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HOPE.py
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HOPE.py
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# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
"""# Import Libraries"""
import numpy as np
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from utils.model import select_model
from utils.options import parse_args_function
from utils.dataset import Dataset
args = parse_args_function()
"""# Load Dataset"""
root = args.input_file
#mean = np.array([120.46480086, 107.89070987, 103.00262132])
#std = np.array([5.9113948 , 5.22646725, 5.47829601])
transform = transforms.Compose([transforms.Resize((224, 224)),
transforms.ToTensor()])
if args.train:
trainset = Dataset(root=root, load_set='train', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=16)
print('Train files loaded')
if args.val:
valset = Dataset(root=root, load_set='val', transform=transform)
valloader = torch.utils.data.DataLoader(valset, batch_size=args.batch_size, shuffle=False, num_workers=8)
print('Validation files loaded')
if args.test:
testset = Dataset(root=root, load_set='test', transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size, shuffle=False, num_workers=8)
print('Test files loaded')
"""# Model"""
use_cuda = False
if args.gpu:
use_cuda = True
model = select_model(args.model_def)
if use_cuda and torch.cuda.is_available():
model = model.cuda()
model = nn.DataParallel(model, device_ids=args.gpu_number)
"""# Load Snapshot"""
if args.pretrained_model != '':
model.load_state_dict(torch.load(args.pretrained_model))
losses = np.load(args.pretrained_model[:-4] + '-losses.npy').tolist()
start = len(losses)
else:
losses = []
start = 0
"""# Optimizer"""
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step, gamma=args.lr_step_gamma)
scheduler.last_epoch = start
lambda_1 = 0.01
lambda_2 = 1
"""# Train"""
if args.train:
print('Begin training the network...')
for epoch in range(start, args.num_iterations): # loop over the dataset multiple times
running_loss = 0.0
train_loss = 0.0
for i, tr_data in enumerate(trainloader):
# get the inputs
inputs, labels2d, labels3d = tr_data
# wrap them in Variable
inputs = Variable(inputs)
labels2d = Variable(labels2d)
labels3d = Variable(labels3d)
if use_cuda and torch.cuda.is_available():
inputs = inputs.float().cuda(device=args.gpu_number[0])
labels2d = labels2d.float().cuda(device=args.gpu_number[0])
labels3d = labels3d.float().cuda(device=args.gpu_number[0])
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs2d_init, outputs2d, outputs3d = model(inputs)
loss2d_init = criterion(outputs2d_init, labels2d)
loss2d = criterion(outputs2d, labels2d)
loss3d = criterion(outputs3d, labels3d)
loss = (lambda_1)*loss2d_init + (lambda_1)*loss2d + (lambda_2)*loss3d
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.data
train_loss += loss.data
if (i+1) % args.log_batch == 0: # print every log_iter mini-batches
print('[%d, %5d] loss: %.5f' % (epoch + 1, i + 1, running_loss / args.log_batch))
running_loss = 0.0
if args.val and (epoch+1) % args.val_epoch == 0:
val_loss = 0.0
for v, val_data in enumerate(valloader):
# get the inputs
inputs, labels2d, labels3d = val_data
# wrap them in Variable
inputs = Variable(inputs)
labels2d = Variable(labels2d)
labels3d = Variable(labels3d)
if use_cuda and torch.cuda.is_available():
inputs = inputs.float().cuda(device=args.gpu_number[0])
labels2d = labels2d.float().cuda(device=args.gpu_number[0])
labels3d = labels3d.float().cuda(device=args.gpu_number[0])
outputs2d_init, outputs2d, outputs3d = model(inputs)
loss2d_init = criterion(outputs2d_init, labels2d)
loss2d = criterion(outputs2d, labels2d)
loss3d = criterion(outputs3d, labels3d)
loss = (lambda_1)*loss2d_init + (lambda_1)*loss2d + (lambda_2)*loss3d
val_loss += loss.data
print('val error: %.5f' % (val_loss / (v+1)))
losses.append((train_loss / (i+1)).cpu().numpy())
if (epoch+1) % args.snapshot_epoch == 0:
torch.save(model.state_dict(), args.output_file+str(epoch+1)+'.pkl')
np.save(args.output_file+str(epoch+1)+'-losses.npy', np.array(losses))
# Decay Learning Rate
scheduler.step()
print('Finished Training')
"""# Test"""
if args.test:
print('Begin testing the network...')
running_loss = 0.0
for i, ts_data in enumerate(testloader):
# get the inputs
inputs, labels2d, labels3d = ts_data
# wrap them in Variable
inputs = Variable(inputs)
labels2d = Variable(labels2d)
labels3d = Variable(labels3d)
if use_cuda and torch.cuda.is_available():
inputs = inputs.float().cuda(device=args.gpu_number[0])
labels2d = labels2d.float().cuda(device=args.gpu_number[0])
labels3d = labels3d.float().cuda(device=args.gpu_number[0])
outputs2d_init, outputs2d, outputs3d = model(inputs)
loss = criterion(outputs3d, labels3d)
running_loss += loss.data
print('test error: %.5f' % (running_loss / (i+1)))