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Runtime error
| from options.train_options import TrainOptions | |
| from data.data_loader import CreateDataLoader | |
| from models.models import create_model | |
| import os | |
| import util.util as util | |
| from torch.autograd import Variable | |
| import torch.nn as nn | |
| opt = TrainOptions().parse() | |
| opt.nThreads = 1 | |
| opt.batchSize = 1 | |
| opt.serial_batches = True | |
| opt.no_flip = True | |
| opt.instance_feat = True | |
| name = 'features' | |
| save_path = os.path.join(opt.checkpoints_dir, opt.name) | |
| ############ Initialize ######### | |
| data_loader = CreateDataLoader(opt) | |
| dataset = data_loader.load_data() | |
| dataset_size = len(data_loader) | |
| model = create_model(opt) | |
| util.mkdirs(os.path.join(opt.dataroot, opt.phase + '_feat')) | |
| ######## Save precomputed feature maps for 1024p training ####### | |
| for i, data in enumerate(dataset): | |
| print('%d / %d images' % (i+1, dataset_size)) | |
| feat_map = model.module.netE.forward(Variable(data['image'].cuda(), volatile=True), data['inst'].cuda()) | |
| feat_map = nn.Upsample(scale_factor=2, mode='nearest')(feat_map) | |
| image_numpy = util.tensor2im(feat_map.data[0]) | |
| save_path = data['path'][0].replace('/train_label/', '/train_feat/') | |
| util.save_image(image_numpy, save_path) |