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example2.py
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from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torchvision import models, transforms
import copy
#from train_model import train_model
import time
import os
from torch.utils.data import Dataset
from PIL import Image
import os
#os.environ["CUDA_VISIBLE_DEVICES"] = "2,3"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# use PIL Image to read image
def default_loader(path):
try:
img = Image.open(path)
return img.convert('RGB')
except:
print("Cannot read image: {}".format(path))
# define your Dataset. Assume each line in your .txt file is [name/one space/label], for example:0001.jpg 1
class customData(Dataset):
def __init__(self, img_path, txt_path, dataset = '', data_transforms=None, loader = default_loader):
with open(txt_path) as input_file:
lines = input_file.readlines()
self.img_name = [os.path.join(img_path, line.strip().split('\t')[0]) for line in lines]
self.img_label = [int(line.strip().split('\t')[-1]) for line in lines]
#self.img_name = [os.path.join(img_path, line.strip()[:-2]) for line in lines]
#self.img_label = [int(line.strip()[-1:]) for line in lines]
#print(len(self.img_name))
#print(self.img_name)
#print(len(self.img_label))
#print(self.img_label)
self.data_transforms = data_transforms
self.dataset = dataset
self.loader = loader
def __len__(self):
return len(self.img_name)
def __getitem__(self, item):
img_name = self.img_name[item]
label = self.img_label[item]
img = self.loader(img_name)
if self.data_transforms is not None:
try:
img = self.data_transforms[self.dataset](img)
except:
print("Cannot transform image: {}".format(img_name))
return img, label
# train_model(dataloaders, image_datasets, model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)
def train_model(dataloaders, image_datasets, model, criterion, optimizer, scheduler, num_epochs=25):
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 30)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
def train(dataloaders, image_datasets):
model_ft = models.resnet18(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
#model_ft = torch.nn.DataParallel(model_ft)#, device_ids=[0,1])
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
model_ft = train_model(dataloaders, image_datasets, model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)
torch.save(model_ft,"models/best_resnet.pkl")
def Data_loader():
batch_size = 4
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
image_datasets = {x: customData(img_path='hymenoptera_data_cp/',
txt_path=(x + '.txt'),
data_transforms=data_transforms,
dataset=x) for x in ['train', 'val']}
# wrap your data and label into Tensor
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=batch_size,
shuffle=True) for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
return image_datasets, dataloaders
def Test_dataloaders(dataloaders):
print("train"*20)
for inputs, labels in dataloaders['train']:
print('{} {} {} {}'.format(type(inputs), inputs.shape, type(labels), labels.detach()))
#labels = torch.IntTensor([labels])
#print('{} {} {}'.format(type(inputs), inputs.shape, type(labels), labels.detach()))
#print('{}'.format(labels))
print("test"*20)
for inputs, labels in dataloaders['val']:
print('{} {} {} {}'.format(type(inputs), inputs.shape, type(labels), labels.detach()))
if __name__ == '__main__':
image_datasets, dataloaders = Data_loader()
#Test_dataloaders(dataloaders)
train(dataloaders, image_datasets)