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googlenet support imagenet dataset on Ascend

This commit is contained in:
caojian05 2020-09-09 15:11:14 +08:00 committed by CaoJian
parent fc8bd0dd03
commit 145545a9cb
11 changed files with 398 additions and 38 deletions
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46
model_zoo/official/cv/googlenet/eval.py
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@ -25,35 +25,57 @@ from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.common import set_seed
from src.config import cifar_cfg as cfg
from src.dataset import create_dataset
from src.config import cifar_cfg, imagenet_cfg
from src.dataset import create_dataset_cifar10, create_dataset_imagenet
from src.googlenet import GoogleNet
set_seed(1)
parser = argparse.ArgumentParser(description='googlenet')
parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['imagenet', 'cifar10'],
help='dataset name.')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
args_opt = parser.parse_args()
if __name__ == '__main__':
if args_opt.dataset_name == 'cifar10':
cfg = cifar_cfg
dataset = create_dataset_cifar10(cfg.data_path, 1, False)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
net = GoogleNet(num_classes=cfg.num_classes)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
weight_decay=cfg.weight_decay)
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
elif args_opt.dataset_name == "imagenet":
cfg = imagenet_cfg
dataset = create_dataset_imagenet(cfg.val_data_path, 1, False)
if not cfg.use_label_smooth:
cfg.label_smooth_factor = 0.0
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean",
smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
net = GoogleNet(num_classes=cfg.num_classes)
model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
else:
raise ValueError("dataset is not support.")
device_target = cfg.device_target
context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
if device_target == "Ascend":
context.set_context(device_id=cfg.device_id)
net = GoogleNet(num_classes=cfg.num_classes)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
weight_decay=cfg.weight_decay)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
if device_target == "Ascend":
param_dict = load_checkpoint(cfg.checkpoint_path)
else: # GPU
if args_opt.checkpoint_path is not None:
param_dict = load_checkpoint(args_opt.checkpoint_path)
print("load checkpoint from [{}].".format(args_opt.checkpoint_path))
else:
param_dict = load_checkpoint(cfg.checkpoint_path)
print("load checkpoint from [{}].".format(cfg.checkpoint_path))
load_param_into_net(net, param_dict)
net.set_train(False)
dataset = create_dataset(cfg.data_path, 1, False)
acc = model.eval(dataset)
print("accuracy: ", acc)

18
model_zoo/official/cv/googlenet/export.py
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@ -16,18 +16,32 @@
##############export checkpoint file into air and onnx models#################
python export.py
"""
import argparse
import numpy as np
import mindspore as ms
from mindspore import Tensor
from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
from src.config import cifar_cfg as cfg
from src.config import cifar_cfg, imagenet_cfg
from src.googlenet import GoogleNet
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Classification')
parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['imagenet', 'cifar10'],
help='dataset name.')
args_opt = parser.parse_args()
if args_opt.dataset_name == 'cifar10':
cfg = cifar_cfg
elif args_opt.dataset_name == 'imagenet':
cfg = imagenet_cfg
else:
raise ValueError("dataset is not support.")
net = GoogleNet(num_classes=cfg.num_classes)
assert cfg.checkpoint_path is not None, "cfg.checkpoint_path is None."
param_dict = load_checkpoint(cfg.checkpoint_path)
load_param_into_net(net, param_dict)

21
model_zoo/official/cv/googlenet/scripts/run_train.sh
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@ -14,9 +14,9 @@
# limitations under the License.
# ============================================================================
if [ $# != 1 ]
if [ $# != 1 ] && [ $# != 2 ]
then
echo "Usage: sh run_train.sh [RANK_TABLE_FILE]"
echo "Usage: sh run_train.sh [RANK_TABLE_FILE] [cifar10|imagenet]"
exit 1
fi
@ -26,6 +26,19 @@ then
exit 1
fi
dataset_type='cifar10'
if [ $# == 2 ]
then
if [ $2 != "cifar10" ] && [ $2 != "imagenet" ]
then
echo "error: the selected dataset is neither cifar10 nor imagenet"
exit 1
fi
dataset_type=$2
fi
ulimit -u unlimited
export DEVICE_NUM=8
export RANK_SIZE=8
@ -43,9 +56,9 @@ do
mkdir ./train_parallel$i
cp -r ./src ./train_parallel$i
cp ./train.py ./train_parallel$i
echo "start training for rank $RANK_ID, device $DEVICE_ID"
echo "start training for rank $RANK_ID, device $DEVICE_ID, $dataset_type"
cd ./train_parallel$i ||exit
env > env.log
python train.py --device_id=$i > log 2>&1 &
python train.py --device_id=$i --dataset_name=$dataset_type> log 2>&1 &
cd ..
done

39
model_zoo/official/cv/googlenet/src/config.py
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@ -18,6 +18,7 @@ network config setting, will be used in main.py
from easydict import EasyDict as edict
cifar_cfg = edict({
'name': 'cifar10',
'pre_trained': False,
'num_classes': 10,
'lr_init': 0.1,
@ -30,9 +31,45 @@ cifar_cfg = edict({
'image_width': 224,
'data_path': './cifar10',
'device_target': 'Ascend',
'device_id': 4,
'device_id': 0,
'keep_checkpoint_max': 10,
'checkpoint_path': './train_googlenet_cifar10-125_390.ckpt',
'onnx_filename': 'googlenet.onnx',
'air_filename': 'googlenet.air'
})
imagenet_cfg = edict({
'name': 'imagenet',
'pre_trained': False,
'num_classes': 1000,
'lr_init': 0.1,
'batch_size': 256,
'epoch_size': 300,
'momentum': 0.9,
'weight_decay': 1e-4,
'buffer_size': None, # invalid parameter
'image_height': 224,
'image_width': 224,
'data_path': './ImageNet_Original/train/',
'val_data_path': './ImageNet_Original/val/',
'device_target': 'Ascend',
'device_id': 0,
'keep_checkpoint_max': 10,
'checkpoint_path': None,
'onnx_filename': 'googlenet.onnx',
'air_filename': 'googlenet.air',
# optimizer and lr related
'lr_scheduler': 'exponential',
'lr_epochs': [70, 140, 210, 280],
'lr_gamma': 0.3,
'eta_min': 0.0,
'T_max': 150,
'warmup_epochs': 0,
# loss related
'is_dynamic_loss_scale': 0,
'loss_scale': 1024,
'label_smooth_factor': 0.1,
'use_label_smooth': True,
})

73
model_zoo/official/cv/googlenet/src/dataset.py
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@ -21,10 +21,10 @@ import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as vision
from src.config import cifar_cfg as cfg
from src.config import cifar_cfg, imagenet_cfg
def create_dataset(data_home, repeat_num=1, training=True):
def create_dataset_cifar10(data_home, repeat_num=1, training=True):
"""Data operations."""
ds.config.set_seed(1)
data_dir = os.path.join(data_home, "cifar-10-batches-bin")
@ -37,14 +37,14 @@ def create_dataset(data_home, repeat_num=1, training=True):
else:
data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id, shuffle=False)
resize_height = cfg.image_height
resize_width = cfg.image_width
resize_height = cifar_cfg.image_height
resize_width = cifar_cfg.image_width
# define map operations
random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
random_horizontal_op = vision.RandomHorizontalFlip()
resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR
rescale_op = vision.Rescale(1.0/255.0, 0.0)
rescale_op = vision.Rescale(1.0 / 255.0, 0.0)
normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
changeswap_op = vision.HWC2CHW()
type_cast_op = C.TypeCast(mstype.int32)
@ -59,7 +59,7 @@ def create_dataset(data_home, repeat_num=1, training=True):
data_set = data_set.map(input_columns="image", operations=c_trans)
# apply batch operations
data_set = data_set.batch(batch_size=cfg.batch_size, drop_remainder=True)
data_set = data_set.batch(batch_size=cifar_cfg.batch_size, drop_remainder=True)
# apply repeat operations
data_set = data_set.repeat(repeat_num)
@ -67,6 +67,67 @@ def create_dataset(data_home, repeat_num=1, training=True):
return data_set
def create_dataset_imagenet(dataset_path, repeat_num=1, training=True,
num_parallel_workers=None, shuffle=None):
"""
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
Returns:
dataset
"""
device_num, rank_id = _get_rank_info()
if device_num == 1:
data_set = ds.ImageFolderDatasetV2(dataset_path, num_parallel_workers=num_parallel_workers, shuffle=shuffle)
else:
data_set = ds.ImageFolderDatasetV2(dataset_path, num_parallel_workers=num_parallel_workers, shuffle=shuffle,
num_shards=device_num, shard_id=rank_id)
assert imagenet_cfg.image_height == imagenet_cfg.image_width, "image_height not equal image_width"
image_size = imagenet_cfg.image_height
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
# define map operations
if training:
transform_img = [
vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
vision.RandomHorizontalFlip(prob=0.5),
vision.RandomColorAdjust(0.4, 0.4, 0.4, 0.1),
vision.Normalize(mean=mean, std=std),
vision.HWC2CHW()
]
else:
transform_img = [
vision.Decode(),
vision.Resize(256),
vision.CenterCrop(image_size),
vision.Normalize(mean=mean, std=std),
vision.HWC2CHW()
]
transform_label = [C.TypeCast(mstype.int32)]
data_set = data_set.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
data_set = data_set.map(input_columns="label", num_parallel_workers=8, operations=transform_label)
# apply batch operations
data_set = data_set.batch(imagenet_cfg.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

1
model_zoo/official/cv/googlenet/src/googlenet.py
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@ -112,6 +112,7 @@ class GoogleNet(nn.Cell):
def construct(self, x):
"""construct"""
x = self.conv1(x)
x = self.maxpool1(x)

0
model_zoo/official/cv/googlenet/src/lr_scheduler/__init__.py Normal file
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20
model_zoo/official/cv/googlenet/src/lr_scheduler/linear_warmup.py Normal file
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@ -0,0 +1,20 @@
# Copyright 2020 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.
# ============================================================================
"""lr"""
def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
lr = float(init_lr) + lr_inc * current_step
return lr

39
model_zoo/official/cv/googlenet/src/lr_scheduler/warmup_cosine_annealing_lr.py Normal file
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@ -0,0 +1,39 @@
# Copyright 2020 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.
# ============================================================================
"""lr"""
import math
import numpy as np
from .linear_warmup import linear_warmup_lr
def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
""" warmup cosine annealing lr"""
base_lr = lr
warmup_init_lr = 0
total_steps = int(max_epoch * steps_per_epoch)
warmup_steps = int(warmup_epochs * steps_per_epoch)
lr_each_step = []
for i in range(total_steps):
last_epoch = i // steps_per_epoch
if i < warmup_steps:
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
else:
lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi * last_epoch / T_max)) / 2
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)

59
model_zoo/official/cv/googlenet/src/lr_scheduler/warmup_step_lr.py Normal file
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@ -0,0 +1,59 @@
# Copyright 2020 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.
# ============================================================================
"""lr"""
from collections import Counter
import numpy as np
from .linear_warmup import linear_warmup_lr
def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
"""warmup step lr"""
base_lr = lr
warmup_init_lr = 0
total_steps = int(max_epoch * steps_per_epoch)
warmup_steps = int(warmup_epochs * steps_per_epoch)
milestones = lr_epochs
milestones_steps = []
for milestone in milestones:
milestones_step = milestone * steps_per_epoch
milestones_steps.append(milestones_step)
lr_each_step = []
lr = base_lr
milestones_steps_counter = Counter(milestones_steps)
for i in range(total_steps):
if i < warmup_steps:
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
else:
lr = lr * gamma ** milestones_steps_counter[i]
lr_each_step.append(lr)
return np.array(lr_each_step).astype(np.float32)
def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
"""lr"""
return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)
def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
"""lr"""
lr_epochs = []
for i in range(1, max_epoch):
if i % epoch_size == 0:
lr_epochs.append(i)
return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)

116
model_zoo/official/cv/googlenet/train.py
View File

@ -27,18 +27,19 @@ from mindspore import context
from mindspore.communication.management import init, get_rank
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager
from mindspore.train.model import Model
from mindspore.context import ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.common import set_seed
from src.config import cifar_cfg as cfg
from src.dataset import create_dataset
from src.config import cifar_cfg, imagenet_cfg
from src.dataset import create_dataset_cifar10, create_dataset_imagenet
from src.googlenet import GoogleNet
set_seed(1)
def lr_steps(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None):
def lr_steps_cifar10(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None):
"""Set learning rate."""
lr_each_step = []
total_steps = steps_per_epoch * total_epochs
@ -59,11 +60,46 @@ def lr_steps(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None):
return learning_rate
def lr_steps_imagenet(_cfg, steps_per_epoch):
"""lr step for imagenet"""
from src.lr_scheduler.warmup_step_lr import warmup_step_lr
from src.lr_scheduler.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
if _cfg.lr_scheduler == 'exponential':
_lr = warmup_step_lr(_cfg.lr_init,
_cfg.lr_epochs,
steps_per_epoch,
_cfg.warmup_epochs,
_cfg.epoch_size,
gamma=_cfg.lr_gamma,
)
elif _cfg.lr_scheduler == 'cosine_annealing':
_lr = warmup_cosine_annealing_lr(_cfg.lr_init,
steps_per_epoch,
_cfg.warmup_epochs,
_cfg.epoch_size,
_cfg.T_max,
_cfg.eta_min)
else:
raise NotImplementedError(_cfg.lr_scheduler)
return _lr
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Cifar10 classification')
parser = argparse.ArgumentParser(description='Classification')
parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['imagenet', 'cifar10'],
help='dataset name.')
parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
args_opt = parser.parse_args()
if args_opt.dataset_name == "cifar10":
cfg = cifar_cfg
elif args_opt.dataset_name == "imagenet":
cfg = imagenet_cfg
else:
raise ValueError("Unsupport dataset.")
# set context
device_target = cfg.device_target
context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
@ -90,7 +126,13 @@ if __name__ == '__main__':
else:
raise ValueError("Unsupported platform.")
dataset = create_dataset(cfg.data_path, 1)
if args_opt.dataset_name == "cifar10":
dataset = create_dataset_cifar10(cfg.data_path, 1)
elif args_opt.dataset_name == "imagenet":
dataset = create_dataset_imagenet(cfg.data_path, 1)
else:
raise ValueError("Unsupport dataset.")
batch_num = dataset.get_dataset_size()
net = GoogleNet(num_classes=cfg.num_classes)
@ -98,23 +140,75 @@ if __name__ == '__main__':
if cfg.pre_trained:
param_dict = load_checkpoint(cfg.checkpoint_path)
load_param_into_net(net, param_dict)
lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=batch_num)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum,
loss_scale_manager = None
if args_opt.dataset_name == 'cifar10':
lr = lr_steps_cifar10(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=batch_num)
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
learning_rate=Tensor(lr),
momentum=cfg.momentum,
weight_decay=cfg.weight_decay)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
elif args_opt.dataset_name == 'imagenet':
lr = lr_steps_imagenet(cfg, batch_num)
def get_param_groups(network):
""" get param groups """
decay_params = []
no_decay_params = []
for x in network.trainable_params():
parameter_name = x.name
if parameter_name.endswith('.bias'):
# all bias not using weight decay
# print('no decay:{}'.format(parameter_name))
no_decay_params.append(x)
elif parameter_name.endswith('.gamma'):
# bn weight bias not using weight decay, be carefully for now x not include BN
# print('no decay:{}'.format(parameter_name))
no_decay_params.append(x)
elif parameter_name.endswith('.beta'):
# bn weight bias not using weight decay, be carefully for now x not include BN
# print('no decay:{}'.format(parameter_name))
no_decay_params.append(x)
else:
decay_params.append(x)
return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]
if cfg.is_dynamic_loss_scale:
cfg.loss_scale = 1
opt = Momentum(params=get_param_groups(net),
learning_rate=Tensor(lr),
momentum=cfg.momentum,
weight_decay=cfg.weight_decay,
loss_scale=cfg.loss_scale)
if not cfg.use_label_smooth:
cfg.label_smooth_factor = 0.0
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean",
smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
if cfg.is_dynamic_loss_scale == 1:
loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536, scale_factor=2, scale_window=2000)
else:
loss_scale_manager = FixedLossScaleManager(cfg.loss_scale, drop_overflow_update=False)
if device_target == "Ascend":
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=loss_scale_manager)
ckpt_save_dir = "./"
else: # GPU
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=True, loss_scale_manager=None)
amp_level="O2", keep_batchnorm_fp32=True, loss_scale_manager=loss_scale_manager)
ckpt_save_dir = "./ckpt_" + str(get_rank()) + "/"
config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max)
time_cb = TimeMonitor(data_size=batch_num)
ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_cifar10", directory=ckpt_save_dir, config=config_ck)
ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_" + args_opt.dataset_name, directory=ckpt_save_dir,
config=config_ck)
loss_cb = LossMonitor()
model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
print("train success")