!15807 add resnet infer

From: @jiangzg001
Reviewed-by: @oacjiewen,@wuxuejian,@c_34
Signed-off-by: @c_34

model_zoo/official/cv/resnet/infer.py

@@ -0,0 +1,111 @@
+# 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.
+# ============================================================================
+"""train resnet."""
+import os
+import argparse
+import numpy as np
+from mindspore import Tensor
+from mindspore import context
+from mindspore.common import set_seed
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+parser = argparse.ArgumentParser(description='Image classification')
+parser.add_argument('--net', type=str, default=None, help='Resnet Model, either resnet18, '
+                                                          'resnet50 or resnet101')
+parser.add_argument('--dataset', type=str, default=None, help='Dataset, imagenet2012')
+
+parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
+parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+parser.add_argument('--device_target', type=str, default='Ascend', choices=("Ascend", "GPU", "CPU"),
+                    help="Device target, support Ascend, GPU and CPU.")
+args_opt = parser.parse_args()
+
+set_seed(1)
+
+if args_opt.dataset != "imagenet2012":
+    raise ValueError("Currently only support imagenet2012 dataset format")
+if args_opt.net in ("resnet18", "resnet50"):
+    if args_opt.net == "resnet18":
+        from src.resnet import resnet18 as resnet
+    if args_opt.net == "resnet50":
+        from src.resnet import resnet50 as resnet
+    from src.config import config2 as config
+    from src.dataset_infer import create_dataset
+
+elif args_opt.net == "resnet101":
+    from src.resnet import resnet101 as resnet
+    from src.config import config3 as config
+    from src.dataset_infer import create_dataset2 as create_dataset
+else:
+    from src.resnet import se_resnet50 as resnet
+    from src.config import config4 as config
+    from src.dataset_infer import create_dataset3 as create_dataset
+
+
+def show_predict_info(label_list, prediction_list, filename_list, predict_ng):
+    label_index = 0
+    for label_index, predict_index, filename in zip(label_list, prediction_list, filename_list):
+        filename = np.array(filename).tostring().decode('utf8')
+        if label_index == -1:
+            print("file: '{}' predict class id is: {}".format(filename, predict_index))
+            continue
+        if predict_index != label_index:
+            predict_ng.append((filename, predict_index, label_index))
+            print("file: '{}' predict wrong, predict class id is: {}, "
+                  "label is {}".format(filename, predict_index, label_index))
+    return predict_ng, label_index
+
+
+if __name__ == '__main__':
+    target = args_opt.device_target
+
+    # init context
+    context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
+    if target == "Ascend":
+        device_id = int(os.getenv('DEVICE_ID'))
+        context.set_context(device_id=device_id)
+
+    # create dataset
+    dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
+                             target=target)
+    step_size = dataset.get_dataset_size()
+
+    # define net
+    net = resnet(class_num=config.class_num)
+
+    # load checkpoint
+    param_dict = load_checkpoint(args_opt.checkpoint_path)
+    load_param_into_net(net, param_dict)
+    net.set_train(False)
+
+    print("start infer")
+    predict_negative = []
+    total_sample = step_size * config.batch_size
+    only_file = 0
+    data_loader = dataset.create_dict_iterator(output_numpy=True, num_epochs=1)
+    for i, data in enumerate(data_loader):
+        images = data["image"]
+        label = data["label"]
+        file_name = data["filename"]
+        res = net(Tensor(images))
+        res = res.asnumpy()
+        predict_id = np.argmax(res, axis=1)
+        predict_negative, only_file = show_predict_info(label.tolist(), predict_id.tolist(),
+                                                        file_name.tolist(), predict_negative)
+
+    if only_file != -1:
+        print(f"total {total_sample} data, top1 acc is {(total_sample - len(predict_negative)) * 1.0 / total_sample}")
+    else:
+        print("infer completed")

model_zoo/official/cv/resnet/scripts/run_infer.sh

@@ -0,0 +1,78 @@
+#!/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 [ $# != 4 ]
+then
+    echo "Usage: bash run_eval.sh [resnet18|resnet50|resnet101|se-resnet50] [imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]"
+exit 1
+fi
+
+if [ $1 != "resnet18" ] && [ $1 != "resnet50" ] && [ $1 != "resnet101" ] && [ $1 != "se-resnet50" ]
+then
+    echo "error: the selected net is neither resnet50 nor resnet101 nor se-resnet50"
+exit 1
+fi
+
+if [ $2 != "imagenet2012" ]
+then
+    echo "error: only support imagenet2012"
+exit 1
+fi
+
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $3)
+PATH2=$(get_real_path $4)
+
+
+if [ ! -d $PATH1 ]
+then
+    echo "error: DATASET_PATH=$PATH1 is not a directory"
+exit 1
+fi
+
+if [ ! -f $PATH2 ]
+then
+    echo "error: CHECKPOINT_PATH=$PATH2 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_NUM=1
+export DEVICE_ID=0
+export RANK_SIZE=$DEVICE_NUM
+export RANK_ID=0
+
+if [ -d "infer" ];
+then
+    rm -rf ./infer
+fi
+mkdir ./infer
+cp ../*.py ./infer
+cp *.sh ./infer
+cp -r ../src ./infer
+cd ./infer || exit
+env > env.log
+echo "start evaluation for device $DEVICE_ID"
+python infer.py --net=$1 --dataset=$2 --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
+cd ..

model_zoo/official/cv/resnet/src/config.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -17,8 +17,10 @@ network config setting, will be used in train.py and eval.py
 """
 from easydict import EasyDict as ed
 # config optimizer for resnet50, imagenet2012. Momentum is default, Thor is optional.
+# infer_label is a directory and label mapping table. such as 'infer_label': {"directory0": 0, "directory1": 1, ...}
 cfg = ed({
     'optimizer': 'Momentum',
+    'infer_label': {}
     })
 
 # config for resent50, cifar10

model_zoo/official/cv/resnet/src/dataset_infer.py

@@ -0,0 +1,319 @@
+# 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.
+# ============================================================================
+"""
+create train or eval dataset.
+"""
+import os
+import numpy as np
+import mindspore.common.dtype as mstype
+import mindspore.dataset as ds
+import mindspore.dataset.vision.c_transforms as C
+import mindspore.dataset.transforms.c_transforms as C2
+from mindspore.communication.management import init, get_rank, get_group_size
+from src.config import cfg
+
+
+class ImgDataset:
+    """
+    create img dataset.
+
+    Args:
+    Returns:
+        de_dataset.
+    """
+
+    def __init__(self, dataset_path):
+        super(ImgDataset, self).__init__()
+        self.data = []
+        self.dir_label_dict = {}
+        self.img_format = (".bmp", ".png", ".jpg", ".jpeg")
+        self.dir_label = cfg.infer_label
+        dataset_list = sorted(os.listdir(dataset_path))
+        file_exist = dir_exist = False
+        for index, data_name in enumerate(dataset_list):
+            data_path = os.path.join(dataset_path, data_name)
+            if os.path.isdir(data_path):
+                dir_exist = True
+                self.dir_label_dict = self.get_file_label(data_name, data_path, index)
+            if os.path.isfile(data_path):
+                file_exist = True
+                self.dir_label_dict = self.get_file_label(data_name, data_path, index=-1)
+            if dir_exist and file_exist:
+                raise ValueError(f"{dataset_path} can not concurrently have image file and directory")
+
+        for data_name, img_label in self.dir_label_dict.items():
+            if os.path.isfile(data_name):
+                if not data_name.lower().endswith(self.img_format):
+                    continue
+                img_data, file_name = self.read_image_data(data_name)
+                self.data.append((img_label, img_data, file_name))
+            else:
+                for file in os.listdir(data_name):
+                    if not file.lower().endswith(self.img_format):
+                        continue
+                    file_path = os.path.join(data_name, file)
+                    img_data, file_name = self.read_image_data(file_path)
+                    self.data.append((img_label, img_data, file_name))
+
+    def get_file_label(self, data_name, data_path, index):
+        if self.dir_label and data_name not in self.dir_label:
+            return self.dir_label_dict
+        if self.dir_label and os.path.isdir(data_name):
+            data_path_name = os.path.split(data_path)[-1]
+            self.dir_label_dict[data_path] = self.dir_label[data_path_name]
+        else:
+            self.dir_label_dict[data_path] = index
+        return self.dir_label_dict
+
+    def read_image_data(self, file_path):
+        file_name = os.path.split(file_path)[-1]
+        img_data = np.fromfile(file_path, np.uint8)
+        file_name = np.fromstring(file_name, np.uint8)
+        file_name = np.pad(file_name, (0, 300 - file_name.shape[0]))
+        return img_data, file_name
+
+    def __getitem__(self, index):
+        return self.data[index]
+
+    def __len__(self):
+        return len(self.data)
+
+
+def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
+    """
+    create a train or eval imagenet2012 dataset for resnet50
+
+    Args:
+        dataset_path(string): the path of dataset.
+        do_train(bool): whether dataset is used for train or eval.
+        repeat_num(int): the repeat times of dataset. Default: 1
+        batch_size(int): the batch size of dataset. Default: 32
+        target(str): the device target. Default: Ascend
+        distribute(bool): data for distribute or not. Default: False
+
+    Returns:
+        dataset
+    """
+    if target == "Ascend":
+        device_num, rank_id = _get_rank_info()
+    else:
+        if distribute:
+            init()
+            rank_id = get_rank()
+            device_num = get_group_size()
+        else:
+            device_num = 1
+
+    dataset_generator = ImgDataset(dataset_path)
+    if device_num == 1:
+        data_set = ds.GeneratorDataset(source=dataset_generator, column_names=["label", "image", "filename"],
+                                       num_parallel_workers=8, shuffle=True)
+    else:
+        data_set = ds.GeneratorDataset(source=dataset_generator, column_names=["label", "image", "filename"],
+                                       num_parallel_workers=8, shuffle=True,
+                                       num_shards=device_num, shard_id=rank_id)
+
+    image_size = 224
+    mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
+    std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
+
+    # define map operations
+    if do_train:
+        trans = [
+            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomHorizontalFlip(prob=0.5),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+    else:
+        trans = [
+            C.Decode(),
+            C.Resize(256),
+            C.CenterCrop(image_size),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+
+    data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
+    data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
+
+    if do_train:
+        data_set = data_set.project(["image", "label"])
+    else:
+        data_set = data_set.project(["image", "label", "filename"])
+
+    # apply batch operations
+    data_set = data_set.batch(batch_size, drop_remainder=True)
+
+    # apply dataset repeat operation
+    data_set = data_set.repeat(repeat_num)
+
+    return data_set
+
+
+def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
+    """
+    create a train or eval imagenet2012 dataset for resnet101
+    Args:
+        dataset_path(string): the path of dataset.
+        do_train(bool): whether dataset is used for train or eval.
+        repeat_num(int): the repeat times of dataset. Default: 1
+        batch_size(int): the batch size of dataset. Default: 32
+        target(str): the device target. Default: Ascend
+        distribute(bool): data for distribute or not. Default: False
+
+    Returns:
+        dataset
+    """
+    if target == "Ascend":
+        device_num, rank_id = _get_rank_info()
+    else:
+        if distribute:
+            init()
+            rank_id = get_rank()
+            device_num = get_group_size()
+        else:
+            device_num = 1
+            rank_id = 1
+    dataset_generator = ImgDataset(dataset_path)
+    if device_num == 1:
+        data_set = ds.GeneratorDataset(source=dataset_generator, column_names=["label", "image", "filename"],
+                                       num_parallel_workers=8, shuffle=True)
+    else:
+        data_set = ds.GeneratorDataset(source=dataset_generator, column_names=["label", "image", "filename"],
+                                       num_parallel_workers=8, shuffle=True,
+                                       num_shards=device_num, shard_id=rank_id)
+    image_size = 224
+    mean = [0.475 * 255, 0.451 * 255, 0.392 * 255]
+    std = [0.275 * 255, 0.267 * 255, 0.278 * 255]
+
+    # define map operations
+    if do_train:
+        trans = [
+            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomHorizontalFlip(rank_id / (rank_id + 1)),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+    else:
+        trans = [
+            C.Decode(),
+            C.Resize(256),
+            C.CenterCrop(image_size),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+
+    data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
+    data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
+    if do_train:
+        data_set = data_set.project(["image", "label"])
+    else:
+        data_set = data_set.project(["image", "label", "filename"])
+    # apply batch operations
+    data_set = data_set.batch(batch_size, drop_remainder=True)
+    # apply dataset repeat operation
+    data_set = data_set.repeat(repeat_num)
+
+    return data_set
+
+
+def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False):
+    """
+    create a train or eval imagenet2012 dataset for se-resnet50
+
+    Args:
+        dataset_path(string): the path of dataset.
+        do_train(bool): whether dataset is used for train or eval.
+        repeat_num(int): the repeat times of dataset. Default: 1
+        batch_size(int): the batch size of dataset. Default: 32
+        target(str): the device target. Default: Ascend
+        distribute(bool): data for distribute or not. Default: False
+
+    Returns:
+        dataset
+    """
+    if target == "Ascend":
+        device_num, rank_id = _get_rank_info()
+    else:
+        if distribute:
+            init()
+            rank_id = get_rank()
+            device_num = get_group_size()
+        else:
+            device_num = 1
+    dataset_generator = ImgDataset(dataset_path)
+    if device_num == 1:
+        data_set = ds.GeneratorDataset(source=dataset_generator, column_names=["label", "image", "filename"],
+                                       num_parallel_workers=8, shuffle=True)
+    else:
+        data_set = ds.GeneratorDataset(source=dataset_generator, column_names=["label", "image", "filename"],
+                                       num_parallel_workers=8, shuffle=True,
+                                       num_shards=device_num, shard_id=rank_id)
+    image_size = 224
+    mean = [123.68, 116.78, 103.94]
+    std = [1.0, 1.0, 1.0]
+
+    # define map operations
+    if do_train:
+        trans = [
+            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomHorizontalFlip(prob=0.5),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+    else:
+        trans = [
+            C.Decode(),
+            C.Resize(292),
+            C.CenterCrop(256),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+    data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=12)
+    data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=12)
+    if do_train:
+        data_set = data_set.project(["image", "label"])
+    else:
+        data_set = data_set.project(["image", "label", "filename"])
+    # apply batch operations
+    data_set = data_set.batch(batch_size, drop_remainder=True)
+
+    # apply dataset repeat operation
+    data_set = data_set.repeat(repeat_num)
+
+    return data_set
+
+
+def _get_rank_info():
+    """
+    get rank size and rank id
+    """
+    rank_size = int(os.environ.get("RANK_SIZE", 1))
+
+    if rank_size > 1:
+        rank_size = get_group_size()
+        rank_id = get_rank()
+    else:
+        rank_size = 1
+        rank_id = 0
+
+    return rank_size, rank_id