def eval_batch(image, dst, evaluator, eval_mode): if eval_mode: # evaluation mode on validation set targets = dst outputs = evaluator.parallel_forward(image) batch_inter, batch_union, batch_correct, batch_label = 0, 0, 0, 0 for output, target in zip(outputs, targets): correct, labeled = utils.batch_pix_accuracy( output.data.cpu(), target) inter, union = utils.batch_intersection_union( output.data.cpu(), target, testset.num_class) batch_correct += correct batch_label += labeled batch_inter += inter batch_union += union return batch_correct, batch_label, batch_inter, batch_union else: # Visualize and dump the results im_paths = dst outputs = evaluator.parallel_forward(image) predicts = [ torch.max(output, 1)[1].cpu().numpy() + testset.pred_offset for output in outputs ] for predict, impath in zip(predicts, im_paths): mask = utils.get_mask_pallete(predict, args.dataset) outname = os.path.splitext(impath)[0] + '.png' mask.save(os.path.join(outdir, outname)) # dummy outputs for compatible with eval mode return 0, 0, 0, 0
def test(args): # data transforms input_transform = transform.Compose([ transform.ToTensor(), transform.Normalize([.485, .456, .406], [.229, .224, .225]) ]) # model if args.model_zoo is not None: model = get_model(args.model_zoo, pretrained=True) else: model = get_segmentation_model(args.model, dataset=args.dataset, backbone=args.backbone, dilated=args.dilated, lateral=args.lateral, jpu=args.jpu, aux=args.aux, se_loss=args.se_loss, norm_layer=BatchNorm, base_size=args.base_size, crop_size=args.crop_size) # resuming checkpoint if args.resume is None or not os.path.isfile(args.resume): raise RuntimeError("=> no checkpoint found at '{}'".format( args.resume)) checkpoint = torch.load(args.resume) # strict=False, so that it is compatible with old pytorch saved models model.load_state_dict(checkpoint['state_dict'], strict=False) print("=> loaded checkpoint '{}' (epoch {})".format( args.resume, checkpoint['epoch'])) print(model) scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \ [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] if not args.ms: scales = [1.0] num_classes = datasets[args.dataset.lower()].NUM_CLASS evaluator = MultiEvalModule(model, num_classes, scales=scales, flip=args.ms).cuda() evaluator.eval() img = input_transform(Image.open( args.input_path).convert('RGB')).unsqueeze(0) with torch.no_grad(): output = evaluator.parallel_forward(img)[0] predict = torch.max(output, 1)[1].cpu().numpy() mask = utils.get_mask_pallete(predict, args.dataset) mask.save(args.save_path)
def eval_batch(image, dst, evaluator, eval_mode, hist, names): if eval_mode: # evaluation mode on validation set targets = dst outputs = evaluator.parallel_forward(image) batch_inter, batch_union, batch_correct, batch_label = 0, 0, 0, 0 for output, target, name in zip(outputs, targets, names): correct, labeled = utils.batch_pix_accuracy( output.data.cpu(), target) inter, union = utils.batch_intersection_union( output.data.cpu(), target, testset.num_class) batch_correct += correct batch_label += labeled batch_inter += inter batch_union += union a = target.numpy().flatten() b = output.data.cpu() _, b = torch.max(b, 1) b = b.numpy().flatten() n = testset.num_class k = (a >= 0) & (a < n) hist += np.bincount(n * a[k].astype(int) + b[k], minlength=n**2).reshape(n, n) output = output.data.cpu().numpy()[0] output = output.transpose(1, 2, 0) output = np.asarray(np.argmax(output, axis=2), dtype=np.uint8) output_col = colorize_mask(output) output = Image.fromarray(output) name = name.split('/')[-1] output.save('%s/%s' % (args.save, name)) output_col.save('%s/%s_color.png' % (args.save, name.split('.')[0])) return batch_correct, batch_label, batch_inter, batch_union, hist else: # test mode, dump the results im_paths = dst outputs = evaluator.parallel_forward(image) predicts = [ torch.max(output, 1)[1].cpu().numpy() + testset.pred_offset for output in outputs ] for predict, impath in zip(predicts, im_paths): mask = utils.get_mask_pallete(predict, args.dataset) outname = os.path.splitext(impath)[0] + '.png' mask.save(os.path.join(outdir, outname)) # dummy outputs for compatible with eval mode return 0, 0, 0, 0
def test(args): # output folder outdir = 'outdir' if not os.path.exists(outdir): os.makedirs(outdir) # data transforms input_transform = transform.Compose([ transform.ToTensor(), transform.Normalize([.485, .456, .406], [.229, .224, .225]) ]) # dataset if args.eval: testset = get_dataset(args.dataset, split='val', mode='testval', transform=input_transform) elif args.test_val: testset = get_dataset(args.dataset, split='val', mode='test', transform=input_transform) else: testset = get_dataset(args.dataset, split='test', mode='test', transform=input_transform) # dataloader loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \ if args.cuda else {} test_data = data.DataLoader(testset, batch_size=args.test_batch_size, drop_last=False, shuffle=False, collate_fn=test_batchify_fn, **loader_kwargs) # model pretrained = args.resume is None and args.verify is None if args.model_zoo is not None: model = get_model(args.model_zoo, pretrained=pretrained) model.base_size = args.base_size model.crop_size = args.crop_size else: # my model_kwargs = {} if args.choice_indices is not None: assert 'alone_resnest50' in args.backbone model_kwargs['choice_indices'] = args.choice_indices # model = get_segmentation_model( args.model, dataset=args.dataset, backbone=args.backbone, aux=args.aux, se_loss=args.se_loss, norm_layer=torch.nn.BatchNorm2d if args.acc_bn else SyncBatchNorm, base_size=args.base_size, crop_size=args.crop_size, **model_kwargs) # resuming checkpoint if args.verify is not None and os.path.isfile(args.verify): print("=> loading checkpoint '{}'".format(args.verify)) model.load_state_dict(torch.load(args.verify, map_location='cpu')) elif args.resume is not None and os.path.isfile(args.resume): checkpoint = torch.load(args.resume, map_location='cpu') # strict=False, so that it is compatible with old pytorch saved models model.load_state_dict(checkpoint['state_dict']) print("=> loaded checkpoint '{}'".format(args.resume)) elif not pretrained: raise RuntimeError("=> no checkpoint found") print(model) if args.acc_bn: from encoding.utils.precise_bn import update_bn_stats data_kwargs = { 'transform': input_transform, 'base_size': args.base_size, 'crop_size': args.crop_size } trainset = get_dataset(args.dataset, split=args.train_split, mode='train', **data_kwargs) trainloader = data.DataLoader(ReturnFirstClosure(trainset), batch_size=args.batch_size, drop_last=True, shuffle=True, **loader_kwargs) print('Reseting BN statistics') #model.apply(reset_bn_statistics) model.cuda() update_bn_stats(model, trainloader) if args.export: torch.save(model.state_dict(), args.export + '.pth') return scales = [0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \ [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]#, 2.0 evaluator = MultiEvalModule(model, testset.num_class, scales=scales).cuda() evaluator.eval() metric = utils.SegmentationMetric(testset.num_class) tbar = tqdm(test_data) for i, (image, dst) in enumerate(tbar): if args.eval: with torch.no_grad(): predicts = evaluator.parallel_forward(image) metric.update(dst, predicts) pixAcc, mIoU = metric.get() tbar.set_description('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU)) else: with torch.no_grad(): outputs = evaluator.parallel_forward(image) predicts = [ testset.make_pred(torch.max(output, 1)[1].cpu().numpy()) for output in outputs ] for predict, impath in zip(predicts, dst): mask = utils.get_mask_pallete(predict, args.dataset) outname = os.path.splitext(impath)[0] + '.png' mask.save(os.path.join(outdir, outname)) if args.eval: print('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU))
def test(args): # output folder outdir = args.save_folder if not os.path.exists(outdir): os.makedirs(outdir) # data transforms input_transform = transform.Compose([ transform.ToTensor(), transform.Normalize([.485, .456, .406], [.229, .224, .225]) ]) # dataset testset = get_segmentation_dataset(args.dataset, split=args.split, mode=args.mode, transform=input_transform) # dataloader loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \ if args.cuda else {} test_data = data.DataLoader(testset, batch_size=args.test_batch_size, drop_last=False, shuffle=False, collate_fn=test_batchify_fn, **loader_kwargs) # model if args.model_zoo is not None: model = get_model(args.model_zoo, pretrained=True) else: model = get_segmentation_model(args.model, dataset=args.dataset, backbone=args.backbone, dilated=args.dilated, lateral=args.lateral, jpu=args.jpu, aux=args.aux, se_loss=args.se_loss, norm_layer=BatchNorm, base_size=args.base_size, crop_size=args.crop_size) # resuming checkpoint if args.resume is None or not os.path.isfile(args.resume): raise RuntimeError("=> no checkpoint found at '{}'".format( args.resume)) checkpoint = torch.load(args.resume) # strict=False, so that it is compatible with old pytorch saved models model.load_state_dict(checkpoint['state_dict']) print("=> loaded checkpoint '{}' (epoch {})".format( args.resume, checkpoint['epoch'])) print(model) scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \ [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] if not args.ms: scales = [1.0] evaluator = MultiEvalModule(model, testset.num_class, scales=scales, flip=args.ms).cuda() evaluator.eval() metric = utils.SegmentationMetric(testset.num_class) tbar = tqdm(test_data) for i, (image, dst) in enumerate(tbar): if 'val' in args.mode: with torch.no_grad(): predicts = evaluator.parallel_forward(image) metric.update(dst, predicts) pixAcc, mIoU = metric.get() tbar.set_description('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU)) else: with torch.no_grad(): outputs = evaluator.parallel_forward(image) predicts = [ testset.make_pred(torch.max(output, 1)[1].cpu().numpy()) for output in outputs ] for predict, impath in zip(predicts, dst): mask = utils.get_mask_pallete(predict, args.dataset) outname = os.path.splitext(impath)[0] + '.png' mask.save(os.path.join(outdir, outname))
def test(args): directory = "runs/val_summary/%s/%s/%s/" % (args.dataset, args.model, args.resume) if not os.path.exists(directory): os.makedirs(directory) writer = SummaryWriter(directory) # output folder outdir = 'outdir' if not os.path.exists(outdir): os.makedirs(outdir) # data transforms input_transform = transform.Compose([ transform.ToTensor(), transform.Normalize([.485, .456, .406], [.229, .224, .225]) ]) # dataset if args.eval: testset = get_segmentation_dataset(args.dataset, split='val', mode='testval', transform=input_transform) elif args.test_val: testset = get_segmentation_dataset(args.dataset, split='val', mode='test', transform=input_transform) else: testset = get_segmentation_dataset(args.dataset, split='test', mode='test', transform=input_transform) # dataloader loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \ if args.cuda else {} test_data = data.DataLoader(testset, batch_size=args.test_batch_size, drop_last=False, shuffle=False, collate_fn=test_batchify_fn, **loader_kwargs) Norm_method = torch.nn.BatchNorm2d # model if args.model_zoo is not None: model = get_model(args.model_zoo, pretrained=True) #model.base_size = args.base_size #model.crop_size = args.crop_size else: model = get_segmentation_model(args.model, dataset=args.dataset, backbone=args.backbone, aux=args.aux, multi_grid=args.multi_grid, num_center=args.num_center, norm_layer=Norm_method, root=args.backbone_path, base_size=args.base_size, crop_size=args.crop_size) # resuming checkpoint if args.resume is None or not os.path.isfile(args.resume): raise RuntimeError("=> no checkpoint found at '{}'".format( args.resume)) checkpoint = torch.load(args.resume) # strict=False, so that it is compatible with old pytorch saved models #model.module.load_state_dict(checkpoint['state_dict']) old_state_dict = checkpoint['state_dict'] new_state_dict = dict() for k, v in old_state_dict.items(): if k.startswith('module.'): #new_state_dict[k[len('module.'):]] = old_state_dict[k] new_state_dict[k[len('model.module.'):]] = old_state_dict[k] #new_state_dict[k] = old_state_dict[k] else: new_state_dict[k] = old_state_dict[k] #new_k = 'module.' + k #new_state_dict[new_k] = old_state_dict[k] model.load_state_dict(new_state_dict) print("=> loaded checkpoint '{}' (epoch {})".format( args.resume, checkpoint['epoch'])) print(model) scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \ [0.75, 1.0, 1.25, 1.5, 1.75, 2.0] if args.dataset == 'ade20k': scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0] if not args.ms: scales = [1.0] if args.dataset == 'ade20k': evaluator = MultiEvalModule2(model, testset.num_class, scales=scales, flip=args.ms).cuda() else: evaluator = MultiEvalModule(model, testset.num_class, scales=scales, flip=args.ms).cuda() evaluator.eval() metric = utils.SegmentationMetric(testset.num_class) tbar = tqdm(test_data) for i, (image, dst) in enumerate(tbar): if args.eval: with torch.no_grad(): predicts = evaluator.parallel_forward(image) metric.update(dst, predicts) pixAcc, mIoU = metric.get() tbar.set_description('pixAcc: %.4f, mIoU: %.4f' % (pixAcc, mIoU)) writer.add_scalar('pixAcc', pixAcc, i) writer.add_scalar('mIoU', mIoU, i) else: with torch.no_grad(): outputs = evaluator.parallel_forward(image) predicts = [ testset.make_pred(torch.max(output, 1)[1].cpu().numpy()) for output in outputs ] for predict, impath in zip(predicts, dst): mask = utils.get_mask_pallete(predict, args.dataset) outname = os.path.splitext(impath)[0] + '.png' mask.save(os.path.join(outdir, outname)) writer.close()
origin_locs = [32 * 8, 64 * 8] for imname in imfiles: imname = imname.strip() im_path = os.path.join(imroot, imname) img = Image.open(im_path).convert('RGB') # img = img.resize((1024,512),Image.BILINEAR) img_tensor = input_transform(img).unsqueeze(0).cuda() outputs, attention = model(img_tensor) # outputs = torch.nn.functional.interpolate(outputs, size=[1024,2048], scale_factor=None, mode='bilinear', align_corners=True) predicts = torch.max(outputs[0], 1)[1].cpu().numpy() mask = utils.get_mask_pallete(predicts, args.dataset) outname = im_path.split('/')[-1].split('.')[0] + '.png' mask.save(os.path.join(outdir, outname)) # eval mask with labelid # eval_mask = _convert_train_id_to_eval_id(predicts, _CITYSCAPES_TRAIN_ID_TO_EVAL_ID) # eval_mask = Image.fromarray(eval_mask.squeeze().astype('uint8')) # outname = im_path.split('/')[-1].split('.')[0] + '.png' # eval_mask.save(os.path.join(outdir_eval, outname)) attention_np = ( attention / torch.max(attention, 1)[0]).squeeze().detach().cpu().numpy() attention_map = (attention_np * 255).astype('uint8') # attention_p=attention_map[vloc,:].reshape((64,128)) # eval_mask = Image.fromarray(attention_p) eval_mask = Image.fromarray(attention_map) outname = im_path.split('/')[-1].split('.')[0] + '.png'
def test(args): # output folder outdir = args.save_folder if not os.path.exists(outdir): os.makedirs(outdir) # data transforms input_transform = transform.Compose([ transform.ToTensor(), transform.Normalize([.485, .456, .406], [.229, .224, .225]) ]) # dataset testset = get_segmentation_dataset(args.dataset, split=args.split, mode=args.mode, transform=input_transform) # dataloader loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \ if args.cuda else {} test_data = data.DataLoader(testset, batch_size=args.test_batch_size, drop_last=False, shuffle=False, collate_fn=test_batchify_fn, **loader_kwargs) # model if args.model_zoo is not None: model = get_model(args.model_zoo, pretrained=True) else: model = get_segmentation_model(args.model, dataset=args.dataset, backbone=args.backbone, dilated=args.dilated, multi_grid=args.multi_grid, stride=args.stride, lateral=args.lateral, jpu=args.jpu, aux=args.aux, se_loss=args.se_loss, norm_layer=BatchNorm, base_size=args.base_size, crop_size=args.crop_size) # resuming checkpoint if args.resume is None or not os.path.isfile(args.resume): raise RuntimeError("=> no checkpoint found at '{}'".format( args.resume)) checkpoint = torch.load(args.resume) # strict=False, so that it is compatible with old pytorch saved models model.load_state_dict(checkpoint['state_dict'], strict=False) print("=> loaded checkpoint '{}' (epoch {})".format( args.resume, checkpoint['epoch'])) # print(model) scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \ [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] if not args.ms: scales = [1.0] evaluator = MultiEvalModule(model, testset.num_class, scales=scales, flip=args.ms).cuda() evaluator.eval() tbar = tqdm(test_data) total_inter, total_union, total_correct, total_label = 0, 0, 0, 0 result = [] for i, (image, dst) in enumerate(tbar): # print(dst) with torch.no_grad(): if i > 20: st = time.time() outputs = evaluator.forward(image[0].unsqueeze(0).cuda()) if i > 20: result.append(1 / (time.time() - st)) print(np.mean(result), np.std(result)) if 'val' in args.mode: # compute image IoU metric inter, union, area_pred, area_lab = batch_intersection_union( outputs, dst[0], testset.num_class) total_label += area_lab total_inter += inter total_union += union class_pixAcc = 1.0 * inter / (np.spacing(1) + area_lab) class_IoU = 1.0 * inter / (np.spacing(1) + union) print("img Classes pixAcc:", class_pixAcc) print("img Classes IoU:", class_IoU) else: # save prediction results predict = testset.make_pred( torch.max(output, 1)[1].cpu().numpy()) mask = utils.get_mask_pallete(predict, args.dataset) outname = os.path.splitext(dst[0])[0] + '.png' mask.save(os.path.join(outdir, outname)) if 'val' in args.mode: # compute set IoU metric pixAcc = 1.0 * total_inter / (np.spacing(1) + total_label) IoU = 1.0 * total_inter / (np.spacing(1) + total_union) mIoU = IoU.mean() print("set Classes pixAcc:", pixAcc) print("set Classes IoU:", IoU) print("set mean IoU:", mIoU)
def semseg(input_path, output_path=None, with_L0=False): """ param: input_path: str, path of input image output_path: str, path to save output image return: tuple, [animal_name, "background"] if pixels of "background" dominate, ["background", animal_name] else. """ sys.argv = sys.argv[:1] option = Options() args = option.parse() args.aux = True args.se_loss = True args.resume = "./checkpoints/encnet_jpu_res101_pcontext.pth.tar" # model checkpoint torch.manual_seed(args.seed) # data transforms input_transform = transform.Compose([ transform.ToTensor(), transform.Normalize([.485, .456, .406], [.229, .224, .225]) ]) # using L0_smooth to transform the orignal picture if with_L0: mid_result = os.path.join(os.path.dirname(input_path), "L0_result.png") L0_smooth(input_path, mid_result) input_path = mid_result # model model = get_segmentation_model(args.model, dataset=args.dataset, backbone=args.backbone, dilated=args.dilated, lateral=args.lateral, jpu=args.jpu, aux=args.aux, se_loss=args.se_loss, norm_layer=BatchNorm, base_size=args.base_size, crop_size=args.crop_size) # resuming checkpoint if args.resume is None or not os.path.isfile(args.resume): raise RuntimeError("=> no checkpoint found at '{}'".format( args.resume)) checkpoint = torch.load(args.resume, map_location=torch.device('cpu')) # strict=False, so that it is compatible with old pytorch saved models model.load_state_dict(checkpoint['state_dict'], strict=False) print("semseg model loaded successfully!") scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \ [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] if not args.ms: scales = [1.0] num_classes = datasets[args.dataset.lower()].NUM_CLASS evaluator = MultiEvalModule(model, num_classes, scales=scales, flip=args.ms).cuda() evaluator.eval() classes = np.array([ 'empty', 'aeroplane', 'bag', 'bed', 'bedclothes', 'bench', 'bicycle', 'bird', 'boat', 'book', 'bottle', 'building', 'bus', 'cabinet', 'car', 'cat', 'ceiling', 'chair', 'cloth', 'computer', 'cow', 'cup', 'curtain', 'dog', 'door', 'fence', 'floor', 'flower', 'food', 'grass', 'ground', 'horse', 'keyboard', 'light', 'motorbike', 'mountain', 'mouse', 'person', 'plate', 'platform', 'pottedplant', 'road', 'rock', 'sheep', 'shelves', 'sidewalk', 'sign', 'sky', 'snow', 'sofa', 'table', 'track', 'train', 'tree', 'truck', 'tvmonitor', 'wall', 'water', 'window', 'wood' ]) animals = ['bird', 'cat', 'cow', 'dog', 'horse', 'mouse', 'sheep'] img = input_transform(Image.open(input_path).convert('RGB')).unsqueeze(0) with torch.no_grad(): output = evaluator.parallel_forward(img)[0] predict = torch.max(output, 1)[1].cpu().numpy() + 1 pred_idx = np.unique(predict) pred_label = classes[pred_idx] print("[SemSeg] ", input_path, ": ", pred_label, sep='') main_pixels = 0 main_idx = -1 for idx, label in zip(pred_idx, pred_label): if label in animals: pixels = np.sum(predict == idx) if pixels > main_pixels: main_pixels = pixels main_idx = idx background_pixels = np.sum(predict != main_idx) main_animal = classes[main_idx] predict[predict != main_idx] = 29 mask_matrix = predict.copy() if output_path is not None: mask_matrix[np.where(mask_matrix != 29)] = 1 mask_matrix[np.where(mask_matrix == 29)] = 0 mask = utils.get_mask_pallete(mask_matrix, args.dataset) mask.save(output_path) if main_idx < 29: return predict, (main_animal, "background") else: return predict, ("background", main_animal)