Create canonical folder for classifier
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classification/classifier/train.ipynb
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429
classification/classifier/train.ipynb
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from __future__ import print_function, division
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.optim import lr_scheduler
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import torch.backends.cudnn as cudnn
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import numpy as np
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import torchvision
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from torchvision import datasets, models, transforms
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import matplotlib.pyplot as plt
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import time
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import os
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import copy
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cudnn.benchmark = True
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plt.ion() # interactive mode
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data_transforms = {
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'train': transforms.Compose([
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transforms.RandomResizedCrop(224),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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'val': transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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}
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data_dir = 'hymenoptera_data'
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image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
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data_transforms[x])
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for x in ['train', 'val']}
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dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
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shuffle=True, num_workers=4)
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for x in ['train', 'val']}
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dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
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class_names = image_datasets['train'].classes
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device = torch.device("cpu")
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def imshow(inp, title=None):
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"""Imshow for Tensor."""
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inp = inp.numpy().transpose((1, 2, 0))
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mean = np.array([0.485, 0.456, 0.406])
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std = np.array([0.229, 0.224, 0.225])
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inp = std * inp + mean
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inp = np.clip(inp, 0, 1)
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plt.imshow(inp)
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if title is not None:
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plt.title(title)
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plt.pause(0.001) # pause a bit so that plots are updated
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def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
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since = time.time()
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best_model_wts = copy.deepcopy(model.state_dict())
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best_acc = 0.0
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for epoch in range(num_epochs):
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print(f'Epoch {epoch}/{num_epochs - 1}')
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print('-' * 10)
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# Each epoch has a training and validation phase
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for phase in ['train', 'val']:
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if phase == 'train':
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model.train() # Set model to training mode
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else:
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model.eval() # Set model to evaluate mode
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running_loss = 0.0
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running_corrects = 0
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# Iterate over data.
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for inputs, labels in dataloaders[phase]:
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inputs = inputs.to(device)
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labels = labels.to(device)
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# zero the parameter gradients
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optimizer.zero_grad()
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# forward
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# track history if only in train
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with torch.set_grad_enabled(phase == 'train'):
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outputs = model(inputs)
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_, preds = torch.max(outputs, 1)
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loss = criterion(outputs, labels)
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# backward + optimize only if in training phase
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if phase == 'train':
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loss.backward()
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optimizer.step()
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# statistics
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running_loss += loss.item() * inputs.size(0)
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running_corrects += torch.sum(preds == labels.data)
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if phase == 'train':
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scheduler.step()
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epoch_loss = running_loss / dataset_sizes[phase]
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epoch_acc = running_corrects.double() / dataset_sizes[phase]
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print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
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# deep copy the model
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if phase == 'val' and epoch_acc > best_acc:
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best_acc = epoch_acc
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best_model_wts = copy.deepcopy(model.state_dict())
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print()
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time_elapsed = time.time() - since
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print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
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print(f'Best val Acc: {best_acc:4f}')
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# load best model weights
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model.load_state_dict(best_model_wts)
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return model
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def visualize_model(model, num_images=6):
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was_training = model.training
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model.eval()
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images_so_far = 0
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fig = plt.figure()
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with torch.no_grad():
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for i, (inputs, labels) in enumerate(dataloaders['val']):
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inputs = inputs.to(device)
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labels = labels.to(device)
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outputs = model(inputs)
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_, preds = torch.max(outputs, 1)
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for j in range(inputs.size()[0]):
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images_so_far += 1
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ax = plt.subplot(num_images//2, 2, images_so_far)
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ax.axis('off')
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ax.set_title(f'predicted: {class_names[preds[j]]}')
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imshow(inputs.cpu().data[j])
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if images_so_far == num_images:
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model.train(mode=was_training)
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return
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model.train(mode=was_training)
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model_ft = models.resnet18(pretrained=True)
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num_ftrs = model_ft.fc.in_features
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# Here the size of each output sample is set to 2.
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# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
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model_ft.fc = nn.Linear(num_ftrs, 2)
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model_ft = model_ft.to(device)
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criterion = nn.CrossEntropyLoss()
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# Observe that all parameters are being optimized
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optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
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# Decay LR by a factor of 0.1 every 7 epochs
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exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
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model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
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num_epochs=25)
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visualize_model(model_ft)
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