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[输入输出数据](#section540883920406) + + + +- [推理环境准备](#ZH-CN_TOPIC_0000001126281702) + +- [快速上手](#ZH-CN_TOPIC_0000001126281700) + + - [获取源码](#section4622531142816) + - [准备数据集](#section183221994411) + - [模型推理](#section741711594517) + +- [模型推理性能&精度](#ZH-CN_TOPIC_0000001172201573) + + ****** + +# 概述 + +BasicVSR++通过设计二阶网格传播和流引导的可变形对齐来重新设计BasicVSR，使信息能够更有效地传播和聚合，[论文链接](https://arxiv.org/abs/2104.13371)。 + +- 参考实现： + + ``` + url=https://github.com/open-mmlab/mmediting.git + commit_id=6ac402ec4545398662f043186688a40fb2e97d5f + code_path=https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/basicvsr_plusplus + model_name=BasicVSR_plusplus + ``` + +## 输入输出数据 + +- 输入数据 + + | 输入数据 | 数据类型 | 大小 | 数据排布格式 | + | -------- | -------- | ------------------------- | ------------ | + | input | RGB_FP32 | 1 x 14 x 3 x 64 x 112 | NCDHW | + + +- 输出数据 + + | 输出数据 | 数据类型 | 大小 | 数据排布格式 | + | -------- | -------- | -------- | ------------ | + | output1 | FLOAT32 | 1 x 14 x 3 x 256 x 448| NCDHW | + + +# 推理环境准备 + +- 该模型需要以下插件与驱动 + + **表 1** 版本配套表 + + | 配套 | 版本 | 环境准备指导 | + | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | + | 固件与驱动 | 22.0.3 | [Pytorch框架推理环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/pies) | + | CANN | 6.0.RC1 | - | + | Python | 3.7.5 | - | + | PyTorch | 1.8.1 | - | + | 说明：Atlas 300I Duo 推理卡请以CANN版本选择实际固件与驱动版本。 | \ | \ | + + + +# 快速上手 + +## 获取源码 + +1. 获取源码。 + + ``` + git clone https://github.com/open-mmlab/mmediting.git + ``` + +2. 安装依赖。 + + ``` + pip3 install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html + pip3 install -U openmim + mim install mmcv-full + + cd mmediting + git reset --hard 6ac402ec4545398662f043186688a40fb2e97d5f + git apply ../basicvsr_plusplus.patch + pip3 install -r requirements.txt + pip3 install -v -e . + cd .. + pip3 install -r requirements.txt + ``` + +## 准备数据集 + +1. 获取原始数据集。（解压命令参考tar –xvf \*.tar与 unzip \*.zip） + + 模型推理使用Vimeo90K数据集，数据集介绍参考[官网链接](http://toflow.csail.mit.edu/)，测试数据集[下载链接](http://data.csail.mit.edu/tofu/testset/vimeo_super_resolution_test.zip) + + 下载后将vimeo_super_resolution_test.zip放在当前工作目录。 + +2. 数据预处理，将原始数据集转换为模型输入的数据。 + + 数据预处理命令如下。 + + ``` + unzip -d ./data/ vimeo_super_resolution_test.zip + mv ./data/vimeo_super_resolution_test ./data/vimeo90k + mv ./data/vimeo90k/low_resolution ./data/vimeo90k/BIx4 + mv ./data/vimeo90k/target ./data/vimeo90k/GT + + python3 ./mmediting/tools/data/super-resolution/vimeo90k/preprocess_vimeo90k_dataset.py ./data/vimeo90k/sep_testlist.txt + mv ./data/vimeo90k/meta_info_Vimeo90K_GT.txt ./data/vimeo90k/meta_info_Vimeo90K_test_GT.txt + + python3 basicvsr_plusplus_preprocess.py \ + ./mmediting/configs/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi.py \ + ./pre_data + ``` + + 生成并保存为bin文件，保存目录为`./pre_data`。 + + +## 模型推理 + +1. 模型转换。 + + 使用PyTorch将模型权重文件.pkl转换为.onnx文件，再使用ATC工具将.onnx文件转为离线推理模型文件.om文件。 + + 1. 获取权重文件。 + + 权重文件为：basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth，[获取链接](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) + 将获取的权重文件放在当前工作路径下，参考命令： + + ``` + wget https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth + ``` + + 2. 导出onnx文件。 + + 1. 使用pytorch2onnx.py导出onnx文件。 + + 运行pytorch2onnx.py脚本。 + + ``` + python3 mmediting/tools/pytorch2onnx.py \ + mmediting/configs/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi.py \ + ./basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth \ + restorer \ + data/vimeo90k/BIx4/00001/0266/ \ + --output-file ./basicvsr_plusplus.onnx + ``` + + 获得basicvsr_plusplus.onnx文件。 + + 3. 使用ATC工具将ONNX模型转OM模型。 + + 1. 配置环境变量。 + + ``` + source /usr/local/Ascend/ascend-toolkit/set_env.sh + ``` + + 2. 执行命令查看芯片名称（$\{chip\_name\}）。 + + ``` + npu-smi info + #该设备芯片名为Ascend310P3 （自行替换） + 回显如下： + +-------------------+-----------------+------------------------------------------------------+ + | NPU Name | Health | Power(W) Temp(C) Hugepages-Usage(page) | + | Chip Device | Bus-Id | AICore(%) Memory-Usage(MB) | + +===================+=================+======================================================+ + | 0 310P3 | OK | 15.8 42 0 / 0 | + | 0 0 | 0000:82:00.0 | 0 1074 / 21534 | + +===================+=================+======================================================+ + | 1 310P3 | OK | 15.4 43 0 / 0 | + | 0 1 | 0000:89:00.0 | 0 1070 / 21534 | + +===================+=================+======================================================+ + ``` + + 3. 执行ATC命令。 + + ``` + atc --framework=5 \ + --model=./basicvsr_plusplus.onnx \ + --input_shape="input:1,14,3,64,112" \ + --output=./basicvsr_plusplus_bs1 \ + --input_format=NCHW \ + --log=error \ + --soc_version=Ascend${chip_name} + ``` + + - 参数说明： + + - --model：为ONNX模型文件。 + - --framework：5代表ONNX模型。 + - --output：输出的OM模型。 + - --input\_format：输入数据的格式。 + - --input\_shape：输入数据的shape。 + - --log：日志级别。 + - --soc\_version：处理器型号。 + + 运行成功后生成`basicvsr_plusplus_bs1.om`模型文件。 + +2. 开始推理验证。 + + 1. 安装ais_bench推理工具。 + + 请访问[ais_bench推理工具](https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_infer)代码仓，根据readme文档进行工具安装。 + + 2. 执行推理。 + + ``` + python3 -m ais_bench --model=basicvsr_plusplus_bs1.om --input=./pre_data/vimeo90k/BIx4 --output=./ --outfmt=NPY --batchsize=1 + ``` + + - 参数说明： + + - --model：om模型。 + - --input：输入数据路径。 + - --output：推理结果路径。 + - --batchsize：om模型的batchsize。 + + 推理后的输出默认在当前目录下。 + + + 3. 精度验证。 + + 调用`basicvsr_plusplus_postprocess.py`脚本将om模型的推理结果。 + + ``` + python3 basicvsr_plusplus_postprocess.py ./pre_data ${output_path} + ``` + + 第一个参数为预处理数据路径，第二个参数${output_path}为推理工具生成的推理结果路径。 + + 4. 性能验证。 + + 可使用ais_bench推理工具的纯推理模式验证不同batch_size的om模型的性能，参考命令如下： + + ``` + python3 -m ais_bench --model=basicvsr_plusplus_bs1.om --loop=20 --batchsize=1 + ``` + + - 参数说明： + - --model：om模型。 + - --loop：模型推理次数。 + - --batchsize：om模型的batchsize。 + + +# 模型推理性能&精度 + +调用ACL接口推理计算，性能参考下列数据。 + +| 芯片型号 | Batch Size | 数据集 | 精度 | 性能 | +| --------- | ---------- | ---------- | ---------- | --------------- | +| Ascend310P | 1 | Vimeo90K | PSNR=35.04, SSIM=0.9198 | 2.27 fps | diff --git a/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/basicvsr_plusplus.patch b/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/basicvsr_plusplus.patch new file mode 100644 index 0000000000000000000000000000000000000000..24c347f2ff5a30d39e811b35809c661b299b5b7d --- /dev/null +++ b/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/basicvsr_plusplus.patch @@ -0,0 +1,161 @@ +diff --git a/mmedit/models/backbones/sr_backbones/basicvsr_pp.py b/mmedit/models/backbones/sr_backbones/basicvsr_pp.py +index 082cf618..790a1f34 100644 +--- a/mmedit/models/backbones/sr_backbones/basicvsr_pp.py ++++ b/mmedit/models/backbones/sr_backbones/basicvsr_pp.py +@@ -368,6 +368,52 @@ class BasicVSRPlusPlus(nn.Module): + f'But received {type(pretrained)}.') + + ++class DeformConv2dFunction(torch.autograd.Function): ++ @staticmethod ++ def forward(ctx, ++ input, ++ weight, ++ offset, ++ bias, ++ stride=1, ++ padding=0, ++ dilation=1, ++ groups=1, ++ deform_groups=1): ++ output_size = [input.shape[0], weight.shape[0]] ++ for d in range(input.dim() - 2): ++ in_size = input.size(d + 2) ++ kernel = dilation * (weight.size(d + 2) - 1) + 1 ++ output_size.append((in_size + (2 * padding) - kernel) // stride + 1) ++ out = torch.randn(output_size).to(input.dtype) ++ return out ++ ++ @staticmethod ++ def symbolic(g, ++ input, ++ weight, ++ offset, ++ bias, ++ stride, ++ padding, ++ dilation, ++ groups, ++ deform_groups): ++ return g.op( ++ "DeformableConv2D", ++ input, ++ weight, ++ offset, ++ bias, ++ strides_i=[stride,stride], ++ pads_i=[padding,padding], ++ dilations_i=dilation, ++ groups_i=groups, ++ deformable_groups_i=deform_groups) ++ ++deform_conv2d = DeformConv2dFunction.apply ++ ++ + class SecondOrderDeformableAlignment(ModulatedDeformConv2d): + """Second-order deformable alignment module. + +@@ -425,8 +471,15 @@ class SecondOrderDeformableAlignment(ModulatedDeformConv2d): + + # mask + mask = torch.sigmoid(mask) +- +- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias, +- self.stride, self.padding, +- self.dilation, self.groups, +- self.deform_groups) ++ offset_x = offset[:, 0::2, :, :] ++ offset_y = offset[:, 1::2, :, :] ++ offset = torch.cat([offset_y, offset_x, mask], dim=1) ++ return deform_conv2d(x, ++ self.weight, ++ offset, ++ self.bias, ++ self.stride[0], ++ self.padding[0], ++ self.dilation[0], ++ self.groups, ++ self.deform_groups) +diff --git a/mmedit/models/common/flow_warp.py b/mmedit/models/common/flow_warp.py +index 7083230d..d34c9cd8 100644 +--- a/mmedit/models/common/flow_warp.py ++++ b/mmedit/models/common/flow_warp.py +@@ -3,6 +3,36 @@ import torch + import torch.nn.functional as F + + ++class GridSampler(torch.autograd.Function): ++ @staticmethod ++ def forward(ctx, ++ x, ++ grid, ++ mode='bilinear', ++ padding_mode='zeros', ++ align_corners=False): ++ output_size = x.shape ++ out = torch.randn(output_size).to(x.dtype) ++ return out ++ ++ @staticmethod ++ def symbolic(g, ++ x, ++ grid, ++ mode, ++ padding_mode, ++ align_corners): ++ return g.op( ++ "GridSample", ++ x, ++ grid, ++ mode_s=mode, ++ padding_mode_s=padding_mode, ++ align_corners_i=align_corners) ++ ++grid_sample = GridSampler.apply ++ ++ + def flow_warp(x, + flow, + interpolation='bilinear', +@@ -41,10 +71,5 @@ def flow_warp(x, + grid_flow_x = 2.0 * grid_flow[:, :, :, 0] / max(w - 1, 1) - 1.0 + grid_flow_y = 2.0 * grid_flow[:, :, :, 1] / max(h - 1, 1) - 1.0 + grid_flow = torch.stack((grid_flow_x, grid_flow_y), dim=3) +- output = F.grid_sample( +- x, +- grid_flow, +- mode=interpolation, +- padding_mode=padding_mode, +- align_corners=align_corners) ++ output = grid_sample(x, grid_flow, interpolation, padding_mode, align_corners) + return output +diff --git a/tools/pytorch2onnx.py b/tools/pytorch2onnx.py +index 4bf524c7..46eafa33 100644 +--- a/tools/pytorch2onnx.py ++++ b/tools/pytorch2onnx.py +@@ -73,7 +73,8 @@ def pytorch2onnx(model, + keep_initializers_as_inputs=False, + verbose=show, + opset_version=opset_version, +- dynamic_axes=dynamic_axes) ++ dynamic_axes=dynamic_axes, ++ enable_onnx_checker=False) + print(f'Successfully exported ONNX model: {output_file}') + if verify: + # check by onnx +@@ -192,6 +193,15 @@ if __name__ == '__main__': + data = dict(merged_path=args.img_path, trimap_path=args.trimap_path) + elif model_type == 'restorer': + data = dict(lq_path=args.img_path) ++ data['lq_path'] = [ ++ 'data/vimeo90k/BIx4/00001/0266/im1.png', ++ 'data/vimeo90k/BIx4/00001/0266/im2.png', ++ 'data/vimeo90k/BIx4/00001/0266/im3.png', ++ 'data/vimeo90k/BIx4/00001/0266/im4.png', ++ 'data/vimeo90k/BIx4/00001/0266/im5.png', ++ 'data/vimeo90k/BIx4/00001/0266/im6.png', ++ 'data/vimeo90k/BIx4/00001/0266/im7.png'] ++ data['key'] = '00001/0266' + data = test_pipeline(data) + + # convert model to onnx file diff --git a/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/basicvsr_plusplus_postprocess.py b/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/basicvsr_plusplus_postprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..81f3d41df9fcbb89df2e0f25884444f3145de7e6 --- /dev/null +++ b/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/basicvsr_plusplus_postprocess.py @@ -0,0 +1,67 @@ +# Copyright 2023 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 torch +import argparse +import numpy as np + +from tqdm import tqdm +from mmedit.core import tensor2img +from mmedit.core.evaluation import psnr, ssim + + +def parse_args(): + parser = argparse.ArgumentParser(description='basicvsr_plusplus data preprocess.') + parser.add_argument('pre_path', help='save bin path') + parser.add_argument('res_path', help='results path') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + gt_path = "{}/vimeo90k/GT".format(args.pre_path) + res_path = args.res_path + + eval_result = dict() + eval_result['PSNR'] = 0 + eval_result['SSIM'] = 0 + + for root, dirs, files in os.walk(res_path): + for idx, f in enumerate(tqdm(files)): + res = torch.from_numpy(np.load(f"{res_path}/{f}")) + gt = torch.from_numpy(np.load(f"{gt_path}/{f.replace('_0.', '.')}")) + + t = res.size(1) + res = 0.5 * (res[:, t // 4] + res[:, -1 - t // 4]) + + res_img = tensor2img(res) + gt_img = tensor2img(gt) + + psnr_val = psnr(res_img, gt_img, 0, convert_to='y') + ssim_val = ssim(res_img, gt_img, 0, convert_to='y') + + eval_result['PSNR'] += psnr_val + eval_result['SSIM'] += ssim_val + + psnr_res = eval_result['PSNR'] / len(files) + ssim_res = eval_result['SSIM'] / len(files) + + print(f"The results of postprocess: PSNR={psnr_res}, SSIM={ssim_res}") + + +if __name__ == '__main__': + main() diff --git a/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/basicvsr_plusplus_preprocess.py b/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/basicvsr_plusplus_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..c733cee85362f53bbb8b21929048dced1ed14f4a --- /dev/null +++ b/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/basicvsr_plusplus_preprocess.py @@ -0,0 +1,72 @@ +# Copyright 2023 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 mmcv +import torch +import shutil +import argparse +import numpy as np + +from tqdm import tqdm +from mmcv import Config +from mmedit.datasets import build_dataloader, build_dataset + + +def parse_args(): + parser = argparse.ArgumentParser(description='basicvsr_plusplus data preprocess.') + parser.add_argument('config', help='config file path') + parser.add_argument('pre_path', help='save bin path') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + data_path = "{}/vimeo90k".format(args.pre_path) + lq_path = "{}/vimeo90k/BIx4".format(args.pre_path) + gt_path = "{}/vimeo90k/GT".format(args.pre_path) + + if not os.path.exists(data_path): + os.makedirs(lq_path) + os.makedirs(gt_path) + else: + shutil.rmtree(data_path) + os.makedirs(lq_path) + os.makedirs(gt_path) + + cfg = Config.fromfile(args.config) + dataset = build_dataset(cfg.data.test) + + loader_cfg = { + **dict((k, cfg.data[k]) for k in ['workers_per_gpu'] if k in cfg.data), + **dict( + samples_per_gpu=1, + drop_last=False, + shuffle=False, + dist=False), + **cfg.data.get('test_dataloader', {}) + } + + data_loader = build_dataloader(dataset, **loader_cfg) + + for idx, data in enumerate(tqdm(data_loader)): + data_name = data['meta'].data[0][0]['key'].replace('/', '_') + np.save(f"{lq_path}/{data_name}.npy", data['lq'].numpy()) + np.save(f"{gt_path}/{data_name}.npy", data['gt'].numpy()) + + +if __name__ == '__main__': + main() diff --git a/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/modelzoo_level.txt b/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/modelzoo_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..55d7316449715cd754664e780e12ebef020b48be --- /dev/null +++ b/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/modelzoo_level.txt @@ -0,0 +1,4 @@ +FuncStatus:OK +PerfStatus:PERFECT +PrecisionStatus:OK +ModelConvert:OK \ No newline at end of file diff --git a/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/requirements.txt b/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..334c8b67f270ca5cd4c806ada2607b13b95c9b16 --- /dev/null +++ b/ACL_PyTorch/contrib/cv/super_resolution/BasicVSR_plusplus/requirements.txt @@ -0,0 +1,3 @@ +-r mmediting/requirements/runtime.txt +-r mmediting/requirements/tests.txt +onnx==1.13.0 \ No newline at end of file