upload c3d

official/cv/c3d/ascend310_infer/CMakeLists.txt

+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)

official/cv/c3d/ascend310_infer/build.sh

+#!/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

official/cv/c3d/ascend310_infer/inc/utils.h

+/**
+ * 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"
+
+std::vector<std::string> GetAllFiles(std::string_view dirName);
+DIR *OpenDir(std::string_view dirName);
+std::string RealPath(std::string_view path);
+mindspore::MSTensor ReadFileToTensor(const std::string &file);
+int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
+#endif

official/cv/c3d/ascend310_infer/src/main.cc

+/**
+ * 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/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::MSTensor;
+using mindspore::dataset::Execute;
+using mindspore::ModelType;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(input0_path, ".", "input0 path");
+DEFINE_int32(device_id, 0, "device id");
+
+int main(int argc, char **argv) {
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+  if (RealPath(FLAGS_mindir_path).empty()) {
+    std::cout << "Invalid mindir" << std::endl;
+    return 1;
+  }
+
+  auto context = std::make_shared<Context>();
+  auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
+  ascend310->SetDeviceID(FLAGS_device_id);
+  context->MutableDeviceInfo().push_back(ascend310);
+  mindspore::Graph graph;
+  Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
+
+  std::cout << "Start Build Model." << std::endl;
+  Model model;
+  Status ret = model.Build(GraphCell(graph), context);
+  if (ret != kSuccess) {
+    std::cout << "ERROR: Build failed." << std::endl;
+    return 1;
+  }
+
+  std::vector<MSTensor> model_inputs = model.GetInputs();
+  if (model_inputs.empty()) {
+    std::cout << "Invalid model, inputs is empty." << std::endl;
+    return 1;
+  }
+
+  auto input0_files = GetAllFiles(FLAGS_input0_path);
+  if (input0_files.empty()) {
+    std::cout << "ERROR: input data empty." << std::endl;
+    return 1;
+  }
+
+  std::map<double, double> costTime_map;
+  size_t size = input0_files.size();
+
+  for (size_t i = 0; i < size; ++i) {
+    struct timeval start = {0};
+    struct timeval end = {0};
+    double startTimeMs;
+    double endTimeMs;
+    std::vector<MSTensor> inputs;
+    std::vector<MSTensor> outputs;
+    std::cout << "Start predict input files:" << input0_files[i] << std::endl;
+
+    auto input0 = ReadFileToTensor(input0_files[i]);
+    std::cout << "666" << input0_files[i] << std::endl;
+    inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
+                        input0.Data().get(), input0.DataSize());
+
+    gettimeofday(&start, nullptr);
+    ret = model.Predict(inputs, &outputs);
+    gettimeofday(&end, nullptr);
+    if (ret != kSuccess) {
+      std::cout << "Predict " << input0_files[i] << " failed." << std::endl;
+      return 1;
+    }
+    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));
+    WriteResult(input0_files[i], outputs);
+  }
+  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;
+}

official/cv/c3d/ascend310_infer/src/utils.cc

+/**
+ * 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 <fstream>
+#include <algorithm>
+#include <iostream>
+#include "inc/utils.h"
+
+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);
+    fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
+    std::string outFileName = homePath + "/" + fileName;
+    FILE * outputFile = fopen(outFileName.c_str(), "wb");
+    fwrite(netOutput.get(), outputSize, sizeof(char), 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;
+}
+
+
+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;
+}

official/cv/c3d/default_config.yaml

+# Device
+device_target:     "Ascend"
+# ==============================================================================
+# Dataset Setup
+clip_length:       16                    # Number of frames within a clip
+clip_offset:       0                     # Frame offset between beginning of video and clip (1st clip only)
+clip_stride:       1                     # Frame offset between successive clips, must be >= 1
+crop_shape:        [112,112]             # (Height, Width) of frame
+crop_type:         Random                # Type of cropping operation (Random, Central and None)
+final_shape:       [112,112]             # (Height, Width) of input to be given to CNN
+num_clips:         -1                    # Number clips to be generated from a video (<0: uniform sampling, 0: Divide entire video into clips, >0: Defines number of clips)
+random_offset:     0                     # Boolean switch to generate a clip length sized clip from a video
+resize_shape:      [128,171]             # (Height, Width) to resize original data
+subtract_mean:     ''                    # Subtract mean (R,G,B) from all frames during preprocessing
+preprocess:        default               # String argument to select preprocessing type
+
+# Experiment Setup
+model:             C3D                   # Name of model to be loaded
+acc_metric:        Accuracy              # Accuracy metric
+batch_size:        16                    # Numbers of videos in a mini-batch
+dataset:           HMDB51                # Name of dataset, 'HMDB51' or 'UCF101'
+epoch:             30                    # Total number of epochs
+gamma:             0.1                   # Multiplier with which to change learning rate
+json_path:         /Data/hmdb-51_json/   # Path to the json file for the given dataset
+img_path:          /Data/hmdb-51_img/    # Path to the img file for the given dataset
+pre_trained:       1                     # Load pretrained network
+pre_trained_ckpt_path : ./pretrained_model/pre_trained_model.ckpt # pre_trained model ckpt file path
+sport1m_mean_file_path: ./pretrained_model/sport1m_train16_128_mean.npy # Sport1m data distribution information
+num_classes:       51                    # Number of total classes in the dataset, 51 for HMDB51 and 101 for UCF101
+loss_type:         M_XENTROPY            # Loss function
+lr:                1e-4                  # Learning rate
+milestones:        [10, 20]              # Epoch values to change learning rate
+momentum:          0.9                   # Momentum value in optimizer
+loss_scale:        1.0
+num_workers:       8                     # Number of CPU worker used to load data
+opt:               sgd                   # Name of optimizer
+save_dir:          ./results             # Path to results directory
+seed:              666                   # Seed for reproducibility
+weight_decay:      0.0005                # Weight decay
+
+# Train Setup
+is_distributed:    0                     # Distributed training or not
+is_save_on_master: 1                     # Only save ckpt on master device
+device_id:         0                     # Device id
+ckpt_path:         ./result/ckpt         # Ckpt save path
+ckpt_interval:     1                     # Saving ckpt interval
+keep_checkpoint_max: 40                  # Max ckpt file number
+
+# Modelarts Setup
+enable_modelarts:  0                     # Is training on modelarts
+result_url:        ''                    # Result save path
+data_url:          ''                    # Data path
+train_url:         ''                    # Train path
+
+# Export Setup
+ckpt_file:         './C3D.ckpt'          # Mindspore ckpt file path
+mindir_file_name:  './C3D'               # Save file path
+file_format:       'mindir'              # Save file format

official/cv/c3d/eval.py

+# 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 datetime
+import numpy as np
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+from mindspore.train.serialization import load_param_into_net, load_checkpoint
+from mindspore.common import set_seed
+
+from src.dataset import classification_dataset
+from src.c3d_model import C3D
+from src.model_utils.config import config
+
+
+class TestOneStepCell(nn.Cell):
+    def __init__(self, network, criterion):
+        super(TestOneStepCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.criterion = criterion
+
+    def construct(self, data, label):
+        output = self.network(data)
+        loss = self.criterion(output, label)
+        return output, loss
+
+def test_net():
+    '''run test'''
+    config.load_type = 'test'
+    set_seed(config.seed)
+    context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=False,
+                        device_target=config.device_target)
+    if config.device_target == "Ascend":
+        context.set_context(device_id=config.device_id)
+
+    # logger
+    config.outputs_dir = os.path.join(config.save_dir, datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
+    dataset, _ = classification_dataset(config.batch_size, shuffle=False, repeat_num=1,
+                                        drop_remainder=False)
+
+    batch_num = dataset.get_dataset_size()
+    config.steps_per_epoch = dataset.get_dataset_size()
+
+    # network
+    print('start create network')
+    network = C3D(config.num_classes)
+
+    # pre_trained
+    param_dict = load_checkpoint(config.ckpt_path)
+    _ = load_param_into_net(network, param_dict)
+    print('pre_trained model:', config.ckpt_path)
+
+    network.set_train(mode=False)
+    acc_sum, sample_num = 0, 0
+    for index, (input_data, label) in enumerate(dataset):
+        predictions = network(Tensor(input_data))
+        predictions, label = predictions.asnumpy(), label.asnumpy()
+        acc = np.sum(np.argmax(predictions, 1) == label[:, -1])
+        batch_size = label.shape[0]
+        acc_sum += acc
+        sample_num += batch_size
+        if index % 20 == 0:
+            print("setep: {}/{}, acc: {}".format(index + 1, batch_num, acc / batch_size))
+
+    accuracy_top1 = acc_sum / sample_num
+    print('eval result: top_1 {:.3f}%'.format(accuracy_top1 * 100))
+
+
+if __name__ == '__main__':
+    test_net()

official/cv/c3d/export.py

+# 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
+from mindspore import dtype as mstype
+from mindspore import context, Tensor, export
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from src.c3d_model import C3D
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
+
+
+@moxing_wrapper()
+def export_model(ckpt_path):
+    network = C3D(num_classes=config.num_class)
+    network.set_train(False)
+    param_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(network, param_dict)
+    image_shape = [config.batch_size, 3, config.clip_length] + config.final_shape
+    window_image = Tensor(np.zeros(image_shape), mstype.float32)
+    export(network, window_image, file_name=config.mindir_file_name, file_format=config.file_format)
+
+
+if __name__ == '__main__':
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target,
+                        save_graphs=False, device_id=config.device_id)
+    export_model(ckpt_path=config.ckpt_file)

official/cv/c3d/requirements.txt

+nibabel
+pyyaml
+opencv-python
\ No newline at end of file

official/cv/c3d/scripts/run_ckpt_convert.sh

+#!/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 1 ] || [ $# -gt 2 ]; then
+    echo "Usage: bash run_ckpt_convert.sh [PYTORCH_FILE_PATH] [MINDSPORE_FILE_PATH]
+    PYTORCH_FILE_PATH is pytorch pretrained model ckpt file path.
+    MINDSPORE_FILE_PATH is mindspore pretrained model ckpt file path."
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+
+torch_file_path=$(get_real_path $1)
+
+if [ ! -f ${torch_file_path} ]; then
+    echo "Pytorch pretrained model ckpt file path does not exist."
+exit 1
+fi
+
+mindspore_file_path=$(get_real_path $2)
+cd ..
+python3.7  -m src.tools.ckpt_convert ${torch_file_path} ${mindspore_file_path}

official/cv/c3d/scripts/run_dataset_preprocess.sh

+#!/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 2 ] || [ $# -gt 3 ]; then
+    echo "Usage: bash run_dataset_preprocess.sh [DATASET_NAME] [RAR_FILE_PATH] [SPLIT_NUMBER]
+    DATASET_NAME is the dataset name, it's value is 'HMDB51' or 'UCF101'.
+    RAR_FILE_PATH is raw rar(or zip) file path
+    SPLIT_NUMBER is the split number, it's value is '1', '2' or '3'."
+exit 1
+fi
+
+if [ $1 != 'HMDB51' ] && [ $1 != 'UCF101' ]; then
+    echo "DATASET_NAME must be 'HMDB51' or 'UCF101'."
+exit 1
+fi
+
+if [ "${2:0:1}" != "/" ]; then
+    echo "Please change $2 to an absolute path."
+exit 1
+fi
+
+if [ ! -f $2 ]; then
+    echo "dataset rar file path does not exist."
+exit 1
+fi
+
+if [ $3 -ne 1 ] && [ $3 -ne 2 ] && [ $3 -ne 3 ]; then
+    echo "SPLIT_NUMBER must be '1', '2' or '3'."
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo $1
+    else
+        echo "$(realpath -m ${PWD}/$1)"
+    fi
+}
+
+dataset_rar_file_path=$(get_real_path $2)
+
+if [ ! -f ${dataset_rar_file_path} ]; then
+    echo "Dataset rar file path does not exist!"
+    exit 1
+fi
+
+python3.7  ../src/tools/dataset_preprocess.py $1 ${dataset_rar_file_path} $3

official/cv/c3d/scripts/run_distribute_train_ascend.sh

+#!/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.
+# ============================================================================
+
+# bash run_distribute_train_ascend.sh [RANK_TABLE_FILE]
+
+if [ $# -ne 1 ]; then
+    echo "Usage: bash run_distribute_train_ascend.sh [RANK_TABLE_FILE]"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo $1
+    else
+        echo "$(realpath -m ${PWD}/$1)"
+    fi
+}
+
+PATH1=$(get_real_path $1)
+
+if [ ! -f ${PATH1} ]; then
+    echo "error: RANK_TABLE_FILE=$PATH1 is not a file."
+exit 1
+fi
+
+cd ..
+export RANK_SIZE=8
+export RANK_TABLE_FILE=$PATH1
+
+echo 'start training'
+for((i=0;i<=$RANK_SIZE-1;i++));
+do
+    export DEVICE_ID=$i
+    export RANK_ID=$i
+    rm -rf ./train_parallel$i
+    mkdir ./train_parallel$i
+    cp ./*.py ./train_parallel$i
+    cp ./*.yaml ./train_parallel$i
+    cp -r ./src ./train_parallel$i
+    cd ./train_parallel$i || exit
+    echo "start training for rank $RANK_ID, device $DEVICE_ID"
+    env > env.log
+    python train.py --is_distributed 1 > ./train$i.log  2>&1 &
+    cd ../
+done

official/cv/c3d/scripts/run_infer_310.sh

+#!/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 2 || $# -gt 3 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+    NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+
+if [ "$2" == "y" ] || [ "$2" == "n" ];then
+    need_preprocess=$2
+else
+  echo "weather need preprocess or not, it's value must be in [y, n]"
+  exit 1
+fi
+
+device_id=0
+if [ $# == 3 ]; then
+    device_id=$3
+fi
+
+echo "mindir name: "$model
+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=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+    if [ -d preprocess_Result ]; then
+        rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+    python3.7 ../preprocess.py
+}
+
+function compile_app()
+{
+    cd ../ascend310_infer || exit
+    bash build.sh &> build.log
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+
+    ../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/image --device_id=$device_id &> infer.log
+
+}
+
+function cal_acc()
+{
+    python3.7 ../postprocess.py &> acc.log
+}
+
+if [ $need_preprocess == "y" ]; then
+    preprocess_data
+    if [ $? -ne 0 ]; then
+        echo "preprocess dataset 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
+# cal_acc
+if [ $? -ne 0 ]; then
+    echo "calculate accuracy failed"
+    exit 1
+fi
\ No newline at end of file

official/cv/c3d/scripts/run_standalone_eval_ascend.sh

+#!/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 [ $# -lt 1 ]; then
+    echo "Usage: bash run_standalone_eval_ascend.sh  [CKPT_FILE_PATH]
+    CKPT_FILE_PATH is saved model ckpt file path."
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+
+ckpt_path=$(get_real_path $1)
+
+if [ ! -f ${ckpt_path} ]; then
+    echo "Ckpt file does not exist."
+exit 1
+fi
+
+cd ../
+python eval.py --ckpt_path ${ckpt_path} > eval.log 2>&1 &

official/cv/c3d/scripts/run_standalone_train_ascend.sh

+#!/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.
+# ============================================================================
+
+# bash run_standalone_train_ascend.sh
+cd ..
+python train.py > train.log 2>&1 &

official/cv/c3d/src/c3d_model.py

+# 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 math
+import mindspore.nn as nn
+import mindspore.ops as P
+from mindspore.common import initializer as init
+from src.utils import default_recurisive_init, KaimingNormal
+
+
+class C3D(nn.Cell):
+    """
+    C3D network definition.
+
+    Args:
+        num_classes (int): Class numbers. Default: 1000.
+    Returns:
+        Tensor, infer output tensor.
+
+    Examples:
+        >>> C3D(num_classes=1000)
+    """
+
+    def __init__(self, num_classes=1000):
+        super(C3D, self).__init__()
+
+        self.conv1 = nn.Conv3d(in_channels=3, out_channels=64, kernel_size=(3, 3, 3),
+                               padding=(1, 1, 1, 1, 1, 1), pad_mode='pad', has_bias=True)
+        self.pool1 = P.MaxPool3D(kernel_size=(1, 2, 2), strides=(1, 2, 2), pad_mode='same')
+
+        self.conv2 = nn.Conv3d(in_channels=64, out_channels=128, kernel_size=(3, 3, 3),
+                               padding=(1, 1, 1, 1, 1, 1), pad_mode='pad', has_bias=True)
+        self.pool2 = P.MaxPool3D(kernel_size=(2, 2, 2), strides=(2, 2, 2), pad_mode='same')
+
+        self.conv3a = nn.Conv3d(in_channels=128, out_channels=256, kernel_size=(3, 3, 3),
+                                padding=(1, 1, 1, 1, 1, 1), pad_mode='pad', has_bias=True)
+        self.conv3b = nn.Conv3d(in_channels=256, out_channels=256, kernel_size=(3, 3, 3),
+                                padding=(1, 1, 1, 1, 1, 1), pad_mode='pad', has_bias=True)
+        self.pool3 = P.MaxPool3D(kernel_size=(2, 2, 2), strides=(2, 2, 2), pad_mode='same')
+
+        self.conv4a = nn.Conv3d(in_channels=256, out_channels=512, kernel_size=(3, 3, 3),
+                                padding=(1, 1, 1, 1, 1, 1), pad_mode='pad', has_bias=True)
+        self.conv4b = nn.Conv3d(in_channels=512, out_channels=512, kernel_size=(3, 3, 3),
+                                padding=(1, 1, 1, 1, 1, 1), pad_mode='pad', has_bias=True)
+        self.pool4 = P.MaxPool3D(kernel_size=(2, 2, 2), strides=(2, 2, 2), pad_mode='same')
+
+        self.conv5a = nn.Conv3d(in_channels=512, out_channels=512, kernel_size=(3, 3, 3),
+                                padding=(1, 1, 1, 1, 1, 1), pad_mode='pad', has_bias=True)
+        self.conv5b = nn.Conv3d(in_channels=512, out_channels=512, kernel_size=(3, 3, 3),
+                                padding=(1, 1, 1, 1, 1, 1), pad_mode='pad', has_bias=True)
+        self.pool5 = P.MaxPool3D(kernel_size=(2, 2, 2), strides=(2, 2, 2), pad_mode='same')
+
+        self.fc6 = nn.Dense(in_channels=8192, out_channels=4096)
+        self.fc7 = nn.Dense(in_channels=4096, out_channels=4096)
+        self.fc8 = nn.Dense(in_channels=4096, out_channels=num_classes, bias_init=init.Normal(0.02))
+
+        self.dropout = nn.Dropout(keep_prob=0.5)
+        self.relu = nn.ReLU()
+        self.pad = nn.Pad(paddings=((0, 0), (0, 0), (1, 0), (1, 0)), mode="CONSTANT")
+
+        self.__init_weight()
+
+    def __init_weight(self):
+        default_recurisive_init(self)
+        self.custom_init_weight()
+
+    def construct(self, x):
+
+        x = self.relu(self.conv1(x))
+        x = self.pool1(x)
+
+        x = self.relu(self.conv2(x))
+        x = self.pool2(x)
+
+        x = self.relu(self.conv3a(x))
+        x = self.relu(self.conv3b(x))
+        x = self.pool3(x)
+
+        x = self.relu(self.conv4a(x))
+        x = self.relu(self.conv4b(x))
+        x = self.pool4(x)
+
+        x = self.relu(self.conv5a(x))
+        x = self.relu(self.conv5b(x))
+        x = x.view(-1, 512 * 2, 7, 7)
+        x = self.pad(x)
+        x = x.view(-1, 512, 2, 8, 8)
+        x = self.pool5(x)
+
+        x = x.view(-1, 8192)
+        x = self.relu(self.fc6(x))
+        x = self.dropout(x)
+        x = self.relu(self.fc7(x))
+        x = self.dropout(x)
+
+        logits = self.fc8(x)
+
+        return logits
+
+    def custom_init_weight(self):
+        """
+        Init the weight of Conv3d and Dense in the net.
+        """
+        for _, cell in self.cells_and_names():
+            if isinstance(cell, nn.Conv3d):
+                cell.weight.set_data(init.initializer(
+                    KaimingNormal(a=math.sqrt(5), mode='fan_out', nonlinearity='relu'),
+                    cell.weight.shape, cell.weight.dtype))
+                if cell.bias is not None:
+                    cell.bias.set_data(init.initializer(
+                        'zeros', cell.bias.shape, cell.bias.dtype))
+            elif isinstance(cell, nn.Dense):
+                cell.weight.set_data(init.initializer(
+                    init.Normal(0.01), cell.weight.shape, cell.weight.dtype))
+                if cell.bias is not None:
+                    cell.bias.set_data(init.initializer(
+                        'zeros', cell.bias.shape, cell.bias.dtype))

official/cv/c3d/src/dataset.py

+# 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 json
+import cv2
+import numpy as np
+from PIL import ImageFile
+import mindspore.dataset as de
+
+from src.model_utils.config import config
+from src.transform import PreprocessTrainC3D, PreprocessEvalC3D
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+class video_Dataset():
+    def __init__(self):
+        self.load_type = config.load_type
+        self.crop_shape = config.crop_shape
+        self.final_shape = config.final_shape
+        self.preprocess = config.preprocess
+        self.json_path = config.json_path
+        self.img_path = config.img_path
+
+        # Clips processing arguments
+        self.clip_length = config.clip_length
+        self.clip_offset = config.clip_offset
+        self.clip_stride = config.clip_stride
+        self.num_clips = config.num_clips
+        self.random_offset = config.random_offset
+
+        # Frame-wise processing arguments
+        self.resize_shape = config.resize_shape
+        self.crop_type = config.crop_type
+
+        # Experiment arguments
+        self.batch_size = config.batch_size
+        self.class_number = config.num_classes
+
+        self._index = 0
+
+        if self.load_type == 'train':
+            self.transforms = PreprocessTrainC3D()
+        else:
+            self.transforms = PreprocessEvalC3D()
+
+        self._getClips()
+
+    def __getitem__(self, idx):
+        vid_info = self.samples[idx]
+        base_path = os.path.join(self.img_path, vid_info['base_path'])
+
+        vid_length = len(vid_info['frames'])
+        labels = np.zeros((vid_length)) - 1
+        input_data = []
+
+        for frame_ind in range(len(vid_info['frames'])):
+            frame_path = os.path.join(base_path, vid_info['frames'][frame_ind]['img_path'])
+
+            for frame_labels in vid_info['frames'][frame_ind]['actions']:
+                labels[frame_ind] = frame_labels['action_class']
+
+            # Load frame image data and preprocess image accordingly
+            input_data.append(cv2.imread(frame_path)[..., ::-1] / 1.)
+
+        # Preprocess data
+        vid_data = self.transforms(input_data)
+        vid_data = np.transpose(vid_data, (3, 0, 1, 2))
+
+        if self.load_type == 'train':
+            one_hot = np.zeros((self.class_number))
+            one_hot[labels.astype('int32')[-1]] = 1.
+            labels = one_hot
+        return (vid_data.astype(np.float32), labels.astype(np.int32))
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __iter__(self):
+        self._index = 0
+        return self
+
+    def __next__(self):
+        # pylint: disable = no-else-return
+        if self._index < len(self.samples):
+            item = self.__getitem__(self._index)
+            self._index += 1
+            return item
+        else:
+            raise StopIteration
+
+    def _getClips(self):
+        self.samples = []
+
+        if self.load_type == 'train':
+            full_json_path = os.path.join(self.json_path, 'train.json')
+
+        elif self.load_type == 'val':
+            full_json_path = os.path.join(self.json_path, 'val.json')
+
+            if not os.path.exists(full_json_path):
+                full_json_path = os.path.join(self.json_path, 'test.json')
+
+        else:
+            full_json_path = os.path.join(self.json_path, 'test.json')
+
+        json_file = open(full_json_path, 'r')
+        json_data = json.load(json_file)
+        json_file.close()
+
+        # Load the information for each video and process it into clips
+        for video_info in json_data:
+            clips = self._extractClips(video_info['frames'])
+
+            # Each clip is a list of dictionaries per frame containing information
+            # Example info: object bbox annotations, object classes, frame img path
+            for clip in clips:
+                self.samples.append(dict(frames=clip, base_path=video_info['base_path']))
+
+    def _extractClips(self, video):
+        """
+        Processes a single video into uniform sized clips that will be loaded by __getitem__
+        Args:
+            video:       List containing a dictionary of annontations per frame
+
+        Additional Parameters:
+            self.clip_length: Number of frames extracted from each clip
+            self.num_clips:   Number of clips to extract from each video
+            (-1 uses the entire video, 0 paritions the entire video in clip_length clips)
+            self.clip_offset: Number of frames from beginning of video to start extracting clips
+            self.clip_stride: Number of frames between clips when extracting them from videos
+            self.random_offset: Randomly select a clip_length sized clip from a video
+        """
+        if self.clip_offset > 0:
+            if len(video) - self.clip_offset >= self.clip_length:
+                video = video[self.clip_offset:]
+
+        if self.num_clips < 0:
+            if len(video) >= self.clip_length:
+                # Uniformly sample one clip from the video
+                final_video = [video[_idx] for _idx in np.linspace(0, len(video) - 1, self.clip_length, dtype='int32')]
+                final_video = [final_video]
+
+            else:
+                indices = np.ceil(self.clip_length / float(len(video)))
+                indices = indices.astype('int32')
+                indices = np.tile(np.arange(0, len(video), 1, dtype='int32'), indices)
+                indices = indices[np.linspace(0, len(indices) - 1, self.clip_length, dtype='int32')]
+
+                final_video = [video[_idx] for _idx in indices]
+                final_video = [final_video]
+
+        elif self.num_clips == 0:
+            if len(video) >= self.clip_length:
+                indices = np.arange(start=0, stop=len(video) - self.clip_length + 1, step=self.clip_stride)
+                final_video = []
+
+                for _idx in indices:
+                    if _idx + self.clip_length <= len(video):
+                        final_video.append([video[true_idx] for true_idx in range(_idx, _idx + self.clip_length)])
+            else:
+                indices = np.ceil(self.clip_length / float(len(video)))
+                indices = indices.astype('int32')
+                indices = np.tile(np.arange(0, len(video), 1, dtype='int32'), indices)
+                indices = indices[:self.clip_length]
+
+                final_video = [video[_idx] for _idx in indices]
+                final_video = [final_video]
+
+        else:
+            if self.clip_length == -1:
+                # This is a special case where we will return the entire video
+                # Batch size must equal one or dataloader items may have varying lengths
+                # and can't be stacked i.e. throws an error
+                assert self.batch_size == 1
+                return [video]
+
+            required_length = (self.num_clips - 1) * (self.clip_stride) + self.clip_length
+
+            if self.random_offset:
+                if len(video) >= required_length:
+                    vid_start = np.random.choice(np.arange(len(video) - required_length + 1), 1)
+                    video = video[int(vid_start):]
+
+            if len(video) >= required_length:
+                # Get indices of sequential clips overlapped by a clip_stride number of frames
+                indices = np.arange(0, len(video), self.clip_stride)
+
+                # Select only the first num clips
+                indices = indices.astype('int32')[:self.num_clips]
+
+                video = np.array(video)
+                final_video = [video[np.arange(_idx, _idx + self.clip_length).astype('int32')].tolist() for _idx in
+                               indices]
+            else:
+                indices = np.ceil(required_length / float(len(video)))
+                indices = indices.astype('int32')
+                indices = np.tile(np.arange(0, len(video), 1, dtype='int32'), indices)
+
+                # Starting index of each clip
+                clip_starts = np.arange(0, len(indices), self.clip_stride).astype('int32')[:self.num_clips]
+
+                video = np.array(video)
+                final_video = [video[indices[_idx:_idx + self.clip_length]].tolist() for _idx in clip_starts]
+        return final_video
+
+
+def classification_dataset(per_batch_size, shuffle=None, repeat_num=1, drop_remainder=True):
+    dataset = video_Dataset()
+    dataset_len = len(dataset)
+    if shuffle:
+        num_parallel_workers = config.num_workers
+        if config.is_distributed:
+            de_dataset = de.GeneratorDataset(dataset, ["video", "label"], num_parallel_workers=num_parallel_workers,
+                                             shuffle=shuffle, num_shards=config.group_size, shard_id=config.rank)
+        else:
+            de_dataset = de.GeneratorDataset(dataset, ["video", "label"], num_parallel_workers=num_parallel_workers,
+                                             shuffle=shuffle)
+    else:
+        num_parallel_workers = 1
+        de_dataset = de.GeneratorDataset(dataset, ["video", "label"], num_parallel_workers=num_parallel_workers,
+                                         shuffle=shuffle)
+    columns_to_project = ["video", "label"]
+    de_dataset = de_dataset.project(columns=columns_to_project)
+
+    de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
+    de_dataset = de_dataset.repeat(repeat_num)
+
+    return de_dataset, dataset_len

official/cv/c3d/src/loss.py

+# 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 mindspore.nn.loss.loss import LossBase
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+
+class Max_Entropy(LossBase):
+
+    def __init__(self, reduction='mean'):
+        super(Max_Entropy, self).__init__(reduction)
+        self.logsoftmax = nn.LogSoftmax(axis=1)
+        self.sum = P.ReduceSum()
+        self.mean = P.ReduceMean()
+
+    def construct(self, predictions, targets):
+        log = self.logsoftmax(predictions)
+        temp_ = self.sum(-targets * log, 1)
+        loss = self.mean(temp_)
+
+        return self.get_loss(loss)

official/cv/c3d/src/lr_schedule.py

+# 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
+
+
+def linear_warmup_learning_rate(lr_max, epoch_step, global_step=0, lr_init=1e-8,
+                                warmup_epochs=0, total_epochs=1, steps_per_epoch=1):
+    """Set learning rate."""
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    warmup_steps = steps_per_epoch * warmup_epochs
+    if warmup_steps != 0:
+        inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
+    else:
+        inc_each_step = 0
+    lr_value = lr_max
+    for i in range(total_steps):
+        if i <= warmup_steps:
+            lr_value = float(lr_init) + inc_each_step * float(i)
+        else:
+            if i // steps_per_epoch in epoch_step and i % steps_per_epoch == 0:
+                lr_value *= 0.1
+            if lr_value < 0.0:
+                lr_value = 0.0
+        lr_each_step.append(lr_value)
+
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+    learning_rate = lr_each_step[global_step:]
+
+    return learning_rate