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utils.py
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utils.py
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from __future__ import print_function
import math
import numpy as np
import torch
import torch.optim as optim
import os
from sklearn.metrics import f1_score
from model import ResNet
import torch.backends.cudnn as cudnn
from torchvision import transforms, datasets
from datasets import OLIVES, RECOVERY, RECOVERY_TEST
import torch.nn as nn
def set_model(opt):
device = opt.device
model = ResNet(name=opt.model,num_classes = opt.ncls)
criterion = torch.nn.BCEWithLogitsLoss()
model = model.to(device)
criterion = criterion.to(device)
return model, criterion
def set_loader(opt):
# construct data loader
if opt.dataset == 'OLIVES' or opt.dataset == 'RECOVERY':
mean = (.1706)
std = (.2112)
else:
raise ValueError('dataset not supported: {}'.format(opt.dataset))
normalize = transforms.Normalize(mean=mean, std=std)
train_transform = transforms.Compose([
transforms.RandomResizedCrop(size=224, scale=(0.2, 1.)),
transforms.RandomHorizontalFlip(),
transforms.RandomApply([
transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)
], p=0.8),
transforms.RandomGrayscale(p=0.2),
transforms.ToTensor(),
normalize,
])
val_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
normalize,
])
if opt.dataset =='OLIVES':
csv_path_train = opt.train_csv_path
csv_path_test = opt.test_csv_path
data_path_train = opt.train_image_path
data_path_test = opt.test_image_path
train_dataset = OLIVES(csv_path_train,data_path_train,transforms = train_transform)
test_dataset = RECOVERY(csv_path_test,data_path_test,transforms = val_transform)
else:
raise ValueError(opt.dataset)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=opt.batch_size, shuffle=True,
num_workers=opt.num_workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=1, shuffle=False,
num_workers=0, pin_memory=True,drop_last=False)
return train_loader, test_loader
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def adjust_learning_rate(args, optimizer, epoch):
lr = args.learning_rate
if args.cosine:
eta_min = lr * (args.lr_decay_rate ** 3)
lr = eta_min + (lr - eta_min) * (
1 + math.cos(math.pi * epoch / args.epochs)) / 2
else:
steps = np.sum(epoch > np.asarray(args.lr_decay_epochs))
if steps > 0:
lr = lr * (args.lr_decay_rate ** steps)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def warmup_learning_rate(args, epoch, batch_id, total_batches, optimizer):
if args.warm and epoch <= args.warm_epochs:
p = (batch_id + (epoch - 1) * total_batches) / \
(args.warm_epochs * total_batches)
lr = args.warmup_from + p * (args.warmup_to - args.warmup_from)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def set_optimizer(opt, model):
optimizer = optim.SGD(model.parameters(),
lr=opt.learning_rate,
momentum=opt.momentum,
weight_decay=opt.weight_decay)
return optimizer
def save_model(model, optimizer, opt, epoch, save_file):
print('==> Saving...')
state = {
'opt': opt,
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
}
torch.save(state, save_file)
del state