forked from OSSInnovation/mindspore
!2553 add mindrecord to mobilenetv2_quant && resnet50_quant
Merge pull request !2553 from wandongdong/r0.3
This commit is contained in:
Gitee
commit
8c30045178
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@ -22,7 +22,7 @@ from mindspore import nn
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.mobilenetV2_quant import mobilenet_v2_quant
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from src.dataset import create_dataset_py
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from src.dataset import create_dataset
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from src.config import config_ascend
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parser = argparse.ArgumentParser(description='Image classification')
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@ -46,11 +46,11 @@ if __name__ == '__main__':
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loss = nn.SoftmaxCrossEntropyWithLogits(
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is_grad=False, sparse=True, reduction='mean')
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dataset = create_dataset_py(dataset_path=args_opt.dataset_path,
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do_train=False,
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config=config_platform,
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platform=args_opt.platform,
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batch_size=config_platform.batch_size)
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=False,
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config=config_platform,
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platform=args_opt.platform,
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batch_size=config_platform.batch_size)
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step_size = dataset.get_dataset_size()
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if args_opt.checkpoint_path:
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@ -22,6 +22,7 @@ config_ascend = ed({
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"image_height": 224,
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"image_width": 224,
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"batch_size": 192,
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"data_load_mode": "mindrecord",
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"epoch_size": 60,
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"start_epoch": 200,
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"warmup_epochs": 1,
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@ -16,11 +16,14 @@
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create train or eval dataset.
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"""
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import os
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from functools import partial
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import mindspore.common.dtype as mstype
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import mindspore.dataset.engine as de
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import mindspore.dataset.transforms.vision.c_transforms as C
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import mindspore.dataset.transforms.c_transforms as C2
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import mindspore.dataset.transforms.vision.py_transforms as P
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from src.config import config_ascend
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def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch_size=32):
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"""
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@ -38,14 +41,19 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
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if platform == "Ascend":
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rank_size = int(os.getenv("RANK_SIZE"))
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rank_id = int(os.getenv("RANK_ID"))
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columns_list = ['image', 'label']
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if config_ascend.data_load_mode == "mindrecord":
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load_func = partial(de.MindDataset, dataset_path, columns_list)
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else:
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load_func = partial(de.ImageFolderDatasetV2, dataset_path)
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if do_train:
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if rank_size == 1:
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
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ds = load_func(num_parallel_workers=8, shuffle=True)
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else:
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
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num_shards=rank_size, shard_id=rank_id)
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ds = load_func(num_parallel_workers=8, shuffle=True,
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num_shards=rank_size, shard_id=rank_id)
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else:
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=False)
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ds = load_func(num_parallel_workers=8, shuffle=False)
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else:
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raise ValueError("Unsupport platform.")
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@ -63,7 +71,8 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
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resize_op = C.Resize(256)
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center_crop = C.CenterCrop(resize_height)
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normalize_op = C.Normalize(mean=[0.485*255, 0.456*255, 0.406*255], std=[0.229*255, 0.224*255, 0.225*255])
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normalize_op = C.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
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std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
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change_swap_op = C.HWC2CHW()
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if do_train:
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@ -84,6 +93,7 @@ def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch
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return ds
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def create_dataset_py(dataset_path, do_train, config, platform, repeat_num=1, batch_size=32):
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"""
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create a train or eval dataset
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@ -32,7 +32,7 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
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from mindspore.train.serialization import load_checkpoint
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from mindspore.communication.management import init
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import mindspore.dataset.engine as de
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from src.dataset import create_dataset_py
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from src.dataset import create_dataset
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from src.lr_generator import get_lr
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from src.config import config_ascend
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from src.mobilenetV2_quant import mobilenet_v2_quant
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@ -197,12 +197,12 @@ if __name__ == '__main__':
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else:
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loss = SoftmaxCrossEntropyWithLogits(
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is_grad=False, sparse=True, reduction='mean')
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dataset = create_dataset_py(dataset_path=args_opt.dataset_path,
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do_train=True,
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config=config_ascend,
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platform=args_opt.platform,
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repeat_num=epoch_size,
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batch_size=config_ascend.batch_size)
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=True,
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config=config_ascend,
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platform=args_opt.platform,
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repeat_num=epoch_size,
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batch_size=config_ascend.batch_size)
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step_size = dataset.get_dataset_size()
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if args_opt.pre_trained:
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param_dict = load_checkpoint(args_opt.pre_trained)
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@ -17,7 +17,7 @@ eval.
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"""
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import os
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import argparse
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from src.dataset import create_dataset_py
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from src.dataset import create_dataset
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from src.config import config
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from src.crossentropy import CrossEntropy
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from src.utils import _load_param_into_net
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@ -49,8 +49,8 @@ if __name__ == '__main__':
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loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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if args_opt.do_eval:
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dataset = create_dataset_py(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
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target=target)
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dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
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target=target)
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step_size = dataset.get_dataset_size()
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if args_opt.checkpoint_path:
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@ -20,7 +20,7 @@ from mindspore import Tensor
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from mindspore.nn import FakeQuantWithMinMax, Conv2dBatchNormQuant
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_ema_decay = 0.999
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_symmetric = False
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_symmetric = True
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_fake = True
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_per_channel = True
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@ -28,6 +28,7 @@ config = ed({
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"buffer_size": 1000,
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"image_height": 224,
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"image_width": 224,
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"data_load_mode": "mindrecord",
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"save_checkpoint": True,
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"save_checkpoint_epochs": 1,
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"keep_checkpoint_max": 50,
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@ -16,12 +16,15 @@
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create train or eval dataset.
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"""
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import os
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from functools import partial
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import mindspore.common.dtype as mstype
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import mindspore.dataset.engine as de
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import mindspore.dataset.transforms.vision.c_transforms as C
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import mindspore.dataset.transforms.c_transforms as C2
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import mindspore.dataset.transforms.vision.py_transforms as P
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from mindspore.communication.management import init, get_rank, get_group_size
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from src.config import config
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def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
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"""
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@ -45,11 +48,16 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="
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rank_id = get_rank()
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device_num = get_group_size()
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if device_num == 1:
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
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columns_list = ['image', 'label']
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if config.data_load_mode == "mindrecord":
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load_func = partial(de.MindDataset, dataset_path, columns_list)
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else:
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
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num_shards=device_num, shard_id=rank_id)
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load_func = partial(de.ImageFolderDatasetV2, dataset_path)
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if device_num == 1:
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ds = load_func(num_parallel_workers=8, shuffle=True)
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else:
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ds = load_func(num_parallel_workers=8, shuffle=True,
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num_shards=device_num, shard_id=rank_id)
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image_size = 224
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mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
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@ -66,7 +74,7 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="
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else:
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trans = [
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C.Decode(),
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C.Resize((256, 256)),
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C.Resize(256),
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C.CenterCrop(image_size),
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C.Normalize(mean=mean, std=std),
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C.HWC2CHW()
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@ -85,6 +93,7 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="
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return ds
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def create_dataset_py(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
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"""
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create a train or eval dataset
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@ -27,7 +27,7 @@ from mindspore.communication.management import init
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import mindspore.nn as nn
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import mindspore.common.initializer as weight_init
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from models.resnet_quant import resnet50_quant
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from src.dataset import create_dataset_py
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from src.dataset import create_dataset
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from src.lr_generator import get_lr
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from src.config import config
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from src.crossentropy import CrossEntropy
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@ -62,7 +62,6 @@ if __name__ == '__main__':
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epoch_size = config.epoch_size
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net = resnet50_quant(class_num=config.class_num)
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net.set_train(True)
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print("========resnet50:\r\n{}".format(net))
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# weight init
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if args_opt.pre_trained:
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@ -85,8 +84,8 @@ if __name__ == '__main__':
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loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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if args_opt.do_train:
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dataset = create_dataset_py(dataset_path=args_opt.dataset_path, do_train=True,
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repeat_num=epoch_size, batch_size=config.batch_size, target=target)
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dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
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repeat_num=epoch_size, batch_size=config.batch_size, target=target)
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step_size = dataset.get_dataset_size()
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loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
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@ -94,6 +93,7 @@ if __name__ == '__main__':
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total_epochs=config.epoch_size, steps_per_epoch=step_size, lr_decay_mode='cosine')
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if args_opt.pre_trained:
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lr = lr[config.pretrained_epoch_size * step_size:]
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print("========resnet50:\r\n{}\r\nlr: \r\n{}".format(net, lr))
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lr = Tensor(lr)
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
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@ -104,7 +104,7 @@ if __name__ == '__main__':
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loss_cb = LossMonitor()
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cb = [time_cb, loss_cb]
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if config.save_checkpoint:
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config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs*step_size,
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config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck)
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cb += [ckpt_cb]
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