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tl_cifar100_to_pascal.py
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tl_cifar100_to_pascal.py
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import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision
import torchvision.transforms as transforms
import numpy as np
from scripts.create_model import create_feedbacknet
from scripts.create_model import save
from scripts.create_model import load_checkpoint
from scripts.load_data import load_train_data
from scripts.load_data import load_test_data
from transfer_learning.feedback48_features import Feedback48Features
import torch.optim as optim
from multiprocessing import freeze_support
if __name__ == '__main__':
freeze_support()
cuda = True
no_checkpoints = False
epoch_start = 0
epochs = 31
gamma = 1.2
feedback_net, optimizer, epoch = create_feedbacknet('feedback48_4', cuda)
epoch = load_checkpoint(feedback_net, optimizer, 'checkpoint38_feedback4_cifar100.pth.tar')
for p in feedback_net.parameters():
p.requires_grad = False
#feedback_net.linear = nn.Linear(64, 10)
feedback_net.linear = nn.Linear(256, 256)
feedback_net.output = nn.Sequential(
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 20),
)
optimizer = optim.Adam([{'params': feedback_net.linear.parameters()}, {'params': feedback_net.output.parameters()}])
#epoch_start = load_checkpoint(feedback_net, optimizer, 'checkpoint14.pth.tar')
feedback_net.cuda()
criterion = nn.MultiLabelSoftMarginLoss()
dataset = 'pascal'
trainloader, valloader = load_train_data(dataset)
testloader = load_test_data(dataset)
print('hi')
for epoch in range(epoch_start, epochs):
running_losses = np.zeros(feedback_net.num_iterations)
running_loss = 0.0
train_correct = np.zeros(feedback_net.num_iterations)
train_total = 0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = Variable(inputs), Variable(labels).float()
if cuda:
inputs = inputs.cuda(device_id=0)
labels = labels.cuda(device_id=0)
optimizer.zero_grad()
outputs = feedback_net(inputs)
losses = [criterion(out, labels) for out in outputs]
loss = Variable(torch.from_numpy(np.zeros(1))).float().cuda()
for it in range(len(losses)):
loss += (gamma ** it) * losses[it]
#loss = sum(losses)
loss.backward(retain_graph=True)
optimizer.step()
running_losses += [l.data[0] for l in losses]
running_loss += loss.data[0]
train_total += labels.size(0)
for i in range(feedback_net.num_iterations):
_, predicted = torch.max(outputs[i].data, 1)
if cuda:
predicted = predicted.cpu()
if dataset != 'pascal':
train_correct[i] += (predicted == labels).sum()
else:
for p in range(predicted.size(0)):
if (labels.data[p, predicted[p]] > 0):
train_correct[i] += 1
if i == 0:
print('Epoch %d, iteration %d: loss=%f'% (epoch, i, running_loss))
print('Running losses:')
print([r for r in running_losses])
elif i % 40 == 0:
print('Epoch %d, iteration %d: loss=%f'% (epoch, i, running_loss/100.0))
print('Running losses:')
print([r/100.0 for r in running_losses])
running_loss = 0.0
running_losses = np.zeros(feedback_net.num_iterations)
for it in range(feedback_net.num_iterations):
train_acc = train_correct[it] / train_total
print('Training accuracy for iteration %i: %f %%' % (it, 100 * train_acc))
# Print val % accuracy
correct = np.zeros(feedback_net.num_iterations)
total = 0
for data in valloader:
inputs, labels = data
inputs= Variable(inputs)
if cuda:
inputs = inputs.cuda(device_id=0)
outputs = feedback_net(inputs)
total += labels.size(0)
for i in range(feedback_net.num_iterations):
_, predicted = torch.max(outputs[i].data, 1)
if cuda:
predicted = predicted.cpu()
if dataset != 'pascal':
correct[i] += (predicted == labels).sum()
else:
for p in range(predicted.size(0)):
if labels[p, predicted[p]] > 0:
correct[i] += 1
for it in range(feedback_net.num_iterations):
val_acc = correct[it] / total
print('Validation accuracy for iteration %i: %f %%' % (it, 100 * val_acc))
save(feedback_net, optimizer, epoch)
print('done!')
correct = np.zeros(feedback_net.num_iterations)
total = 0
feedback_net.train(True)
for data in testloader:
inputs, labels = data
inputs= Variable(inputs, volatile=True)
if cuda:
inputs = inputs.cuda(device_id=0)
outputs = feedback_net(inputs)
total += labels.size(0)
for i in range(feedback_net.num_iterations):
_, predicted = torch.max(outputs[i].data, 1)
if cuda:
predicted = predicted.cpu()
if dataset != 'pascal':
correct[i] += (predicted == labels).sum()
else:
for p in range(predicted.size(0)):
if labels[p, predicted[p]] > 0:
correct[i] += 1
for i in range(feedback_net.num_iterations):
print('Accuracy for iteration %i: %f %%' % (i, 100 * correct[i] / total))