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prototype conditional pipeline

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luni
2022-06-10 06:59:58 +00:00
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convnext/models/convnext.py
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# copied from https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
from timm.models.registry import register_model
class Block(nn.Module):
r""" ConvNeXt Block. There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
We use (2) as we find it slightly faster in PyTorch
Args:
dim (int): Number of input channels.
drop_path (float): Stochastic depth rate. Default: 0.0
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
"""
def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
super().__init__()
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
self.norm = LayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
self.act = nn.GELU()
self.pwconv2 = nn.Linear(4 * dim, dim)
self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),
requires_grad=True) if layer_scale_init_value > 0 else None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
input = x
x = self.dwconv(x)
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.norm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
if self.gamma is not None:
x = self.gamma * x
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
x = input + self.drop_path(x)
return x
class ConvNeXt(nn.Module):
r""" ConvNeXt
A PyTorch impl of : `A ConvNet for the 2020s` -
https://arxiv.org/pdf/2201.03545.pdf
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000
depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
drop_path_rate (float): Stochastic depth rate. Default: 0.
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
"""
def __init__(self, in_chans=3, num_classes=1000,
depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0.,
layer_scale_init_value=1e-6, head_init_scale=1.,
):
super().__init__()
self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers
stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
)
self.downsample_layers.append(stem)
for i in range(3):
downsample_layer = nn.Sequential(
LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
)
self.downsample_layers.append(downsample_layer)
self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks
dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
cur = 0
for i in range(4):
stage = nn.Sequential(
*[Block(dim=dims[i], drop_path=dp_rates[cur + j],
layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]
)
self.stages.append(stage)
cur += depths[i]
self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer
self.head = nn.Linear(dims[-1], num_classes)
self.apply(self._init_weights)
self.head.weight.data.mul_(head_init_scale)
self.head.bias.data.mul_(head_init_scale)
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
trunc_normal_(m.weight, std=.02)
nn.init.constant_(m.bias, 0)
def forward_features(self, x):
for i in range(4):
x = self.downsample_layers[i](x)
x = self.stages[i](x)
return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C)
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
class LayerNorm(nn.Module):
r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width, channels) while channels_first corresponds to inputs
with shape (batch_size, channels, height, width).
"""
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
super().__init__()
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.data_format = data_format
if self.data_format not in ["channels_last", "channels_first"]:
raise NotImplementedError
self.normalized_shape = (normalized_shape, )
def forward(self, x):
if self.data_format == "channels_last":
return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
elif self.data_format == "channels_first":
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
model_urls = {
"convnext_tiny_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
"convnext_small_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth",
"convnext_base_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth",
"convnext_large_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth",
"convnext_base_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth",
"convnext_large_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth",
"convnext_xlarge_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth",
}
@register_model
def convnext_tiny(pretrained=False, **kwargs):
model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
if pretrained:
url = model_urls['convnext_tiny_1k']
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
model.load_state_dict(checkpoint["model"])
return model
@register_model
def convnext_small(pretrained=False, **kwargs):
model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
if pretrained:
url = model_urls['convnext_small_1k']
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
model.load_state_dict(checkpoint["model"])
return model
@register_model
def convnext_base(pretrained=False, in_22k=False, **kwargs):
model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
if pretrained:
url = model_urls['convnext_base_22k'] if in_22k else model_urls['convnext_base_1k']
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
model.load_state_dict(checkpoint["model"])
return model
@register_model
def convnext_large(pretrained=False, in_22k=False, **kwargs):
model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
if pretrained:
url = model_urls['convnext_large_22k'] if in_22k else model_urls['convnext_large_1k']
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
model.load_state_dict(checkpoint["model"])
return model
@register_model
def convnext_xlarge(pretrained=False, in_22k=False, **kwargs):
model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
if pretrained:
url = model_urls['convnext_xlarge_22k'] if in_22k else model_urls['convnext_xlarge_1k']
checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
model.load_state_dict(checkpoint["model"])
return model
convnext/models/convnext_tf.py
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import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import (Conv2D, Dense, DepthwiseConv2D,
GlobalAveragePooling2D, Layer,
LayerNormalization)
def drop_path(inputs, drop_prob, is_training):
# borrowed from https://github.com/rishigami/Swin-Transformer-TF/blob/main/swintransformer/model.py
if (not is_training) or (drop_prob == 0.):
return inputs
# Compute keep_prob
keep_prob = 1.0 - drop_prob
# Compute drop_connect tensor
random_tensor = keep_prob
shape = (tf.shape(inputs)[0],) + (1,) * \
(len(tf.shape(inputs)) - 1)
random_tensor += tf.random.uniform(shape, dtype=inputs.dtype)
binary_tensor = tf.floor(random_tensor)
output = tf.math.divide(inputs, keep_prob) * binary_tensor
return output
class DropPath(tf.keras.layers.Layer):
# borrowed from https://github.com/rishigami/Swin-Transformer-TF/blob/main/swintransformer/model.py
def __init__(self, drop_prob=None):
super().__init__()
self.drop_prob = drop_prob
def call(self, x, training=None):
return drop_path(x, self.drop_prob, training)
class Block(Layer):
r""" ConvNeXt Block. There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
We use (2) as we find it slightly faster in PyTorch
Args:
dim (int): Number of input channels.
drop_path (float): Stochastic depth rate. Default: 0.0
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
"""
def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6, prefix=''):
super().__init__()
self.dwconv = DepthwiseConv2D(
kernel_size=7, padding='same') # depthwise conv
self.norm = LayerNormalization(epsilon=1e-6)
# pointwise/1x1 convs, implemented with linear layers
self.pwconv1 = Dense(4 * dim)
self.act = tf.keras.activations.gelu
self.pwconv2 = Dense(dim)
self.drop_path = DropPath(drop_path)
self.dim = dim
self.layer_scale_init_value = layer_scale_init_value
self.prefix = prefix
def build(self, input_shape):
self.gamma = tf.Variable(
initial_value=self.layer_scale_init_value * tf.ones((self.dim)),
trainable=True,
name=f'{self.prefix}/gamma')
self.built = True
def call(self, x):
input = x
x = self.dwconv(x)
# x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.norm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
if self.gamma is not None:
x = self.gamma * x
# x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
x = input + self.drop_path(x)
return x
class ConvNeXt(tf.keras.Model):
r""" ConvNeXt
A Tensorflow keras impl of : `A ConvNet for the 2020s` -
https://arxiv.org/pdf/2201.03545.pdf
Args:
num_classes (int): Number of classes for classification head. Default: 1000
depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
include_top (bool): whether to add head or just use it as feature extractor. Default: True
drop_path_rate (float): Stochastic depth rate. Default: 0.
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
"""
def __init__(self, num_classes=1000,
depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], include_top=True,
drop_path_rate=0., layer_scale_init_value=1e-6, head_init_scale=1.,
):
super().__init__()
self.include_top = include_top
self.downsample_layers = [] # stem and 3 intermediate downsampling conv layers
stem = tf.keras.Sequential([
Conv2D(dims[0], kernel_size=4, strides=4, padding='same'),
LayerNormalization(epsilon=1e-6)]
)
self.downsample_layers.append(stem)
for i in range(3):
downsample_layer = tf.keras.Sequential([
LayerNormalization(epsilon=1e-6),
Conv2D(dims[i+1], kernel_size=2, strides=2, padding='same')]
)
self.downsample_layers.append(downsample_layer)
self.stages = [] # 4 feature resolution stages, each consisting of multiple residual blocks
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]
if self.include_top:
self.avg = GlobalAveragePooling2D()
self.norm = LayerNormalization(epsilon=1e-6) # final norm layer
self.head = Dense(num_classes)
else:
self.avg = None
self.norm = None
self.head = None
def forward_features(self, x):
for i in range(4):
x = self.downsample_layers[i](x)
x = self.stages[i](x)
return x
def call(self, x):
x = self.forward_features(x)
if self.include_top:
x = self.avg(x)
x = self.norm(x)
x = self.head(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