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train_ofa_net.py
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train_ofa_net.py
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# Once for All: Train One Network and Specialize it for Efficient Deployment
# Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han
# International Conference on Learning Representations (ICLR), 2020.
import argparse
import os
import random
import numpy as np
import torch
from ofa.imagenet_classification.elastic_nn.modules.dynamic_op import \
DynamicSeparableConv2d
from ofa.imagenet_classification.elastic_nn.networks import (OFAMobileNetV3,
OFAResNets)
from ofa.imagenet_classification.elastic_nn.training.progressive_shrinking import \
load_models
from ofa.imagenet_classification.networks import MobileNetV3Large
from ofa.imagenet_classification.run_manager import \
DistributedImageNetRunConfig, ImagenetRunConfig, CifarRunConfig
from ofa.imagenet_classification.run_manager.run_manager import (
RunManager)
from ofa.utils import MyRandomResizedCrop, download_url
parser = argparse.ArgumentParser()
parser.add_argument('--task', type=str, default='expand', choices=[
'kernel', 'depth', 'expand',
])
parser.add_argument('--phase', type=int, default=1, choices=[1, 2])
parser.add_argument('--resume', action='store_true')
args = parser.parse_args()
'''Task
task = Kernel : kernel size search
task = Depth : model depth search
task = expand : kernel depth -> kernel depth width
'''
if args.task == 'kernel':
args.path = 'exp/normal2kernel'
args.dynamic_batch_size = 1
args.n_epochs = 120
args.base_lr = 3e-2
args.warmup_epochs = 5
args.warmup_lr = -1
args.ks_list = '3,5' # 原3 5 7
args.expand_list = '6'
args.depth_list = '4'
elif args.task == 'depth':
args.path = 'exp/kernel2kernel_depth/phase%d' % args.phase
args.dynamic_batch_size = 2
if args.phase == 1:
args.n_epochs = 25
args.base_lr = 2.5e-3
args.warmup_epochs = 0
args.warmup_lr = -1
args.ks_list = '3,5,7'
args.expand_list = '6'
args.depth_list = '3,4'
else:
args.n_epochs = 120
args.base_lr = 7.5e-3
args.warmup_epochs = 5
args.warmup_lr = -1
args.ks_list = '3,5,7'
args.expand_list = '6'
args.depth_list = '2,3,4'
elif args.task == 'expand':
args.path = 'exp/kernel_depth2kernel_depth_width/phase%d' % args.phase
args.dynamic_batch_size = 4
if args.phase == 1:
args.n_epochs = 25
args.base_lr = 2.5e-3
args.warmup_epochs = 0
args.warmup_lr = -1
args.ks_list = '3,5'
args.expand_list = '2,4'
args.depth_list = '2,3'
else:
args.n_epochs = 120
args.base_lr = 7.5e-3
args.warmup_epochs = 5
args.warmup_lr = -1
args.ks_list = '3,5,7'
args.expand_list = '3,4,6'
args.depth_list = '2,3,4'
else:
raise NotImplementedError
args.manual_seed = 0
args.lr_schedule_type = 'cosine'
args.base_batch_size = 64
args.valid_size = 10000
args.opt_type = 'sgd'
args.momentum = 0.9
args.no_nesterov = False
args.weight_decay = 3e-5
args.label_smoothing = 0.1
args.no_decay_keys = 'bn#bias'
args.fp16_allreduce = False
args.model_init = 'he_fout'
args.validation_frequency = 1
args.print_frequency = 10
args.n_worker = 3
args.resize_scale = 0.08
args.distort_color = 'tf'
args.image_size = '24,32'
args.continuous_size = True
args.not_sync_distributed_image_size = False
args.bn_momentum = 0.1
args.bn_eps = 1e-5
args.dropout = 0.1
args.base_stage_width = 'proxyless'
args.width_mult_list = '1.0'
args.dy_conv_scaling_mode = 1
args.independent_distributed_sampling = False
args.kd_ratio = 1.0
args.kd_type = 'ce'
if __name__ == '__main__':
os.makedirs(args.path, exist_ok=True)
if args.kd_ratio > 0:
args.teacher_path = args.teacher_path = "/home/pdluser/project/once-for-all/exp/teachernet/checkpoint/model_best.pth.tar"
num_gpus = 1
torch.manual_seed(args.manual_seed)
torch.cuda.manual_seed_all(args.manual_seed)
np.random.seed(args.manual_seed)
random.seed(args.manual_seed)
# image size
args.image_size = [int(img_size)
for img_size in args.image_size.split(',')]
if len(args.image_size) == 1:
args.image_size = args.image_size[0]
MyRandomResizedCrop.CONTINUOUS = args.continuous_size
MyRandomResizedCrop.SYNC_DISTRIBUTED = not args.not_sync_distributed_image_size
# build run config from args
args.lr_schedule_param = None
args.opt_param = {
'momentum': args.momentum,
'nesterov': not args.no_nesterov,
}
args.init_lr = args.base_lr # linearly rescale the learning rate
if args.warmup_lr < 0:
args.warmup_lr = args.base_lr
args.train_batch_size = args.base_batch_size
args.test_batch_size = args.base_batch_size * 4
run_config = CifarRunConfig(
**args.__dict__, num_replicas=num_gpus, rank=0)
# print run config information
print('Run config:')
for k, v in run_config.config.items():
print('\t%s: %s' % (k, v))
if args.dy_conv_scaling_mode == -1:
args.dy_conv_scaling_mode = None
DynamicSeparableConv2d.KERNEL_TRANSFORM_MODE = args.dy_conv_scaling_mode
# build net from args
args.width_mult_list = [float(
width_mult) for width_mult in args.width_mult_list.split(',')] # 类似slimmable network
args.ks_list = [int(ks) for ks in args.ks_list.split(',')]
args.expand_list = [int(e) for e in args.expand_list.split(',')]
args.depth_list = [int(d) for d in args.depth_list.split(',')]
args.width_mult_list = args.width_mult_list[0] if len(
args.width_mult_list) == 1 else args.width_mult_list
# net = OFAResNets(
# n_classes=run_config.data_provider.n_classes, bn_param=(
# args.bn_momentum, args.bn_eps),
# dropout_rate=args.dropout, width_mult_list=args.width_mult_list
# )
net = OFAMobileNetV3(
n_classes=run_config.data_provider.n_classes, bn_param=(
args.bn_momentum, args.bn_eps),
dropout_rate=args.dropout, ks_list=args.ks_list, expand_ratio_list=args.expand_list, depth_list=args.expand_list
)
# teacher model
if args.kd_ratio > 0:
args.teacher_model = MobileNetV3Large(
n_classes=run_config.data_provider.n_classes, bn_param=(
args.bn_momentum, args.bn_eps),
dropout_rate=0, width_mult=1.0, ks=7, expand_ratio=6, depth_param=4,
)
args.teacher_model.cuda()
""" RunManager """
run_manager = RunManager(args.path, net, run_config)
run_manager.save_config()
# load teacher net weights
if args.kd_ratio > 0:
load_models(run_manager, args.teacher_model,
model_path=args.teacher_path)
# training
from ofa.imagenet_classification.elastic_nn.training.progressive_shrinking import (
train, validate)
validate_func_dict = {'image_size_list': {32} if isinstance(args.image_size, int) else sorted({24, 32}),
'ks_list': sorted({min(args.ks_list), max(args.ks_list)}),
'expand_ratio_list': sorted({min(args.expand_list), max(args.expand_list)}),
'depth_list': sorted({min(net.depth_list), max(net.depth_list)})}
if args.task == 'kernel':
validate_func_dict['ks_list'] = sorted(args.ks_list)
if run_manager.start_epoch == 0:
args.ofa_checkpoint_path = download_url(
'https://hanlab.mit.edu/files/OnceForAll/ofa_checkpoints/ofa_D4_E6_K7',
model_dir='.torch/ofa_checkpoints/%d' % 0
)
# load_models(run_manager, run_manager.net, args.ofa_checkpoint_path)
run_manager.write_log(
'%.3f\t%.3f\t%.3f\t%s' % validate(run_manager, is_test=True, **validate_func_dict), 'valid')
else:
assert args.resume
train(run_manager, args,
lambda _run_manager, epoch, is_test: validate(_run_manager, epoch, is_test, **validate_func_dict))
elif args.task == 'depth':
from ofa.imagenet_classification.elastic_nn.training.progressive_shrinking import \
train_elastic_depth
if args.phase == 1:
args.ofa_checkpoint_path = download_url(
'https://hanlab.mit.edu/files/OnceForAll/ofa_checkpoints/ofa_D4_E6_K357',
model_dir='.torch/ofa_checkpoints/%d' % 0
)
else:
args.ofa_checkpoint_path = download_url(
'https://hanlab.mit.edu/files/OnceForAll/ofa_checkpoints/ofa_D34_E6_K357',
model_dir='.torch/ofa_checkpoints/%d' % 0
)
train_elastic_depth(train, run_manager, args, validate_func_dict)
elif args.task == 'expand':
from ofa.imagenet_classification.elastic_nn.training.progressive_shrinking import \
train_elastic_expand
if args.phase == 1:
args.ofa_checkpoint_path = "/home/pdluser/project/once-for-all/exp/teachernet/checkpoint/model_best.pth.tar"
else:
args.ofa_checkpoint_path = "/home/pdluser/project/once-for-all/exp/teachernet/checkpoint/model_best.pth.tar"
train_elastic_expand(train, run_manager, args, validate_func_dict)
else:
raise NotImplementedError