prototype conditional pipeline
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# copied from https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from timm.models.layers import trunc_normal_, DropPath
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from timm.models.registry import register_model
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class Block(nn.Module):
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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, drop_path=0., layer_scale_init_value=1e-6):
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super().__init__()
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self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
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self.norm = LayerNorm(dim, eps=1e-6)
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self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
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self.act = nn.GELU()
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self.pwconv2 = nn.Linear(4 * dim, dim)
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self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),
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requires_grad=True) if layer_scale_init_value > 0 else None
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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def forward(self, x):
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input = x
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x = self.dwconv(x)
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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)
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x = self.pwconv1(x)
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x = self.act(x)
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x = self.pwconv2(x)
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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)
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return x
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class ConvNeXt(nn.Module):
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r""" ConvNeXt
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A PyTorch 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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in_chans (int): Number of input image channels. Default: 3
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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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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, in_chans=3, num_classes=1000,
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depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0.,
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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 = nn.ModuleList() # stem and 3 intermediate downsampling conv layers
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stem = nn.Sequential(
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nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
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LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
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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 = nn.Sequential(
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LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
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nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
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)
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self.downsample_layers.append(downsample_layer)
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self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks
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dp_rates=[x.item() for x in torch.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 = nn.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) 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.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer
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self.head = nn.Linear(dims[-1], num_classes)
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self.apply(self._init_weights)
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self.head.weight.data.mul_(head_init_scale)
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self.head.bias.data.mul_(head_init_scale)
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def _init_weights(self, m):
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if isinstance(m, (nn.Conv2d, nn.Linear)):
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trunc_normal_(m.weight, std=.02)
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nn.init.constant_(m.bias, 0)
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def forward_features(self, x):
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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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return self.norm(x.mean([-2, -1])) # global average pooling, (N, C, H, W) -> (N, C)
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def forward(self, x):
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x = self.forward_features(x)
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x = self.head(x)
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return x
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class LayerNorm(nn.Module):
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r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
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The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
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shape (batch_size, height, width, channels) while channels_first corresponds to inputs
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with shape (batch_size, channels, height, width).
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"""
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def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(normalized_shape))
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self.bias = nn.Parameter(torch.zeros(normalized_shape))
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self.eps = eps
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self.data_format = data_format
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if self.data_format not in ["channels_last", "channels_first"]:
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raise NotImplementedError
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self.normalized_shape = (normalized_shape, )
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def forward(self, x):
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if self.data_format == "channels_last":
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return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
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elif self.data_format == "channels_first":
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u = x.mean(1, keepdim=True)
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s = (x - u).pow(2).mean(1, keepdim=True)
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x = (x - u) / torch.sqrt(s + self.eps)
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x = self.weight[:, None, None] * x + self.bias[:, None, None]
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return x
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model_urls = {
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"convnext_tiny_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
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"convnext_small_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth",
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"convnext_base_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth",
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"convnext_large_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth",
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"convnext_base_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth",
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"convnext_large_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth",
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"convnext_xlarge_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth",
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}
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@register_model
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def convnext_tiny(pretrained=False, **kwargs):
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model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
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if pretrained:
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url = model_urls['convnext_tiny_1k']
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checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
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model.load_state_dict(checkpoint["model"])
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return model
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@register_model
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def convnext_small(pretrained=False, **kwargs):
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model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
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if pretrained:
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url = model_urls['convnext_small_1k']
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checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
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model.load_state_dict(checkpoint["model"])
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return model
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@register_model
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def convnext_base(pretrained=False, in_22k=False, **kwargs):
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model = ConvNeXt(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], **kwargs)
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if pretrained:
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url = model_urls['convnext_base_22k'] if in_22k else model_urls['convnext_base_1k']
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checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
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model.load_state_dict(checkpoint["model"])
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return model
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@register_model
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def convnext_large(pretrained=False, in_22k=False, **kwargs):
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model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], **kwargs)
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if pretrained:
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url = model_urls['convnext_large_22k'] if in_22k else model_urls['convnext_large_1k']
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checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
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model.load_state_dict(checkpoint["model"])
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return model
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@register_model
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def convnext_xlarge(pretrained=False, in_22k=False, **kwargs):
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model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs)
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if pretrained:
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url = model_urls['convnext_xlarge_22k'] if in_22k else model_urls['convnext_xlarge_1k']
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checkpoint = torch.hub.load_state_dict_from_url(url=url, map_location="cpu", check_hash=True)
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model.load_state_dict(checkpoint["model"])
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return model
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