def fake_input(img):
image = PaddleTensor()
image.name = "image"
image.shape = img.shape
image.dtype = PaddleDType.FLOAT32
image.data = PaddleBuf(img.flatten().tolist())
return [image]
def copyToTensor(self, batch_size):
tensor = PaddleTensor()
tensor.name = self.name
tensor.shape = [batch_size, self.shape_size]
tensor.dtype = self.list[self.dtype]
tensor.data = PaddleBuf(self.data)
return tensor
def array2tensor(ndarray):
""" convert numpy array to PaddleTensor"""
assert isinstance(ndarray, np.ndarray), "input type must be np.ndarray"
tensor = PaddleTensor()
tensor.name = "data"
tensor.shape = ndarray.shape
if "float" in str(ndarray.dtype):
tensor.dtype = PaddleDType.FLOAT32
elif "int" in str(ndarray.dtype):
tensor.dtype = PaddleDType.INT64
else:
raise ValueError("{} type ndarray is unsupported".format(tensor.dtype))
tensor.data = PaddleBuf(ndarray.flatten().tolist())
return tensor
def preprocess(img):
img = cv2.resize(img, (input_size, input_size))
img = img.transpose((2, 0, 1))
if modelname == "mobilenet-ssd":
img = (img - 127.5) * 0.007843
else:
mean = np.array([103.94, 116.669, 123.68],
np.float32).reshape([3, 1, 1])
img = img - mean
image = PaddleTensor()
image.name = "data"
image.shape = [1, 3, input_size, input_size]
image.dtype = PaddleDType.FLOAT32
image.data = PaddleBuf(img.flatten().astype("float32").tolist())
return [image]
def warp_input(image_data, input_size):
"""
deal input to paddle tensor
:param image_data: 输入的图像
:param image_shape: 原始图像的大小
:param input_size: 输入图像的大小
:return:
"""
# image data
image = PaddleTensor()
image.name = 'image'
image.shape = input_size
image.dtype = PaddleDType.FLOAT32
image.data = PaddleBuf(image_data.flatten().astype(np.float32).tolist())
return image
def texts2tensor(self, texts):
"""
Tranform the texts(dict) to PaddleTensor
Args:
texts(dict): texts
Returns:
tensor(PaddleTensor): tensor with texts data
"""
lod = [0]
data = []
for i, text in enumerate(texts):
data += text['processed']
lod.append(len(text['processed']) + lod[i])
tensor = PaddleTensor(np.array(data).astype('int64'))
tensor.name = "words"
tensor.lod = [lod]
tensor.shape = [lod[-1], 1]
return tensor
def texts2tensor(self, texts):
"""
Tranform the texts(dict) to PaddleTensor
Args:
texts(list): each element is a dict that must have a named 'processed' key whose value is word_ids, such as
texts = [{'processed': [23, 89, 43, 906]}]
Returns:
tensor(PaddleTensor): tensor with texts data
"""
lod = [0]
data = []
for i, text in enumerate(texts):
data += text['processed']
lod.append(len(text['processed']) + lod[i])
tensor = PaddleTensor(np.array(data).astype('int64'))
tensor.name = "words"
tensor.lod = [lod]
tensor.shape = [lod[-1], 1]
return tensor
def texts2tensor(self, texts):
"""
Tranform the texts(list) to PaddleTensor
Args:
texts(list): texts
Returns:
tensor(PaddleTensor): tensor with texts data
"""
lod = [0]
data = []
for i, text in enumerate(texts):
text_inds = word_to_ids(text,
self.word2id_dict,
self.word_replace_dict,
oov_id=self.oov_id)
data += text_inds
lod.append(len(text_inds) + lod[i])
tensor = PaddleTensor(np.array(data).astype('int64'))
tensor.name = "words"
tensor.lod = [lod]
tensor.shape = [lod[-1], 1]
return tensor
