def __init__(self, data_path, batch_size, num_thread, crop, rali_cpu=True): super(trainPipeline, self).__init__(batch_size, num_thread, rali_cpu=rali_cpu) world_size = 1 local_rank = 0 self.input = ops.FileReader(file_root=data_path, shard_id=local_rank, num_shards=world_size, random_shuffle=True) rali_device = 'cpu' if rali_cpu else 'gpu' decoder_device = 'cpu' if rali_cpu else 'mixed' 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=rali_device, resize_x=crop, resize_y=crop) 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('rali "{0}" variant'.format(rali_device))
def __init__(self, batch_size, num_threads, device_id, data_dir, crop, rali_cpu = True,rali_type=True ): super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id,rali_cpu=rali_cpu) self.box = False if rali_type else True self.input = ops.CaffeReader(path = data_dir, bbox=self.box,random_shuffle=True) self.rali_type = rali_type rali_device = 'cpu' if rali_cpu else 'gpu' decoder_device = 'cpu' if rali_cpu else 'mixed' 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=rali_device, resize_x=crop, resize_y=crop) 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.one_hot_labels = ops.OneHot(num_classes=1000) self.coin = ops.CoinFlip(probability=0.5) print('rali "{0}" variant'.format(rali_device))
def __init__(self, batch_size, num_threads, device_id, data_dir, crop, rali_cpu=True): super(HybridPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id, rali_cpu=rali_cpu) self.input = ops.TFRecordReader(path=data_dir, index_path="", features={ 'image/encoded': tf.FixedLenFeature((), tf.string, ""), 'image/class/label': tf.FixedLenFeature([1], tf.int64, -1), 'image/class/text': tf.FixedLenFeature([], tf.string, ''), 'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32), 'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32) }) rali_device = 'cpu' if rali_cpu else 'gpu' decoder_device = 'cpu' if rali_cpu else 'mixed' 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.cmnp = ops.CropMirrorNormalize( device="cpu", 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('rali "{0}" variant'.format(rali_device))
def __init__(self, batch_size, num_threads, device_id, data_dir, crop, rali_cpu=True): super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id, rali_cpu=rali_cpu) world_size = 1 local_rank = 0 self.input = ops.FileReader(file_root=data_dir, shard_id=local_rank, num_shards=world_size, random_shuffle=True) rali_device = 'cpu' if rali_cpu else 'gpu' decoder_device = 'cpu' if rali_cpu else 'mixed' 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=rali_device, resize_x=crop, resize_y=crop) # #self.res = ops.Crop(crop=(crop, crop)) # self.rain = ops.Rain(rain=0.5) # self.blur = ops.Blur(blur=0.5) # self.jitter = ops.Jitter() # self.contrast =ops.Rotate(angle=20) # self.hue = ops.Hue() # self.blend = ops.Blend(blend=0.5) # self.snp = ops.SnPNoise(snpNoise = 0.5) # self.ving = ops.Vignette(vignette = 0.2) # self.exp = ops.Exposure(exposure = 0.2) # #self.wf = ops.WarpAffine() # self.sat = ops.Saturation() 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('rali "{0}" variant'.format(rali_device))