Migrating code to TF2 and not getting the ModuleNotFoundError: No module named 'tensorflow.contrib.framework'
Migrating code to TF2 and not getting the ModuleNotFoundError: No module named 'tensorflow.contrib.framework'
我从 https://github.com/skokec/DAU-ConvNet, which is based on this paper https://arxiv.org/abs/1902.07474 那里获得了这段代码，它用自适应滤波器版本替换了卷积块中基于网格的标准滤波器。当我尝试导入包时出现此错误：
ModuleNotFoundError: No module named 'tensorflow.contrib.framework'.
我知道 "tensorflow.contrib" 显然在 2.0 版中被删除了，我需要恢复到 <= 1.14 版才能使其正常工作。但我想看看是否有人可以让这段代码在较新的 TF.2 版本中工作，因为这是一篇非常有趣的论文，结果非常好，通过将这段代码迁移到 TF2，它会鼓励其他人尝试和试验与这一层不同 architectures/setups。下面是源代码：
import os
import numpy as np
import tensorflow as tf
from tensorflow.python.layers import base
from tensorflow.python.layers import utils
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import init_ops
class DAUConv2dTF(base.Layer):
def __init__(self, filters,
dau_units,
max_kernel_size,
strides=1,
data_format='channels_first',
activation=None,
use_bias=True,
weight_initializer=init_ops.random_normal_initializer(stddev=0.1),
mu1_initializer=None,
mu2_initializer=None,
sigma_initializer=None,
bias_initializer=init_ops.zeros_initializer(),
weight_regularizer=None,
mu1_regularizer=None,
mu2_regularizer=None,
sigma_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
weight_constraint=None,
mu1_constraint=None,
mu2_constraint=None,
sigma_constraint=None,
bias_constraint=None,
trainable=True,
mu_learning_rate_factor=500,
dau_unit_border_bound=0.01,
dau_sigma_trainable=False,
name=None,
**kwargs):
super(DAUConv2dTF, self).__init__(trainable=trainable, name=name,
activity_regularizer=activity_regularizer,
**kwargs)
self.rank = 2
self.filters = filters
self.dau_units = utils.normalize_tuple(dau_units, self.rank, 'dau_components')
self.max_kernel_size = max_kernel_size
self.padding = np.floor(self.max_kernel_size/2.0)
self.strides = strides
self.data_format = utils.normalize_data_format(data_format)
self.activation = activation
self.use_bias = use_bias
self.bias_initializer = bias_initializer
self.bias_regularizer = bias_regularizer
self.bias_constraint = bias_constraint
self.weight_initializer = weight_initializer
self.weight_regularizer = weight_regularizer
self.weight_constraint = weight_constraint
self.mu1_initializer = mu1_initializer
self.mu1_regularizer = mu1_regularizer
self.mu1_constraint = mu1_constraint
self.mu2_initializer = mu2_initializer
self.mu2_regularizer = mu2_regularizer
self.mu2_constraint = mu2_constraint
self.sigma_initializer = sigma_initializer
self.sigma_regularizer = sigma_regularizer
self.sigma_constraint = sigma_constraint
if self.mu1_initializer is None:
raise Exception("Must initialize MU1")
if self.mu2_initializer is None:
raise Exception("Must initialize MU2")
if self.sigma_initializer is None:
self.sigma_initializer=init_ops.constant_initializer(0.5)
self.mu_learning_rate_factor = mu_learning_rate_factor
self.input_spec = base.InputSpec(ndim=self.rank + 2)
self.dau_unit_border_bound = dau_unit_border_bound
self.num_dau_units_all = np.int32(np.prod(self.dau_units))
self.dau_weights = None
self.dau_mu1 = None
self.dau_mu2 = None
self.dau_sigma = None
self.dau_sigma_trainable = dau_sigma_trainable
def set_dau_variables_manually(self, w = None, mu1 = None, mu2 = None, sigma = None):
""" Manually set w,mu1,mu2 and/or sigma variables with custom tensor. Call before build() or __call__().
The shape must match the expecated shape as returned by the get_dau_variable_shape(input_shape)
otherwise the build() function will fail."""
if w is not None:
self.dau_weights = w
if mu1 is not None:
self.dau_mu1 = mu1
if mu2 is not None:
self.dau_mu2 = mu2
if sigma is not None:
self.dau_sigma = sigma
def _get_input_channel_axis(self):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
return channel_axis
def _get_input_channels(self, input_shape):
channel_axis = self._get_input_channel_axis()
if input_shape[channel_axis].value is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
return input_shape[channel_axis].value
def get_dau_variable_shape(self, input_shape):
# get input
num_input_channels = self._get_input_channels(input_shape)
dau_params_shape_ = (num_input_channels, self.dau_units[0], self.dau_units[1], self.filters)
dau_params_shape = (1, num_input_channels, self.num_dau_units_all, self.filters)
return dau_params_shape
def add_dau_weights_var(self, input_shape):
dau_params_shape = self.get_dau_variable_shape(input_shape)
return self.add_variable(name='weights',
shape=dau_params_shape,
initializer=self.weight_initializer,
regularizer=self.weight_regularizer,
constraint=self.weight_constraint,
trainable=True,
dtype=self.dtype)
def add_dau_mu1_var(self, input_shape):
dau_params_shape = self.get_dau_variable_shape(input_shape)
mu1_var = self.add_variable(name='mu1',
shape=dau_params_shape,
initializer=self.mu1_initializer,
regularizer=self.mu1_regularizer,
constraint=self.mu1_constraint,
trainable=True,
dtype=self.dtype)
# limit max offset based on self.dau_unit_border_bound and kernel size
mu1_var = tf.minimum(tf.maximum(mu1_var,
-(self.max_kernel_size - self.dau_unit_border_bound)),
self.max_kernel_size - self.dau_unit_border_bound)
return mu1_var
def add_dau_mu2_var(self, input_shape):
dau_params_shape = self.get_dau_variable_shape(input_shape)
mu2_var = self.add_variable(name='mu2',
shape=dau_params_shape,
initializer=self.mu2_initializer,
regularizer=self.mu2_regularizer,
constraint=self.mu2_constraint,
trainable=True,
dtype=self.dtype)
# limit max offset based on self.dau_unit_border_bound and kernel size
mu2_var = tf.minimum(tf.maximum(mu2_var,
-(self.max_kernel_size - self.dau_unit_border_bound)),
self.max_kernel_size - self.dau_unit_border_bound)
return mu2_var
def add_dau_sigma_var(self, input_shape, trainable=False):
dau_params_shape = self.get_dau_variable_shape(input_shape)
# create single sigma variable
sigma_var = self.add_variable(name='sigma',
shape=dau_params_shape,
initializer=self.sigma_initializer,
regularizer=self.sigma_regularizer,
constraint=self.sigma_constraint,
trainable=self.dau_sigma_trainable,
dtype=self.dtype)
# but make variable shared across all channels as required for the efficient DAU implementation
return sigma_var
def add_bias_var(self):
return self.add_variable(name='bias',
shape=(self.filters,),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
trainable=True,
dtype=self.dtype)
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
dau_params_shape = self.get_dau_variable_shape(input_shape)
if self.dau_weights is None:
self.dau_weights = self.add_dau_weights_var(input_shape)
elif np.any(self.dau_weights.shape != dau_params_shape):
raise ValueError('Shape mismatch for variable `dau_weights`')
if self.dau_mu1 is None:
self.dau_mu1 = self.add_dau_mu1_var(input_shape)
elif np.any(self.dau_mu1.shape != dau_params_shape):
raise ValueError('Shape mismatch for variable `dau_mu1`')
if self.dau_mu2 is None:
self.dau_mu2 = self.add_dau_mu2_var(input_shape)
elif np.any(self.dau_mu2.shape != dau_params_shape):
raise ValueError('Shape mismatch for variable `dau_mu2`')
if self.dau_sigma is None:
self.dau_sigma = self.add_dau_sigma_var(input_shape, trainable=self.dau_sigma_trainable)
elif np.any(self.dau_sigma.shape != dau_params_shape):
raise ValueError('Shape mismatch for variable `dau_sigma`')
if self.use_bias:
self.bias = self.add_bias_var()
else:
self.bias = None
input_channel_axis = self._get_input_channel_axis()
num_input_channels = self._get_input_channels(input_shape)
self.input_spec = base.InputSpec(ndim=self.rank + 2,
axes={input_channel_axis: num_input_channels})
kernel_shape = tf.TensorShape((self.max_kernel_size, self.max_kernel_size, num_input_channels, self.filters))
self._convolution_op = nn_ops.Convolution(
input_shape,
filter_shape=kernel_shape,
dilation_rate=(1,1),
strides=(self.strides,self.strides),
padding="SAME",
data_format=utils.convert_data_format(self.data_format,
self.rank + 2))
self.built = True
def call(self, inputs):
def get_kernel_fn(dau_w, dau_mu1, dau_mu2, dau_sigma, max_kernel_size, mu_learning_rate_factor=1):
# add mu1/mu2 gradient multiplyer
if mu_learning_rate_factor != 1:
dau_mu1 = mu_learning_rate_factor * dau_mu1 + (1 - mu_learning_rate_factor) * tf.stop_gradient(dau_mu1)
dau_mu2 = mu_learning_rate_factor * dau_mu2 + (1 - mu_learning_rate_factor) * tf.stop_gradient(dau_mu2)
[X,Y] = np.meshgrid(np.arange(max_kernel_size),np.arange(max_kernel_size))
X = np.reshape(X,(max_kernel_size*max_kernel_size,1,1,1)) - int(max_kernel_size/2)
Y = np.reshape(Y,(max_kernel_size*max_kernel_size,1,1,1)) - int(max_kernel_size/2)
X = X.astype(np.float32)
Y = Y.astype(np.float32)
# Gaussian kernel
X = tf.convert_to_tensor(X,name='X',dtype=tf.float32)
Y = tf.convert_to_tensor(Y,name='Y',dtype=tf.float32)
gauss_kernel = tf.exp(-1* (tf.pow(X - dau_mu1,2.0) + tf.pow(Y - dau_mu2,2.0)) / (2.0*tf.pow(dau_sigma,2.0)),name='gauss_kernel')
gauss_kernel_sum = tf.reduce_sum(gauss_kernel,axis=0, keep_dims=True,name='guass_kernel_sum')
gauss_kernel_norm = tf.divide(gauss_kernel, gauss_kernel_sum ,name='gauss_kernel_norm')
# normalize to sum of 1 and add weight
gauss_kernel_norm = tf.multiply(dau_w, gauss_kernel_norm,name='gauss_kernel_weight')
# sum over Gaussian units
gauss_kernel_norm = tf.reduce_sum(gauss_kernel_norm, axis=2, keep_dims=True,name='gauss_kernel_sum_units')
# convert to [Kw,Kh,S,F] shape
gauss_kernel_norm = tf.reshape(gauss_kernel_norm, (max_kernel_size, max_kernel_size, gauss_kernel_norm.shape[1], gauss_kernel_norm.shape[3]),name='gauss_kernel_reshape')
return gauss_kernel_norm
try:
# try with XLA if exists
from tensorflow.contrib.compiler import xla
gauss_kernel_norm = xla.compile(computation=get_kernel_fn, inputs=(self.dau_weights, self.dau_mu1, self.dau_mu2, self.dau_sigma, self.max_kernel_size, self.mu_learning_rate_factor))[0]
except:
# otherwise revert to direct method call
gauss_kernel_norm = get_kernel_fn(self.dau_weights, self.dau_mu1, self.dau_mu2, self.dau_sigma, self.max_kernel_size, self.mu_learning_rate_factor)
outputs = self._convolution_op(inputs, gauss_kernel_norm)
if self.use_bias:
if self.data_format == 'channels_first':
if self.rank == 1:
# nn.bias_add does not accept a 1D input tensor.
bias = array_ops.reshape(self.bias, (1, self.filters, 1))
outputs += bias
if self.rank == 2:
outputs = nn.bias_add(outputs, self.bias, data_format='NCHW')
if self.rank == 3:
# As of Mar 2017, direct addition is significantly slower than
# bias_add when computing gradients. To use bias_add, we collapse Z
# and Y into a single dimension to obtain a 4D input tensor.
outputs_shape = outputs.shape.as_list()
if outputs_shape[0] is None:
outputs_shape[0] = -1
outputs_4d = array_ops.reshape(outputs,
[outputs_shape[0], outputs_shape[1],
outputs_shape[2] * outputs_shape[3],
outputs_shape[4]])
outputs_4d = nn.bias_add(outputs_4d, self.bias, data_format='NCHW')
outputs = array_ops.reshape(outputs_4d, outputs_shape)
else:
outputs = nn.bias_add(outputs, self.bias, data_format='NHWC')
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
if self.data_format == 'channels_last':
space = input_shape[1:-1]
new_space = []
for i in range(len(space)):
new_dim = utils.conv_output_length(
space[i],
self.max_kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=1)
new_space.append(new_dim)
return tensor_shape.TensorShape([input_shape[0]] + new_space +
[self.filters])
else:
space = input_shape[2:]
new_space = []
for i in range(len(space)):
new_dim = utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides,
dilation=1)
new_space.append(new_dim)
return tensor_shape.TensorShape([input_shape[0], self.filters] +
new_space)
from tensorflow.contrib.framework.python.ops import add_arg_scope
from tensorflow.python.ops import variable_scope
from tensorflow.contrib.layers.python.layers import layers as layers_contrib
from tensorflow.contrib.layers.python.layers import utils as utils_contrib
@add_arg_scope
def dau_conv2d_tf(inputs,
filters,
dau_units,
max_kernel_size,
stride=1,
mu_learning_rate_factor=500,
data_format=None,
activation_fn=nn.relu,
normalizer_fn=None,
normalizer_params=None,
weights_initializer=init_ops.random_normal_initializer(stddev=0.1), #init_ops.glorot_uniform_initializer(),
weights_regularizer=None,
weights_constraint=None,
mu1_initializer=None,
mu1_regularizer=None,
mu1_constraint=None,
mu2_initializer=None,
mu2_regularizer=None,
mu2_constraint=None,
sigma_initializer=None,
sigma_regularizer=None,
sigma_constraint=None,
biases_initializer=init_ops.zeros_initializer(),
biases_regularizer=None,
biases_constraint=None,
dau_unit_border_bound=0.01,
dau_sigma_trainable=False,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
scope=None):
if data_format not in [None, 'NWC', 'NCW', 'NHWC', 'NCHW', 'NDHWC', 'NCDHW']:
raise ValueError('Invalid data_format: %r' % (data_format,))
layer_variable_getter = layers_contrib._build_variable_getter({
'bias': 'biases',
'weight': 'weights',
'mu1': 'mu1',
'mu2': 'mu2',
'sigma': 'sigma'
})
with variable_scope.variable_scope(
scope, 'DAUConv', [inputs], reuse=reuse,
custom_getter=layer_variable_getter) as sc:
inputs = ops.convert_to_tensor(inputs)
input_rank = inputs.get_shape().ndims
if input_rank != 4:
raise ValueError('DAU convolution not supported for input with rank',
input_rank)
df = ('channels_first'
if data_format and data_format.startswith('NC') else 'channels_last')
layer = DAUConv2dTF(filters,
dau_units,
max_kernel_size,
strides=stride,
data_format=df,
activation=None,
use_bias=not normalizer_fn and biases_initializer,
mu_learning_rate_factor=mu_learning_rate_factor,
weight_initializer=weights_initializer,
mu1_initializer=mu1_initializer,
mu2_initializer=mu2_initializer,
sigma_initializer=sigma_initializer,
bias_initializer=biases_initializer,
weight_regularizer=weights_regularizer,
mu1_regularizer=mu1_regularizer,
mu2_regularizer=mu2_regularizer,
sigma_regularizer=sigma_regularizer,
bias_regularizer=biases_regularizer,
activity_regularizer=None,
weight_constraint=weights_constraint,
mu1_constraint=mu1_constraint,
mu2_constraint=mu2_constraint,
sigma_constraint=sigma_constraint,
bias_constraint=biases_constraint,
dau_unit_border_bound=dau_unit_border_bound,
dau_sigma_trainable=dau_sigma_trainable,
trainable=trainable,
name=sc.name,
_scope=sc,
_reuse=reuse)
outputs = layer.apply(inputs)
# Add variables to collections.
layers_contrib._add_variable_to_collections(layer.dau_weights, variables_collections, 'weights')
layers_contrib._add_variable_to_collections(layer.dau_mu1, variables_collections, 'mu1')
layers_contrib._add_variable_to_collections(layer.dau_mu2, variables_collections, 'mu2')
layers_contrib._add_variable_to_collections(layer.dau_sigma, variables_collections, 'sigma')
if layer.use_bias:
layers_contrib._add_variable_to_collections(layer.bias, variables_collections, 'biases')
if normalizer_fn is not None:
normalizer_params = normalizer_params or {}
outputs = normalizer_fn(outputs, **normalizer_params)
if activation_fn is not None:
outputs = activation_fn(outputs)
return utils_contrib.collect_named_outputs(outputs_collections, sc.name, outputs)
如有任何帮助，我们将不胜感激。
干杯，
H
在 TensorFlow 2.x 中，创建可重用神经网络层的推荐方法是创建一个新的 tf.keras.layers.Layer subclass。 TensorFlow 提供了 great tutorial on this。您可以在 tf.keras 层 class 中重用您发布的示例中的绝大部分代码。您也许还可以从 tensorflow.python.keras.layers.convolutional.Conv 继承以减少样板代码的数量。
至于有些模块找不到，你应该使用TensorFlow暴露的别名。这是一个不完整的列表：
array_ops.reshape -> tf.reshapeinit_ops.constant_initializer -> tf.initializers.constanttensor_shape -> tf.TensorShapenn_ops.Convolution -> tf.nn.convolution 或 tf.keras.layers.Conv?D
我从 https://github.com/skokec/DAU-ConvNet, which is based on this paper https://arxiv.org/abs/1902.07474 那里获得了这段代码，它用自适应滤波器版本替换了卷积块中基于网格的标准滤波器。当我尝试导入包时出现此错误：
ModuleNotFoundError: No module named 'tensorflow.contrib.framework'.
我知道 "tensorflow.contrib" 显然在 2.0 版中被删除了，我需要恢复到 <= 1.14 版才能使其正常工作。但我想看看是否有人可以让这段代码在较新的 TF.2 版本中工作，因为这是一篇非常有趣的论文，结果非常好，通过将这段代码迁移到 TF2，它会鼓励其他人尝试和试验与这一层不同 architectures/setups。下面是源代码：
import os
import numpy as np
import tensorflow as tf
from tensorflow.python.layers import base
from tensorflow.python.layers import utils
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import init_ops
class DAUConv2dTF(base.Layer):
def __init__(self, filters,
dau_units,
max_kernel_size,
strides=1,
data_format='channels_first',
activation=None,
use_bias=True,
weight_initializer=init_ops.random_normal_initializer(stddev=0.1),
mu1_initializer=None,
mu2_initializer=None,
sigma_initializer=None,
bias_initializer=init_ops.zeros_initializer(),
weight_regularizer=None,
mu1_regularizer=None,
mu2_regularizer=None,
sigma_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
weight_constraint=None,
mu1_constraint=None,
mu2_constraint=None,
sigma_constraint=None,
bias_constraint=None,
trainable=True,
mu_learning_rate_factor=500,
dau_unit_border_bound=0.01,
dau_sigma_trainable=False,
name=None,
**kwargs):
super(DAUConv2dTF, self).__init__(trainable=trainable, name=name,
activity_regularizer=activity_regularizer,
**kwargs)
self.rank = 2
self.filters = filters
self.dau_units = utils.normalize_tuple(dau_units, self.rank, 'dau_components')
self.max_kernel_size = max_kernel_size
self.padding = np.floor(self.max_kernel_size/2.0)
self.strides = strides
self.data_format = utils.normalize_data_format(data_format)
self.activation = activation
self.use_bias = use_bias
self.bias_initializer = bias_initializer
self.bias_regularizer = bias_regularizer
self.bias_constraint = bias_constraint
self.weight_initializer = weight_initializer
self.weight_regularizer = weight_regularizer
self.weight_constraint = weight_constraint
self.mu1_initializer = mu1_initializer
self.mu1_regularizer = mu1_regularizer
self.mu1_constraint = mu1_constraint
self.mu2_initializer = mu2_initializer
self.mu2_regularizer = mu2_regularizer
self.mu2_constraint = mu2_constraint
self.sigma_initializer = sigma_initializer
self.sigma_regularizer = sigma_regularizer
self.sigma_constraint = sigma_constraint
if self.mu1_initializer is None:
raise Exception("Must initialize MU1")
if self.mu2_initializer is None:
raise Exception("Must initialize MU2")
if self.sigma_initializer is None:
self.sigma_initializer=init_ops.constant_initializer(0.5)
self.mu_learning_rate_factor = mu_learning_rate_factor
self.input_spec = base.InputSpec(ndim=self.rank + 2)
self.dau_unit_border_bound = dau_unit_border_bound
self.num_dau_units_all = np.int32(np.prod(self.dau_units))
self.dau_weights = None
self.dau_mu1 = None
self.dau_mu2 = None
self.dau_sigma = None
self.dau_sigma_trainable = dau_sigma_trainable
def set_dau_variables_manually(self, w = None, mu1 = None, mu2 = None, sigma = None):
""" Manually set w,mu1,mu2 and/or sigma variables with custom tensor. Call before build() or __call__().
The shape must match the expecated shape as returned by the get_dau_variable_shape(input_shape)
otherwise the build() function will fail."""
if w is not None:
self.dau_weights = w
if mu1 is not None:
self.dau_mu1 = mu1
if mu2 is not None:
self.dau_mu2 = mu2
if sigma is not None:
self.dau_sigma = sigma
def _get_input_channel_axis(self):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
return channel_axis
def _get_input_channels(self, input_shape):
channel_axis = self._get_input_channel_axis()
if input_shape[channel_axis].value is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
return input_shape[channel_axis].value
def get_dau_variable_shape(self, input_shape):
# get input
num_input_channels = self._get_input_channels(input_shape)
dau_params_shape_ = (num_input_channels, self.dau_units[0], self.dau_units[1], self.filters)
dau_params_shape = (1, num_input_channels, self.num_dau_units_all, self.filters)
return dau_params_shape
def add_dau_weights_var(self, input_shape):
dau_params_shape = self.get_dau_variable_shape(input_shape)
return self.add_variable(name='weights',
shape=dau_params_shape,
initializer=self.weight_initializer,
regularizer=self.weight_regularizer,
constraint=self.weight_constraint,
trainable=True,
dtype=self.dtype)
def add_dau_mu1_var(self, input_shape):
dau_params_shape = self.get_dau_variable_shape(input_shape)
mu1_var = self.add_variable(name='mu1',
shape=dau_params_shape,
initializer=self.mu1_initializer,
regularizer=self.mu1_regularizer,
constraint=self.mu1_constraint,
trainable=True,
dtype=self.dtype)
# limit max offset based on self.dau_unit_border_bound and kernel size
mu1_var = tf.minimum(tf.maximum(mu1_var,
-(self.max_kernel_size - self.dau_unit_border_bound)),
self.max_kernel_size - self.dau_unit_border_bound)
return mu1_var
def add_dau_mu2_var(self, input_shape):
dau_params_shape = self.get_dau_variable_shape(input_shape)
mu2_var = self.add_variable(name='mu2',
shape=dau_params_shape,
initializer=self.mu2_initializer,
regularizer=self.mu2_regularizer,
constraint=self.mu2_constraint,
trainable=True,
dtype=self.dtype)
# limit max offset based on self.dau_unit_border_bound and kernel size
mu2_var = tf.minimum(tf.maximum(mu2_var,
-(self.max_kernel_size - self.dau_unit_border_bound)),
self.max_kernel_size - self.dau_unit_border_bound)
return mu2_var
def add_dau_sigma_var(self, input_shape, trainable=False):
dau_params_shape = self.get_dau_variable_shape(input_shape)
# create single sigma variable
sigma_var = self.add_variable(name='sigma',
shape=dau_params_shape,
initializer=self.sigma_initializer,
regularizer=self.sigma_regularizer,
constraint=self.sigma_constraint,
trainable=self.dau_sigma_trainable,
dtype=self.dtype)
# but make variable shared across all channels as required for the efficient DAU implementation
return sigma_var
def add_bias_var(self):
return self.add_variable(name='bias',
shape=(self.filters,),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
trainable=True,
dtype=self.dtype)
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
dau_params_shape = self.get_dau_variable_shape(input_shape)
if self.dau_weights is None:
self.dau_weights = self.add_dau_weights_var(input_shape)
elif np.any(self.dau_weights.shape != dau_params_shape):
raise ValueError('Shape mismatch for variable `dau_weights`')
if self.dau_mu1 is None:
self.dau_mu1 = self.add_dau_mu1_var(input_shape)
elif np.any(self.dau_mu1.shape != dau_params_shape):
raise ValueError('Shape mismatch for variable `dau_mu1`')
if self.dau_mu2 is None:
self.dau_mu2 = self.add_dau_mu2_var(input_shape)
elif np.any(self.dau_mu2.shape != dau_params_shape):
raise ValueError('Shape mismatch for variable `dau_mu2`')
if self.dau_sigma is None:
self.dau_sigma = self.add_dau_sigma_var(input_shape, trainable=self.dau_sigma_trainable)
elif np.any(self.dau_sigma.shape != dau_params_shape):
raise ValueError('Shape mismatch for variable `dau_sigma`')
if self.use_bias:
self.bias = self.add_bias_var()
else:
self.bias = None
input_channel_axis = self._get_input_channel_axis()
num_input_channels = self._get_input_channels(input_shape)
self.input_spec = base.InputSpec(ndim=self.rank + 2,
axes={input_channel_axis: num_input_channels})
kernel_shape = tf.TensorShape((self.max_kernel_size, self.max_kernel_size, num_input_channels, self.filters))
self._convolution_op = nn_ops.Convolution(
input_shape,
filter_shape=kernel_shape,
dilation_rate=(1,1),
strides=(self.strides,self.strides),
padding="SAME",
data_format=utils.convert_data_format(self.data_format,
self.rank + 2))
self.built = True
def call(self, inputs):
def get_kernel_fn(dau_w, dau_mu1, dau_mu2, dau_sigma, max_kernel_size, mu_learning_rate_factor=1):
# add mu1/mu2 gradient multiplyer
if mu_learning_rate_factor != 1:
dau_mu1 = mu_learning_rate_factor * dau_mu1 + (1 - mu_learning_rate_factor) * tf.stop_gradient(dau_mu1)
dau_mu2 = mu_learning_rate_factor * dau_mu2 + (1 - mu_learning_rate_factor) * tf.stop_gradient(dau_mu2)
[X,Y] = np.meshgrid(np.arange(max_kernel_size),np.arange(max_kernel_size))
X = np.reshape(X,(max_kernel_size*max_kernel_size,1,1,1)) - int(max_kernel_size/2)
Y = np.reshape(Y,(max_kernel_size*max_kernel_size,1,1,1)) - int(max_kernel_size/2)
X = X.astype(np.float32)
Y = Y.astype(np.float32)
# Gaussian kernel
X = tf.convert_to_tensor(X,name='X',dtype=tf.float32)
Y = tf.convert_to_tensor(Y,name='Y',dtype=tf.float32)
gauss_kernel = tf.exp(-1* (tf.pow(X - dau_mu1,2.0) + tf.pow(Y - dau_mu2,2.0)) / (2.0*tf.pow(dau_sigma,2.0)),name='gauss_kernel')
gauss_kernel_sum = tf.reduce_sum(gauss_kernel,axis=0, keep_dims=True,name='guass_kernel_sum')
gauss_kernel_norm = tf.divide(gauss_kernel, gauss_kernel_sum ,name='gauss_kernel_norm')
# normalize to sum of 1 and add weight
gauss_kernel_norm = tf.multiply(dau_w, gauss_kernel_norm,name='gauss_kernel_weight')
# sum over Gaussian units
gauss_kernel_norm = tf.reduce_sum(gauss_kernel_norm, axis=2, keep_dims=True,name='gauss_kernel_sum_units')
# convert to [Kw,Kh,S,F] shape
gauss_kernel_norm = tf.reshape(gauss_kernel_norm, (max_kernel_size, max_kernel_size, gauss_kernel_norm.shape[1], gauss_kernel_norm.shape[3]),name='gauss_kernel_reshape')
return gauss_kernel_norm
try:
# try with XLA if exists
from tensorflow.contrib.compiler import xla
gauss_kernel_norm = xla.compile(computation=get_kernel_fn, inputs=(self.dau_weights, self.dau_mu1, self.dau_mu2, self.dau_sigma, self.max_kernel_size, self.mu_learning_rate_factor))[0]
except:
# otherwise revert to direct method call
gauss_kernel_norm = get_kernel_fn(self.dau_weights, self.dau_mu1, self.dau_mu2, self.dau_sigma, self.max_kernel_size, self.mu_learning_rate_factor)
outputs = self._convolution_op(inputs, gauss_kernel_norm)
if self.use_bias:
if self.data_format == 'channels_first':
if self.rank == 1:
# nn.bias_add does not accept a 1D input tensor.
bias = array_ops.reshape(self.bias, (1, self.filters, 1))
outputs += bias
if self.rank == 2:
outputs = nn.bias_add(outputs, self.bias, data_format='NCHW')
if self.rank == 3:
# As of Mar 2017, direct addition is significantly slower than
# bias_add when computing gradients. To use bias_add, we collapse Z
# and Y into a single dimension to obtain a 4D input tensor.
outputs_shape = outputs.shape.as_list()
if outputs_shape[0] is None:
outputs_shape[0] = -1
outputs_4d = array_ops.reshape(outputs,
[outputs_shape[0], outputs_shape[1],
outputs_shape[2] * outputs_shape[3],
outputs_shape[4]])
outputs_4d = nn.bias_add(outputs_4d, self.bias, data_format='NCHW')
outputs = array_ops.reshape(outputs_4d, outputs_shape)
else:
outputs = nn.bias_add(outputs, self.bias, data_format='NHWC')
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
if self.data_format == 'channels_last':
space = input_shape[1:-1]
new_space = []
for i in range(len(space)):
new_dim = utils.conv_output_length(
space[i],
self.max_kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=1)
new_space.append(new_dim)
return tensor_shape.TensorShape([input_shape[0]] + new_space +
[self.filters])
else:
space = input_shape[2:]
new_space = []
for i in range(len(space)):
new_dim = utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides,
dilation=1)
new_space.append(new_dim)
return tensor_shape.TensorShape([input_shape[0], self.filters] +
new_space)
from tensorflow.contrib.framework.python.ops import add_arg_scope
from tensorflow.python.ops import variable_scope
from tensorflow.contrib.layers.python.layers import layers as layers_contrib
from tensorflow.contrib.layers.python.layers import utils as utils_contrib
@add_arg_scope
def dau_conv2d_tf(inputs,
filters,
dau_units,
max_kernel_size,
stride=1,
mu_learning_rate_factor=500,
data_format=None,
activation_fn=nn.relu,
normalizer_fn=None,
normalizer_params=None,
weights_initializer=init_ops.random_normal_initializer(stddev=0.1), #init_ops.glorot_uniform_initializer(),
weights_regularizer=None,
weights_constraint=None,
mu1_initializer=None,
mu1_regularizer=None,
mu1_constraint=None,
mu2_initializer=None,
mu2_regularizer=None,
mu2_constraint=None,
sigma_initializer=None,
sigma_regularizer=None,
sigma_constraint=None,
biases_initializer=init_ops.zeros_initializer(),
biases_regularizer=None,
biases_constraint=None,
dau_unit_border_bound=0.01,
dau_sigma_trainable=False,
reuse=None,
variables_collections=None,
outputs_collections=None,
trainable=True,
scope=None):
if data_format not in [None, 'NWC', 'NCW', 'NHWC', 'NCHW', 'NDHWC', 'NCDHW']:
raise ValueError('Invalid data_format: %r' % (data_format,))
layer_variable_getter = layers_contrib._build_variable_getter({
'bias': 'biases',
'weight': 'weights',
'mu1': 'mu1',
'mu2': 'mu2',
'sigma': 'sigma'
})
with variable_scope.variable_scope(
scope, 'DAUConv', [inputs], reuse=reuse,
custom_getter=layer_variable_getter) as sc:
inputs = ops.convert_to_tensor(inputs)
input_rank = inputs.get_shape().ndims
if input_rank != 4:
raise ValueError('DAU convolution not supported for input with rank',
input_rank)
df = ('channels_first'
if data_format and data_format.startswith('NC') else 'channels_last')
layer = DAUConv2dTF(filters,
dau_units,
max_kernel_size,
strides=stride,
data_format=df,
activation=None,
use_bias=not normalizer_fn and biases_initializer,
mu_learning_rate_factor=mu_learning_rate_factor,
weight_initializer=weights_initializer,
mu1_initializer=mu1_initializer,
mu2_initializer=mu2_initializer,
sigma_initializer=sigma_initializer,
bias_initializer=biases_initializer,
weight_regularizer=weights_regularizer,
mu1_regularizer=mu1_regularizer,
mu2_regularizer=mu2_regularizer,
sigma_regularizer=sigma_regularizer,
bias_regularizer=biases_regularizer,
activity_regularizer=None,
weight_constraint=weights_constraint,
mu1_constraint=mu1_constraint,
mu2_constraint=mu2_constraint,
sigma_constraint=sigma_constraint,
bias_constraint=biases_constraint,
dau_unit_border_bound=dau_unit_border_bound,
dau_sigma_trainable=dau_sigma_trainable,
trainable=trainable,
name=sc.name,
_scope=sc,
_reuse=reuse)
outputs = layer.apply(inputs)
# Add variables to collections.
layers_contrib._add_variable_to_collections(layer.dau_weights, variables_collections, 'weights')
layers_contrib._add_variable_to_collections(layer.dau_mu1, variables_collections, 'mu1')
layers_contrib._add_variable_to_collections(layer.dau_mu2, variables_collections, 'mu2')
layers_contrib._add_variable_to_collections(layer.dau_sigma, variables_collections, 'sigma')
if layer.use_bias:
layers_contrib._add_variable_to_collections(layer.bias, variables_collections, 'biases')
if normalizer_fn is not None:
normalizer_params = normalizer_params or {}
outputs = normalizer_fn(outputs, **normalizer_params)
if activation_fn is not None:
outputs = activation_fn(outputs)
return utils_contrib.collect_named_outputs(outputs_collections, sc.name, outputs)
如有任何帮助，我们将不胜感激。 干杯， H
在 TensorFlow 2.x 中，创建可重用神经网络层的推荐方法是创建一个新的 tf.keras.layers.Layer subclass。 TensorFlow 提供了 great tutorial on this。您可以在 tf.keras 层 class 中重用您发布的示例中的绝大部分代码。您也许还可以从 tensorflow.python.keras.layers.convolutional.Conv 继承以减少样板代码的数量。
至于有些模块找不到，你应该使用TensorFlow暴露的别名。这是一个不完整的列表：
array_ops.reshape->tf.reshapeinit_ops.constant_initializer->tf.initializers.constanttensor_shape->tf.TensorShapenn_ops.Convolution->tf.nn.convolution或tf.keras.layers.Conv?D