# coding=utf-8 # Copyright 2022 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. """Convert ResNet checkpoints from timm.""" import argparse import json from dataclasses import dataclass, field from functools import partial from pathlib import Path from typing import List import timm import torch import torch.nn as nn from huggingface_hub import hf_hub_download from torch import Tensor from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger() @dataclass class Tracker: module: nn.Module traced: List[nn.Module] = field(default_factory=list) handles: list = field(default_factory=list) def _forward_hook(self, m, inputs: Tensor, outputs: Tensor): has_not_submodules = len(list(m.modules())) == 1 or isinstance(m, nn.Conv2d) or isinstance(m, nn.BatchNorm2d) if has_not_submodules: self.traced.append(m) def __call__(self, x: Tensor): for m in self.module.modules(): self.handles.append(m.register_forward_hook(self._forward_hook)) self.module(x) [x.remove() for x in self.handles] return self @property def parametrized(self): # check the len of the state_dict keys to see if we have learnable params return list(filter(lambda x: len(list(x.state_dict().keys())) > 0, self.traced)) @dataclass class ModuleTransfer: src: nn.Module dest: nn.Module verbose: int = 0 src_skip: List = field(default_factory=list) dest_skip: List = field(default_factory=list) def __call__(self, x: Tensor): """ Transfer the weights of `self.src` to `self.dest` by performing a forward pass using `x` as input. Under the hood we tracked all the operations in both modules. """ dest_traced = Tracker(self.dest)(x).parametrized src_traced = Tracker(self.src)(x).parametrized src_traced = list(filter(lambda x: type(x) not in self.src_skip, src_traced)) dest_traced = list(filter(lambda x: type(x) not in self.dest_skip, dest_traced)) if len(dest_traced) != len(src_traced): raise Exception( f"Numbers of operations are different. Source module has {len(src_traced)} operations while" f" destination module has {len(dest_traced)}." ) for dest_m, src_m in zip(dest_traced, src_traced): dest_m.load_state_dict(src_m.state_dict()) if self.verbose == 1: print(f"Transfered from={src_m} to={dest_m}") def convert_weight_and_push(name: str, config: ResNetConfig, save_directory: Path, push_to_hub: bool = True): print(f"Converting {name}...") with torch.no_grad(): from_model = timm.create_model(name, pretrained=True).eval() our_model = ResNetForImageClassification(config).eval() module_transfer = ModuleTransfer(src=from_model, dest=our_model) x = torch.randn((1, 3, 224, 224)) module_transfer(x) assert torch.allclose(from_model(x), our_model(x).logits), "The model logits don't match the original one." checkpoint_name = f"resnet{'-'.join(name.split('resnet'))}" print(checkpoint_name) if push_to_hub: our_model.push_to_hub( repo_path_or_name=save_directory / checkpoint_name, commit_message="Add model", use_temp_dir=True, ) # we can use the convnext one image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k") image_processor.push_to_hub( repo_path_or_name=save_directory / checkpoint_name, commit_message="Add image processor", use_temp_dir=True, ) print(f"Pushed {checkpoint_name}") def convert_weights_and_push(save_directory: Path, model_name: str = None, push_to_hub: bool = True): filename = "imagenet-1k-id2label.json" num_labels = 1000 expected_shape = (1, num_labels) repo_id = "huggingface/label-files" num_labels = num_labels id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} id2label = id2label label2id = {v: k for k, v in id2label.items()} ImageNetPreTrainedConfig = partial(ResNetConfig, num_labels=num_labels, id2label=id2label, label2id=label2id) names_to_config = { "resnet18": ImageNetPreTrainedConfig( depths=[2, 2, 2, 2], hidden_sizes=[64, 128, 256, 512], layer_type="basic" ), "resnet26": ImageNetPreTrainedConfig( depths=[2, 2, 2, 2], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck" ), "resnet34": ImageNetPreTrainedConfig( depths=[3, 4, 6, 3], hidden_sizes=[64, 128, 256, 512], layer_type="basic" ), "resnet50": ImageNetPreTrainedConfig( depths=[3, 4, 6, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck" ), "resnet101": ImageNetPreTrainedConfig( depths=[3, 4, 23, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck" ), "resnet152": ImageNetPreTrainedConfig( depths=[3, 8, 36, 3], hidden_sizes=[256, 512, 1024, 2048], layer_type="bottleneck" ), } if model_name: convert_weight_and_push(model_name, names_to_config[model_name], save_directory, push_to_hub) else: for model_name, config in names_to_config.items(): convert_weight_and_push(model_name, config, save_directory, push_to_hub) return config, expected_shape if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default=None, type=str, help=( "The name of the model you wish to convert, it must be one of the supported resnet* architecture," " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted." ), ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=Path, required=True, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", default=True, type=bool, required=False, help="If True, push model and image processor to the hub.", ) args = parser.parse_args() pytorch_dump_folder_path: Path = args.pytorch_dump_folder_path pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True) convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)