mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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241 lines
9.4 KiB
241 lines
9.4 KiB
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 environemnt 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 environemnt 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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