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- # Copyright 2021-2022 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 as ms
- import mindspore.dataset as ds
- from mindspore.communication.management import init, get_rank, get_group_size
- from src.model_utils.config import config
-
-
- 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 = config.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 = [
- ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.RandomHorizontalFlip(prob=0.5),
- ds.vision.Normalize(mean=mean, std=std),
- ds.vision.HWC2CHW()
- ]
- else:
- trans = [
- ds.vision.Decode(),
- ds.vision.Resize(256),
- ds.vision.CenterCrop(image_size),
- ds.vision.Normalize(mean=mean, std=std),
- ds.vision.HWC2CHW()
- ]
-
- type_cast_op = ds.transforms.transforms.TypeCast(ms.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 = [
- ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.RandomHorizontalFlip(rank_id / (rank_id + 1)),
- ds.vision.Normalize(mean=mean, std=std),
- ds.vision.HWC2CHW()
- ]
- else:
- trans = [
- ds.vision.Decode(),
- ds.vision.Resize(256),
- ds.vision.CenterCrop(image_size),
- ds.vision.Normalize(mean=mean, std=std),
- ds.vision.HWC2CHW()
- ]
-
- type_cast_op = ds.transforms.transforms.TypeCast(ms.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 = [
- ds.vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- ds.vision.RandomHorizontalFlip(prob=0.5),
- ds.vision.Normalize(mean=mean, std=std),
- ds.vision.HWC2CHW()
- ]
- else:
- trans = [
- ds.vision.Decode(),
- ds.vision.Resize(292),
- ds.vision.CenterCrop(256),
- ds.vision.Normalize(mean=mean, std=std),
- ds.vision.HWC2CHW()
- ]
-
- type_cast_op = ds.transforms.transforms.TypeCast(ms.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
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