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from __future__ import print_function, division
import torch
from torch.autograd import Variable
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
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
import torch.utils.data as data_utils
from torch.utils import data
torch.manual_seed(2809)
def train_model(model, criterion, optimizer, scheduler,
train_input, train_label, num_epochs=25):
since = time.time()
model.train() # Set model to training mode
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
scheduler.step()
running_loss = 0.0
running_corrects = 0
# Iterate over data.
train_input = train_input.to(device)
train_label = train_label.to(device)
# zero the parameter gradients
optimizer.zero_grad()
output = model(train_input)
_, pred = torch.max(output, 1)
loss = criterion(output, train_label)
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * train_input.size(0)
running_corrects += torch.sum(pred == train_label.data)
epoch_loss = running_loss / dataset_size['train']
epoch_acc = running_corrects.double() / dataset_size['train']
print('train Loss: {:.4f} Acc: {:.4f}'.format(epoch_loss, epoch_acc))
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
return model
data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(20),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'test': transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
}
data_dir = "test_images"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_ft = models.resnet50(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
# Save a clone of initial model to restore later
initial_model = copy.deepcopy(model_ft)
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 = model_ft.cuda()
nb_samples = 10
nb_classes = 2
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train']}
dataset_size = {x: len(image_datasets[x]) for x in ['train']}
class_names = image_datasets['train'].classes
# LOOCV
loocv_preds = []
loocv_targets = []
for idx in range(nb_samples):
print('Using sample {} as test data'.format(idx))
print('Resetting model')
model_ft = copy.deepcopy(initial_model)
# Get all indices and remove test sample
train_indices = list(range(len(image_datasets['train'])))
del train_indices[idx]
# Create new sampler
sampler = data.SubsetRandomSampler(train_indices)
dataloader = data.DataLoader(
image_datasets['train'],
num_workers=2,
batch_size=1,
sampler=sampler
)
# Train model
for batch_idx, (sample, target) in enumerate(dataloader):
print('Batch {}'.format(batch_idx))
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, sample, target, num_epochs=2) # do I add this line here?
# Test on LOO sample
model_ft.eval()
test_data, test_target = image_datasets['train'][idx]
# Apply test preprocessing on data
test_data = data_transforms['test'](test_data)
test_data = test_data.cuda()
test_target = torch.tensor(test_target)
test_target = test_target.cuda()
test_data.unsqueeze_(0)
test_target.unsqueeze_(0)
print(test_data.shape)
output = model_ft(test_data)
pred = torch.argmax(output, 1)
loocv_preds.append(pred)
loocv_targets.append(test_target.item())
print("loocv preds: ", loocv_preds)
print("loocv targets: ", loocv_targets)
print(accuracy_score(loocv_targets, loocv_preds))
print(confusion_matrix(loocv_targets, loocv_preds))
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