convolution.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Optional

class ResNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = downsample
    
    def forward(self, x):
        identity = x
        
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        
        out = self.conv2(out)
        out = self.bn2(out)
        
        if self.downsample is not None:
            identity = self.downsample(x)
        
        out += identity
        out = self.relu(out)
        
        return out

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000, in_channels=3):
        super().__init__()
        self.in_channels = 64
        
        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)
    
    def _make_layer(self, block, out_channels, blocks, stride=1):
        downsample = None
        if stride != 1 or self.in_channels != out_channels:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )
        
        layers = []
        layers.append(block(self.in_channels, out_channels, stride, downsample))
        self.in_channels = out_channels
        
        for _ in range(1, blocks):
            layers.append(block(out_channels, out_channels))
        
        return nn.Sequential(*layers)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        
        return x

class EfficientNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride, expand_ratio, se_ratio=0.25):
        super().__init__()
        self.stride = stride
        self.use_residual = (stride == 1 and in_channels == out_channels)
        
        hidden_dim = in_channels * expand_ratio
        self.use_expansion = expand_ratio != 1
        
        if self.use_expansion:
            self.expand_conv = nn.Sequential(
                nn.Conv2d(in_channels, hidden_dim, 1, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.SiLU(inplace=True)
            )
        
        self.depthwise_conv = nn.Sequential(
            nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, kernel_size // 2, groups=hidden_dim, bias=False),
            nn.BatchNorm2d(hidden_dim),
            nn.SiLU(inplace=True)
        )
        
        se_channels = max(1, int(in_channels * se_ratio))
        self.se = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(hidden_dim, se_channels, 1),
            nn.SiLU(inplace=True),
            nn.Conv2d(se_channels, hidden_dim, 1),
            nn.Sigmoid()
        )
        
        self.project_conv = nn.Sequential(
            nn.Conv2d(hidden_dim, out_channels, 1, bias=False),
            nn.BatchNorm2d(out_channels)
        )
    
    def forward(self, x):
        identity = x
        
        if self.use_expansion:
            x = self.expand_conv(x)
        
        x = self.depthwise_conv(x)
        
        se_weight = self.se(x)
        x = x * se_weight
        
        x = self.project_conv(x)
        
        if self.use_residual:
            x = x + identity
        
        return x

class UNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, down=True):
        super().__init__()
        self.down = down
        
        if down:
            self.conv = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 3, padding=1),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True),
                nn.Conv2d(out_channels, out_channels, 3, padding=1),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True)
            )
            self.pool = nn.MaxPool2d(2)
        else:
            self.conv = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 3, padding=1),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True),
                nn.Conv2d(out_channels, out_channels, 3, padding=1),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True)
            )
            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, 2, stride=2)
    
    def forward(self, x, skip=None):
        if self.down:
            x = self.conv(x)
            pool = self.pool(x)
            return x, pool
        else:
            x = self.up(x)
            if skip is not None:
                x = torch.cat([x, skip], dim=1)
            x = self.conv(x)
            return x

class ConvNeXtBlock(nn.Module):
    def __init__(self, dim, drop_path=0.0, layer_scale_init_value=1e-6):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)
        self.norm = nn.LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, 4 * dim)
        self.act = nn.GELU()
        self.pwconv2 = nn.Linear(4 * dim, dim)
        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(dim)) if layer_scale_init_value > 0 else None
        
        from .modules import DropPath
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
    
    def forward(self, x):
        identity = x
        x = self.dwconv(x)
        x = x.permute(0, 2, 3, 1)
        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)
        x = identity + self.drop_path(x)
        return x

class DilatedConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels, dilation_rates=[1, 2, 4, 8]):
        super().__init__()
        self.convs = nn.ModuleList([
            nn.Sequential(
                nn.Conv2d(in_channels, out_channels // len(dilation_rates), 3, padding=d, dilation=d),
                nn.BatchNorm2d(out_channels // len(dilation_rates)),
                nn.ReLU(inplace=True)
            )
            for d in dilation_rates
        ])
    
    def forward(self, x):
        return torch.cat([conv(x) for conv in self.convs], dim=1)

class PyramidPoolingModule(nn.Module):
    def __init__(self, in_channels, out_channels, pool_sizes=[1, 2, 3, 6]):
        super().__init__()
        self.stages = nn.ModuleList([
            nn.Sequential(
                nn.AdaptiveAvgPool2d(size),
                nn.Conv2d(in_channels, out_channels // len(pool_sizes), 1),
                nn.BatchNorm2d(out_channels // len(pool_sizes)),
                nn.ReLU(inplace=True)
            )
            for size in pool_sizes
        ])
    
    def forward(self, x):
        h, w = x.size(2), x.size(3)
        features = [x]
        for stage in self.stages:
            pooled = stage(x)
            features.append(F.interpolate(pooled, size=(h, w), mode='bilinear', align_corners=False))
        return torch.cat(features, dim=1)