本文整理汇总了Python中tensorflow.contrib.slim.nets.resnet_utils.conv2d_same方法的典型用法代码示例。如果您正苦于以下问题:Python resnet_utils.conv2d_same方法的具体用法?Python resnet_utils.conv2d_same怎么用?Python resnet_utils.conv2d_same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tensorflow.contrib.slim.nets.resnet_utils
的用法示例。
在下文中一共展示了resnet_utils.conv2d_same方法的13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: root_block_fn_for_beta_variant
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def root_block_fn_for_beta_variant(net):
"""Gets root_block_fn for beta variant.
ResNet-v1 beta variant modifies the first original 7x7 convolution to three
3x3 convolutions.
Args:
net: A tensor of size [batch, height, width, channels], input to the model.
Returns:
A tensor after three 3x3 convolutions.
"""
net = resnet_utils.conv2d_same(net, 64, 3, stride=2, scope='conv1_1')
net = resnet_utils.conv2d_same(net, 64, 3, stride=1, scope='conv1_2')
net = resnet_utils.conv2d_same(net, 128, 3, stride=1, scope='conv1_3')
return net
示例2: root_block_fn_for_beta_variant
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def root_block_fn_for_beta_variant(net):
"""Gets root_block_fn for beta variant.
ResNet-v1 beta variant modifies the first original 7x7 convolution to three
3x3 convolutions.
Args:
net: A tensor of size [batch, height, width, channels], input to the model.
Returns:
A tensor after three 3x3 convolutions.
"""
net = resnet_utils.conv2d_same(net, 64, 3, stride=2, scope='conv1_1')
net = resnet_utils.conv2d_same(net, 64, 3, stride=1, scope='conv1_2')
net = resnet_utils.conv2d_same(net, 128, 3, stride=1, scope='conv1_3')
return net
示例3: root_block_fn_for_beta_variant
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def root_block_fn_for_beta_variant(net):
"""Gets root_block_fn for beta variant.
ResNet-v1 beta variant modifies the first original 7x7 convolution to three
3x3 convolutions.
Args:
net: A tensor of size [batch, height, width, channels], input to the model.
Returns:
A tensor after three 3x3 convolutions.
"""
net = resnet_utils.conv2d_same(net, 64, 3, stride=2, scope='conv1_1')
net = resnet_utils.conv2d_same(net, 64, 3, stride=1, scope='conv1_2')
net = resnet_utils.conv2d_same(net, 128, 3, stride=1, scope='conv1_3')
return net
示例4: testConv2DSameEven
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def testConv2DSameEven(self):
n, n2 = 4, 2
# Input image.
x = create_test_input(1, n, n, 1)
# Convolution kernel.
w = create_test_input(1, 3, 3, 1)
w = tf.reshape(w, [3, 3, 1, 1])
tf.get_variable('Conv/weights', initializer=w)
tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
tf.get_variable_scope().reuse_variables()
y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
y1_expected = tf.to_float([[14, 28, 43, 26],
[28, 48, 66, 37],
[43, 66, 84, 46],
[26, 37, 46, 22]])
y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
y2 = resnet_utils.subsample(y1, 2)
y2_expected = tf.to_float([[14, 43],
[43, 84]])
y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
y3_expected = y2_expected
y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
y4_expected = tf.to_float([[48, 37],
[37, 22]])
y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1])
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertAllClose(y1.eval(), y1_expected.eval())
self.assertAllClose(y2.eval(), y2_expected.eval())
self.assertAllClose(y3.eval(), y3_expected.eval())
self.assertAllClose(y4.eval(), y4_expected.eval())
示例5: testConv2DSameOdd
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def testConv2DSameOdd(self):
n, n2 = 5, 3
# Input image.
x = create_test_input(1, n, n, 1)
# Convolution kernel.
w = create_test_input(1, 3, 3, 1)
w = tf.reshape(w, [3, 3, 1, 1])
tf.get_variable('Conv/weights', initializer=w)
tf.get_variable('Conv/biases', initializer=tf.zeros([1]))
tf.get_variable_scope().reuse_variables()
y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
y1_expected = tf.to_float([[14, 28, 43, 58, 34],
[28, 48, 66, 84, 46],
[43, 66, 84, 102, 55],
[58, 84, 102, 120, 64],
[34, 46, 55, 64, 30]])
y1_expected = tf.reshape(y1_expected, [1, n, n, 1])
y2 = resnet_utils.subsample(y1, 2)
y2_expected = tf.to_float([[14, 43, 34],
[43, 84, 55],
[34, 55, 30]])
y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1])
y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
y3_expected = y2_expected
y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
y4_expected = y2_expected
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
self.assertAllClose(y1.eval(), y1_expected.eval())
self.assertAllClose(y2.eval(), y2_expected.eval())
self.assertAllClose(y3.eval(), y3_expected.eval())
self.assertAllClose(y4.eval(), y4_expected.eval())
示例6: __call__
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def __call__(self, features):
""" Define tf graph.
"""
inputs = features['image']
with tf.variable_scope('encoder') as vsc:
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
# conv1
with arg_scope(
[layers_lib.conv2d], activation_fn=None, normalizer_fn=None):
net = resnet_utils.conv2d_same(inputs, 16, 5, stride=2, scope='conv1')
tf.add_to_collection(vsc.original_name_scope, net)
# resnet blocks
blocks = []
for i in range(len(self.encoder_params['block_name'])):
block = resnet_v2.resnet_v2_block(
scope=self.encoder_params['block_name'][i],
base_depth=self.encoder_params['base_depth'][i],
num_units=self.encoder_params['num_units'][i],
stride=self.encoder_params['stride'][i])
blocks.append(block)
net, _ = resnet_v2.resnet_v2(
net,
blocks,
is_training=(self.mode == ModeKeys.TRAIN),
global_pool=False,
output_stride=2,
include_root_block=False,
scope='resnet')
tf.add_to_collection(vsc.original_name_scope, net)
return net
示例7: _nas_stem
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def _nas_stem(inputs,
batch_norm_fn=slim.batch_norm):
"""Stem used for NAS models."""
net = resnet_utils.conv2d_same(inputs, 64, 3, stride=2, scope='conv0')
net = batch_norm_fn(net, scope='conv0_bn')
net = tf.nn.relu(net)
net = resnet_utils.conv2d_same(net, 64, 3, stride=1, scope='conv1')
net = batch_norm_fn(net, scope='conv1_bn')
cell_outputs = [net]
net = tf.nn.relu(net)
net = resnet_utils.conv2d_same(net, 128, 3, stride=2, scope='conv2')
net = batch_norm_fn(net, scope='conv2_bn')
cell_outputs.append(net)
return net, cell_outputs
示例8: resnet_base
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def resnet_base(img_batch, scope_name, is_training=True):
'''
this code is derived from light-head rcnn.
https://github.com/zengarden/light_head_rcnn
It is convenient to freeze blocks. So we adapt this mode.
'''
if scope_name == 'resnet_v1_50':
middle_num_units = 6
elif scope_name == 'resnet_v1_101':
middle_num_units = 23
else:
raise NotImplementedError('We only support resnet_v1_50 or resnet_v1_101. Check your network name....yjr')
blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
# use stride 1 for the last conv4 layer.
resnet_v1_block('block3', base_depth=256, num_units=middle_num_units, stride=1)]
# when use fpn . stride list is [1, 2, 2]
with slim.arg_scope(resnet_arg_scope(is_training=False)):
with tf.variable_scope(scope_name, scope_name):
# Do the first few layers manually, because 'SAME' padding can behave inconsistently
# for images of different sizes: sometimes 0, sometimes 1
net = resnet_utils.conv2d_same(
img_batch, 64, 7, stride=2, scope='conv1')
net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
net = slim.max_pool2d(
net, [3, 3], stride=2, padding='VALID', scope='pool1')
not_freezed = [False] * cfgs.FIXED_BLOCKS + (4-cfgs.FIXED_BLOCKS)*[True]
# Fixed_Blocks can be 1~3
with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[0]))):
C2, _ = resnet_v1.resnet_v1(net,
blocks[0:1],
global_pool=False,
include_root_block=False,
scope=scope_name)
# C2 = tf.Print(C2, [tf.shape(C2)], summarize=10, message='C2_shape')
with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[1]))):
C3, _ = resnet_v1.resnet_v1(C2,
blocks[1:2],
global_pool=False,
include_root_block=False,
scope=scope_name)
# C3 = tf.Print(C3, [tf.shape(C3)], summarize=10, message='C3_shape')
with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[2]))):
C4, _ = resnet_v1.resnet_v1(C3,
blocks[2:3],
global_pool=False,
include_root_block=False,
scope=scope_name)
# C4 = tf.Print(C4, [tf.shape(C4)], summarize=10, message='C4_shape')
return C4
示例9: bottleneck
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
outputs_collections=None, scope=None):
"""Bottleneck residual unit variant with BN before convolutions.
This is the full preactivation residual unit variant proposed in [2]. See
Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck
variant which has an extra bottleneck layer.
When putting together two consecutive ResNet blocks that use this unit, one
should use stride = 2 in the last unit of the first block.
Args:
inputs: A tensor of size [batch, height, width, channels].
depth: The depth of the ResNet unit output.
depth_bottleneck: The depth of the bottleneck layers.
stride: The ResNet unit's stride. Determines the amount of downsampling of
the units output compared to its input.
rate: An integer, rate for atrous convolution.
outputs_collections: Collection to add the ResNet unit output.
scope: Optional variable_scope.
Returns:
The ResNet unit's output.
"""
with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
if depth == depth_in:
shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
else:
shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,
normalizer_fn=None, activation_fn=None,
scope='shortcut')
residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1,
scope='conv1')
residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
rate=rate, scope='conv2')
residual = slim.conv2d(residual, depth, [1, 1], stride=1,
normalizer_fn=None, activation_fn=None,
scope='conv3')
output = shortcut + residual
return slim.utils.collect_named_outputs(outputs_collections,
sc.name,
output)
示例10: bottleneck
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def bottleneck(inputs,
depth,
depth_bottleneck,
stride,
unit_rate=1,
rate=1,
outputs_collections=None,
scope=None):
"""Bottleneck residual unit variant with BN after convolutions.
This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
its definition. Note that we use here the bottleneck variant which has an
extra bottleneck layer.
When putting together two consecutive ResNet blocks that use this unit, one
should use stride = 2 in the last unit of the first block.
Args:
inputs: A tensor of size [batch, height, width, channels].
depth: The depth of the ResNet unit output.
depth_bottleneck: The depth of the bottleneck layers.
stride: The ResNet unit's stride. Determines the amount of downsampling of
the units output compared to its input.
unit_rate: An integer, unit rate for atrous convolution.
rate: An integer, rate for atrous convolution.
outputs_collections: Collection to add the ResNet unit output.
scope: Optional variable_scope.
Returns:
The ResNet unit's output.
"""
with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
if depth == depth_in:
shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
else:
shortcut = slim.conv2d(
inputs,
depth,
[1, 1],
stride=stride,
activation_fn=None,
scope='shortcut')
residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
scope='conv1')
residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
rate=rate * unit_rate, scope='conv2')
residual = slim.conv2d(residual, depth, [1, 1], stride=1,
activation_fn=None, scope='conv3')
output = tf.nn.relu(shortcut + residual)
return slim.utils.collect_named_outputs(outputs_collections,
sc.name,
output)
示例11: bottleneck
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def bottleneck(inputs,
depth,
depth_bottleneck,
stride,
unit_rate=1,
rate=1,
outputs_collections=None,
scope=None):
"""Bottleneck residual unit variant with BN after convolutions.
This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
its definition. Note that we use here the bottleneck variant which has an
extra bottleneck layer.
When putting together two consecutive ResNet blocks that use this unit, one
should use stride = 2 in the last unit of the first block.
Args:
inputs: A tensor of size [batch, height, width, channels].
depth: The depth of the ResNet unit output.
depth_bottleneck: The depth of the bottleneck layers.
stride: The ResNet unit's stride. Determines the amount of downsampling of
the units output compared to its input.
unit_rate: An integer, unit rate for atrous convolution.
rate: An integer, rate for atrous convolution.
outputs_collections: Collection to add the ResNet unit output.
scope: Optional variable_scope.
Returns:
The ResNet unit's output.
"""
with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
if depth == depth_in:
shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
else:
shortcut = slim.conv2d(
inputs,
depth,
[1, 1],
stride=stride,
activation_fn=None,
scope='shortcut')
residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
scope='conv1')
residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
rate=rate*unit_rate, scope='conv2')
residual = slim.conv2d(residual, depth, [1, 1], stride=1,
activation_fn=None, scope='conv3')
output = tf.nn.relu(shortcut + residual)
return slim.utils.collect_named_outputs(outputs_collections,
sc.name,
output)
示例12: _apply_conv_operation
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def _apply_conv_operation(self, net, operation, stride,
is_from_original_input):
"""Applies the predicted conv operation to net."""
if stride > 1 and not is_from_original_input:
stride = 1
input_filters = net.shape[3]
filter_size = self._filter_size
if 'separable' in operation:
num_layers = int(operation.split('_')[-1])
kernel_size = int(operation.split('x')[0][-1])
for layer_num in range(num_layers):
net = tf.nn.relu(net)
net = separable_conv2d_same(
net,
filter_size,
kernel_size,
depth_multiplier=1,
scope='separable_{0}x{0}_{1}'.format(kernel_size, layer_num + 1),
stride=stride)
net = self._batch_norm_fn(
net, scope='bn_sep_{0}x{0}_{1}'.format(kernel_size, layer_num + 1))
stride = 1
elif 'atrous' in operation:
kernel_size = int(operation.split('x')[0][-1])
net = tf.nn.relu(net)
if stride == 2:
scaled_height = scale_dimension(tf.shape(net)[1], 0.5)
scaled_width = scale_dimension(tf.shape(net)[2], 0.5)
net = resize_bilinear(net, [scaled_height, scaled_width], net.dtype)
net = resnet_utils.conv2d_same(
net, filter_size, kernel_size, rate=1, stride=1,
scope='atrous_{0}x{0}'.format(kernel_size))
else:
net = resnet_utils.conv2d_same(
net, filter_size, kernel_size, rate=2, stride=1,
scope='atrous_{0}x{0}'.format(kernel_size))
net = self._batch_norm_fn(net, scope='bn_atr_{0}x{0}'.format(kernel_size))
elif operation in ['none']:
if stride > 1 or (input_filters != filter_size):
net = tf.nn.relu(net)
net = slim.conv2d(net, filter_size, 1, stride=stride, scope='1x1')
net = self._batch_norm_fn(net, scope='bn_1')
elif 'pool' in operation:
pooling_type = operation.split('_')[0]
pooling_shape = int(operation.split('_')[-1].split('x')[0])
if pooling_type == 'avg':
net = slim.avg_pool2d(net, pooling_shape, stride=stride, padding='SAME')
elif pooling_type == 'max':
net = slim.max_pool2d(net, pooling_shape, stride=stride, padding='SAME')
else:
raise ValueError('Unimplemented pooling type: ', pooling_type)
if input_filters != filter_size:
net = slim.conv2d(net, filter_size, 1, stride=1, scope='1x1')
net = self._batch_norm_fn(net, scope='bn_1')
else:
raise ValueError('Unimplemented operation', operation)
if operation != 'none':
net = self._apply_drop_path(net)
return net
示例13: bottleneck
# 需要导入模块: from tensorflow.contrib.slim.nets import resnet_utils [as 别名]
# 或者: from tensorflow.contrib.slim.nets.resnet_utils import conv2d_same [as 别名]
def bottleneck(inputs,
depth,
depth_bottleneck,
stride,
unit_rate=1,
rate=1,
outputs_collections=None,
scope=None):
"""Bottleneck residual unit variant with BN after convolutions.
This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for
its definition. Note that we use here the bottleneck variant which has an
extra bottleneck layer.
When putting together two consecutive ResNet blocks that use this unit, one
should use stride = 2 in the last unit of the first block.
Args:
inputs: A tensor of size [batch, height, width, channels].
depth: The depth of the ResNet unit output.
depth_bottleneck: The depth of the bottleneck layers.
stride: The ResNet unit's stride. Determines the amount of downsampling of
the units output compared to its input.
unit_rate: An integer, unit rate for atrous convolution.
rate: An integer, rate for atrous convolution.
outputs_collections: Collection to add the ResNet unit output.
scope: Optional variable_scope.
Returns:
The ResNet unit's output.
"""
with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
if depth == depth_in:
shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
else:
shortcut = slim.conv2d(
inputs,
depth,
[1, 1],
stride=stride,
activation_fn=None,
scope='shortcut')
residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
scope='conv1')
residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
rate=rate*unit_rate, scope='conv2')
residual = slim.conv2d(residual, depth, [1, 1], stride=1,
activation_fn=None, scope='conv3')
output = tf.nn.relu(shortcut + residual)
return slim.utils.collect_named_outputs(outputs_collections,
sc.name,
output)