import sys
sys.path.append('..')
from PIL import Image
from torchvision import transforms as tfsim = Image.open("cat.jpg")
im
#Resize
print('Before scale, shape: {}'.format(im.size))
new_im = tfs.Resize((100,200))(im)
print('After scale, shape: {}'.format(new_im.size))
new_imBefore scale, shape: (250, 308)
After scale, shape: (200, 100)
#Random Crop
random_im = tfs.RandomCrop(100)(im)#100*100
random_im
#center crop
center_crop = tfs.CenterCrop(100)(im)
center_crop
#Random Flip horizontal
h_flip = tfs.RandomHorizontalFlip()(im)
h_flip
#vertical
v_flip = tfs.RandomVerticalFlip()(im)
v_flip
#Random Rotation
rot_im = tfs.RandomRotation(40)(im)#-40-40
rot_im
#brightness
bright_im = tfs.ColorJitter(2)(im)
bright_im
#contrast
contrast_im = tfs.ColorJitter(contrast=2)(im)
contrast_im
#Hue
hue_im = tfs.ColorJitter(hue = 0.5)(im)
hue_im
#Composite
im_aug = tfs.Compose([
tfs.Resize(120),
tfs.RandomHorizontalFlip(),
tfs.RandomCrop(90),
tfs.ColorJitter(brightness=0.5,contrast=0.5,hue = 0.5)
])import matplotlib.pyplot as plt
%matplotlib inline
nrows = 3
ncols = 3
figsize = (8,8)
_,figs = plt.subplots(nrows, ncols, figsize = figsize)
for i in range(nrows):
for j in range (ncols):
figs[i][j].imshow(im_aug(im))
figs[i][j].axes.get_xaxis().set_visible(False)
figs[i][j].axes.get_yaxis().set_visible(False)
plt.show()
#***Training Model Test***
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision.datasets import CIFAR10
import torchvision
from torchvision import transforms as tfs
from utils import train, resnet
#data augmented
def train_tf(x):
im_aug = tfs.Compose([
tfs.Resize(120),
tfs.RandomHorizontalFlip(),
tfs.RandomCrop(96),
tfs.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5),
tfs.ToTensor(),
tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
x = im_aug(x)
return x
def test_tf(x):
im_aug = tfs.Compose([
tfs.Resize(96),
tfs.ToTensor(),
tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
x = im_aug(x)
return x
train_set = CIFAR10('./data', train=True, transform=train_tf)
train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_set = CIFAR10('./data', train=False, transform=test_tf)
test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)
net = resnet(3, 10)
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()
train(net, train_data, test_data, 10, optimizer, criterion)from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
plt.ion() # interactive mode
#Augmentation and normalization
#Just normalize for validation
data_tfs = {
'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])
])
}
data_dir = 'data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_tfs[x])
for x in ['train','val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x],batch_size = 4,
shuffle = True, num_workers=4)
for x in ['train','val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train','val']}
class_names = image_datasets['train'].classes
device = torch.device("cuda:0" if torch.cuda.is_available() else 'cpu')
def imshow(inp, title = None):
"""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)
if title is not None:
plt.title(title)
plt.pause(0.001)
#get a batch of training data
inputs, classes = next(iter(dataloaders['train']))
#Make a grid from batch
out = torchvision.utils.make_grid(inputs)
imshow(out, title=[class_names[x] for x in classes])
def train_model(model, criterion, optimizer, scheduler, num_epoch=25):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epoch):
print('Epoch{}/{}'.format(epoch,num_epoch-1))
print('-'*10)
#Each epoch in range(num_epochs):
for phase in ['train','val']:
if phase == 'train':
scheduler.step()
model.train() #Training mode
else:
model.eval() #Evaluation 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 gradient
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 mode
if phase == 'train':
loss.backward()
optimizer.step()
#stat
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())
print()
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
#Visualizing the model
def visualize_model(model, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad():
for i, (inputs, labels) in enumerate(dataloaders['val']):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images//2, 2, images_so_far)
ax.axis('off')
ax.set_title('predicted: {}'.format(class_names[preds[j]]))
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
model.train(mode=was_training)
return
model.train(mode=was_training)
#Finetuning the convet
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)
criterion = nn.CrossEntropyLoss()
#Observe the parameters
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(model_ft, criterion, optimizer_ft,exp_lr_scheduler,num_epoch=25)
visualize_model(model_ft)