Spaces:
Runtime error
Runtime error
File size: 3,810 Bytes
0924f30 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
# coding=utf-8
# Copyright 2021 The Deeplab2 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This script contains STEMs for neural networks.
The `STEM` is defined as the first few convolutions that process the input
image to a spatially smaller feature map (e.g., output stride = 2).
Reference code:
https://github.com/tensorflow/models/blob/master/research/deeplab/core/resnet_v1_beta.py
"""
import tensorflow as tf
from deeplab2.model.layers import convolutions
layers = tf.keras.layers
class InceptionSTEM(tf.keras.layers.Layer):
"""A InceptionSTEM layer.
This class builds an InceptionSTEM layer which can be used to as the first
few layers in a neural network. In particular, InceptionSTEM contains three
consecutive 3x3 colutions.
Reference:
- Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi.
"Inception-v4, inception-resnet and the impact of residual connections on
learning." In AAAI, 2017.
"""
def __init__(self,
bn_layer=tf.keras.layers.BatchNormalization,
width_multiplier=1.0,
conv_kernel_weight_decay=0.0,
activation='relu'):
"""Creates the InceptionSTEM layer.
Args:
bn_layer: An optional tf.keras.layers.Layer that computes the
normalization (default: tf.keras.layers.BatchNormalization).
width_multiplier: A float multiplier, controlling the value of
convolution output channels.
conv_kernel_weight_decay: A float, the weight decay for convolution
kernels.
activation: A string specifying an activation function to be used in this
stem.
"""
super(InceptionSTEM, self).__init__(name='stem')
self._conv1_bn_act = convolutions.Conv2DSame(
output_channels=int(64 * width_multiplier),
kernel_size=3,
name='conv1_bn_act',
strides=2,
use_bias=False,
use_bn=True,
bn_layer=bn_layer,
activation=activation,
conv_kernel_weight_decay=conv_kernel_weight_decay)
self._conv2_bn_act = convolutions.Conv2DSame(
output_channels=int(64 * width_multiplier),
kernel_size=3,
name='conv2_bn_act',
strides=1,
use_bias=False,
use_bn=True,
bn_layer=bn_layer,
activation=activation,
conv_kernel_weight_decay=conv_kernel_weight_decay)
self._conv3_bn = convolutions.Conv2DSame(
output_channels=int(128 * width_multiplier),
kernel_size=3,
strides=1,
use_bias=False,
use_bn=True,
bn_layer=bn_layer,
activation='none',
name='conv3_bn',
conv_kernel_weight_decay=conv_kernel_weight_decay)
def call(self, input_tensor, training=False):
"""Performs a forward pass.
Args:
input_tensor: An input tensor of type tf.Tensor with shape [batch, height,
width, channels].
training: A boolean flag indicating whether training behavior should be
used (default: False).
Returns:
Two output tensors. The first output tensor is not activated. The second
tensor is activated.
"""
x = self._conv1_bn_act(input_tensor, training=training)
x = self._conv2_bn_act(x, training=training)
x = self._conv3_bn(x, training=training)
return x
|