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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
class YOLOv5ClsPreprocessor:
def __init__(self):
"""Create a preprocessor for YOLOv5Cls
"""
self._preprocessor = C.vision.classification.YOLOv5ClsPreprocessor()
def run(self, input_ims):
"""Preprocess input images for YOLOv5Cls
:param: input_ims: (list of numpy.ndarray)The input image
:return: list of FDTensor
"""
return self._preprocessor.run(input_ims)
@property
def size(self):
"""
Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [224, 224]
"""
return self._preprocessor.size
@size.setter
def size(self, wh):
assert isinstance(wh, (list, tuple)),\
"The value to set `size` must be type of tuple or list."
assert len(wh) == 2,\
"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
len(wh))
self._preprocessor.size = wh
class YOLOv5ClsPostprocessor:
def __init__(self):
"""Create a postprocessor for YOLOv5Cls
"""
self._postprocessor = C.vision.classification.YOLOv5ClsPostprocessor()
def run(self, runtime_results, ims_info):
"""Postprocess the runtime results for YOLOv5Cls
:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
:param: ims_info: (list of dict)Record input_shape and output_shape
:return: list of ClassifyResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
"""
return self._postprocessor.run(runtime_results, ims_info)
@property
def topk(self):
"""
topk for postprocessing, default is 1
"""
return self._postprocessor.topk
@topk.setter
def topk(self, topk):
assert isinstance(topk, int),\
"The value to set `top k` must be type of int."
self._postprocessor.topk = topk
[docs]class YOLOv5Cls(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a YOLOv5Cls model exported by YOLOv5Cls.
:param model_file: (str)Path of model file, e.g ./YOLOv5Cls.onnx
:param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(YOLOv5Cls, self).__init__(runtime_option)
assert model_format == ModelFormat.ONNX, "YOLOv5Cls only support model format of ModelFormat.ONNX now."
self._model = C.vision.classification.YOLOv5Cls(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "YOLOv5Cls initialize failed."
[docs] def predict(self, input_image):
"""Classify an input image
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: ClassifyResult
"""
assert input_image is not None, "Input image is None."
return self._model.predict(input_image)
[docs] def batch_predict(self, images):
"""Classify a batch of input image
:param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
:return list of ClassifyResult
"""
return self._model.batch_predict(images)
@property
def preprocessor(self):
"""Get YOLOv5ClsPreprocessor object of the loaded model
:return YOLOv5ClsPreprocessor
"""
return self._model.preprocessor
@property
def postprocessor(self):
"""Get YOLOv5ClsPostprocessor object of the loaded model
:return YOLOv5ClsPostprocessor
"""
return self._model.postprocessor