def get_data(data_dir, source, target, height, width, batch_size, re=0, workers=8): dataset = DA(data_dir, source, target) normalizer = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) num_classes = dataset.num_train_ids train_transformer = T.Compose([ T.RandomSizedRectCrop(height, width), T.RandomHorizontalFlip(), T.ToTensor(), normalizer, T.RandomErasing(EPSILON=re), ]) test_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.ToTensor(), normalizer, ]) source_train_loader = DataLoader( Preprocessor(dataset.source_train, root=osp.join(dataset.source_images_dir, dataset.source_train_path), transform=train_transformer), batch_size=batch_size, num_workers=workers, shuffle=True, pin_memory=True, drop_last=True) target_train_loader = DataLoader( UnsupervisedCamStylePreprocessor(dataset.target_train, root=osp.join(dataset.target_images_dir, dataset.target_train_path), camstyle_root=osp.join(dataset.target_images_dir, dataset.target_train_camstyle_path), num_cam=dataset.target_num_cam, transform=train_transformer), batch_size=batch_size, num_workers=workers, shuffle=True, pin_memory=True, drop_last=True) query_loader = DataLoader( Preprocessor(dataset.query, root=osp.join(dataset.target_images_dir, dataset.query_path), transform=test_transformer), batch_size=batch_size, num_workers=workers, shuffle=False, pin_memory=True) gallery_loader = DataLoader( Preprocessor(dataset.gallery, root=osp.join(dataset.target_images_dir, dataset.gallery_path), transform=test_transformer), batch_size=batch_size, num_workers=workers, shuffle=False, pin_memory=True) return dataset, num_classes, source_train_loader, target_train_loader, query_loader, gallery_loader
def get_data(data_dir, source, target, height, width, batch_size, num_instance=2, workers=8): dataset = DA(data_dir, source, target) normalizer = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) num_classes = dataset.num_train_ids train_transformer = T.Compose([ T.Resize((256, 128), interpolation=3), T.Pad(10), T.RandomCrop((256,128)), T.RandomHorizontalFlip(0.5), T.RandomRotation(5), T.ToTensor(), normalizer, ]) test_transformer = T.Compose([ T.Resize((256, 128), interpolation=3), T.ToTensor(), normalizer, ]) source_train_loader = DataLoader( Preprocessor_occluded(dataset.source_train, root=osp.join(dataset.source_images_dir, dataset.source_train_path), transform=train_transformer, train=True), batch_size=batch_size, num_workers=workers, sampler=IdentitySampler(dataset.source_train, num_instance), pin_memory=True, drop_last=True) query_loader = DataLoader( Preprocessor_occluded(dataset.query, root=osp.join(dataset.target_images_dir, dataset.query_path), transform=test_transformer), batch_size=42, num_workers=workers, shuffle=False, pin_memory=True) gallery_loader = DataLoader( Preprocessor_occluded(dataset.gallery, root=osp.join(dataset.target_images_dir, dataset.gallery_path), transform=test_transformer), batch_size=42, num_workers=workers, shuffle=False, pin_memory=True) return dataset, num_classes, source_train_loader, query_loader, gallery_loader
def get_data(data_dir, height, width, batch_size, num_instances, re=0, workers=8): dataset = DA(data_dir) test_dataset = TotalData(data_dir) normalizer = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) num_classes = dataset.num_source_ids train_transformer = T.Compose([ T.Resize((256, 128), interpolation=3), T.Pad(10), T.RandomCrop((256,128)), T.RandomHorizontalFlip(0.5), T.RandomRotation(5), T.ColorJitter(brightness=(0.5, 2.0), saturation=(0.5, 2.0), hue=(-0.1, 0.1)), T.ToTensor(), normalizer, # T.RandomErasing(EPSILON=re), ]) test_transformer = T.Compose([ T.Resize((256, 128), interpolation=3), T.ToTensor(), normalizer, ]) # Train source_train_loader = DataLoader( Preprocessor(dataset.source_train, transform=train_transformer), batch_size=batch_size, num_workers=workers, # shuffle=True, pin_memory=True, drop_last=True) sampler=RandomIdentitySampler(dataset.source_train, batch_size, num_instances), pin_memory=True, drop_last=True) # Test grid_query_loader = DataLoader( Preprocessor(test_dataset.grid_query, root=osp.join(test_dataset.grid_images_dir, test_dataset.query_path), transform=test_transformer), batch_size=64, num_workers=4, shuffle=False, pin_memory=True) grid_gallery_loader = DataLoader( Preprocessor(test_dataset.grid_gallery, root=osp.join(test_dataset.grid_images_dir, test_dataset.gallery_path), transform=test_transformer), batch_size=64, num_workers=4, shuffle=False, pin_memory=True) prid_query_loader = DataLoader( Preprocessor(test_dataset.prid_query, root=osp.join(test_dataset.prid_images_dir, test_dataset.query_path), transform=test_transformer), batch_size=64, num_workers=4, shuffle=False, pin_memory=True) prid_gallery_loader = DataLoader( Preprocessor(test_dataset.prid_gallery, root=osp.join(test_dataset.prid_images_dir, test_dataset.gallery_path), transform=test_transformer), batch_size=64, num_workers=4, shuffle=False, pin_memory=True) viper_query_loader = DataLoader( Preprocessor(test_dataset.viper_query, root=osp.join(test_dataset.viper_images_dir, test_dataset.query_path), transform=test_transformer), batch_size=64, num_workers=4, shuffle=False, pin_memory=True) viper_gallery_loader = DataLoader( Preprocessor(test_dataset.viper_gallery, root=osp.join(test_dataset.viper_images_dir, test_dataset.gallery_path), transform=test_transformer), batch_size=64, num_workers=4, shuffle=False, pin_memory=True) ilid_query_loader = DataLoader( Preprocessor(test_dataset.ilid_query, root=osp.join(test_dataset.ilid_images_dir, "images"), transform=test_transformer), batch_size=64, num_workers=4, shuffle=False, pin_memory=True) ilid_gallery_loader = DataLoader( Preprocessor(test_dataset.ilid_gallery, root=osp.join(test_dataset.ilid_images_dir, "images"), transform=test_transformer), batch_size=64, num_workers=4, shuffle=False, pin_memory=True) return dataset, test_dataset, num_classes, source_train_loader, grid_query_loader, grid_gallery_loader,prid_query_loader, prid_gallery_loader,viper_query_loader, viper_gallery_loader, ilid_query_loader, ilid_gallery_loader
def get_data(data_dir, source, target, height, width, batch_size, re=0, workers=8): dataset = DA(data_dir, source, target) dataset_2 = DA(data_dir, target, source) normalizer = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) num_classes = dataset.num_train_ids train_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.RandomHorizontalFlip(p=0.5), T.Pad(10), T.RandomCrop((height, width)), T.ToTensor(), normalizer, # T.RandomErasing(EPSILON=re), T.RandomErasing(probability=0.4, mean=[0.485, 0.456, 0.406]) ]) train_transformer_2 = T.Compose([ T.Resize((height, width), interpolation=3), T.RandomHorizontalFlip(p=0.5), T.Pad(10), T.RandomCrop((height, width)), T.ToTensor(), normalizer, # T.RandomErasing(EPSILON=re), T.RandomErasing(probability=0.4, mean=[0.485, 0.456, 0.406]) ]) test_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.ToTensor(), normalizer, ]) ''' num_instances=4 rmgs_flag = num_instances > 0 if rmgs_flag: sampler = RandomIdentitySampler(dataset.target_train, num_instances) else: sampler = None ''' source_train_loader = DataLoader( Preprocessor(dataset.source_train, root=osp.join(dataset.source_images_dir, dataset.source_train_path), transform=train_transformer), batch_size=batch_size, num_workers=workers, shuffle=True, pin_memory=True, drop_last=True) num_instances=0 rmgs_flag = num_instances > 0 if rmgs_flag: sampler = RandomIdentitySampler(dataset.target_train, num_instances) else: sampler = None target_train_loader = DataLoader( UnsupervisedCamStylePreprocessor(dataset.target_train, root=osp.join(dataset.target_images_dir, dataset.target_train_path), camstyle_root=osp.join(dataset.target_images_dir, dataset.target_train_camstyle_path), num_cam=dataset.target_num_cam, transform=train_transformer), batch_size=batch_size, num_workers=workers, shuffle=True, pin_memory=True, drop_last=True) query_loader = DataLoader( Preprocessor(dataset.query, root=osp.join(dataset.target_images_dir, dataset.query_path), transform=test_transformer), batch_size=batch_size, num_workers=workers, shuffle=False, pin_memory=True) query_loader_2 = DataLoader( Preprocessor(dataset_2.query, root=osp.join(dataset_2.target_images_dir, dataset_2.query_path), transform=test_transformer), batch_size=batch_size, num_workers=workers, shuffle=False, pin_memory=True) gallery_loader = DataLoader( Preprocessor(dataset.gallery, root=osp.join(dataset.target_images_dir, dataset.gallery_path), transform=test_transformer), batch_size=batch_size, num_workers=workers, shuffle=False, pin_memory=True) gallery_loader_2 = DataLoader( Preprocessor(dataset_2.gallery, root=osp.join(dataset_2.target_images_dir, dataset_2.gallery_path), transform=test_transformer), batch_size=batch_size, num_workers=workers, shuffle=False, pin_memory=True) return dataset,dataset_2, num_classes, source_train_loader, target_train_loader, query_loader, gallery_loader, query_loader_2, gallery_loader_2