import dense_correspondence_manipulation.utils.utils as utils
utils.add_dense_correspondence_to_python_path()
from dense_correspondence.training.training import *
import sys
import logging
#utils.set_default_cuda_visible_devices()
# utils.set_cuda_visible_devices([0]) # use this to manually set CUDA_VISIBLE_DEVICES
from dense_correspondence.training.training import DenseCorrespondenceTraining
from dense_correspondence.dataset.spartan_dataset_masked import SpartanDataset
logging.basicConfig(level=logging.INFO)
from dense_correspondence.evaluation.evaluation import DenseCorrespondenceEvaluation
config_filename = os.path.join(utils.getDenseCorrespondenceSourceDir(), 'config', 'dense_correspondence',
'dataset', 'composite', 'toy.yaml')
config = utils.getDictFromYamlFilename(config_filename)
train_config_file = os.path.join(utils.getDenseCorrespondenceSourceDir(), 'config', 'dense_correspondence',
'training', 'toy_training.yaml')
train_config = utils.getDictFromYamlFilename(train_config_file)
dataset = SpartanDataset(config=config)
logging_dir = "/home/zhouxian/git/pytorch-dense-correspondence/pdc/trained_models/tutorials"
d = 3 # the descriptor dimension
name = "toy_hacker_%d" %(d)
train_config["training"]["logging_dir_name"] = name
train_config["training"]["logging_dir"] = logging_dir
train_config["dense_correspondence_network"]["descriptor_dimension"] = d
#!/usr/bin/python
import sys, os
import numpy as np
import logging
import dense_correspondence_manipulation.utils.utils as utils
utils.add_dense_correspondence_to_python_path()
from PIL import Image
import torch
import torch.nn as nn
from torchvision import transforms
from torch.autograd import Variable
import pytorch_segmentation_detection.models.resnet_dilated as resnet_dilated
from dense_correspondence.dataset.spartan_dataset_masked import SpartanDataset
class DenseCorrespondenceNetwork(nn.Module):
IMAGE_TO_TENSOR = valid_transform = transforms.Compose([transforms.ToTensor(), ])
def __init__(self, fcn, descriptor_dimension, image_width=640,
image_height=480):
super(DenseCorrespondenceNetwork, self).__init__()
self._fcn = fcn
self._descriptor_dimension = descriptor_dimension
