modeling_moco.py

"""Model classes for MoCo models compatible with transformers"""

import sys
import os
from pathlib import Path
import torch
import torch.nn as nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import ImageClassifierOutputWithNoAttention
from safetensors.torch import load_file

# Embed ResNet code directly to avoid import issues when transformers caches modules
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    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 Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    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)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(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=51, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        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,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        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.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        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


# Import configuration
try:
    from configuration_moco import MoCoResNetConfig
except ImportError:
    # Fallback: import from same directory
    import importlib.util
    config_path = Path(__file__).parent / "configuration_moco.py"
    spec = importlib.util.spec_from_file_location("configuration_moco", config_path)
    config_module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(config_module)
    MoCoResNetConfig = config_module.MoCoResNetConfig


class MoCoResNetForImageClassification(PreTrainedModel):
    """MoCo ResNet model for image classification or feature extraction"""
    
    config_class = MoCoResNetConfig
    
    def __init__(self, config):
        super().__init__(config)
        
        # Build ResNet model from config
        if config.block == "Bottleneck":
            block = Bottleneck
        elif config.block == "BasicBlock":
            block = BasicBlock
        else:
            raise ValueError(f"Unsupported block type: {config.block}")
        
        # Create ResNet backbone
        # For MoCo models, we typically want feature extraction (no classification head)
        # But we need to initialize with some num_classes, then replace fc if needed
        self.model = ResNet(
            block=block,
            layers=config.layers,
            num_classes=2048  # Standard ResNet-50 feature dimension
        )
        
        # Replace classification head based on num_labels
        if config.num_labels == 0:
            # Feature extraction mode: replace fc with identity
            self.model.fc = nn.Identity()
        else:
            # Classification mode: replace fc with new classifier
            self.model.fc = nn.Linear(512 * block.expansion, config.num_labels)
    
    def forward(self, pixel_values=None, labels=None, return_dict=None, **kwargs):
        """
        Args:
            pixel_values: Input images (B, C, H, W)
            labels: Optional labels for loss computation (only if num_labels > 0)
            return_dict: Whether to return a ModelOutput instead of a plain tuple
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        
        if pixel_values is None:
            raise ValueError("pixel_values must be provided")
        
        # Forward through ResNet
        features = self.model(pixel_values)
        
        # If num_labels > 0, features are logits; otherwise they're feature vectors
        if self.config.num_labels > 0:
            logits = features
            loss = None
            if labels is not None:
                loss_fct = nn.CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
            
            if not return_dict:
                output = (logits,)
                return (loss,) + output if loss is not None else output
            
            return ImageClassifierOutputWithNoAttention(
                loss=loss,
                logits=logits,
                hidden_states=None,
            )
        else:
            # Feature extraction mode
            if not return_dict:
                return (features,)
            return {"features": features}
    
    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        """Load model from pretrained checkpoint"""
        config = kwargs.pop("config", None)
        if config is None:
            config = MoCoResNetConfig.from_pretrained(pretrained_model_name_or_path)
        
        model = cls(config)
        
        # Load weights from safetensors
        model_path = Path(pretrained_model_name_or_path)
        safetensors_path = model_path / "model.safetensors"
        
        if safetensors_path.exists():
            state_dict = load_file(str(safetensors_path))
            # Remove 'model.' prefix if present
            state_dict_clean = {}
            for k, v in state_dict.items():
                if k.startswith("model."):
                    state_dict_clean[k[6:]] = v
                else:
                    state_dict_clean[k] = v
            model.model.load_state_dict(state_dict_clean, strict=False)
        else:
            raise FileNotFoundError(f"Model weights not found at {safetensors_path}")
        
        return model