training/models/modules.py

# --------------------------------------------------------
# Reversible Column Networks
# Copyright (c) 2022 Megvii Inc.
# Licensed under The Apache License 2.0 [see LICENSE for details]
# Written by Yuxuan Cai
# --------------------------------------------------------

import imp
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import DropPath


class UpSampleConvnext(nn.Module):
    def __init__(self, ratio, inchannel, outchannel):
        super().__init__()
        self.ratio = ratio
        self.channel_reschedule = nn.Sequential(  
                                        # LayerNorm(inchannel, eps=1e-6, data_format="channels_last"),
                                        nn.Linear(inchannel, outchannel),
                                        LayerNorm(outchannel, eps=1e-6, data_format="channels_last"))
        self.upsample  = nn.Upsample(scale_factor=2**ratio, mode='nearest')
    def forward(self, x):
        x = x.permute(0, 2, 3, 1)
        x = self.channel_reschedule(x)
        x = x = x.permute(0, 3, 1, 2)
        
        return self.upsample(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_first", elementwise_affine = True):
        super().__init__()
        self.elementwise_affine = elementwise_affine
        if elementwise_affine:
            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)
            if self.elementwise_affine:
                x = self.weight[:, None, None] * x + self.bias[:, None, None]
            return x


class ConvNextBlock(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, in_channel, hidden_dim, out_channel, kernel_size=3, layer_scale_init_value=1e-6, drop_path= 0.0):
        super().__init__()
        self.dwconv = nn.Conv2d(in_channel, in_channel, kernel_size=kernel_size, padding=(kernel_size - 1) // 2, groups=in_channel) # depthwise conv
        self.norm = nn.LayerNorm(in_channel, eps=1e-6)
        self.pwconv1 = nn.Linear(in_channel, hidden_dim) # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.pwconv2 = nn.Linear(hidden_dim, out_channel)
        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((out_channel)), 
                                    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)
        # print(f"x min: {x.min()}, x max: {x.max()}, input min: {input.min()}, input max: {input.max()}, x mean: {x.mean()}, x var: {x.var()}, ratio: {torch.sum(x>8)/x.numel()}")
        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 Decoder(nn.Module):
    def __init__(self, depth=[2,2,2,2], dim=[112, 72, 40, 24], block_type = None, kernel_size = 3) -> None:
        super().__init__()
        self.depth = depth
        self.dim = dim
        self.block_type = block_type
        self._build_decode_layer(dim, depth, kernel_size)
        self.projback = nn.Sequential(
            nn.Conv2d(
                in_channels=dim[-1],
                out_channels=4 ** 2 * 3, kernel_size=1),
            nn.PixelShuffle(4),
        )

    def _build_decode_layer(self, dim, depth, kernel_size):
        normal_layers = nn.ModuleList()
        upsample_layers = nn.ModuleList()
        proj_layers = nn.ModuleList()

        norm_layer = LayerNorm

        for i in range(1, len(dim)):
            module = [self.block_type(dim[i], dim[i], dim[i], kernel_size) for _ in range(depth[i])]
            normal_layers.append(nn.Sequential(*module))
            upsample_layers.append(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True))
            proj_layers.append(nn.Sequential(
                nn.Conv2d(dim[i-1], dim[i], 1, 1), 
                norm_layer(dim[i]),
                nn.GELU()
                ))
        self.normal_layers = normal_layers
        self.upsample_layers = upsample_layers
        self.proj_layers = proj_layers

    def _forward_stage(self, stage, x):
        x = self.proj_layers[stage](x)
        x = self.upsample_layers[stage](x)
        return self.normal_layers[stage](x)

    def forward(self, c3):
        x = self._forward_stage(0, c3) #14
        x = self._forward_stage(1, x) #28
        x = self._forward_stage(2, x) #56
        x = self.projback(x)
        return x

class SimDecoder(nn.Module):
    def __init__(self, in_channel, encoder_stride) -> None:
        super().__init__()
        self.projback = nn.Sequential(
            LayerNorm(in_channel),
            nn.Conv2d(
                in_channels=in_channel,
                out_channels=encoder_stride ** 2 * 3, kernel_size=1),
            nn.PixelShuffle(encoder_stride),
        )

    def forward(self, c3):
        return self.projback(c3)