Datasets:
jblitzar
/
github-python-corpus

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lab[~mask] = 7.787 * lab[~mask] + 16. / 116.
x, y, z = lab[..., 0:1], lab[..., 1:2], lab[..., 2:3]
# Vector scaling
L = (116. * y) - 16.
a = 500.0 * (x - y)
b = 200.0 * (y - z)
return torch.cat([L, a, b], dim=-1).permute(0,3,1,2)
def label2one_hot_torch(labels, C=14):
# w.r.t http://jacobkimmel.github.io/pytorch_onehot/
'''
Converts an integer label torch.autograd.Variable to a one-hot Variable.
Parameters
----------
labels : torch.autograd.Variable of torch.cuda.LongTensor
N x 1 x H x W, where N is batch size.
Each value is an integer representing correct classification.
C : integer.
number of classes in labels.
Returns
-------
target : torch.cuda.FloatTensor
N x C x H x W, where C is class number. One-hot encoded.
'''
b,_, h, w = labels.shape
one_hot = torch.zeros(b, C, h, w, dtype=torch.long).cuda()
target = one_hot.scatter_(1, labels.type(torch.long).data, 1) #require long type
return target.type(torch.float32)
# <FILESEP>
import torch
import data as Data
import model as Model
import argparse
import logging
import core.logger as Logger
from tensorboardX import SummaryWriter
import os
import numpy as np
from math import *
import time
from util.visualizer import Visualizer
from PIL import Image
def save_image(image_numpy, image_path):
image_pil = Image.fromarray(image_numpy.astype('uint8'))
image_pil.save(image_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, default='config/test.json', help='JSON file for configuration')
parser.add_argument('-p', '--phase', type=str, choices=['train', 'test'],
help='Run either train(training) or test(inference)', default='train')
parser.add_argument('-gpu', '--gpu_ids', type=str, default=None)
parser.add_argument('-debug', '-d', action='store_true')
# parse configs
args = parser.parse_args()
opt = Logger.parse(args)
# Convert to NoneDict, which return None for missing key.
opt = Logger.dict_to_nonedict(opt)
visualizer = Visualizer(opt)
# logging
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
Logger.setup_logger(None, opt['path']['log'], 'train', level=logging.INFO, screen=True)
logger = logging.getLogger('base')
logger.info(Logger.dict2str(opt))
tb_logger = SummaryWriter(log_dir=opt['path']['tb_logger'])
batchSize = opt['datasets']['train']['batch_size']
# dataset
for phase, dataset_opt in opt['datasets'].items():
if phase == 'train':
train_set = Data.create_dataset_xcad(dataset_opt, phase)
train_loader = Data.create_dataloader(train_set, dataset_opt, phase)
training_iters = int(ceil(train_set.data_len / float(batchSize)))
val_set = Data.create_dataset_xcad(dataset_opt, 'val')
val_loader = Data.create_dataloader(val_set, dataset_opt, 'val')
valid_iters = int(ceil(val_set.data_len / float(batchSize)))
elif phase == 'test':
val_set = Data.create_dataset_xcad(dataset_opt, 'test')
val_loader = Data.create_dataloader(val_set, dataset_opt, phase)
valid_iters = int(ceil(val_set.data_len / float(batchSize)))
logger.info('Initial Dataset Finished')
# model
diffusion = Model.create_model(opt)
logger.info('Initial Model Finished')
# Train