def __init__(self,
pipeline,
tensor_layout=types.NCHW,
reverse_channels=False,
multiplier=[1.0, 1.0, 1.0],
offset=[0.0, 0.0, 0.0],
tensor_dtype=types.FLOAT):
self.loader = pipeline
self.tensor_format = tensor_layout
self.multiplier = multiplier
self.offset = offset
self.reverse_channels = reverse_channels
self.tensor_dtype = tensor_dtype
self.w = b.getOutputWidth(self.loader._handle)
self.h = b.getOutputHeight(self.loader._handle)
self.n = b.getOutputImageCount(self.loader._handle)
self.bs = pipeline._batch_size
color_format = b.getOutputColorFormat(self.loader._handle)
self.p = (1 if color_format is types.GRAY else 3)
if self.tensor_dtype == types.FLOAT:
self.out = np.zeros((
self.bs * self.n,
self.p,
int(self.h / self.bs),
self.w,
),
dtype="float32")
elif self.tensor_dtype == types.FLOAT16:
self.out = np.zeros((
self.bs * self.n,
self.p,
int(self.h / self.bs),
self.w,
),
dtype="float16")
def __init__(self, pipeline):
self.loader = pipeline
self.w = b.getOutputWidth(self.loader._handle)
self.h = b.getOutputHeight(self.loader._handle)
self.n = b.getOutputImageCount(self.loader._handle)
color_format = b.getOutputColorFormat(self.loader._handle)
self.p = (1 if (color_format == int(types.GRAY)) else 3)
height = self.h * self.n
self.out_tensor = None
self.out_image = np.zeros((height, self.w, self.p), dtype="uint8")
self.bs = pipeline._batch_size
def __init__(self,
pipeline,
tensor_layout=types.NCHW,
reverse_channels=False,
multiplier=[1.0, 1.0, 1.0],
offset=[0.0, 0.0, 0.0],
tensor_dtype=types.FLOAT):
self.loader = pipeline
self.tensor_format = tensor_layout
self.multiplier = multiplier
self.offset = offset
self.reverse_channels = reverse_channels
self.tensor_dtype = tensor_dtype
self.w = b.getOutputWidth(self.loader._handle)
self.h = b.getOutputHeight(self.loader._handle)
self.n = b.getOutputImageCount(self.loader._handle)
self.bs = pipeline._batch_size
color_format = b.getOutputColorFormat(self.loader._handle)
self.p = (1 if (color_format == int(types.GRAY)) else 3)
if self.tensor_dtype == types.FLOAT:
self.out = np.zeros((
self.bs * self.n,
self.p,
int(self.h / self.bs),
self.w,
),
dtype="float32")
elif self.tensor_dtype == types.FLOAT16:
self.out = np.zeros((
self.bs * self.n,
self.p,
int(self.h / self.bs),
self.w,
),
dtype="float16")
# self.labels = np.zeros((self.bs),dtype = "int32")
if (self.loader._oneHotEncoding == True):
self.labels = np.zeros((self.bs) * (self.loader._numOfClasses),
dtype="int32")
else:
self.labels = np.zeros((self.bs), dtype="int32")
if self.bs != 0:
self.len = b.getRemainingImages(self.loader._handle) // self.bs
else:
self.len = b.getRemainingImages(self.loader._handle)
def getOutputWidth(self):
return b.getOutputWidth(self._handle)
