modeling_resnet.py

# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" ResNet model configuration and model."""

from transformers import PretrainedConfig, PreTrainedModel
from typing import List
import torch
from timm.models.resnet import BasicBlock, Bottleneck, ResNet

BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}

class ResnetConfig(PretrainedConfig):


    def __init__(
        self,
        block_type="bottleneck",
        layers: list[int] = [3, 4, 6, 3],
        num_classes: int = 1000,
        input_channels: int = 3,
        cardinality: int = 1,
        base_width: int = 64,
        stem_width: int = 64,
        stem_type: str = "",
        avg_down: bool = False,
        **kwargs,
    ):
        if block_type not in ["basic", "bottleneck"]:
            raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
        if stem_type not in ["", "deep", "deep-tiered"]:
            raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")

        self.block_type = block_type
        self.layers = layers
        self.num_classes = num_classes
        self.input_channels = input_channels
        self.cardinality = cardinality
        self.base_width = base_width
        self.stem_width = stem_width
        self.stem_type = stem_type
        self.avg_down = avg_down
        super().__init__(**kwargs)

class ResnetModel(PreTrainedModel):
    config_class = ResnetConfig

    def __init__(self, config):
        super().__init__(config)
        block_layer = BLOCK_MAPPING[config.block_type]
        self.model = ResNet(
            block_layer,
            config.layers,
            num_classes=config.num_classes,
            in_chans=config.input_channels,
            cardinality=config.cardinality,
            base_width=config.base_width,
            stem_width=config.stem_width,
            stem_type=config.stem_type,
            avg_down=config.avg_down,
        )

    def forward(self, tensor):
        return self.model.forward_features(tensor)

class ResnetModelForImageClassification(PreTrainedModel):
    config_class = ResnetConfig

    def __init__(self, config):
        super().__init__(config)
        block_layer = BLOCK_MAPPING[config.block_type]
        self.model = ResNet(
            block_layer,
            config.layers,
            num_classes=config.num_classes,
            in_chans=config.input_channels,
            cardinality=config.cardinality,
            base_width=config.base_width,
            stem_width=config.stem_width,
            stem_type=config.stem_type,
            avg_down=config.avg_down,
        )

    def forward(self, tensor, labels=None):
        logits = self.model(tensor)
        if labels is not None:
            loss = torch.nn.functional.cross_entropy(logits, labels)
            return {"loss": loss, "logits": logits}
        return {"logits": logits}