continue for k in xrange(dets.shape[0]): f.write('{:f} {:f} {:f} {:f} {:.32f}\n'.format(\ dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4])) def evaluate_proposals_msr(self, all_boxes, output_dir): # for each image for im_ind, index in enumerate(self.image_index): filename = os.path.join(output_dir, index + '.txt') print 'Writing PASCAL results to file ' + filename with open(filename, 'wt') as f: dets = all_boxes[im_ind] if dets == []: continue for k in xrange(dets.shape[0]): f.write('{:f} {:f} {:f} {:f} {:.32f}\n'.format(dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4])) def competition_mode(self, on): if on: self.config['use_salt'] = False self.config['cleanup'] = False else: self.config['use_salt'] = True self.config['cleanup'] = True if __name__ == '__main__': d = datasets.pascal3d('train') res = d.roidb from IPython import embed; embed()
dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4])) def evaluate_proposals_msr(self, all_boxes, output_dir): # for each image for im_ind, index in enumerate(self.image_index): filename = os.path.join(output_dir, index + '.txt') print 'Writing PASCAL results to file ' + filename with open(filename, 'wt') as f: dets = all_boxes[im_ind] if dets == []: continue for k in xrange(dets.shape[0]): f.write('{:f} {:f} {:f} {:f} {:.32f}\n'.format( dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4])) def competition_mode(self, on): if on: self.config['use_salt'] = False self.config['cleanup'] = False else: self.config['use_salt'] = True self.config['cleanup'] = True if __name__ == '__main__': d = datasets.pascal3d('train') res = d.roidb from IPython import embed embed()
def get_data_loaders(dataset, batch_size, num_workers, model, num_classes=12): image_size = 227 train_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=(0., 0., 0.), std=(1. / 255., 1. / 255., 1. / 255.) ), transforms.Normalize(mean=(104, 116.668, 122.678), std=(1., 1., 1.) ) ]) test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=(0., 0., 0.), std=(1. / 255., 1. / 255., 1. / 255.) ), transforms.Normalize(mean=(104, 116.668, 122.678), std=(1., 1., 1.) ) ]) # # The New transform for ImageNet Stuff # new_transform = transforms.Compose([ # transforms.ToTensor(), # transforms.Normalize(mean=(0.485, 0.456, 0.406), # std=(0.229, 0.224, 0.225))]) if dataset == "pascal": csv_train = os.path.join(root_dir, 'data/pascal3d_train.csv') csv_test = os.path.join(root_dir, 'data/pascal3d_valid.csv') # 生成训练数据集,测试数据集 train_set = pascal3d(csv_train, dataset_root=dataset_root, transform=train_transform, im_size=image_size) test_set = pascal3d(csv_test, dataset_root=dataset_root, transform=test_transform, im_size=image_size) elif dataset == "pascalEasy": csv_train = os.path.join(root_dir, 'data/pascal3d_train_easy.csv') csv_test = os.path.join(root_dir, 'data/pascal3d_valid_easy.csv') train_set = pascal3d(csv_train, dataset_root=dataset_root, transform=train_transform, im_size=image_size) test_set = pascal3d(csv_test, dataset_root=dataset_root, transform=test_transform, im_size=image_size) elif dataset == "pascalFull": csv_train = os.path.join(root_dir, 'data/train.csv') csv_test = os.path.join(root_dir, 'data/val.csv') train_set = pascal3d(csv_train, dataset_root=dataset_root, transform=train_transform, im_size=image_size) test_set = pascal3d(csv_test, dataset_root=dataset_root, transform=test_transform, im_size=image_size) else: print("Error in load_datasets: Dataset name not defined.") # Generate data loaders train_loader = torch.utils.data.DataLoader(dataset=train_set, batch_size=batch_size, shuffle=True, num_workers=num_workers, drop_last=True) test_loader = torch.utils.data.DataLoader(dataset=test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers, drop_last=False) return train_loader, test_loader