Esempio n. 1
0
def convert_dit_checkpoint(checkpoint_url,
                           pytorch_dump_folder_path,
                           push_to_hub=False):
    """
    Copy/paste/tweak model's weights to our BEiT structure.
    """

    # define default BEiT configuration
    has_lm_head = False if "rvlcdip" in checkpoint_url else True
    config = BeitConfig(use_absolute_position_embeddings=True,
                        use_mask_token=has_lm_head)

    # size of the architecture
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        config.hidden_size = 1024
        config.intermediate_size = 4096
        config.num_hidden_layers = 24
        config.num_attention_heads = 16

    # labels
    if "rvlcdip" in checkpoint_url:
        config.num_labels = 16
        repo_id = "datasets/huggingface/label-files"
        filename = "rvlcdip-id2label.json"
        id2label = json.load(
            open(cached_download(hf_hub_url(repo_id, filename)), "r"))
        id2label = {int(k): v for k, v in id2label.items()}
        config.id2label = id2label
        config.label2id = {v: k for k, v in id2label.items()}

    # load state_dict of original model, remove and rename some keys
    state_dict = torch.hub.load_state_dict_from_url(
        checkpoint_url, map_location="cpu")["model"]

    rename_keys = create_rename_keys(config, has_lm_head=has_lm_head)
    for src, dest in rename_keys:
        rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict, config, has_lm_head=has_lm_head)

    # load HuggingFace model
    model = BeitForMaskedImageModeling(
        config) if has_lm_head else BeitForImageClassification(config)
    model.eval()
    model.load_state_dict(state_dict)

    # Check outputs on an image
    feature_extractor = BeitFeatureExtractor(size=config.image_size,
                                             resample=Image.BILINEAR,
                                             do_center_crop=False)
    image = prepare_img()

    encoding = feature_extractor(images=image, return_tensors="pt")
    pixel_values = encoding["pixel_values"]

    outputs = model(pixel_values)
    logits = outputs.logits

    # verify logits
    expected_shape = [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
    assert logits.shape == torch.Size(
        expected_shape), "Shape of logits not as expected"

    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 feature extractor to {pytorch_dump_folder_path}")
    feature_extractor.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        if has_lm_head:
            model_name = "dit-base" if "base" in checkpoint_url else "dit-large"
        else:
            model_name = "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
        feature_extractor.push_to_hub(
            repo_path_or_name=Path(pytorch_dump_folder_path, model_name),
            organization="nielsr",
            commit_message="Add feature extractor",
            use_temp_dir=True,
        )
        model.push_to_hub(
            repo_path_or_name=Path(pytorch_dump_folder_path, model_name),
            organization="nielsr",
            commit_message="Add model",
            use_temp_dir=True,
        )
def convert_beit_checkpoint(checkpoint_url, pytorch_dump_folder_path):
    """
    Copy/paste/tweak model's weights to our BEiT structure.
    """

    # define default BEiT configuration
    config = BeitConfig()
    has_lm_head = False
    is_semantic = False
    repo_id = "datasets/huggingface/label-files"
    # set config parameters based on URL
    if checkpoint_url[-9:-4] == "pt22k":
        # masked image modeling
        config.use_shared_relative_position_bias = True
        config.use_mask_token = True
        has_lm_head = True
    elif checkpoint_url[-9:-4] == "ft22k":
        # intermediate fine-tuning on ImageNet-22k
        config.use_relative_position_bias = True
        config.num_labels = 21841
        filename = "imagenet-22k-id2label.json"
        id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
        id2label = {int(k): v for k, v in id2label.items()}
        # 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()}
    elif checkpoint_url[-8:-4] == "to1k":
        # fine-tuning on ImageNet-1k
        config.use_relative_position_bias = True
        config.num_labels = 1000
        filename = "imagenet-1k-id2label.json"
        id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
        id2label = {int(k): v for k, v in id2label.items()}
        config.id2label = id2label
        config.label2id = {v: k for k, v in id2label.items()}
        if "384" in checkpoint_url:
            config.image_size = 384
        if "512" in checkpoint_url:
            config.image_size = 512
    elif "ade20k" in checkpoint_url:
        # fine-tuning
        config.use_relative_position_bias = True
        config.num_labels = 150
        filename = "ade20k-id2label.json"
        id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
        id2label = {int(k): v for k, v in id2label.items()}
        config.id2label = id2label
        config.label2id = {v: k for k, v in id2label.items()}
        config.image_size = 640
        is_semantic = True
    else:
        raise ValueError(
            "Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"
        )

    # size of the architecture
    if "base" in checkpoint_url:
        pass
    elif "large" in checkpoint_url:
        config.hidden_size = 1024
        config.intermediate_size = 4096
        config.num_hidden_layers = 24
        config.num_attention_heads = 16
        if "ade20k" in checkpoint_url:
            config.image_size = 640
            config.out_indices = [7, 11, 15, 23]
    else:
        raise ValueError(
            "Should either find 'base' or 'large' in checkpoint URL")

    # load state_dict of original model, remove and rename some keys
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url,
                                                    map_location="cpu",
                                                    check_hash=True)
    state_dict = state_dict[
        "model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

    rename_keys = create_rename_keys(config,
                                     has_lm_head=has_lm_head,
                                     is_semantic=is_semantic)
    for src, dest in rename_keys:
        rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict,
                  config,
                  has_lm_head=has_lm_head,
                  is_semantic=is_semantic)
    if is_semantic:
        # add prefix to decoder keys
        for key, val in state_dict.copy().items():
            val = state_dict.pop(key)
            if key.startswith("backbone.fpn"):
                key = key.replace("backbone.fpn", "fpn")
            state_dict[key] = val

    # load HuggingFace model
    if checkpoint_url[-9:-4] == "pt22k":
        model = BeitForMaskedImageModeling(config)
    elif "ade20k" in checkpoint_url:
        model = BeitForSemanticSegmentation(config)
    else:
        model = BeitForImageClassification(config)
    model.eval()
    model.load_state_dict(state_dict)

    # Check outputs on an image
    if is_semantic:
        feature_extractor = BeitFeatureExtractor(size=config.image_size,
                                                 do_center_crop=False)
        ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
        image = Image.open(ds[0]["file"])
    else:
        feature_extractor = BeitFeatureExtractor(size=config.image_size,
                                                 resample=Image.BILINEAR,
                                                 do_center_crop=False)
        image = prepare_img()

    encoding = feature_extractor(images=image, return_tensors="pt")
    pixel_values = encoding["pixel_values"]

    outputs = model(pixel_values)
    logits = outputs.logits

    # verify logits
    expected_shape = torch.Size([1, 1000])
    if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"):
        expected_shape = torch.Size([1, 196, 8192])
    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"):
        expected_shape = torch.Size([1, 196, 8192])
    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"):
        expected_shape = torch.Size([1, 21841])
        expected_logits = torch.tensor([2.2288, 2.4671, 0.7395])
        expected_class_idx = 2397
    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"):
        expected_shape = torch.Size([1, 21841])
        expected_logits = torch.tensor([1.6881, -0.2787, 0.5901])
        expected_class_idx = 2396
    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"):
        expected_logits = torch.tensor([0.1241, 0.0798, -0.6569])
        expected_class_idx = 285
    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-1.2385, -1.0987, -1.0108])
        expected_class_idx = 281
    elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-1.5303, -0.9484, -0.3147])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"):
        expected_logits = torch.tensor([0.4610, -0.0928, 0.2086])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith(
            "beit_large_patch16_224_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-0.4804, 0.6257, -0.1837])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith(
            "beit_large_patch16_384_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([[-0.5122, 0.5117, -0.2113]])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith(
            "beit_large_patch16_512_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-0.3062, 0.7261, 0.4852])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith(
            "beit_base_patch16_640_pt22k_ft22ktoade20k"):
        expected_shape = (1, 150, 160, 160)
        expected_logits = torch.tensor([
            [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561],
             [-2.9549, -1.3228, -2.1347]],
            [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277],
             [-3.8356, -2.4643, -3.3535]],
            [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035],
             [3.2413, 4.7813, 4.9969]],
        ])
    elif checkpoint_url[:-4].endswith(
            "beit_large_patch16_640_pt22k_ft22ktoade20k"):
        expected_shape = (1, 150, 160, 160)
        expected_logits = torch.tensor([
            [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251],
             [-3.4198, -1.8004, -2.9062]],
            [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755],
             [-4.2791, -3.1874, -4.1681]],
            [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518],
             [4.5550, 6.2495, 6.5154]],
        ])
    else:
        raise ValueError("Can't verify logits as model is not supported")

    assert logits.shape == expected_shape, "Shape of logits not as expected"
    if not has_lm_head:
        if is_semantic:
            assert torch.allclose(
                logits[0, :3, :3, :3], expected_logits,
                atol=1e-3), "First elements of logits not as expected"
        else:
            print("Predicted class idx:", logits.argmax(-1).item())
            assert torch.allclose(
                logits[0, :3], expected_logits,
                atol=1e-3), "First elements of logits not as expected"
            assert logits.argmax(-1).item(
            ) == expected_class_idx, "Predicted class index not as expected"

    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 feature extractor to {pytorch_dump_folder_path}")
    feature_extractor.save_pretrained(pytorch_dump_folder_path)
Esempio n. 3
0
def convert_beit_checkpoint(checkpoint_url, pytorch_dump_folder_path):
    """
    Copy/paste/tweak model's weights to our BEiT structure.
    """

    # define default BEiT configuration
    config = BeitConfig()
    has_lm_head = False
    repo_id = "datasets/huggingface/label-files"
    # set config parameters based on URL
    if checkpoint_url[-9:-4] == "pt22k":
        # masked image modeling
        config.use_shared_relative_position_bias = True
        config.use_mask_token = True
        has_lm_head = True
    elif checkpoint_url[-9:-4] == "ft22k":
        # intermediate fine-tuning on ImageNet-22k
        config.use_relative_position_bias = True
        config.num_labels = 21841
        filename = "imagenet-22k-id2label.json"
        id2label = json.load(
            open(cached_download(hf_hub_url(repo_id, filename)), "r"))
        id2label = {int(k): v for k, v in id2label.items()}
        # 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()}
    elif checkpoint_url[-8:-4] == "to1k":
        # fine-tuning on ImageNet-1k
        config.use_relative_position_bias = True
        config.num_labels = 1000
        filename = "imagenet-1k-id2label.json"
        id2label = json.load(
            open(cached_download(hf_hub_url(repo_id, filename)), "r"))
        id2label = {int(k): v for k, v in id2label.items()}
        config.id2label = id2label
        config.label2id = {v: k for k, v in id2label.items()}
        if "384" in checkpoint_url:
            config.image_size = 384
        if "512" in checkpoint_url:
            config.image_size = 512
    else:
        raise ValueError(
            "Checkpoint not supported, URL should either end with 'pt22k', 'ft22k' or 'to1k'"
        )

    # size of the architecture
    if "base" in checkpoint_url:
        pass
    elif "large" in checkpoint_url:
        config.hidden_size = 1024
        config.intermediate_size = 4096
        config.num_hidden_layers = 24
        config.num_attention_heads = 16
    else:
        raise ValueError(
            "Should either find 'base' or 'large' in checkpoint URL")

    # load state_dict of original model, remove and rename some keys
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url,
                                                    map_location="cpu",
                                                    check_hash=True)["model"]
    rename_keys = create_rename_keys(config, has_lm_head=has_lm_head)
    for src, dest in rename_keys:
        rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict, config, has_lm_head=has_lm_head)

    # load HuggingFace model
    if checkpoint_url[-9:-4] == "pt22k":
        model = BeitForMaskedImageModeling(config)
    else:
        model = BeitForImageClassification(config)
    model.eval()
    model.load_state_dict(state_dict)

    # Check outputs on an image
    feature_extractor = BeitFeatureExtractor(size=config.image_size,
                                             resample=Image.BILINEAR,
                                             do_center_crop=False)
    encoding = feature_extractor(images=prepare_img(), return_tensors="pt")
    pixel_values = encoding["pixel_values"]

    outputs = model(pixel_values)
    logits = outputs.logits

    # verify logits
    expected_shape = torch.Size([1, 1000])
    if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"):
        expected_shape = torch.Size([1, 196, 8192])
    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"):
        expected_shape = torch.Size([1, 196, 8192])
    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"):
        expected_shape = torch.Size([1, 21841])
        expected_logits = torch.tensor([2.2288, 2.4671, 0.7395])
        expected_class_idx = 2397
    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"):
        expected_shape = torch.Size([1, 21841])
        expected_logits = torch.tensor([1.6881, -0.2787, 0.5901])
        expected_class_idx = 2396
    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"):
        expected_logits = torch.tensor([0.1241, 0.0798, -0.6569])
        expected_class_idx = 285
    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-1.2385, -1.0987, -1.0108])
        expected_class_idx = 281
    elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-1.5303, -0.9484, -0.3147])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"):
        expected_logits = torch.tensor([0.4610, -0.0928, 0.2086])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith(
            "beit_large_patch16_224_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-0.4804, 0.6257, -0.1837])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith(
            "beit_large_patch16_384_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([[-0.5122, 0.5117, -0.2113]])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith(
            "beit_large_patch16_512_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-0.3062, 0.7261, 0.4852])
        expected_class_idx = 761
    else:
        raise ValueError("Can't verify logits as model is not supported")

    assert logits.shape == expected_shape, "Shape of logits not as expected"
    print("Shape of logits:", logits.shape)
    if not has_lm_head:
        print("Predicted class idx:", logits.argmax(-1).item())
        assert torch.allclose(
            logits[0, :3], expected_logits,
            atol=1e-3), "First elements of logits not as expected"
        assert logits.argmax(-1).item(
        ) == expected_class_idx, "Predicted class index not as expected"

    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 feature extractor to {pytorch_dump_folder_path}")
    feature_extractor.save_pretrained(pytorch_dump_folder_path)