248 lines
9.5 KiB
Python
248 lines
9.5 KiB
Python
|
from __future__ import print_function
|
||
|
|
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 environment 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 environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
|
||
|
|
|
||
|
|
try:
|
||
|
|
xrange # Python 2
|
||
|
|
except NameError:
|
||
|
|
xrange = range # Python 3
|
||
|
|
|
||
|
|
|
||
|
|
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 = sys.stdout
|
||
|
|
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(samples_handled, 'Accuracy for', frameworks[i].get_name() + ':', fw_accuracy[i], file=self.log)
|
||
|
|
print("Inference time, ms ", \
|
||
|
|
frameworks[i].get_name(), inference_time[i] / samples_handled * 1000, file=self.log)
|
||
|
|
|
||
|
|
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(samples_handled, "L1 difference", log_str, l1_diff, file=self.log)
|
||
|
|
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(samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i], file=self.log)
|
||
|
|
|
||
|
|
self.log.flush()
|
||
|
|
|
||
|
|
for i in range(1, len(blobs_l1_diff)):
|
||
|
|
log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
|
||
|
|
print('Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i], file=self.log)
|
||
|
|
|
||
|
|
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)
|