Python 3 compatibility

testdata/dnn/darknet/generate_darknet_models.py

@@ -1,5 +1,6 @@
 # export PYTHONPATH=/path/to/darknet/python/:$PYTHONPATH
 # export LD_LIBRARY_PATH=/path/to/darknet/:$LD_LIBRARY_PATH
+from builtins import range
 import darknet as dn
 import numpy as np
 

testdata/dnn/download_models.py

@@ -1,18 +1,23 @@
 #!/usr/bin/env python
 
 from __future__ import print_function
+from __future__ import division
+from future import standard_library
+standard_library.install_aliases()
+from builtins import object
+from past.utils import old_div
 import hashlib
 import sys
 import tarfile
 import requests
 
 if sys.version_info[0] < 3:
-    from urllib2 import urlopen
+    from urllib.request import urlopen
 else:
     from urllib.request import urlopen
 
 
-class Model:
+class Model(object):
     MB = 1024*1024
     BUFSIZE = 10*MB
 
@@ -33,7 +38,7 @@ def getMB(r):
             d = dict(r.info())
             for c in ['content-length', 'Content-Length']:
                 if c in d:
-                    return int(d[c]) / self.MB
+                    return old_div(int(d[c]), self.MB)
             return '<unknown>'
         print('  {} {} [{} Mb]'.format(r.getcode(), r.msg, getMB(r)))
 
@@ -72,7 +77,7 @@ def get(self):
             assert self.downloader
             print('  hash check failed - downloading')
             sz = self.downloader(self.filename)
-            print('  size = %.2f Mb' % (sz / (1024.0 * 1024)))
+            print('  size = %.2f Mb' % (old_div(sz, (1024.0 * 1024))))
 
         print(' done')
         print(' file {}'.format(self.filename))
@@ -115,7 +120,7 @@ def download_gdrive(dst):
         response = session.get(URL, params = { 'id' : gid }, stream = True)
 
         def get_confirm_token(response):  # in case of large files
-            for key, value in response.cookies.items():
+            for key, value in list(response.cookies.items()):
                 if key.startswith('download_warning'):
                     return value
             return None

testdata/dnn/layers/run.py

@@ -1,5 +1,6 @@
 # coding: utf-8
 
+from __future__ import print_function
 import sys, os, glob
 
 CAFFE_ROOT = "/home/vitaliy/opencv/caffe/"
@@ -20,7 +21,7 @@ def get_cafe_output(inp_blob, proto_name, caffemodel_name):
     out_blob = net.blobs['output'].data[...];
 
     if net.params.get('output'):
-        print "Params count:", len(net.params['output'])
+        print("Params count:", len(net.params['output']))
         net.save(caffemodel_name)
 
     return out_blob
@@ -37,9 +38,9 @@ def get_cafe_output(inp_blob, proto_name, caffemodel_name):
         inp_blob_name = proto_basename + ".input.npy"
         inp_blob = np.load(inp_blob_name) if os.path.exists(inp_blob_name) else np.load('blob.npy')
 
-        print "\nGenerate data for:"
-        print cfmod_basename, inp_blob.shape
+        print("\nGenerate data for:")
+        print(cfmod_basename, inp_blob.shape)
 
         out_blob = get_cafe_output(inp_blob, proto_filename, cfmod_basename)
-        print out_blob.shape
+        print(out_blob.shape)
         np.save(npy_filename, out_blob)

testdata/dnn/onnx/generate_onnx_models.py

@@ -1,4 +1,7 @@
 from __future__ import print_function
+from __future__ import division
+from builtins import str
+from past.utils import old_div
 import torch
 from torch.autograd import Variable
 import torch.nn.init as init
@@ -12,7 +15,7 @@
 
 
 def assertExpected(s):
-    if not (isinstance(s, str) or (sys.version_info[0] == 2 and isinstance(s, unicode))):
+    if not (isinstance(s, str) or (sys.version_info[0] == 2 and isinstance(s, str))):
         raise TypeError("assertExpected is strings only")
 
 def assertONNXExpected(binary_pb):
@@ -556,7 +559,7 @@ def __init__(self):
     def forward(self, x):
         exp = torch.exp(x)
         sum = torch.sum(exp, dim=2, keepdim=True)
-        return exp / sum
+        return old_div(exp, sum)
 
 input = Variable(torch.randn(1, 2, 4, 3))
 model = SoftMaxUnfused()
@@ -587,7 +590,7 @@ def forward(self, image):
         channels = image.size(1)
         h = image.size(2)
         w = image.size(3)
-        image = image.view(batch_size, channels*h* (w / 2), -1)
+        image = image.view(batch_size, channels*h* (old_div(w, 2)), -1)
         return image
 
 input = Variable(torch.randn(1, 2, 3, 4))
@@ -600,7 +603,7 @@ def __init__(self):
         super(Broadcast, self).__init__()
 
     def forward(self, x, y):
-        return x * y + (x - x) / y - y
+        return x * y + old_div((x - x), y) - y
 
 input1 = Variable(torch.randn(1, 4, 1, 2))
 input2 = Variable(torch.randn(1, 4, 1, 1))
@@ -746,7 +749,7 @@ def forward(self, x):
       norm = torch.norm(x, p=2, dim=1, keepdim=True)
       clip = torch.clamp(norm, min=0)
       expand = clip.expand_as(x)
-      return x / expand
+      return old_div(x, expand)
 
 model = NormL2()
 x = Variable(torch.randn(1, 2, 3, 4))
@@ -813,7 +816,7 @@ def forward(self, x, y):
 save_data_and_model_multy_inputs("upsample_unfused_two_inputs_opset9_torch1.4", UpsampleUnfusedTwoInput(), input_0, input_1, version=9)
 save_data_and_model_multy_inputs("upsample_unfused_two_inputs_opset11_torch1.4", UpsampleUnfusedTwoInput(), input_0, input_1, version=11)
 
- class FrozenBatchNorm2d(nn.Module):
+class FrozenBatchNorm2d(nn.Module):
     def __init__(self, n):
         super(FrozenBatchNorm2d, self).__init__()
         self.register_buffer("weight", torch.ones(n))
@@ -832,15 +835,15 @@ def forward(self, x):
 model = FrozenBatchNorm2d(2)
 save_data_and_model("batch_norm_subgraph", x, model)
 
- class GatherScalar(nn.Module):
+class GatherScalar(nn.Module):
     def forward(self, x):
         return x[1]
 
 x = Variable(torch.randn(2))
 model = GatherScalar()
 save_data_and_model("gather_scalar", x, model)
 
- class Gather(nn.Module):
+class Gather(nn.Module):
     def forward(self, x):
         return x[..., 1]
 

testdata/dnn/tensorflow/generate_tf2_models.py

@@ -1,4 +1,5 @@
 # This script is used to generate test data for OpenCV deep learning module.
+from builtins import next
 import numpy as np
 import tensorflow as tf
 import shutil
@@ -37,7 +38,7 @@ def save(model, name, **kwargs):
 
     assert(len(kwargs) == 1)
 
-    inputData = gen_data(next(iter(kwargs.values())))
+    inputData = gen_data(next(iter(list(kwargs.values()))))
     outputData = model(inputData)
 
     writeBlob(inputData, name + '_in')

testdata/dnn/tensorflow/generate_tf_models.py

@@ -1,4 +1,7 @@
+from __future__ import division
 # This script is used to generate test data for OpenCV deep learning module.
+from builtins import zip
+from past.utils import old_div
 import numpy as np
 import tensorflow as tf
 import os
@@ -869,8 +872,8 @@ def pad_depth(x, desired_channels):
 inp = tf.placeholder(tf.float32, [1, 9, 6, 2], 'input')
 conv = tf.layers.conv2d(inp, filters=2, kernel_size=[1, 1])
 shape_input = tf.shape(inp)
-hi = shape_input[1] / 3
-wi = shape_input[2] / 2
+hi = old_div(shape_input[1], 3)
+wi = old_div(shape_input[2], 2)
 input_down = tf.image.resize(conv, size=[hi,wi], method=0, name='resize_down')
 save(inp, input_down, 'resize_bilinear_down')
 ################################################################################

testdata/perf/append.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 import sys, re, os.path
 from xml.dom.minidom import parse
 
@@ -22,7 +23,7 @@ def processLogFile(outname, inname):
         if case.nodeName in tests:
             del tests[case.nodeName]
 
-    for case in tests.items():
+    for case in list(tests.items()):
         fstorage.appendChild(case[1])
 
     if tests:
@@ -39,7 +40,7 @@ def processLogFile(outname, inname):
 
 if __name__ == "__main__":
     if len(sys.argv) < 3:
-        print "Usage:\n", os.path.basename(sys.argv[0]), "<log_name>.xml <new_log_name>.xml"
+        print("Usage:\n", os.path.basename(sys.argv[0]), "<log_name>.xml <new_log_name>.xml")
         exit(0)
 
     processLogFile(sys.argv[1], sys.argv[2])

testdata/perf/clean_regex.py

@@ -1,43 +1,44 @@
-import sys, re, os.path
-from xml.dom.minidom import parse
-
-def parseLogFile(filename):
-    tests = []
-    log = parse(open(filename, 'rb'))
-    fstorage = log.firstChild
-    #print help(log)
-    for case in fstorage.childNodes:
-        if case.nodeName == "#text":
-            continue
-        #print case.nodeName
-        tests.append(case.nodeName)
-    return tests
-
-def process(filename, expr, save_results):
-    log = parse(open(filename, 'rb'))
-    fstorage = log.firstChild
-    for case in fstorage.childNodes:
-        if case.nodeName == "#text":
-            continue
-        if expr.search(case.nodeName):
-            print case.nodeName
-            fstorage.removeChild(case)
-
-    if save_results:
-        xmlstr = log.toxml()
-        xmlstr = re.sub(r"(\s*\n)+", "\n", xmlstr)
-        xmlstr = re.sub(r"(\s*\r\n)+", "\r\n", xmlstr)
-        xmlstr = re.sub(r"<(\w*)/>", "<\\1></\\1>", xmlstr)
-        xmlstr = xmlstr.replace("&quot;", "\"")
-        f = open(filename, 'wb')
-        f.write(xmlstr)
-        f.close()
-
-if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print "This script is used to remove entries from sanity xml"
-        print "  Usage:\n", os.path.basename(sys.argv[0]), "<name>.xml <regex>"
-        exit(0)
-
-    process(sys.argv[1], re.compile(sys.argv[2]), len(sys.argv) == 4)
-
+from __future__ import print_function
+import sys, re, os.path
+from xml.dom.minidom import parse
+
+def parseLogFile(filename):
+    tests = []
+    log = parse(open(filename, 'rb'))
+    fstorage = log.firstChild
+    #print help(log)
+    for case in fstorage.childNodes:
+        if case.nodeName == "#text":
+            continue
+        #print case.nodeName
+        tests.append(case.nodeName)
+    return tests
+
+def process(filename, expr, save_results):
+    log = parse(open(filename, 'rb'))
+    fstorage = log.firstChild
+    for case in fstorage.childNodes:
+        if case.nodeName == "#text":
+            continue
+        if expr.search(case.nodeName):
+            print(case.nodeName)
+            fstorage.removeChild(case)
+
+    if save_results:
+        xmlstr = log.toxml()
+        xmlstr = re.sub(r"(\s*\n)+", "\n", xmlstr)
+        xmlstr = re.sub(r"(\s*\r\n)+", "\r\n", xmlstr)
+        xmlstr = re.sub(r"<(\w*)/>", "<\\1></\\1>", xmlstr)
+        xmlstr = xmlstr.replace("&quot;", "\"")
+        f = open(filename, 'wb')
+        f.write(xmlstr)
+        f.close()
+
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print("This script is used to remove entries from sanity xml")
+        print("  Usage:\n", os.path.basename(sys.argv[0]), "<name>.xml <regex>")
+        exit(0)
+
+    process(sys.argv[1], re.compile(sys.argv[2]), len(sys.argv) == 4)
+

testdata/perf/clean_unused.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 import sys, re, os.path
 from xml.dom.minidom import parse
 
@@ -33,7 +34,7 @@ def processLogFile(outname, inname, tests):
 
 if __name__ == "__main__":
     if len(sys.argv) < 3:
-        print "Usage:\n", os.path.basename(sys.argv[0]), "<log_name>.xml <log_name>.backup.xml"
+        print("Usage:\n", os.path.basename(sys.argv[0]), "<log_name>.xml <log_name>.backup.xml")
         exit(0)
 
     tests = parseLogFile(sys.argv[1])