本文整理汇总了Python中tensorflow.python.ops.image_ops.resize_bilinear函数的典型用法代码示例。如果您正苦于以下问题:Python resize_bilinear函数的具体用法?Python resize_bilinear怎么用?Python resize_bilinear使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了resize_bilinear函数的10个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: preprocess_image
def preprocess_image(
images, height=INCEPTION_DEFAULT_IMAGE_SIZE,
width=INCEPTION_DEFAULT_IMAGE_SIZE, scope=None):
"""Prepare a batch of images for evaluation.
This is the preprocessing portion of the graph from
http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz.
Note that it expects Tensors in [0, 255]. This function maps pixel values to
[-1, 1] and resizes to match the InceptionV1 network.
Args:
images: 3-D or 4-D Tensor of images. Values are in [0, 255].
height: Integer. Height of resized output image.
width: Integer. Width of resized output image.
scope: Optional scope for name_scope.
Returns:
3-D or 4-D float Tensor of prepared image(s). Values are in [-1, 1].
"""
is_single = images.shape.ndims == 3
with ops.name_scope(scope, 'preprocess', [images, height, width]):
if not images.dtype.is_floating:
images = math_ops.to_float(images)
if is_single:
images = array_ops.expand_dims(images, axis=0)
resized = image_ops.resize_bilinear(images, [height, width])
resized = (resized - 128.0) / 128.0
if is_single:
resized = array_ops.squeeze(resized, axis=0)
return resized
开发者ID:changchunli,项目名称:compare_gan,代码行数:31,代码来源:classifier_metrics_impl.py
示例2: testFuseResizeAndConv
def testFuseResizeAndConv(self):
with self.cached_session() as sess:
inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6]
input_op = constant_op.constant(
np.array(inputs), shape=[1, 2, 3, 2], dtype=dtypes.float32)
resize_op = image_ops.resize_bilinear(
input_op, [12, 4], align_corners=False)
weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4]
weights_op = constant_op.constant(
np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32)
nn_ops.conv2d(
resize_op, weights_op, [1, 1, 1, 1], padding="VALID", name="output")
original_graph_def = sess.graph_def
original_result = sess.run(["output:0"])
optimized_graph_def = optimize_for_inference_lib.fuse_resize_and_conv(
original_graph_def, ["output"])
with self.cached_session() as sess:
_ = importer.import_graph_def(
optimized_graph_def, input_map={}, name="optimized")
optimized_result = sess.run(["optimized/output:0"])
self.assertAllClose(original_result, optimized_result)
for node in optimized_graph_def.node:
self.assertNotEqual("Conv2D", node.op)
self.assertNotEqual("MirrorPad", node.op)
开发者ID:Wajih-O,项目名称:tensorflow,代码行数:27,代码来源:optimize_for_inference_test.py
示例3: testGradOnUnsupportedType
def testGradOnUnsupportedType(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.uint8)
with self.test_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
grad = gradients_impl.gradients(input_tensor, [resize_out])
self.assertEqual([None], grad)
开发者ID:1000sprites,项目名称:tensorflow,代码行数:11,代码来源:image_grad_test.py
示例4: testGradFromResizeToSmallerInBothDims
def testGradFromResizeToSmallerInBothDims(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.float32)
with self.test_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
err = gradient_checker.compute_gradient_error(
input_tensor, in_shape, resize_out, out_shape, x_init_value=x)
self.assertLess(err, 1e-3)
开发者ID:1000sprites,项目名称:tensorflow,代码行数:12,代码来源:image_grad_test.py
示例5: testShapeIsCorrectAfterOp
def testShapeIsCorrectAfterOp(self):
in_shape = [1, 2, 2, 1]
out_shape = [1, 4, 6, 1]
x = np.arange(0, 4).reshape(in_shape).astype(np.float32)
with self.test_session() as sess:
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
self.assertEqual(out_shape, list(resize_out.get_shape()))
resize_out = sess.run(resize_out)
self.assertEqual(out_shape, list(resize_out.shape))
开发者ID:1000sprites,项目名称:tensorflow,代码行数:13,代码来源:image_grad_test.py
示例6: testCompareGpuVsCpu
def testCompareGpuVsCpu(self):
in_shape = [2, 4, 6, 3]
out_shape = [2, 8, 16, 3]
size = np.prod(in_shape)
x = 1.0 / size * np.arange(0, size).reshape(in_shape).astype(np.float32)
for align_corners in [True, False]:
grad = {}
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
input_tensor = constant_op.constant(x, shape=in_shape)
resized_tensor = image_ops.resize_bilinear(
input_tensor, out_shape[1:3], align_corners=align_corners)
grad[use_gpu] = gradient_checker.compute_gradient(
input_tensor, in_shape, resized_tensor, out_shape, x_init_value=x)
self.assertAllClose(grad[False], grad[True], rtol=1e-4, atol=1e-4)
开发者ID:1000sprites,项目名称:tensorflow,代码行数:17,代码来源:image_grad_test.py
示例7: testTypes
def testTypes(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape)
with self.cached_session() as sess:
for dtype in [np.float16, np.float32, np.float64]:
input_tensor = constant_op.constant(x.astype(dtype), shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
grad = sess.run(gradients_impl.gradients(resize_out, input_tensor))[0]
self.assertAllEqual(in_shape, grad.shape)
# Not using gradient_checker.compute_gradient as I didn't work out
# the changes required to compensate for the lower precision of
# float16 when computing the numeric jacobian.
# Instead, we just test the theoretical jacobian.
self.assertAllEqual([[[[1.], [0.], [1.], [0.], [1.], [0.]], [[0.], [
0.
], [0.], [0.], [0.], [0.]], [[1.], [0.], [1.], [0.], [1.], [0.]],
[[0.], [0.], [0.], [0.], [0.], [0.]]]], grad)
开发者ID:Wajih-O,项目名称:tensorflow,代码行数:19,代码来源:image_grad_test.py
示例8: testCompareGpuVsCpu
def testCompareGpuVsCpu(self):
in_shape = [2, 4, 6, 3]
out_shape = [2, 8, 16, 3]
size = np.prod(in_shape)
x = 1.0 / size * np.arange(0, size).reshape(in_shape).astype(np.float32)
# Align corners will be deprecated for tf2.0 and the false version is not
# supported by XLA.
align_corner_options = [True
] if test_util.is_xla_enabled() else [True, False]
for align_corners in align_corner_options:
grad = {}
for use_gpu in [False, True]:
with self.cached_session(use_gpu=use_gpu):
input_tensor = constant_op.constant(x, shape=in_shape)
resized_tensor = image_ops.resize_bilinear(
input_tensor, out_shape[1:3], align_corners=align_corners)
grad[use_gpu] = gradient_checker.compute_gradient(
input_tensor, in_shape, resized_tensor, out_shape, x_init_value=x)
self.assertAllClose(grad[False], grad[True], rtol=1e-4, atol=1e-4)
开发者ID:aritratony,项目名称:tensorflow,代码行数:22,代码来源:image_grad_test.py
示例9: preprocess_image
def preprocess_image(
image, height=INCEPTION_V3_DEFAULT_IMG_SIZE,
width=INCEPTION_V3_DEFAULT_IMG_SIZE, central_fraction=0.875, scope=None):
"""Prepare one image for evaluation.
If height and width are specified it would output an image with that size by
applying resize_bilinear.
If central_fraction is specified it would crop the central fraction of the
input image.
Args:
image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
[0, 1], otherwise it would converted to tf.float32 assuming that the range
is [0, MAX], where MAX is largest positive representable number for
int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
height: integer
width: integer
central_fraction: Optional Float, fraction of the image to crop.
scope: Optional scope for name_scope.
Returns:
3-D float Tensor of prepared image.
"""
with ops.name_scope(scope, 'eval_image', [image, height, width]):
if image.dtype != dtypes.float32:
image = image_ops.convert_image_dtype(image, dtype=dtypes.float32)
# Crop the central region of the image with an area containing 87.5% of
# the original image.
image = image_ops.central_crop(image, central_fraction=central_fraction)
# Resize the image to the specified height and width.
image = array_ops.expand_dims(image, 0)
image = image_ops.resize_bilinear(image, [height, width],
align_corners=False)
image = array_ops.squeeze(image, [0])
image = (image - 0.5) * 2.0
return image
开发者ID:Crazyonxh,项目名称:tensorflow,代码行数:37,代码来源:classifier_metrics_impl.py
示例10: _resize_image
def _resize_image(image, height, width):
image = array_ops.expand_dims(image, 0)
image = image_ops.resize_bilinear(image, [height, width])
return array_ops.squeeze(image, [0])
开发者ID:1000sprites,项目名称:tensorflow,代码行数:4,代码来源:dataset_data_provider_test.py
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