mirror of
https://github.com/opencv/opencv.git
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242 lines
9.4 KiB
Python
242 lines
9.4 KiB
Python
from abc import ABCMeta, abstractmethod
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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 time
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try:
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import caffe
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except ImportError:
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raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, '
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'configure environment variable PYTHONPATH to "git/caffe/python" directory')
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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 environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
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class DataFetch(object):
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imgs_dir = ''
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frame_size = 0
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bgr_to_rgb = False
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__metaclass__ = ABCMeta
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@abstractmethod
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def preprocess(self, img):
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pass
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def get_batch(self, imgs_names):
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assert type(imgs_names) is list
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batch = np.zeros((len(imgs_names), 3, self.frame_size, self.frame_size)).astype(np.float32)
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for i in range(len(imgs_names)):
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img_name = imgs_names[i]
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img_file = self.imgs_dir + img_name
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assert os.path.exists(img_file)
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img = cv.imread(img_file, cv.IMREAD_COLOR)
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min_dim = min(img.shape[-3], img.shape[-2])
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resize_ratio = self.frame_size / float(min_dim)
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img = cv.resize(img, (0, 0), fx=resize_ratio, fy=resize_ratio)
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cols = img.shape[1]
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rows = img.shape[0]
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y1 = (rows - self.frame_size) / 2
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y2 = y1 + self.frame_size
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x1 = (cols - self.frame_size) / 2
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x2 = x1 + self.frame_size
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img = img[y1:y2, x1:x2]
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if self.bgr_to_rgb:
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img = img[..., ::-1]
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image_data = img[:, :, 0:3].transpose(2, 0, 1)
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batch[i] = self.preprocess(image_data)
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return batch
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class MeanBlobFetch(DataFetch):
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mean_blob = np.ndarray(())
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def __init__(self, frame_size, mean_blob_path, imgs_dir):
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self.imgs_dir = imgs_dir
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self.frame_size = frame_size
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blob = caffe.proto.caffe_pb2.BlobProto()
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data = open(mean_blob_path, 'rb').read()
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blob.ParseFromString(data)
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self.mean_blob = np.array(caffe.io.blobproto_to_array(blob))
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start = (self.mean_blob.shape[2] - self.frame_size) / 2
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stop = start + self.frame_size
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self.mean_blob = self.mean_blob[:, :, start:stop, start:stop][0]
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def preprocess(self, img):
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return img - self.mean_blob
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class MeanChannelsFetch(MeanBlobFetch):
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def __init__(self, frame_size, imgs_dir):
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self.imgs_dir = imgs_dir
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self.frame_size = frame_size
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self.mean_blob = np.ones((3, self.frame_size, self.frame_size)).astype(np.float32)
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self.mean_blob[0] *= 104
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self.mean_blob[1] *= 117
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self.mean_blob[2] *= 123
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class MeanValueFetch(MeanBlobFetch):
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def __init__(self, frame_size, imgs_dir, bgr_to_rgb):
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self.imgs_dir = imgs_dir
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self.frame_size = frame_size
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self.mean_blob = np.ones((3, self.frame_size, self.frame_size)).astype(np.float32)
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self.mean_blob *= 117
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self.bgr_to_rgb = bgr_to_rgb
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def get_correct_answers(img_list, img_classes, net_output_blob):
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correct_answers = 0
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for i in range(len(img_list)):
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indexes = np.argsort(net_output_blob[i])[-5:]
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correct_index = img_classes[img_list[i]]
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if correct_index in indexes:
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correct_answers += 1
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return correct_answers
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class Framework(object):
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in_blob_name = ''
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out_blob_name = ''
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__metaclass__ = ABCMeta
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@abstractmethod
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def get_name(self):
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pass
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@abstractmethod
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def get_output(self, input_blob):
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pass
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class CaffeModel(Framework):
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net = caffe.Net
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need_reshape = False
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def __init__(self, prototxt, caffemodel, in_blob_name, out_blob_name, need_reshape=False):
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caffe.set_mode_cpu()
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self.net = caffe.Net(prototxt, caffemodel, caffe.TEST)
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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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self.need_reshape = need_reshape
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def get_name(self):
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return 'Caffe'
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def get_output(self, input_blob):
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if self.need_reshape:
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self.net.blobs[self.in_blob_name].reshape(*input_blob.shape)
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return self.net.forward_all(**{self.in_blob_name: input_blob})[self.out_blob_name]
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class DnnCaffeModel(Framework):
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net = object
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def __init__(self, prototxt, caffemodel, in_blob_name, out_blob_name):
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self.net = cv.dnn.readNetFromCaffe(prototxt, caffemodel)
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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_name(self):
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return 'DNN'
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def get_output(self, input_blob):
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self.net.setInput(input_blob, self.in_blob_name)
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return self.net.forward(self.out_blob_name)
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class ClsAccEvaluation:
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log = file
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img_classes = {}
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batch_size = 0
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def __init__(self, log_path, img_classes_file, batch_size):
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self.log = open(log_path, 'w')
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self.img_classes = self.read_classes(img_classes_file)
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self.batch_size = batch_size
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@staticmethod
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def read_classes(img_classes_file):
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result = {}
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with open(img_classes_file) as file:
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for l in file.readlines():
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result[l.split()[0]] = int(l.split()[1])
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return result
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def process(self, frameworks, data_fetcher):
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sorted_imgs_names = sorted(self.img_classes.keys())
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correct_answers = [0] * len(frameworks)
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samples_handled = 0
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blobs_l1_diff = [0] * len(frameworks)
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blobs_l1_diff_count = [0] * len(frameworks)
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blobs_l_inf_diff = [sys.float_info.min] * len(frameworks)
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inference_time = [0.0] * len(frameworks)
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for x in xrange(0, len(sorted_imgs_names), self.batch_size):
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sublist = sorted_imgs_names[x:x + self.batch_size]
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batch = data_fetcher.get_batch(sublist)
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samples_handled += len(sublist)
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frameworks_out = []
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fw_accuracy = []
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for i in range(len(frameworks)):
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start = time.time()
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out = frameworks[i].get_output(batch)
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end = time.time()
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correct_answers[i] += get_correct_answers(sublist, self.img_classes, out)
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fw_accuracy.append(100 * correct_answers[i] / float(samples_handled))
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frameworks_out.append(out)
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inference_time[i] += end - start
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print >> self.log, samples_handled, 'Accuracy for', frameworks[i].get_name() + ':', fw_accuracy[i]
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print >> self.log, "Inference time, ms ", \
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frameworks[i].get_name(), inference_time[i] / samples_handled * 1000
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for i in range(1, len(frameworks)):
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log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
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diff = np.abs(frameworks_out[0] - frameworks_out[i])
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l1_diff = np.sum(diff) / diff.size
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print >> self.log, samples_handled, "L1 difference", log_str, l1_diff
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blobs_l1_diff[i] += l1_diff
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blobs_l1_diff_count[i] += 1
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if np.max(diff) > blobs_l_inf_diff[i]:
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blobs_l_inf_diff[i] = np.max(diff)
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print >> self.log, samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i]
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self.log.flush()
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for i in range(1, len(blobs_l1_diff)):
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log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
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print >> self.log, 'Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i]
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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, val.txt file from this "
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"archive: http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz")
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parser.add_argument("--prototxt", help="path to caffe prototxt, download it here: "
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"https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/deploy.prototxt")
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parser.add_argument("--caffemodel", help="path to caffemodel file, download it here: "
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"http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel")
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parser.add_argument("--log", help="path to logging file")
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parser.add_argument("--mean", help="path to ImageNet mean blob caffe file, imagenet_mean.binaryproto file from"
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"this archive: http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz")
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parser.add_argument("--batch_size", help="size of images in batch", default=1000)
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parser.add_argument("--frame_size", help="size of input image", default=227)
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parser.add_argument("--in_blob", help="name for input blob", default='data')
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parser.add_argument("--out_blob", help="name for output blob", default='prob')
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args = parser.parse_args()
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data_fetcher = MeanBlobFetch(args.frame_size, args.mean, args.imgs_dir)
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frameworks = [CaffeModel(args.prototxt, args.caffemodel, args.in_blob, args.out_blob),
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DnnCaffeModel(args.prototxt, args.caffemodel, '', 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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