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train.py
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train.py
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import argparse
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from models import modules, net, resnet, densenet
import net_mask
import loaddata
import util
import numpy as np
import sobel
import net_mask
import os
import pdb
parser = argparse.ArgumentParser(description='single depth estimation')
parser.add_argument('--epochs', default=10, type=int,
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int,
help='manual epoch number (useful on restarts)')
parser.add_argument('--lr', '--learning-rate', default=0.0001, type=float,
help='initial learning rate')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
help='weight decay (default: 1e-4)')
def define_model(encoder='resnet'):
if encoder is 'resnet':
original_model = resnet.resnet50(pretrained = True)
Encoder = modules.E_resnet(original_model)
model = net.model(Encoder, num_features=2048, block_channel = [256, 512, 1024, 2048])
if encoder is 'densenet':
original_model = densenet.densenet161(pretrained=True)
Encoder = modules.E_densenet(original_model)
model = net.model(Encoder, num_features=2208, block_channel = [192, 384, 1056, 2208])
if encoder is 'senet':
original_model = senet.senet154(pretrained='imagenet')
Encoder = modules.E_senet(original_model)
model = net.model(Encoder, num_features=2048, block_channel = [256, 512, 1024, 2048])
return model
def main():
global args
args = parser.parse_args()
model_selection = 'resnet'
model = define_model(encoder = model_selection)
original_model2 = net_mask.drn_d_22(pretrained=True)
model2 = net_mask.AutoED(original_model2)
if torch.cuda.device_count() == 8:
model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3, 4, 5, 6, 7]).cuda()
model2 = torch.nn.DataParallel(model2, device_ids=[0, 1, 2, 3, 4, 5, 6, 7]).cuda()
batch_size = 64
elif torch.cuda.device_count() == 4:
model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3]).cuda()
model2 = torch.nn.DataParallel(model2, device_ids=[0, 1, 2, 3]).cuda()
batch_size = 32
else:
model = torch.nn.DataParallel(model).cuda()
model2 = torch.nn.DataParallel(model2).cuda()
batch_size = 8
model.load_state_dict(torch.load('./pretrained_model/model_' + model_selection))
cudnn.benchmark = True
optimizer = torch.optim.Adam(model2.parameters(), args.lr, weight_decay=args.weight_decay)
train_loader = loaddata.getTrainingData(batch_size)
for epoch in range(args.start_epoch, args.epochs):
train(train_loader, model, model2, optimizer, epoch)
torch.save(model2.state_dict(), './net_mask/mask_'+model_selection)
def train(train_loader, model, model2, optimizer, epoch):
batch_time = AverageMeter()
losses = AverageMeter()
totalNumber = 0
errorSum = {'MSE': 0, 'RMSE': 0, 'ABS_REL': 0, 'LG10': 0,
'MAE': 0, 'DELTA1': 0, 'DELTA2': 0, 'DELTA3': 0}
model.eval()
model2.train()
cos = nn.CosineSimilarity(dim=1, eps=0)
get_gradient = sobel.Sobel().cuda()
end = time.time()
for i, sample_batched in enumerate(train_loader):
image, depth_ = sample_batched['image'], sample_batched['depth']
image = torch.autograd.Variable(image).cuda()
depth_ = torch.autograd.Variable(depth_).cuda()
ones = torch.ones(depth_.size(0), 1, depth_.size(2),depth_.size(3)).float().cuda()
ones = torch.autograd.Variable(ones)
depth = model(image.clone()).detach()
optimizer.zero_grad()
mask = model2(image)
output = model(image*mask)
depth_grad = get_gradient(depth)
output_grad = get_gradient(output)
depth_grad_dx = depth_grad[:, 0, :, :].contiguous().view_as(depth)
depth_grad_dy = depth_grad[:, 1, :, :].contiguous().view_as(depth)
output_grad_dx = output_grad[:, 0, :, :].contiguous().view_as(depth)
output_grad_dy = output_grad[:, 1, :, :].contiguous().view_as(depth)
depth_normal = torch.cat((-depth_grad_dx, -depth_grad_dy, ones), 1)
output_normal = torch.cat((-output_grad_dx, -output_grad_dy, ones), 1)
loss_depth = torch.log(torch.abs(output - depth) + 0.5).mean()
loss_dx = torch.log(torch.abs(output_grad_dx - depth_grad_dx) + 0.5).mean()
loss_dy = torch.log(torch.abs(output_grad_dy - depth_grad_dy) + 0.5).mean()
loss_normal = torch.abs(1 - cos(output_normal, depth_normal)).mean()
loss_rec = loss_depth + loss_normal + (loss_dx + loss_dy)
loss_sparse = mask.mean()
loss = loss_rec + loss_sparse*5
losses.update(loss_sparse.data[0], image.size(0))
loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
end = time.time()
batchSize = depth.size(0)
errors = util.evaluateError(output,depth)
errorSum = util.addErrors(errorSum, errors, batchSize)
totalNumber = totalNumber + batchSize
averageError = util.averageErrors(errorSum, totalNumber)
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.sum:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})'
.format(epoch, i, len(train_loader), batch_time=batch_time, loss=losses))
print('errors: ', averageError)
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
if __name__ == '__main__':
main()