lot of things
This commit is contained in:
+305
-19
@@ -1,9 +1,9 @@
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from unittest import skip
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras.layers import (Conv2D, Dense, DepthwiseConv2D,
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from tensorflow.keras.layers import (Conv2D, Conv2DTranspose, Dense, DepthwiseConv2D,
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GlobalAveragePooling2D, Layer,
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LayerNormalization)
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LayerNormalization, Flatten, Reshape, UpSampling2D)
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def drop_path(inputs, drop_prob, is_training):
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# borrowed from https://github.com/rishigami/Swin-Transformer-TF/blob/main/swintransformer/model.py
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@@ -109,6 +109,299 @@ class ConvNeXt(tf.keras.Model):
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LayerNormalization(epsilon=1e-6)]
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)
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self.downsample_layers.append(stem)
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for i in range(len(depths) - 1):
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downsample_layer = tf.keras.Sequential([
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LayerNormalization(epsilon=1e-6),
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Conv2D(dims[i+1], kernel_size=2, strides=2, padding='same')]
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)
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self.downsample_layers.append(downsample_layer)
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self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks
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dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))]
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cur = 0
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for i in range(len(depths)):
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stage = tf.keras.Sequential(
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[Block(dim=dims[i], drop_path=dp_rates[cur + j],
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layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])]
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)
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self.stages.append(stage)
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cur += depths[i]
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if self.include_top:
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self.avg = GlobalAveragePooling2D()
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self.norm = LayerNormalization(epsilon=1e-6) # final norm layer
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self.head = Dense(num_classes)
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else:
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self.avg = None
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self.norm = None
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self.head = None
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def forward_features(self, x):
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for i in range(len(self.downsample_layers)):
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x = self.downsample_layers[i](x)
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x = self.stages[i](x)
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return x
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def call(self, x):
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x = self.forward_features(x)
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if self.include_top:
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x = self.avg(x)
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x = self.norm(x)
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x = self.head(x)
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return x
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class ConvNeXtWithSkip(tf.keras.Model):
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r""" ConvNeXt
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A Tensorflow keras impl of : `A ConvNet for the 2020s` -
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https://arxiv.org/pdf/2201.03545.pdf
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Args:
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num_classes (int): Number of classes for classification head. Default: 1000
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depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
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dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
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include_top (bool): whether to add head or just use it as feature extractor. Default: True
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drop_path_rate (float): Stochastic depth rate. Default: 0.
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layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
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head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
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"""
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def __init__(self,
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depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
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drop_path_rate=0., layer_scale_init_value=1e-6,
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):
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super().__init__()
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self.downsample_layers = [] # stem and 3 intermediate downsampling conv layers
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stem = tf.keras.Sequential([
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Conv2D(dims[0], kernel_size=4, strides=4, padding='same'),
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LayerNormalization(epsilon=1e-6)]
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)
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self.downsample_layers.append(stem)
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for i in range(len(depths) - 1):
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downsample_layer = tf.keras.Sequential([
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LayerNormalization(epsilon=1e-6),
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Conv2D(dims[i+1], kernel_size=2, strides=2, padding='same')]
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)
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self.downsample_layers.append(downsample_layer)
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self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks
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dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))]
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cur = 0
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for i in range(len(depths)):
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stage = tf.keras.Sequential(
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[Block(dim=dims[i], drop_path=dp_rates[cur + j],
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layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])]
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)
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self.stages.append(stage)
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cur += depths[i]
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def call(self, x):
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skip_features = []
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for i in range(len(self.downsample_layers)):
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x = self.downsample_layers[i](x)
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x = self.stages[i](x)
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skip_features.append(x)
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return x, skip_features
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class ConvNeXtReverse(tf.keras.Model):
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r""" ConvNeXt
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A Tensorflow keras impl of : `A ConvNet for the 2020s` -
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https://arxiv.org/pdf/2201.03545.pdf
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Args:
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num_classes (int): Number of classes for classification head. Default: 1000
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depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
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dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
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include_top (bool): whether to add head or just use it as feature extractor. Default: True
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drop_path_rate (float): Stochastic depth rate. Default: 0.
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layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
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head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
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"""
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def __init__(self,
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depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
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drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1.,
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):
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super().__init__()
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self.upsample_layers = [] # stem and 3 intermediate downsampling conv layers
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stem = tf.keras.Sequential([
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Conv2DTranspose(dims[0], kernel_size=4, strides=4, padding='same'),
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# UpSampling2D((4, 4), interpolation='nearest'),
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# Conv2D(dims[0], kernel_size=4, strides=1, padding='same'),
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# Dense(dims[0]),
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LayerNormalization(epsilon=1e-6)]
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)
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self.upsample_layers.append(stem)
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for i in range(len(depths) - 1):
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downsample_layer = tf.keras.Sequential([
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LayerNormalization(epsilon=1e-6),
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Conv2DTranspose(dims[i+1], kernel_size=2, strides=2, padding='same')
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# UpSampling2D((2,2), interpolation='nearest'),
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# Conv2D(dims[i+1], kernel_size=2, strides=1, padding='same'),
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# Dense(dims[i+1]),
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])
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self.upsample_layers.append(downsample_layer)
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self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks
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dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))]
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cur = 0
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for i in range(len(depths)):
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stage = tf.keras.Sequential(
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[Block(dim=dims[i], drop_path=dp_rates[cur + j],
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layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])]
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)
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self.stages.append(stage)
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cur += depths[i]
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self.last = Dense(3, activation='tanh')
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def call(self, x):
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for i in reversed(range(len(self.upsample_layers))):
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x = self.upsample_layers[i](x)
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x = self.stages[i](x)
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x = self.last(x)
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return x
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class ConvNeXtReverseWithSkip(tf.keras.Model):
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r""" ConvNeXt
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A Tensorflow keras impl of : `A ConvNet for the 2020s` -
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https://arxiv.org/pdf/2201.03545.pdf
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Args:
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num_classes (int): Number of classes for classification head. Default: 1000
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depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
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dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
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include_top (bool): whether to add head or just use it as feature extractor. Default: True
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drop_path_rate (float): Stochastic depth rate. Default: 0.
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layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
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head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
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"""
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def __init__(self,
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depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
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drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1.,
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):
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super().__init__()
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assert len(depths) == len(dims)
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self.upsample_layers = [] # stem and 3 intermediate downsampling conv layers
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stem = tf.keras.Sequential([
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Conv2DTranspose(dims[0], kernel_size=4, strides=4, padding='same'),
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LayerNormalization(epsilon=1e-6)]
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)
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self.upsample_layers.append(stem)
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for i in range(len(depths) - 1):
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downsample_layer = tf.keras.Sequential([
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LayerNormalization(epsilon=1e-6),
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Conv2DTranspose(dims[i+1], kernel_size=2, strides=2, padding='same')]
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)
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self.upsample_layers.append(downsample_layer)
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self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks
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dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))]
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cur = 0
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for i in range(len(depths)):
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stage = tf.keras.Sequential(
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[Block(dim=dims[i], drop_path=dp_rates[cur + j],
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layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])]
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)
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self.stages.append(stage)
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cur += depths[i]
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self.last = Dense(3, activation='tanh')
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def call(self, x):
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x, skip_features = x
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for i in reversed(range(len(self.upsample_layers))):
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x = tf.concat((x, skip_features[i]), axis=-1)
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x = self.upsample_layers[i](x)
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x = self.stages[i](x)
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x = self.last(x)
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return x
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class ConvNeXtReverseLabels(tf.keras.Model):
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r""" ConvNeXt
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A Tensorflow keras impl of : `A ConvNet for the 2020s` -
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https://arxiv.org/pdf/2201.03545.pdf
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Args:
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num_classes (int): Number of classes for classification head. Default: 1000
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depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
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dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
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include_top (bool): whether to add head or just use it as feature extractor. Default: True
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drop_path_rate (float): Stochastic depth rate. Default: 0.
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layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
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head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
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"""
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def __init__(self,
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depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
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drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1.,
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nr_of_classes=7):
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super().__init__()
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self.downsample_layers = [] # stem and 3 intermediate downsampling conv layers
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stem = tf.keras.Sequential([
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Conv2DTranspose(dims[0], kernel_size=4, strides=4, padding='same'),
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LayerNormalization(epsilon=1e-6)]
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)
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self.downsample_layers.append(stem)
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for i in range(3):
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downsample_layer = tf.keras.Sequential([
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LayerNormalization(epsilon=1e-6),
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Conv2DTranspose(dims[i+1], kernel_size=2, strides=2, padding='same')]
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)
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self.downsample_layers.append(downsample_layer)
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self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks
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dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))]
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cur = 0
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for i in range(4):
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stage = tf.keras.Sequential(
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[Block(dim=dims[i], drop_path=dp_rates[cur + j],
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layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])]
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)
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self.stages.append(stage)
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cur += depths[i]
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self.last = Dense(nr_of_classes, activation='softmax')
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def call(self, x):
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for i in reversed(range(4)):
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x = self.downsample_layers[i](x)
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x = self.stages[i](x)
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x = self.last(x)
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return x
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class ConvNeXtDiscriminator(tf.keras.Model):
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r""" ConvNeXt
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A Tensorflow keras impl of : `A ConvNet for the 2020s` -
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https://arxiv.org/pdf/2201.03545.pdf
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Args:
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num_classes (int): Number of classes for classification head. Default: 1000
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depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
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dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
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include_top (bool): whether to add head or just use it as feature extractor. Default: True
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drop_path_rate (float): Stochastic depth rate. Default: 0.
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layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
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head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
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"""
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def __init__(self, num_classes=1000,
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depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], include_top=True,
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drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1.,
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):
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super().__init__()
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self.downsample_layers = [] # stem and 3 intermediate downsampling conv layers
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stem = tf.keras.Sequential([
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Conv2D(dims[0], kernel_size=4, strides=4, padding='same'),
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LayerNormalization(epsilon=1e-6)]
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)
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self.downsample_layers.append(stem)
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for i in range(3):
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downsample_layer = tf.keras.Sequential([
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LayerNormalization(epsilon=1e-6),
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@@ -127,28 +420,21 @@ class ConvNeXt(tf.keras.Model):
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self.stages.append(stage)
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cur += depths[i]
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if self.include_top:
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self.avg = GlobalAveragePooling2D()
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self.norm = LayerNormalization(epsilon=1e-6) # final norm layer
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self.head = Dense(num_classes)
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else:
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self.avg = None
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self.norm = None
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self.head = None
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self.flatten = Flatten()
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self.last = Dense(1)
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def forward_features(self, x):
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def call(self, x):
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x = tf.transpose(x, [0,2,3,1])
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for i in range(4):
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x = self.downsample_layers[i](x)
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x = self.stages[i](x)
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x = self.flatten(x)
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x = self.last(x)
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return x
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def call(self, x):
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x = self.forward_features(x)
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if self.include_top:
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x = self.avg(x)
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x = self.norm(x)
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x = self.head(x)
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return x
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model_urls = {
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