Skip to content

M
Models

曾经的那一瞬间 / Models
大约 1 年 前同步成功

通知 1
Star 0
Fork 0
M
Models

体验新版 GitCode，发现更多精彩内容 >>

提交 65174bdc 编写于 作者: S Scott Zhu 提交者： A. Unique TensorFlower
浏览文件
操作

Internal change 

PiperOrigin-RevId: 447077996
上级 fd015075
隐藏空白更改
内联 并排
Showing with 137 addition and 82 deletion
official/vision/modeling/decoders/nasfpn.py
浏览文件 @ 65174bdc
......@@ -135,25 +135,6 @@ class NASFPN(tf.keras.Model):
self._conv_op = (tf.keras.layers.SeparableConv2D
if self._config_dict['use_separable_conv']
else tf.keras.layers.Conv2D)
if self._config_dict['use_separable_conv']:
self._conv_kwargs = {
'depthwise_initializer': tf.keras.initializers.VarianceScaling(
scale=2, mode='fan_out', distribution='untruncated_normal'),
'pointwise_initializer': tf.keras.initializers.VarianceScaling(
scale=2, mode='fan_out', distribution='untruncated_normal'),
'bias_initializer': tf.zeros_initializer(),
'depthwise_regularizer': self._config_dict['kernel_regularizer'],
'pointwise_regularizer': self._config_dict['kernel_regularizer'],
'bias_regularizer': self._config_dict['bias_regularizer'],
}
else:
self._conv_kwargs = {
'kernel_initializer': tf.keras.initializers.VarianceScaling(
scale=2, mode='fan_out', distribution='untruncated_normal'),
'bias_initializer': tf.zeros_initializer(),
'kernel_regularizer': self._config_dict['kernel_regularizer'],
'bias_regularizer': self._config_dict['bias_regularizer'],
}
self._norm_op = (tf.keras.layers.experimental.SyncBatchNormalization
if self._config_dict['use_sync_bn']
else tf.keras.layers.BatchNormalization)
......@@ -240,6 +221,28 @@ class NASFPN(tf.keras.Model):
else:
return x
@property
def _conv_kwargs(self):
if self._config_dict['use_separable_conv']:
return {
'depthwise_initializer': tf.keras.initializers.VarianceScaling(
scale=2, mode='fan_out', distribution='untruncated_normal'),
'pointwise_initializer': tf.keras.initializers.VarianceScaling(
scale=2, mode='fan_out', distribution='untruncated_normal'),
'bias_initializer': tf.zeros_initializer(),
'depthwise_regularizer': self._config_dict['kernel_regularizer'],
'pointwise_regularizer': self._config_dict['kernel_regularizer'],
'bias_regularizer': self._config_dict['bias_regularizer'],
}
else:
return {
'kernel_initializer': tf.keras.initializers.VarianceScaling(
scale=2, mode='fan_out', distribution='untruncated_normal'),
'bias_initializer': tf.zeros_initializer(),
'kernel_regularizer': self._config_dict['kernel_regularizer'],
'bias_regularizer': self._config_dict['bias_regularizer'],
}
def _global_attention(self, feat0, feat1):
m = tf.math.reduce_max(feat0, axis=[1, 2], keepdims=True)
m = tf.math.sigmoid(m)
......
official/vision/modeling/heads/dense_prediction_heads.py
浏览文件 @ 65174bdc
......@@ -140,6 +140,9 @@ class RetinaNetHead(tf.keras.layers.Layer):
for i in range(self._config_dict['num_convs']):
if level == self._config_dict['min_level']:
cls_conv_name = 'classnet-conv_{}'.format(i)
if 'kernel_initializer' in conv_kwargs:
conv_kwargs['kernel_initializer'] = tf_utils.clone_initializer(
conv_kwargs['kernel_initializer'])
self._cls_convs.append(conv_op(name=cls_conv_name, **conv_kwargs))
cls_norm_name = 'classnet-conv-norm_{}_{}'.format(level, i)
this_level_cls_norms.append(bn_op(name=cls_norm_name, **bn_kwargs))
......@@ -170,6 +173,9 @@ class RetinaNetHead(tf.keras.layers.Layer):
for i in range(self._config_dict['num_convs']):
if level == self._config_dict['min_level']:
box_conv_name = 'boxnet-conv_{}'.format(i)
if 'kernel_initializer' in conv_kwargs:
conv_kwargs['kernel_initializer'] = tf_utils.clone_initializer(
conv_kwargs['kernel_initializer'])
self._box_convs.append(conv_op(name=box_conv_name, **conv_kwargs))
box_norm_name = 'boxnet-conv-norm_{}_{}'.format(level, i)
this_level_box_norms.append(bn_op(name=box_norm_name, **bn_kwargs))
......@@ -211,6 +217,9 @@ class RetinaNetHead(tf.keras.layers.Layer):
for i in range(self._config_dict['num_convs']):
if level == self._config_dict['min_level']:
att_conv_name = '{}-conv_{}'.format(att_name, i)
if 'kernel_initializer' in conv_kwargs:
conv_kwargs['kernel_initializer'] = tf_utils.clone_initializer(
conv_kwargs['kernel_initializer'])
att_convs_i.append(conv_op(name=att_conv_name, **conv_kwargs))
att_norm_name = '{}-conv-norm_{}_{}'.format(att_name, level, i)
this_level_att_norms.append(bn_op(name=att_norm_name, **bn_kwargs))
......@@ -436,6 +445,9 @@ class RPNHead(tf.keras.layers.Layer):
for i in range(self._config_dict['num_convs']):
if level == self._config_dict['min_level']:
conv_name = 'rpn-conv_{}'.format(i)
if 'kernel_initializer' in conv_kwargs:
conv_kwargs['kernel_initializer'] = tf_utils.clone_initializer(
conv_kwargs['kernel_initializer'])
self._convs.append(conv_op(name=conv_name, **conv_kwargs))
norm_name = 'rpn-conv-norm_{}_{}'.format(level, i)
this_level_norms.append(bn_op(name=norm_name, **bn_kwargs))
......
official/vision/modeling/heads/instance_heads.py
浏览文件 @ 65174bdc
......@@ -133,6 +133,9 @@ class DetectionHead(tf.keras.layers.Layer):
self._conv_norms = []
for i in range(self._config_dict['num_convs']):
conv_name = 'detection-conv_{}'.format(i)
if 'kernel_initializer' in conv_kwargs:
conv_kwargs['kernel_initializer'] = tf_utils.clone_initializer(
conv_kwargs['kernel_initializer'])
self._convs.append(conv_op(name=conv_name, **conv_kwargs))
bn_name = 'detection-conv-bn_{}'.format(i)
self._conv_norms.append(bn_op(name=bn_name, **bn_kwargs))
......@@ -324,6 +327,11 @@ class MaskHead(tf.keras.layers.Layer):
self._conv_norms = []
for i in range(self._config_dict['num_convs']):
conv_name = 'mask-conv_{}'.format(i)
for initializer_name in ['kernel_initializer', 'depthwise_initializer',
'pointwise_initializer']:
if initializer_name in conv_kwargs:
conv_kwargs[initializer_name] = tf_utils.clone_initializer(
conv_kwargs[initializer_name])
self._convs.append(conv_op(name=conv_name, **conv_kwargs))
bn_name = 'mask-conv-bn_{}'.format(i)
self._conv_norms.append(bn_op(name=bn_name, **bn_kwargs))
......
official/vision/modeling/heads/segmentation_heads.py
浏览文件 @ 65174bdc
......@@ -118,6 +118,9 @@ class MaskScoring(tf.keras.Model):
self._conv_norms = []
for i in range(self._config_dict['num_convs']):
conv_name = 'mask-scoring_{}'.format(i)
if 'kernel_initializer' in conv_kwargs:
conv_kwargs['kernel_initializer'] = tf_utils.clone_initializer(
conv_kwargs['kernel_initializer'])
self._convs.append(conv_op(name=conv_name, **conv_kwargs))
bn_name = 'mask-scoring-bn_{}'.format(i)
self._conv_norms.append(bn_op(name=bn_name, **bn_kwargs))
......@@ -297,15 +300,7 @@ class SegmentationHead(tf.keras.layers.Layer):
def build(self, input_shape: Union[tf.TensorShape, List[tf.TensorShape]]):
"""Creates the variables of the segmentation head."""
use_depthwise_convolution = self._config_dict['use_depthwise_convolution']
random_initializer = tf.keras.initializers.RandomNormal(stddev=0.01)
conv_op = tf.keras.layers.Conv2D
conv_kwargs = {
'kernel_size': 3 if not use_depthwise_convolution else 1,
'padding': 'same',
'use_bias': False,
'kernel_initializer': random_initializer,
'kernel_regularizer': self._config_dict['kernel_regularizer'],
}
bn_op = (tf.keras.layers.experimental.SyncBatchNormalization
if self._config_dict['use_sync_bn']
else tf.keras.layers.BatchNormalization)
......@@ -352,7 +347,8 @@ class SegmentationHead(tf.keras.layers.Layer):
kernel_size=3,
padding='same',
use_bias=False,
depthwise_initializer=random_initializer,
depthwise_initializer=tf.keras.initializers.RandomNormal(
stddev=0.01),
depthwise_regularizer=self._config_dict['kernel_regularizer'],
depth_multiplier=1))
norm_name = 'segmentation_head_depthwise_norm_{}'.format(i)
......@@ -362,7 +358,12 @@ class SegmentationHead(tf.keras.layers.Layer):
conv_op(
name=conv_name,
filters=self._config_dict['num_filters'],
**conv_kwargs))
kernel_size=3 if not use_depthwise_convolution else 1,
padding='same',
use_bias=False,
kernel_initializer=tf.keras.initializers.RandomNormal(
stddev=0.01),
kernel_regularizer=self._config_dict['kernel_regularizer']))
norm_name = 'segmentation_head_norm_{}'.format(i)
self._norms.append(bn_op(name=norm_name, **bn_kwargs))
......
official/vision/modeling/layers/deeplab.py
浏览文件 @ 65174bdc
......@@ -16,6 +16,8 @@
import tensorflow as tf
from official.modeling import tf_utils
class SpatialPyramidPooling(tf.keras.layers.Layer):
"""Implements the Atrous Spatial Pyramid Pooling.
......@@ -103,8 +105,10 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
conv_sequential = tf.keras.Sequential([
tf.keras.layers.Conv2D(
filters=self.output_channels, kernel_size=(1, 1),
kernel_initializer=self.kernel_initializer,
filters=self.output_channels,
kernel_size=(1, 1),
kernel_initializer=tf_utils.clone_initializer(
self.kernel_initializer),
kernel_regularizer=self.kernel_regularizer,
use_bias=False),
bn_op(
......@@ -121,21 +125,32 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
if self.use_depthwise_convolution:
leading_layers += [
tf.keras.layers.DepthwiseConv2D(
depth_multiplier=1, kernel_size=kernel_size,
padding='same', depthwise_regularizer=self.kernel_regularizer,
depthwise_initializer=self.kernel_initializer,
dilation_rate=dilation_rate, use_bias=False)
depth_multiplier=1,
kernel_size=kernel_size,
padding='same',
depthwise_regularizer=self.kernel_regularizer,
depthwise_initializer=tf_utils.clone_initializer(
self.kernel_initializer),
dilation_rate=dilation_rate,
use_bias=False)
]
kernel_size = (1, 1)
conv_sequential = tf.keras.Sequential(leading_layers + [
tf.keras.layers.Conv2D(
filters=self.output_channels, kernel_size=kernel_size,
padding='same', kernel_regularizer=self.kernel_regularizer,
kernel_initializer=self.kernel_initializer,
dilation_rate=dilation_rate, use_bias=False),
bn_op(axis=bn_axis, momentum=self.batchnorm_momentum,
epsilon=self.batchnorm_epsilon),
tf.keras.layers.Activation(self.activation)])
filters=self.output_channels,
kernel_size=kernel_size,
padding='same',
kernel_regularizer=self.kernel_regularizer,
kernel_initializer=tf_utils.clone_initializer(
self.kernel_initializer),
dilation_rate=dilation_rate,
use_bias=False),
bn_op(
axis=bn_axis,
momentum=self.batchnorm_momentum,
epsilon=self.batchnorm_epsilon),
tf.keras.layers.Activation(self.activation)
])
self.aspp_layers.append(conv_sequential)
if self.pool_kernel_size is None:
......@@ -151,7 +166,8 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
tf.keras.layers.Conv2D(
filters=self.output_channels,
kernel_size=(1, 1),
kernel_initializer=self.kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self.kernel_initializer),
kernel_regularizer=self.kernel_regularizer,
use_bias=False),
bn_op(
......@@ -170,8 +186,10 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
self.projection = tf.keras.Sequential([
tf.keras.layers.Conv2D(
filters=self.output_channels, kernel_size=(1, 1),
kernel_initializer=self.kernel_initializer,
filters=self.output_channels,
kernel_size=(1, 1),
kernel_initializer=tf_utils.clone_initializer(
self.kernel_initializer),
kernel_regularizer=self.kernel_regularizer,
use_bias=False),
bn_op(
......@@ -179,7 +197,8 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
momentum=self.batchnorm_momentum,
epsilon=self.batchnorm_epsilon),
tf.keras.layers.Activation(self.activation),
tf.keras.layers.Dropout(rate=self.dropout)])
tf.keras.layers.Dropout(rate=self.dropout)
])
def call(self, inputs, training=None):
if training is None:
......
official/vision/modeling/layers/nn_blocks.py
浏览文件 @ 65174bdc
......@@ -141,7 +141,8 @@ class ResidualBlock(tf.keras.layers.Layer):
kernel_size=1,
strides=self._strides,
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm0 = self._norm(
......@@ -162,7 +163,7 @@ class ResidualBlock(tf.keras.layers.Layer):
strides=self._strides,
padding=conv1_padding,
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm1 = self._norm(
......@@ -177,7 +178,7 @@ class ResidualBlock(tf.keras.layers.Layer):
strides=1,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm2 = self._norm(
......@@ -191,7 +192,8 @@ class ResidualBlock(tf.keras.layers.Layer):
in_filters=self._filters,
out_filters=self._filters,
se_ratio=self._se_ratio,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
else:
......@@ -338,7 +340,8 @@ class BottleneckBlock(tf.keras.layers.Layer):
kernel_size=1,
strides=1,
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
else:
......@@ -347,7 +350,8 @@ class BottleneckBlock(tf.keras.layers.Layer):
kernel_size=1,
strides=self._strides,
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
......@@ -362,7 +366,7 @@ class BottleneckBlock(tf.keras.layers.Layer):
kernel_size=1,
strides=1,
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm1 = self._norm(
......@@ -380,7 +384,7 @@ class BottleneckBlock(tf.keras.layers.Layer):
dilation_rate=self._dilation_rate,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm2 = self._norm(
......@@ -396,7 +400,7 @@ class BottleneckBlock(tf.keras.layers.Layer):
kernel_size=1,
strides=1,
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm3 = self._norm(
......@@ -412,7 +416,8 @@ class BottleneckBlock(tf.keras.layers.Layer):
in_filters=self._filters * 4,
out_filters=self._filters * 4,
se_ratio=self._se_ratio,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
else:
......@@ -616,7 +621,8 @@ class InvertedBottleneckBlock(tf.keras.layers.Layer):
strides=expand_stride,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm0 = self._norm(
......@@ -635,7 +641,8 @@ class InvertedBottleneckBlock(tf.keras.layers.Layer):
depth_multiplier=1,
dilation_rate=self._dilation_rate,
use_bias=False,
depthwise_initializer=self._kernel_initializer,
depthwise_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
depthwise_regularizer=self._depthsize_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm1 = self._norm(
......@@ -657,7 +664,8 @@ class InvertedBottleneckBlock(tf.keras.layers.Layer):
se_ratio=self._se_ratio,
divisible_by=self._divisible_by,
round_down_protect=self._se_round_down_protect,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activation=self._se_inner_activation,
......@@ -672,7 +680,7 @@ class InvertedBottleneckBlock(tf.keras.layers.Layer):
strides=1,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm2 = self._norm(
......@@ -829,7 +837,7 @@ class ResidualInner(tf.keras.layers.Layer):
strides=self.strides,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer)
self._batch_norm_1 = self._norm(
......@@ -843,7 +851,7 @@ class ResidualInner(tf.keras.layers.Layer):
strides=1,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer)
super(ResidualInner, self).build(input_shape)
......@@ -954,7 +962,7 @@ class BottleneckResidualInner(tf.keras.layers.Layer):
strides=self.strides,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer)
self._batch_norm_1 = self._norm(
axis=self._bn_axis,
......@@ -966,7 +974,7 @@ class BottleneckResidualInner(tf.keras.layers.Layer):
strides=1,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer)
self._batch_norm_2 = self._norm(
axis=self._bn_axis,
......@@ -978,7 +986,7 @@ class BottleneckResidualInner(tf.keras.layers.Layer):
strides=1,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer)
super(BottleneckResidualInner, self).build(input_shape)
......@@ -1286,7 +1294,7 @@ class DepthwiseSeparableConvBlock(tf.keras.layers.Layer):
padding='same',
depth_multiplier=1,
dilation_rate=self._dilation_rate,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._depthsize_regularizer,
use_bias=False)
self._norm0 = self._norm(
......@@ -1300,7 +1308,7 @@ class DepthwiseSeparableConvBlock(tf.keras.layers.Layer):
strides=1,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer)
self._norm1 = self._norm(
axis=self._bn_axis,
......@@ -1411,7 +1419,7 @@ class TuckerConvBlock(tf.keras.layers.Layer):
strides=1,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm0 = self._norm(
......@@ -1432,7 +1440,7 @@ class TuckerConvBlock(tf.keras.layers.Layer):
strides=self._strides,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm1 = self._norm(
......@@ -1449,7 +1457,7 @@ class TuckerConvBlock(tf.keras.layers.Layer):
strides=1,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm2 = self._norm(
......
official/vision/modeling/layers/nn_blocks_3d.py
浏览文件 @ 65174bdc
......@@ -155,7 +155,7 @@ class BottleneckBlock3D(tf.keras.layers.Layer):
self._temporal_strides, self._spatial_strides, self._spatial_strides
],
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm0 = self._norm(
......@@ -169,7 +169,7 @@ class BottleneckBlock3D(tf.keras.layers.Layer):
strides=[self._temporal_strides, 1, 1],
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm1 = self._norm(
......@@ -183,7 +183,7 @@ class BottleneckBlock3D(tf.keras.layers.Layer):
strides=[1, self._spatial_strides, self._spatial_strides],
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm2 = self._norm(
......@@ -197,7 +197,7 @@ class BottleneckBlock3D(tf.keras.layers.Layer):
strides=[1, 1, 1],
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm3 = self._norm(
......@@ -211,7 +211,8 @@ class BottleneckBlock3D(tf.keras.layers.Layer):
out_filters=self._filters * 4,
se_ratio=self._se_ratio,
use_3d_input=True,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
else:
......
official/vision/modeling/layers/nn_layers.py
浏览文件 @ 65174bdc
......@@ -163,7 +163,7 @@ class SqueezeExcitation(tf.keras.layers.Layer):
strides=1,
padding='same',
use_bias=True,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
......@@ -173,7 +173,7 @@ class SqueezeExcitation(tf.keras.layers.Layer):
strides=1,
padding='same',
use_bias=True,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
......@@ -1155,7 +1155,7 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
conv1 = tf.keras.layers.Conv2D(
filters=self._output_channels,
kernel_size=(1, 1),
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
use_bias=False)
norm1 = self._bn_op(
......@@ -1175,7 +1175,8 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
kernel_size=kernel_size,
padding='same',
depthwise_regularizer=self._kernel_regularizer,
depthwise_initializer=self._kernel_initializer,
depthwise_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
dilation_rate=dilation_rate,
use_bias=False)
]
......@@ -1186,7 +1187,8 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
kernel_size=kernel_size,
padding='same',
kernel_regularizer=self._kernel_regularizer,
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
dilation_rate=dilation_rate,
use_bias=False)
]
......@@ -1208,7 +1210,7 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
conv2 = tf.keras.layers.Conv2D(
filters=self._output_channels,
kernel_size=(1, 1),
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
use_bias=False)
norm2 = self._bn_op(
......@@ -1225,7 +1227,8 @@ class SpatialPyramidPooling(tf.keras.layers.Layer):
tf.keras.layers.Conv2D(
filters=self._output_channels,
kernel_size=(1, 1),
kernel_initializer=self._kernel_initializer,
kernel_initializer=tf_utils.clone_initializer(
self._kernel_initializer),
kernel_regularizer=self._kernel_regularizer,
use_bias=False),
self._bn_op(
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或