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modeltrain.py
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modeltrain.py
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from __future__ import print_function, division
import torch
import torch.nn as nn
import time
import os
import argparse
#import piexif
from utee import quant, misc
from imagenet import dataset
#from read_ImageNetData import ImageNetData
import torch.optim as optim
from torch.optim import lr_scheduler
ds_fetcher = dataset.get
def train_model(args, model, criterion, optimizer, scheduler, num_epochs, dataset_sizes, dataloders, device_ids):
since = time.time()
resumed = False
best_model_wts = model.state_dict()
val_ds_tmp = ds_fetcher(batch_size=8,
data_root=args.data_root,
train=False,
val = True,
shuffle=args.shuffle,
input_size=args.input_size)
for epoch in range(args.start_epoch, num_epochs + 1):
print("qauntize activation")
model = model.module
model = torch.nn.DataParallel(model.cuda(), device_ids=[device_ids[0]])
quant.add_counter(model, args.n_sample)
misc.eval_model(model, val_ds_tmp, device_ids=device_ids[0], n_sample=args.n_sample)
model = model.module
model = torch.nn.DataParallel(model.cuda(), device_ids=device_ids)
for phase in ['train','val']:
if phase == 'train':
print("train phase")
scheduler.step(epoch)
model.train(True) # Set model to training mode
running_loss = 0.0
running_corrects = 0
tic_batch = time.time()
# Iterate over data for 1 epoch
for i, (inputs, labels) in enumerate(dataloders[phase]):
inputs = inputs.cuda()
labels = labels.cuda()
# zero the parameter gradients
optimizer.zero_grad()
# forward
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item()
running_corrects += torch.sum(preds == labels.data)
batch_loss = running_loss / ((i+1)*args.batch_size)
batch_acc = float(running_corrects) / ((i+1)*args.batch_size)
if i % args.print_freq == 0:
print('[Epoch {}/{}]-[batch:{}/{}] lr:{:.8f} {} Loss: {:.6f} Acc: {:.4f} Time: {:.4f}batch/sec'.format(
epoch, num_epochs,
i, round(dataset_sizes[phase])-1,
scheduler.get_lr()[0], phase, batch_loss, batch_acc,
args.print_freq/(time.time()-tic_batch)))
tic_batch = time.time()
#if i>=3:
# break
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = float(running_corrects) / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
else:
print("val phase")
model.eval() # Set model to evaluate mode
acc1, acc5 = misc.eval_model(model, dataloders[phase], device_ids=device_ids)
res_str = "epoch={}, type={}, quant_method={}, \n \
param_bits={}, fwd_bits={},\n \
acc1={:.4f}, acc5={:.4f}".format(
epoch, args.type, args.quant_method,
args.param_bits,args.fwd_bits, acc1, acc5)
print(res_str)
with open(str(args.param_bits)+"-"+str(args.fwd_bits)+'bits_quant_acc1_acc5.txt','a') as f:
f.write(res_str + '\n')
if (epoch+1) % args.save_epoch_freq == 0:
if not os.path.exists(args.save_path):
os.makedirs(args.save_path)
torch.save(model, os.path.join(args.save_path, "epoch_" + str(epoch) + ".pth.tar"))
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
# load best model weights
model.load_state_dict(best_model_wts)
return model
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="PyTorch implementation of SENet")
parser.add_argument('--data-dir', type=str, default="/home/jcwang/dataset/imagenet")
parser.add_argument('--batch_size', type=int, default=16)
parser.add_argument('--num_class', type=int, default=1000)
parser.add_argument('--num_epochs', type=int, default=100)
parser.add_argument('--lr', type=float, default=0.045)
parser.add_argument('--gpus', type=str, default=0)
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")
parser.add_argument('--start_epoch', type=int, default=0, help="Corresponding to the epoch of resume ")
args = parser.parse_args()
# read data
# dataloders, dataset_sizes = ImageNetData(args)
# use gpu or not
use_gpu = torch.cuda.is_available()
print("use_gpu:{}".format(use_gpu))
# get model
#model = mobilenetv2_19(num_classes = args.num_class)
if args.resume:
if os.path.isfile(args.resume):
print(("=> loading checkpoint '{}'".format(args.resume)))
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)
else:
print(("=> no checkpoint found at '{}'".format(args.resume)))
if use_gpu:
model = model.cuda()
model = torch.nn.DataParallel(model, device_ids=[int(i) for i in args.gpus.strip().split(',')])
# 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=1, gamma=0.98)
model = train_model(args=args,
model=model,
criterion=criterion,
optimizer=optimizer_ft,
scheduler=exp_lr_scheduler,
num_epochs=args.num_epochs,
dataset_sizes=dataset_sizes)