cc @ydshieh - this will probably break some things, but if I can make it work it should reduce the memory usage during building a lot
Make the TF dummies even smaller. cc @ydshieh - this will probably break some things, but if I can make it work it should reduce the memory usage during building a lot
# 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 RegNet 10B checkpoints vissl."""
# You need to install a specific version of classy vision
# pip install git+https://github.com/FrancescoSaverioZuppichini/ClassyVision.git@convert_weights

import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoFeatureExtractor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
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)
    name2module: Dict[str, nn.Module] = field(default_factory=OrderedDict)

    def _forward_hook(self, m, inputs: Tensor, outputs: Tensor, name: str):
        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)
            self.name2module[name] = m

    def __call__(self, x: Tensor):
        for name, m in self.module.named_modules():
            self.handles.append(m.register_forward_hook(partial(self._forward_hook, name=name)))
        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 {k: v for k, v in self.name2module.items() if len(list(v.state_dict().keys())) > 0}


class FakeRegNetVisslWrapper(nn.Module):
    """
    Fake wrapper for RegNet that mimics what vissl does without the need to pass a config file.
    """

    def __init__(self, model: nn.Module):
        super().__init__()

        feature_blocks: List[Tuple[str, nn.Module]] = []
        # - get the stem
        feature_blocks.append(("conv1", model.stem))
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
            assert k.startswith("block"), f"Unexpected layer name {k}"
            block_index = len(feature_blocks) + 1
            feature_blocks.append((f"res{block_index}", v))

        self._feature_blocks = nn.ModuleDict(feature_blocks)

    def forward(self, x: Tensor):
        return get_trunk_forward_outputs(
            x,
            out_feat_keys=None,
            feature_blocks=self._feature_blocks,
        )


class FakeRegNetParams(RegNetParams):
    """
    Used to instantiace a RegNet model from classy vision with the same depth as the 10B one but with super small
    parameters, so we can trace it in memory.
    """

    def get_expanded_params(self):
        return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]


def get_from_to_our_keys(model_name: str) -> Dict[str, str]:
    """
    Returns a dictionary that maps from original model's key -> our implementation's keys
    """

    # create our model (with small weights)
    our_config = RegNetConfig(depths=[2, 7, 17, 1], hidden_sizes=[8, 8, 8, 8], groups_width=8)
    if "in1k" in model_name:
        our_model = RegNetForImageClassification(our_config)
    else:
        our_model = RegNetModel(our_config)
    # create from model (with small weights)
    from_model = FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=27, group_width=1010, w_0=1744, w_a=620.83, w_m=2.52))
    )

    with torch.no_grad():
        from_model = from_model.eval()
        our_model = our_model.eval()

        x = torch.randn((1, 3, 32, 32))
        # trace both
        dest_tracker = Tracker(our_model)
        dest_traced = dest_tracker(x).parametrized

        pprint(dest_tracker.name2module)
        src_tracker = Tracker(from_model)
        src_traced = src_tracker(x).parametrized

    # convert the keys -> module dict to keys -> params
    def to_params_dict(dict_with_modules):
        params_dict = OrderedDict()
        for name, module in dict_with_modules.items():
            for param_name, param in module.state_dict().items():
                params_dict[f"{name}.{param_name}"] = param
        return params_dict

    from_to_ours_keys = {}

    src_state_dict = to_params_dict(src_traced)
    dst_state_dict = to_params_dict(dest_traced)

    for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(), dst_state_dict.items()):
        from_to_ours_keys[src_key] = dest_key
        logger.info(f"{src_key} -> {dest_key}")
    # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
    if "in1k" in model_name:
        from_to_ours_keys["0.clf.0.weight"] = "classifier.1.weight"
        from_to_ours_keys["0.clf.0.bias"] = "classifier.1.bias"

    return from_to_ours_keys


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

    repo_id = "huggingface/label-files"
    num_labels = num_labels
    id2label = json.load(open(cached_download(hf_hub_url(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(RegNetConfig, num_labels=num_labels, id2label=id2label, label2id=label2id)

    names_to_config = {
        "regnet-y-10b-seer": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010
        ),
        # finetuned on imagenet
        "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010
        ),
    }

    # add seer weights logic
    def load_using_classy_vision(checkpoint_url: str) -> Tuple[Dict, Dict]:
        files = torch.hub.load_state_dict_from_url(checkpoint_url, model_dir=str(save_directory), map_location="cpu")
        # check if we have a head, if yes add it
        model_state_dict = files["classy_state_dict"]["base_model"]["model"]
        return model_state_dict["trunk"], model_state_dict["heads"]

    names_to_from_model = {
        "regnet-y-10b-seer": partial(
            load_using_classy_vision,
            "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch",
        ),
        "regnet-y-10b-seer-in1k": partial(
            load_using_classy_vision,
            "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch",
        ),
    }

    from_to_ours_keys = get_from_to_our_keys(model_name)

    if not (save_directory / f"{model_name}.pth").exists():
        logger.info("Loading original state_dict.")
        from_state_dict_trunk, from_state_dict_head = names_to_from_model[model_name]()
        from_state_dict = from_state_dict_trunk
        if "in1k" in model_name:
            # add the head
            from_state_dict = {**from_state_dict_trunk, **from_state_dict_head}
        logger.info("Done!")

        converted_state_dict = {}

        not_used_keys = list(from_state_dict.keys())
        regex = r"\.block.-part."
        # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
        for key in from_state_dict.keys():
            # remove the weird "block[0,1]-part" from the key
            src_key = re.sub(regex, "", key)
            # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
            dest_key = from_to_ours_keys[src_key]
            # store the parameter with our key
            converted_state_dict[dest_key] = from_state_dict[key]
            not_used_keys.remove(key)
        # check that all keys have been updated
        assert len(not_used_keys) == 0, f"Some keys where not used {','.join(not_used_keys)}"

        logger.info(f"The following keys were not used: {','.join(not_used_keys)}")

        # save our state dict to disk
        torch.save(converted_state_dict, save_directory / f"{model_name}.pth")

        del converted_state_dict
    else:
        logger.info("The state_dict was already stored on disk.")
    if push_to_hub:
        logger.info(f"Token is {os.environ['HF_TOKEN']}")
        logger.info("Loading our model.")
        # create our model
        our_config = names_to_config[model_name]
        our_model_func = RegNetModel
        if "in1k" in model_name:
            our_model_func = RegNetForImageClassification
        our_model = our_model_func(our_config)
        # place our model to the meta device (so remove all the weights)
        our_model.to(torch.device("meta"))
        logger.info("Loading state_dict in our model.")
        # load state dict
        state_dict_keys = our_model.state_dict().keys()
        PreTrainedModel._load_pretrained_model_low_mem(
            our_model, state_dict_keys, [save_directory / f"{model_name}.pth"]
        )
        logger.info("Finally, pushing!")
        # push it to hub
        our_model.push_to_hub(
            repo_path_or_name=save_directory / model_name,
            commit_message="Add model",
            output_dir=save_directory / model_name,
        )
        size = 384
        # we can use the convnext one
        feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/convnext-base-224-22k-1k", size=size)
        feature_extractor.push_to_hub(
            repo_path_or_name=save_directory / model_name,
            commit_message="Add feature extractor",
            output_dir=save_directory / model_name,
        )


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 regnet* architecture,"
            " currently: regnetx-*, regnety-*. 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 feature extractor 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)

# 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 RegNet 10B checkpoints vissl."""
# You need to install a specific version of classy vision
# pip install git+https://github.com/FrancescoSaverioZuppichini/ClassyVision.git@convert_weights

import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoFeatureExtractor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
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)
    name2module: Dict[str, nn.Module] = field(default_factory=OrderedDict)

    def _forward_hook(self, m, inputs: Tensor, outputs: Tensor, name: str):
        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)
            self.name2module[name] = m

    def __call__(self, x: Tensor):
        for name, m in self.module.named_modules():
            self.handles.append(m.register_forward_hook(partial(self._forward_hook, name=name)))
        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 {k: v for k, v in self.name2module.items() if len(list(v.state_dict().keys())) > 0}


class FakeRegNetVisslWrapper(nn.Module):
    """
    Fake wrapper for RegNet that mimics what vissl does without the need to pass a config file.
    """

    def __init__(self, model: nn.Module):
        super().__init__()

        feature_blocks: List[Tuple[str, nn.Module]] = []
        # - get the stem
        feature_blocks.append(("conv1", model.stem))
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
            assert k.startswith("block"), f"Unexpected layer name {k}"
            block_index = len(feature_blocks) + 1
            feature_blocks.append((f"res{block_index}", v))

        self._feature_blocks = nn.ModuleDict(feature_blocks)

    def forward(self, x: Tensor):
        return get_trunk_forward_outputs(
            x,
            out_feat_keys=None,
            feature_blocks=self._feature_blocks,
        )


class FakeRegNetParams(RegNetParams):
    """
    Used to instantiace a RegNet model from classy vision with the same depth as the 10B one but with super small
    parameters, so we can trace it in memory.
    """

    def get_expanded_params(self):
        return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]


def get_from_to_our_keys(model_name: str) -> Dict[str, str]:
    """
    Returns a dictionary that maps from original model's key -> our implementation's keys
    """

    # create our model (with small weights)
    our_config = RegNetConfig(depths=[2, 7, 17, 1], hidden_sizes=[8, 8, 8, 8], groups_width=8)
    if "in1k" in model_name:
        our_model = RegNetForImageClassification(our_config)
    else:
        our_model = RegNetModel(our_config)
    # create from model (with small weights)
    from_model = FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=27, group_width=1010, w_0=1744, w_a=620.83, w_m=2.52))
    )

    with torch.no_grad():
        from_model = from_model.eval()
        our_model = our_model.eval()

        x = torch.randn((1, 3, 32, 32))
        # trace both
        dest_tracker = Tracker(our_model)
        dest_traced = dest_tracker(x).parametrized

        pprint(dest_tracker.name2module)
        src_tracker = Tracker(from_model)
        src_traced = src_tracker(x).parametrized

    # convert the keys -> module dict to keys -> params
    def to_params_dict(dict_with_modules):
        params_dict = OrderedDict()
        for name, module in dict_with_modules.items():
            for param_name, param in module.state_dict().items():
                params_dict[f"{name}.{param_name}"] = param
        return params_dict

    from_to_ours_keys = {}

    src_state_dict = to_params_dict(src_traced)
    dst_state_dict = to_params_dict(dest_traced)

    for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(), dst_state_dict.items()):
        from_to_ours_keys[src_key] = dest_key
        logger.info(f"{src_key} -> {dest_key}")
    # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
    if "in1k" in model_name:
        from_to_ours_keys["0.clf.0.weight"] = "classifier.1.weight"
        from_to_ours_keys["0.clf.0.bias"] = "classifier.1.bias"

    return from_to_ours_keys


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

    repo_id = "huggingface/label-files"
    num_labels = num_labels
    id2label = json.load(open(cached_download(hf_hub_url(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(RegNetConfig, num_labels=num_labels, id2label=id2label, label2id=label2id)

    names_to_config = {
        "regnet-y-10b-seer": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010
        ),
        # finetuned on imagenet
        "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1], hidden_sizes=[2020, 4040, 11110, 28280], groups_width=1010
        ),
    }

    # add seer weights logic
    def load_using_classy_vision(checkpoint_url: str) -> Tuple[Dict, Dict]:
        files = torch.hub.load_state_dict_from_url(checkpoint_url, model_dir=str(save_directory), map_location="cpu")
        # check if we have a head, if yes add it
        model_state_dict = files["classy_state_dict"]["base_model"]["model"]
        return model_state_dict["trunk"], model_state_dict["heads"]

    names_to_from_model = {
        "regnet-y-10b-seer": partial(
            load_using_classy_vision,
            "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch",
        ),
        "regnet-y-10b-seer-in1k": partial(
            load_using_classy_vision,
            "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch",
        ),
    }

    from_to_ours_keys = get_from_to_our_keys(model_name)

    if not (save_directory / f"{model_name}.pth").exists():
        logger.info("Loading original state_dict.")
        from_state_dict_trunk, from_state_dict_head = names_to_from_model[model_name]()
        from_state_dict = from_state_dict_trunk
        if "in1k" in model_name:
            # add the head
            from_state_dict = {**from_state_dict_trunk, **from_state_dict_head}
        logger.info("Done!")

        converted_state_dict = {}

        not_used_keys = list(from_state_dict.keys())
        regex = r"\.block.-part."
        # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
        for key in from_state_dict.keys():
            # remove the weird "block[0,1]-part" from the key
            src_key = re.sub(regex, "", key)
            # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
            dest_key = from_to_ours_keys[src_key]
            # store the parameter with our key
            converted_state_dict[dest_key] = from_state_dict[key]
            not_used_keys.remove(key)
        # check that all keys have been updated
        assert len(not_used_keys) == 0, f"Some keys where not used {','.join(not_used_keys)}"

        logger.info(f"The following keys were not used: {','.join(not_used_keys)}")

        # save our state dict to disk
        torch.save(converted_state_dict, save_directory / f"{model_name}.pth")

        del converted_state_dict
    else:
        logger.info("The state_dict was already stored on disk.")
    if push_to_hub:
        logger.info(f"Token is {os.environ['HF_TOKEN']}")
        logger.info("Loading our model.")
        # create our model
        our_config = names_to_config[model_name]
        our_model_func = RegNetModel
        if "in1k" in model_name:
            our_model_func = RegNetForImageClassification
        our_model = our_model_func(our_config)
        # place our model to the meta device (so remove all the weights)
        our_model.to(torch.device("meta"))
        logger.info("Loading state_dict in our model.")
        # load state dict
        state_dict_keys = our_model.state_dict().keys()
        PreTrainedModel._load_pretrained_model_low_mem(
            our_model, state_dict_keys, [save_directory / f"{model_name}.pth"]
        )
        logger.info("Finally, pushing!")
        # push it to hub
        our_model.push_to_hub(
            repo_path_or_name=save_directory / model_name,
            commit_message="Add model",
            output_dir=save_directory / model_name,
        )
        size = 384
        # we can use the convnext one
        feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/convnext-base-224-22k-1k", size=size)
        feature_extractor.push_to_hub(
            repo_path_or_name=save_directory / model_name,
            commit_message="Add feature extractor",
            output_dir=save_directory / model_name,
        )


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 regnet* architecture,"
            " currently: regnetx-*, regnety-*. 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 feature extractor 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)
