models/core/extractor.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

# -- Added by Chu King on 16th November 2025 for debugging purposes.
import os, signal
import logging
import torch.distributed as dist

class ResidualBlock(nn.Module):
    def __init__(self, in_planes, planes, norm_fn="group", stride=1):
        super(ResidualBlock, self).__init__()

        self.conv1 = nn.Conv2d(
            in_planes, planes, kernel_size=3, padding=1, stride=stride
        )
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
        self.relu = nn.ReLU(inplace=True)

        num_groups = planes // 8

        if norm_fn == "group":
            self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
            self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
            self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)

        elif norm_fn == "batch":
            self.norm1 = nn.BatchNorm2d(planes)
            self.norm2 = nn.BatchNorm2d(planes)
            self.norm3 = nn.BatchNorm2d(planes)

        elif norm_fn == "instance":
            self.norm1 = nn.InstanceNorm2d(planes, affine=False)
            self.norm2 = nn.InstanceNorm2d(planes, affine=False)
            self.norm3 = nn.InstanceNorm2d(planes, affine=False)

        elif norm_fn == "none":
            self.norm1 = nn.Sequential()
            self.norm2 = nn.Sequential()
            self.norm3 = nn.Sequential()

        self.downsample = nn.Sequential(
            nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3
        )

    def forward(self, x):
        y = x
        y = self.relu(self.norm1(self.conv1(y)))
        y = self.relu(self.norm2(self.conv2(y)))

        # -- ensures that x is transformed to the correct shape so it can be added to y.
        x = self.downsample(x)

        return self.relu(x + y)


class BasicEncoder(nn.Module):
    def __init__(self, output_dim=128, norm_fn="batch", dropout=0.0):
        super(BasicEncoder, self).__init__()
        self.norm_fn = norm_fn

        if self.norm_fn == "group":
            self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)

        elif self.norm_fn == "batch":
            self.norm1 = nn.BatchNorm2d(64)

        elif self.norm_fn == "instance":
            self.norm1 = nn.InstanceNorm2d(64, affine=False)

        elif self.norm_fn == "none":
            self.norm1 = nn.Sequential()

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.relu1 = nn.ReLU(inplace=True)

        self.in_planes = 64
        self.layer1 = self._make_layer(64, stride=1)
        self.layer2 = self._make_layer(96, stride=2)
        self.layer3 = self._make_layer(128, stride=1)

        # output convolution
        self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1)

        self.dropout = None
        if dropout > 0:
            self.dropout = nn.Dropout2d(p=dropout)

        # -- self.modules() is a PyTorch utility function that returns all submodules of this nn.Module recursively.
        # -- This means it will looop through every layer: conv1, layer1, layer2, layer3, conv2 and so on.
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
                if m.weight is not None:
                    nn.init.constant_(m.weight, 1)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def _make_layer(self, dim, stride=1):
        layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
        layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
        layers = (layer1, layer2)

        self.in_planes = dim
        return nn.Sequential(*layers)

    def forward(self, x):
        # -- x = [L, R]
        # -- L, R ~ (b*t, c, h, w)

        # if input is list, combine batch dimension
        is_list = isinstance(x, tuple) or isinstance(x, list)
        if is_list:
            batch_dim = x[0].shape[0]
            x = torch.cat(x, dim=0)

        x = self.conv1(x)
        x = self.norm1(x)
        x = self.relu1(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)

        x = self.conv2(x)

        if self.dropout is not None:
            x = self.dropout(x)

        if is_list:
            x = torch.split(x, x.shape[0] // 2, dim=0)

        return x