!22516 AttGAN ascend310 commit

Merge pull request !22516 from MR.D/dev

model_zoo/research/cv/AttGAN/README_CN.md

@@ -17,6 +17,8 @@
         - [评估](#评估)
     - [推理过程](#推理过程)
         - [导出MindIR](#导出MindIR)
+        - [在Ascend310执行推理](#在Ascend310执行推理)
+        - [结果](#结果)
 - [模型描述](#模型描述)
     - [性能](#性能)
         - [评估性能](#评估性能)
@@ -53,7 +55,7 @@ CelebFaces Attributes Dataset (CelebA) 是一个大规模的人脸属性数据
 - 硬件（Ascend）
     - 使用Ascend来搭建硬件环境。
 - 框架
-    - [MindSpore](https://www.mindspore.cn/install)
+    - [MindSpore](https://www.mindspore.cn/install/en)
 - 如需查看详情，请参见如下资源：
     - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
     - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
@@ -70,17 +72,17 @@ CelebFaces Attributes Dataset (CelebA) 是一个大规模的人脸属性数据
   export RANK_SIZE=1
   python train.py --experiment_name 128_shortcut1_inject1_none --data_path /path/data/img_align_celeba --attr_path /path/data/list_attr_celeba.txt
   OR
-  bash run_single_train.sh experiment_name /path/data/img_align_celeba /path/data/list_attr_celeba
+  bash run_single_train.sh experiment_name /path/data/img_align_celeba /path/data/list_attr_celeba.txt
 
   # 运行分布式训练示例
-  bash run_distribute_train.sh /path/hccl_config_file.json /path/data/img_align_celeba /path/data/list_attr_celeba
+  bash run_distribute_train.sh /path/hccl_config_file.json /path/data/img_align_celeba /path/data/list_attr_celeba.txt
 
   # 运行评估示例
   export DEVICE_ID=0
   export RANK_SIZE=1
-  python eval.py --experiment_name 128_shortcut1_inject1_none --test_int 1.0 --custom_data /path/data/custom/ --custom_attr /path/data/list_attr_custom.txt --custom_img --enc_ckpt_name encoder-119_84999.ckpt --dec_ckpt_name decoder-119_84999.ckpt
+  python eval.py --experiment_name 128_shortcut1_inject1_none --test_int 1.0 --custom_data /path/data/custom/ --custom_attr /path/data/list_attr_custom.txt --custom_img --gen_ckpt_name generator-119_84999.ckpt
   OR
-  bash run_eval.sh experiment_name /path/data/custom/ /path/data/list_attr_custom enc_ckpt_name dec_ckpt_name
+  bash run_eval.sh experiment_name /path/data/custom/ /path/data/list_attr_custom.txt gen_ckpt_name
   ```
 
   对于分布式训练，需要提前创建JSON格式的hccl配置文件。该配置文件的绝对路径作为运行分布式脚本的第一个参数。
@@ -90,7 +92,7 @@ CelebFaces Attributes Dataset (CelebA) 是一个大规模的人脸属性数据
  <https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.>
 
   对于评估脚本，需要提前创建存放自定义图片(jpg)的目录以及属性编辑文件，关于属性编辑文件的说明见[脚本及样例代码](#脚本及样例代码)。目录以及属性编辑文件分别对应参数`custom_data`和`custom_attr`。checkpoint文件被训练脚本默认放置在
-  `/output/{experiment_name}/checkpoint`目录下，执行脚本时需要将两个检查点文件（Encoder和Decoder）的名称作为参数传入。
+  `/output/{experiment_name}/checkpoint`目录下，执行脚本时需要将检查点文件（Generator）的名称作为参数传入。
 
   [注意] 以上路径均应设置为绝对路径
 
@@ -102,10 +104,12 @@ CelebFaces Attributes Dataset (CelebA) 是一个大规模的人脸属性数据
 .
 └─ cv
   └─ AttGAN
+    ├── ascend310_infer                    # 310推理目录
     ├── scripts
       ├──run_distribute_train.sh           # 分布式训练的shell脚本
       ├──run_single_train.sh               # 单卡训练的shell脚本
-      ├──run_eval.sh                       # 推理脚本
+      ├──run_eval.sh                       # 评估脚本
+      ├──run_infer_310.sh                  # 推理脚本
     ├─ src
       ├─ __init__.py                       # 初始化文件
       ├─ block.py                          # 基础cell
@@ -117,6 +121,9 @@ CelebFaces Attributes Dataset (CelebA) 是一个大规模的人脸属性数据
       ├─ loss.py                           # loss计算
     ├─ eval.py                             # 测试脚本
     ├─ train.py                            # 训练脚本
+    ├─ export.py                           # MINDIR模型导出脚本
+    ├─ preprocess.py                       # 310推理预处理脚本
+    ├─ postprocess.py                      # 310推理后处理脚本
     └─ README_CN.md                        # AttGAN的文件描述
 ```
 
@@ -141,7 +148,7 @@ CelebFaces Attributes Dataset (CelebA) 是一个大规模的人脸属性数据
 - Ascend处理器环境运行
 
   ```bash
-  bash run_distribute_train.sh /path/hccl_config_file.json /path/data/img_align_celeba /path/data/list_attr_celeba
+  bash run_distribute_train.sh /path/hccl_config_file.json /path/data/img_align_celeba /path/data/list_attr_celeba.txt
   ```
 
   上述shell脚本将在后台运行分布式训练。该脚本将在脚本目录下生成相应的LOG{RANK_ID}目录，每个进程的输出记录在相应LOG{RANK_ID}目录下的log.txt文件中。checkpoint文件保存在output/experiment_name/rank{RANK_ID}下。
@@ -153,12 +160,12 @@ CelebFaces Attributes Dataset (CelebA) 是一个大规模的人脸属性数据
 - 在Ascend环境运行时评估自定义数据集
   该网络可以用于修改面部属性，用户将希望修改的图片放在自定义的图片目录下，并根据自己期望修改的属性来修改属性编辑文件(文件的具体参数参照CelebA数据集及属性编辑文件)。完成后，需要将自定义图片目录和属性编辑文件作为参数传入测试脚本，分别对应custom_data以及custom_attr。
 
-  评估时选择已经生成好的检查点文件，作为参数传入测试脚本，对应参数为`enc_ckpt_name`和`dec_ckpt_name`(分别保存了编码器和解码器的参数)
+  评估时选择已经生成好的检查点文件，作为参数传入测试脚本，对应参数为`gen_ckpt_name`(保存了编码器和解码器的参数)
 
   ```bash
   export DEVICE_ID=0
   export RANK_SIZE=1
-  python eval.py --experiment_name 128_shortcut1_inject1_none --test_int 1.0 --custom_data /path/data/custom/ --custom_attr /path/data/list_attr_custom.txt --custom_img --enc_ckpt_name encoder-119_84999.ckpt --dec_ckpt_name decoder-119_84999.ckpt
+  python eval.py --experiment_name 128_shortcut1_inject1_none --test_int 1.0 --custom_data /path/data/custom/ --custom_attr /path/data/list_attr_custom.txt --custom_img --gen_ckpt_name generator-119_84999.ckpt
   ```
 
   测试脚本执行完成后，用户进入当前目录下的`output/{experiment_name}/custom_img`下查看修改好的图片。
@@ -168,7 +175,7 @@ CelebFaces Attributes Dataset (CelebA) 是一个大规模的人脸属性数据
 ### 导出MindIR
 
 ```shell
-python export.py --experiment_name [EXPERIMENT_NAME] --enc_ckpt_name [ENCODER_CKPT_NAME] --dec_ckpt_name [DECODER_CKPT_NAME] --file_format [FILE_FORMAT]
+python export.py --experiment_name [EXPERIMENT_NAME] --gen_ckpt_name [GENERATOR_CKPT_NAME] --file_format [FILE_FORMAT]
 ```
 
 `file_format` 必须在 ["AIR", "MINDIR"]中选择。
@@ -176,6 +183,26 @@ python export.py --experiment_name [EXPERIMENT_NAME] --enc_ckpt_name [ENCODER_CK
 
 脚本会在当前目录下生成对应的MINDIR文件。
 
+### 在Ascend310执行推理
+
+在执行推理前，必须通过export脚本导出MINDIR模型。以下命令展示了如何通过命令在Ascend310上对图片进行属性编辑：
+
+```bash
+bash run_infer_310.sh [GEN_MINDIR_PATH] [ATTR_FILE_PATH] [DATA_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+```
+
+- `MINDIR_PATH` MINDIR文件的路径
+- `ATTR_FILE_PATH` 属性编辑文件的路径，路径应当为绝对路径
+- `DATA_PATH` 需要进行推理的数据集目录，图像格式应当为jpg
+- `NEED_PREPROCESS` 表示属性编辑文件是否需要预处理，可以在y或者n中选择，如果选择y，表示进行预处理（在第一次执行推理时需要对属性编辑文件进行预处理,图片较多的话需要一些时间）
+- `DEVICE_ID` 可选，默认值为0.
+
+[注] 属性编辑文件的格式可以参考celeba数据集中的list_attr_celeba.txt文件，第一行为要推理的图片数目，第二行为要编辑的属性，接下来的是要编辑的图片名称和属性tag。属性编辑文件中的图片数目必须和数据集目录中的图片数相同。
+
+### 结果
+
+推理结果保存在脚本执行的目录下，属性编辑后的图片保存在`result_Files/`目录下，推理的时间统计结果保存在`time_Result/`目录下。编辑后的图片以`imgName_attrId.jpg`的格式保存，如`182001_1.jpg`表示对名称为182001的第一个属性进行编辑后的结果，是否对该属性进行编辑根据属性编辑文件的内容决定。
+
 # 模型描述
 
 ## 性能

model_zoo/research/cv/AttGAN/ascend310_infer/CMakeLists.txt

@@ -0,0 +1,14 @@
+cmake_minimum_required(VERSION 3.14.1)
+project(Ascend310Infer)
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
+set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
+option(MINDSPORE_PATH "mindspore install path" "")
+include_directories(${MINDSPORE_PATH})
+include_directories(${MINDSPORE_PATH}/include)
+include_directories(${PROJECT_SRC_ROOT})
+find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
+file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
+
+add_executable(main src/main.cc src/utils.cc)
+target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)

model_zoo/research/cv/AttGAN/ascend310_infer/build.sh

@@ -0,0 +1,29 @@
+#!/bin/bash
+# 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.
+# ============================================================================
+if [ -d out ]; then
+    rm -rf out
+fi
+
+mkdir out
+cd out || exit
+
+if [ -f "Makefile" ]; then
+  make clean
+fi
+
+cmake .. \
+    -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

model_zoo/research/cv/AttGAN/ascend310_infer/inc/utils.h

@@ -0,0 +1,33 @@
+/**
+ * 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.
+ */
+#ifndef MINDSPORE_INFERENCE_UTILS_H_
+#define MINDSPORE_INFERENCE_UTILS_H_
+
+#include <sys/stat.h>
+#include <dirent.h>
+#include <vector>
+#include <string>
+#include <memory>
+#include "include/api/types.h"
+
+DIR *OpenDir(std::string_view dirName);
+void Denorm(std::vector<mindspore::MSTensor> *outputs);
+std::string RealPath(std::string_view path);
+std::vector<std::string> GetAllFiles(std::string_view dirName);
+std::vector<mindspore::MSTensor> ReadCfgToTensor(const std::string &file, size_t *n_ptr);
+mindspore::MSTensor ReadFileToTensor(const std::string &file);
+int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
+#endif

model_zoo/research/cv/AttGAN/ascend310_infer/src/main.cc

@@ -0,0 +1,149 @@
+/**
+ * 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.
+ */
+#include <sys/time.h>
+#include <gflags/gflags.h>
+#include <dirent.h>
+#include <iostream>
+#include <string>
+#include <algorithm>
+#include <iosfwd>
+#include <vector>
+#include <fstream>
+#include <sstream>
+
+#include "include/api/model.h"
+#include "include/api/context.h"
+#include "include/api/types.h"
+#include "include/api/serialization.h"
+#include "include/dataset/vision_ascend.h"
+#include "include/dataset/execute.h"
+#include "include/dataset/vision.h"
+#include "inc/utils.h"
+
+using mindspore::Context;
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Status;
+using mindspore::ModelType;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::MSTensor;
+using mindspore::dataset::Execute;
+using mindspore::dataset::TensorTransform;
+using mindspore::dataset::vision::Resize;
+using mindspore::dataset::vision::HWC2CHW;
+using mindspore::dataset::vision::Normalize;
+using mindspore::dataset::vision::Decode;
+using color_rep_type = std::underlying_type<mindspore::DataType>::type;
+
+DEFINE_string(gen_mindir_path, "", "generator mindir path");
+DEFINE_string(dataset_path, "", "dataset path");
+DEFINE_string(attr_file_path, "", "attribute file path");
+DEFINE_int32(device_id, 0, "device id");
+DEFINE_int32(image_height, 128, "image height");
+DEFINE_int32(image_width, 128, "image width");
+
+int main(int argc, char **argv) {
+    gflags::ParseCommandLineFlags(&argc, &argv, true);
+    if (RealPath(FLAGS_gen_mindir_path).empty()) {
+        std::cout << "Invalid generator mindir" << std::endl;
+        return 1;
+    }
+    auto context = std::make_shared<Context>();
+    auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
+    ascend310->SetDeviceID(FLAGS_device_id);
+    ascend310->SetBufferOptimizeMode("off_optimize");
+    context->MutableDeviceInfo().push_back(ascend310);
+
+    mindspore::Graph gen_graph;
+    Serialization::Load(FLAGS_gen_mindir_path, ModelType::kMindIR, &gen_graph);
+
+    Model gen_model;
+    Status gen_ret = gen_model.Build(GraphCell(gen_graph), context);
+
+    if (gen_ret != kSuccess) {
+        std::cout << "ERROR: Generator build failed." << std::endl;
+        return 1;
+    }
+
+    size_t n_attrs = 13;
+    auto all_cfg = ReadCfgToTensor(FLAGS_attr_file_path, &n_attrs);
+    auto all_files = GetAllFiles(FLAGS_dataset_path);
+    std::map<double, double> costTime_map;
+    double startTimeMs;
+    double endTimeMs;
+    size_t size = all_files.size();
+
+    for (size_t i = 0; i < size; ++i) {
+        struct timeval start = {0};
+        struct timeval end = {0};
+        std::cout << "Start predict input files:" << all_files[i] << std::endl;
+
+        auto img = std::make_shared<MSTensor>();
+        std::shared_ptr<TensorTransform> decode(new Decode());
+        std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
+        auto resizeShape = {FLAGS_image_height, FLAGS_image_width};
+        std::shared_ptr<TensorTransform> resize(new Resize(resizeShape));
+        std::shared_ptr<TensorTransform> normalize(
+        new Normalize({127.5, 127.5, 127.5}, {127.5, 127.5, 127.5}));
+        Execute composeDecode({decode, resize, normalize, hwc2chw});
+        auto image = ReadFileToTensor(all_files[i]);
+        composeDecode(image, img.get());
+
+        for (size_t k = 0; k < n_attrs; ++k) {
+            gettimeofday(&start, nullptr);
+            std::vector<MSTensor> inputs;
+            std::vector<MSTensor> outputs;
+            size_t index = i * n_attrs + k;
+            std::cout << static_cast<color_rep_type>(img->DataType()) << std::endl;
+            inputs.emplace_back(img->Name(), img->DataType(), img->Shape(), img->Data().get(), img->DataSize());
+            inputs.emplace_back(all_cfg[index].Name(), all_cfg[index].DataType(), all_cfg[index].Shape(),
+                                all_cfg[index].Data().get(), all_cfg[index].DataSize());
+            Status gen_model_ret = gen_model.Predict(inputs, &outputs);
+            if (gen_model_ret != kSuccess) {
+                std::cout << "Generator inference " << all_files[i] << " failed." << std::endl;
+                return 1;
+            }
+            Denorm(&outputs);
+            int pos = all_files[i].find('.');
+            std::string fileName = all_files[i].substr(0, pos);
+            WriteResult(fileName + "_" + std::to_string(k) + ".jpg", outputs);
+            gettimeofday(&end, nullptr);
+            startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
+            endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
+            costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
+        }
+    }
+    double average = 0.0;
+    int inferCount = 0;
+
+    for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+        double diff = 0.0;
+        diff = iter->second - iter->first;
+        average += diff;
+        inferCount++;
+    }
+    average = average / inferCount;
+    std::stringstream timeCost;
+    timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
+    std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
+    std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
+    std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
+    fileStream << timeCost.str();
+    fileStream.close();
+    costTime_map.clear();
+    return 0;
+}

model_zoo/research/cv/AttGAN/ascend310_infer/src/utils.cc

@@ -0,0 +1,201 @@
+/**
+ * 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.
+ */
+#include "inc/utils.h"
+
+#include <fstream>
+#include <algorithm>
+#include <iostream>
+#include <cstring>
+#include <sstream>
+
+using mindspore::MSTensor;
+using mindspore::DataType;
+
+std::vector<std::string> GetAllFiles(std::string_view dirName) {
+    struct dirent *filename;
+    DIR *dir = OpenDir(dirName);
+    if (dir == nullptr) {
+        return {};
+    }
+    std::vector<std::string> res;
+    while ((filename = readdir(dir)) != nullptr) {
+        std::string dName = std::string(filename->d_name);
+        if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
+            continue;
+        }
+        res.emplace_back(std::string(dirName) + "/" + filename->d_name);
+    }
+    std::sort(res.begin(), res.end());
+    for (auto &f : res) {
+        std::cout << "image file: " << f << std::endl;
+    }
+    return res;
+}
+
+int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
+    std::string homePath = "./result_Files";
+    for (size_t i = 0; i < outputs.size(); ++i) {
+        size_t outputSize;
+        std::shared_ptr<const void> netOutput;
+        netOutput = outputs[i].Data();
+        outputSize = outputs[i].DataSize();
+        int pos = imageFile.rfind('/');
+        std::string fileName(imageFile, pos + 1);
+        std::cout << fileName << std::endl;
+        fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), ".bin");
+        std::string outFileName = homePath + "/" + fileName;
+        FILE * outputFile = fopen(outFileName.c_str(), "wb");
+        fwrite(netOutput.get(), sizeof(char), outputSize, outputFile);
+        fclose(outputFile);
+        outputFile = nullptr;
+    }
+    return 0;
+}
+
+mindspore::MSTensor ReadFileToTensor(const std::string &file) {
+    if (file.empty()) {
+        std::cout << "Pointer file is nullptr" << std::endl;
+        return mindspore::MSTensor();
+    }
+
+    std::ifstream ifs(file);
+    if (!ifs.good()) {
+        std::cout << "File: " << file << " is not exist" << std::endl;
+        return mindspore::MSTensor();
+    }
+
+    if (!ifs.is_open()) {
+        std::cout << "File: " << file << "open failed" << std::endl;
+        return mindspore::MSTensor();
+    }
+
+    ifs.seekg(0, std::ios::end);
+    size_t size = ifs.tellg();
+    mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8,
+                               {static_cast<int64_t>(size)}, nullptr, size);
+    ifs.seekg(0, std::ios::beg);
+    ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
+    ifs.close();
+    return buffer;
+}
+
+std::vector<std::string> split(std::string inputs) {
+    std::vector<std::string> line;
+    std::stringstream stream(inputs);
+    std::string result;
+    while ( stream >> result ) {
+        line.push_back(result);
+    }
+    return line;
+}
+
+std::vector<mindspore::MSTensor> ReadCfgToTensor(const std::string &file, size_t *n_ptr) {
+    std::vector<mindspore::MSTensor> res;
+    if (file.empty()) {
+        std::cout << "Pointer file is nullptr." << std::endl;
+        exit(1);
+    }
+
+    std::ifstream ifs(file);
+    if (!ifs.good()) {
+        std::cout << "File: " << file << " is not exist." << std::endl;
+        exit(1);
+    }
+
+    if (!ifs.is_open()) {
+        std::cout << "File: " << file << " open failed." << std::endl;
+        exit(1);
+    }
+
+    std::string n_images;
+    std::string n_attrs;
+    getline(ifs, n_images);
+    getline(ifs, n_attrs);
+
+    auto n_images_ = std::stoi(n_images);
+    auto n_attrs_ = std::stoi(n_attrs);
+    *n_ptr = n_attrs_;
+    std::cout << "Image number is " << n_images << std::endl;
+    std::cout << "Attribute number is " << n_attrs << std::endl;
+
+    auto all_lines = n_images_ * n_attrs_;
+    for (auto i = 0; i < all_lines; i++) {
+        std::string val;
+        getline(ifs, val);
+        std::vector<std::string> val_split = split(val);
+        void *data = malloc(sizeof(float)*n_attrs_);
+        float *elements = reinterpret_cast<float *>(data);
+        for (auto j = 0; j < n_attrs_; j++) elements[j] = atof(val_split[j].c_str());
+        auto size = sizeof(float) * n_attrs_;
+        mindspore::MSTensor buffer(file + std::to_string(i), mindspore::DataType::kNumberTypeFloat32,
+                                   {static_cast<int64_t>(size)}, nullptr, size);
+        memcpy(buffer.MutableData(), elements, size);
+        res.emplace_back(buffer);
+    }
+    ifs.close();
+    return res;
+}
+
+DIR *OpenDir(std::string_view dirName) {
+    if (dirName.empty()) {
+        std::cout << " dirName is null ! " << std::endl;
+        return nullptr;
+    }
+    std::string realPath = RealPath(dirName);
+    struct stat s;
+    lstat(realPath.c_str(), &s);
+    if (!S_ISDIR(s.st_mode)) {
+        std::cout << "dirName is not a valid directory !" << std::endl;
+        return nullptr;
+    }
+    DIR *dir;
+    dir = opendir(realPath.c_str());
+    if (dir == nullptr) {
+        std::cout << "Can not open dir " << dirName << std::endl;
+        return nullptr;
+    }
+    std::cout << "Successfully opened the dir " << dirName << std::endl;
+    return dir;
+}
+
+std::string RealPath(std::string_view path) {
+    char realPathMem[PATH_MAX] = {0};
+    char *realPathRet = nullptr;
+    realPathRet = realpath(path.data(), realPathMem);
+
+    if (realPathRet == nullptr) {
+        std::cout << "File: " << path << " is not exist.";
+        return "";
+    }
+
+    std::string realPath(realPathMem);
+    std::cout << path << " realpath is: " << realPath << std::endl;
+    return realPath;
+}
+
+void Denorm(std::vector<MSTensor> *outputs) {
+    for (size_t i = 0; i < outputs->size(); ++i) {
+        size_t outputSize = (*outputs)[i].DataSize();
+        float* netOutput = reinterpret_cast<float *>((*outputs)[i].MutableData());
+        size_t outputLen = outputSize / sizeof(float);
+
+        for (size_t j = 0; j < outputLen; ++j) {
+            netOutput[j] = (netOutput[j] + 1) / 2 * 255;
+            netOutput[j] = (netOutput[j] < 0) ? 0 : netOutput[j];
+            netOutput[j] = (netOutput[j] > 255) ? 255 : netOutput[j];
+        }
+    }
+}

model_zoo/research/cv/AttGAN/eval.py

@@ -28,12 +28,12 @@ import mindspore.dataset as de
 from mindspore import context, Tensor, ops
 from mindspore.train.serialization import load_param_into_net
 
-from src.attgan import Genc, Gdec
+from src.attgan import Gen
 from src.cell import init_weights
 from src.data import check_attribute_conflict
 from src.data import get_loader, Custom
 from src.helpers import Progressbar
-from src.utils import resume_model, denorm
+from src.utils import resume_generator, denorm
 
 device_id = int(os.getenv('DEVICE_ID'))
 context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)
@@ -45,8 +45,7 @@ def parse(arg=None):
     parser.add_argument('--experiment_name', dest='experiment_name', required=True)
     parser.add_argument('--test_int', dest='test_int', type=float, default=1.0)
     parser.add_argument('--num_test', dest='num_test', type=int)
-    parser.add_argument('--enc_ckpt_name', type=str, default='')
+    parser.add_argument('--gen_ckpt_name', type=str, default='')
-    parser.add_argument('--dec_ckpt_name', type=str, default='')
     parser.add_argument('--custom_img', action='store_true')
     parser.add_argument('--custom_data', type=str, default='../data/custom')
     parser.add_argument('--custom_attr', type=str, default='../data/list_attr_custom.txt')
@@ -62,8 +61,7 @@ with open(join('output', args_.experiment_name, 'setting.txt'), 'r') as f:
     args = json.load(f, object_hook=lambda d: argparse.Namespace(**d))
 args.test_int = args_.test_int
 args.num_test = args_.num_test
-args.enc_ckpt_name = args_.enc_ckpt_name
+args.gen_ckpt_name = args_.gen_ckpt_name
-args.dec_ckpt_name = args_.dec_ckpt_name
 args.custom_img = args_.custom_img
 args.custom_data = args_.custom_data
 args.custom_attr = args_.custom_attr
@@ -101,15 +99,15 @@ else:
     print('Testing images:', min(test_len, args.num_test))
 
 # Model loader
-genc = Genc(mode='test')
+gen = Gen(args.enc_dim, args.enc_layers, args.enc_norm, args.enc_acti, args.dec_dim, args.dec_layers, args.dec_norm,
-gdec = Gdec(shortcut_layers=args.shortcut_layers, inject_layers=args.inject_layers, mode='test')
+          args.dec_acti, args.n_attrs, args.shortcut_layers, args.inject_layers, args.img_size, mode='test')
 
 # Initialize network
-init_weights(genc, 'KaimingUniform', math.sqrt(5))
+init_weights(gen, 'KaimingUniform', math.sqrt(5))
-init_weights(gdec, 'KaimingUniform', math.sqrt(5))
+para_gen = resume_generator(args, gen, args.gen_ckpt_name)
-para_genc, para_gdec = resume_model(args, genc, gdec, args.enc_ckpt_name, args.dec_ckpt_name)
+load_param_into_net(gen, para_gen)
-load_param_into_net(genc, para_genc)
+
-load_param_into_net(gdec, para_gdec)
+print("Network initializes successfully.")
 
 progressbar = Progressbar()
 it = 0
@@ -134,8 +132,7 @@ for data in test_dataset_iter:
         att_b_ = (att_b * 2 - 1) * args.thres_int
         if i > 0:
             att_b_[..., i - 1] = att_b_[..., i - 1] * args.test_int / args.thres_int
-        a_enc = genc(img_a)
+        samples.append(gen(img_a, att_b_, mode="enc-dec"))
-        samples.append(gdec(a_enc, att_b_))
     cat = ops.Concat(axis=3)
     samples = cat(samples).asnumpy()
     result = denorm(samples)

model_zoo/research/cv/AttGAN/export.py

@@ -21,14 +21,13 @@ import numpy as np
 from mindspore import context, Tensor
 from mindspore.train.serialization import export, load_param_into_net
 
-from src.utils import resume_model
+from src.utils import resume_generator
-from src.attgan import Genc, Gdec
+from src.attgan import Gen
 
 parser = argparse.ArgumentParser(description='Attribute Edit')
 parser.add_argument("--device_id", type=int, default=0, help="Device id")
 parser.add_argument("--batch_size", type=int, default=1, help="batch size")
-parser.add_argument('--enc_ckpt_name', type=str, default='')
+parser.add_argument('--gen_ckpt_name', type=str, default='')
-parser.add_argument('--dec_ckpt_name', type=str, default='')
 parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default='AIR', help='file format')
 parser.add_argument('--experiment_name', dest='experiment_name', required=True)
 
@@ -39,27 +38,20 @@ with open(join('output', args_.experiment_name, 'setting.txt'), 'r') as f:
     args = json.load(f, object_hook=lambda d: argparse.Namespace(**d))
 args.device_id = args_.device_id
 args.batch_size = args_.batch_size
-args.enc_ckpt_name = args_.enc_ckpt_name
+args.gen_ckpt_name = args_.gen_ckpt_name
-args.dec_ckpt_name = args_.dec_ckpt_name
 args.file_format = args_.file_format
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
 
 if __name__ == '__main__':
 
-    genc = Genc(mode='test')
+    gen = Gen(mode="test")
-    gdec = Gdec(mode='test')
 
-    para_genc, para_gdec = resume_model(args, genc, gdec, args.enc_ckpt_name, args.dec_ckpt_name)
+    para_gen = resume_generator(args, gen, args.gen_ckpt_name)
-    load_param_into_net(genc, para_genc)
+    load_param_into_net(gen, para_gen)
-    load_param_into_net(gdec, para_gdec)
 
-    enc_array = Tensor(np.random.uniform(-1.0, 1.0, size=(1, 3, 128, 128)).astype(np.float32))
+    input_array = Tensor(np.random.uniform(-1.0, 1.0, size=(1, 3, 128, 128)).astype(np.float32))
-    dec_array = genc(enc_array)
     input_label = Tensor(np.random.uniform(-1.0, 1.0, size=(1, 13)).astype(np.float32))
 
-    G_enc_file = f"AttGAN_Generator_Encoder"
+    Gen_file = f"attgan_mindir"
-    export(genc, enc_array, file_name=G_enc_file, file_format=args.file_format)
+    export(gen, *(input_array, input_label), file_name=Gen_file, file_format=args.file_format)
-
-    G_dec_file = f"AttGAN_Generator_Decoder"
-    export(gdec, *(dec_array, input_label), file_name=G_dec_file, file_format=args.file_format)

model_zoo/research/cv/AttGAN/postprocess.py

@@ -0,0 +1,60 @@
+# 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.
+# ============================================================================
+"""post process for 310 inference"""
+import os
+import argparse
+import numpy as np
+from PIL import Image
+
+def parse(arg=None):
+    """Define configuration of postprocess"""
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--bin_path', type=str, default='./result_Files/')
+    parser.add_argument('--target_path', type=str, default='./result_Files/')
+    return parser.parse_args(arg)
+
+def load_bin_file(bin_file, shape=None, dtype="float32"):
+    """Load data from bin file"""
+    data = np.fromfile(bin_file, dtype=dtype)
+    if shape:
+        data = np.reshape(data, shape)
+    return data
+
+def save_bin_to_image(data, out_name):
+    """Save bin file to image arrays"""
+    image = np.transpose(data, (1, 2, 0))
+    im = Image.fromarray(np.uint8(image))
+    im.save(out_name)
+    print("Successfully save image in " + out_name)
+
+def scan_dir(bin_path):
+    """Scan directory"""
+    out = os.listdir(bin_path)
+    return out
+
+def postprocess(bin_path):
+    """Post process bin file"""
+    file_list = scan_dir(bin_path)
+    for file in file_list:
+        data = load_bin_file(bin_path + file, shape=(3, 128, 128), dtype="float32")
+        pos = file.find(".")
+        file_name = file[0:pos] + "." + "jpg"
+        outfile = os.path.join(args.target_path, file_name)
+        save_bin_to_image(data, outfile)
+
+if __name__ == "__main__":
+
+    args = parse()
+    postprocess(args.bin_path)

model_zoo/research/cv/AttGAN/preprocess.py

@@ -0,0 +1,123 @@
+# 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.
+# ============================================================================
+"""pre process for 310 inference"""
+import os
+from os.path import join
+
+import argparse
+import numpy as np
+
+selected_attrs = [
+    'Bald', 'Bangs', 'Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Bushy_Eyebrows',
+    'Eyeglasses', 'Male', 'Mouth_Slightly_Open', 'Mustache', 'No_Beard', 'Pale_Skin', 'Young'
+]
+
+def parse(arg=None):
+    """Define configuration of preprocess"""
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--attrs', dest='attrs', default=selected_attrs, nargs='+', help='attributes to learn')
+    parser.add_argument('--attrs_path', type=str, default='../data/list_attr_custom.txt')
+    parser.add_argument('--test_int', dest='test_int', type=float, default=1.0)
+    parser.add_argument('--thres_int', dest='thres_int', type=float, default=0.5)
+    return parser.parse_args(arg)
+
+args = parse()
+args.n_attrs = len(args.attrs)
+
+def check_attribute_conflict(att_batch, att_name, att_names):
+    """Check Attributes"""
+    def _set(att, att_name):
+        if att_name in att_names:
+            att[att_names.index(att_name)] = 0.0
+
+    att_id = att_names.index(att_name)
+    for att in att_batch:
+        if att_name in ['Bald', 'Receding_Hairline'] and att[att_id] != 0:
+            _set(att, 'Bangs')
+        elif att_name == 'Bangs' and att[att_id] != 0:
+            _set(att, 'Bald')
+            _set(att, 'Receding_Hairline')
+        elif att_name in ['Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Gray_Hair'] and att[att_id] != 0:
+            for n in ['Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Gray_Hair']:
+                if n != att_name:
+                    _set(att, n)
+        elif att_name in ['Straight_Hair', 'Wavy_Hair'] and att[att_id] != 0:
+            for n in ['Straight_Hair', 'Wavy_Hair']:
+                if n != att_name:
+                    _set(att, n)
+        elif att_name in ['Mustache', 'No_Beard'] and att[att_id] != 0:
+            for n in ['Mustache', 'No_Beard']:
+                if n != att_name:
+                    _set(att, n)
+    return att_batch
+
+def read_cfg_file(attr_path):
+    """Read configuration from attribute file"""
+    attr_list = open(attr_path, "r", encoding="utf-8").readlines()[1].split()
+    atts = [attr_list.index(att) + 1 for att in selected_attrs]
+    labels = np.loadtxt(attr_path, skiprows=2, usecols=atts, dtype=np.int)
+    attr_number = int(open(attr_path, "r", encoding="utf-8").readlines()[0])
+    labels = [labels] if attr_number == 1 else labels[0:]
+    new_attr = []
+    for index in range(attr_number):
+        att = [np.asarray((labels[index] + 1) // 2)]
+        new_attr.append(att)
+    new_attr = np.array(new_attr)
+    return new_attr, attr_number
+
+def preprocess_cfg(attrs, numbers):
+    """Preprocess attribute file"""
+    new_attr = []
+    for index in range(numbers):
+        attr = attrs[index]
+        att_b_list = [attr]
+        for i in range(args.n_attrs):
+            tmp = attr.copy()
+            tmp[:, i] = 1 - tmp[:, i]
+            tmp = check_attribute_conflict(tmp, selected_attrs[i], selected_attrs)
+            att_b_list.append(tmp)
+        for i, att_b in enumerate(att_b_list):
+            att_b_ = (att_b * 2 - 1) * args.thres_int
+            if i > 0:
+                att_b_[..., i - 1] = att_b_[..., i - 1] * args.test_int / args.thres_int
+            new_attr.append(att_b_)
+    return new_attr
+
+def write_cfg_file(attrs, numbers):
+    """Write attribute file"""
+    cur_dir = os.getcwd()
+    print(cur_dir)
+    path = join(cur_dir, 'attrs.txt')
+    with open(path, "w") as f:
+        f.writelines(str(numbers))
+        f.writelines("\n")
+        f.writelines(str(args.n_attrs))
+        f.writelines("\n")
+        counts = numbers * args.n_attrs
+        for index in range(counts):
+            attrs_list = attrs[index][0]
+            new_attrs_list = ["%s" % x for x in attrs_list]
+            sequence = " ".join(new_attrs_list)
+            f.writelines(sequence)
+            f.writelines("\n")
+    print("Generate cfg file successfully.")
+
+if __name__ == "__main__":
+
+    if args.attrs_path is None:
+        print("Path is not correct!")
+    attributes, n_images = read_cfg_file(args.attrs_path)
+    new_attrs = preprocess_cfg(attributes, n_images)
+    write_cfg_file(new_attrs, n_images)

model_zoo/research/cv/AttGAN/scripts/run_eval.sh

@@ -14,17 +14,16 @@
 # limitations under the License.
 # ============================================================================
 
-if [ $# != 5 ]
+if [ $# != 4 ]
 then
-    echo "Usage: sh run_eval.sh [EXPERIMENT_NAME] [CUSTOM_DATA_PATH] [CUSTOM_ATTR_PATH] [ENC_CKPT_NAME] [DEC_CKPT_NAME]"
+    echo "Usage: sh run_eval.sh [EXPERIMENT_NAME] [CUSTOM_DATA_PATH] [CUSTOM_ATTR_PATH] [GEN_CKPT_NAME]"
     exit 1
 fi
 
 experiment_name=$1
 data_path=$2
 attr_path=$3
-enc_name=$4
+gen_ckpt_name=$4
-dec_name=$5
 
 cores=`cat /proc/cpuinfo|grep "processor" |wc -l`
 echo "The number of logical core" $cores
@@ -47,5 +46,4 @@ python eval.py  \
 --custom_data $data_path \
 --custom_attr $attr_path \
 --custom_img \
---enc_ckpt_name $enc_name \
+--gen_ckpt_name $gen_ckpt_name > ./scripts/EVAL_LOG/log.txt 2>&1 &
---dec_ckpt_name $dec_name > ./scripts/EVAL_LOG/log.txt 2>&1 &

model_zoo/research/cv/AttGAN/scripts/run_infer_310.sh

@@ -0,0 +1,128 @@
+#!/bin/bash
+# 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.
+# ============================================================================
+
+if [[ $# -lt 4 || $# -gt 5 ]]; then
+    echo "Usage: bash run_infer_310.sh [GEN_MINDIR_PATH] [ATTR_FILE_PATH] [DATA_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+    NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+
+gen_model=$(get_real_path $1)
+attr_path=$(get_real_path $2)
+data_path=$(get_real_path $3)
+
+if [ "$4" == "y" ] || [ "$4" == "n" ];then
+    need_preprocess=$4
+else
+  echo "weather need preprocess or not, it's value must be in [y, n]"
+  exit 1
+fi
+
+device_id=0
+if [ $# == 5 ]; then
+    device_id=$5
+fi
+
+echo "generator mindir name: "$gen_model
+echo "attribute file path: "$attr_path
+echo "dataset path: "$data_path
+echo "need preprocess: "$need_preprocess
+echo "device id: "$device_id
+
+export ASCEND_HOME=/usr/local/Ascend/
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+    export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+    export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
+    export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+    export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+    export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+    echo "Start to preprocess attr file..."
+    python ../preprocess.py --attrs_path=$attr_path --test_int=1.0 --thres_int=0.5 &> preprocess.log
+    echo "Attribute file generates successfully!"
+}
+
+function compile_app()
+{
+    echo "Start to compile source code..."
+    cd ../ascend310_infer || exit
+    bash build.sh &> build.log
+    echo "Compile successfully."
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+    echo "Start to execute inference..."
+    ../ascend310_infer/out/main --gen_mindir_path=$gen_model --dataset_path=$data_path --attr_file_path="attrs.txt" --device_id=$device_id --image_height=128 --image_width=128 &> infer.log
+}
+
+function postprocess_data()
+{
+    echo "Start to postprocess image file..."
+    python ../postprocess.py --bin_path="./result_Files/" --target_path="./result_Files/"
+}
+
+if [ $need_preprocess == "y" ]; then
+    preprocess_data
+    if [ $? -ne 0 ]; then
+        echo "preprocess attrs failed"
+        exit 1
+    fi
+fi
+
+compile_app
+if [ $? -ne 0 ]; then
+    echo "compile app code failed"
+    exit 1
+fi
+
+infer
+if [ $? -ne 0 ]; then
+    echo "execute inference failed"
+    exit 1
+fi
+
+postprocess_data
+if [ $? -ne 0 ]; then
+    echo "postprocess images failed"
+    exit 1
+fi

model_zoo/research/cv/AttGAN/src/attgan.py

@@ -22,12 +22,15 @@ from src.block import LinearBlock, Conv2dBlock, ConvTranspose2dBlock
 # Image size 128 x 128
 MAX_DIM = 64 * 16
 
-
+class Gen(nn.Cell):
-class Genc(nn.Cell):
+    """Generator"""
-    """Generator encoder"""
     def __init__(self, enc_dim=64, enc_layers=5, enc_norm_fn="batchnorm", enc_acti_fn="lrelu",
-                 mode='test'):
+                 dec_dim=64, dec_layers=5, dec_norm_fn="batchnorm", dec_acti_fn="relu",
+                 n_attrs=13, shortcut_layers=1, inject_layers=1, img_size=128, mode="test"):
         super().__init__()
+        self.shortcut_layers = min(shortcut_layers, dec_layers - 1)
+        self.inject_layers = min(inject_layers, dec_layers - 1)
+        self.f_size = img_size // 2 ** dec_layers  # f_size = 4 for 128x128
 
         layers = []
         n_in = 3
@@ -39,27 +42,7 @@ class Genc(nn.Cell):
             n_in = n_out
         self.enc_layers = nn.CellList(layers)
 
-    def construct(self, x):
-        """Encoder construct"""
-        z = x
-        zs = []
-        for layer in self.enc_layers:
-            z = layer(z)
-            zs.append(z)
-        return zs
-
-
-class Gdec(nn.Cell):
-    """Generator decoder"""
-    def __init__(self, dec_dim=64, dec_layers=5, dec_norm_fn="batchnorm", dec_acti_fn="relu", n_attrs=13,
-                 shortcut_layers=1, inject_layers=1, img_size=128, mode='test'):
-        super().__init__()
-        self.shortcut_layers = min(shortcut_layers, dec_layers - 1)
-        self.inject_layers = min(inject_layers, dec_layers - 1)
-        self.f_size = img_size // 2 ** dec_layers  # f_size = 4 for 128x128
-
         layers = []
-        n_in = 1024
         n_in = n_in + n_attrs  # 1024 + 13
         for i in range(dec_layers):
             if i < dec_layers - 1:
@@ -80,7 +63,16 @@ class Gdec(nn.Cell):
         self.repeat = P.Tile()
         self.cat = P.Concat(1)
 
-    def construct(self, zs, a):
+    def encoder(self, x):
+        """Encoder construct"""
+        z = x
+        zs = []
+        for layer in self.enc_layers:
+            z = layer(z)
+            zs.append(z)
+        return zs
+
+    def decoder(self, zs, a):
         """Decoder construct"""
         a_tile = self.view(a, (a.shape[0], -1, 1, 1))
         multiples = (1, 1, self.f_size, self.f_size)
@@ -100,6 +92,16 @@ class Gdec(nn.Cell):
             i = i + 1
         return z
 
+    def construct(self, x, a=None, mode="enc-dec"):
+        result = None
+        if mode == "enc-dec":
+            out = self.encoder(x)
+            result = self.decoder(out, a)
+        if mode == "enc":
+            result = self.encoder(x)
+        if mode == "dec":
+            result = self.decoder(x, a)
+        return result
 
 class Dis(nn.Cell):
     """Discriminator"""

model_zoo/research/cv/AttGAN/src/cell.py

@@ -116,8 +116,7 @@ class TrainOneStepCellGen(nn.Cell):
         grads = self.grad(self.network, weights)(img_a, att_a, att_a_, att_b, att_b_, sens)
         if self.reducer_flag:
             grads = self.grad_reducer(grads)
-        self.optimizer(grads)
+        return F.depend(loss, self.optimizer(grads)), gf_loss, gc_loss, gr_loss
-        return loss, gf_loss, gc_loss, gr_loss
 
 
 class TrainOneStepCellDis(nn.Cell):
@@ -153,5 +152,4 @@ class TrainOneStepCellDis(nn.Cell):
         if self.reducer_flag:
             grads = self.grad_reducer(grads)
 
-        self.optimizer(grads)
+        return F.depend(loss, self.optimizer(grads)), d_real_loss, d_fake_loss, dc_loss, df_gp
-        return loss, d_real_loss, d_fake_loss, dc_loss, df_gp

model_zoo/research/cv/AttGAN/src/loss.py

@@ -90,10 +90,9 @@ class WGANGPGradientPenalty(nn.Cell):
 class GenLoss(nn.Cell):
     """Define total Generator loss"""
 
-    def __init__(self, args, encoder, decoder, discriminator):
+    def __init__(self, args, generator, discriminator):
         super().__init__()
-        self.encoder = encoder
+        self.generator = generator
-        self.decoder = decoder
         self.discriminator = discriminator
 
         self.lambda_1 = Tensor(args.lambda_1, mstype.float32)
@@ -108,9 +107,9 @@ class GenLoss(nn.Cell):
     def construct(self, img_a, att_a, att_a_, att_b, att_b_):
         """Get generator loss"""
         # generate
-        zs_a = self.encoder(img_a)
+        zs_a = self.generator(img_a, mode="enc")
-        img_fake = self.decoder(zs_a, att_b_)
+        img_fake = self.generator(zs_a, att_b_, mode="dec")
-        img_recon = self.decoder(zs_a, att_a_)
+        img_recon = self.generator(zs_a, att_a_, mode="dec")
 
         # discriminate
         d_fake, dc_fake = self.discriminator(img_fake)
@@ -128,10 +127,9 @@ class GenLoss(nn.Cell):
 class DisLoss(nn.Cell):
     """Define total discriminator loss"""
 
-    def __init__(self, args, encoder, decoder, discriminator):
+    def __init__(self, args, generator, discriminator):
         super().__init__()
-        self.encoder = encoder
+        self.generator = generator
-        self.decoder = decoder
         self.discriminator = discriminator
 
         self.cyc_loss = P.ReduceMean()
@@ -146,8 +144,7 @@ class DisLoss(nn.Cell):
     def construct(self, img_a, att_a, att_a_, att_b, att_b_):
         """Get discriminator loss"""
         # generate
-        z = self.encoder(img_a)
+        img_fake = self.generator(img_a, att_b_, mode="enc-dec")
-        img_fake = self.decoder(z, att_b_)
 
         # discriminate
         d_real, dc_real = self.discriminator(img_a)

model_zoo/research/cv/AttGAN/src/utils.py

@@ -60,16 +60,13 @@ class DistributedSampler:
     def __len__(self):
         return self.num_samples
 
-def resume_model(args, encoder, decoder, enc_ckpt_name, dec_ckpt_name):
+def resume_generator(args, generator, gen_ckpt_name):
-    """Restore the trained generator and discriminator"""
+    """Restore the trained generator"""
     print("Loading the trained models from step {}...".format(args.save_interval))
-    encoder_path = os.path.join('output', args.experiment_name, 'checkpoint/rank0', enc_ckpt_name)
+    generator_path = os.path.join('output', args.experiment_name, 'checkpoint/rank0', gen_ckpt_name)
-    decoder_path = os.path.join('output', args.experiment_name, 'checkpoint/rank0', dec_ckpt_name)
+    param_generator = load_checkpoint(generator_path, generator)
 
-    param_encoder = load_checkpoint(encoder_path, encoder)
+    return param_generator
-    param_decoder = load_checkpoint(decoder_path, decoder)
-
-    return param_encoder, param_decoder
 
 def resume_discriminator(args, discriminator, dis_ckpt_name):
     """Restore the trained discriminator"""

model_zoo/research/cv/AttGAN/train.py

@@ -11,7 +11,7 @@
 # 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.
-# ============================================================================sss
+# ============================================================================
 """Entry point for training AttGAN network"""
 
 import argparse
@@ -32,12 +32,12 @@ from mindspore.context import ParallelMode
 from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, _InternalCallbackParam, RunContext
 from mindspore.train.serialization import load_param_into_net
 
-from src.attgan import Genc, Gdec, Dis
+from src.attgan import Gen, Dis
 from src.cell import TrainOneStepCellGen, TrainOneStepCellDis, init_weights
 from src.data import data_loader
 from src.helpers import Progressbar
 from src.loss import GenLoss, DisLoss
-from src.utils import resume_model, resume_discriminator
+from src.utils import resume_generator, resume_discriminator
 
 attrs_default = [
     'Bald', 'Bangs', 'Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Bushy_Eyebrows',
@@ -94,8 +94,7 @@ def parse(arg=None):
                         default=datetime.datetime.now().strftime("%I:%M%p on %B %d, %Y"))
     parser.add_argument("--run_distribute", type=int, default=0, help="Run distribute, default: false.")
     parser.add_argument('--resume_model', action='store_true')
-    parser.add_argument('--enc_ckpt_name', type=str, default='')
+    parser.add_argument('--gen_ckpt_name', type=str, default='')
-    parser.add_argument('--dec_ckpt_name', type=str, default='')
     parser.add_argument('--dis_ckpt_name', type=str, default='')
 
     return parser.parse_args(arg)
@@ -150,32 +149,28 @@ if __name__ == '__main__':
     print('Training images:', train_length)
 
     # Define network
-    genc = Genc(args.enc_dim, args.enc_layers, args.enc_norm, args.enc_acti, mode='train')
+    gen = Gen(args.enc_dim, args.enc_layers, args.enc_norm, args.enc_acti, args.dec_dim, args.dec_layers, args.dec_norm,
-    gdec = Gdec(args.dec_dim, args.dec_layers, args.dec_norm, args.dec_acti, args.n_attrs, args.shortcut_layers,
+              args.dec_acti, args.n_attrs, args.shortcut_layers, args.inject_layers, args.img_size, mode='train')
-                args.inject_layers, args.img_size, mode='train')
     dis = Dis(args.dis_dim, args.dis_norm, args.dis_acti, args.dis_fc_dim, args.dis_fc_norm, args.dis_fc_acti,
               args.dis_layers, args.img_size, mode='train')
 
     # Initialize network
-    init_weights(genc, 'KaimingUniform', math.sqrt(5))
+    init_weights(gen, 'KaimingUniform', math.sqrt(5))
-    init_weights(gdec, 'KaimingUniform', math.sqrt(5))
     init_weights(dis, 'KaimingUniform', math.sqrt(5))
 
     # Resume from checkpoint
     if args.resume_model:
-        para_genc, para_gdec = resume_model(args, genc, gdec, args.enc_ckpt_name, args.dec_ckpt_name)
+        para_gen = resume_generator(args, gen, args.gen_ckpt_name)
         para_dis = resume_discriminator(args, dis, args.dis_ckpt_name)
-        load_param_into_net(genc, para_genc)
+        load_param_into_net(gen, para_gen)
-        load_param_into_net(gdec, para_gdec)
         load_param_into_net(dis, para_dis)
 
     # Define network with loss
-    G_loss_cell = GenLoss(args, genc, gdec, dis)
+    G_loss_cell = GenLoss(args, gen, dis)
-    D_loss_cell = DisLoss(args, genc, gdec, dis)
+    D_loss_cell = DisLoss(args, gen, dis)
 
     # Define Optimizer
-    G_trainable_params = genc.trainable_params() + gdec.trainable_params()
+    optimizer_G = nn.Adam(params=gen.trainable_params(), learning_rate=args.lr, beta1=args.beta1, beta2=args.beta2)
-    optimizer_G = nn.Adam(params=G_trainable_params, learning_rate=args.lr, beta1=args.beta1, beta2=args.beta2)
     optimizer_D = nn.Adam(params=dis.trainable_params(), learning_rate=args.lr, beta1=args.beta1, beta2=args.beta2)
 
     # Define One Step Train
@@ -193,21 +188,14 @@ if __name__ == '__main__':
 
     if rank == 0:
         local_train_url = os.path.join('output', args.experiment_name, 'checkpoint/rank{}'.format(rank))
-        ckpt_cb_genc = ModelCheckpoint(config=ckpt_config, directory=local_train_url, prefix='encoder')
+        ckpt_cb_gen = ModelCheckpoint(config=ckpt_config, directory=local_train_url, prefix='generator')
-        ckpt_cb_gdec = ModelCheckpoint(config=ckpt_config, directory=local_train_url, prefix='decoder')
         ckpt_cb_dis = ModelCheckpoint(config=ckpt_config, directory=local_train_url, prefix='discriminator')
 
-        cb_params_genc = _InternalCallbackParam()
+        cb_params_gen = _InternalCallbackParam()
-        cb_params_genc.train_network = genc
+        cb_params_gen.train_network = gen
-        cb_params_genc.cur_epoch_num = 0
+        cb_params_gen.cur_epoch_num = 0
-        genc_run_context = RunContext(cb_params_genc)
+        gen_run_context = RunContext(cb_params_gen)
-        ckpt_cb_genc.begin(genc_run_context)
+        ckpt_cb_gen.begin(gen_run_context)
-
-        cb_params_gdec = _InternalCallbackParam()
-        cb_params_gdec.train_network = gdec
-        cb_params_gdec.cur_epoch_num = 0
-        gdec_run_context = RunContext(cb_params_gdec)
-        ckpt_cb_gdec.begin(gdec_run_context)
 
         cb_params_dis = _InternalCallbackParam()
         cb_params_dis.train_network = dis
@@ -243,16 +231,12 @@ if __name__ == '__main__':
                                 gr_loss=gr_loss, dc_loss=dc_loss, df_gp=df_gp)
 
             if (epoch + 1) % 5 == 0 and (it + 1) % args.save_interval == 0 and rank == 0:
-                cb_params_genc.cur_epoch_num = epoch + 1
+                cb_params_gen.cur_epoch_num = epoch + 1
-                cb_params_gdec.cur_epoch_num = epoch + 1
                 cb_params_dis.cur_epoch_num = epoch + 1
-                cb_params_genc.cur_step_num = it + 1
+                cb_params_gen.cur_step_num = it + 1
-                cb_params_gdec.cur_step_num = it + 1
                 cb_params_dis.cur_step_num = it + 1
-                cb_params_genc.batch_num = it + 2
+                cb_params_gen.batch_num = it + 2
-                cb_params_gdec.batch_num = it + 2
                 cb_params_dis.batch_num = it + 2
-                ckpt_cb_genc.step_end(genc_run_context)
+                ckpt_cb_gen.step_end(gen_run_context)
-                ckpt_cb_gdec.step_end(gdec_run_context)
                 ckpt_cb_dis.step_end(dis_run_context)
             it += 1