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78 lines
3.4 KiB
Python
78 lines
3.4 KiB
Python
import numpy as np
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import sys
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import os
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import argparse
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import tensorflow as tf
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from tensorflow.python.platform import gfile
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from imagenet_cls_test_alexnet import MeanValueFetch, DnnCaffeModel, Framework, ClsAccEvaluation
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try:
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import cv2 as cv
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except ImportError:
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raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
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'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
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# If you've got an exception "Cannot load libmkl_avx.so or libmkl_def.so" or similar, try to export next variable
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# before runnigng the script:
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# LD_PRELOAD=/opt/intel/mkl/lib/intel64/libmkl_core.so:/opt/intel/mkl/lib/intel64/libmkl_sequential.so
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class TensorflowModel(Framework):
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sess = tf.Session
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output = tf.Graph
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def __init__(self, model_file, in_blob_name, out_blob_name):
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self.in_blob_name = in_blob_name
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self.sess = tf.Session()
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with gfile.FastGFile(model_file, 'rb') as f:
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graph_def = tf.GraphDef()
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graph_def.ParseFromString(f.read())
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self.sess.graph.as_default()
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tf.import_graph_def(graph_def, name='')
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self.output = self.sess.graph.get_tensor_by_name(out_blob_name + ":0")
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def get_name(self):
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return 'Tensorflow'
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def get_output(self, input_blob):
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assert len(input_blob.shape) == 4
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batch_tf = input_blob.transpose(0, 2, 3, 1)
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out = self.sess.run(self.output,
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{self.in_blob_name+':0': batch_tf})
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out = out[..., 1:1001]
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return out
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class DnnTfInceptionModel(DnnCaffeModel):
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net = cv.dnn.Net()
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def __init__(self, model_file, in_blob_name, out_blob_name):
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self.net = cv.dnn.readNetFromTensorflow(model_file)
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self.in_blob_name = in_blob_name
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self.out_blob_name = out_blob_name
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def get_output(self, input_blob):
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return super(DnnTfInceptionModel, self).get_output(input_blob)[..., 1:1001]
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--imgs_dir", help="path to ImageNet validation subset images dir, ILSVRC2012_img_val dir")
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parser.add_argument("--img_cls_file", help="path to file with classes ids for images, download it here:"
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"https://github.com/opencv/opencv_extra/tree/master/testdata/dnn/img_classes_inception.txt")
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parser.add_argument("--model", help="path to tensorflow model, download it here:"
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"https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip")
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parser.add_argument("--log", help="path to logging file")
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parser.add_argument("--batch_size", help="size of images in batch", default=1)
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parser.add_argument("--frame_size", help="size of input image", default=224)
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parser.add_argument("--in_blob", help="name for input blob", default='input')
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parser.add_argument("--out_blob", help="name for output blob", default='softmax2')
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args = parser.parse_args()
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data_fetcher = MeanValueFetch(args.frame_size, args.imgs_dir, True)
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frameworks = [TensorflowModel(args.model, args.in_blob, args.out_blob),
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DnnTfInceptionModel(args.model, '', args.out_blob)]
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acc_eval = ClsAccEvaluation(args.log, args.img_cls_file, args.batch_size)
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acc_eval.process(frameworks, data_fetcher)
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