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creating resnet101

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luni
2023-02-21 22:17:40 +00:00
parent e302deab7e
commit 8447b7c505
7 changed files with 370 additions and 57 deletions
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models/base.py
+6 -8
View File
@@ -1,6 +1,6 @@
import tensorflow as tf
from convnext.models.convnext_tf import ConvNeXt, ConvNeXtReverse, Block, ConvNeXtReverseLabels
from models.resnet import resnet_18, make_basic_block_layer
from models.resnet import resnet_18, make_basic_block_layer, resnet_34, resnet_50, resnet_101, resnet_152
class Bottleneck(tf.keras.Model):
@@ -116,7 +116,7 @@ class VariationalEncoderNew(tf.keras.Model):
def __init__(self, features):
super(VariationalEncoderNew, self).__init__()
self.encoder = resnet_18(features)
self.encoder = resnet_101(features)
self.mean_branch = tf.keras.Sequential([
tf.keras.layers.Conv2D(filters=features,
@@ -166,7 +166,7 @@ class AEEncoderNew(tf.keras.Model):
def __init__(self, features, activation='tanh'):
super(AEEncoderNew, self).__init__()
self.encoder = resnet_18(features)
self.encoder = resnet_101(features)
self.act = tf.keras.layers.Activation(activation)
@@ -187,17 +187,15 @@ class AEEncoderNew(tf.keras.Model):
class DecoderNew(tf.keras.Model):
def __init__(self, features, is_label=False, is_cvae=False):
def __init__(self, depths, dims, kernel_size=7, is_label=False, learn_weights=False):
def __init__(self, features, is_label=False, is_cvae=False, learn_weights=False):
super(DecoderNew, self).__init__()
self.is_label = is_label
if not is_label:
self.decoder = resnet_18(features, reversed=True, is_cvae=is_cvae)
self.decoder = ConvNeXtReverse(depths=depths, dims=dims, kernel_size=kernel_size, learn_weights=learn_weights)
self.decoder = resnet_101(features, reversed=True, is_cvae=is_cvae)
else:
self.decoder = resnet_18(features, reversed=True, labels=6, is_cvae=is_cvae)
self.decoder = resnet_101(features, reversed=True, labels=6, is_cvae=is_cvae)
def call(self, inputs, training):
models/resnet.py
+83 -45
View File
@@ -54,17 +54,23 @@ class BasicBlock(tf.keras.layers.Layer):
class BottleNeck(tf.keras.layers.Layer):
def __init__(self, filter_num, stride=1):
def __init__(self, filter_num, stride=1, upsample=False):
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')
if not upsample:
self.conv2 = tf.keras.layers.Conv2D(filters=filter_num,
kernel_size=(3, 3),
strides=stride,
padding='same')
else:
self.conv2 = tf.keras.layers.Conv2DTranspose(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),
@@ -72,14 +78,19 @@ class BottleNeck(tf.keras.layers.Layer):
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())
self.updownsample = tf.keras.Sequential()
if not upsample:
self.updownsample.add(tf.keras.layers.Conv2D(filters=filter_num * 4,
kernel_size=(1, 1),
strides=stride))
else:
self.updownsample.add(tf.keras.layers.Conv2DTranspose(filters=filter_num * 4,
kernel_size=(1, 1),
strides=stride))
self.updownsample.add(tf.keras.layers.BatchNormalization())
def call(self, inputs, training=None, **kwargs):
residual = self.downsample(inputs)
residual = self.updownsample(inputs)
x = self.conv1(inputs)
x = self.bn1(x, training=training)
@@ -105,9 +116,9 @@ def make_basic_block_layer(filter_num, blocks, stride=1, upsample=False):
return res_block
def make_bottleneck_layer(filter_num, blocks, stride=1):
def make_bottleneck_layer(filter_num, blocks, stride=1, upsample=False):
res_block = tf.keras.Sequential()
res_block.add(BottleNeck(filter_num, stride=stride))
res_block.add(BottleNeck(filter_num, stride=stride, upsample=upsample))
for _ in range(1, blocks):
res_block.add(BottleNeck(filter_num, stride=1))
@@ -136,9 +147,6 @@ class ResNetTypeI(tf.keras.Model):
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,
@@ -193,45 +201,75 @@ class ResNetTypeI(tf.keras.Model):
class ResNetTypeII(tf.keras.Model):
def __init__(self, layer_params):
def __init__(self, layer_params, features, reversed=False, labels=None, is_cvae=False):
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.reversed = reversed
self.labels = labels
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 if labels is None else labels,
kernel_size=(7, 7),
strides=2,
padding="same")
self.bn1 = tf.keras.layers.BatchNormalization()
self.pool1 = tf.keras.layers.UpSampling2D()
self.layer1 = make_bottleneck_layer(filter_num=64,
blocks=layer_params[0])
blocks=layer_params[0],
stride=1 if not reversed else 2,
upsample=reversed)
self.layer2 = make_bottleneck_layer(filter_num=128,
blocks=layer_params[1],
stride=2)
stride=2,
upsample=reversed)
self.layer3 = make_bottleneck_layer(filter_num=256,
blocks=layer_params[2],
stride=2)
stride=2,
upsample=reversed)
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)
stride=2,
upsample=reversed)
self.layer5 = make_bottleneck_layer(filter_num=features if not is_cvae else features*2,
blocks=layer_params[4],
stride=2 if not reversed else 1,
upsample=reversed)
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)
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)
x = self.layer5(x, training=training)
else:
x = self.layer5(inputs, training=training)
x = self.layer4(x, training=training)
x = self.layer3(x, training=training)
x = self.layer2(x, training=training)
x = self.layer1(x, training=training)
return output
x = self.pool1(x)
x = self.bn1(x, training=training)
x = self.conv1(x)
if self.labels is None:
x = tf.nn.tanh(x)
return x
def resnet_18(features, reversed=False, labels=None, is_cvae=False):
@@ -246,8 +284,8 @@ def resnet_50():
return ResNetTypeII(layer_params=[3, 4, 6, 3])
def resnet_101():
return ResNetTypeII(layer_params=[3, 4, 23, 3])
def resnet_101(features, reversed=False, labels=None, is_cvae=False):
return ResNetTypeII(layer_params=[3, 4, 23, 3, 3], features=features, reversed=reversed, labels=labels, is_cvae=is_cvae)
def resnet_152():