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,
feature_key_map,
tfrecordreader_type,
batch_size,
num_threads,
device_id,
data_dir,
crop,
oneHotLabels=0,
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,
"")
})
self._oneHotLabels = oneHotLabels
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(
user_feature_key_map=feature_key_map,
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.one_hot_labels = ops.OneHot(num_classes=1000)
self.coin = ops.CoinFlip(probability=0.5)
print('rali "{0}" variant'.format(rali_device))