!19566 push icnet code add 310inference

Merge pull request !19566 from bigpingping/master

model_zoo/research/cv/ICNet/README.md

@@ -13,6 +13,7 @@
     - [Evaluation Process](#evaluation-process)
         - [Evaluation](#evaluation)
         - [Evaluation Result](#evaluation-result)
+    - [310 infer](#310-inference)
 - [Model Description](#model-description)
 - [Description of Random Situation](#description-of-random-situation)
 - [ModelZoo Homepage](#modelzoo-homepage)
@@ -50,27 +51,39 @@ It contains 5,000 finely annotated images split into training, validation and te
 ```python
 .
 └─ICNet
-  ├─configs
+    ├── ascend310_infer
-    ├─icnet.yaml                          # config file
+    │   ├── build.sh
-  ├─models
+    │   ├── CMakeLists.txt
-    ├─base_models
+    │   ├── inc
-      ├─resnt50_v1.py                     # used resnet50
+    │   │   └── utils.h
-    ├─__init__.py
+    │   └── src
-    ├─icnet.py                            # validation network
+    │       ├── main.cc
-    ├─icnet_dc.py                         # training network
+    │       └── utils.cc
-  ├─scripts
+    ├── eval.py                                    # validation
-    ├─run_distribute_train8p.sh           # Multi card distributed training in ascend
+    ├── export.py                                  # export mindir
-    ├─run_eval.sh                         # validation script
+    ├── postprocess.py                             # 310 infer calculate accuracy
-  ├─utils
+    ├── README.md                                  # descriptions about ICNet
-    ├─__init__.py
+    ├── scripts
-    ├─logger.py                           # logger
+    │   ├── run_distribute_train8p.sh              # multi cards distributed training in ascend
-    ├─loss.py                             # loss
+    │   ├── run_eval.sh                            # validation script
-    ├─losses.py                           # SoftmaxCrossEntropyLoss
+    │   └── run_infer_310.sh                       # 310 infer script
-    ├─lr_scheduler.py                     # lr
+    ├── src
-    └─metric.py                           # metric
+    │   ├── cityscapes_mindrecord.py               # create mindrecord dataset
-  ├─eval.py                               # validation
+    │   ├── __init__.py
-  ├─train.py                              # train
+    │   ├── logger.py                              # logger
-  └─visualize.py                          # inference visualization
+    │   ├── losses.py                              # used losses
+    │   ├── loss.py                                # loss
+    │   ├── lr_scheduler.py                        # lr
+    │   ├── metric.py                              # metric
+    │   ├── models
+    │   │   ├── icnet_1p.py                        # net single card
+    │   │   ├── icnet_dc.py                        # net multi cards
+    │   │   ├── icnet.py                           # validation card
+    │   │   └── resnet50_v1.py                     # backbone
+    │   ├── model_utils
+    │   │   └── icnet.yaml                         # config
+    │   └── visualize.py                           # inference visualization
+    └── train.py                                   # train
 ```
 
 ## Script Parameters
@@ -169,6 +182,14 @@ avg_pixacc 0.94285786
 avgtime 0.19648232793807982
 ````
 
+## 310 infer
+
+```shell
+    bash run_infer_310.sh [The path of the MINDIR for 310 infer] [The path of the dataset for 310 infer]  0
+```
+
+Note:: Before executing 310 infer, create the MINDIR/AIR model using "python export.py --ckpt-file [The path of the CKPT for exporting]".
+
 # [Model Description](#Content)
 
 ## Performance

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

@@ -0,0 +1,23 @@
+#!/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
+  mkdir out
+fi
+cd out || exit
+cmake .. \
+    -DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

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

@@ -0,0 +1,32 @@
+/**
+ * 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

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

@@ -0,0 +1,162 @@
+/**
+ * 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 "../inc/utils.h"
+#include "minddata/dataset/include/execute.h"
+#include "minddata/dataset/include/transforms.h"
+#include "minddata/dataset/include/vision.h"
+#include "minddata/dataset/include/vision_ascend.h"
+#include "include/api/types.h"
+#include "include/api/model.h"
+#include "include/api/serialization.h"
+#include "include/api/context.h"
+
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Context;
+using mindspore::Status;
+using mindspore::ModelType;
+using mindspore::Graph;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::MSTensor;
+using mindspore::DataType;
+using mindspore::dataset::Execute;
+using mindspore::dataset::TensorTransform;
+using mindspore::dataset::vision::Decode;
+using mindspore::dataset::vision::Resize;
+using mindspore::dataset::vision::Rescale;
+using mindspore::dataset::vision::Normalize;
+using mindspore::dataset::vision::HWC2CHW;
+
+using mindspore::dataset::transforms::TypeCast;
+
+DEFINE_string(model_path, "/root/ICNet.mindir", "model path");
+DEFINE_string(dataset_path, "/data/cityscapes/leftImg8bit/val", "dataset path");
+DEFINE_int32(input_width, 2048, "input width");
+DEFINE_int32(input_height, 1024, "inputheight");
+DEFINE_int32(device_id, 0, "device id");
+DEFINE_string(precision_mode, "allow_fp32_to_fp16", "precision mode");
+DEFINE_string(op_select_impl_mode, "", "op select impl mode");
+DEFINE_string(device_target, "Ascend310", "device target");
+
+int main(int argc, char **argv) {
+    gflags::ParseCommandLineFlags(&argc, &argv, true);
+    if (RealPath(FLAGS_model_path).empty()) {
+        std::cout << "Invalid model" << std::endl;
+        return 1;
+    }
+
+    auto context = std::make_shared<Context>();
+    auto ascend310_info = std::make_shared<mindspore::Ascend310DeviceInfo>();
+    ascend310_info->SetDeviceID(FLAGS_device_id);
+    context->MutableDeviceInfo().push_back(ascend310_info);
+
+    Graph graph;
+    Status ret = Serialization::Load(FLAGS_model_path, ModelType::kMindIR, &graph);
+    if (ret != kSuccess) {
+        std::cout << "Load model failed." << std::endl;
+        return 1;
+    }
+
+    Model model;
+    ret = model.Build(GraphCell(graph), context);
+    if (ret != kSuccess) {
+        std::cout << "ERROR: Build failed." << std::endl;
+        return 1;
+    }
+
+    std::vector<MSTensor> modelInputs = model.GetInputs();
+
+    auto all_files = GetAllFiles(FLAGS_dataset_path);
+    if (all_files.empty()) {
+        std::cout << "ERROR: no input data." << std::endl;
+        return 1;
+    }
+
+    auto decode = Decode();
+    auto normalize = Normalize({0.485, 0.456, 0.406}, {0.229, 0.224, 0.225});
+    auto hwc2chw = HWC2CHW();
+    auto rescale = Rescale(1.0 / 255.0, 0);
+    auto typeCast = TypeCast("float32");
+
+    mindspore::dataset::Execute transformDecode(decode);
+    mindspore::dataset::Execute transform({rescale, normalize, hwc2chw});
+    mindspore::dataset::Execute transformCast(typeCast);
+
+    std::map<double, double> costTime_map;
+
+    size_t size = all_files.size();
+    for (size_t i = 0; i < size; ++i) {
+        struct timeval start;
+        struct timeval end;
+        double startTime_ms;
+        double endTime_ms;
+        std::vector<MSTensor> inputs;
+        std::vector<MSTensor> outputs;
+
+        std::cout << "Start predict input files:" << all_files[i] << std::endl;
+        mindspore::MSTensor image =  ReadFileToTensor(all_files[i]);
+
+        transformDecode(image, &image);
+        std::vector<int64_t> shape = image.Shape();
+        transform(image, &image);
+
+        inputs.emplace_back(modelInputs[0].Name(), modelInputs[0].DataType(), modelInputs[0].Shape(),
+                            image.Data().get(), image.DataSize());
+
+        gettimeofday(&start, NULL);
+        model.Predict(inputs, &outputs);
+        gettimeofday(&end, NULL);
+
+        startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
+        endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
+        costTime_map.insert(std::pair<double, double>(startTime_ms, endTime_ms));
+        WriteResult(all_files[i], outputs);
+    }
+    double average = 0.0;
+    int infer_cnt = 0;
+
+    for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+        double diff = 0.0;
+        diff = iter->second - iter->first;
+        average += diff;
+        infer_cnt++;
+    }
+
+    average = average / infer_cnt;
+
+    std::stringstream timeCost;
+    timeCost << "NN inference cost average time: " << average << " ms of infer_count " << infer_cnt << std::endl;
+    std::cout << "NN inference cost average time: " << average << "ms of infer_count " << infer_cnt << std::endl;
+    std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
+    std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
+    file_stream << timeCost.str();
+    file_stream.close();
+    costTime_map.clear();
+    return 0;
+}

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

@@ -0,0 +1,127 @@
+/**
+ * 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>
+
+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 = 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 = 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;
+}

model_zoo/research/cv/ICNet/postprocess.py

@@ -0,0 +1,119 @@
+# 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.
+# ============================================================================
+"""Evaluate mIou and Pixacc"""
+import os
+import time
+import sys
+import argparse
+import yaml
+import numpy as np
+from PIL import Image
+
+parser = argparse.ArgumentParser(description="ICNet Evaluation")
+parser.add_argument("--dataset_path", type=str, default="/home/dataset",
+                    help="dataset path for evaluation")
+parser.add_argument("--project_path", type=str, default='/home/ICNet',
+                    help="project_path")
+parser.add_argument("--device_id", type=int, default=5, help="Device id, default is 5.")
+parser.add_argument("--result_path", type=str, default="", help="Image path.")
+
+args_opt = parser.parse_args()
+
+
+class Evaluator:
+    """evaluate"""
+
+    def __init__(self, config):
+        self.cfg = config
+
+        self.mask_folder = '/home/data'
+
+        # evaluation metrics
+        self.metric = SegmentationMetric(19)
+
+    def eval(self):
+        """evaluate"""
+        self.metric.reset()
+
+        list_time = []
+
+        for root, _, files in os.walk(args_opt.dataset_path):
+            for filename in files:
+                if filename.endswith('.png'):
+                    img_path = os.path.join(root, filename)
+                    file_name = filename.split('.')[0]
+                    output_file = os.path.join(args_opt.result_path, file_name + "_0.bin")
+                    output = np.fromfile(output_file, dtype=np.float32).reshape(1, 19, 1024, 2048)
+                    folder_name = os.path.basename(os.path.dirname(img_path))
+                    mask_name = filename.replace('leftImg8bit', 'gtFine_labelIds')
+                    mask_file = os.path.join(self.mask_folder, folder_name, mask_name)
+                    mask = Image.open(mask_file)  # mask shape: (W,H)
+
+                    mask = self._mask_transform(mask)  # mask shape: (H,w)
+
+                    start_time = time.time()
+                    end_time = time.time()
+                    step_time = end_time - start_time
+
+                    mask = np.expand_dims(mask, axis=0)
+                    self.metric.update(output, mask)
+                    list_time.append(step_time)
+
+        mIoU, pixAcc = self.metric.get()
+
+        average_time = sum(list_time) / len(list_time)
+
+        print("avgmiou", mIoU)
+        print("avg_pixacc", pixAcc)
+        print("avgtime", average_time)
+
+    def _mask_transform(self, mask):
+        mask = self._class_to_index(np.array(mask).astype('int32'))
+        return np.array(mask).astype('int32')
+
+    def _class_to_index(self, mask):
+        """assert the value"""
+        values = np.unique(mask)
+        self._key = np.array([-1, -1, -1, -1, -1, -1,
+                              -1, -1, 0, 1, -1, -1,
+                              2, 3, 4, -1, -1, -1,
+                              5, -1, 6, 7, 8, 9,
+                              10, 11, 12, 13, 14, 15,
+                              -1, -1, 16, 17, 18])
+        self._mapping = np.array(range(-1, len(self._key) - 1)).astype('int32')
+        for value in values:
+            assert value in self._mapping
+        # Get the index of each pixel value in the mask corresponding to _mapping
+        index = np.digitize(mask.ravel(), self._mapping, right=True)
+        # According to the above index index, according to _key, the corresponding mask image is obtained
+        return self._key[index].reshape(mask.shape)
+
+
+if __name__ == '__main__':
+    sys.path.append(args_opt.project_path)
+    from src.metric import SegmentationMetric
+    from src.logger import SetupLogger
+    # Set config file
+    config_file = "/src/model_utils/icnet.yaml"
+    config_path = os.path.join(args_opt.project_path, config_file)
+    with open(config_path, "r") as yaml_file:
+        cfg = yaml.load(yaml_file.read())
+    logger = SetupLogger(name="semantic_segmentation",
+                         save_dir=cfg["train"]["ckpt_dir"],
+                         distributed_rank=0,
+                         filename='{}_{}_evaluate_log.txt'.format(cfg["model"]["name"], cfg["model"]["backbone"]))
+
+    evaluator = Evaluator(cfg)
+    evaluator.eval()

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

@@ -0,0 +1,106 @@
+#!/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 [ $# != 3 ]; then
+    echo "Usage: sh run_infer_310.sh [MODEL_PATH] [DATA_PATH] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+model=$(get_real_path $1)
+data_path=$(get_real_path $2)
+device_id=$3
+
+echo $model
+echo $data_path
+echo $device_id
+
+export ASCEND_HOME=/usr/local/Ascend/
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+    export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+    export LD_LIBRARY_PATH=/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=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+    export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function compile_app()
+{
+    cd ../ascend310_infer || exit
+    if [ -f "Makefile" ]; then
+        make clean
+    fi
+    sh build.sh &> build.log
+
+    if [ $? -ne 0 ]; then
+        echo "compile app code failed"
+        exit 1
+    fi
+    cd - || exit
+}
+
+function infer()
+{
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+     if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+    ../ascend310_infer/out/main --model_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
+
+    if [ $? -ne 0 ]; then
+        echo "execute inference failed"
+        exit 1
+    fi
+}
+
+function cal_acc()
+{
+    if [ -d output ]; then
+        rm -rf ./output
+    fi
+     if [ -d output_img ]; then
+        rm -rf ./output_img
+    fi
+    mkdir output
+    mkdir output_img
+    python ../postprocess.py --dataset_path=$data_path --result_path=result_Files &> acc.log
+    if [ $? -ne 0 ]; then
+        echo "calculate accuracy failed"
+        exit 1
+    fi
+
+}
+
+compile_app
+infer
+cal_acc

model_zoo/research/cv/ICNet/src/__init__.py

@@ -0,0 +1,2 @@
+""""init"""
+from .loss import ICNetLoss

model_zoo/research/cv/ICNet/src/cityscapes_mindrecord.py

@@ -50,7 +50,7 @@ def _get_city_pairs(folder, split='train'):
         img_folder = os.path.join(folder, 'leftImg8bit/' + split)
         # "./Cityscapes/gtFine/train" or "./Cityscapes/gtFine/val"
         mask_folder = os.path.join(folder, 'gtFine/' + split)
-
+        # The order of img_paths and mask_paths is one-to-one correspondence
         img_paths, mask_paths = get_path_pairs(img_folder, mask_folder)
         return img_paths, mask_paths
 

model_zoo/research/cv/ICNet/src/metric.py

@@ -13,9 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """Evaluation Metrics for Semantic Segmentation"""
-from mindspore import Tensor
+import numpy as np
-import mindspore.ops as ops
-import mindspore.common.dtype as dtype
 
 __all__ = ['SegmentationMetric', 'batch_pix_accuracy', 'batch_intersection_union']
 
@@ -41,11 +39,11 @@ class SegmentationMetric:
         correct, labeled = batch_pix_accuracy(pred, label)
         inter, union = batch_intersection_union(pred, label, self.nclass)
 
-        self.total_correct += correct
+        self.total_correct = correct + self.total_correct
-        self.total_label += labeled
+        self.total_label = labeled + self.total_label
 
-        self.total_inter += inter
+        self.total_inter = inter + self.total_inter
-        self.total_union += union
+        self.total_union = union + self.total_union
 
     def get(self):
         """Gets the current evaluation result.
@@ -55,19 +53,17 @@ class SegmentationMetric:
         metrics : tuple of float
             pixAcc and mIoU
         """
-        mean = ops.ReduceMean(keep_dims=False)
+        pixAcc = np.true_divide(self.total_correct, (2.220446049250313e-16 + self.total_label))  # remove c.spacing(1)
-        pixAcc = 1.0 * self.total_correct / (2.220446049250313e-16 + self.total_label)  # remove c.spacing(1)
+        IoU = np.true_divide(self.total_inter, (2.220446049250313e-16 + self.total_union))
-        IoU = 1.0 * self.total_inter / (2.220446049250313e-16 + self.total_union)
 
-        mIoU = mean(IoU, axis=0)
+        mIoU = np.mean(IoU)
 
-        return pixAcc, mIoU
+        return mIoU, pixAcc
 
     def reset(self):
         """Resets the internal evaluation result to initial state."""
-        zeros = ops.Zeros()
+        self.total_inter = np.zeros(self.nclass, dtype=np.float)
-        self.total_inter = zeros(self.nclass, dtype.float32)
+        self.total_union = np.zeros(self.nclass, dtype=np.float)
-        self.total_union = zeros(self.nclass, dtype.float32)
         self.total_correct = 0
         self.total_label = 0
 
@@ -75,19 +71,15 @@ class SegmentationMetric:
 def batch_pix_accuracy(output, target):
     """PixAcc"""
 
-    predict = ops.Argmax(output_type=dtype.int32, axis=1)(output) + 1
+    predict = np.argmax(output, axis=1) + 1
     # （1，19， 1024，2048）-->(1, 1024,2048)
     target = target + 1
 
-    typetrue = dtype.float32
+    labeled = np.array(target > 0).astype(int)
-    cast = ops.Cast()
+    pixel_labeled = np.sum(labeled)  # sum of pixels without 0
-    sumtarget = ops.ReduceSum()
-    sumcorrect = ops.ReduceSum()
 
-    labeled = cast(target > 0, typetrue)
+    pixel_correct = np.sum(np.array(predict == target).astype(int) * np.array(target > 0).astype(int))
-    pixel_labeled = sumtarget(labeled)  # sum of pixels without 0
+    # Quantity of correct pixels
-
-    pixel_correct = sumcorrect(cast(predict == target, typetrue) * cast(target > 0, typetrue))  # 标记正确的像素和
 
     assert pixel_correct <= pixel_labeled, "Correct area should be smaller than Labeled"
     return pixel_correct, pixel_labeled
@@ -96,26 +88,19 @@ def batch_pix_accuracy(output, target):
 def batch_intersection_union(output, target, nclass):
     """mIoU"""
     # inputs are numpy array, output 4D, target 3D
-    predict = ops.Argmax(output_type=dtype.int32, axis=1)(output) + 1  # [N,H,W]
+    predict = np.argmax(output, axis=1) + 1  # [N,H,W]
-    target = target.astype(dtype.float32) + 1  # [N,H,W]
+    target = target.astype(float) + 1  # [N,H,W]
 
-    typetrue = dtype.float32
+    predict = predict.astype(float) * np.array(target > 0).astype(float)
-    cast = ops.Cast()
+    intersection = predict * np.array(predict == target).astype(float)
-    predict = cast(predict, typetrue) * cast(target > 0, typetrue)
-    intersection = cast(predict, typetrue) * cast(predict == target, typetrue)
     # areas of intersection and union
     # element 0 in intersection occur the main difference from np.bincount. set boundary to -1 is necessary.
 
-    Range = Tensor([0.0, 20.0], dtype.float32)
+    area_inter, _ = np.array(np.histogram(intersection, bins=nclass, range=(1, nclass+1)))
-    hist = ops.HistogramFixedWidth(nclass + 1)
+    area_pred, _ = np.array(np.histogram(predict, bins=nclass, range=(1, nclass+1)))
-    area_inter = hist(intersection, Range)
+    area_lab, _ = np.array(np.histogram(target, bins=nclass, range=(1, nclass+1)))
-    area_pred = hist(predict, Range)
-    area_lab = hist(target, Range)
 
-    area_union = area_pred + area_lab - area_inter
+    area_all = area_pred + area_lab
+    area_union = area_all - area_inter
 
-    area_inter = area_inter[1:]
+    return area_inter, area_union
-    area_union = area_union[1:]
-    Sum = ops.ReduceSum()
-    assert Sum(cast(area_inter > area_union, typetrue)) == 0, "Intersection area should be smaller than Union area"
-    return cast(area_inter, typetrue), cast(area_union, typetrue)

model_zoo/research/cv/ICNet/src/model_utils/icnet.yaml

@@ -19,7 +19,7 @@ train:
   epochs: 160
   val_epoch: 1        # run validation every val-epoch
   ckpt_dir: "./ckpt/" # ckpt and training log will be saved here
-  mindrecord_dir: '/root/mindrecord'
+  mindrecord_dir: '/root/ICNet/mindrecord'
   save_checkpoint_epochs: 5
   keep_checkpoint_max: 10
 

model_zoo/research/cv/ICNet/src/models/__init__.py

@@ -1,3 +1,4 @@
 """__init__"""
 from .icnet import ICNet
 from .icnet_dc import ICNetdc
+from .icnet_1p import ICNet1p

model_zoo/research/cv/ICNet/src/models/icnet.py

@@ -67,7 +67,7 @@ class ICNet(nn.Cell):
 
         output = self.head(x_sub1, x_sub2, x_sub4)
 
-        return output
+        return output[0]
 
 
 class PyramidPoolingModule(nn.Cell):

model_zoo/research/cv/ICNet/src/visualize.py

@@ -22,7 +22,7 @@ from mindspore import Tensor
 from mindspore import load_param_into_net
 from mindspore import load_checkpoint
 import mindspore.dataset.vision.py_transforms as transforms
-from src.models.icnet import ICNet
+from models.icnet import ICNet
 
 __all__ = ['get_color_palette', 'set_img_color',
            'show_prediction', 'show_colorful_images', 'save_colorful_images']
@@ -115,28 +115,6 @@ def _getvocpalette(num_cls):
 
 vocpalette = _getvocpalette(256)
 
-cityspalette = [
-    128, 64, 128,
-    244, 35, 232,
-    70, 70, 70,
-    102, 102, 156,
-    190, 153, 153,
-    153, 153, 153,
-    250, 170, 30,
-    220, 220, 0,
-    107, 142, 35,
-    152, 251, 152,
-    0, 130, 180,
-    220, 20, 60,
-    255, 0, 0,
-    0, 0, 142,
-    0, 0, 70,
-    0, 60, 100,
-    0, 80, 100,
-    0, 0, 230,
-    119, 11, 32,
-]
-
 
 def _class_to_index(mask):
     """assert the value"""
@@ -150,9 +128,9 @@ def _class_to_index(mask):
     _mapping = np.array(range(-1, len(_key) - 1)).astype('int32')
     for value in values:
         assert value in _mapping
-
+    # Get the index of each pixel value in the mask corresponding to _mapping
     index = np.digitize(mask.ravel(), _mapping, right=True)
-
+    # According to the above index index, according to _key, the corresponding mask image is obtained
     return _key[index].reshape(mask.shape)
 
 
@@ -167,7 +145,7 @@ if __name__ == '__main__':
     ckpt_file_name = '/root/ICNet/ckpt/ICNet-160_93_699.ckpt'
     param_dict = load_checkpoint(ckpt_file_name)
     load_param_into_net(model, param_dict)
-    image_path = 'Test/val_lindau_000023_000019_leftImg8bit.png'
+    image_path = '../Test/val_lindau_000023_000019_leftImg8bit.png'
     image = Image.open(image_path).convert('RGB')
     image = _img_transform(image)
     image = Tensor(image)
@@ -181,4 +159,4 @@ if __name__ == '__main__':
     pred = pred.asnumpy()
     pred = pred.squeeze(0)
     pred = get_color_palette(pred, "citys")
-    pred.save('Test/visual_pred.png')
+    pred.save('Test/visual_pred_random.png')