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replace backbone with resnet18

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luni
2023-01-25 16:49:01 +00:00
parent ffdc60062d
commit 2305a0ee23
4 changed files with 303 additions and 3 deletions
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models/base.py
+6 -2
View File
@@ -1,5 +1,7 @@
import tensorflow as tf
from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block, ConvNeXtReverseLabels
from models.resnet import resnet_18
class Bottleneck(tf.keras.Model):
@@ -150,7 +152,8 @@ class AEEncoderNew(tf.keras.Model):
def __init__(self, depths, dims, kernel_size=7, activation='tanh'):
super(AEEncoderNew, self).__init__()
self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size)
# self.encoder = ConvNeXt(depths=depths, dims=dims, include_top=False, kernel_size=kernel_size)
self.encoder = resnet_18()
self.act = tf.keras.layers.Activation(activation)
@@ -177,7 +180,8 @@ class DecoderNew(tf.keras.Model):
self.is_label = is_label
if not is_label:
self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size)
# self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size)
self.decoder = resnet_18(reversed=True)
else:
self.decoder = ConvNeXtReverseLabels(depths=depths, dims=dims, nr_of_classes=7)
models/resnet.py
+243
View File
@@ -0,0 +1,243 @@
# taken from https://github.com/calmisential/TensorFlow2.0_ResNet/blob/master/models/resnet.py
import tensorflow as tf
class BasicBlock(tf.keras.layers.Layer):
def __init__(self, filter_num, stride=1, upsample=False):
super(BasicBlock, self).__init__()
if not upsample:
self.conv1 = tf.keras.layers.Conv2D(filters=filter_num,
kernel_size=(3, 3),
strides=stride,
padding="same")
else:
self.conv1 = tf.keras.layers.Conv2DTranspose(filters=filter_num,
kernel_size=(3, 3),
strides=stride,
padding="same")
self.bn1 = tf.keras.layers.BatchNormalization()
self.conv2 = tf.keras.layers.Conv2D(filters=filter_num,
kernel_size=(3, 3),
strides=1,
padding="same")
self.bn2 = tf.keras.layers.BatchNormalization()
if stride != 1:
if not upsample:
self.updownsample = tf.keras.Sequential()
self.updownsample.add(tf.keras.layers.Conv2D(filters=filter_num,
kernel_size=(1, 1),
strides=stride))
self.updownsample.add(tf.keras.layers.BatchNormalization())
else:
self.updownsample = tf.keras.Sequential()
self.updownsample.add(tf.keras.layers.Conv2DTranspose(filters=filter_num,
kernel_size=(1, 1),
strides=stride))
self.updownsample.add(tf.keras.layers.BatchNormalization())
else:
self.updownsample = lambda x: x
def call(self, inputs, training=None, **kwargs):
residual = self.updownsample(inputs)
x = self.conv1(inputs)
x = self.bn1(x, training=training)
x = tf.nn.relu(x)
x = self.conv2(x)
x = self.bn2(x, training=training)
output = tf.nn.relu(tf.keras.layers.add([residual, x]))
return output
class BottleNeck(tf.keras.layers.Layer):
def __init__(self, filter_num, stride=1):
super(BottleNeck, self).__init__()
self.conv1 = tf.keras.layers.Conv2D(filters=filter_num,
kernel_size=(1, 1),
strides=1,
padding='same')
self.bn1 = tf.keras.layers.BatchNormalization()
self.conv2 = tf.keras.layers.Conv2D(filters=filter_num,
kernel_size=(3, 3),
strides=stride,
padding='same')
self.bn2 = tf.keras.layers.BatchNormalization()
self.conv3 = tf.keras.layers.Conv2D(filters=filter_num * 4,
kernel_size=(1, 1),
strides=1,
padding='same')
self.bn3 = tf.keras.layers.BatchNormalization()
self.downsample = tf.keras.Sequential()
self.downsample.add(tf.keras.layers.Conv2D(filters=filter_num * 4,
kernel_size=(1, 1),
strides=stride))
self.downsample.add(tf.keras.layers.BatchNormalization())
def call(self, inputs, training=None, **kwargs):
residual = self.downsample(inputs)
x = self.conv1(inputs)
x = self.bn1(x, training=training)
x = tf.nn.relu(x)
x = self.conv2(x)
x = self.bn2(x, training=training)
x = tf.nn.relu(x)
x = self.conv3(x)
x = self.bn3(x, training=training)
output = tf.nn.relu(tf.keras.layers.add([residual, x]))
return output
def make_basic_block_layer(filter_num, blocks, stride=1, upsample=False):
res_block = tf.keras.Sequential()
res_block.add(BasicBlock(filter_num, stride=stride, upsample=upsample))
for _ in range(1, blocks):
res_block.add(BasicBlock(filter_num, stride=1))
return res_block
def make_bottleneck_layer(filter_num, blocks, stride=1):
res_block = tf.keras.Sequential()
res_block.add(BottleNeck(filter_num, stride=stride))
for _ in range(1, blocks):
res_block.add(BottleNeck(filter_num, stride=1))
return res_block
class ResNetTypeI(tf.keras.Model):
def __init__(self, layer_params, reversed=False):
super(ResNetTypeI, self).__init__()
self.reversed = reversed
if not reversed:
self.conv1 = tf.keras.layers.Conv2D(filters=64,
kernel_size=(7, 7),
strides=2,
padding="same")
self.bn1 = tf.keras.layers.BatchNormalization()
self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=2,
padding="same")
else:
self.conv1 = tf.keras.layers.Conv2DTranspose(filters=3,
kernel_size=(7, 7),
strides=2,
padding="same")
self.bn1 = tf.keras.layers.BatchNormalization()
# self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
# strides=2,
# padding="same")
self.pool1 = tf.keras.layers.UpSampling2D()
self.layer1 = make_basic_block_layer(filter_num=64,
blocks=layer_params[0],
stride=1 if not reversed else 2,
upsample=reversed)
self.layer2 = make_basic_block_layer(filter_num=128,
blocks=layer_params[1],
stride=2,
upsample=reversed)
self.layer3 = make_basic_block_layer(filter_num=256,
blocks=layer_params[2],
stride=2,
upsample=reversed)
self.layer4 = make_basic_block_layer(filter_num=512,
blocks=layer_params[3],
stride=2 if not reversed else 1,
upsample=reversed)
def call(self, inputs, training=None, mask=None):
if not self.reversed:
x = self.conv1(inputs)
x = self.bn1(x, training=training)
x = tf.nn.relu(x)
x = self.pool1(x)
x = self.layer1(x, training=training)
x = self.layer2(x, training=training)
x = self.layer3(x, training=training)
x = self.layer4(x, training=training)
else:
x = self.layer4(inputs, training=training)
x = self.layer3(x, training=training)
x = self.layer2(x, training=training)
x = self.layer1(x, training=training)
x = self.pool1(x)
x = self.bn1(x, training=training)
x = tf.nn.relu(x)
x = self.conv1(x)
return x
class ResNetTypeII(tf.keras.Model):
def __init__(self, layer_params):
super(ResNetTypeII, self).__init__()
self.conv1 = tf.keras.layers.Conv2D(filters=64,
kernel_size=(7, 7),
strides=2,
padding="same")
self.bn1 = tf.keras.layers.BatchNormalization()
self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=2,
padding="same")
self.layer1 = make_bottleneck_layer(filter_num=64,
blocks=layer_params[0])
self.layer2 = make_bottleneck_layer(filter_num=128,
blocks=layer_params[1],
stride=2)
self.layer3 = make_bottleneck_layer(filter_num=256,
blocks=layer_params[2],
stride=2)
self.layer4 = make_bottleneck_layer(filter_num=512,
blocks=layer_params[3],
stride=2)
self.avgpool = tf.keras.layers.GlobalAveragePooling2D()
self.fc = tf.keras.layers.Dense(units=NUM_CLASSES, activation=tf.keras.activations.softmax)
def call(self, inputs, training=None, mask=None):
x = self.conv1(inputs)
x = self.bn1(x, training=training)
x = tf.nn.relu(x)
x = self.pool1(x)
x = self.layer1(x, training=training)
x = self.layer2(x, training=training)
x = self.layer3(x, training=training)
x = self.layer4(x, training=training)
x = self.avgpool(x)
output = self.fc(x)
return output
def resnet_18(reversed=False):
return ResNetTypeI(layer_params=[2, 2, 2, 2], reversed=reversed)
def resnet_34():
return ResNetTypeI(layer_params=[3, 4, 6, 3])
def resnet_50():
return ResNetTypeII(layer_params=[3, 4, 6, 3])
def resnet_101():
return ResNetTypeII(layer_params=[3, 4, 23, 3])
def resnet_152():
return ResNetTypeII(layer_params=[3, 8, 36, 3])