File image_ops_test.py has 5118 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

ResizeImagesV2Test has 27 functions (exceeds 20 allowed). Consider refactoring.

Open

class ResizeImagesV2Test(test_util.TensorFlowTestCase, parameterized.TestCase):

  METHODS = [
      image_ops.ResizeMethod.BILINEAR, image_ops.ResizeMethod.NEAREST_NEIGHBOR,
      image_ops.ResizeMethod.BICUBIC, image_ops.ResizeMethod.AREA,

ResizeImagesTest has 26 functions (exceeds 20 allowed). Consider refactoring.

Open

class ResizeImagesTest(test_util.TensorFlowTestCase,
                       parameterized.TestCase):

  METHODS = [
      image_ops.ResizeMethodV1.BILINEAR,

FlipTransposeRotateTest has 26 functions (exceeds 20 allowed). Consider refactoring.

Open

class FlipTransposeRotateTest(test_util.TensorFlowTestCase,
                              parameterized.TestCase):

  def testInvolutionLeftRight(self):
    x_np = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.uint8).reshape([2, 3, 1])

Function testReturnDtypeV2 has a Cognitive Complexity of 17 (exceeds 5 allowed). Consider refactoring.

Open

  def testReturnDtypeV2(self, run_func_eagerly):
    if not context.executing_eagerly() and run_func_eagerly:
      # Skip running tf.function eagerly in V1 mode.
      self.skipTest("Skip test that runs tf.function eagerly in V1 mode.")
    else:

• Read up
  Read up

Function testReturnDtypeV2 has a Cognitive Complexity of 15 (exceeds 5 allowed). Consider refactoring.

Open

  def testReturnDtypeV2(self, run_func_eagerly):
    if not context.executing_eagerly() and run_func_eagerly:
      # Skip running tf.function eagerly in V1 mode.
      self.skipTest("Skip test that runs tf.function eagerly in V1 mode.")
    else:

• Read up
  Read up

Function testDataTypes has a Cognitive Complexity of 14 (exceeds 5 allowed). Consider refactoring.

Open

  def testDataTypes(self):
    # Test case for GitHub issue 20199.
    boxes_np = [[0, 0, 1, 1], [0, 0.1, 1, 1.1], [0, -0.1, 1, 0.9],
                [0, 10, 1, 11], [0, 10.1, 1, 11.1], [0, 100, 1, 101]]
    scores_np = [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]

• Read up
  Read up

Function testRandomFlipStatelessWithBatch has a Cognitive Complexity of 13 (exceeds 5 allowed). Consider refactoring.

Open

  def testRandomFlipStatelessWithBatch(self, func):
    with test_util.use_gpu():
      batch_size = 16

      # create single item of test data

• Read up
  Read up

Function testReturnDtypeV1 has a Cognitive Complexity of 12 (exceeds 5 allowed). Consider refactoring.

Open

  def testReturnDtypeV1(self):
    # Shape inference in V1.
    with ops.Graph().as_default():
      target_shapes = [[6, 4], [3, 2], [
          array_ops.placeholder(dtypes.int32),

• Read up
  Read up

Function testReturnDtypeV1 has a Cognitive Complexity of 11 (exceeds 5 allowed). Consider refactoring.

Open

  def testReturnDtypeV1(self):
    # Shape inference in V1.
    with ops.Graph().as_default():
      target_shapes = [[6, 4], [3, 2],
                       [

• Read up
  Read up

Function testResizeDown has a Cognitive Complexity of 11 (exceeds 5 allowed). Consider refactoring.

Open

  def testResizeDown(self):
    # This test is also conducted with int8, so 127 is the maximum
    # value that can be used.
    data = [
        127, 127, 64, 64, 127, 127, 64, 64, 64, 64, 127, 127, 64, 64, 127, 127,

• Read up
  Read up

Function testAdjustRandomSaturation has a Cognitive Complexity of 11 (exceeds 5 allowed). Consider refactoring.

Open

  def testAdjustRandomSaturation(self):
    x_shapes = [
        [2, 2, 3],
        [4, 2, 3],
        [2, 4, 3],

• Read up
  Read up

Function testAdjustRandomHue has a Cognitive Complexity of 11 (exceeds 5 allowed). Consider refactoring.

Open

  def testAdjustRandomHue(self):
    x_shapes = [
        [2, 2, 3],
        [4, 2, 3],
        [2, 4, 3],

• Read up
  Read up

Function testResizeDown has a Cognitive Complexity of 10 (exceeds 5 allowed). Consider refactoring.

Open

  def testResizeDown(self):
    # This test is also conducted with int8, so 127 is the maximum
    # value that can be used.
    data = [
        127, 127, 64, 64, 127, 127, 64, 64, 64, 64, 127, 127, 64, 64, 127, 127,

• Read up
  Read up

Function testCompareBilinear has a Cognitive Complexity of 10 (exceeds 5 allowed). Consider refactoring.

Open

  def testCompareBilinear(self):
    if test.is_gpu_available():
      input_shape = [1, 5, 6, 3]
      target_height = 8
      target_width = 12

• Read up
  Read up

Function _assertRaises has 8 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertRaises(self,

Function _assertRaises has 8 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertRaises(self,

Function testRandomFlipStateless has a Cognitive Complexity of 9 (exceeds 5 allowed). Consider refactoring.

Open

  def testRandomFlipStateless(self, func):
    with test_util.use_gpu():
      x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8).reshape([2, 3, 1])
      y_np = np.array([[3, 2, 1], [6, 5, 4]], dtype=np.uint8).reshape([2, 3, 1])
      if "RandomFlipUpDown" in self.id():

• Read up
  Read up

Function _assertReturns has 7 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertReturns(self,

Function _assertReturns has 7 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertReturns(self,

Function _assertReturns has 7 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertReturns(self,

Avoid deeply nested control flow statements.

Open

            with self.cached_session(use_gpu=use_gpu):
              image = constant_op.constant(img_np, shape=input_shape)
              new_size = constant_op.constant([target_height, target_width])
              out_op = image_ops.resize_images(
                  image,

Function _assertResizeEqual has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertResizeEqual(self, x, x_shape, y, y_shape,

Avoid deeply nested control flow statements.

Open

            with self.cached_session():
              image = constant_op.constant(img_np, shape=img_shape)
              y = image_ops.resize_images(image, [target_height, target_width],
                                          method)
              expected = np.array(expected_data).reshape(target_shape)

Avoid deeply nested control flow statements.

Open

            if y_tf_eval[j][0][0] == 1:
              self.assertAllEqual(y_tf_eval[j], x_np[j])
              count_unflipped += 1
              flip_seq += "U"
            else:

Function _PadToBoundingBox has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _PadToBoundingBox(self, x, offset_height, offset_width, target_height,

Avoid deeply nested control flow statements.

Open

            with test_util.run_functions_eagerly(run_func_eagerly):
              test_dtype.get_concrete_function(image, target_shape, method)

Avoid deeply nested control flow statements.

Open

            with test_util.run_functions_eagerly(run_func_eagerly):
              test_dtype.get_concrete_function(image, target_shape, method)

  # half_pixel_centers not supported by XLA
  @test_util.disable_xla("b/127616992")

Function _assertRaises has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertRaises(self,

Function _assertRaises has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertRaises(self,

Function _assertRaises has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertRaises(self,

Function _RGBToGrayscale has a Cognitive Complexity of 8 (exceeds 5 allowed). Consider refactoring.

Open

  def _RGBToGrayscale(self, images):
    is_batch = True
    if len(images.shape) == 3:
      is_batch = False
      images = np.expand_dims(images, axis=0)

• Read up
  Read up

Function _CropToBoundingBox has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _CropToBoundingBox(self, x, offset_height, offset_width, target_height,

Function _assertResizeCheckShape has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertResizeCheckShape(self,

Function _InternalPadToBoundingBox has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _InternalPadToBoundingBox(self, x, offset_height, offset_width,

Function _assertResizeEqual has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertResizeEqual(self,

Function _assertResizeCheckShape has 6 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertResizeCheckShape(self, x, x_shape, target_shape,

Avoid deeply nested control flow statements.

Open

            with self.cached_session():
              image = constant_op.constant(img_np, shape=img_shape)
              y = image_ops.resize_images_v2(
                  image, [target_height, target_width], method)
              expected = np.array(expected_data).reshape(target_shape)

Avoid deeply nested control flow statements.

Open

            if method == image_ops.ResizeMethod.NEAREST_NEIGHBOR:
              expected_dtype = image.dtype
            else:
              expected_dtype = dtypes.float32
            self.assertEqual(y.dtype, expected_dtype)

Avoid deeply nested control flow statements.

Open

            if (method == image_ops.ResizeMethodV1.NEAREST_NEIGHBOR or
                target_shape == image.shape[1:3]):
              expected_dtype = image.dtype
            else:
              expected_dtype = dtypes.float32

Function _ResizeImageCall has 5 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _ResizeImageCall(self, x, max_h, max_w, preserve_aspect_ratio,

Function _testStatelessSampleDistortedBoundingBox has 5 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _testStatelessSampleDistortedBoundingBox(self, image, bounding_box,

Function _assertReturns has 5 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertReturns(self,

Function _testSampleDistortedBoundingBox has 5 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _testSampleDistortedBoundingBox(self, image, bounding_box,

Function _assertReturns has 5 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertReturns(self,

Function _ResizeImageCall has 5 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _ResizeImageCall(self, x, max_h, max_w, preserve_aspect_ratio,

Function _assertReturns has 5 arguments (exceeds 4 allowed). Consider refactoring.

Open

  def _assertReturns(self,

Function testRandomFlipLeftRightWithBatch has a Cognitive Complexity of 7 (exceeds 5 allowed). Consider refactoring.

Open

  def testRandomFlipLeftRightWithBatch(self):
    batch_size = 16
    seed = 42

    # create single item of test data

• Read up
  Read up

Function testRandomFlipUpDownWithBatch has a Cognitive Complexity of 7 (exceeds 5 allowed). Consider refactoring.

Open

  def testRandomFlipUpDownWithBatch(self):
    batch_size = 16
    seed = 42

    # create single item of test data

• Read up
  Read up

Function testExisting has a Cognitive Complexity of 7 (exceeds 5 allowed). Consider refactoring.

Open

  def testExisting(self):
    # Read some real PNGs, converting to different channel numbers
    prefix = "tensorflow/core/lib/png/testdata/"
    inputs = ((1, "lena_gray.png"), (4, "lena_rgba.png"),
              (3, "lena_palette.png"), (4, "lena_palette_trns.png"))

• Read up
  Read up

Function testCompareNearestNeighbor has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

  def testCompareNearestNeighbor(self):
    if test.is_gpu_available():
      input_shape = [1, 5, 6, 3]
      target_height = 8
      target_width = 12

• Read up
  Read up

Function testFormats has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

  def testFormats(self):
    prefix = "tensorflow/core/lib"
    paths = ("png/testdata/lena_gray.png", "jpeg/testdata/jpeg_merge_test1.jpg",
             "gif/testdata/lena.gif")
    decoders = {

• Read up
  Read up

Function testCompareBilinear has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

  def testCompareBilinear(self):
    if test.is_gpu_available():
      input_shape = [1, 5, 6, 3]
      target_height = 8
      target_width = 12

• Read up
  Read up

Function testNoOp has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

  def testNoOp(self):
    img_shape = [1, 6, 4, 1]
    single_shape = [6, 4, 1]
    # This test is also conducted with int8, so 127 is the maximum
    # value that can be used.

• Read up
  Read up