Python cudnn.fastest方法代码示例

# 需要导入模块: from torch.backends import cudnn [as 别名]
# 或者: from torch.backends.cudnn import fastest [as 别名]
def main():

    args = parse_args()
    cfg.set_args(args.gpu_ids)
    cudnn.fastest = True
    cudnn.benchmark = True

    tester = Tester(args.test_epoch)
    tester._make_batch_generator()
    tester._make_model()

    preds = []
    with torch.no_grad():
        for itr, (input_img, cam_param) in enumerate(tqdm(tester.batch_generator)):
            
            coord_out = tester.model(input_img, cam_param)
            coord_out = coord_out.cpu().numpy()
            preds.append(coord_out)
            
    # evaluate
    preds = np.concatenate(preds, axis=0)
    tester._evaluate(preds, cfg.result_dir) 

# 需要导入模块: from torch.backends import cudnn [as 别名]
# 或者: from torch.backends.cudnn import fastest [as 别名]
def benchmark_inference(model, opts):
    """Benchmarks inference phase.

    :param obj model: A model to benchmark
    :param dict opts: A dictionary of parameters.
    :rtype: tuple
    :return: A tuple of (model_name, list of batch times)
    """
    if opts['phase'] != 'inference':
        raise "Phase in benchmark_inference func is '%s'" % opts['phase']
    if opts['device'] == 'gpu' and opts['world_size'] != 1:
        raise "GPU inference can only be used with one GPU (world_size: %d)." % opts['world_size']

    # Batch, Channels, Height, Width
    data = autograd.Variable(torch.randn((opts['batch_size'],) + model.input_shape))
    if opts['device'] == 'gpu':
        # TODO: Is it good to enable cuDNN autotuning (batch size is fixed)?
        #   https://github.com/soumith/cudnn.torch#modes
        #   https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936
        # How many iterations do we need to get cuDNN decide what kernels to use?
        cudnn.benchmark = opts['cudnn_benchmark']
        cudnn.fastest = opts['cudnn_fastest']

        data = data.cuda()
        model = model.cuda()
    if opts['dtype'] == 'float16':
        data = data.half()
        model = model.half()
    model.eval()
    # Do warmup round
    for i in range(opts['num_warmup_batches']):
        model(data)
    # Do benchmark round
    batch_times = np.zeros(opts['num_batches'])
    for i in range(opts['num_batches']):
        start_time = timeit.default_timer()
        model(data)
        batch_times[i] = timeit.default_timer() - start_time
    return (model.name, batch_times) 

# 需要导入模块: from torch.backends import cudnn [as 别名]
# 或者: from torch.backends.cudnn import fastest [as 别名]
def main():

    args = parse_args()
    cfg.set_args(args.gpu_ids)
    cudnn.fastest = True
    cudnn.benchmark = True
    cudnn.deterministic = False
    cudnn.enabled = True

    tester = Tester(args.test_epoch)
    tester._make_batch_generator()
    tester._make_model()

    preds = []

    with torch.no_grad():
        for itr, input_img in enumerate(tqdm(tester.batch_generator)):
            
            # forward
            coord_out = tester.model(input_img)

            if cfg.flip_test:
                flipped_input_img = flip(input_img, dims=3)
                flipped_coord_out = tester.model(flipped_input_img)
                flipped_coord_out[:, :, 0] = cfg.output_shape[1] - flipped_coord_out[:, :, 0] - 1
                for pair in tester.flip_pairs:
                    flipped_coord_out[:, pair[0], :], flipped_coord_out[:, pair[1], :] = flipped_coord_out[:, pair[1], :].clone(), flipped_coord_out[:, pair[0], :].clone()
                coord_out = (coord_out + flipped_coord_out)/2.

            vis = False
            if vis:
                filename = str(itr)
                tmpimg = input_img[0].cpu().numpy()
                tmpimg = tmpimg * np.array(cfg.pixel_std).reshape(3,1,1) + np.array(cfg.pixel_mean).reshape(3,1,1)
                tmpimg = tmpimg.astype(np.uint8)
                tmpimg = tmpimg[::-1, :, :]
                tmpimg = np.transpose(tmpimg,(1,2,0)).copy()
                tmpkps = np.zeros((3,tester.joint_num))
                tmpkps[:2,:] = coord_out[0,:,:2].cpu().numpy().transpose(1,0) / cfg.output_shape[0] * cfg.input_shape[0]
                tmpkps[2,:] = 1
                tmpimg = vis_keypoints(tmpimg, tmpkps, tester.skeleton)
                cv2.imwrite(filename + '_output.jpg', tmpimg)

            coord_out = coord_out.cpu().numpy()
            preds.append(coord_out)
            
    # evaluate
    preds = np.concatenate(preds, axis=0)
    tester._evaluate(preds, cfg.result_dir) 

# 需要导入模块: from torch.backends import cudnn [as 别名]
# 或者: from torch.backends.cudnn import fastest [as 别名]
def main():
    
    # argument parse and create log
    args = parse_args()
    cfg.set_args(args.gpu_ids, args.continue_train)
    cudnn.fastest = True
    cudnn.benchmark = True

    trainer = Trainer()
    trainer._make_batch_generator()
    trainer._make_model()

    # train
    for epoch in range(trainer.start_epoch, cfg.end_epoch):
        
        trainer.set_lr(epoch)
        trainer.tot_timer.tic()
        trainer.read_timer.tic()

        for itr, (input_img, joint_img, joint_vis, joints_have_depth) in enumerate(trainer.batch_generator):
            trainer.read_timer.toc()
            trainer.gpu_timer.tic()

            # forward
            trainer.optimizer.zero_grad()
            target = {'coord': joint_img, 'vis': joint_vis, 'have_depth': joints_have_depth}
            loss_coord = trainer.model(input_img, target)
            loss_coord = loss_coord.mean()

            # backward
            loss = loss_coord
            loss.backward()
            trainer.optimizer.step()
            
            trainer.gpu_timer.toc()
            screen = [
                'Epoch %d/%d itr %d/%d:' % (epoch, cfg.end_epoch, itr, trainer.itr_per_epoch),
                'lr: %g' % (trainer.get_lr()),
                'speed: %.2f(%.2fs r%.2f)s/itr' % (
                    trainer.tot_timer.average_time, trainer.gpu_timer.average_time, trainer.read_timer.average_time),
                '%.2fh/epoch' % (trainer.tot_timer.average_time / 3600. * trainer.itr_per_epoch),
                '%s: %.4f' % ('loss_coord', loss_coord.detach()),
                ]
            trainer.logger.info(' '.join(screen))
            trainer.tot_timer.toc()
            trainer.tot_timer.tic()
            trainer.read_timer.tic()

        trainer.save_model({
            'epoch': epoch,
            'network': trainer.model.state_dict(),
            'optimizer': trainer.optimizer.state_dict(),
        }, epoch) 

# 需要导入模块: from torch.backends import cudnn [as 别名]
# 或者: from torch.backends.cudnn import fastest [as 别名]
def main():
    
    # argument parse and create log
    args = parse_args()
    cfg.set_args(args.gpu_ids, args.continue_train)
    cudnn.fastest = True
    cudnn.benchmark = True

    trainer = Trainer()
    trainer._make_batch_generator()
    trainer._make_model()

    # train
    for epoch in range(trainer.start_epoch, cfg.end_epoch):
        
        trainer.set_lr(epoch)
        trainer.tot_timer.tic()
        trainer.read_timer.tic()

        for itr, (input_img, k_value, root_img, root_vis, joints_have_depth) in enumerate(trainer.batch_generator):
            trainer.read_timer.toc()
            trainer.gpu_timer.tic()

            # forward
            trainer.optimizer.zero_grad()
            target = {'coord': root_img, 'vis': root_vis, 'have_depth': joints_have_depth}
            loss_coord = trainer.model(input_img, k_value, target)
            loss_coord = loss_coord.mean();

            # backward
            loss = loss_coord

            loss.backward()
            trainer.optimizer.step()
            
            trainer.gpu_timer.toc()

            screen = [
                'Epoch %d/%d itr %d/%d:' % (epoch, cfg.end_epoch, itr, trainer.itr_per_epoch),
                'lr: %g' % (trainer.get_lr()),
                'speed: %.2f(%.2fs r%.2f)s/itr' % (
                    trainer.tot_timer.average_time, trainer.gpu_timer.average_time, trainer.read_timer.average_time),
                '%.2fh/epoch' % (trainer.tot_timer.average_time / 3600. * trainer.itr_per_epoch),
                '%s: %.4f' % ('loss_coord', loss_coord.detach()),
                ]
            trainer.logger.info(' '.join(screen))

            trainer.tot_timer.toc()
            trainer.tot_timer.tic()
            trainer.read_timer.tic()

        trainer.save_model({
            'epoch': epoch,
            'network': trainer.model.state_dict(),
            'optimizer': trainer.optimizer.state_dict(),
        }, epoch) 

# 需要导入模块: from torch.backends import cudnn [as 别名]
# 或者: from torch.backends.cudnn import fastest [as 别名]
def __init__(self, ARCH, DATA, datadir, logdir, modeldir):
    # parameters
    self.ARCH = ARCH
    self.DATA = DATA
    self.datadir = datadir
    self.logdir = logdir
    self.modeldir = modeldir

    # get the data
    parserModule = imp.load_source("parserModule",
                                   booger.TRAIN_PATH + '/tasks/semantic/dataset/' +
                                   self.DATA["name"] + '/parser.py')
    self.parser = parserModule.Parser(root=self.datadir,
                                      train_sequences=self.DATA["split"]["train"],
                                      valid_sequences=self.DATA["split"]["valid"],
                                      test_sequences=self.DATA["split"]["test"],
                                      labels=self.DATA["labels"],
                                      color_map=self.DATA["color_map"],
                                      learning_map=self.DATA["learning_map"],
                                      learning_map_inv=self.DATA["learning_map_inv"],
                                      sensor=self.ARCH["dataset"]["sensor"],
                                      max_points=self.ARCH["dataset"]["max_points"],
                                      batch_size=1,
                                      workers=self.ARCH["train"]["workers"],
                                      gt=True,
                                      shuffle_train=False)

    # concatenate the encoder and the head
    with torch.no_grad():
      self.model = Segmentator(self.ARCH,
                               self.parser.get_n_classes(),
                               self.modeldir)

    # use knn post processing?
    self.post = None
    if self.ARCH["post"]["KNN"]["use"]:
      self.post = KNN(self.ARCH["post"]["KNN"]["params"],
                      self.parser.get_n_classes())

    # GPU?
    self.gpu = False
    self.model_single = self.model
    self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print("Infering in device: ", self.device)
    if torch.cuda.is_available() and torch.cuda.device_count() > 0:
      cudnn.benchmark = True
      cudnn.fastest = True
      self.gpu = True
      self.model.cuda() 

# 需要导入模块: from torch.backends import cudnn [as 别名]
# 或者: from torch.backends.cudnn import fastest [as 别名]
def __init__(self, path):
    # parameters
    self.path = path

    # config from path
    try:
      yaml_path = self.path + "/cfg.yaml"
      print("Opening config file %s" % yaml_path)
      self.CFG = yaml.safe_load(open(yaml_path, 'r'))
    except Exception as e:
      print(e)
      print("Error opening cfg.yaml file from trained model.")
      quit()

    # make a colorizer
    self.colorizer = Colorizer(self.CFG["dataset"]["color_map"])

    # get the data
    parserModule = imp.load_source("parserModule",
                                   booger.TRAIN_PATH + '/tasks/segmentation/dataset/' +
                                   self.CFG["dataset"]["name"] + '/parser.py')
    self.parser = parserModule.Parser(img_prop=self.CFG["dataset"]["img_prop"],
                                      img_means=self.CFG["dataset"]["img_means"],
                                      img_stds=self.CFG["dataset"]["img_stds"],
                                      classes=self.CFG["dataset"]["labels"],
                                      train=False)

    # some useful data
    self.data_h, self.data_w, self.data_d = self.parser.get_img_size()
    self.means, self.stds = self.parser.get_means_stds()
    self.means = torch.tensor(self.means)
    self.stds = torch.tensor(self.stds)
    self.nclasses = self.parser.get_n_classes()

    # architecture definition
    # get weights?
    try:
      self.pytorch_path = os.path.join(self.path, "model.pytorch")
      self.model = torch.jit.load(self.pytorch_path)
      print("Successfully Pytorch-traced model from ", self.pytorch_path)
    except Exception as e:
      print("Couldn't load Pytorch-traced network. Error: ", e)
      quit()

    # GPU?
    self.gpu = False
    self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    if torch.cuda.is_available() and torch.cuda.device_count() > 0:
      self.gpu = True
      cudnn.benchmark = True
      cudnn.fastest = True
      self.model.cuda()
      self.means = self.means.cuda()
      self.stds = self.stds.cuda() 

# 需要导入模块: from torch.backends import cudnn [as 别名]
# 或者: from torch.backends.cudnn import fastest [as 别名]
def __init__(self, path):
    # parameters
    self.path = path

    # config from path
    try:
      yaml_path = self.path + "/cfg.yaml"
      print("Opening config file %s" % yaml_path)
      self.CFG = yaml.safe_load(open(yaml_path, 'r'))
    except Exception as e:
      print(e)
      print("Error opening cfg.yaml file from trained model.")
      quit()

    # get the data
    parserModule = imp.load_source("parserModule",
                                   booger.TRAIN_PATH + '/tasks/classification/dataset/' +
                                   self.CFG["dataset"]["name"] + '/parser.py')
    self.parser = parserModule.Parser(img_prop=self.CFG["dataset"]["img_prop"],
                                      img_means=self.CFG["dataset"]["img_means"],
                                      img_stds=self.CFG["dataset"]["img_stds"],
                                      classes=self.CFG["dataset"]["labels"],
                                      train=False)

    # some useful data
    self.data_h, self.data_w, self.data_d = self.parser.get_img_size()
    self.means, self.stds = self.parser.get_means_stds()
    self.means = torch.tensor(self.means)
    self.stds = torch.tensor(self.stds)
    self.nclasses = self.parser.get_n_classes()

    # architecture definition
    # get weights?
    try:
      self.pytorch_path = os.path.join(self.path, "model.pytorch")
      self.model = torch.jit.load(self.pytorch_path)
      print("Successfully Pytorch-traced model from ", self.pytorch_path)
    except Exception as e:
      print("Couldn't load Pytorch-traced network. Error: ", e)
      quit()

    # GPU?
    self.gpu = False
    self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    if torch.cuda.is_available() and torch.cuda.device_count() > 0:
      self.gpu = True
      cudnn.benchmark = True
      cudnn.fastest = True
      self.model.cuda()
      self.means = self.means.cuda()
      self.stds = self.stds.cuda()