from abc import ABCMeta, abstractmethod
import numpy as np
import sys
import os
import argparse
import time
# sys.path.append('<path to git/caffe/python dir>')
sys.path.append('/home/arrybn/git/caffe/python')
try:
import caffe
except ImportError:
raise ImportError('Can\'t find caffe. If you\'ve built it from sources without installation, '
'uncomment the line before and insert there path to git/caffe/python dir')
# sys.path.append('<path to opencv_build_dir/lib>')
sys.path.append('/home/arrybn/build/opencv_w_contrib/lib')
try:
import cv2 as cv
except ImportError:
raise ImportError('Can\'t find opencv. If you\'ve built it from sources without installation, '
'uncomment the line before and insert there path to opencv_build_dir/lib dir')
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.setBlob(self.in_blob_name, input_blob)
self.net.forward()
return self.net.getBlob(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)