def __init__(self, batch_size, num_threads, device_id): super(TFRecordPipeline, self).__init__(batch_size, num_threads, device_id) self.input = ops.TFRecordReader(path = tfrecord, index_path = tfrecord_idx, features = {"image/encoded" : tfrec.FixedLenFeature((), tfrec.string, ""), 'image/filename': tfrec.FixedLenFeature([ ], tfrec.string, ''), 'image/height': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/width': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/colorspace': tfrec.FixedLenFeature([ ], tfrec.string, ''), 'image/channels': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/format': tfrec.FixedLenFeature([ ], tfrec.string, ''), 'image/class/label': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/class/synset': tfrec.FixedLenFeature([ ], tfrec.string, ''), 'image/class/text': tfrec.FixedLenFeature([ ], tfrec.string, ''), 'image/object/bbox/xmin': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/bbox/ymin': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/bbox/xmax': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/bbox/ymax': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/bbox/label': tfrec.FixedLenFeature([1], tfrec.int64, -1)}) self.decode = ops.ImageDecoder(device = "mixed", output_type = types.RGB) self.resize = ops.Resize(device = "gpu", resize_x = 1920.,resize_y=1920.) self.vert_flip = ops.Flip(device = "gpu", horizontal=0) self.vert_coin = ops.CoinFlip(probability=0.5) self.cmnp = ops.CropMirrorNormalize(device = "gpu", output_dtype = types.FLOAT, crop = (1920, 1920), image_type = types.RGB, mean = [0., 0., 0.], std = [1., 1., 1.]) self.uniform = ops.Uniform(range = (0.0, 1.0)) self.iter = 0
def __init__(self, batch_size, num_threads, device_id): super(TFRecordPipeline, self).__init__(batch_size, num_threads, device_id) self.input = ops.TFRecordReader( path=tfrecord, index_path=tfrecord_idx, features={ "image/encoded": tfrec.FixedLenFeature((), tfrec.string, ""), 'image/class/label': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/class/text': tfrec.FixedLenFeature([], tfrec.string, ''), 'image/object/bbox/xmin': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/bbox/ymin': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/bbox/xmax': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/bbox/ymax': tfrec.VarLenFeature(tfrec.float32, 0.0) }) self.decode = ops.ImageDecoder(device="mixed", output_type=types.RGB) self.resize = ops.Resize(device="gpu", resize_shorter=256.) self.cmnp = ops.CropMirrorNormalize(device="gpu", output_dtype=types.FLOAT, crop=(224, 224), image_type=types.RGB, mean=[0., 0., 0.], std=[1., 1., 1.]) self.uniform = ops.Uniform(range=(0.0, 1.0)) self.iter = 0
def __init__(self, batch_size, num_threads, device_id, **kwargs): super(TFRecordTrain, self).__init__(batch_size, num_threads, device_id) self.dim = kwargs["dim"] self.seed = kwargs["seed"] self.oversampling = kwargs["oversampling"] self.input = ops.TFRecordReader( path=kwargs["tfrecords"], index_path=kwargs["tfrecords_idx"], features={ "X_shape": tfrec.FixedLenFeature([self.dim + 1], tfrec.int64, 0), "Y_shape": tfrec.FixedLenFeature([self.dim + 1], tfrec.int64, 0), "X": tfrec.VarLenFeature([], tfrec.float32, 0.0), "Y": tfrec.FixedLenFeature([], tfrec.string, ""), "fname": tfrec.FixedLenFeature([], tfrec.string, ""), }, num_shards=kwargs["gpus"], shard_id=device_id, random_shuffle=True, pad_last_batch=True, read_ahead=True, seed=self.seed, ) self.patch_size = kwargs["patch_size"] self.crop_shape = types.Constant(np.array(self.patch_size), dtype=types.INT64) self.crop_shape_float = types.Constant(np.array(self.patch_size), dtype=types.FLOAT) self.layout = "CDHW" if self.dim == 3 else "CHW" self.axis_name = "DHW" if self.dim == 3 else "HW"
def __init__(self, batch_size, num_threads, device_id, data_dir, crop, size): super(HybridValPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id) # self.input = ops.FileReader(file_root=data_dir, shard_id=args.local_rank, num_shards=args.world_size, random_shuffle=False) index_path = [] for path in os.listdir("/home/guojia/idx_files/val"): index_path.append(os.path.join("/home/guojia/idx_files/val", path)) index_path = sorted(index_path) self.input = ops.TFRecordReader(path=data_dir, index_path=index_path, shard_id=args.local_rank, num_shards=args.world_size, random_shuffle=True, features={ 'image/height': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/width': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/colorspace': tfrec.FixedLenFeature([], tfrec.string, ''), 'image/channels': tfrec.FixedLenFeature([], tfrec.int64, -1), 'image/class/label': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/class/synset': tfrec.FixedLenFeature([], tfrec.string, ''), 'image/format': tfrec.FixedLenFeature((), tfrec.string, ""), 'image/filename': tfrec.FixedLenFeature((), tfrec.string, ""), 'image/encoded': tfrec.FixedLenFeature((), tfrec.string, "") }) self.decode = ops.ImageDecoder(device="mixed", output_type=types.RGB) self.res = ops.Resize(device="gpu", resize_shorter=size, interp_type=types.INTERP_TRIANGULAR) self.cmnp = ops.CropMirrorNormalize(device="gpu", output_dtype=types.FLOAT, output_layout=types.NCHW, crop=(crop, crop), image_type=types.RGB, mean=[0.485 * 255,0.456 * 255,0.406 * 255], std=[0.229 * 255,0.224 * 255,0.225 * 255])
def __init__(self, batch_size, num_threads, device_id, num_gpus, data, data_idx): super(TFRecordPipeline, self).__init__(batch_size, num_threads, device_id) self.input = ops.TFRecordReader(path = data, index_path = data_idx, features = {"image/encoded" : tfrec.FixedLenFeature((), tfrec.string, ""), "image/class/label": tfrec.FixedLenFeature([1], tfrec.int64, -1) })
def __init__(self, batch_size, num_threads, device_id, data_dir, crop, dali_cpu=False): super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id) self.input = ops.TFRecordReader( path=data_dir + ".tfrecord", index_path=data_dir + ".index", features={ "image": tfrec.FixedLenFeature([], tfrec.string, ""), 'label': tfrec.FixedLenFeature([], tfrec.float32, 0.0), 'index': tfrec.FixedLenFeature([], tfrec.int64, 0), }) self.decode = ops.ImageDecoder( device="cpu", output_type=types.RGB, split_stages=True, ) self.res = ops.RandomResizedCrop(device="gpu", size=(224, 224)) self.cmnp = ops.CropMirrorNormalize( device="gpu", output_dtype=types.FLOAT, output_layout=types.NCHW, crop=(crop, crop), image_type=types.RGB, mean=[0.485 * 255, 0.456 * 255, 0.406 * 255], std=[0.229 * 255, 0.224 * 255, 0.225 * 255]) self.coin = ops.CoinFlip(probability=0.5) print('DALI "{0}" variant'.format('gpu'))
def __init__(self, args): super(TFRecordDetectionPipeline, self).__init__(args.batch_size, args.num_workers, 0, 0) self.input = ops.TFRecordReader( path=os.path.join(test_dummy_data_path, 'small_coco.tfrecord'), index_path=os.path.join(test_dummy_data_path, 'small_coco_index.idx'), features={ 'image/encoded': tfrec.FixedLenFeature((), tfrec.string, ""), 'image/object/class/label': tfrec.VarLenFeature([1], tfrec.int64, 0), 'image/object/bbox': tfrec.VarLenFeature([4], tfrec.float32, 0.0), }, shard_id=0, num_shards=1, random_shuffle=False) self.decode_gpu = ops.ImageDecoder(device="mixed", output_type=types.RGB) self.cast = ops.Cast(dtype=types.INT32) self.box_encoder = ops.BoxEncoder(device="cpu", criteria=0.5, anchors=coco_anchors())
def __init__(self, batch_size, num_threads, device_id, tfrecord, tfrecord_idx): super(TFRecordPipeline, self).__init__(batch_size, num_threads, device_id) self.input = ops.TFRecordReader(path=tfrecord, index_path=tfrecord_idx, features={ "image/encoded": tfrec.FixedLenFeature( (), tfrec.string, ""), 'image/class/label': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/class/text': tfrec.FixedLenFeature([], tfrec.string, ''), }) self.decode = ops.nvJPEGDecoder(device="mixed", output_type=types.RGB) self.resize = ops.Resize(device="gpu", resize_a=256, resize_b=256) self.cmnp = ops.CropMirrorNormalize(device="gpu", output_dtype=types.FLOAT, crop=(224, 224), image_type=types.RGB, mean=[0., 0., 0.], std=[1., 1., 1.], output_layout=types.NHWC) self.uniform = ops.Uniform(range=(0.0, 1.0)) self.iter = 0
def __init__(self, tfrecord_files, idx_files, batch_size, device_id=0, rank=0, total_devices=1, num_threads=4): super(TFRecordPipeline, self).__init__(batch_size, num_threads, device_id) self.input = ops.TFRecordReader(path = tfrecord_files, index_path = idx_files, shard_id = rank, num_shards = total_devices, random_shuffle = True, features = {"image/encoded" : tfrec.FixedLenFeature((), tfrec.string, ""), 'image/class/label': tfrec.FixedLenFeature([1], tfrec.int64, -1), }) self.decode = ops.ImageDecoder(device = "mixed", output_type = types.RGB) self.resize = ops.Resize(device = "gpu", resize_shorter = 256) self.cmnp = ops.CropMirrorNormalize(device = "gpu", output_dtype = types.FLOAT16, crop = (224, 224), image_type = types.RGB, mean = [0, 0, 0], std = [1., 1., 1.], output_layout='HWC') self.uniform = ops.Uniform(range = (0.0, 1.0)) self.flip = ops.CoinFlip() self.brightness = ops.Uniform(range = (0.5, 1.5)) self.contrast = ops.Uniform(range = (0.8, 1.3)) self.cast = ops.Cast(device = "gpu", dtype = types.FLOAT16) self.iter = 0
def __init__(self, tfrec_filenames, tfrec_idx_filenames, batch_size, num_threads, device_id, set_affinity, prefetch_queue_depth): super(Dali_CPU_Pipe, self).__init__(batch_size, num_threads, device_id, set_affinity=set_affinity, prefetch_queue_depth=prefetch_queue_depth) self.input = ops.TFRecordReader(path=tfrec_filenames, index_path=tfrec_idx_filenames, initial_fill=10000, features={ "image/encoded": tfrec.FixedLenFeature( (), tfrec.string, ""), 'image/class/label': tfrec.FixedLenFeature([1], tfrec.int64, -1) }) self.decode = ops.HostDecoder(output_type=types.RGB) self.resize = ops.Resize(device="cpu", resize_shorter=_RESIZE_MIN) self.cmnp = ops.CropMirrorNormalize(device="cpu", output_dtype=types.FLOAT, crop=(INPUT_SIZE, INPUT_SIZE), image_type=types.RGB, mean=_CHANNEL_MEANS, std=[58.395, 57.120, 57.375], output_layout=dali.types.NCHW) self.iter = 0
def __init__(self, batch_size, num_threads, device_id, num_gpus, data_paths): super(TFRecordPipeline, self).__init__(batch_size, num_threads, device_id) tfrecord = sorted(glob.glob(data_paths[0])) tfrecord_idx = sorted(glob.glob(data_paths[1])) self.input = ops.TFRecordReader(path = tfrecord, index_path = tfrecord_idx, shard_id = device_id, num_shards = num_gpus, features = {"image/encoded" : tfrec.FixedLenFeature((), tfrec.string, ""), "image/class/label": tfrec.FixedLenFeature([1], tfrec.int64, -1) })
def __init__(self, **kwargs): super(TFRecordPipeline, self).__init__(**kwargs) tfrecord = sorted(glob.glob(kwargs['data_paths'][0])) tfrecord_idx = sorted(glob.glob(kwargs['data_paths'][1])) cache_enabled = kwargs['decoder_cache_params']['cache_enabled'] self.input = ops.TFRecordReader(path = tfrecord, index_path = tfrecord_idx, shard_id = kwargs['device_id'], num_shards = kwargs['num_gpus'], stick_to_shard = cache_enabled, #skip_cached_images = cache_enabled, features = {"image/encoded" : tfrec.FixedLenFeature((), tfrec.string, ""), "image/class/label": tfrec.FixedLenFeature([1], tfrec.int64, -1) })
def __init__(self, batch_size, num_threads, device_id, data_dir, crop, dali_cpu=False): super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id) # self.input = ops.FileReader(file_root=data_dir, shard_id=args.local_rank, num_shards=args.world_size, random_shuffle=True) index_path = [] for path in os.listdir("/home/guojia/idx_files/train"): index_path.append(os.path.join("/home/guojia/idx_files/train", path)) index_path = sorted(index_path) self.input = ops.TFRecordReader(path=data_dir, index_path=index_path, shard_id=args.local_rank, num_shards=args.world_size, random_shuffle=True, features={ 'image/height': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/width': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/colorspace': tfrec.FixedLenFeature([ ], tfrec.string, ''), 'image/channels': tfrec.FixedLenFeature([], tfrec.int64, -1), 'image/class/label': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/class/synset': tfrec.FixedLenFeature([ ], tfrec.string, ''), # 'image/class/text': tfrec.FixedLenFeature([ ], tfrec.string, ''), # 'image/object/bbox/xmin': tfrec.VarLenFeature(tfrec.float32, 0.0), # 'image/object/bbox/xmax': tfrec.VarLenFeature(tfrec.float32, 0.0), # 'image/object/bbox/ymin': tfrec.VarLenFeature(tfrec.float32, 0.0), # 'image/object/bbox/ymax': tfrec.VarLenFeature(tfrec.float32, 0.0), # 'image/object/bbox/label': tfrec.FixedLenFeature([1], tfrec.int64,-1), 'image/format': tfrec.FixedLenFeature((), tfrec.string, ""), 'image/filename': tfrec.FixedLenFeature((), tfrec.string, ""), 'image/encoded': tfrec.FixedLenFeature((), tfrec.string, "") }) #let user decide which pipeline works him bets for RN version he runs dali_device = 'cpu' if dali_cpu else 'gpu' decoder_device = 'cpu' if dali_cpu else 'mixed' # This padding sets the size of the internal nvJPEG buffers to be able to handle all images from full-sized ImageNet # without additional reallocations device_memory_padding = 211025920 if decoder_device == 'mixed' else 0 host_memory_padding = 140544512 if decoder_device == 'mixed' else 0 self.decode = ops.ImageDecoderRandomCrop(device=decoder_device, output_type=types.RGB, device_memory_padding=device_memory_padding, host_memory_padding=host_memory_padding, random_aspect_ratio=[0.8, 1.25], random_area=[0.1, 1.0], num_attempts=100) self.res = ops.Resize(device=dali_device, resize_x=crop, resize_y=crop, interp_type=types.INTERP_TRIANGULAR) self.cmnp = ops.CropMirrorNormalize(device="gpu", output_dtype=types.FLOAT, output_layout=types.NCHW, crop=(crop, crop), image_type=types.RGB, mean=[0.485 * 255,0.456 * 255,0.406 * 255], std=[0.229 * 255,0.224 * 255,0.225 * 255]) self.coin = ops.CoinFlip(probability=0.5) print('DALI "{0}" variant'.format(dali_device))
def __init__(self, batch_size, num_threads, device_id, tfrecords, idx_paths): super(ResnetPipeline, self).__init__(batch_size, num_threads, device_id) # Transformation operations below. # From https://docs.nvidia.com/deeplearning/sdk/dali-developer-guide/docs/supported_ops.html self.input = ops.TFRecordReader( path=tfrecords, index_path=idx_paths, features={ "image/encoded": tfrec.FixedLenFeature([], tfrec.string, ""), "image/class/label": tfrec.FixedLenFeature([1], tfrec.float32, 0.0), "image/class/text": tfrec.FixedLenFeature([], tfrec.string, ""), "image/object/bbox/xmin": tfrec.VarLenFeature(tfrec.float32, 0.0), "image/object/bbox/ymin": tfrec.VarLenFeature(tfrec.float32, 0.0), "image/object/bbox/xmax": tfrec.VarLenFeature(tfrec.float32, 0.0), "image/object/bbox/ymax": tfrec.VarLenFeature(tfrec.float32, 0.0) }) self.decode = ops.nvJPEGDecoder(device="mixed", cache_debug=True, output_type=types.RGB) self.resize = ops.Resize(device="gpu", image_type=types.RGB, interp_type=types.INTERP_LINEAR, resize_shorter=256.) self.cmn = ops.CropMirrorNormalize(device="gpu", output_dtype=types.FLOAT, crop=(224, 224), image_type=types.RGB, mean=[0., 0., 0.], std=[1., 1., 1]) self.uniform = ops.Uniform(range=(0.0, 1.0)) self.transpose = ops.Transpose(device="gpu", perm=[0, 3, 1, 2]) self.cast = ops.Cast(device="gpu", dtype=types.INT32) self.iter = 0
def __init__(self, batch_size, num_threads, device_id, **kwargs): super(TFRecordTest, self).__init__(batch_size, num_threads, device_id) self.input = ops.TFRecordReader( path=kwargs["tfrecords"], index_path=kwargs["tfrecords_idx"], features={ "X_shape": tfrec.FixedLenFeature([4], tfrec.int64, 0), "X": tfrec.VarLenFeature([], tfrec.float32, 0.0), "fname": tfrec.FixedLenFeature([], tfrec.string, ""), }, shard_id=device_id, num_shards=kwargs["gpus"], read_ahead=True, random_shuffle=False, pad_last_batch=True, )
def __init__(self, file_tfrecord, batch_size, num_workers, device_id=0): super().__init__(batch_size, num_workers, device_id) self.input = ops.TFRecordReader( path=file_tfrecord.as_posix(), index_path=(file_tfrecord.parent / (file_tfrecord.stem + '.idx')).as_posix(), features={'encoded': tfrec.FixedLenFeature((), tfrec.string, "")}) self.decode = ops.ImageDecoder(device='mixed', output_type=types.RGB) self.cmnp = ops.CropMirrorNormalize(device='gpu', output_dtype=types.FLOAT, output_layout=types.NCHW, image_type=types.RGB, mean=[124, 116, 104], std=[58, 57, 57])
def __init__( self, batch_size, num_threads, device_id, size=1024, path="/home/guyuchao/ssd/dataset/cityscape/leftImg8bit/dalirecord/dataset-r10.tfrecords", index_path="/home/guyuchao/ssd/dataset/cityscape/leftImg8bit/dalirecord/dataset-r10.idx" ): super(TFRecordPipeline, self).__init__(batch_size, num_threads, device_id, size) self.input = ops.TFRecordReader(path=path, index_path=index_path, features={ "image/encoded": tfrec.FixedLenFeature( (), tfrec.string, "") })
def __init__(self, batch_size, num_threads, device_id, **kwargs): super(TFRecordBenchmark, self).__init__(batch_size, num_threads, device_id) self.dim = kwargs["dim"] self.input = ops.TFRecordReader( path=kwargs["tfrecords"], index_path=kwargs["tfrecords_idx"], features={ "X_shape": tfrec.FixedLenFeature([self.dim + 1], tfrec.int64, 0), "Y_shape": tfrec.FixedLenFeature([self.dim + 1], tfrec.int64, 0), "X": tfrec.VarLenFeature([], tfrec.float32, 0.0), "Y": tfrec.FixedLenFeature([], tfrec.string, ""), "fname": tfrec.FixedLenFeature([], tfrec.string, ""), }, shard_id=device_id, num_shards=kwargs["gpus"], read_ahead=True, ) self.patch_size = kwargs["patch_size"] self.layout = "CDHW" if self.dim == 3 else "CHW"
def _input(self, tfrecord_path, index_path, shard_id=0): return ops.TFRecordReader( path=tfrecord_path, index_path=index_path, random_shuffle=True, features={ 'image/encoded': tfrec.FixedLenFeature((), tfrec.string, ""), 'image/filename': tfrec.FixedLenFeature((), tfrec.string, ""), 'image/format': tfrec.FixedLenFeature((), tfrec.string, ""), 'image/height': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/width': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/channels': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/segmentation/class/encoded': (tfrec.FixedLenFeature( (), tfrec.string, "")), 'image/segmentation/class/format': (tfrec.FixedLenFeature( (), tfrec.string, "")) })
def __new__(cls, path, shard_id=0, num_shards=1, random_shuffle=False, initial_fill=1024, **kwargs): """Create a ``TFRecordReader``. Parameters ---------- path : str The folder of record files. shard_id : int, optional, default=0 The index of partition to read. num_shards : int, optional, default=1 The total number of partitions over dataset. random_shuffle : bool, optional, default=False Whether to shuffle the data. initial_fill : int, optional, default=1024 The length of sampling sequence for shuffle. Returns ------- nvidia.dali.ops.TFRecordReader The reader instance. """ path, index_path, features = cls.check_files(path) return ops.TFRecordReader(path=path, index_path=index_path, shard_id=shard_id, num_shards=num_shards, features=features, random_shuffle=random_shuffle, initial_fill=initial_fill, **kwargs)
def __init__(self, tfrecords, batch_size, target_size, preproc_param, num_threads, num_shards, device_ids, training=False): Pipeline.__init__(self, batch_size=batch_size, num_threads=num_threads, device_id=device_ids, prefetch_queue_depth=num_threads, seed=42, exec_async=False, exec_pipelined=False) DaliPipeline.__init__(self, target_size=target_size, preproc_param=preproc_param, training=training) tfrecords_idx = [tfrecord + "_idx" for tfrecord in tfrecords] for tfrecord, tfrecord_idx in zip(tfrecords, tfrecords_idx): if os.path.exists(tfrecord_idx): continue call(["tfrecord2idx", tfrecord, tfrecord + "_idx"]) self.length = sum([len(open(f).readlines()) for f in tfrecords_idx]) self.input = ops.TFRecordReader( path=tfrecords, index_path=tfrecords_idx, features={ 'image/height': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/width': tfrec.FixedLenFeature([1], tfrec.int64, -1), 'image/encoded': tfrec.FixedLenFeature((), tfrec.string, ""), 'image/format': tfrec.FixedLenFeature((), tfrec.string, ""), 'image/object/bbox/xmin': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/bbox/ymin': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/bbox/xmax': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/bbox/ymax': tfrec.VarLenFeature(tfrec.float32, 0.0), 'image/object/class/text': tfrec.FixedLenFeature([], tfrec.string, ''), 'image/object/class/label': tfrec.VarLenFeature(tfrec.int64, -1) }, num_shards=num_shards, random_shuffle=training) self.training = training self.cat = dalitorch.TorchPythonFunction( function=lambda l, t, r, b: torch.cat([l, t, r, b]).view( 4, -1).permute(1, 0)) #[l*w,t*h,r*w,b*h], [l,t,r,b] self.cast = ops.Cast(dtype=types.DALIDataType.INT32)
def test_python_operator_and_custom_plugin(self): plugin_manager.load_library(test_bin_dir + "/libcustomdummyplugin.so") ops.TFRecordReader(path="dummy", index_path="dummy", features={})