + +class BaseSolver(object): + root_logger = logging.getLogger('solver') + def logger(self, suffix): + return self.root_logger.getChild(suffix) + + + def clear_folder(self): + """clear weight and log dir""" + logger = self.logger('clear_folder') + + for f in os.listdir(self.log_dir): + logger.warning('Deleted log file ' + f) + os.remove(os.path.join(self.log_dir, f)) + + for f in os.listdir(self.weight_dir): + logger.warning('Deleted weight file ' + f) + os.remove(os.path.join(self.weight_dir, f)) + + + def snapshot(self, sess, iter, filenames = None): + """save checkpoint""" + if not os.path.exists(self.weight_dir): + os.makedirs(self.weight_dir) + + if filenames is None: + filename = 'snapshot_epoch_{}.ckpt'.format(iter) + else: + filename = filenames + pth = os.path.join(self.weight_dir, filename) + self.saver.save(sess, pth) + + self.logger('snapshot').info('Wrote snapshot to: {}'.format(filename)) + + + def initialize(self, sess): + """weight initialization""" + logger = self.logger('initialize') + + if self.trained_weight is None: + sess.run(tf.global_variables_initializer()) + else: + sess.run(tf.global_variables_initializer()) + logger.info('Restoring whole model snapshots from {:s}'.format(self.trained_weight)) + saver_restore = tf.train.Saver() + saver_restore.restore(sess, self.trained_weight) + + + + + def set_lr_decay(self, global_step): + if self.args.lr_decay_type == 'no': + lr = self.args.lr + elif self.args.lr_decay_type == 'exp': + decay_stepsize = len(self.train_dataloader)*self.args.lr_decay_step + lr = tf.train.exponential_decay( + self.args.lr, + global_step, + decay_stepsize, + self.args.lr_decay_rate, + staircase=True) + elif self.args.lr_decay_type == 'cos': + decay_stepsize = len(self.train_dataloader)*self.args.lr_decay_step + lr = tf.train.cosine_decay_restarts( + self.args.lr, + global_step, + decay_stepsize, + t_mul=2.0, + m_mul=0.8, + alpha=0.1 + ) + + return lr + + + + def set_optimizer(self, lr): + if self.args.optimizer == 'sgd': + optimizer = tf.train.GradientDescentOptimizer(lr) + elif self.args.optimizer == 'momentum': + optimizer = tf.train.MomentumOptimizer(lr, 0.9) + logger.info('Using momentum optimizer') + elif self.args.optimizer == 'adam': + optimizer = tf.train.AdamOptimizer(lr) + logger.info('Using Adam optimizer') + elif self.args.optimizer == 'adamw': + optimizer = tf.contrib.opt.AdamWOptimizer(5e-5, lr) + logger.info('Using AdamW optimizer') + elif self.args.optimizer == 'rmsprop': + optimizer = tf.train.RMSPropOptimizer(lr) + logger.info('Using RMSProp optimizer') + + return optimizer diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/config.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..d3211f4d14fe71cac8397081692f5b8d4a685bc3 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/config.py @@ -0,0 +1,63 @@ +# 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 logging, os + +logging.basicConfig(format='[%(asctime)s] %(name)s: %(message)s', level=logging.INFO) + + +RANDOM_SEED = None + +SUMMARY_INTERVAL = 'auto' # int(of iter) or 'auto' +IMAGE_SUMMARY_INTERVAL = 'auto' # int(of iter) or 'auto' + + +ROOT_DIR = "." # change this to the project folder + + +WEIGHT_ROOT_DIR = ROOT_DIR+"/SymNet_NPU/weights/" +LOG_ROOT_DIR = ROOT_DIR+"/SymNet_NPU/output_log/" +DATA_ROOT_DIR = ROOT_DIR+"/data" + + +CZSL_DS_ROOT = { + 'MIT': DATA_ROOT_DIR+'/mit-states-original', + 'UT': 'ut-zap50k-original', +} + +GCZSL_DS_ROOT = { + 'MIT': DATA_ROOT_DIR+'/mit-states-natural', + 'UT': DATA_ROOT_DIR+'/ut-zap50k-natural', +} + +GRADIENT_CLIPPING = 5 + + +# if not os.path.exists(WEIGHT_ROOT_DIR): +# os.makedirs(WEIGHT_ROOT_DIR) +# if not os.path.exists(LOG_ROOT_DIR): +# os.makedirs(LOG_ROOT_DIR) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/CZSL_dataset.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/CZSL_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..5efad8565683d28f173767af85147a111d7523b0 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/CZSL_dataset.py @@ -0,0 +1,261 @@ +# 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. +"""Modified from attributes-as-operators""" +import numpy as np +import torch, torchvision +import os, logging, pickle, json +import tqdm +import os.path as osp + + +try: + from . import data_utils + from .. import config as cfg +except (ValueError, ImportError): + import data_utils + + +class CompositionDatasetActivations(torch.utils.data.Dataset): + + def __init__(self, train_url, name, root, phase, feat_file, split='compositional-split', with_image=False, obj_pred=None, transform_type='normal'): + # root: /home/ma-user/modelarts/inputs/data_url_0/ut-zap50k-original + self.train_url = train_url + self.root = root + self.phase = phase + self.split = split + self.with_image = with_image + + self.feat_dim = None + self.transform = data_utils.imagenet_transform(phase, transform_type) + self.loader = data_utils.ImageLoader(self.root+'/images/') + print("loader",self.loader) + + feat_file = os.path.join(self.root, feat_file) + print("feat_file",feat_file) + activation_data = torch.load(feat_file) + + self.activation_dict = dict(zip(activation_data['files'], activation_data['features'])) + self.feat_dim = activation_data['features'].size(1) + print ('%d activations loaded'%(len(self.activation_dict))) + print("#####################11") + print("root:",self.root) + print("#####################21") + print("root2",root) + # pair = (attr, obj) + self.attrs, self.objs, self.pairs, self.train_pairs, self.test_pairs = self.parse_split() + self.attr2idx = {attr: idx for idx, attr in enumerate(self.attrs)} + self.obj2idx = {obj: idx for idx, obj in enumerate(self.objs)} + self.pair2idx = {pair: idx for idx, pair in enumerate(self.pairs)} + + + self.train_data, self.test_data = self.get_split_info() + + self.data = self.train_data if self.phase=='train' else self.test_data # list of [img_name, attr, obj, attr_id, obj_id, feat] + print ('#images = %d'%len(self.data)) + + + # return {object: all attrs that occur with obj} + self.obj_affordance_mask = [] + for _obj in self.objs: + candidates = [attr for (_,attr,obj,_,_,_) in self.train_data+self.test_data if obj==_obj] + affordance = set(candidates) + mask = [1 if x in affordance else 0 for x in self.attrs] + self.obj_affordance_mask.append(mask) + + + # negative image pool + samples_grouped_by_obj = [[] for _ in range(len(self.objs))] + for i,x in enumerate(self.train_data): + samples_grouped_by_obj[x[4]].append(i) + + self.neg_pool = [] # [obj_id][attr_id] => list of sample id + for obj_id in range(len(self.objs)): + self.neg_pool.append([]) + for attr_id in range(len(self.attrs)): + self.neg_pool[obj_id].append( + [i for i in samples_grouped_by_obj[obj_id] if + self.train_data[i][3] != attr_id ] + ) + # print("#####################31") + # print(root) + # print("#####################32") + aux_data_root = os.path.join(self.root, 'aux_data') + print("aux_data_root",aux_data_root) + gamma = json.load(open(aux_data_root + "/%s_gamma.json"%name)) + gamma = {k:np.array(v, dtype=np.float32) for k,v in gamma.items()} + self.comp_gamma = {'a':gamma['comp_a'], 'b':gamma['comp_b']} + self.attr_gamma = {'a':gamma['attr_a'], 'b':gamma['attr_b']} + + + if obj_pred is None: + self.obj_pred = None + else: + obj_pred_path = os.path.join(self.train_url, 'obj_scores', obj_pred) + print("Loading object prediction from %s"%obj_pred_path) + with open(obj_pred_path, 'rb') as fp: + self.obj_pred = np.array(pickle.load(fp), dtype=np.float32) + + + def get_split_info(self): + data = torch.load(self.root+'/metadata.t7') + train_pair_set = set(self.train_pairs) + test_pair_set = set(self.test_pairs) + train_data, test_data = [], [] + + for instance in data: + + image, attr, obj = instance['image'], instance['attr'], instance['obj'] + + if attr=='NA' or (attr, obj) not in self.pairs: + # ignore instances with unlabeled attributes + # ignore instances that are not in current split + continue + + data_i = [image, attr, obj, self.attr2idx[attr], self.obj2idx[obj], self.activation_dict[image]] + if (attr, obj) in train_pair_set: + train_data.append(data_i) + else: + test_data.append(data_i) + + return train_data, test_data + + def parse_split(self): + + def parse_pairs(pair_list): + with open(pair_list,'r') as f: + pairs = f.read().strip().split('\n') + pairs = [t.split() for t in pairs] + pairs = list(map(tuple, pairs)) + attrs, objs = zip(*pairs) + return attrs, objs, pairs + + tr_attrs, tr_objs, tr_pairs = parse_pairs('%s/%s/train_pairs.txt'%(self.root, self.split)) + ts_attrs, ts_objs, ts_pairs = parse_pairs('%s/%s/test_pairs.txt'%(self.root, self.split)) + + all_attrs, all_objs = sorted(list(set(tr_attrs+ts_attrs))), sorted(list(set(tr_objs+ts_objs))) + all_pairs = sorted(list(set(tr_pairs + ts_pairs))) + + return all_attrs, all_objs, all_pairs, tr_pairs, ts_pairs + + + def sample_negative(self, attr_id, obj_id): + return np.random.choice(self.neg_pool[obj_id][attr_id]) + + + def __getitem__(self, index): + def get_sample(i): + image, attr, obj, attr_id, obj_id, feat = self.data[i] + if self.with_image: + img = self.loader(image) + img = self.transform(img) + else: + img = None + + return [img, attr_id, obj_id, self.pair2idx[(attr, obj)], feat] + + pos = get_sample(index) + + mask = np.array(self.obj_affordance_mask[pos[2]], dtype=np.float32) + + + if self.phase=='train': + negid = self.sample_negative(pos[1], pos[2]) # negative example + neg = get_sample(negid) + + data = pos + neg + [mask] + else: + data = pos + [mask] + + # train [img, attr_id, obj_id, pair_id, img_feature, img, attr_id, obj_id, pair_id, img_feature, aff_mask] + # test [img, attr_id, obj_id, pair_id, img_feature, aff_mask] + + if self.obj_pred is not None: + data.append(self.obj_pred[index,:]) + + return data + + def __len__(self): + return len(self.data) + + + + + + + +class CompositionDatasetActivationsGenerator(CompositionDatasetActivations): + + def __init__(self, root, feat_file, split='compositional-split', feat_extractor=None, transform_type='normal'): + super(CompositionDatasetActivationsGenerator, self).__init__(root, 'train', feat_file, split, transform_type=transform_type) + + assert os.path.exists(root) + with torch.no_grad(): + self.generate_features(feat_file, feat_extractor, transform_type) + print('Features generated.') + + + def generate_features(self, out_file, feat_extractor, transform_type): + + data = self.train_data+self.test_data + transform = data_utils.imagenet_transform('test', transform_type) + + if feat_extractor is None: + feat_extractor = torchvision.models.resnet18(pretrained=True) + feat_extractor.fc = torch.nn.Sequential() + feat_extractor.eval().cuda() + + image_feats = [] + image_files = [] + for chunk in tqdm.tqdm(data_utils.chunks(data, 512), total=len(data)//512): + files, attrs, objs = zip(*chunk) + imgs = list(map(self.loader, files)) + imgs = list(map(transform, imgs)) + feats = feat_extractor(torch.stack(imgs, 0).cuda()) + image_feats.append(feats.data.cpu()) + image_files += files + image_feats = torch.cat(image_feats, 0) + print ('features for %d images generated'%(len(image_files))) + + torch.save({'features': image_feats, 'files': image_files}, out_file) + + + + + +if __name__=='__main__': + """example code for generating new features for MIT states and UT Zappos + CompositionDatasetActivationsGenerator( + root = 'data-dir', + feat_file = 'filename-to-save', + feat_extractor = torchvision.models.resnet18(pretrained=True), + ) + """ + CompositionDatasetActivationsGenerator( + root = 'data/attributes-as-operators/data/mit-states', + feat_file = 'data/attributes-as-operators/data/mit-states/features.t7', + ) \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__init__.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4e9997cf2fd96dcca3a7163b10d4e0e553ad09a3 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__init__.py @@ -0,0 +1,62 @@ +# 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. +from .. import config as cfg +from utils.dataset import CZSL_dataset +from torch.utils.data import DataLoader +import numpy as np +import os.path as osp +import os +cwd = os.getcwd() +def get_dataloader(train_url, dataset_name, phase, feature_file="features.t7", batchsize=1, num_workers=1, shuffle=None, **kwargs): + print("url:",train_url) + print("url:", cfg.CZSL_DS_ROOT[dataset_name]) + dt_path = osp.join(train_url, cfg.CZSL_DS_ROOT[dataset_name]) + # /home/ma-user/modelarts/inputs/data_url_0/ut-zap50k-original + print("dt_path",dt_path) + + print("Current working directory: {0}".format(cwd)) + + dataset = CZSL_dataset.CompositionDatasetActivations( + train_url = train_url, + name = dataset_name, + root = dt_path, #data/mit-states-original/features.t7 + phase = phase, + feat_file = feature_file, + **kwargs) + + + if shuffle is None: + shuffle = (phase=='train') + + return DataLoader(dataset, batchsize, shuffle, num_workers=num_workers, + collate_fn = lambda data: [np.stack(d, axis=0) for d in zip(*data)] + ) + + + + diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/CZSL_dataset.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/CZSL_dataset.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..637614fbaae3eeb53a5eee42d6d991024f1023e1 Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/CZSL_dataset.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/GCZSL_dataset.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/GCZSL_dataset.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..91b03dbd379755915110e2295e5d77eea3170158 Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/GCZSL_dataset.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/__init__.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9a2cf26963efab6883e3e6a37e406c78761dcc4a Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/__init__.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/data_utils.cpython-37.pyc b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/data_utils.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d0f0b00284b74c14861ee01715e06d6609349c2d Binary files /dev/null and b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/__pycache__/data_utils.cpython-37.pyc differ diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/data_utils.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..890e245aa04b4e5b8909485a04f7952c5a2debb8 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/dataset/data_utils.py @@ -0,0 +1,94 @@ +# 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 torch +import torchvision.transforms as transforms +from PIL import Image +import scipy.io as sio +import os.path as osp +import numpy as np + + + +class ImageLoader: + def __init__(self, root): + self.img_dir = root + + def __call__(self, img): + str_types = [str] + try: + str_types.append(unicode) + except NameError: + pass + + if type(img) in str_types: + f = '%s/%s'%(self.img_dir, img) + img = Image.open(f).convert('RGB') + elif type(img) in [list, tuple]: + f = '%s/%s'%(self.img_dir, img[0]) + x,y,w,h = img[1:] # bbox + img = Image.open(f).convert('RGB') + img = img.crop((x, y, x+w, y+h)) + else: + raise NotImplementedError(str(type(img))) + return img + + +def imagenet_transform(phase, transform_type): + mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225] + + if transform_type == 'normal': + if phase=='train': + transform = transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(mean, std) + ]) + elif phase in ['test', 'val']: + transform = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize(mean, std) + ]) + elif transform_type == 'fixed': + transform = transforms.Compose([ + transforms.Resize(224), + transforms.ToTensor(), + transforms.Normalize(mean, std) + ]) + else: + raise NotImplementedError("transform_type %s"%transform_type) + + return transform + + +def chunks(l, n): + """Yield successive n-sized chunks from l.""" + for i in range(0, len(l), n): + yield l[i:i + n] \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/evaluator.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..eff09727b55ae5b4bc6f248dd47cc3019af2e01a --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/evaluator.py @@ -0,0 +1,307 @@ +# 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 torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.models as tmodels +import numpy as np +from . import utils +import itertools +import math +import collections +import logging + + +class CZSL_Evaluator: + """modified from AttrOperator""" + + def __init__(self, dset, model): + + self.dset = dset + + # convert text pairs to idx tensors: [('sliced', 'apple'), ('ripe', 'apple'), ...] --> torch.LongTensor([[0,1],[1,1], ...]) + pairs = [(dset.attr2idx[attr], dset.obj2idx[obj]) + for attr, obj in dset.pairs] + self.pairs = torch.LongTensor(pairs) + + # mask over pairs that occur in closed world + test_pair_set = set(dset.test_pairs) + mask = [1 if pair in test_pair_set else 0 for pair in dset.pairs] + self.closed_mask = torch.ByteTensor(mask) + + # object specific mask over which pairs occur in the object oracle setting + oracle_obj_mask = [] + for _obj in dset.objs: + mask = [1 if _obj==obj else 0 for attr, obj in dset.pairs] + oracle_obj_mask.append(torch.ByteTensor(mask)) + self.oracle_obj_mask = torch.stack(oracle_obj_mask, 0) + + + # generate masks for each setting, mask scores, and get prediction labels + def generate_predictions(self, scores, obj_truth): # (B, #pairs) + + def get_pred_from_scores(_scores): + _, pair_pred = _scores.max(1) + attr_pred, obj_pred = self.pairs[pair_pred][:,0], self.pairs[pair_pred][:,1] + return (attr_pred, obj_pred) # attr/obj word id (not name) + + def get_pred_from_scores_and_mask_best(_scores): + _, pair_pred = _scores.max(1) + attr_pred, obj_pred = self.pairs[pair_pred][:,0], self.pairs[pair_pred][:,1] + _scores[range(pair_pred.shape[0]),pair_pred] = -1e10 + return _scores, (attr_pred, obj_pred) # attr/obj word id (not name) + + results = {} + + # open world setting -- no mask + results.update({'open': get_pred_from_scores(scores)}) + + + # closed world setting - set the score for all NON test-pairs to -1e10 + mask = self.closed_mask.repeat(scores.shape[0], 1) + closed_scores = scores.clone() + if hasattr(mask, 'bool'): + closed_scores[(1-mask).bool()] = -1e10 + else: + closed_scores[(1-mask).byte()] = -1e10 + closed_scores, closed1 = get_pred_from_scores_and_mask_best(closed_scores) + results.update({'closed1': closed1}) + closed_scores, closed2 = get_pred_from_scores_and_mask_best(closed_scores) + results.update({'closed2': closed2}) + closed_scores, closed3 = get_pred_from_scores_and_mask_best(closed_scores) + results.update({'closed3': closed3}) + + + # object_oracle setting - set the score to -1e10 for all pairs where the true object does NOT participate + mask = self.oracle_obj_mask[obj_truth] + oracle_obj_scores = scores.clone() + if hasattr(mask, 'bool'): + oracle_obj_scores[(1-mask).bool()] = -1e10 + else: + oracle_obj_scores[(1-mask).byte()] = -1e10 + + results.update({'object_oracle': get_pred_from_scores(oracle_obj_scores)}) + + return results + + def score_model(self, scores, obj_truth): + + # put everything on CPU + #scores = {k:v.cpu() for k,v in scores.items()} + #obj_truth = obj_truth.cpu() + + # gather scores for all relevant (a,o) pairs + scores = torch.stack([ + scores[(self.dset.attr2idx[attr], self.dset.obj2idx[obj])] + for attr, obj in self.dset.pairs + ], 1) # (B, #pairs) + results = self.generate_predictions(scores, obj_truth) + return results + + def evaluate_predictions(self, predictions, attr_truth, obj_truth, histogram=False, synonym_mode=False): + assert not histogram + + # put everything on cpu + #attr_truth, obj_truth = attr_truth.cpu(), obj_truth.cpu() + + # top 1 pair accuracy + # open world: attribute, object and pair + attr_match = (attr_truth==predictions['open'][0]).float() + obj_match = (obj_truth==predictions['open'][1]).float() + open_match = attr_match*obj_match + + # closed world, obj_oracle: pair + closed_1_match = (attr_truth==predictions['closed1'][0]).float() * (obj_truth==predictions['closed1'][1]).float() + closed_2_match = (attr_truth==predictions['closed2'][0]).float() * (obj_truth==predictions['closed2'][1]).float() + closed_1_match + closed_3_match = (attr_truth==predictions['closed3'][0]).float() * (obj_truth==predictions['closed3'][1]).float() + closed_2_match + + if synonym_mode: + closed_2_match[closed_2_match>1] = 1 + closed_3_match[closed_3_match>1] = 1 + + assert torch.max(closed_1_match).item()<=1, torch.max(closed_1_match).item() + assert torch.max(closed_2_match).item()<=1, torch.max(closed_2_match).item() + assert torch.max(closed_3_match).item()<=1, torch.max(closed_3_match).item() + + + obj_oracle_match = (attr_truth==predictions['object_oracle'][0]).float() * (obj_truth==predictions['object_oracle'][1]).float() + + return attr_match, obj_match, closed_1_match, closed_2_match, closed_3_match, open_match, obj_oracle_match + + + def evaluate_only_attr_obj(self, prob_a, gt_a, prob_o, gt_o): + prob_a, prob_o = torch.from_numpy(prob_a), torch.from_numpy(prob_o) + _, pred_a = prob_a.max(1) + _, pred_o = prob_o.max(1) + + attr_match = (pred_a == gt_a).float() + obj_match = (pred_o == gt_o).float() + + return attr_match, obj_match + + + + + + + +class GCZSL_Evaluator: + """modified from TMN""" + + def __init__(self, dset): + + self.dset = dset + + # convert text pairs to idx tensors: [('sliced', 'apple'), ('ripe', 'apple'), ...] --> torch.LongTensor([[0,1],[1,1], ...]) + pairs = [(dset.attr2idx[attr], dset.obj2idx[obj]) + for attr, obj in dset.pairs] + self.train_pairs = [(dset.attr2idx[attr], dset.obj2idx[obj]) + for attr, obj in dset.train_pairs] + self.pairs = torch.LongTensor(pairs) + + # mask over pairs that occur in closed world + if dset.phase == 'train': + print('Evaluating with train pairs') + test_pair_set = set(dset.train_pairs) + elif dset.phase == 'val': + print('Evaluating with val pairs') + test_pair_set = set(dset.val_pairs + dset.train_pairs) + else: + print('Evaluating with test pairs') + test_pair_set = set(dset.test_pairs + dset.train_pairs) + self.test_pairs = [(dset.attr2idx[attr], dset.obj2idx[obj]) + for attr, obj in list(test_pair_set)] + mask = [1 if pair in test_pair_set else 0 for pair in dset.pairs] + self.closed_mask = torch.ByteTensor(mask) + + seen_pair_set = set(dset.train_pairs) + mask = [1 if pair in seen_pair_set else 0 for pair in dset.pairs] + self.seen_mask = torch.ByteTensor(mask) + + # object specific mask over which pairs occur in the object oracle setting + oracle_obj_mask = [] + for _obj in dset.objs: + mask = [1 if _obj == obj else 0 for attr, obj in dset.pairs] + oracle_obj_mask.append(torch.ByteTensor(mask)) + self.oracle_obj_mask = torch.stack(oracle_obj_mask, 0) + + + # generate masks for each setting, mask scores, and get prediction labels + def generate_predictions(self, scores, obj_truth): # (B, #pairs) + def get_pred_from_scores(_scores): + _, pair_pred = _scores.topk(10, dim=1) #sort(1, descending=True) + pair_pred = pair_pred[:, :10].contiguous().view(-1) + attr_pred, obj_pred = self.pairs[pair_pred][:, 0].view( + -1, 10), self.pairs[pair_pred][:, 1].view(-1, 10) + return (attr_pred, obj_pred) + + results = {} + + # open world setting -- no mask + mask = self.closed_mask.repeat(scores.shape[0], 1) + mask = 1 - mask + if hasattr(mask, "bool"): + mask = mask.bool() + closed_scores = scores.clone() + closed_scores[mask] = -1e10 + results.update({'open': get_pred_from_scores(closed_scores)}) + + # closed world setting - set the score for all NON test-pairs to -1e10 + #results.update({'closed': get_pred_from_scores(closed_scores)}) + results.update({'closed': results['open']}) + + # object_oracle setting - set the score to -1e10 for all pairs where the true object does NOT participate + mask = self.oracle_obj_mask[obj_truth] + oracle_obj_scores = scores.clone() + + mask = 1 - mask + if hasattr(mask, "bool"): + mask = mask.bool() + oracle_obj_scores[mask] = -1e10 + + results.update({ + 'object_oracle': get_pred_from_scores(oracle_obj_scores) + }) + + return results + + + def score_model(self, scores, obj_truth, bias=0.0): + # put everything on CPU + scores = {k: v.cpu() for k, v in scores.items()} + obj_truth = obj_truth.cpu() + # gather scores for all relevant (a,o) pairs + scores = torch.stack( + [scores[(self.dset.attr2idx[attr], self.dset.obj2idx[obj])] for attr, obj in self.dset.pairs], + 1) # (B, #pairs) + orig_scores = scores.clone() + mask = self.seen_mask.repeat(scores.shape[0], 1) + mask = 1 - mask + if hasattr(mask, "bool"): + mask = mask.bool() + scores[mask] += bias + results = self.generate_predictions(scores, obj_truth) + results['biased_scores'] = scores + results['scores'] = orig_scores + return results + + def evaluate_predictions(self, predictions, attr_truth, obj_truth, topk=1): + + # put everything on cpu + attr_truth, obj_truth = attr_truth.cpu(), obj_truth.cpu() + pairs = list( + zip(list(attr_truth.cpu().numpy()), list(obj_truth.cpu().numpy()))) + seen_ind = torch.LongTensor([ + i for i in range(len(attr_truth)) if pairs[i] in self.train_pairs + ]) + unseen_ind = torch.LongTensor([ + i for i in range(len(attr_truth)) + if pairs[i] not in self.train_pairs + ]) + + # top 1 pair accuracy + # open world: attribute, object and pair + attr_match = (attr_truth.unsqueeze(1).repeat( + 1, topk) == predictions['open'][0][:, :topk]) + obj_match = (obj_truth.unsqueeze(1).repeat( + 1, topk) == predictions['open'][1][:, :topk]) + open_match = (attr_match * obj_match).any(1).float() + attr_match = attr_match.any(1).float() + obj_match = obj_match.any(1).float() + open_seen_match = open_match[seen_ind] + open_unseen_match = open_match[unseen_ind] + + # closed world, obj_oracle: pair + closed_match = (attr_truth == predictions['closed'][0][:, 0]).float( + ) * (obj_truth == predictions['closed'][1][:, 0]).float() + + obj_oracle_match = ( + attr_truth == predictions['object_oracle'][0][:, 0]).float() * ( + obj_truth == predictions['object_oracle'][1][:, 0]).float() + + return attr_match, obj_match, closed_match, open_match, obj_oracle_match, open_seen_match, open_unseen_match \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/ops_info.json b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/ops_info.json new file mode 100644 index 0000000000000000000000000000000000000000..31b9f61387d11504a1c677f9f06af7bb152f9af2 --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/ops_info.json @@ -0,0 +1,7 @@ +{ + "white-list": { // 白名单 + "to-add": [ // 黑名单或灰名单算子转换为白名单算子 + "matmulv2" + ] + } +} \ No newline at end of file diff --git a/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/utils.py b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c7f6f952148f458e181bb7731597165a8260821b --- /dev/null +++ b/TensorFlow/contrib/cv/SYMNET_ID1292_for_Tensorflow/utils/utils.py @@ -0,0 +1,195 @@ +# 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 tensorflow as tf +import time, cv2, os, argparse +import os.path as osp +from PIL import Image +import numpy as np + +from . import config as cfg +from . import aux_data +from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig +from npu_bridge.npu_init import * +################################################################################ +# tools for solvers # +################################################################################ + +def create_session(): + """create tensorflow session""" + ####################### add ######################## + configs = tf.ConfigProto() + custom_op = configs.graph_options.rewrite_options.custom_optimizers.add() + custom_op.name = "NpuOptimizer" + custom_op.parameter_map["use_off_line"].b = True + + custom_op.parameter_map["dynamic_input"].b = True + custom_op.parameter_map["dynamic_graph_execute_mode"].s = tf.compat.as_bytes("lazy_recompile") + + # mix add white + custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") + # custom_op.parameter_map["modify_mixlist"].s = tf.compat.as_bytes("/home/test/ops_info.json") + + custom_op.parameter_map["profiling_mode"].b = True + custom_op.parameter_map["profiling_options"].s = tf.compat.as_bytes( + '{"output":"/cache/profiling","task_trace":"on", "aicpu":"on"}') + # custom_op.parameter_map["auto_tune_mode"].s = tf.compat.as_bytes("RL,GA") + + # close + configs.graph_options.rewrite_options.remapping = RewriterConfig.OFF + # close + configs.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF + ####################### add ######################## + return tf.Session(config=configs) + + + +def display_args(args, logger, verbose=False): + """print some essential arguments""" + if verbose: + ignore = [] + for k,v in args.__dict__.items(): + if not callable(v) and not k.startswith('__') and k not in ignore: + logger.info("{:30s}{}".format(k,v)) + else: + logger.info('Name: %s'%args.name) + logger.info('Network: %s'%args.network) + logger.info('Data: %s'%args.data) + logger.info('FC layers: At {fc_att}, Cm {fc_compress}, Cls {fc_cls}'.format( + **args.__dict__)) + + + +def duplication_check(args): + if args.force: + return + elif args.trained_weight is None or args.trained_weight.split('/')[0] != args.name: + assert not osp.exists(osp.join(cfg.WEIGHT_ROOT_DIR, args.name)), \ + "weight dir with same name exists (%s)"%(args.name) + assert not osp.exists(osp.join(cfg.LOG_ROOT_DIR, args.name)), \ + "log dir with same name exists (%s)"%(args.name) + + +def formated_czsl_result(report): + fstr = '[{name}/{epoch}] rA:{real_attr_acc:.4f}|rO:{real_obj_acc:.4f}|Cl/T1:{top1_acc:.4f}|T2:{top2_acc:.4f}|T3:{top3_acc:.4f}' + + return fstr.format(**report) + + +################################################################################ +# glove embedder # +################################################################################ + +class Embedder: + """word embedder (for various vector type) + __init__(self) + """ + + def __init__(self, vec_type, vocab, data): + self.vec_type = vec_type + + if vec_type != 'onehot': + self.embeds = self.load_word_embeddings(vec_type, vocab, data) + self.emb_dim = self.embeds.shape[1] + else: + self.emb_dim = len(vocab) + + def get_embedding(self, i): + """actually implements __getitem__() function""" + if self.vec_type == 'onehot': + return tf.one_hot(i, depth=self.emb_dim, axis=1) + else: + i_onehot = tf.one_hot(i, depth=self.embeds.shape[0], axis=1) + return tf.matmul(i_onehot, self.embeds) + + + def load_word_embeddings(self, vec_type, vocab, data): + tmp = aux_data.load_wordvec_dict(data, vec_type) + if type(tmp) == tuple: + attr_dict, obj_dict = tmp + attr_dict.update(obj_dict) + embeds = attr_dict + else: + embeds = tmp + + embeds_list = [] + for k in vocab: + if k in embeds: + embeds_list.append(embeds[k]) + else: + raise NotImplementedError('some vocabs are not in dictionary: %s'%k) + + embeds = np.array(embeds_list, dtype=np.float32) + + print ('Embeddings shape = %s'%str(embeds.shape)) + return embeds + + + + + +################################################################################ +# network utils # +################################################################################ + + +def repeat_tensor(tensor, axis, multiple): + """e.g. (1,2,3)x3 = (1,1,1,2,2,2,3,3,3)""" + + result_shape = tensor.shape.as_list() + for i,v in enumerate(result_shape): + if v is None: + result_shape[i] = tf.shape(tensor)[i] + result_shape[axis] *= multiple + + tensor = tf.expand_dims(tensor, axis+1) + mul = [1]*len(tensor.shape) + mul[axis+1] = multiple + tensor = tf.tile(tensor, mul) + tensor = tf.reshape(tensor, result_shape) + + return tensor + + +def tile_tensor(tensor, axis, multiple): + """e.g. (1,2,3)x3 = (1,2,3,1,2,3,1,2,3)""" + mul = [1]*len(tensor.shape) + mul[axis] = multiple + + return tf.tile(tensor, mul) + + +def activation_func(name): + if name == "none": + return (lambda x:x) + elif name == "sigmoid": + return tf.sigmoid + elif name == "relu": + return tf.nn.relu + else: + raise NotImplementedError("activation function %s not implemented"%name) +