本文整理汇总了Python中object_detection.exporter.rewrite_nn_resize_op方法的典型用法代码示例。如果您正苦于以下问题：Python exporter.rewrite_nn_resize_op方法的具体用法？Python exporter.rewrite_nn_resize_op怎么用？Python exporter.rewrite_nn_resize_op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.exporter的用法示例。
在下文中一共展示了exporter.rewrite_nn_resize_op方法的6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 import exporter [as 别名]
# 或者: from object_detection.exporter import rewrite_nn_resize_op [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 import exporter [as 别名]
# 或者: from object_detection.exporter import rewrite_nn_resize_op [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 import exporter [as 别名]
# 或者: from object_detection.exporter import rewrite_nn_resize_op [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_rewrite_nn_resize_op_quantized
# 需要导入模块: from object_detection import exporter [as 别名]
# 或者: from object_detection.exporter import rewrite_nn_resize_op [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5: test_rewrite_nn_resize_op_quantized
# 需要导入模块: from object_detection import exporter [as 别名]
# 或者: from object_detection.exporter import rewrite_nn_resize_op [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 = 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6: test_rewrite_nn_resize_op_multiple_path
# 需要导入模块: from object_detection import exporter [as 别名]
# 或者: from object_detection.exporter import rewrite_nn_resize_op [as 别名]
def test_rewrite_nn_resize_op_multiple_path(self):
g = tf.Graph()
with g.as_default():
with tf.name_scope('nearest_upsampling'):
x = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
x_stack = tf.stack([tf.stack([x] * 2, axis=3)] * 2, axis=2)
x_reshape = tf.reshape(x_stack, [8, 20, 20, 8])
with tf.name_scope('nearest_upsampling'):
x_2 = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
x_stack_2 = tf.stack([tf.stack([x_2] * 2, axis=3)] * 2, axis=2)
x_reshape_2 = tf.reshape(x_stack_2, [8, 20, 20, 8])
t = x_reshape + x_reshape_2
exporter.rewrite_nn_resize_op()
graph_def = g.as_graph_def()
graph_def = strip_unused_lib.strip_unused(
graph_def,
input_node_names=[
'nearest_upsampling/Placeholder', 'nearest_upsampling_1/Placeholder'
],
output_node_names=['add'],
placeholder_type_enum=dtypes.float32.as_datatype_enum)
counter_resize_op = 0
t_input_ops = [op.name for op in t.op.inputs]
for node in graph_def.node:
# Make sure Stacks are replaced.
self.assertNotEqual(node.op, 'Pack')
if node.op == 'ResizeNearestNeighbor':
counter_resize_op += 1
self.assertIn(six.ensure_str(node.name) + ':0', t_input_ops)
self.assertEqual(counter_resize_op, 2)