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quantize-retrain.py
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quantize-retrain.py
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import argparse
from utee import misc, quant, selector
import torch
import torch.backends.cudnn as cudnn
cudnn.benchmark =True
from collections import OrderedDict
import pprint
import os
import torch.nn as nn
from torch.nn.parameter import Parameter
import copy
known_models = [
'mnist', 'svhn', # 28x28
'cifar10', 'cifar100', # 32x32
'stl10', # 96x96
'alexnet', # 224x224
'vgg16', 'vgg16_bn', 'vgg19', 'vgg19_bn', # 224x224
'resnet18', 'resnet34', 'resnet50', 'resnet101','resnet152', # 224x224
'squeezenet_v0', 'squeezenet_v1', #224x224
'inception_v3', # 299x299
]
parser = argparse.ArgumentParser(description='PyTorch Quantization')
parser.add_argument('--use_model_zoo', type=int, default=1, help='decide if use model_zoo')
parser.add_argument('--type', default='alexnet', help='|'.join(selector.known_models))
parser.add_argument('--data_root', default='~/dataset/imagenet', help='folder to save the model')
parser.add_argument('--model_root', default='~/.torch/models/', help='the path of pre-trained parammeters')
parser.add_argument('--net_root', default='~/pytorch-mobilenet-v2/MobileNetV2.py', help='the path of pre-trained parammeters')
parser.add_argument('--test', type=int, default=1, help='test data distribution')
parser.add_argument('--batch_size', type=int, default=50, help='input batch size for training')
parser.add_argument('--n_sample', type=int, default=4, help='number of samples to infer the scaling factor')
parser.add_argument('--gpu', default="0", help='index of gpus to use')
parser.add_argument('--ngpu', type=int, default=4, help='number of gpus to use')
parser.add_argument('--logdir', default='./log/default', help='folder to save to the log')
parser.add_argument('--replace_bn', type=int, default=0, help='decide if replace bn layer')
parser.add_argument('--map_bn', type=int, default=1, help='decide if map bn layer to conv layer')
parser.add_argument('--input_size', type=int, default=224, help='input size of image')
parser.add_argument('--shuffle', type=int, default=1, help='data shuffle')
parser.add_argument('--overflow_rate', type=float, default=0.0, help='overflow rate')
parser.add_argument('--quant_method', default='linear', help='linear|minmax|log|tanh|scale')
parser.add_argument('--param_bits', type=int, default=8, help='bit-width for parameters')
parser.add_argument('--fwd_bits', type=int, default=8, help='bit-width for layer output')
#training param
parser.add_argument('--workers', type=int, default=4)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--num_epochs', type=int, default=2)
parser.add_argument('--start_epoch', type=int, default=0, help="Corresponding to the epoch of resume ")
parser.add_argument('--print_freq', type=int, default=10)
parser.add_argument('--save_epoch_freq', type=int, default=1)
parser.add_argument('--save_path', type=str, default="./output")
parser.add_argument('--resume', type=str, default="", help="For training from one checkpoint")
args = parser.parse_args()
assert torch.cuda.is_available(), 'no cuda'
#os.environ["CUDA_VISIBLE_DEVICES"]=args.gpu
args.gpu = misc.auto_select_gpu(utility_bound=0, num_gpu=args.ngpu, selected_gpus=args.gpu)
misc.ensure_dir(args.logdir)
args.model_root = misc.expand_user(args.model_root)
args.data_root = misc.expand_user(args.data_root)
args.input_size = 299 if 'inception' in args.type else args.input_size
assert args.quant_method in ['linear', 'minmax', 'log', 'tanh', 'scale']
print("=================FLAGS==================")
for k, v in args.__dict__.items():
print('{}: {}'.format(k, v))
print("========================================")
# load model and dataset fetcher
if args.use_model_zoo:
args.model_root = os.path.expanduser('~/.torch/models/')
model, ds_fetcher, is_imagenet = selector.select(model_name=args.type, model_root=args.model_root)
args.ngpu = args.ngpu if is_imagenet else 1
else:
args.model_root = '~/pytorch-mobilenet-v2/mobilenetv2_718.pth'
#args.model_root = None
model, ds_fetcher = selector.find(
model_name = args.type,
model_root = args.model_root,
net_root = args.net_root)
if args.model_root is None:
for m in model.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform(m.weight)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
#model_raw = copy.deepcopy(model)
# replace bn layer
if args.replace_bn:
quant.replace_bn(model)
# map bn to conv
if args.map_bn:
quant.bn2conv(model)
device_ids=[int(i) for i in args.gpu]
if args.resume == "":
model = quant.combine_cb(model, param_bits=args.param_bits, fwd_bits=args.fwd_bits, counter=args.n_sample)
model = torch.nn.DataParallel(model.cuda(), device_ids=[device_ids[0]])
#model = model.cuda()
print(model)
#retrain quantized model
print("===================retrain model======================")
import torch.optim as optim
from torch.optim import lr_scheduler
from imagenet import dataset
import modeltrain
if args.resume != "":
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
checkpoint = torch.load(args.resume, map_location='cpu')
#checkpoint = torch.load(args.resume)
#base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint.state_dict().items())}
#model.load_state_dict(base_dict)
model = checkpoint
print(model)
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
#model = torch.nn.DataParallel(model.cuda(), device_ids=device_ids)
# define loss function
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9, weight_decay=0.00004)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=2, gamma=0.8)
# read data
dataloders, dataset_sizes = dataset.ImageNetData(args)
#dataloders, dataset_sizes = dataset.lmdb(args)
#print("dataset_sizes:{}".format(dataset_sizes))
model = modeltrain.train_model(args=args,
model=model,
criterion=criterion,
optimizer=optimizer_ft,
scheduler=exp_lr_scheduler,
num_epochs=args.num_epochs,
dataset_sizes=dataset_sizes,
dataloders=dataloders,
device_ids=device_ids)
print("======================================================")
'''
print("===================eval model=========================")
#print(model)
if args.test:
args.batch_size = 1
val_ds = ds_fetcher(batch_size=args.batch_size,
data_root=args.data_root,
train=False,
val = True,
shuffle=args.shuffle,
input_size=args.input_size)
if args.test:
acc1, acc5 = misc.eval_model(model, val_ds, device_ids=device_ids, n_sample=1)
else:
acc1, acc5 = misc.eval_model(model, val_ds, device_ids=device_ids)
print("======================================================")
res_str = "type={}, quant_method={}, \n \
param_bits={}, fwd_bits={},\n \
acc1={:.4f}, acc5={:.4f}".format(
args.type, args.quant_method, args.param_bits,
args.fwd_bits, acc1, acc5)
print(res_str)
'''