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util.py
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import shutil
import datetime
import torch
from torch.autograd import Variable
from path import Path
import numpy as np
def set_arguments(parser):
parser.add_argument('data', metavar='DIR',
help='path to dataset')
parser.add_argument('--activation-function', default=None,
help='activation function to apply to DepthNet')
parser.add_argument('--bn', action='store_true',
help='activate batchNorm (overwritten if pretrained model)')
parser.add_argument('--clamp', action='store_true',
help='activate depth clamping to (10,60) in forward pass')
parser.add_argument('--solver', default='sgd', choices=['adam', 'sgd'],
help='solvers: adam | sgd')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=55, type=int, metavar='N',
help='number of total epochs to run (default: 55')
parser.add_argument('--epoch-size', default=0, type=int, metavar='N',
help='manual epoch size (will match dataset size if not set)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.01, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum for sgd, alpha parameter for adam')
parser.add_argument('--beta', default=0.999, type=float, metavar='M',
help='beta parameters for adam')
parser.add_argument('--weight-decay', '--wd', default=4e-4, type=float,
metavar='W', help='weight decay (default: 4e-4)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', default=None,
help='path to pre-trained model')
parser.add_argument('--seed', default=0, type=int, help='seed for random functions, test/train split, network initialization')
parser.add_argument('-s', '--split', default=90, type=float, metavar='%',
help='split percentage of train samples vs test (default: 90)')
parser.add_argument('--log-summary', default='progress_log_summary.csv',
help='csv where to save per-epoch train and test stats')
parser.add_argument('--log-full', default='progress_log_full.csv',
help='csv where to save per-gradient descent train stats')
parser.add_argument('--no-date', action='store_true',
help='don\'t append date timestamp to folder')
parser.add_argument('--loss', default='L1', help='loss function to apply to multiScaleCriterion : L1 (default)| SmoothL1| MSE')
parser.add_argument('--log-output', action='store_true', help='logs in tensorboard some outputs of the network during test phase. Needs OpenCV 3')
def set_params(parser, with_confidence=False):
args = parser.parse_args()
args.data = Path(args.data)
folder_name = args.data.normpath().name
arch_string = 'DepthNet'
if with_confidence:
arch_string += '_confidence'
if args.activation_function is not None:
arch_string += '_'+args.activation_function
if args.bn:
arch_string += '_bn'
if args.clamp:
arch_string += '_clamp'
args.arch = arch_string
save_path = '{},{}epochs{},b{},lr{}'.format(
args.solver,
args.epochs,
',epochSize'+str(args.epoch_size) if args.epoch_size > 0 else '',
args.batch_size,
args.lr)
save_path = Path(save_path)
if not args.no_date:
timestamp = datetime.datetime.now().strftime("%m-%d-%H:%M")
save_path = save_path/timestamp
args.save_path = Path('Results')/arch_string/folder_name/save_path
print('=> will save everything to {}'.format(save_path))
args.save_path.makedirs_p()
return args
def save_checkpoint(save_path, state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, save_path/filename)
if is_best:
shutil.copyfile(save_path/filename, save_path/'model_best.pth.tar')
class AverageMeter(object):
"""Computes and stores the average and current value."""
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
def adjust_learning_rate(optimizer, epoch):
# Set the learning rate to the initial LR decayed by 2 after 300K iterations, 400K and 500K
if epoch == 19 or epoch == 44:
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['lr']/2
if epoch == 30 or epoch == 53:
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['lr']/5
def tensor2array(tensor, max_value=255, colormap='rainbow'):
tensor = tensor.detach().cpu()
if max_value is None:
max_value = tensor.max().item()
if tensor.ndimension() == 2 or tensor.size(0) == 1:
try:
import cv2
if int(cv2.__version__[0]) >= 3:
color_cvt = cv2.COLOR_BGR2RGB
else: # 2.4
color_cvt = cv2.cv.CV_BGR2RGB
if colormap == 'rainbow':
colormap = cv2.COLORMAP_RAINBOW
elif colormap == 'bone':
colormap = cv2.COLORMAP_BONE
array = (255*tensor.squeeze().numpy()/max_value).clip(0, 255).astype(np.uint8)
colored_array = cv2.applyColorMap(array, colormap)
array = cv2.cvtColor(colored_array, color_cvt).astype(np.float32)/255
except ImportError:
if tensor.ndimension() == 2:
tensor.unsqueeze_(2)
array = (tensor.expand(tensor.size(0), tensor.size(1), 3).numpy()/max_value).clip(0,1)
array = array.transpose(2, 0, 1)
elif tensor.ndimension() == 3:
assert(tensor.size(0) == 3)
array = 0.5 + tensor.numpy()*0.5
return array