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CatDogResnet.py
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CatDogResnet.py
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from glob import glob
import os
import numpy as np
import matplotlib.pyplot as plt
import PIL
import shutil
from torchvision import transforms
from torchvision import models
import torch
from torch.autograd import Variable
import torch.nn as nn
from torch.optim import lr_scheduler
from torch import optim
from torchvision.datasets import ImageFolder
from torchvision.utils import make_grid
import time
def imshow(inp):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
def imshow(inp):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
datadir = '/home/andrew/PycharmProjects/DeepLearning/CatDog/CatDogWorking/'
simple_transform = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
train = ImageFolder(os.path.join(datadir, 'train'), simple_transform)
valid = ImageFolder(os.path.join(datadir, 'val'), simple_transform)
train_data_gen = torch.utils.data.DataLoader(train, shuffle=True, batch_size=32, num_workers=15)
valid_data_gen = torch.utils.data.DataLoader(valid, batch_size=32, num_workers=15)
dataset_sizes = {'train': len(train_data_gen.dataset), 'valid': len(valid_data_gen.dataset)}
dataloaders = {'train': train_data_gen, 'valid': valid_data_gen}
model_ft = models.resnet50(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)
if torch.cuda.is_available():
model_ft = model_ft.cuda()
# Loss and Optimizer
learning_rate = 1e-2
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=learning_rate, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
def train_model(model, criterion, optimizer, scheduler, num_epochs=5):
since = time.time()
best_model_wts = model.state_dict()
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch+1, num_epochs))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'valid']:
if phase == 'train':
model.train(True) # Set model to training mode
else:
model.train(False) # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for data in dataloaders[phase]:
# get the inputs
inputs, labels = data
# wrap them in Variable
if torch.cuda.is_available():
inputs = inputs.cuda()
labels = labels.cuda()
else:
inputs, labels = inputs, labels
# 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()
scheduler.step()
# statistics
running_loss += loss.data
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss.item() / dataset_sizes[phase]
epoch_acc = running_corrects.item() / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'valid' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = 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
ST = time.time()
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=2)
print("Total time:", time.time()-ST, "seconds.")