502 lines
21 KiB
Python
502 lines
21 KiB
Python
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, Conv2DTranspose, Dense, DepthwiseConv2D,
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GlobalAveragePooling2D, Layer, Activation, Lambda,
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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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if (not is_training) or (drop_prob == 0.):
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return inputs
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# Compute keep_prob
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keep_prob = 1.0 - drop_prob
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# Compute drop_connect tensor
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random_tensor = keep_prob
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shape = (tf.shape(inputs)[0],) + (1,) * \
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(len(tf.shape(inputs)) - 1)
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random_tensor += tf.random.uniform(shape, dtype=inputs.dtype)
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binary_tensor = tf.floor(random_tensor)
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output = tf.math.divide(inputs, keep_prob) * binary_tensor
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return output
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class DropPath(tf.keras.layers.Layer):
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# borrowed from https://github.com/rishigami/Swin-Transformer-TF/blob/main/swintransformer/model.py
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def __init__(self, drop_prob=None):
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super().__init__()
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self.drop_prob = drop_prob
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def call(self, x, training):
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return drop_path(x, self.drop_prob, training)
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class Block(Layer):
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r""" ConvNeXt Block. There are two equivalent implementations:
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(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
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(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
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We use (2) as we find it slightly faster in PyTorch
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Args:
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dim (int): Number of input channels.
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drop_path (float): Stochastic depth rate. Default: 0.0
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layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
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"""
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def __init__(self, dim, kernel_size=7, drop_path=0., layer_scale_init_value=1e-6, prefix=''):
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super().__init__()
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self.dwconv = DepthwiseConv2D(
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kernel_size=kernel_size, padding='same') # depthwise conv
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self.norm = LayerNormalization(epsilon=1e-6)
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# pointwise/1x1 convs, implemented with linear layers
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self.pwconv1 = Dense(4 * dim)
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self.act = tf.keras.activations.gelu
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self.pwconv2 = Dense(dim)
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self.drop_path = DropPath(drop_path)
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self.dim = dim
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self.layer_scale_init_value = layer_scale_init_value
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self.prefix = prefix
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def build(self, input_shape):
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self.gamma = tf.Variable(
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initial_value=self.layer_scale_init_value * tf.ones((self.dim)),
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trainable=True,
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name=f'{self.prefix}/gamma')
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self.built = True
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def call(self, x, training):
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input = x
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x = self.dwconv(x, training=training)
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# x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
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x = self.norm(x, training=training)
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x = self.pwconv1(x, training=training)
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x = self.act(x)
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x = self.pwconv2(x, training=training)
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if self.gamma is not None:
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x = self.gamma * x
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# x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
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x = input + self.drop_path(x, training=training)
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return x
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class ConvNeXt(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, kernel_size=7,
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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.include_top = include_top
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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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stem = [Conv2D(dims[0], kernel_size=4, strides=4, padding='same'), LayerNormalization(epsilon=1e-6)]
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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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downsample_layer = [LayerNormalization(epsilon=1e-6), Conv2D(dims[i+1], kernel_size=2, strides=2, padding='same')]
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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], kernel_size=kernel_size,
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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, training):
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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.downsample_layers[i][0](x, training=training)
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x = self.downsample_layers[i][1](x, training=training)
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x = self.stages[i](x, training=training)
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return x
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def call(self, x, training):
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x = self.forward_features(x, training=training)
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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], kernel_size=7,
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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], kernel_size=kernel_size,
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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], kernel_size=7,
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drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1., learn_weights=False,
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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], kernel_size=kernel_size,
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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 not learn_weights:
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self.last = Dense(3, activation='tanh')
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else:
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# self.last = Activation('softmax')
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self.last = lambda x, training: x
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# self.last = Dense(24, activation='softmax')
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def call(self, x, training):
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for i in reversed(range(len(self.upsample_layers))):
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x = self.upsample_layers[i](x, training=training)
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x = self.stages[i](x, training=training)
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x = self.last(x, training=training)
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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], kernel_size=7,
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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], kernel_size=kernel_size, 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(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.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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# self.last = Dense(nr_of_classes, activation='softmax')
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self.last = Dense(nr_of_classes - 1)
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def call(self, x, training):
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for i in reversed(range(len(self.downsample_layers))):
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x = self.downsample_layers[i](x, training=training)
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x = self.stages[i](x, training=training)
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x = self.last(x, training=training)
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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([
|
|
LayerNormalization(epsilon=1e-6),
|
|
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)
|
|
|
|
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))]
|
|
cur = 0
|
|
for i in range(4):
|
|
stage = tf.keras.Sequential(
|
|
[Block(dim=dims[i], drop_path=dp_rates[cur + j],
|
|
layer_scale_init_value=layer_scale_init_value, prefix=f'block{i}') for j in range(depths[i])]
|
|
)
|
|
self.stages.append(stage)
|
|
cur += depths[i]
|
|
|
|
self.flatten = Flatten()
|
|
self.last = Dense(1)
|
|
|
|
|
|
def call(self, x):
|
|
x = tf.transpose(x, [0,2,3,1])
|
|
for i in range(4):
|
|
x = self.downsample_layers[i](x)
|
|
x = self.stages[i](x)
|
|
x = self.flatten(x)
|
|
x = self.last(x)
|
|
return x
|
|
|
|
|
|
|
|
|
|
|
|
model_urls = {
|
|
"convnext_tiny_224": "https://github.com/bamps53/convnext-tf/releases/download/v0.1/convnext_tiny_1k_224_ema.h5",
|
|
"convnext_small_224": "https://github.com/bamps53/convnext-tf/releases/download/v0.1/convnext_small_1k_224_ema.h5",
|
|
"convnext_base_224": "https://github.com/bamps53/convnext-tf/releases/download/v0.1/convnext_base_22k_1k_224.h5",
|
|
"convnext_base_384": "https://github.com/bamps53/convnext-tf/releases/download/v0.1/convnext_base_22k_1k_384.h5",
|
|
"convnext_large_224": "https://github.com/bamps53/convnext-tf/releases/download/v0.1/convnext_large_22k_1k_224.h5",
|
|
"convnext_large_384": "https://github.com/bamps53/convnext-tf/releases/download/v0.1/convnext_large_22k_1k_384.h5",
|
|
"convnext_xlarge_224": "https://github.com/bamps53/convnext-tf/releases/download/v0.1/convnext_xlarge_22k_1k_224_ema.h5",
|
|
"convnext_xlarge_384": "https://github.com/bamps53/convnext-tf/releases/download/v0.1/convnext_xlarge_22k_1k_384_ema.h5",
|
|
}
|
|
|
|
|
|
model_configs = dict(
|
|
convnext_tiny=dict(
|
|
depths=[3, 3, 9, 3],
|
|
dims=[8, 16, 32, 512]
|
|
),
|
|
convnext_small=dict(
|
|
depths=[3, 3, 27, 3],
|
|
dims=[96, 192, 384, 768]
|
|
),
|
|
convnext_base=dict(
|
|
depths=[3, 3, 27, 3],
|
|
dims=[128, 256, 512, 1024]
|
|
),
|
|
convnext_large=dict(
|
|
depths=[3, 3, 27, 3],
|
|
dims=[192, 384, 768, 1536]
|
|
),
|
|
convnext_xlarge=dict(
|
|
depths=[3, 3, 27, 3],
|
|
dims=[256, 512, 1024, 2048]
|
|
),
|
|
)
|
|
|
|
|
|
def create_model(model_name='convnext_tiny_1k', input_shape=(224, 224), num_classes=1000, include_top=True, pretrained=True, use_tpu=False, **kwargs):
|
|
cfg = model_configs['_'.join(model_name.split('_')[:2])]
|
|
net = ConvNeXt(num_classes, cfg['depths'],
|
|
cfg['dims'], include_top, **kwargs)
|
|
net(tf.keras.Input(shape=(*input_shape, 3)))
|
|
if pretrained is True:
|
|
url = model_urls[model_name]
|
|
pretrained_ckpt = tf.keras.utils.get_file(
|
|
f'{model_name}.h5', url, untar=False)
|
|
if use_tpu:
|
|
load_locally = tf.saved_model.LoadOptions(
|
|
experimental_io_device='/job:localhost')
|
|
net.load_weights(
|
|
pretrained_ckpt, options=load_locally, skip_mismatch=True)
|
|
else:
|
|
net.load_weights(pretrained_ckpt, skip_mismatch=True, by_name=True)
|
|
|
|
return net
|