mae-cnn/models/convnext.py

# Copyright (c) ByteDance, Inc. and its 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.
#
# This file is basically a copy of: https://github.com/facebookresearch/ConvNeXt/blob/06f7b05f922e21914916406141f50f82b4a15852/models/convnext.py

import torch
import torch.nn as nn
from timm.models.layers import trunc_normal_
from timm.models.registry import register_model

from encoder import SparseConvNeXtBlock, SparseConvNeXtLayerNorm


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., global_pool='avg',
                 sparse=True,
                 ):
        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),
            SparseConvNeXtLayerNorm(dims[0], eps=1e-6, data_format="channels_first", sparse=sparse)
        )
        self.downsample_layers.append(stem)
        for i in range(3):
            downsample_layer = nn.Sequential(
                SparseConvNeXtLayerNorm(dims[i], eps=1e-6, data_format="channels_first", sparse=sparse),
                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
        self.drop_path_rate = drop_path_rate
        self.layer_scale_init_value = layer_scale_init_value
        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(
                *[SparseConvNeXtBlock(dim=dims[i], drop_path=dp_rates[cur + j],
                                      layer_scale_init_value=layer_scale_init_value, sparse=sparse) for j in range(depths[i])]
            )
            self.stages.append(stage)
            cur += depths[i]
        self.depths = depths
        
        self.apply(self._init_weights)
        if num_classes > 0:
            self.norm = SparseConvNeXtLayerNorm(dims[-1], eps=1e-6, sparse=False)  # final norm layer for LE/FT; should not be sparse
            self.fc = nn.Linear(dims[-1], num_classes)
        else:
            self.norm = nn.Identity()
            self.fc = nn.Identity()
    
    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(self, x, hierarchy=0):
        ls = []
        for i in range(4):
            x = self.downsample_layers[i](x)
            x = self.stages[i](x)
            ls.append(x if hierarchy >= 4-i else None)
        if hierarchy:
            return ls
        else:
            return self.fc(self.norm(x.mean([-2, -1]))) # (B, C, H, W) =mean=> (B, C) =norm&fc=> (B, NumCls)
    
    def get_classifier(self):
        return self.fc
    
    def extra_repr(self):
        return f'drop_path_rate={self.drop_path_rate}, layer_scale_init_value={self.layer_scale_init_value:g}'


@register_model
def convnext_tiny(pretrained=False, in_22k=False, **kwargs):
    model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
    return model


@register_model
def convnext_small(pretrained=False, in_22k=False, **kwargs):
    model = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], **kwargs)
    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)
    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)
    return model


if __name__ == '__main__':
    from timm.models import create_model
    cnx = create_model('convnext_small', sparse=False)
    
    def prt(lst):
        print([tuple(t.shape) if t is not None else '(None)' for t in lst])
    with torch.no_grad():
        inp = torch.rand(2, 3, 224, 224)
        prt(cnx(inp))
        prt(cnx(inp, hierarchy=1))
        prt(cnx(inp, hierarchy=2))
        prt(cnx(inp, hierarchy=3))
        prt(cnx(inp, hierarchy=4))