from abc import ABCMeta, abstractmethod import numpy as np import sys import os import argparse import time try: import caffe except ImportError: raise ImportError('Can\'t find Caffe Python module. If you\'ve built it from sources without installation, ' 'configure environemnt variable PYTHONPATH to "git/caffe/python" directory') try: import cv2 as cv except ImportError: raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, ' 'configure environemnt variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)') class DataFetch(object): imgs_dir = '' frame_size = 0 bgr_to_rgb = False __metaclass__ = ABCMeta @abstractmethod def preprocess(self, img): pass def get_batch(self, imgs_names): assert type(imgs_names) is list batch = np.zeros((len(imgs_names), 3, self.frame_size, self.frame_size)).astype(np.float32) for i in range(len(imgs_names)): img_name = imgs_names[i] img_file = self.imgs_dir + img_name assert os.path.exists(img_file) img = cv.imread(img_file, cv.IMREAD_COLOR) min_dim = min(img.shape[-3], img.shape[-2]) resize_ratio = self.frame_size / float(min_dim) img = cv.resize(img, (0, 0), fx=resize_ratio, fy=resize_ratio) cols = img.shape[1] rows = img.shape[0] y1 = (rows - self.frame_size) / 2 y2 = y1 + self.frame_size x1 = (cols - self.frame_size) / 2 x2 = x1 + self.frame_size img = img[y1:y2, x1:x2] if self.bgr_to_rgb: img = img[..., ::-1] image_data = img[:, :, 0:3].transpose(2, 0, 1) batch[i] = self.preprocess(image_data) return batch class MeanBlobFetch(DataFetch): mean_blob = np.ndarray(()) def __init__(self, frame_size, mean_blob_path, imgs_dir): self.imgs_dir = imgs_dir self.frame_size = frame_size blob = caffe.proto.caffe_pb2.BlobProto() data = open(mean_blob_path, 'rb').read() blob.ParseFromString(data) self.mean_blob = np.array(caffe.io.blobproto_to_array(blob)) start = (self.mean_blob.shape[2] - self.frame_size) / 2 stop = start + self.frame_size self.mean_blob = self.mean_blob[:, :, start:stop, start:stop][0] def preprocess(self, img): return img - self.mean_blob class MeanChannelsFetch(MeanBlobFetch): def __init__(self, frame_size, imgs_dir): self.imgs_dir = imgs_dir self.frame_size = frame_size self.mean_blob = np.ones((3, self.frame_size, self.frame_size)).astype(np.float32) self.mean_blob[0] *= 104 self.mean_blob[1] *= 117 self.mean_blob[2] *= 123 class MeanValueFetch(MeanBlobFetch): def __init__(self, frame_size, imgs_dir, bgr_to_rgb): self.imgs_dir = imgs_dir self.frame_size = frame_size self.mean_blob = np.ones((3, self.frame_size, self.frame_size)).astype(np.float32) self.mean_blob *= 117 self.bgr_to_rgb = bgr_to_rgb def get_correct_answers(img_list, img_classes, net_output_blob): correct_answers = 0 for i in range(len(img_list)): indexes = np.argsort(net_output_blob[i])[-5:] correct_index = img_classes[img_list[i]] if correct_index in indexes: correct_answers += 1 return correct_answers class Framework(object): in_blob_name = '' out_blob_name = '' __metaclass__ = ABCMeta @abstractmethod def get_name(self): pass @abstractmethod def get_output(self, input_blob): pass class CaffeModel(Framework): net = caffe.Net need_reshape = False def __init__(self, prototxt, caffemodel, in_blob_name, out_blob_name, need_reshape=False): caffe.set_mode_cpu() self.net = caffe.Net(prototxt, caffemodel, caffe.TEST) self.in_blob_name = in_blob_name self.out_blob_name = out_blob_name self.need_reshape = need_reshape def get_name(self): return 'Caffe' def get_output(self, input_blob): if self.need_reshape: self.net.blobs[self.in_blob_name].reshape(*input_blob.shape) return self.net.forward_all(**{self.in_blob_name: input_blob})[self.out_blob_name] class DnnCaffeModel(Framework): net = object def __init__(self, prototxt, caffemodel, in_blob_name, out_blob_name): self.net = cv.dnn.readNetFromCaffe(prototxt, caffemodel) self.in_blob_name = in_blob_name self.out_blob_name = out_blob_name def get_name(self): return 'DNN' def get_output(self, input_blob): self.net.setInput(input_blob, self.in_blob_name) return self.net.forward(self.out_blob_name) class ClsAccEvaluation: log = file img_classes = {} batch_size = 0 def __init__(self, log_path, img_classes_file, batch_size): self.log = open(log_path, 'w') self.img_classes = self.read_classes(img_classes_file) self.batch_size = batch_size @staticmethod def read_classes(img_classes_file): result = {} with open(img_classes_file) as file: for l in file.readlines(): result[l.split()[0]] = int(l.split()[1]) return result def process(self, frameworks, data_fetcher): sorted_imgs_names = sorted(self.img_classes.keys()) correct_answers = [0] * len(frameworks) samples_handled = 0 blobs_l1_diff = [0] * len(frameworks) blobs_l1_diff_count = [0] * len(frameworks) blobs_l_inf_diff = [sys.float_info.min] * len(frameworks) inference_time = [0.0] * len(frameworks) for x in xrange(0, len(sorted_imgs_names), self.batch_size): sublist = sorted_imgs_names[x:x + self.batch_size] batch = data_fetcher.get_batch(sublist) samples_handled += len(sublist) frameworks_out = [] fw_accuracy = [] for i in range(len(frameworks)): start = time.time() out = frameworks[i].get_output(batch) end = time.time() correct_answers[i] += get_correct_answers(sublist, self.img_classes, out) fw_accuracy.append(100 * correct_answers[i] / float(samples_handled)) frameworks_out.append(out) inference_time[i] += end - start print >> self.log, samples_handled, 'Accuracy for', frameworks[i].get_name() + ':', fw_accuracy[i] print >> self.log, "Inference time, ms ", \ frameworks[i].get_name(), inference_time[i] / samples_handled * 1000 for i in range(1, len(frameworks)): log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':' diff = np.abs(frameworks_out[0] - frameworks_out[i]) l1_diff = np.sum(diff) / diff.size print >> self.log, samples_handled, "L1 difference", log_str, l1_diff blobs_l1_diff[i] += l1_diff blobs_l1_diff_count[i] += 1 if np.max(diff) > blobs_l_inf_diff[i]: blobs_l_inf_diff[i] = np.max(diff) print >> self.log, samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i] self.log.flush() for i in range(1, len(blobs_l1_diff)): log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':' print >> self.log, 'Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i] if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--imgs_dir", help="path to ImageNet validation subset images dir, ILSVRC2012_img_val dir") parser.add_argument("--img_cls_file", help="path to file with classes ids for images, val.txt file from this " "archive: http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz") parser.add_argument("--prototxt", help="path to caffe prototxt, download it here: " "https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/deploy.prototxt") parser.add_argument("--caffemodel", help="path to caffemodel file, download it here: " "http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel") parser.add_argument("--log", help="path to logging file") parser.add_argument("--mean", help="path to ImageNet mean blob caffe file, imagenet_mean.binaryproto file from" "this archive: http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz") parser.add_argument("--batch_size", help="size of images in batch", default=1000) parser.add_argument("--frame_size", help="size of input image", default=227) parser.add_argument("--in_blob", help="name for input blob", default='data') parser.add_argument("--out_blob", help="name for output blob", default='prob') args = parser.parse_args() data_fetcher = MeanBlobFetch(args.frame_size, args.mean, args.imgs_dir) frameworks = [CaffeModel(args.prototxt, args.caffemodel, args.in_blob, args.out_blob), DnnCaffeModel(args.prototxt, args.caffemodel, '', args.out_blob)] acc_eval = ClsAccEvaluation(args.log, args.img_cls_file, args.batch_size) acc_eval.process(frameworks, data_fetcher)