def getModel(sourcemodel): net = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem( n_classes=20, hidden_layers=128, source_classes=7, ) x = torch.load(sourcemodel) net.load_source_model(x) net.cuda() return net
def inference_batch(sourcemodel, imgPaths): # launch gpu model net = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem( n_classes=20, hidden_layers=128, source_classes=7, ) x = torch.load(sourcemodel) net.load_source_model(x) net.cuda() n = len(imgPaths) for i, imgpath in enumerate(imgPaths): print('Running Segmentation on {}: No. {}/{}'.format( imgpath, i + 1, n)) dirname, basename = os.path.split(imgpath) basename = '.'.join(basename.split('.')[:-1]) inferenceWrite(net, imgpath, dirname, basename)
if __name__ == "__main__": """argparse begin""" parser = argparse.ArgumentParser() # parser.add_argument('--loadmodel',default=None,type=str) parser.add_argument("--loadmodel", default="", type=str) parser.add_argument("--img_path", default="", type=str) parser.add_argument("--output_path", default="", type=str) parser.add_argument("--output_name", default="", type=str) parser.add_argument("--use_gpu", default=1, type=int) opts = parser.parse_args() net = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem( n_classes=20, hidden_layers=128, source_classes=7, ) if not opts.loadmodel == "": x = torch.load(opts.loadmodel) net.load_source_model(x) print("load model:", opts.loadmodel) else: print("no model load !!!!!!!!") raise RuntimeError("No model!!!!") if opts.use_gpu > 0: net.cuda() use_gpu = True else: use_gpu = False
def main(opts): adj2_ = torch.from_numpy(graph.cihp2pascal_nlp_adj).float() adj2_test = (adj2_.unsqueeze(0).unsqueeze(0).expand(1, 1, 7, 20).cuda().transpose( 2, 3)) adj1_ = Variable( torch.from_numpy(graph.preprocess_adj(graph.pascal_graph)).float()) adj3_test = adj1_.unsqueeze(0).unsqueeze(0).expand(1, 1, 7, 7).cuda() cihp_adj = graph.preprocess_adj(graph.cihp_graph) adj3_ = Variable(torch.from_numpy(cihp_adj).float()) adj1_test = adj3_.unsqueeze(0).unsqueeze(0).expand(1, 1, 20, 20).cuda() p = OrderedDict() # Parameters to include in report p["trainBatch"] = opts.batch # Training batch size p["nAveGrad"] = 1 # Average the gradient of several iterations p["lr"] = opts.lr # Learning rate p["lrFtr"] = 1e-5 p["lraspp"] = 1e-5 p["lrpro"] = 1e-5 p["lrdecoder"] = 1e-5 p["lrother"] = 1e-5 p["wd"] = 5e-4 # Weight decay p["momentum"] = 0.9 # Momentum p["epoch_size"] = 10 # How many epochs to change learning rate p["num_workers"] = opts.numworker backbone = "xception" # Use xception or resnet as feature extractor, with open(opts.txt_file, "r") as f: img_list = f.readlines() max_id = 0 save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__))) exp_name = os.path.dirname(os.path.abspath(__file__)).split("/")[-1] runs = glob.glob(os.path.join(save_dir_root, "run", "run_*")) for r in runs: run_id = int(r.split("_")[-1]) if run_id >= max_id: max_id = run_id + 1 # run_id = int(runs[-1].split('_')[-1]) + 1 if runs else 0 # Network definition if backbone == "xception": net = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem( n_classes=opts.classes, os=16, hidden_layers=opts.hidden_layers, source_classes=7, ) elif backbone == "resnet": # net = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True) raise NotImplementedError else: raise NotImplementedError if gpu_id >= 0: net.cuda() # net load weights if not opts.loadmodel == "": x = torch.load(opts.loadmodel) net.load_source_model(x) print("load model:", opts.loadmodel) else: print("no model load !!!!!!!!") ## multi scale scale_list = [1, 0.5, 0.75, 1.25, 1.5, 1.75] testloader_list = [] testloader_flip_list = [] for pv in scale_list: composed_transforms_ts = transforms.Compose( [tr.Scale_(pv), tr.Normalize_xception_tf(), tr.ToTensor_()]) composed_transforms_ts_flip = transforms.Compose([ tr.Scale_(pv), tr.HorizontalFlip(), tr.Normalize_xception_tf(), tr.ToTensor_(), ]) voc_val = cihp.VOCSegmentation(split="test", transform=composed_transforms_ts) voc_val_f = cihp.VOCSegmentation(split="test", transform=composed_transforms_ts_flip) testloader = DataLoader(voc_val, batch_size=1, shuffle=False, num_workers=p["num_workers"]) testloader_flip = DataLoader(voc_val_f, batch_size=1, shuffle=False, num_workers=p["num_workers"]) testloader_list.append(copy.deepcopy(testloader)) testloader_flip_list.append(copy.deepcopy(testloader_flip)) print("Eval Network") if not os.path.exists(opts.output_path + "cihp_output_vis/"): os.makedirs(opts.output_path + "cihp_output_vis/") if not os.path.exists(opts.output_path + "cihp_output/"): os.makedirs(opts.output_path + "cihp_output/") start_time = timeit.default_timer() # One testing epoch total_iou = 0.0 net.eval() for ii, large_sample_batched in enumerate( zip(*testloader_list, *testloader_flip_list)): print(ii) # 1 0.5 0.75 1.25 1.5 1.75 ; flip: sample1 = large_sample_batched[:6] sample2 = large_sample_batched[6:] for iii, sample_batched in enumerate(zip(sample1, sample2)): inputs, labels = sample_batched[0]["image"], sample_batched[0][ "label"] inputs_f, _ = sample_batched[1]["image"], sample_batched[1][ "label"] inputs = torch.cat((inputs, inputs_f), dim=0) if iii == 0: _, _, h, w = inputs.size() # assert inputs.size() == inputs_f.size() # Forward pass of the mini-batch inputs, labels = Variable(inputs, requires_grad=False), Variable(labels) with torch.no_grad(): if gpu_id >= 0: inputs, labels = inputs.cuda(), labels.cuda() # outputs = net.forward(inputs) # pdb.set_trace() outputs = net.forward(inputs, adj1_test.cuda(), adj3_test.cuda(), adj2_test.cuda()) outputs = (outputs[0] + flip(flip_cihp(outputs[1]), dim=-1)) / 2 outputs = outputs.unsqueeze(0) if iii > 0: outputs = F.upsample(outputs, size=(h, w), mode="bilinear", align_corners=True) outputs_final = outputs_final + outputs else: outputs_final = outputs.clone() ################ plot pic predictions = torch.max(outputs_final, 1)[1] prob_predictions = torch.max(outputs_final, 1)[0] results = predictions.cpu().numpy() prob_results = prob_predictions.cpu().numpy() vis_res = decode_labels(results) parsing_im = Image.fromarray(vis_res[0]) parsing_im.save(opts.output_path + "cihp_output_vis/{}.png".format(img_list[ii][:-1])) cv2.imwrite( opts.output_path + "cihp_output/{}.png".format(img_list[ii][:-1]), results[0, :, :], ) # np.save('../../cihp_prob_output/{}.npy'.format(img_list[ii][:-1]), prob_results[0, :, :]) # pred_list.append(predictions.cpu()) # label_list.append(labels.squeeze(1).cpu()) # loss = criterion(outputs, labels, batch_average=True) # running_loss_ts += loss.item() # total_iou += utils.get_iou(predictions, labels) end_time = timeit.default_timer() print("time use for " + str(ii) + " is :" + str(end_time - start_time)) # Eval pred_path = opts.output_path + "cihp_output/" eval_( pred_path=pred_path, gt_path=opts.gt_path, classes=opts.classes, txt_file=opts.txt_file, )
def main(opts): p = OrderedDict() # Parameters to include in report p['trainBatch'] = opts.batch # Training batch size testBatch = 1 # Testing batch size useTest = True # See evolution of the test set when training nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs snapshot = 1 # Store a model every snapshot epochs p['nAveGrad'] = 1 # Average the gradient of several iterations p['lr'] = opts.lr # Learning rate p['lrFtr'] = 1e-5 p['lraspp'] = 1e-5 p['lrpro'] = 1e-5 p['lrdecoder'] = 1e-5 p['lrother'] = 1e-5 p['wd'] = 5e-4 # Weight decay p['momentum'] = 0.9 # Momentum p['epoch_size'] = opts.step # How many epochs to change learning rate p['num_workers'] = opts.numworker model_path = opts.pretrainedModel backbone = 'xception' # Use xception or resnet as feature extractor, nEpochs = opts.epochs max_id = 0 save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__))) exp_name = os.path.dirname(os.path.abspath(__file__)).split('/')[-1] runs = glob.glob(os.path.join(save_dir_root, 'run_cihp', 'run_*')) for r in runs: run_id = int(r.split('_')[-1]) if run_id >= max_id: max_id = run_id + 1 save_dir = os.path.join(save_dir_root, 'run_cihp', 'run_' + str(max_id)) # Device if (opts.device == "gpu"): use_cuda = torch.cuda.is_available() if (use_cuda == True): device = torch.device("cuda") #torch.cuda.set_device(args.gpu_ids[0]) print("実行デバイス :", device) print("GPU名 :", torch.cuda.get_device_name(device)) print("torch.cuda.current_device() =", torch.cuda.current_device()) else: print("can't using gpu.") device = torch.device("cpu") print("実行デバイス :", device) else: device = torch.device("cpu") print("実行デバイス :", device) # Network definition if backbone == 'xception': net_ = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem( n_classes=opts.classes, os=16, hidden_layers=opts.hidden_layers, source_classes=7, ) elif backbone == 'resnet': # net_ = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True) raise NotImplementedError else: raise NotImplementedError modelName = 'deeplabv3plus-' + backbone + '-voc' + datetime.now().strftime( '%b%d_%H-%M-%S') criterion = util.cross_entropy2d if gpu_id >= 0: # torch.cuda.set_device(device=gpu_id) #net_.cuda() net_.to(device) # net load weights if not model_path == '': x = torch.load(model_path) net_.load_state_dict_new(x) print('load pretrainedModel:', model_path) else: print('no pretrainedModel.') if not opts.loadmodel == '': x = torch.load(opts.loadmodel) net_.load_source_model(x) print('load model:', opts.loadmodel) else: print('no model load !!!!!!!!') log_dir = os.path.join( save_dir, 'models', datetime.now().strftime('%b%d_%H-%M-%S') + '_' + socket.gethostname()) writer = SummaryWriter(log_dir=log_dir) writer.add_text('load model', opts.loadmodel, 1) writer.add_text('setting', sys.argv[0], 1) if opts.freezeBN: net_.freeze_bn() print(net_) # Use the following optimizer optimizer = optim.SGD(net_.parameters(), lr=p['lr'], momentum=p['momentum'], weight_decay=p['wd']) composed_transforms_tr = transforms.Compose([ tr.RandomSized_new(opts.image_size), tr.Normalize_xception_tf(), tr.ToTensor_() ]) composed_transforms_ts = transforms.Compose( [tr.Normalize_xception_tf(), tr.ToTensor_()]) composed_transforms_ts_flip = transforms.Compose( [tr.HorizontalFlip(), tr.Normalize_xception_tf(), tr.ToTensor_()]) #voc_train = cihp.VOCSegmentation(split='train', transform=composed_transforms_tr, flip=True) #voc_val = cihp.VOCSegmentation(split='val', transform=composed_transforms_ts) #voc_val_flip = cihp.VOCSegmentation(split='val', transform=composed_transforms_ts_flip) voc_train = cihp.VOCSegmentation(base_dir="../../data/datasets/CIHP_4w", split='train', transform=composed_transforms_tr, flip=True) voc_val = cihp.VOCSegmentation(base_dir="../../data/datasets/CIHP_4w", split='val', transform=composed_transforms_ts) voc_val_flip = cihp.VOCSegmentation(base_dir="../../data/datasets/CIHP_4w", split='val', transform=composed_transforms_ts_flip) trainloader = DataLoader(voc_train, batch_size=p['trainBatch'], shuffle=True, num_workers=p['num_workers'], drop_last=True) testloader = DataLoader(voc_val, batch_size=testBatch, shuffle=False, num_workers=p['num_workers']) testloader_flip = DataLoader(voc_val_flip, batch_size=testBatch, shuffle=False, num_workers=p['num_workers']) num_img_tr = len(trainloader) num_img_ts = len(testloader) running_loss_tr = 0.0 running_loss_ts = 0.0 aveGrad = 0 global_step = 0 print("Training Network") print("num_img_tr : ", num_img_tr) net = torch.nn.DataParallel(net_) train_graph, test_graph = get_graphs(opts, device) adj1, adj2, adj3 = train_graph # Main Training and Testing Loop for epoch in range(resume_epoch, nEpochs): start_time = timeit.default_timer() if epoch % p['epoch_size'] == p['epoch_size'] - 1: lr_ = util.lr_poly(p['lr'], epoch, nEpochs, 0.9) optimizer = optim.SGD(net_.parameters(), lr=lr_, momentum=p['momentum'], weight_decay=p['wd']) writer.add_scalar('data/lr_', lr_, epoch) print('(poly lr policy) learning rate: ', lr_) net.train() for ii, sample_batched in enumerate(trainloader): inputs, labels = sample_batched['image'], sample_batched['label'] # Forward-Backward of the mini-batch inputs, labels = Variable(inputs, requires_grad=True), Variable(labels) global_step += inputs.data.shape[0] if gpu_id >= 0: #inputs, labels = inputs.cuda(), labels.cuda() inputs, labels = inputs.to(device), labels.to(device) #print( "inputs.shape : ", inputs.shape ) # torch.Size([batch, 3, 512, 512]) #print( "adj1.shape : ", adj1.shape ) # torch.Size([8, 1, 20, 20]) #print( "adj2.shape : ", adj2.shape ) # torch.Size([8, 1, 20, 7]) #print( "adj3.shape : ", adj3.shape ) # torch.Size([8, 1, 7, 7]) outputs = net.forward(inputs, adj1, adj3, adj2) #print( "outputs.shape : ", outputs.shape ) # torch.Size([2, 20, 512, 512]) loss = criterion(outputs, labels, batch_average=True) running_loss_tr += loss.item() # Print stuff if ii % num_img_tr == (num_img_tr - 1): running_loss_tr = running_loss_tr / num_img_tr writer.add_scalar('data/total_loss_epoch', running_loss_tr, epoch) print('[Epoch: %d, numImages: %5d]' % (epoch, ii * p['trainBatch'] + inputs.data.shape[0])) print('Loss: %f' % running_loss_tr) running_loss_tr = 0 stop_time = timeit.default_timer() print("Execution time: " + str(stop_time - start_time) + "\n") # Backward the averaged gradient loss /= p['nAveGrad'] loss.backward() aveGrad += 1 # Update the weights once in p['nAveGrad'] forward passes if aveGrad % p['nAveGrad'] == 0: writer.add_scalar('data/total_loss_iter', loss.item(), ii + num_img_tr * epoch) optimizer.step() optimizer.zero_grad() aveGrad = 0 # Show 10 * 3 images results each epoch if ii % (num_img_tr // 10) == 0: # if ii % (num_img_tr // 4000) == 0: grid_image = make_grid(inputs[:3].clone().cpu().data, 3, normalize=True) writer.add_image('Image', grid_image, global_step) grid_image = make_grid(util.decode_seg_map_sequence( torch.max(outputs[:3], 1)[1].detach().cpu().numpy()), 3, normalize=False, range=(0, 255)) writer.add_image('Predicted label', grid_image, global_step) grid_image = make_grid(util.decode_seg_map_sequence( torch.squeeze(labels[:3], 1).detach().cpu().numpy()), 3, normalize=False, range=(0, 255)) writer.add_image('Groundtruth label', grid_image, global_step) print('loss is ', loss.cpu().item(), flush=True) # Save the model if (epoch % snapshot) == snapshot - 1: torch.save( net_.state_dict(), os.path.join(save_dir, 'models', modelName + '_epoch-' + str(epoch) + '.pth')) print("Save model at {}\n".format( os.path.join(save_dir, 'models', modelName + '_epoch-' + str(epoch) + '.pth'))) torch.cuda.empty_cache() # One testing epoch if useTest and epoch % nTestInterval == (nTestInterval - 1): val_cihp(net_, testloader=testloader, testloader_flip=testloader_flip, test_graph=test_graph, epoch=epoch, writer=writer, criterion=criterion, classes=opts.classes, device=device) torch.cuda.empty_cache()
def main(opts): p = OrderedDict() # Parameters to include in report p["trainBatch"] = opts.batch # Training batch size testBatch = 1 # Testing batch size useTest = True # See evolution of the test set when training nTestInterval = opts.testInterval # Run on test set every nTestInterval epochs snapshot = 1 # Store a model every snapshot epochs p["nAveGrad"] = 1 # Average the gradient of several iterations p["lr"] = opts.lr # Learning rate p["lrFtr"] = 1e-5 p["lraspp"] = 1e-5 p["lrpro"] = 1e-5 p["lrdecoder"] = 1e-5 p["lrother"] = 1e-5 p["wd"] = 5e-4 # Weight decay p["momentum"] = 0.9 # Momentum p["epoch_size"] = opts.step # How many epochs to change learning rate p["num_workers"] = opts.numworker model_path = opts.pretrainedModel backbone = "xception" # Use xception or resnet as feature extractor, nEpochs = opts.epochs max_id = 0 save_dir_root = os.path.join(os.path.dirname(os.path.abspath(__file__))) exp_name = os.path.dirname(os.path.abspath(__file__)).split("/")[-1] runs = glob.glob(os.path.join(save_dir_root, "run_cihp", "run_*")) for r in runs: run_id = int(r.split("_")[-1]) if run_id >= max_id: max_id = run_id + 1 save_dir = os.path.join(save_dir_root, "run_cihp", "run_" + str(max_id)) # Network definition if backbone == "xception": net_ = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem( n_classes=opts.classes, os=16, hidden_layers=opts.hidden_layers, source_classes=7, ) elif backbone == "resnet": # net_ = deeplab_resnet.DeepLabv3_plus(nInputChannels=3, n_classes=7, os=16, pretrained=True) raise NotImplementedError else: raise NotImplementedError modelName = ("deeplabv3plus-" + backbone + "-voc" + datetime.now().strftime("%b%d_%H-%M-%S")) criterion = util.cross_entropy2d if gpu_id >= 0: # torch.cuda.set_device(device=gpu_id) net_.cuda() # net load weights if not model_path == "": x = torch.load(model_path) net_.load_state_dict_new(x) print("load pretrainedModel:", model_path) else: print("no pretrainedModel.") if not opts.loadmodel == "": x = torch.load(opts.loadmodel) net_.load_source_model(x) print("load model:", opts.loadmodel) else: print("no model load !!!!!!!!") log_dir = os.path.join( save_dir, "models", datetime.now().strftime("%b%d_%H-%M-%S") + "_" + socket.gethostname(), ) writer = SummaryWriter(log_dir=log_dir) writer.add_text("load model", opts.loadmodel, 1) writer.add_text("setting", sys.argv[0], 1) if opts.freezeBN: net_.freeze_bn() # Use the following optimizer optimizer = optim.SGD(net_.parameters(), lr=p["lr"], momentum=p["momentum"], weight_decay=p["wd"]) composed_transforms_tr = transforms.Compose( [tr.RandomSized_new(512), tr.Normalize_xception_tf(), tr.ToTensor_()]) composed_transforms_ts = transforms.Compose( [tr.Normalize_xception_tf(), tr.ToTensor_()]) composed_transforms_ts_flip = transforms.Compose( [tr.HorizontalFlip(), tr.Normalize_xception_tf(), tr.ToTensor_()]) voc_train = cihp.VOCSegmentation(split="train", transform=composed_transforms_tr, flip=True) voc_val = cihp.VOCSegmentation(split="val", transform=composed_transforms_ts) voc_val_flip = cihp.VOCSegmentation(split="val", transform=composed_transforms_ts_flip) trainloader = DataLoader( voc_train, batch_size=p["trainBatch"], shuffle=True, num_workers=p["num_workers"], drop_last=True, ) testloader = DataLoader(voc_val, batch_size=testBatch, shuffle=False, num_workers=p["num_workers"]) testloader_flip = DataLoader(voc_val_flip, batch_size=testBatch, shuffle=False, num_workers=p["num_workers"]) num_img_tr = len(trainloader) num_img_ts = len(testloader) running_loss_tr = 0.0 running_loss_ts = 0.0 aveGrad = 0 global_step = 0 print("Training Network") net = torch.nn.DataParallel(net_) train_graph, test_graph = get_graphs(opts) adj1, adj2, adj3 = train_graph # Main Training and Testing Loop for epoch in range(resume_epoch, nEpochs): start_time = timeit.default_timer() if epoch % p["epoch_size"] == p["epoch_size"] - 1: lr_ = util.lr_poly(p["lr"], epoch, nEpochs, 0.9) optimizer = optim.SGD(net_.parameters(), lr=lr_, momentum=p["momentum"], weight_decay=p["wd"]) writer.add_scalar("data/lr_", lr_, epoch) print("(poly lr policy) learning rate: ", lr_) net.train() for ii, sample_batched in enumerate(trainloader): inputs, labels = sample_batched["image"], sample_batched["label"] # Forward-Backward of the mini-batch inputs, labels = Variable(inputs, requires_grad=True), Variable(labels) global_step += inputs.data.shape[0] if gpu_id >= 0: inputs, labels = inputs.cuda(), labels.cuda() outputs = net.forward(inputs, adj1, adj3, adj2) loss = criterion(outputs, labels, batch_average=True) running_loss_tr += loss.item() # Print stuff if ii % num_img_tr == (num_img_tr - 1): running_loss_tr = running_loss_tr / num_img_tr writer.add_scalar("data/total_loss_epoch", running_loss_tr, epoch) print("[Epoch: %d, numImages: %5d]" % (epoch, ii * p["trainBatch"] + inputs.data.shape[0])) print("Loss: %f" % running_loss_tr) running_loss_tr = 0 stop_time = timeit.default_timer() print("Execution time: " + str(stop_time - start_time) + "\n") # Backward the averaged gradient loss /= p["nAveGrad"] loss.backward() aveGrad += 1 # Update the weights once in p['nAveGrad'] forward passes if aveGrad % p["nAveGrad"] == 0: writer.add_scalar("data/total_loss_iter", loss.item(), ii + num_img_tr * epoch) optimizer.step() optimizer.zero_grad() aveGrad = 0 # Show 10 * 3 images results each epoch if ii % (num_img_tr // 10) == 0: grid_image = make_grid(inputs[:3].clone().cpu().data, 3, normalize=True) writer.add_image("Image", grid_image, global_step) grid_image = make_grid( util.decode_seg_map_sequence( torch.max(outputs[:3], 1)[1].detach().cpu().numpy()), 3, normalize=False, range=(0, 255), ) writer.add_image("Predicted label", grid_image, global_step) grid_image = make_grid( util.decode_seg_map_sequence( torch.squeeze(labels[:3], 1).detach().cpu().numpy()), 3, normalize=False, range=(0, 255), ) writer.add_image("Groundtruth label", grid_image, global_step) print("loss is ", loss.cpu().item(), flush=True) # Save the model if (epoch % snapshot) == snapshot - 1: torch.save( net_.state_dict(), os.path.join(save_dir, "models", modelName + "_epoch-" + str(epoch) + ".pth"), ) print("Save model at {}\n".format( os.path.join(save_dir, "models", modelName + "_epoch-" + str(epoch) + ".pth"))) torch.cuda.empty_cache() # One testing epoch if useTest and epoch % nTestInterval == (nTestInterval - 1): val_cihp( net_, testloader=testloader, testloader_flip=testloader_flip, test_graph=test_graph, epoch=epoch, writer=writer, criterion=criterion, classes=opts.classes, ) torch.cuda.empty_cache()