本文整理汇总了Python中object_detection.utils.ops.nearest_neighbor_upsampling方法的典型用法代码示例。如果您正苦于以下问题:Python ops.nearest_neighbor_upsampling方法的具体用法?Python ops.nearest_neighbor_upsampling怎么用?Python ops.nearest_neighbor_upsampling使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.utils.ops
的用法示例。
在下文中一共展示了ops.nearest_neighbor_upsampling方法的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,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_rewrite_nn_resize_op
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import nearest_neighbor_upsampling [as 别名]
def test_rewrite_nn_resize_op(self):
g = tf.Graph()
with g.as_default():
x = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
y = array_ops.placeholder(dtypes.float32, shape=(8, 20, 20, 8))
s = ops.nearest_neighbor_upsampling(x, 2)
t = s + y
exporter.rewrite_nn_resize_op()
resize_op_found = False
for op in g.get_operations():
if op.type == 'ResizeNearestNeighbor':
resize_op_found = True
self.assertEqual(op.inputs[0], x)
self.assertEqual(op.outputs[0].consumers()[0], t.op)
break
self.assertTrue(resize_op_found)
示例2: test_rewrite_nn_resize_op_odd_size
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import nearest_neighbor_upsampling [as 别名]
def test_rewrite_nn_resize_op_odd_size(self):
g = tf.Graph()
with g.as_default():
x = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
s = ops.nearest_neighbor_upsampling(x, 2)
t = s[:, :19, :19, :]
exporter.rewrite_nn_resize_op()
resize_op_found = False
for op in g.get_operations():
if op.type == 'ResizeNearestNeighbor':
resize_op_found = True
self.assertEqual(op.inputs[0], x)
self.assertEqual(op.outputs[0].consumers()[0], t.op)
break
self.assertTrue(resize_op_found)
示例3: test_rewrite_nn_resize_op_quantized_odd_size
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import nearest_neighbor_upsampling [as 别名]
def test_rewrite_nn_resize_op_quantized_odd_size(self):
g = tf.Graph()
with g.as_default():
x = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
x_conv = slim.conv2d(x, 8, 1)
s = ops.nearest_neighbor_upsampling(x_conv, 2)
t = s[:, :19, :19, :]
graph_rewriter_config = graph_rewriter_pb2.GraphRewriter()
graph_rewriter_config.quantization.delay = 500000
graph_rewriter_fn = graph_rewriter_builder.build(
graph_rewriter_config, is_training=False)
graph_rewriter_fn()
exporter.rewrite_nn_resize_op(is_quantized=True)
resize_op_found = False
for op in g.get_operations():
if op.type == 'ResizeNearestNeighbor':
resize_op_found = True
self.assertEqual(op.inputs[0].op.type, 'FakeQuantWithMinMaxVars')
self.assertEqual(op.outputs[0].consumers()[0], t.op)
break
self.assertTrue(resize_op_found)
示例4: test_upsampling_with_single_scale
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import nearest_neighbor_upsampling [as 别名]
def test_upsampling_with_single_scale(self):
def graph_fn(inputs):
custom_op_output = ops.nearest_neighbor_upsampling(inputs, scale=2)
return custom_op_output
inputs = np.reshape(np.arange(4).astype(np.float32), [1, 2, 2, 1])
custom_op_output = self.execute(graph_fn, [inputs])
expected_output = [[[[0], [0], [1], [1]],
[[0], [0], [1], [1]],
[[2], [2], [3], [3]],
[[2], [2], [3], [3]]]]
self.assertAllClose(custom_op_output, expected_output)
示例5: test_upsampling_with_separate_height_width_scales
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import nearest_neighbor_upsampling [as 别名]
def test_upsampling_with_separate_height_width_scales(self):
def graph_fn(inputs):
custom_op_output = ops.nearest_neighbor_upsampling(inputs,
height_scale=2,
width_scale=3)
return custom_op_output
inputs = np.reshape(np.arange(4).astype(np.float32), [1, 2, 2, 1])
custom_op_output = self.execute(graph_fn, [inputs])
expected_output = [[[[0], [0], [0], [1], [1], [1]],
[[0], [0], [0], [1], [1], [1]],
[[2], [2], [2], [3], [3], [3]],
[[2], [2], [2], [3], [3], [3]]]]
self.assertAllClose(custom_op_output, expected_output)
示例6: test_rewrite_nn_resize_op_quantized
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import nearest_neighbor_upsampling [as 别名]
def test_rewrite_nn_resize_op_quantized(self):
g = tf.Graph()
with g.as_default():
x = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
x_conv = tf.contrib.slim.conv2d(x, 8, 1)
y = array_ops.placeholder(dtypes.float32, shape=(8, 20, 20, 8))
s = ops.nearest_neighbor_upsampling(x_conv, 2)
t = s + y
graph_rewriter_config = graph_rewriter_pb2.GraphRewriter()
graph_rewriter_config.quantization.delay = 500000
graph_rewriter_fn = graph_rewriter_builder.build(
graph_rewriter_config, is_training=False)
graph_rewriter_fn()
exporter.rewrite_nn_resize_op(is_quantized=True)
resize_op_found = False
for op in g.get_operations():
if op.type == 'ResizeNearestNeighbor':
resize_op_found = True
self.assertEqual(op.inputs[0].op.type, 'FakeQuantWithMinMaxVars')
self.assertEqual(op.outputs[0].consumers()[0], t.op)
break
self.assertTrue(resize_op_found)
示例7: test_upsampling
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import nearest_neighbor_upsampling [as 别名]
def test_upsampling(self):
def graph_fn(inputs):
custom_op_output = ops.nearest_neighbor_upsampling(inputs, scale=2)
return custom_op_output
inputs = np.reshape(np.arange(4).astype(np.float32), [1, 2, 2, 1])
custom_op_output = self.execute(graph_fn, [inputs])
expected_output = [[[[0], [0], [1], [1]],
[[0], [0], [1], [1]],
[[2], [2], [3], [3]],
[[2], [2], [3], [3]]]]
self.assertAllClose(custom_op_output, expected_output)
示例8: test_upsampling
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import nearest_neighbor_upsampling [as 别名]
def test_upsampling(self):
def graph_fn(inputs):
custom_op_output = ops.nearest_neighbor_upsampling(inputs, scale=2)
tf_op_output = tf.image.resize_images(
inputs, [4, 4], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return (custom_op_output, tf_op_output)
inputs = np.reshape(np.arange(2**4), [2, 2, 2, 2])
(custom_op_output, tf_op_output) = self.execute(graph_fn, [inputs])
self.assertAllClose(custom_op_output, tf_op_output)
示例9: test_upsampling
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import nearest_neighbor_upsampling [as 别名]
def test_upsampling(self):
def graph_fn(inputs):
custom_op_output = ops.nearest_neighbor_upsampling(inputs, scale=2)
return custom_op_output
inputs = np.reshape(np.arange(4).astype(np.float32), [1, 2, 2, 1])
custom_op_output = self.execute(graph_fn, [inputs])
expected_output = [[[[0], [0], [1], [1]],
[[0], [0], [1], [1]],
[[2], [2], [3], [3]],
[[2], [2], [3], [3]]]]
self.assertAllClose(custom_op_output, expected_output)
示例10: create_upsample_feature_map_ops
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import nearest_neighbor_upsampling [as 别名]
def create_upsample_feature_map_ops(scale, use_native_resize_op, name):
"""Creates Keras layers for upsampling feature maps.
Args:
scale: Int. The scale factor by which to upsample input feature maps. For
example, in the case of a typical feature map pyramid, the scale factor
between level_i and level_i-1 is 2.
use_native_resize_op: If True, uses tf.image.resize_nearest_neighbor op for
the upsampling process instead of reshape and broadcasting implementation.
name: String. The name used to prefix the constructed layers.
Returns:
A list of Keras layers which will upsample input feature maps by the
desired scale factor.
"""
layers = []
if use_native_resize_op:
def resize_nearest_neighbor(image):
image_shape = shape_utils.combined_static_and_dynamic_shape(image)
return tf.image.resize_nearest_neighbor(
image, [image_shape[1] * scale, image_shape[2] * scale])
layers.append(
tf.keras.layers.Lambda(
resize_nearest_neighbor,
name=name + 'nearest_neighbor_upsampling_x{}'.format(scale)))
else:
def nearest_neighbor_upsampling(image):
return ops.nearest_neighbor_upsampling(image, scale=scale)
layers.append(
tf.keras.layers.Lambda(
nearest_neighbor_upsampling,
name=name + 'nearest_neighbor_upsampling_x{}'.format(scale)))
return layers