def main(conf, test_set, test_part=-1): gulp_path = os.path.join(conf.gulp_test_dir, conf.modality.lower(), 'test', test_set) gulp_path = os.path.realpath(gulp_path) gulp_path = Path(gulp_path) classes_map = pickle.load(open(conf.classes_map, "rb")) conf.num_classes = count_num_classes(classes_map) net = TSN(conf.num_classes, 1, conf.modality, base_model=conf.arch, consensus_type=conf.crop_fusion_type, dropout=conf.dropout) checkpoint = torch.load(conf.weights) print("Model epoch {} best prec@1: {}".format(checkpoint['epoch'], checkpoint['best_prec1'])) base_dict = { '.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items()) } net.load_state_dict(base_dict) if conf.test_crops == 1: cropping = torchvision.transforms.Compose([ GroupScale(net.scale_size), GroupCenterCrop(net.input_size), ]) elif conf.test_crops == 10: cropping = torchvision.transforms.Compose( [GroupOverSample(net.input_size, net.scale_size)]) else: raise ValueError( "Only 1 and 10 crops are supported while we got {}".format( conf.test_crops)) class_type = 'verb+noun' if conf.class_type == 'action' else conf.class_type if conf.modality == 'Flow': dataset = EpicVideoFlowDataset(gulp_path=gulp_path, class_type=class_type) else: dataset = EpicVideoDataset(gulp_path=gulp_path, class_type=class_type) data_loader = torch.utils.data.DataLoader(EpicTSNTestDataset( dataset, classes_map, num_segments=conf.test_segments, new_length=1 if conf.modality == "RGB" else 5, modality=conf.modality, transform=torchvision.transforms.Compose([ cropping, Stack(roll=conf.arch == 'BNInception'), ToTorchFormatTensor(div=conf.arch != 'BNInception'), GroupNormalize(net.input_mean, net.input_std), ]), part=test_part), batch_size=1, shuffle=False, num_workers=conf.workers * 2, pin_memory=True) net = torch.nn.DataParallel(net, device_ids=conf.gpus).cuda() net.eval() total_num = len(data_loader.dataset) output = [] proc_start_time = time.time() for i, (keys, input_) in enumerate(data_loader): rst = eval_video(conf, (i, keys, input_), net) output.append(rst[1:]) cnt_time = time.time() - proc_start_time print('video {} done, total {}/{}, average {} sec/video'.format( i, i + 1, total_num, float(cnt_time) / (i + 1))) video_index = [x[0] for x in output] scores = [x[1] for x in output] save_scores = './{}/tsn_{}_{}_testset_{}_{}_lr_{}_model_{:03d}.npz'.format( conf.checkpoint, conf.class_type, conf.modality.lower(), test_set, conf.arch, conf.lr, checkpoint['epoch']) if test_part > 0: save_scores = save_scores.replace('.npz', '_part-{}.npz'.format(test_part)) np.savez(save_scores, segment_indices=video_index, scores=scores)
# Define the transform batch_size = 1 snippet_length = 1 # Number of frames composing the snippet, 1 for RGB, 5 for optical flow snippet_channels = 3 # Number of channels in a frame, 3 for RGB, 2 for optical flow height, width = 224, 224 crop_count = 10 if crop_count == 1: cropping = Compose([ GroupScale(model.scale_size), GroupCenterCrop(model.input_size), ]) elif crop_count == 10: cropping = GroupOverSample(model.input_size, model.scale_size) else: raise ValueError("Only 1 and 10 crop_count are supported while we got {}".format(crop_count)) transform = Compose([ cropping, Stack(roll=base_model == base_model), ToTorchFormatTensor(div=base_model != base_model), GroupNormalize(model.input_mean, model.input_std), ]) pred_verb_indices = [] pred_noun_indices = [] pred_verb_classes = [] pred_noun_classes = [] gt_verb_indices = []
def main(): parser = argparse.ArgumentParser( description="Action Recognition with Coviar (CVPR2018)") parser.add_argument('--video-name', type=str, default=None) parser.add_argument('--orig-video', type=str, default=None) parser.add_argument('--num-class', type=int, default=5) parser.add_argument('--num-segments', type=int, default=3) parser.add_argument('--representation', type=str, choices=['iframe', 'mv', 'residual'], default=None) parser.add_argument('--arch', type=str, default=None) parser.add_argument('--model', type=str, default=None) parser.add_argument( '--no-accumulation', action='store_true', help='disable accumulation of motion vectors and residuals.') args = parser.parse_args() #載入model net = Model(args.num_class, args.num_segments, args.representation, base_model=args.arch) checkpoint = torch.load(args.model) print("model epoch {} best prec@1: {}".format(checkpoint['epoch'], checkpoint['best_prec1'])) base_dict = { '.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items()) } net.load_state_dict(base_dict) #data argumentaion預設每個segment產生10個 cropping = torchvision.transforms.Compose([ GroupOverSample(net.crop_size, net.scale_size, is_mv=(args.representation == 'mv')) ]) #我們只有一個GPU, 所以只能設cudo(0) => GPU0 devices = [0] net = torch.nn.DataParallel(net.cuda(devices[0]), device_ids=devices) net.eval() #姿態辨識 result, scores = actionRecongnize(args, net, cropping) np.savez(args.representation + "_recognizer_score.npz", scores=np.array(scores), labels=np.array(result)) #print(len(result)) #讀檔 cap = cv2.VideoCapture(args.orig_video) if cap.isOpened() is False: print('Error opening video stream or file') return frame_num = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) copy_list = cal_complementary_frames(frame_num, 1500) action_list = cal_complementary_segments(result, 38) #寫檔 fourcc = cv2.VideoWriter_fourcc(*'XVID') out = cv2.VideoWriter('./result.avi', fourcc, 30.0, (1920, 1080)) #產生影片 frame_cnt = 0 frame = np.array([]) for i in range(len(copy_list)): # if not copy_list[i]: _, frame = cap.read() # else: #當實際幀數比預期幀數多的時候,透過平均跳過的方式把多的幀數丟掉 if frame_num > 1500: _, frame = cap.read() _, frame = cap.read() # if action_list[(i // SEG_SIZE)] == 1: cv2.putText(frame, "WritingOnBoard", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2) else: cv2.putText(frame, "Non-WritingOnBoard", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 2) # out.write(frame) frame_cnt += 1 print('\rcurrent/total frame = {}/{} / Progress: {:.1f}%'.format( frame_cnt, frame_num, (frame_cnt / frame_num) * 100), end='') sys.stdout.flush() out.release() cap.release()
def main(): net = Model(num_class, args.test_segments, args.representation, base_model=args.arch) checkpoint = torch.load(args.weights) print("model epoch {} best prec@1: {}".format(checkpoint['epoch'], checkpoint['best_prec1'])) base_dict = { '.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items()) } net.load_state_dict(base_dict) if args.test_crops == 1: cropping = torchvision.transforms.Compose([ GroupScale(net.scale_size), GroupCenterCrop(net.crop_size), ]) elif args.test_crops == 10: cropping = torchvision.transforms.Compose([ GroupOverSample(net.crop_size, net.scale_size, is_mv=(args.representation == 'mv')) ]) else: raise ValueError( "Only 1 and 10 crops are supported, but got {}.".format( args.test_crops)) data_loader = torch.utils.data.DataLoader(CoviarDataSet( args.data_root, args.data_name, video_list=args.test_list, num_segments=args.test_segments, representation=args.representation, transform=cropping, is_train=False, accumulate=(not args.no_accumulation), ), batch_size=1, shuffle=False, num_workers=args.workers * 2, pin_memory=True) if args.gpus is not None: devices = [args.gpus[i] for i in range(args.workers)] else: devices = list(range(args.workers)) net = torch.nn.DataParallel(net.cuda(devices[0]), device_ids=devices) net.eval() data_gen = enumerate(data_loader) total_num = len(data_loader.dataset) output = [] def forward_video(data): input_var = torch.autograd.Variable(data, volatile=True) scores = net(input_var) scores = scores.view((-1, args.test_segments * args.test_crops) + scores.size()[1:]) scores = torch.mean(scores, dim=1) return scores.data.cpu().numpy().copy() proc_start_time = time.time() for i, (data, label) in data_gen: video_scores = forward_video(data) output.append((video_scores, label[0])) cnt_time = time.time() - proc_start_time if (i + 1) % 100 == 0: print('video {} done, total {}/{}, average {} sec/video'.format( i, i + 1, total_num, float(cnt_time) / (i + 1))) video_pred = [np.argmax(x[0]) for x in output] video_labels = [x[1] for x in output] print('Accuracy {:.02f}% ({})'.format( float(np.sum(np.array(video_pred) == np.array(video_labels))) / len(video_pred) * 100.0, len(video_pred))) if args.save_scores is not None: name_list = [x.strip().split()[0] for x in open(args.test_list)] order_dict = {e: i for i, e in enumerate(sorted(name_list))} reorder_output = [None] * len(output) reorder_label = [None] * len(output) reorder_name = [None] * len(output) for i in range(len(output)): idx = order_dict[name_list[i]] reorder_output[idx] = output[i] reorder_label[idx] = video_labels[i] reorder_name[idx] = name_list[i] np.savez(args.save_scores, scores=reorder_output, labels=reorder_label, names=reorder_name)
def main(): # define the model net = Model(num_class, args.test_segments, args.representation, base_model=args.arch, new_length=args.new_length, use_databn=args.use_databn, gen_flow_or_delta=args.gen_flow_or_delta, gen_flow_ds_factor=args.gen_flow_ds_factor, arch_estimator=args.arch_estimator, att=args.att) # load the trained model checkpoint = torch.load(args.weights, map_location=lambda storage, loc: storage) print("model epoch {} best prec@1: {}".format(checkpoint['epoch'], checkpoint['best_prec1'])) base_dict = { '.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items()) } net.load_state_dict(base_dict, strict=False) # setup the data loader if args.test_crops == 1: cropping = torchvision.transforms.Compose([ GroupScale(net.scale_size), GroupCenterCrop(net.crop_size), ]) elif args.test_crops == 10: cropping = torchvision.transforms.Compose( [GroupOverSample(net.crop_size, net.scale_size)]) else: raise ValueError( "Only 1 and 10 crops are supported, but got {}.".format( args.test_crops)) data_loader = torch.utils.data.DataLoader(CoviarDataSet( args.data_root, args.flow_root, args.data_name, video_list=args.test_list, num_segments=args.test_segments, representation=args.representation, new_length=args.new_length, flow_ds_factor=args.flow_ds_factor, upsample_interp=args.upsample_interp, transform=cropping, is_train=False, accumulate=(not args.no_accumulation), gop=args.gop, flow_folder=args.data_flow, viz=args.viz), batch_size=1, shuffle=False, num_workers=args.workers * 2, pin_memory=True) # deploy model on gpu if args.gpus is not None: devices = [args.gpus[i] for i in range(args.workers)] else: devices = list(range(args.workers)) net.cuda(devices[0]) #net.base_model.cuda(devices[-1]) net = torch.nn.DataParallel(net, device_ids=devices) # switch to inference model and start to iterate over the test set net.eval() total_num = len(data_loader.dataset) output = [] # process each video to obtain its predictions def forward_video(input_mv, input_residual, att=0): input_mv_var = torch.autograd.Variable(input_mv, volatile=True) input_residual_var = torch.autograd.Variable(input_residual, volatile=True) if att == 0: scores, gen_flow = net(input_mv_var, input_residual_var) if att == 1: scores, gen_flow, att_flow = net(input_mv_var, input_residual_var) scores = scores.view((-1, args.test_segments * args.test_crops) + scores.size()[1:]) scores = torch.mean(scores, dim=1) if att == 0: return scores.data.cpu().numpy().copy(), gen_flow if att == 1: return scores.data.cpu().numpy().copy(), gen_flow, att_flow proc_start_time = time.time() # iterate over the whole test set for i, (input_flow, input_mv, input_residual, label) in enumerate(data_loader): input_mv = input_mv.cuda(args.gpus[-1], async=True) input_residual = input_residual.cuda(args.gpus[0], async=True) input_flow = input_flow.cuda(args.gpus[-1], async=True) # print("input_flow shape:") # print(input_flow.shape) # torch.Size([batch_size, num_crops*num_segments, 2, 224, 224]) # print("input_flow type:") # print(input_flow.type()) # torch.cuda.FloatTensor if args.att == 0: video_scores, gen_flow = forward_video(input_mv, input_residual) if args.att == 1: video_scores, gen_flow, att_flow = forward_video( input_mv, input_residual, args.att) output.append((video_scores, label[0])) cnt_time = time.time() - proc_start_time if (i + 1) % 100 == 0: print('video {} done, total {}/{}, average {} sec/video'.format( i, i + 1, total_num, float(cnt_time) / (i + 1))) video_pred = [np.argmax(x[0]) for x in output] video_labels = [x[1] for x in output] print('Accuracy {:.02f}% ({})'.format( float(np.sum(np.array(video_pred) == np.array(video_labels))) / len(video_pred) * 100.0, len(video_pred))) if args.save_scores is not None: name_list = [x.strip().split()[0] for x in open(args.test_list)] order_dict = {e: i for i, e in enumerate(sorted(name_list))} reorder_output = [None] * len(output) reorder_label = [None] * len(output) reorder_name = [None] * len(output) for i in range(len(output)): idx = order_dict[name_list[i]] reorder_output[idx] = output[i] reorder_label[idx] = video_labels[i] reorder_name[idx] = name_list[i] np.savez(args.save_scores, scores=reorder_output, labels=reorder_label, names=reorder_name)
def main(): # load trained model ''' @Param num_class: total number of classes num_segments: number of TSN segments, test default = 25 representation: iframe, mv, residual base_model: base architecture ''' net = Model(num_class, args.test_segments, args.representation, base_model=args.arch, mv_stack_size=args.mv_stack_size) # -----------------------------MODIFIED_CODE_START------------------------------- # print(net) # -----------------------------MODIFIED_CODE_END--------------------------------- # checkpoint trained model ? (not best model checkpoint = torch.load(args.weights) print("model epoch {} best prec@1: {}".format(checkpoint['epoch'], checkpoint['best_prec1'])) base_dict = { '.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items()) } net.load_state_dict(base_dict) # ----------------------- # CLASS torchvision.transforms.Compose(transforms)[SOURCE] # Composes several transforms together. # Parameters: transforms (list of Transform objects) – list of transforms to compose. # ----------------------- # ----------------------- # TSN: # if args.test_crops == 1: # cropping = torchvision.transforms.Compose([ # GroupScale(net.scale_size), # GroupCenterCrop(net.input_size), # ]) # ----------------------- if args.test_crops == 1: cropping = torchvision.transforms.Compose([ GroupScale(net.scale_size), GroupCenterCrop(net.crop_size), ]) # ??? what's difference between net.input_size and net.crop_size # line 70 in model.py # def crop_size(self): # return self._input_size # seems they are same here # ----------------------- # TSN: # elif args.test_crops == 10: # cropping = torchvision.transforms.Compose([ # GroupOverSample(net.input_size, net.scale_size) # ]) # ----------------------- # is_mv=(args.representation == 'mv') seems quite important elif args.test_crops == 10: cropping = torchvision.transforms.Compose([ GroupOverSample(net.crop_size, net.scale_size, is_mv=(args.representation == 'mv')) ]) # --test-crops specifies how many crops per segment. # The value should be 1 or 10. # 1 means using only one center crop. # 10 means using 5 crops for both (horizontal) flips. else: raise ValueError( "Only 1 and 10 crops are supported, but got {}.".format( args.test_crops)) data_loader = torch.utils.data.DataLoader( CoviarDataSet( args.data_root, args.data_name, video_list=args.test_list, num_segments=args.test_segments, representation=args.representation, transform=cropping, # seems important to stacking # test_crops == 1: GroupScale + GroupCenterCrop # the same as val_data_loader in train.py # seems np.stack in resize_mv() called in GroupCenterCrop # has the same effects as Stack() in TSN # test_crops == 10: GroupOverSample # ----------------------- # TSN: # transform=torchvision.transforms.Compose([ # cropping, # Stack(roll=args.arch == 'BNInception'), # this line seems important # ToTorchFormatTensor(div=args.arch != 'BNInception'), # GroupNormalize(net.input_mean, net.input_std), # ])), # ----------------------- is_train=False, accumulate=(not args.no_accumulation), mv_stack_size=args.mv_stack_size), batch_size=1, shuffle=False, # -----------------------------ORIGINAL_CODE_START----------------------------- # num_workers=args.workers * 2, pin_memory=True) # -----------------------------ORIGINAL_CODE_END------------------------------- # -----------------------------MODIFIED_CODE_START----------------------------- num_workers=args.workers, pin_memory=True) # -----------------------------MODIFIED_CODE_END------------------------------- if args.gpus is not None: devices = [args.gpus[i] for i in range(args.workers)] else: devices = list(range(args.workers)) net = torch.nn.DataParallel(net.cuda(devices[0]), device_ids=devices) net.eval() data_gen = enumerate(data_loader) total_num = len(data_loader.dataset) output = [] def forward_video(data): # torch.Size([batch_size, num_segment, 2*MV_STACK_SIZE, height, width]) # -----------------------------MODIFIED_CODE_START------------------------------- # print("data.shape"+str(data.shape)) # testing: torch.Size([1, 25, 10, 224, 224]) # training: torch.Size([40, 3, 10, 224, 224]) # original:data.shape:torch.Size([1, 250, 2, 224, 224]) # so it seems that the format of input data in this function is not correct # -----------------------------MODIFIED_CODE_END--------------------------------- input_var = torch.autograd.Variable(data, volatile=True) # -----------------------------MODIFIED_CODE_START------------------------------- # print("input_var:"+str(input_var.shape)) # input_var:torch.Size([1, 25, 10, 224, 224]) # original: input_var.shape:torch.Size([1, 250, 2, 224, 224]) # -----------------------------MODIFIED_CODE_END--------------------------------- # compute output scores = net(input_var) # -----------------------------MODIFIED_CODE_START------------------------------- # torch.Size([batch_size*num_segment, num_class]) # print("scores: "+str(scores.shape)) # testing: torch.Size([25, 101]) # training: torch.Size([120, 101]) # print("scores.size()") # print(scores.size()) # torch.Size([25, 101]) # -----------------------------MODIFIED_CODE_END--------------------------------- # what does args.test_segments * args.test_crops mean?? # view(*shape) → Tensor: Returns a new tensor with the same data as the self tensor but of a different shape. # Parameters shape (torch.Size or int...) – the desired size scores = scores.view((-1, args.test_segments * args.test_crops) + scores.size()[1:]) scores = torch.mean(scores, dim=1) return scores.data.cpu().numpy().copy() proc_start_time = time.time() for i, (data, label) in data_gen: video_scores = forward_video(data) output.append((video_scores, label[0])) cnt_time = time.time() - proc_start_time if (i + 1) % 100 == 0: print('video {} done, total {}/{}, average {} sec/video'.format( i, i + 1, total_num, float(cnt_time) / (i + 1))) video_pred = [np.argmax(x[0]) for x in output] video_labels = [x[1] for x in output] print('Accuracy {:.02f}% ({})'.format( float(np.sum(np.array(video_pred) == np.array(video_labels))) / len(video_pred) * 100.0, len(video_pred))) if args.save_scores is not None: name_list = [x.strip().split()[0] for x in open(args.test_list)] order_dict = {e: i for i, e in enumerate(sorted(name_list))} reorder_output = [None] * len(output) reorder_label = [None] * len(output) reorder_name = [None] * len(output) for i in range(len(output)): idx = order_dict[name_list[i]] reorder_output[idx] = output[i] reorder_label[idx] = video_labels[i] reorder_name[idx] = name_list[i] np.savez(args.save_scores, scores=reorder_output, labels=reorder_label, names=reorder_name)
def main(): writter = SummaryWriter('./log/test', comment='') net = Model(2, args.num_segments, args.representation, base_model=args.arch) checkpoint = torch.load(args.weights) # print("model epoch {} best prec@1: {}".format(checkpoint['epoch'], checkpoint['best_prec1'])) print("model epoch {} lowest loss {}".format(checkpoint['epoch'], checkpoint['loss_min'])) base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items())} net.load_state_dict(base_dict) if args.test_crops == 1: cropping = torchvision.transforms.Compose([ GroupScale(net.scale_size), GroupCenterCrop(net.crop_size), ]) elif args.test_crops == 10: cropping = torchvision.transforms.Compose([ GroupOverSample(net.crop_size, net.scale_size, is_mv=(args.representation == 'mv')) ]) else: raise ValueError("Only 1 and 10 crops are supported, but got {}.".format(args.test_crops)) data_loader = torch.utils.data.DataLoader( CoviarDataSet( args.data_root, video_list=args.test_list, num_segments=args.num_segments, representation=args.representation, transform=cropping, is_train=False, accumulate=(not args.no_accumulation), ), batch_size=1, shuffle=False, num_workers=args.workers * 2, pin_memory=True) devices = [torch.device("cuda:%d" % device) for device in args.gpus] net = torch.nn.DataParallel(net.cuda(devices[0]), device_ids=devices) net.eval() total_num = len(data_loader.dataset) scores = [] labels = [] proc_start_time = time.time() correct_nums = 0 for i, (input_pairs, label) in enumerate(data_loader): with torch.no_grad: input_pairs[0] = input_pairs[0].float().to(devices[0]) input_pairs[1] = input_pairs[1].float().to(devices[0]) label = label.float().to(devices[0]) outputs, y = net(input_pairs) _, predicts = torch.max(y, 1) scores.append(y.detach().cpu().numpy()) labels.append(label.detach().cpu().numpy()) correct_nums += (predicts == label.clone().long()).sum() cnt_time = time.time() - proc_start_time if (i + 1) % 100 == 0: print('video {} done, total {}/{}, average {} sec/video'.format(i, i + 1, total_num, float(cnt_time) / (i + 1))) predits = np.argmax(scores, 1) labels = np.around(labels).astype(np.long).ravel() acc = 100 * correct_nums / len(data_loader.dataset) target_names = ['Copy', 'Not Copy'] # writter.add_pr_curve('Precision/Recall', labels, predits) writter.add_text('Accuracy', '%.3f%%' % acc) writter.add_text(classification_report(labels, predits, target_names=target_names)) print(('Validating Results: accuracy: {accuracy:.3f}%'.format(accuracy=acc))) if args.save_scores is not None: with open(args.save_scores + '_scores.pkl', 'wb') as fp: pickle.dump(scores, fp) with open(args.save_scores + '_labels.pkl', 'wb') as fp: pickle.dump(labels, fp)
def main(): net = Model(num_class, base_model=args.arch) checkpoint = torch.load(args.weights) print("model epoch {} best prec@1: {}".format(checkpoint['epoch'], checkpoint['best_prec1'])) base_dict = { '.'.join(k.split('.')[1:]): v for k, v in list(checkpoint['state_dict'].items()) } net.load_state_dict(base_dict) if args.test_crops == 1: cropping = torchvision.transforms.Compose([ GroupScale(net.scale_size), GroupCenterCrop(net.crop_size), ]) elif args.test_crops == 10: cropping = torchvision.transforms.Compose([ GroupOverSample(net.crop_size, net.scale_size, is_mv=(args.representation == 'mv')) ]) else: raise ValueError( "Only 1 and 10 crops are supported, but got {}.".format( args.test_crops)) data_loader = torch.utils.data.DataLoader(FoodDataSet( args.data_root, img_list=args.test_list, transform=cropping, is_train=False, ), batch_size=1, shuffle=False, num_workers=args.workers * 2, pin_memory=True) if args.gpus is not None: devices = [args.gpus[i] for i in range(args.workers)] else: devices = list(range(args.workers)) net = torch.nn.DataParallel(net.cuda(devices[0]), device_ids=devices) net.eval() data_gen = enumerate(data_loader) total_num = len(data_loader.dataset) output = [] def forward_img(data): """ Args: data (Tensor): size [batch_size, c, h, w] Returns: scores (Tensor) : size [batch_size, num_class] """ with torch.no_grad(): input_var = torch.autograd.Variable(data, volatile=True) scores = net(input_var) scores = scores.view((-1, args.test_crops) + scores.size()[1:]) scores = torch.mean(scores, dim=1) return scores.data.cpu().numpy().copy() proc_start_time = time.time() for i, (data, label) in data_gen: # data = [1, c, h ,w], label = [1] img_scores = forward_img(data) output.append((img_scores[0], label[0])) cnt_time = time.time() - proc_start_time if (i + 1) % 100 == 0: print('image {} done, total {}/{}, average {} sec/image'.format( i, i + 1, total_num, float(cnt_time) / (i + 1))) img_pred = [np.argmax(x[0]) for x in output] img_labels = [x[1] for x in output] print('Accuracy {:.02f}% ({})'.format( float(np.sum(np.array(img_pred) == np.array(img_labels))) / len(img_pred) * 100.0, len(img_pred)))