codingutf8 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 orglicenseslicense2 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 efficientformer checkpoints from the original repository url https github comsnapresearchefficientformer we will verify our results on a coco image prepare image original processing pipeline save checkpoints required parameters coding utf 8 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 efficientformer checkpoints from the original repository url https github com snap research efficientformer we will verify our results on a coco image prepare image original processing pipeline save checkpoints required parameters
import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def rename_key(old_name, num_meta4D_last_stage):
    new_name = old_name

    if "patch_embed" in old_name:
        _, layer, param = old_name.split(".")

        if layer == "0":
            new_name = old_name.replace("0", "convolution1")
        elif layer == "1":
            new_name = old_name.replace("1", "batchnorm_before")
        elif layer == "3":
            new_name = old_name.replace("3", "convolution2")
        else:
            new_name = old_name.replace("4", "batchnorm_after")

    if "network" in old_name and re.search(r"\d\.\d", old_name):
        two_digit_num = r"\b\d{2}\b"
        if bool(re.search(two_digit_num, old_name)):
            match = re.search(r"\d\.\d\d.", old_name).group()
        else:
            match = re.search(r"\d\.\d.", old_name).group()
        if int(match[0]) < 6:
            trimmed_name = old_name.replace(match, "")
            trimmed_name = trimmed_name.replace("network", match[0] + ".meta4D_layers.blocks." + match[2:-1])
            new_name = "intermediate_stages." + trimmed_name
        else:
            trimmed_name = old_name.replace(match, "")
            if int(match[2]) < num_meta4D_last_stage:
                trimmed_name = trimmed_name.replace("network", "meta4D_layers.blocks." + match[2])
            else:
                layer_index = str(int(match[2]) - num_meta4D_last_stage)
                trimmed_name = trimmed_name.replace("network", "meta3D_layers.blocks." + layer_index)
                if "norm1" in old_name:
                    trimmed_name = trimmed_name.replace("norm1", "layernorm1")
                elif "norm2" in old_name:
                    trimmed_name = trimmed_name.replace("norm2", "layernorm2")
                elif "fc1" in old_name:
                    trimmed_name = trimmed_name.replace("fc1", "linear_in")
                elif "fc2" in old_name:
                    trimmed_name = trimmed_name.replace("fc2", "linear_out")

            new_name = "last_stage." + trimmed_name

    elif "network" in old_name and re.search(r".\d.", old_name):
        new_name = old_name.replace("network", "intermediate_stages")

    if "fc" in new_name:
        new_name = new_name.replace("fc", "convolution")
    elif ("norm1" in new_name) and ("layernorm1" not in new_name):
        new_name = new_name.replace("norm1", "batchnorm_before")
    elif ("norm2" in new_name) and ("layernorm2" not in new_name):
        new_name = new_name.replace("norm2", "batchnorm_after")
    if "proj" in new_name:
        new_name = new_name.replace("proj", "projection")
    if "dist_head" in new_name:
        new_name = new_name.replace("dist_head", "distillation_classifier")
    elif "head" in new_name:
        new_name = new_name.replace("head", "classifier")
    elif "patch_embed" in new_name:
        new_name = "efficientformer." + new_name
    elif new_name == "norm.weight" or new_name == "norm.bias":
        new_name = new_name.replace("norm", "layernorm")
        new_name = "efficientformer." + new_name
    else:
        new_name = "efficientformer.encoder." + new_name

    return new_name


def convert_torch_checkpoint(checkpoint, num_meta4D_last_stage):
    for key in checkpoint.copy().keys():
        val = checkpoint.pop(key)
        checkpoint[rename_key(key, num_meta4D_last_stage)] = val

    return checkpoint



def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)

    return image


def convert_efficientformer_checkpoint(
    checkpoint_path: Path, efficientformer_config_file: Path, pytorch_dump_path: Path, push_to_hub: bool
):
    orig_state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
    config = EfficientFormerConfig.from_json_file(efficientformer_config_file)
    model = EfficientFormerForImageClassificationWithTeacher(config)
    model_name = "_".join(checkpoint_path.split("/")[-1].split(".")[0].split("_")[:-1])

    num_meta4D_last_stage = config.depths[-1] - config.num_meta3d_blocks + 1
    new_state_dict = convert_torch_checkpoint(orig_state_dict, num_meta4D_last_stage)

    model.load_state_dict(new_state_dict)
    model.eval()

    pillow_resamplings = {
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    
    image = prepare_img()
    image_size = 256
    crop_size = 224
    processor = EfficientFormerImageProcessor(
        size={"shortest_edge": image_size},
        crop_size={"height": crop_size, "width": crop_size},
        resample=pillow_resamplings["bicubic"],
    )
    pixel_values = processor(images=image, return_tensors="pt").pixel_values

    
    image_transforms = Compose(
        [
            Resize(image_size, interpolation=pillow_resamplings["bicubic"]),
            CenterCrop(crop_size),
            ToTensor(),
            Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
        ]
    )
    original_pixel_values = image_transforms(image).unsqueeze(0)

    assert torch.allclose(original_pixel_values, pixel_values)

    outputs = model(pixel_values)
    logits = outputs.logits

    expected_shape = (1, 1000)

    if "l1" in model_name:
        expected_logits = torch.Tensor(
            [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]
        )
        assert torch.allclose(logits[0, :10], expected_logits, atol=1e-3)
        assert logits.shape == expected_shape
    elif "l3" in model_name:
        expected_logits = torch.Tensor(
            [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]
        )
        assert torch.allclose(logits[0, :10], expected_logits, atol=1e-3)
        assert logits.shape == expected_shape
    elif "l7" in model_name:
        expected_logits = torch.Tensor(
            [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]
        )
        assert logits.shape == expected_shape
    else:
        raise ValueError(
            f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"
        )

    
    Path(pytorch_dump_path).mkdir(exist_ok=True)
    model.save_pretrained(pytorch_dump_path)
    print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}")
    processor.save_pretrained(pytorch_dump_path)
    print(f"Processor successfuly saved at {pytorch_dump_path}")

    if push_to_hub:
        print("Pushing model to the hub...")

        model.push_to_hub(
            repo_id=f"Bearnardd/{pytorch_dump_path}",
            commit_message="Add model",
            use_temp_dir=True,
        )
        processor.push_to_hub(
            repo_id=f"Bearnardd/{pytorch_dump_path}",
            commit_message="Add image processor",
            use_temp_dir=True,
        )


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    
    parser.add_argument(
        "--pytorch_model_path",
        default=None,
        type=str,
        required=True,
        help="Path to EfficientFormer pytorch checkpoint.",
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help="The json file for EfficientFormer model config.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )

    parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
    parser.add_argument(
        "--no-push_to_hub",
        dest="push_to_hub",
        action="store_false",
        help="Do not push model and image processor to the hub",
    )
    parser.set_defaults(push_to_hub=True)

    args = parser.parse_args()
    convert_efficientformer_checkpoint(
        checkpoint_path=args.pytorch_model_path,
        efficientformer_config_file=args.config_file,
        pytorch_dump_path=args.pytorch_dump_path,
        push_to_hub=args.push_to_hub,
    )