def __init__(self, batch_size, num_threads, device_id, data_dir, rali_cpu=True, prefetch_queue_depth=2): super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, rali_cpu=rali_cpu, prefetch_queue_depth=prefetch_queue_depth) world_size = 1 local_rank = 0 resize_width = 300 resize_height = 300 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' self.decode = ops.ImageDecoder(device=decoder_device, output_type=types.RGB) self.res = ops.Resize(device=rali_device, resize_x=resize_width, resize_y=resize_height) self.rain = ops.Rain(rain=0.5)
def __init__(self, data_path, batch_size, num_thread, crop, rali_cpu=True): super(valPipeline, 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.ImageDecoder(device=decoder_device, output_type=types.RGB) self.res = ops.Resize(device=rali_device, resize_x=256, resize_y=256) self.centrecrop = ops.CentreCrop(crop=(crop, 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]) print('rali "{0}" variant'.format(rali_device))
def __init__(self, batch_size, num_threads, device_id, data_dir,ann_dir, crop, rali_cpu = True): super(COCOPipeline, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id,rali_cpu=rali_cpu) self.input = ops.COCOReader(file_root = data_dir, annotations_file = ann_dir) 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.decode = ops.ImageDecoder(device=decoder_device, output_type=types.RGB) self.crop = ops.SSDRandomCrop(num_attempts=5) self.res = ops.Resize(device=rali_device, resize_x=crop, resize_y=crop) self.twist = ops.ColorTwist(device=rali_device) self.cmnp = ops.CropMirrorNormalize(device="gpu", output_dtype=types.FLOAT, output_layout=types.NCHW, crop=(crop, crop), image_type=types.RGB, mirror=0, mean=[0.485 * 255,0.456 * 255,0.406 * 255], std=[0.229 * 255,0.224 * 255,0.225 * 255]) # Random variables self.rng1 = ops.Uniform(range=[0.5, 1.5]) self.rng2 = ops.Uniform(range=[0.875, 1.125]) self.rng3 = ops.Uniform(range=[-0.5, 0.5]) print('rali "{0}" variant'.format(rali_device))
def __init__(self, feature_key_map, tfrecordreader_type, 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="", reader_type=tfrecordreader_type, user_feature_key_map=feature_key_map, features={ 'image/encoded': tf.FixedLenFeature((), tf.string, ""), 'image/class/label': tf.FixedLenFeature([1], tf.int64, -1), 'image/filename': tf.FixedLenFeature((), tf.string, "") }) rali_device = 'cpu' if rali_cpu else 'gpu' decoder_device = 'cpu' if rali_cpu else 'mixed' self.decode = ops.ImageDecoder(user_feature_key_map=feature_key_map, device=decoder_device, output_type=types.RGB) self.res = ops.Resize(device=rali_device, resize_x=crop[0], resize_y=crop[1]) self.cmnp = ops.CropMirrorNormalize(device="cpu", output_dtype=types.FLOAT, output_layout=types.NCHW, crop=crop, image_type=types.RGB, mean=[0, 0, 0], std=[255, 255, 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, ann_dir, default_boxes, crop, rali_cpu=True): super(COCOPipeline, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id, rali_cpu=rali_cpu) self.input = ops.COCOReader(file_root=data_dir, annotations_file=ann_dir) 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.decode = ops.ImageDecoder(device=decoder_device, output_type=types.RGB) self.crop = ops.SSDRandomCrop(num_attempts=5) self.decode_slice = ops.ImageDecoderSlice(device=decoder_device, output_type=types.RGB) self.random_bbox_crop = ops.RandomBBoxCrop( device="cpu", aspect_ratio=[0.5, 2.0], thresholds=[0, 0.1, 0.3, 0.5, 0.7, 0.9], scaling=[0.3, 1.0], ltrb=True, allow_no_crop=True, num_attempts=1) self.res = ops.Resize(device=rali_device, resize_x=crop, resize_y=crop) self.twist = ops.ColorTwist(device=rali_device) self.bbflip = ops.BBFlip(device=rali_device, ltrb=True) self.cmnp = ops.CropMirrorNormalize( device="gpu", output_dtype=types.FLOAT, output_layout=types.NCHW, crop=(crop, crop), image_type=types.RGB, mirror=0, mean=[0.485 * 255, 0.456 * 255, 0.406 * 255], std=[0.229 * 255, 0.224 * 255, 0.225 * 255]) self.boxEncoder = ops.BoxEncoder(device=rali_device, criteria=0.5, anchors=default_boxes) self.cast = ops.Cast(device=rali_device, dtype=types.FLOAT) # Random variables self.rng1 = ops.Uniform(range=[0.5, 1.5]) self.rng2 = ops.Uniform(range=[0.875, 1.125]) self.rng3 = ops.Uniform(range=[-0.5, 0.5]) self.coin_flip = ops.CoinFlip(probability=0.5) print('rali "{0}" variant'.format(rali_device))