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python: better Python 3 support

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This commit is contained in:
Alexander Alekhin
2018-05-11 13:29:28 +03:00
parent df02fe0615
commit 78f205ffa5
7 changed files with 30 additions and 17 deletions
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modules/dnn/misc/quantize_face_detector.py
+4
View File
@@ -1,10 +1,14 @@
from __future__ import print_function
import sys
import argparse
import cv2 as cv
import tensorflow as tf
import numpy as np
import struct
if sys.version_info > (3,):
long = int
from tensorflow.python.tools import optimize_for_inference_lib
from tensorflow.tools.graph_transforms import TransformGraph
from tensorflow.core.framework.node_def_pb2 import NodeDef
modules/dnn/test/imagenet_cls_test_alexnet.py
+8 -7
View File
@@ -1,3 +1,4 @@
from __future__ import print_function
from abc import ABCMeta, abstractmethod
import numpy as np
import sys
@@ -156,7 +157,7 @@ class DnnCaffeModel(Framework):
class ClsAccEvaluation:
log = file
log = sys.stdout
img_classes = {}
batch_size = 0
@@ -198,26 +199,26 @@ class ClsAccEvaluation:
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
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 >> self.log, samples_handled, "L1 difference", log_str, l1_diff
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 >> self.log, samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i]
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 >> self.log, 'Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i]
print('Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i], file=self.log)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
modules/dnn/test/pascal_semsegm_test_fcn.py
+10 -9
View File
@@ -1,3 +1,4 @@
from __future__ import print_function
from abc import ABCMeta, abstractmethod
import numpy as np
import sys
@@ -145,7 +146,7 @@ class PASCALDataFetch(DatasetImageFetch):
class SemSegmEvaluation:
log = file
log = sys.stdout
def __init__(self, log_path,):
self.log = open(log_path, 'w')
@@ -174,28 +175,28 @@ class SemSegmEvaluation:
pix_acc, mean_acc, miou = get_metrics(conf_mats[i])
name = frameworks[i].get_name()
print >> self.log, samples_handled, 'Pixel accuracy, %s:' % name, 100 * pix_acc
print >> self.log, samples_handled, 'Mean accuracy, %s:' % name, 100 * mean_acc
print >> self.log, samples_handled, 'Mean IOU, %s:' % name, 100 * miou
print >> self.log, "Inference time, ms ", \
frameworks[i].get_name(), inference_time[i] / samples_handled * 1000
print(samples_handled, 'Pixel accuracy, %s:' % name, 100 * pix_acc, file=self.log)
print(samples_handled, 'Mean accuracy, %s:' % name, 100 * mean_acc, file=self.log)
print(samples_handled, 'Mean IOU, %s:' % name, 100 * miou, 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 >> self.log, samples_handled, "L1 difference", log_str, l1_diff
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 >> self.log, samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i]
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 >> self.log, 'Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i]
print('Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i], file=self.log)
if __name__ == "__main__":
parser = argparse.ArgumentParser()