def export_weights_to_model(weights_path, model_path, architecture, dataset, area1layers=None): # TODO: instead of passings args, pass them separately class dotdict(dict): def __getattr__(self, name): return self[name] args = {} target_size = default_configs.get_default_target_size(dataset) target_size = (target_size, target_size) args['input_shape'] = get_input_shape(target_size) args['network_name'] = architecture args['num_classes'] = default_configs.get_default_num_classes(dataset) args['area1layers'] = area1layers args = dotdict(args) model = which_architecture(args) model.load_weights(weights_path) model.save(model_path)
def main_worker(ngpus_per_node, args): mean, std = model_utils.get_preprocessing_function(args.colour_space, args.vision_type) if args.gpus is not None: print("Use GPU: {} for training".format(args.gpus)) if args.distributed: if args.dist_url == "env://" and args.rank == -1: args.rank = int(os.environ["RANK"]) if args.multiprocessing_distributed: # For multiprocessing distributed training, rank needs to be the # global rank among all the processes args.rank = args.rank * ngpus_per_node + args.gpus dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank) # create model if args.transfer_weights is not None: print('Transferred model!') (model, _) = model_utils.which_network(args.transfer_weights[0], args.task_type, num_classes=args.old_classes) which_layer = -1 if len(args.transfer_weights) == 2: which_layer = args.transfer_weights[1] model = model_utils.NewClassificationModel(model, which_layer, args.num_classes) elif args.custom_arch: print('Custom model!') supported_customs = ['resnet_basic_custom', 'resnet_bottleneck_custom'] if os.path.isfile(args.network_name): checkpoint = torch.load(args.network_name, map_location='cpu') customs = None if 'customs' in checkpoint: customs = checkpoint['customs'] # TODO: num_classes is just for backward compatibility if 'num_classes' not in customs: customs['num_classes'] = 1000 model = which_architecture(checkpoint['arch'], customs, args.contrast_head) args.network_name = checkpoint['arch'] model.load_state_dict(checkpoint['state_dict'], strict=False) elif args.network_name in supported_customs: model = custom_models.__dict__[args.network_name]( args.blocks, contrast_head=args.contrast_head, pooling_type=args.pooling_type, in_chns=len(mean), num_classes=args.num_classes, inplanes=args.num_kernels, kernel_size=args.kernel_size) elif args.pretrained: print("=> using pre-trained model '{}'".format(args.network_name)) model = models.__dict__[args.network_name](pretrained=True) else: print("=> creating model '{}'".format(args.network_name)) model = models.__dict__[args.network_name]() # TODO: why load weights is False? args.out_dir = prepare_training.prepare_output_directories( dataset_name='contrast', network_name=args.network_name, optimiser='sgd', load_weights=False, experiment_name=args.experiment_name, framework='pytorch') # preparing the output folder create_dir(args.out_dir) json_file_name = os.path.join(args.out_dir, 'args.json') with open(json_file_name, 'w') as fp: json.dump(dict(args._get_kwargs()), fp, sort_keys=True, indent=4) if args.distributed: # For multiprocessing distributed, DistributedDataParallel constructor # should always set the single device scope, otherwise, # DistributedDataParallel will use all available devices. if args.gpus is not None: torch.cuda.set_device(args.gpus) model.cuda(args.gpus) # When using a single GPU per process and per # DistributedDataParallel, we need to divide the batch size # ourselves based on the total number of GPUs we have args.batch_size = int(args.batch_size / ngpus_per_node) args.workers = int(args.workers / ngpus_per_node) model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.gpus]) else: model.cuda() # DistributedDataParallel will divide and allocate batch_size to all # available GPUs if device_ids are not set model = torch.nn.parallel.DistributedDataParallel(model) elif args.gpus is not None: torch.cuda.set_device(args.gpus) model = model.cuda(args.gpus) else: # DataParallel will divide and allocate batch_size to all available GPUs if (args.network_name.startswith('alexnet') or args.network_name.startswith('vgg')): model.features = torch.nn.DataParallel(model.features) model.cuda() else: model = torch.nn.DataParallel(model).cuda() # define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss().cuda(args.gpus) # optimiser if args.transfer_weights is None: optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay) else: # for p in model.features.parameters(): # p.requires_grad = False params_to_optimize = [ { 'params': [p for p in model.features.parameters()], 'lr': 1e-6 }, { 'params': [p for p in model.fc.parameters()] }, ] optimizer = torch.optim.SGD(params_to_optimize, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) model_progress = [] model_progress_path = os.path.join(args.out_dir, 'model_progress.csv') # optionally resume from a checkpoint # TODO: it would be best if resume load the architecture from this file # TODO: merge with which_architecture best_acc1 = 0 if args.resume is not None: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume, map_location='cpu') args.initial_epoch = checkpoint['epoch'] best_acc1 = checkpoint['best_acc1'] model.load_state_dict(checkpoint['state_dict']) if args.gpus is not None: # best_acc1 may be from a checkpoint from a different GPU best_acc1 = best_acc1.to(args.gpus) model = model.cuda(args.gpus) optimizer.load_state_dict(checkpoint['optimizer']) print("=> loaded checkpoint '{}' (epoch {})".format( args.resume, checkpoint['epoch'])) if os.path.exists(model_progress_path): model_progress = np.loadtxt(model_progress_path, delimiter=',') model_progress = model_progress.tolist() else: print("=> no checkpoint found at '{}'".format(args.resume)) cudnn.benchmark = True train_trans = [] valid_trans = [] both_trans = [] if args.mosaic_pattern is not None: mosaic_trans = preprocessing.mosaic_transformation(args.mosaic_pattern) both_trans.append(mosaic_trans) if args.num_augmentations != 0: augmentations = preprocessing.random_augmentation( args.augmentation_settings, args.num_augmentations) train_trans.append(augmentations) target_size = default_configs.get_default_target_size( args.dataset, args.target_size) # loading the training set train_trans = [*both_trans, *train_trans] db_params = { 'colour_space': args.colour_space, 'vision_type': args.vision_type, 'mask_image': args.mask_image } if args.dataset in ['imagenet', 'celeba', 'natural']: path_or_sample = args.data_dir else: path_or_sample = args.train_samples train_dataset = dataloader.train_set(args.dataset, target_size, mean, std, extra_transformation=train_trans, data_dir=path_or_sample, **db_params) if args.dataset == 'natural': train_dataset.num_crops = args.batch_size args.batch_size = 1 if args.distributed: train_sampler = torch.utils.data.distributed.DistributedSampler( train_dataset) else: train_sampler = None train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, pin_memory=True, sampler=train_sampler) # loading validation set valid_trans = [*both_trans, *valid_trans] validation_dataset = dataloader.validation_set( args.dataset, target_size, mean, std, extra_transformation=valid_trans, data_dir=path_or_sample, **db_params) if args.dataset == 'natural': validation_dataset.num_crops = train_dataset.num_crops args.batch_size = 1 val_loader = torch.utils.data.DataLoader(validation_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True) # training on epoch for epoch in range(args.initial_epoch, args.epochs): if args.distributed: train_sampler.set_epoch(epoch) misc_utils.adjust_learning_rate(optimizer, epoch, args) # train for one epoch train_log = train_on_data(train_loader, model, criterion, optimizer, epoch, args) # evaluate on validation set validation_log = validate_on_data(val_loader, model, criterion, args) model_progress.append([*train_log, *validation_log]) # remember best acc@1 and save checkpoint acc1 = validation_log[2] is_best = acc1 > best_acc1 best_acc1 = max(acc1, best_acc1) if misc_utils.is_saving_node(args.multiprocessing_distributed, args.rank, ngpus_per_node): misc_utils.save_checkpoint( { 'epoch': epoch + 1, 'arch': args.network_name, 'customs': { 'pooling_type': args.pooling_type, 'in_chns': len(mean), 'num_classes': args.num_classes, 'blocks': args.blocks, 'num_kernels': args.num_kernels, 'kernel_size': args.kernel_size }, 'preprocessing': { 'mean': mean, 'std': std }, 'state_dict': model.state_dict(), 'best_acc1': best_acc1, 'optimizer': optimizer.state_dict(), 'target_size': target_size, }, is_best, out_folder=args.out_dir) # TODO: get this header directly as a dictionary keys header = 'epoch,t_time,t_loss,t_top1,t_top5,v_time,v_loss,v_top1,v_top5' np.savetxt(model_progress_path, np.array(model_progress), delimiter=',', header=header)
def main_worker(ngpus_per_node, args): mean, std = model_utils.get_preprocessing_function(args.colour_space, args.vision_type) # preparing the output folder create_dir(args.out_dir) if args.gpus is not None: print("Use GPU: {} for training".format(args.gpus)) if args.distributed: if args.dist_url == "env://" and args.rank == -1: args.rank = int(os.environ["RANK"]) if args.multiprocessing_distributed: # For multiprocessing distributed training, rank needs to be the # global rank among all the processes args.rank = args.rank * ngpus_per_node + args.gpus dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank) # create model if args.transfer_weights is not None: print('Transferred model!') model = contrast_utils.AFCModel(args.network_name, args.transfer_weights) elif args.custom_arch: print('Custom model!') supported_customs = ['resnet_basic_custom', 'resnet_bottleneck_custom'] if args.network_name in supported_customs: model = custom_models.__dict__[args.network_name]( args.blocks, pooling_type=args.pooling_type, in_chns=len(mean), num_classes=args.num_classes, inplanes=args.num_kernels, kernel_size=args.kernel_size) elif args.pretrained: print("=> using pre-trained model '{}'".format(args.network_name)) model = models.__dict__[args.network_name](pretrained=True) else: print("=> creating model '{}'".format(args.network_name)) model = models.__dict__[args.network_name]() if args.distributed: # For multiprocessing distributed, DistributedDataParallel constructor # should always set the single device scope, otherwise, # DistributedDataParallel will use all available devices. if args.gpus is not None: torch.cuda.set_device(args.gpus) model.cuda(args.gpus) # When using a single GPU per process and per # DistributedDataParallel, we need to divide the batch size # ourselves based on the total number of GPUs we have args.batch_size = int(args.batch_size / ngpus_per_node) args.workers = int(args.workers / ngpus_per_node) model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.gpus]) else: model.cuda() # DistributedDataParallel will divide and allocate batch_size to all # available GPUs if device_ids are not set model = torch.nn.parallel.DistributedDataParallel(model) elif args.gpus is not None: torch.cuda.set_device(args.gpus) model = model.cuda(args.gpus) else: # DataParallel will divide and allocate batch_size to all available GPUs if (args.network_name.startswith('alexnet') or args.network_name.startswith('vgg')): model.features = torch.nn.DataParallel(model.features) model.cuda() else: model = torch.nn.DataParallel(model).cuda() # define loss function (criterion) and optimizer criterion = soft_cross_entropy # optimiser if args.transfer_weights is None: optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay) else: params_to_optimize = [ { 'params': [p for p in model.parameters() if p.requires_grad] }, ] optimizer = torch.optim.SGD(params_to_optimize, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) model_progress = [] model_progress_path = os.path.join(args.out_dir, 'model_progress.csv') # optionally resume from a checkpoint # TODO: it would be best if resume load the architecture from this file # TODO: merge with which_architecture best_acc1 = 0 if args.resume is not None: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume, map_location='cpu') args.initial_epoch = checkpoint['epoch'] best_acc1 = checkpoint['best_acc1'] model.load_state_dict(checkpoint['state_dict']) if args.gpus is not None: # best_acc1 may be from a checkpoint from a different GPU best_acc1 = best_acc1.to(args.gpus) model = model.cuda(args.gpus) optimizer.load_state_dict(checkpoint['optimizer']) print("=> loaded checkpoint '{}' (epoch {})".format( args.resume, checkpoint['epoch'])) if os.path.exists(model_progress_path): model_progress = np.loadtxt(model_progress_path, delimiter=',') model_progress = model_progress.tolist() else: print("=> no checkpoint found at '{}'".format(args.resume)) cudnn.benchmark = True train_trans = [] valid_trans = [] both_trans = [] if args.mosaic_pattern is not None: mosaic_trans = preprocessing.mosaic_transformation(args.mosaic_pattern) both_trans.append(mosaic_trans) if args.num_augmentations != 0: augmentations = preprocessing.random_augmentation( args.augmentation_settings, args.num_augmentations) train_trans.append(augmentations) target_size = default_configs.get_default_target_size( args.dataset, args.target_size) final_trans = [ cv2_transforms.ToTensor(), cv2_transforms.Normalize(mean, std), ] train_trans.append( cv2_transforms.RandomResizedCrop(target_size, scale=(0.08, 1.0))) # loading the training set train_trans = torch_transforms.Compose( [*both_trans, *train_trans, *final_trans]) train_dataset = image_quality.BAPPS2afc(root=args.data_dir, split='train', transform=train_trans, concat=0.5) if args.distributed: train_sampler = torch.utils.data.distributed.DistributedSampler( train_dataset) else: train_sampler = None train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, pin_memory=True, sampler=train_sampler) valid_trans.extend([ cv2_transforms.Resize(target_size), cv2_transforms.CenterCrop(target_size), ]) # loading validation set valid_trans = torch_transforms.Compose( [*both_trans, *valid_trans, *final_trans]) validation_dataset = image_quality.BAPPS2afc(root=args.data_dir, split='val', transform=valid_trans, concat=0) val_loader = torch.utils.data.DataLoader(validation_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True) # training on epoch for epoch in range(args.initial_epoch, args.epochs): if args.distributed: train_sampler.set_epoch(epoch) misc_utils.adjust_learning_rate(optimizer, epoch, args) # train for one epoch train_log = train_on_data(train_loader, model, criterion, optimizer, epoch, args) # evaluate on validation set validation_log = validate_on_data(val_loader, model, criterion, args) model_progress.append([*train_log, *validation_log]) # remember best acc@1 and save checkpoint acc1 = validation_log[2] is_best = acc1 > best_acc1 best_acc1 = max(acc1, best_acc1) if misc_utils.is_saving_node(args.multiprocessing_distributed, args.rank, ngpus_per_node): misc_utils.save_checkpoint( { 'epoch': epoch + 1, 'arch': args.network_name, 'customs': { 'pooling_type': args.pooling_type, 'in_chns': len(mean), 'num_classes': args.num_classes, 'blocks': args.blocks, 'num_kernels': args.num_kernels, 'kernel_size': args.kernel_size }, 'transfer_weights': args.transfer_weights, 'preprocessing': { 'mean': mean, 'std': std }, 'state_dict': model.state_dict(), 'best_acc1': best_acc1, 'optimizer': optimizer.state_dict(), 'target_size': target_size, }, is_best, out_folder=args.out_dir) # TODO: get this header directly as a dictionary keys header = 'epoch,t_time,t_loss,t_top5,v_time,v_loss,v_top1' np.savetxt(model_progress_path, np.array(model_progress), delimiter=',', header=header)
def main_worker(ngpus_per_node, args): global best_acc1 is_pill_img = 'wcs_xyz_png_1600' in args.data_dir mean, std = model_utils.get_preprocessing_function(args.colour_space, args.vision_type) # preparing the output folder create_dir(args.out_dir) if args.gpus is not None: print("Use GPU: {} for training".format(args.gpus)) if args.distributed: if args.dist_url == "env://" and args.rank == -1: args.rank = int(os.environ["RANK"]) if args.multiprocessing_distributed: # For multiprocessing distributed training, rank needs to be the # global rank among all the processes args.rank = args.rank * ngpus_per_node + args.gpus dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank) # create model if args.prediction: checkpoint = torch.load(args.network_weights, map_location='cpu') blocks = checkpoint['customs']['blocks'] pooling_type = checkpoint['customs']['pooling_type'] num_kernels = checkpoint['customs']['num_kernels'] outputs = checkpoint['customs']['outputs'] for key, val in outputs.items(): if 'area' not in val: outputs[key] = None model = resnet.__dict__[args.network_name](blocks, pooling_type=pooling_type, in_chns=len(mean), inplanes=num_kernels, outputs=outputs) model.load_state_dict(checkpoint['state_dict']) elif args.transfer_weights is not None: print('Transferred model!') (model, _) = model_utils.which_network(args.transfer_weights, args.task_type, num_classes=args.old_classes) model = model_utils.NewClassificationModel(model, args.num_classes) elif args.custom_arch: print('Custom model!') if (args.network_name == 'resnet_basic_custom' or args.network_name == 'resnet_bottleneck_custom'): outputs = {'objects': None, 'munsells': None, 'illuminants': None} imagenet_weights = args.imagenet_weights if args.object_area is not None: outputs['objects'] = { 'num_classes': 2100, 'area': args.object_area } if args.munsell_area is not None: outputs['munsells'] = { 'num_classes': 1600, 'area': args.munsell_area } if args.illuminant_area is not None: outputs['illuminants'] = { 'num_classes': 280, 'area': args.illuminant_area } model = resnet.__dict__[args.network_name]( args.blocks, pooling_type=args.pooling_type, in_chns=len(mean), inplanes=args.num_kernels, outputs=outputs, imagenet_weights=imagenet_weights) elif args.pretrained: print("=> using pre-trained model '{}'".format(args.network_name)) model = models.__dict__[args.network_name](pretrained=True) else: print("=> creating model '{}'".format(args.network_name)) model = models.__dict__[args.network_name]() if args.distributed: # For multiprocessing distributed, DistributedDataParallel constructor # should always set the single device scope, otherwise, # DistributedDataParallel will use all available devices. if args.gpus is not None: torch.cuda.set_device(args.gpus) model.cuda(args.gpus) # When using a single GPU per process and per # DistributedDataParallel, we need to divide the batch size # ourselves based on the total number of GPUs we have args.batch_size = int(args.batch_size / ngpus_per_node) args.workers = int(args.workers / ngpus_per_node) model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.gpus]) else: model.cuda() # DistributedDataParallel will divide and allocate batch_size to all # available GPUs if device_ids are not set model = torch.nn.parallel.DistributedDataParallel(model) elif args.gpus is not None: torch.cuda.set_device(args.gpus) model = model.cuda(args.gpus) else: # DataParallel will divide and allocate batch_size to all available GPUs if (args.network_name.startswith('alexnet') or args.network_name.startswith('vgg')): model.features = torch.nn.DataParallel(model.features) model.cuda() else: model = torch.nn.DataParallel(model).cuda() # define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss().cuda(args.gpus) # optimiser optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay) model_progress = [] model_progress_path = os.path.join(args.out_dir, 'model_progress.csv') # optionally resume from a checkpoint # TODO: it would be best if resume load the architecture from this file # TODO: merge with which_architecture if args.resume is not None: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume, map_location='cpu') args.initial_epoch = checkpoint['epoch'] best_acc1 = checkpoint['best_acc1'] model.load_state_dict(checkpoint['state_dict']) if args.gpus is not None: # best_acc1 may be from a checkpoint from a different GPU best_acc1 = best_acc1.to(args.gpus) model = model.cuda(args.gpus) optimizer.load_state_dict(checkpoint['optimizer']) print("=> loaded checkpoint '{}' (epoch {})".format( args.resume, checkpoint['epoch'])) if os.path.exists(model_progress_path): model_progress = np.loadtxt(model_progress_path, delimiter=',') model_progress = model_progress.tolist() else: print("=> no checkpoint found at '{}'".format(args.resume)) cudnn.benchmark = True normalize = transforms.Normalize(mean=mean, std=std) other_transformations = [] if args.num_augmentations != 0: augmentations = preprocessing.random_augmentation( args.augmentation_settings, args.num_augmentations) other_transformations.append(augmentations) target_size = get_default_target_size(args.dataset, args.target_size) train_dataset, validation_dataset = get_train_val_dataset( args.data_dir, other_transformations, [], normalize, args.imagenet_weights) if args.distributed: train_sampler = torch.utils.data.distributed.DistributedSampler( train_dataset) else: train_sampler = None if args.prediction: manipulation_values = args.parameters['kwargs'][args.manipulation] manipulation_name = args.parameters['f_name'] for j, manipulation_value in enumerate(manipulation_values): args.parameters['kwargs'][args.manipulation] = manipulation_value prediction_transformation = preprocessing.prediction_transformation( args.parameters, args.colour_space, tmp_c_space(manipulation_name)) other_transformations = [prediction_transformation] _, validation_dataset = get_train_val_dataset( args.data_dir, other_transformations, other_transformations, normalize, args.imagenet_weights) val_loader = torch.utils.data.DataLoader( validation_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True) pred_log = predict(val_loader, model, criterion, torch.device(args.gpus)) from kernelphysiology.dl.utils import prepapre_testing prepapre_testing.save_predictions(pred_log, args.experiment_name, args.pred_name, args.dataset, manipulation_name, manipulation_value) return val_loader = torch.utils.data.DataLoader(validation_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, pin_memory=True, sampler=train_sampler) if args.ill_colour is not None: print('Performing with illuminant correction') args.ill_colour = np.loadtxt(args.ill_colour, delimiter=',') # training on epoch for epoch in range(args.initial_epoch, args.epochs): if args.distributed: train_sampler.set_epoch(epoch) if args.imagenet_weights is None: adjust_learning_rate(optimizer, epoch, args) # train for one epoch train_log = train_on_data(train_loader, model, criterion, optimizer, epoch, args) # evaluate on validation set validation_log = validate_on_data(val_loader, model, criterion, args) model_progress.append([*train_log, *validation_log]) # remember best acc@1 and save checkpoint acc1 = validation_log[2] is_best = acc1 > best_acc1 best_acc1 = max(acc1, best_acc1) if not args.multiprocessing_distributed or ( args.multiprocessing_distributed and args.rank % ngpus_per_node == 0): save_checkpoint( { 'epoch': epoch + 1, 'arch': args.network_name, 'customs': { 'pooling_type': args.pooling_type, 'in_chns': len(mean), 'num_classes': args.num_classes, 'blocks': args.blocks, 'num_kernels': args.num_kernels, 'outputs': outputs }, 'state_dict': model.state_dict(), 'best_acc1': best_acc1, 'optimizer': optimizer.state_dict(), 'target_size': target_size, }, is_best, out_folder=args.out_dir) # TODO: get this header directly as a dictionary keys header = 'epoch,t_time,t_loss,t_lo,t_lm,t_li,t_ao,t_am,t_ai,' \ 'v_time,v_loss,v_lo,v_lm,v_li,v_ao,v_am,v_ai' np.savetxt(model_progress_path, np.array(model_progress), delimiter=',', header=header)
def check_common_args(parser, argvs, script_type): # HINT: this is just in order to get rid of EXIF warnings warnings.filterwarnings('ignore', '.*(Possibly )?corrupt EXIF data.*', UserWarning) warnings.filterwarnings('ignore', '.*is a low contrast image.*', UserWarning) args = parser.parse_args(argvs) args.script_type = script_type # checking whether number of classes is provided args.num_classes = default_configs.get_num_classes(args.dataset, args.num_classes) # checking whether traindir and valdir are provided if args.data_dir is not None: if 'cifar' in args.dataset: args.train_dir = args.data_dir args.validation_dir = args.data_dir else: args.train_dir = os.path.join(args.data_dir, 'train') args.validation_dir = os.path.join(args.data_dir, 'validation') else: args.train_dir = args.train_dir args.validation_dir = args.validation_dir # setting task type args.task_type = check_task_type(args.dataset, args.task_type) # setting the target size args.target_size = default_configs.get_default_target_size( args.dataset, args.target_size) args.target_size = (args.target_size, args.target_size) # setting the batch size if args.batch_size is None: if args.dataset == 'imagenet': if args.script_type == 'training': args.batch_size = 32 if args.script_type == 'testing': args.batch_size = 64 if args.script_type == 'activation': args.batch_size = 32 elif 'cifar' in args.dataset or 'stl' in args.dataset: if args.script_type == 'training': args.batch_size = 256 if args.script_type == 'testing': args.batch_size = 512 if args.script_type == 'activation': args.batch_size = 256 else: if args.script_type == 'training': args.batch_size = 32 if args.script_type == 'testing': args.batch_size = 64 if args.script_type == 'activation': args.batch_size = 32 warnings.warn('default batch_size are used for dataset %s' % args.dataset) args.gpus = system_utils.set_visible_gpus(args.gpus) # handling the paths (args.train_dir, args.validation_dir, args.data_dir) = default_configs.get_default_dataset_paths( args.dataset, args.train_dir, args.validation_dir, args.data_dir, args.script_type) return args
def generic_evaluation(args, fn, save_fn=None, **kwargs): manipulation_values = args.parameters['kwargs'][args.manipulation] manipulation_name = args.parameters['f_name'] other_mans = args.parameters['others'] for j, current_network in enumerate(args.network_files): # which architecture (model, target_size) = model_utils.which_network( current_network, args.task_type, num_classes=args.num_classes, kill_kernels=args.kill_kernels, kill_planes=args.kill_planes, kill_lines=args.kill_lines) model.to(args.device) mean, std = model_utils.get_preprocessing_function( args.colour_space, args.network_chromaticities[j]) normalize = transforms.Normalize(mean=mean, std=std) for i, manipulation_value in enumerate(manipulation_values): args.parameters['kwargs'][args.manipulation] = manipulation_value output_file = prepapre_testing._prepare_saving_file( args.experiment_name, args.network_names[j], args.dataset, manipulation_name, manipulation_value, extension='csv') if os.path.exists(output_file): continue if args.task_type == 'segmentation' or 'voc' in args.dataset: prediction_transformation = preprocessing.prediction_transformation_seg( args.parameters, args.colour_space, tmp_c_space(manipulation_name)) else: prediction_transformation = preprocessing.prediction_transformation( args.parameters, args.colour_space, tmp_c_space(manipulation_name)) colour_vision = 'trichromat' if _requires_colour_transform(manipulation_name, args.network_chromaticities[j]): colour_vision = args.network_chromaticities[j] other_transformations = [] for oth_man in other_mans: if args.task_type == 'segmentation' or 'voc' in args.dataset: other_transformations.append( preprocessing.prediction_transformation_seg( oth_man, args.colour_space, tmp_c_space(oth_man['f_name']))) else: other_transformations.append( preprocessing.prediction_transformation( oth_man, args.colour_space, tmp_c_space(oth_man['f_name']))) if args.mosaic_pattern is not None: other_transformations.append( preprocessing.mosaic_transformation(args.mosaic_pattern)) if args.sf_filter is not None: other_transformations.append( preprocessing.sf_transformation(args.sf_filter, args.sf_filter_chn)) other_transformations.append(prediction_transformation) print('Processing network %s and %s %f' % (current_network, manipulation_name, manipulation_value)) # which dataset # reading it after the model, because each might have their own # specific size # loading validation set target_size = get_default_target_size(args.dataset, args.target_size) target_transform = utils_db.ImagenetCategoryTransform( args.categories, args.cat_dir) validation_dataset = utils_db.get_validation_dataset( args.dataset, args.validation_dir, colour_vision, args.colour_space, other_transformations, normalize, target_size, task=args.task_type, target_transform=target_transform) # TODO: nicer solution: if 'sampler' not in args: sampler = None else: sampler = args.sampler(validation_dataset) if 'collate_fn' not in args: args.collate_fn = None # FIXME: add segmentation datasests val_loader = torch.utils.data.DataLoader( validation_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True, sampler=sampler, collate_fn=args.collate_fn) if args.random_images is not None: out_folder = prepapre_testing.prepare_saving_dir( args.experiment_name, args.network_names[j], args.dataset, manipulation_name) normalize_inverse = NormalizeInverse(mean, std) fn(val_loader, out_folder, normalize_inverse, manipulation_value, **kwargs) elif args.activation_map is not None: model = model_utils.LayerActivation(model, args.activation_map) current_results = fn(val_loader, model, **kwargs) save_fn(current_results, args.experiment_name, args.network_names[j], args.dataset, manipulation_name, manipulation_value) else: (_, _, current_results) = fn(val_loader, model, **kwargs) save_fn(current_results, args.experiment_name, args.network_names[j], args.dataset, manipulation_name, manipulation_value)