modelling_pruned_resnet50.py

# modeling_resnet.py
import torch
import torch.nn as nn
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import ImageClassifierOutput


class PrunedResNetConfig(PretrainedConfig):
    model_type = "resnet"

    def __init__(
        self, channel_config: dict[str, int] | None = None, num_classes=1000, **kwargs
    ):
        super().__init__(**kwargs)
        self.channel_config = channel_config
        self.num_classes = num_classes


class PrunedResNet50(PreTrainedModel):
    config_class = PrunedResNetConfig
    _tied_weights_keys = []

    def __init__(self, config: PrunedResNetConfig):
        super().__init__(config)
        self.config = config
        c = config.channel_config
        self.conv1 = nn.Conv2d(
            3, c["conv1"], kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = nn.BatchNorm2d(c["conv1"])
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(c, stage_idx=1, layers=3, stride=1)
        self.layer2 = self._make_layer(c, stage_idx=2, layers=4, stride=2)
        self.layer3 = self._make_layer(c, stage_idx=3, layers=6, stride=2)
        self.layer4 = self._make_layer(c, stage_idx=4, layers=3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        last_channel = c["layer4.2.conv3"]
        self.fc = nn.Linear(last_channel, config.num_classes)
        self.post_init()

    def _make_layer(self, c, stage_idx, layers, stride):
        # Builds a ResNet layer (e.g., layer1) containing multiple Bottleneck blocks
        blocks = []

        # The first block in a layer often handles stride and downsampling
        blocks.append(
            Bottleneck(
                inplanes=c[f"layer{stage_idx}.0.in"],
                planes=[
                    c[f"layer{stage_idx}.0.conv1"],
                    c[f"layer{stage_idx}.0.conv2"],
                    c[f"layer{stage_idx}.0.conv3"],
                ],
                stride=stride,
                downsample_planes=c.get(f"layer{stage_idx}.0.downsample.0", None),
            )
        )

        # Subsequent blocks
        for i in range(1, layers):
            blocks.append(
                Bottleneck(
                    inplanes=c[f"layer{stage_idx}.{i}.in"],
                    planes=[
                        c[f"layer{stage_idx}.{i}.conv1"],
                        c[f"layer{stage_idx}.{i}.conv2"],
                        c[f"layer{stage_idx}.{i}.conv3"],
                    ],
                )
            )

        return nn.Sequential(*blocks)

    def forward(self, pixel_values=None, labels=None, **kwargs):
        x = pixel_values
        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)
        logits = self.fc(x)
        loss = None
        if labels is not None:
            # CrossEntropyLoss handles the Softmax internally
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.num_classes), labels.view(-1))
        return ImageClassifierOutput(logits=logits, loss=loss)


class Bottleneck(nn.Module):
    # Standard Bottleneck but with dynamic channel sizes
    def __init__(self, inplanes, planes, stride=1, downsample_planes=None):
        super().__init__()
        c1, c2, c3 = planes  # The 3 conv widths inside the bottleneck

        self.conv1 = nn.Conv2d(inplanes, c1, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(c1)

        self.conv2 = nn.Conv2d(
            c1, c2, kernel_size=3, stride=stride, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(c2)

        self.conv3 = nn.Conv2d(c2, c3, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(c3)

        self.relu = nn.ReLU(inplace=True)

        self.downsample = None
        if downsample_planes is not None:
            self.downsample = nn.Sequential(
                nn.Conv2d(
                    inplanes,
                    downsample_planes,
                    kernel_size=1,
                    stride=stride,
                    bias=False,
                ),
                nn.BatchNorm2d(downsample_planes),
            )

    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