transformers_4_35_0/models/convnext/convert_convnext_to_pytorch.py

# 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 ConvNext checkpoints from the original repository.

URL: https://github.com/facebookresearch/ConvNeXt"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
logger = logging.get_logger(__name__)


def get_convnext_config(checkpoint_url):
    config = ConvNextConfig()

    if "tiny" in checkpoint_url:
        depths = [3, 3, 9, 3]
        hidden_sizes = [96, 192, 384, 768]
    if "small" in checkpoint_url:
        depths = [3, 3, 27, 3]
        hidden_sizes = [96, 192, 384, 768]
    if "base" in checkpoint_url:
        depths = [3, 3, 27, 3]
        hidden_sizes = [128, 256, 512, 1024]
    if "large" in checkpoint_url:
        depths = [3, 3, 27, 3]
        hidden_sizes = [192, 384, 768, 1536]
    if "xlarge" in checkpoint_url:
        depths = [3, 3, 27, 3]
        hidden_sizes = [256, 512, 1024, 2048]

    if "1k" in checkpoint_url:
        num_labels = 1000
        filename = "imagenet-1k-id2label.json"
        expected_shape = (1, 1000)
    else:
        num_labels = 21841
        filename = "imagenet-22k-id2label.json"
        expected_shape = (1, 21841)

    repo_id = "huggingface/label-files"
    config.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()}
    if "1k" not in checkpoint_url:
        # this dataset contains 21843 labels but the model only has 21841
        # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
        del id2label[9205]
        del id2label[15027]
    config.id2label = id2label
    config.label2id = {v: k for k, v in id2label.items()}
    config.hidden_sizes = hidden_sizes
    config.depths = depths

    return config, expected_shape


def rename_key(name):
    if "downsample_layers.0.0" in name:
        name = name.replace("downsample_layers.0.0", "embeddings.patch_embeddings")
    if "downsample_layers.0.1" in name:
        name = name.replace("downsample_layers.0.1", "embeddings.norm")  # we rename to layernorm later on
    if "downsample_layers.1.0" in name:
        name = name.replace("downsample_layers.1.0", "stages.1.downsampling_layer.0")
    if "downsample_layers.1.1" in name:
        name = name.replace("downsample_layers.1.1", "stages.1.downsampling_layer.1")
    if "downsample_layers.2.0" in name:
        name = name.replace("downsample_layers.2.0", "stages.2.downsampling_layer.0")
    if "downsample_layers.2.1" in name:
        name = name.replace("downsample_layers.2.1", "stages.2.downsampling_layer.1")
    if "downsample_layers.3.0" in name:
        name = name.replace("downsample_layers.3.0", "stages.3.downsampling_layer.0")
    if "downsample_layers.3.1" in name:
        name = name.replace("downsample_layers.3.1", "stages.3.downsampling_layer.1")
    if "stages" in name and "downsampling_layer" not in name:
        # stages.0.0. for instance should be renamed to stages.0.layers.0.
        name = name[: len("stages.0")] + ".layers" + name[len("stages.0") :]
    if "stages" in name:
        name = name.replace("stages", "encoder.stages")
    if "norm" in name:
        name = name.replace("norm", "layernorm")
    if "gamma" in name:
        name = name.replace("gamma", "layer_scale_parameter")
    if "head" in name:
        name = name.replace("head", "classifier")

    return name


# We will verify our results on an image of cute cats
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    im = Image.open(requests.get(url, stream=True).raw)
    return im


@torch.no_grad()
def convert_convnext_checkpoint(checkpoint_url, pytorch_dump_folder_path):
    """
    Copy/paste/tweak model's weights to our ConvNext structure.
    """

    # define ConvNext configuration based on URL
    config, expected_shape = get_convnext_config(checkpoint_url)
    # load original state_dict from URL
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url)["model"]
    # rename keys
    for key in state_dict.copy().keys():
        val = state_dict.pop(key)
        state_dict[rename_key(key)] = val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        val = state_dict.pop(key)
        if not key.startswith("classifier"):
            key = "convnext." + key
        state_dict[key] = val

    # load HuggingFace model
    model = ConvNextForImageClassification(config)
    model.load_state_dict(state_dict)
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    size = 224 if "224" in checkpoint_url else 384
    image_processor = ConvNextImageProcessor(size=size)
    pixel_values = image_processor(images=prepare_img(), return_tensors="pt").pixel_values

    logits = model(pixel_values).logits

    # note: the logits below were obtained without center cropping
    if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
        expected_logits = torch.tensor([-0.1210, -0.6605, 0.1918])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
        expected_logits = torch.tensor([-0.4473, -0.1847, -0.6365])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
        expected_logits = torch.tensor([0.4525, 0.7539, 0.0308])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
        expected_logits = torch.tensor([0.3561, 0.6350, -0.0384])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
        expected_logits = torch.tensor([0.4174, -0.0989, 0.1489])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
        expected_logits = torch.tensor([0.2513, -0.1349, -0.1613])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
        expected_logits = torch.tensor([1.2980, 0.3631, -0.1198])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
        expected_logits = torch.tensor([1.2963, 0.1227, 0.1723])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
        expected_logits = torch.tensor([1.7956, 0.8390, 0.2820])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
        expected_logits = torch.tensor([-0.2822, -0.0502, -0.0878])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
        expected_logits = torch.tensor([-0.5672, -0.0730, -0.4348])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
        expected_logits = torch.tensor([0.2681, 0.2365, 0.6246])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
        expected_logits = torch.tensor([-0.2642, 0.3931, 0.5116])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
        expected_logits = torch.tensor([-0.6677, -0.1873, -0.8379])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
        expected_logits = torch.tensor([-0.7749, -0.2967, -0.6444])
    else:
        raise ValueError(f"Unknown URL: {checkpoint_url}")

    assert torch.allclose(logits[0, :3], expected_logits, atol=1e-3)
    assert logits.shape == expected_shape

    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    print(f"Saving model to {pytorch_dump_folder_path}")
    model.save_pretrained(pytorch_dump_folder_path)
    print(f"Saving image processor to {pytorch_dump_folder_path}")
    image_processor.save_pretrained(pytorch_dump_folder_path)

    print("Pushing model to the hub...")
    model_name = "convnext"
    if "tiny" in checkpoint_url:
        model_name += "-tiny"
    elif "small" in checkpoint_url:
        model_name += "-small"
    elif "base" in checkpoint_url:
        model_name += "-base"
    elif "xlarge" in checkpoint_url:
        model_name += "-xlarge"
    elif "large" in checkpoint_url:
        model_name += "-large"
    if "224" in checkpoint_url:
        model_name += "-224"
    elif "384" in checkpoint_url:
        model_name += "-384"
    if "22k" in checkpoint_url and "1k" not in checkpoint_url:
        model_name += "-22k"
    if "22k" in checkpoint_url and "1k" in checkpoint_url:
        model_name += "-22k-1k"

    model.push_to_hub(
        repo_path_or_name=Path(pytorch_dump_folder_path, model_name),
        organization="nielsr",
        commit_message="Add model",
    )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--checkpoint_url",
        default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
        type=str,
        help="URL of the original ConvNeXT checkpoint you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the output PyTorch model directory.",
    )

    args = parser.parse_args()
    convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)