第一次提交Yolo项目

mobileclip/__init__.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import json
+import os
+from typing import Any, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from torchvision.transforms import (
+    CenterCrop,
+    Compose,
+    InterpolationMode,
+    Resize,
+    ToTensor,
+)
+
+from mobileclip.clip import CLIP
+from mobileclip.modules.common.mobileone import reparameterize_model
+from mobileclip.modules.text.tokenizer import (
+    ClipTokenizer,
+)
+
+
+def create_model_and_transforms(
+    model_name: str,
+    pretrained: str | None = None,
+    reparameterize: bool | None = True,
+    device: str | torch.device = "cpu",
+) -> tuple[nn.Module, Any, Any]:
+    """Method to instantiate model and pre-processing transforms necessary for inference.
+
+    Args:
+        model_name: Model name. Choose from ['mobileclip_s0', 'mobileclip_s1', 'mobileclip_s2', 'mobileclip_b']
+        pretrained: Location of pretrained checkpoint.
+        reparameterize: When set to True, re-parameterizable branches get folded for faster inference.
+        device: Device identifier for model placement.
+
+    Returns:
+        Tuple of instantiated model, and preprocessing transforms for inference.
+    """
+    # Config files
+    root_dir = os.path.dirname(os.path.abspath(__file__))
+    configs_dir = os.path.join(root_dir, "configs")
+    model_cfg_file = os.path.join(configs_dir, model_name + ".json")
+
+    # Get config from yaml file
+    if not os.path.exists(model_cfg_file):
+        raise ValueError(f"Unsupported model name: {model_name}")
+    model_cfg = json.load(open(model_cfg_file))
+
+    # Build preprocessing transforms for inference
+    resolution = model_cfg["image_cfg"]["image_size"]
+    resize_size = resolution
+    centercrop_size = resolution
+    aug_list = [
+        Resize(
+            resize_size,
+            interpolation=InterpolationMode.BILINEAR,
+        ),
+        CenterCrop(centercrop_size),
+        ToTensor(),
+    ]
+    preprocess = Compose(aug_list)
+
+    # Build model
+    model = CLIP(cfg=model_cfg)
+    model.to(device)
+    model.eval()
+
+    # Load checkpoint
+    if pretrained is not None:
+        chkpt = torch.load(pretrained)
+        model.load_state_dict(chkpt)
+
+    # Reparameterize model for inference (if specified)
+    if reparameterize:
+        model = reparameterize_model(model)
+
+    return model, None, preprocess
+
+
+def get_tokenizer(model_name: str) -> nn.Module:
+    # Config files
+    root_dir = os.path.dirname(os.path.abspath(__file__))
+    configs_dir = os.path.join(root_dir, "configs")
+    model_cfg_file = os.path.join(configs_dir, model_name + ".json")
+
+    # Get config from yaml file
+    model_cfg = json.load(open(model_cfg_file))
+
+    # Build tokenizer
+    text_tokenizer = ClipTokenizer(model_cfg)
+    return text_tokenizer

mobileclip/clip.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+"""Model schema in open_clip format for inference only."""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from mobileclip.text_encoder import (
+    TextTransformer,
+)
+
+from .image_encoder import MCi
+
+
+class CLIP(nn.Module):
+    """Base class for multi-modal image-text data."""
+
+    def __init__(self, cfg: dict, output_dict: bool = False, *args, **kwargs) -> None:
+        super().__init__()
+        self.output_dict = output_dict
+        self.projection_dim = cfg["embed_dim"]
+        if self.projection_dim is None:
+            raise ValueError("Please specify `embed_dim` in model config.")
+
+        self.image_encoder = MCi(
+            model_name=cfg["image_cfg"]["model_name"],
+            projection_dim=self.projection_dim,
+        )
+        self.text_encoder = TextTransformer(cfg=cfg["text_cfg"], projection_dim=self.projection_dim)
+        self.logit_scale = nn.Parameter(torch.ones([]) * math.log(1.0 / 0.07))
+
+    def _exponentiate_and_clip_logits(self, max_scale: float = 100.0):
+        scale = self.logit_scale.exp()
+        scale = torch.clamp(scale, 0, max_scale)
+        return scale
+
+    def encode_image(self, image: torch.Tensor, normalize: bool = False):
+        image_encoder_out = self.image_encoder(image)
+        if isinstance(image_encoder_out, dict):
+            features = image_encoder_out["logits"]
+        else:
+            features = image_encoder_out
+        return F.normalize(features, dim=-1) if normalize else features
+
+    def encode_text(self, text: torch.Tensor, normalize: bool = False):
+        text_features = self.text_encoder(text_tokens=text, key_padding_mask=None)
+        return F.normalize(text_features, dim=-1) if normalize else text_features
+
+    def forward(self, image: torch.Tensor | None = None, text: torch.Tensor | None = None, *args, **kwargs) -> Any:
+        image_embeddings = self.encode_image(image, normalize=True) if image is not None else None
+        text_embeddings = self.encode_text(text, normalize=True) if text is not None else None
+
+        if self.output_dict:
+            return {
+                "image_features": image_embeddings,
+                "text_features": text_embeddings,
+                "logit_scale": self._exponentiate_and_clip_logits(),
+            }
+        return image_embeddings, text_embeddings, self._exponentiate_and_clip_logits()

mobileclip/configs/mobileclip_b.json

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+{
+  "embed_dim": 512,
+  "image_cfg": {
+    "image_size": 224,
+    "model_name": "vit_b16"
+  },
+  "text_cfg": {
+    "context_length": 77,
+    "vocab_size": 49408,
+    "dim": 512,
+    "ffn_multiplier_per_layer": 4.0,
+    "n_heads_per_layer": 8,
+    "n_transformer_layers": 12,
+    "norm_layer": "layer_norm_fp32",
+    "causal_masking": true,
+    "model_name": "base"
+  }
+}

mobileclip/configs/mobileclip_s0.json

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+{
+  "embed_dim": 512,
+  "image_cfg": {
+    "image_size": 256,
+    "model_name": "mci0"
+  },
+  "text_cfg": {
+    "context_length": 77,
+    "vocab_size": 49408,
+    "dim": 512,
+    "ffn_multiplier_per_layer": 4.0,
+    "n_heads_per_layer": 8,
+    "n_transformer_layers": 4,
+    "norm_layer": "layer_norm_fp32",
+    "causal_masking": false,
+    "model_name": "mct"
+  }
+}

mobileclip/configs/mobileclip_s1.json

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+{
+  "embed_dim": 512,
+  "image_cfg": {
+    "image_size": 256,
+    "model_name": "mci1"
+  },
+  "text_cfg": {
+    "context_length": 77,
+    "vocab_size": 49408,
+    "dim": 512,
+    "ffn_multiplier_per_layer": 4.0,
+    "n_heads_per_layer": 8,
+    "n_transformer_layers": 12,
+    "norm_layer": "layer_norm_fp32",
+    "causal_masking": false,
+    "model_name": "base"
+  }
+}

mobileclip/configs/mobileclip_s2.json

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+{
+  "embed_dim": 512,
+  "image_cfg": {
+    "image_size": 256,
+    "model_name": "mci2"
+  },
+  "text_cfg": {
+    "context_length": 77,
+    "vocab_size": 49408,
+    "dim": 512,
+    "ffn_multiplier_per_layer": 4.0,
+    "n_heads_per_layer": 8,
+    "n_transformer_layers": 12,
+    "norm_layer": "layer_norm_fp32",
+    "causal_masking": false,
+    "model_name": "base"
+  }
+}

mobileclip/image_encoder.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+from typing import Any
+
+import torch.nn as nn
+from timm.models import create_model
+
+from mobileclip import models  # noqa added to register models
+from mobileclip.modules.image.image_projection import GlobalPool2D
+
+
+class MCi(nn.Module):
+    """This class implements `MCi Models <https://arxiv.org/pdf/2311.17049.pdf>`_."""
+
+    def __init__(self, model_name: str, *args, **kwargs) -> None:
+        super().__init__()
+        self.projection_dim = None
+        if "projection_dim" in kwargs:
+            self.projection_dim = kwargs.get("projection_dim")
+
+        # Create model
+        self.model = create_model(model_name, projection_dim=self.projection_dim)
+
+        # Build out projection head.
+        if self.projection_dim is not None:
+            if hasattr(self.model, "head"):
+                self.model.head = MCi._update_image_classifier(
+                    image_classifier=self.model.head, projection_dim=self.projection_dim
+                )
+
+    def forward(self, x: Any, *args, **kwargs) -> Any:
+        """A forward function of the model."""
+        x = self.model(x)
+        return x
+
+    @staticmethod
+    def _get_in_feature_dimension(image_classifier: nn.Module) -> int:
+        """Return the input feature dimension to the image classification head."""
+        in_features = None
+        if isinstance(image_classifier, nn.Sequential):
+            # Classifier that uses nn.Sequential usually has global pooling and
+            # multiple linear layers. Find the first linear layer and get its
+            # in_features
+            for layer in image_classifier:
+                if isinstance(layer, nn.Linear):
+                    in_features = layer.in_features
+                    break
+        elif isinstance(image_classifier, nn.Linear):
+            in_features = image_classifier.in_features
+
+        if in_features is None:
+            raise NotImplementedError(f"Cannot get input feature dimension of {image_classifier}.")
+        return in_features
+
+    @staticmethod
+    def _update_image_classifier(image_classifier: nn.Module, projection_dim: int, *args, **kwargs) -> nn.Module:
+        in_features = MCi._get_in_feature_dimension(image_classifier)
+        new_img_classifier = GlobalPool2D(in_dim=in_features, out_dim=projection_dim)
+        return new_img_classifier

mobileclip/logger.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import os
+import sys
+import time
+import traceback
+
+text_colors = {
+    "logs": "\033[34m",  # 033 is the escape code and 34 is the color code
+    "info": "\033[32m",
+    "warning": "\033[33m",
+    "debug": "\033[93m",
+    "error": "\033[31m",
+    "bold": "\033[1m",
+    "end_color": "\033[0m",
+    "light_red": "\033[36m",
+}
+
+
+def get_curr_time_stamp() -> str:
+    return time.strftime("%Y-%m-%d %H:%M:%S")
+
+
+def error(message: str) -> None:
+    time_stamp = get_curr_time_stamp()
+    error_str = text_colors["error"] + text_colors["bold"] + "ERROR  " + text_colors["end_color"]
+
+    # exiting with code -1 does not tell any information about the error (e.g., NaN encountered in the loss).
+    # For more descriptive error messages, we replace exit(-1) with sys.exit(ERROR_MESSAGE).
+    # This allows us to handle specific exceptions in the tests.
+
+    # print("{} - {} - {}".format(time_stamp, error_str, message), flush=True)
+    # print("{} - {} - {}".format(time_stamp, error_str, "Exiting!!!"), flush=True)
+    # exit(-1)
+
+    if sys.exc_info()[0] is None:
+        traceback.print_stack()
+    else:
+        traceback.print_exc()
+    sys.exit(f"{time_stamp} - {error_str} - {message}. Exiting!!!")
+
+
+def color_text(in_text: str) -> str:
+    return text_colors["light_red"] + in_text + text_colors["end_color"]
+
+
+def log(message: str, end="\n") -> None:
+    time_stamp = get_curr_time_stamp()
+    log_str = text_colors["logs"] + text_colors["bold"] + "LOGS   " + text_colors["end_color"]
+    print(f"{time_stamp} - {log_str} - {message}", end=end)
+
+
+def warning(message: str | Warning) -> None:
+    if isinstance(message, Warning):
+        message = f"{type(message).__name__}({','.join(map(repr, message.args))}"
+
+    time_stamp = get_curr_time_stamp()
+    warn_str = text_colors["warning"] + text_colors["bold"] + "WARNING" + text_colors["end_color"]
+    print(f"{time_stamp} - {warn_str} - {message}")
+
+
+def ignore_exception_with_warning(message: str) -> None:
+    """After catching a tolerable exception E1 (e.g. when Model.forward() fails during profiling with try-catch, it'll
+    be helpful to log the exception for future investigation. But printing the error stack trace, as is, could be
+    confusing when an uncaught (non-tolerable) exception "E2" raises down the road. Then, the log will contain two
+    stack traces for E1, E2. When looking for errors in logs, users should look for E2, but they may find E1.
+
+    This function appends "(WARNING)" at the end of all lines of the E1 traceback, so that the user can distinguish E1
+    from uncaught exception E2.
+
+    Args:
+        message: Extra explanation and context for debugging. (Note: the exception obj will be automatically fetched
+            from python. No need to pass it as an argument or as message)
+    """
+    warning(f"{message}:\n{traceback.format_exc()}".replace("\n", "\n(WARNING)"))
+
+
+def info(message: str, print_line: bool | None = False) -> None:
+    time_stamp = get_curr_time_stamp()
+    info_str = text_colors["info"] + text_colors["bold"] + "INFO   " + text_colors["end_color"]
+    print(f"{time_stamp} - {info_str} - {message}")
+    if print_line:
+        double_dash_line(dashes=150)
+
+
+def debug(message: str) -> None:
+    time_stamp = get_curr_time_stamp()
+    log_str = text_colors["debug"] + text_colors["bold"] + "DEBUG   " + text_colors["end_color"]
+    print(f"{time_stamp} - {log_str} - {message}")
+
+
+def double_dash_line(dashes: int | None = 75) -> None:
+    print(text_colors["error"] + "=" * dashes + text_colors["end_color"])
+
+
+def singe_dash_line(dashes: int | None = 67) -> None:
+    print("-" * dashes)
+
+
+def print_header(header: str) -> None:
+    double_dash_line()
+    print(text_colors["info"] + text_colors["bold"] + "=" * 50 + str(header) + text_colors["end_color"])
+    double_dash_line()
+
+
+def print_header_minor(header: str) -> None:
+    print(text_colors["warning"] + text_colors["bold"] + "=" * 25 + str(header) + text_colors["end_color"])
+
+
+def disable_printing():
+    sys.stdout = open(os.devnull, "w")
+
+
+def enable_printing():
+    sys.stdout = sys.__stdout__

mobileclip/models/__init__.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#
+from .mci import (
+    mci0,
+    mci1,
+    mci2,
+)
+from .vit import vit_b16

mobileclip/models/mci.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import copy
+from functools import partial
+
+import torch
+import torch.nn as nn
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from timm.models import register_model
+from timm.models.layers import DropPath, trunc_normal_
+
+from mobileclip.modules.common.mobileone import MobileOneBlock
+from mobileclip.modules.image.replknet import ReparamLargeKernelConv
+
+
+def _cfg(url="", **kwargs):
+    return {
+        "url": url,
+        "num_classes": 1000,
+        "input_size": (3, 256, 256),
+        "pool_size": None,
+        "crop_pct": 0.95,
+        "interpolation": "bicubic",
+        "mean": IMAGENET_DEFAULT_MEAN,
+        "std": IMAGENET_DEFAULT_STD,
+        "classifier": "head",
+        **kwargs,
+    }
+
+
+default_cfgs = {
+    "fastvit_t": _cfg(crop_pct=0.9),
+    "fastvit_s": _cfg(crop_pct=0.9),
+    "fastvit_m": _cfg(crop_pct=0.95),
+}
+
+
+def convolutional_stem(in_channels: int, out_channels: int, inference_mode: bool = False) -> nn.Sequential:
+    """Build convolutional stem with MobileOne blocks.
+
+    Args:
+        in_channels: Number of input channels.
+        out_channels: Number of output channels.
+        inference_mode: Flag to instantiate model in inference mode. Default: ``False``
+
+    Returns:
+        nn.Sequential object with stem elements.
+    """
+    return nn.Sequential(
+        MobileOneBlock(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            groups=1,
+            inference_mode=inference_mode,
+            use_se=False,
+            num_conv_branches=1,
+        ),
+        MobileOneBlock(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            groups=out_channels,
+            inference_mode=inference_mode,
+            use_se=False,
+            num_conv_branches=1,
+        ),
+        MobileOneBlock(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            groups=1,
+            inference_mode=inference_mode,
+            use_se=False,
+            num_conv_branches=1,
+        ),
+    )
+
+
+class MHSA(nn.Module):
+    """Multi-headed Self Attention module.
+
+    Source modified from:
+    https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        head_dim: int = 32,
+        qkv_bias: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        """Build MHSA module that can handle 3D or 4D input tensors.
+
+        Args:
+            dim: Number of embedding dimensions.
+            head_dim: Number of hidden dimensions per head. Default: ``32``
+            qkv_bias: Use bias or not. Default: ``False``
+            attn_drop: Dropout rate for attention tensor.
+            proj_drop: Dropout rate for projection tensor.
+        """
+        super().__init__()
+        assert dim % head_dim == 0, "dim should be divisible by head_dim"
+        self.head_dim = head_dim
+        self.num_heads = dim // head_dim
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shape = x.shape
+        B, C, H, W = shape
+        N = H * W
+        if len(shape) == 4:
+            x = torch.flatten(x, start_dim=2).transpose(-2, -1)  # (B, N, C)
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        # trick here to make q@k.t more stable
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        if len(shape) == 4:
+            x = x.transpose(-2, -1).reshape(B, C, H, W)
+
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """Convolutional patch embedding layer."""
+
+    def __init__(
+        self,
+        patch_size: int,
+        stride: int,
+        in_channels: int,
+        embed_dim: int,
+        inference_mode: bool = False,
+        use_se: bool = False,
+    ) -> None:
+        """Build patch embedding layer.
+
+        Args:
+            patch_size: Patch size for embedding computation.
+            stride: Stride for convolutional embedding layer.
+            in_channels: Number of channels of input tensor.
+            embed_dim: Number of embedding dimensions.
+            inference_mode: Flag to instantiate model in inference mode. Default: ``False``
+            use_se: If ``True`` SE block will be used.
+        """
+        super().__init__()
+        block = list()
+        block.append(
+            ReparamLargeKernelConv(
+                in_channels=in_channels,
+                out_channels=embed_dim,
+                kernel_size=patch_size,
+                stride=stride,
+                groups=in_channels,
+                small_kernel=3,
+                inference_mode=inference_mode,
+                use_se=use_se,
+            )
+        )
+        block.append(
+            MobileOneBlock(
+                in_channels=embed_dim,
+                out_channels=embed_dim,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                groups=1,
+                inference_mode=inference_mode,
+                use_se=False,
+                num_conv_branches=1,
+            )
+        )
+        self.proj = nn.Sequential(*block)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        return x
+
+
+class RepMixer(nn.Module):
+    """Reparameterizable token mixer.
+
+    For more details, please refer to our paper: `FastViT: A Fast Hybrid Vision Transformer using Structural
+    Reparameterization <https://arxiv.org/pdf/2303.14189.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim,
+        kernel_size=3,
+        use_layer_scale=True,
+        layer_scale_init_value=1e-5,
+        inference_mode: bool = False,
+    ):
+        """Build RepMixer Module.
+
+        Args:
+            dim: Input feature map dimension. :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, H, W)`.
+            kernel_size: Kernel size for spatial mixing. Default: 3
+            use_layer_scale: If True, learnable layer scale is used. Default: ``True``
+            layer_scale_init_value: Initial value for layer scale. Default: 1e-5
+            inference_mode: If True, instantiates model in inference mode. Default: ``False``
+        """
+        super().__init__()
+        self.dim = dim
+        self.kernel_size = kernel_size
+        self.inference_mode = inference_mode
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=self.dim,
+                out_channels=self.dim,
+                kernel_size=self.kernel_size,
+                stride=1,
+                padding=self.kernel_size // 2,
+                groups=self.dim,
+                bias=True,
+            )
+        else:
+            self.norm = MobileOneBlock(
+                dim,
+                dim,
+                kernel_size,
+                padding=kernel_size // 2,
+                groups=dim,
+                use_act=False,
+                use_scale_branch=False,
+                num_conv_branches=0,
+            )
+            self.mixer = MobileOneBlock(
+                dim,
+                dim,
+                kernel_size,
+                padding=kernel_size // 2,
+                groups=dim,
+                use_act=False,
+            )
+            self.use_layer_scale = use_layer_scale
+            if use_layer_scale:
+                self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "reparam_conv"):
+            x = self.reparam_conv(x)
+            return x
+        else:
+            if self.use_layer_scale:
+                x = x + self.layer_scale * (self.mixer(x) - self.norm(x))
+            else:
+                x = x + self.mixer(x) - self.norm(x)
+            return x
+
+    def reparameterize(self) -> None:
+        """Reparameterize mixer and norm into a single convolutional layer for efficient inference."""
+        if self.inference_mode:
+            return
+
+        self.mixer.reparameterize()
+        self.norm.reparameterize()
+
+        if self.use_layer_scale:
+            w = self.mixer.id_tensor + self.layer_scale.unsqueeze(-1) * (
+                self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
+            )
+            b = torch.squeeze(self.layer_scale) * (self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias)
+        else:
+            w = self.mixer.id_tensor + self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
+            b = self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
+
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.dim,
+            out_channels=self.dim,
+            kernel_size=self.kernel_size,
+            stride=1,
+            padding=self.kernel_size // 2,
+            groups=self.dim,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = w
+        self.reparam_conv.bias.data = b
+
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("mixer")
+        self.__delattr__("norm")
+        if self.use_layer_scale:
+            self.__delattr__("layer_scale")
+
+
+class ConvFFN(nn.Module):
+    """Convolutional FFN Module."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        hidden_channels: int | None = None,
+        out_channels: int | None = None,
+        act_layer: nn.Module = nn.GELU,
+        drop: float = 0.0,
+    ) -> None:
+        """Build convolutional FFN module.
+
+        Args:
+            in_channels: Number of input channels.
+            hidden_channels: Number of channels after expansion. Default: None
+            out_channels: Number of output channels. Default: None
+            act_layer: Activation layer. Default: ``GELU``
+            drop: Dropout rate. Default: ``0.0``.
+        """
+        super().__init__()
+        out_channels = out_channels or in_channels
+        hidden_channels = hidden_channels or in_channels
+        self.conv = nn.Sequential()
+        self.conv.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=7,
+                padding=3,
+                groups=in_channels,
+                bias=False,
+            ),
+        )
+        self.conv.add_module("bn", nn.BatchNorm2d(num_features=out_channels))
+        self.fc1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=1)
+        self.act = act_layer()
+        self.fc2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=1)
+        self.drop = nn.Dropout(drop)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m: nn.Module) -> None:
+        if isinstance(m, nn.Conv2d):
+            trunc_normal_(m.weight, std=0.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv(x)
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class RepCPE(nn.Module):
+    """Implementation of conditional positional encoding.
+
+    For more details refer to paper: `Conditional Positional Encodings for Vision Transformers
+    <https://arxiv.org/pdf/2102.10882.pdf>`_
+
+    In our implementation, we can reparameterize this module to eliminate a skip connection.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        embed_dim: int = 768,
+        spatial_shape: int | tuple[int, int] = (7, 7),
+        inference_mode=False,
+    ) -> None:
+        """Build reparameterizable conditional positional encoding.
+
+        Args:
+            in_channels: Number of input channels.
+            embed_dim: Number of embedding dimensions. Default: 768
+            spatial_shape: Spatial shape of kernel for positional encoding. Default: (7, 7)
+            inference_mode: Flag to instantiate block in inference mode. Default: ``False``
+        """
+        super().__init__()
+        if isinstance(spatial_shape, int):
+            spatial_shape = tuple([spatial_shape] * 2)
+        assert isinstance(spatial_shape, tuple), (
+            f'"spatial_shape" must by a sequence or int, get {type(spatial_shape)} instead.'
+        )
+        assert len(spatial_shape) == 2, f'Length of "spatial_shape" should be 2, got {len(spatial_shape)} instead.'
+
+        self.spatial_shape = spatial_shape
+        self.embed_dim = embed_dim
+        self.in_channels = in_channels
+        self.groups = embed_dim
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.embed_dim,
+                kernel_size=self.spatial_shape,
+                stride=1,
+                padding=int(self.spatial_shape[0] // 2),
+                groups=self.embed_dim,
+                bias=True,
+            )
+        else:
+            self.pe = nn.Conv2d(
+                in_channels,
+                embed_dim,
+                spatial_shape,
+                1,
+                int(spatial_shape[0] // 2),
+                bias=True,
+                groups=embed_dim,
+            )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "reparam_conv"):
+            x = self.reparam_conv(x)
+            return x
+        else:
+            x = self.pe(x) + x
+            return x
+
+    def reparameterize(self) -> None:
+        # Build equivalent Id tensor
+        input_dim = self.in_channels // self.groups
+        kernel_value = torch.zeros(
+            (
+                self.in_channels,
+                input_dim,
+                self.spatial_shape[0],
+                self.spatial_shape[1],
+            ),
+            dtype=self.pe.weight.dtype,
+            device=self.pe.weight.device,
+        )
+        for i in range(self.in_channels):
+            kernel_value[
+                i,
+                i % input_dim,
+                self.spatial_shape[0] // 2,
+                self.spatial_shape[1] // 2,
+            ] = 1
+        id_tensor = kernel_value
+
+        # Reparameterize Id tensor and conv
+        w_final = id_tensor + self.pe.weight
+        b_final = self.pe.bias
+
+        # Introduce reparam conv
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.in_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.spatial_shape,
+            stride=1,
+            padding=int(self.spatial_shape[0] // 2),
+            groups=self.embed_dim,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = w_final
+        self.reparam_conv.bias.data = b_final
+
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("pe")
+
+
+class RepMixerBlock(nn.Module):
+    """Implementation of Metaformer block with RepMixer as token mixer.
+
+    For more details on Metaformer structure, please refer to: `MetaFormer Is Actually What You Need for Vision
+    <https://arxiv.org/pdf/2111.11418.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        kernel_size: int = 3,
+        mlp_ratio: float = 4.0,
+        act_layer: nn.Module = nn.GELU,
+        drop: float = 0.0,
+        drop_path: float = 0.0,
+        use_layer_scale: bool = True,
+        layer_scale_init_value: float = 1e-5,
+        inference_mode: bool = False,
+    ):
+        """Build RepMixer Block.
+
+        Args:
+            dim: Number of embedding dimensions.
+            kernel_size: Kernel size for repmixer. Default: 3
+            mlp_ratio: MLP expansion ratio. Default: 4.0
+            act_layer: Activation layer. Default: ``nn.GELU``
+            drop: Dropout rate. Default: 0.0
+            drop_path: Drop path rate. Default: 0.0
+            use_layer_scale: Flag to turn on layer scale. Default: ``True``
+            layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
+            inference_mode: Flag to instantiate block in inference mode. Default: ``False``
+        """
+        super().__init__()
+
+        self.token_mixer = RepMixer(
+            dim,
+            kernel_size=kernel_size,
+            use_layer_scale=use_layer_scale,
+            layer_scale_init_value=layer_scale_init_value,
+            inference_mode=inference_mode,
+        )
+
+        assert mlp_ratio > 0, f"MLP ratio should be greater than 0, found: {mlp_ratio}"
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.convffn = ConvFFN(
+            in_channels=dim,
+            hidden_channels=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        # Drop Path
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        # Layer Scale
+        self.use_layer_scale = use_layer_scale
+        if use_layer_scale:
+            self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+
+    def forward(self, x):
+        if self.use_layer_scale:
+            x = self.token_mixer(x)
+            x = x + self.drop_path(self.layer_scale * self.convffn(x))
+        else:
+            x = self.token_mixer(x)
+            x = x + self.drop_path(self.convffn(x))
+        return x
+
+
+class AttentionBlock(nn.Module):
+    """Implementation of metaformer block with MHSA as token mixer.
+
+    For more details on Metaformer structure, please refer to: `MetaFormer Is Actually What You Need for Vision
+    <https://arxiv.org/pdf/2111.11418.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        mlp_ratio: float = 4.0,
+        act_layer: nn.Module = nn.GELU,
+        norm_layer: nn.Module = nn.BatchNorm2d,
+        drop: float = 0.0,
+        drop_path: float = 0.0,
+        use_layer_scale: bool = True,
+        layer_scale_init_value: float = 1e-5,
+    ):
+        """Build Attention Block.
+
+        Args:
+            dim: Number of embedding dimensions.
+            mlp_ratio: MLP expansion ratio. Default: 4.0
+            act_layer: Activation layer. Default: ``nn.GELU``
+            norm_layer: Normalization layer. Default: ``nn.BatchNorm2d``
+            drop: Dropout rate. Default: 0.0
+            drop_path: Drop path rate. Default: 0.0
+            use_layer_scale: Flag to turn on layer scale. Default: ``True``
+            layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
+        """
+        super().__init__()
+
+        self.norm = norm_layer(dim)
+        self.token_mixer = MHSA(dim=dim)
+
+        assert mlp_ratio > 0, f"MLP ratio should be greater than 0, found: {mlp_ratio}"
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.convffn = ConvFFN(
+            in_channels=dim,
+            hidden_channels=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        # Drop path
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        # Layer Scale
+        self.use_layer_scale = use_layer_scale
+        if use_layer_scale:
+            self.layer_scale_1 = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+            self.layer_scale_2 = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+
+    def forward(self, x):
+        if self.use_layer_scale:
+            x = x + self.drop_path(self.layer_scale_1 * self.token_mixer(self.norm(x)))
+            x = x + self.drop_path(self.layer_scale_2 * self.convffn(x))
+        else:
+            x = x + self.drop_path(self.token_mixer(self.norm(x)))
+            x = x + self.drop_path(self.convffn(x))
+        return x
+
+
+def basic_blocks(
+    dim: int,
+    block_index: int,
+    num_blocks: list[int],
+    token_mixer_type: str,
+    kernel_size: int = 3,
+    mlp_ratio: float = 4.0,
+    act_layer: nn.Module = nn.GELU,
+    norm_layer: nn.Module = nn.BatchNorm2d,
+    drop_rate: float = 0.0,
+    drop_path_rate: float = 0.0,
+    use_layer_scale: bool = True,
+    layer_scale_init_value: float = 1e-5,
+    inference_mode=False,
+) -> nn.Sequential:
+    """Build FastViT blocks within a stage.
+
+    Args:
+        dim: Number of embedding dimensions.
+        block_index: block index.
+        num_blocks: List containing number of blocks per stage.
+        token_mixer_type: Token mixer type.
+        kernel_size: Kernel size for repmixer.
+        mlp_ratio: MLP expansion ratio.
+        act_layer: Activation layer.
+        norm_layer: Normalization layer.
+        drop_rate: Dropout rate.
+        drop_path_rate: Drop path rate.
+        use_layer_scale: Flag to turn on layer scale regularization.
+        layer_scale_init_value: Layer scale value at initialization.
+        inference_mode: Flag to instantiate block in inference mode.
+
+    Returns:
+        nn.Sequential object of all the blocks within the stage.
+    """
+    blocks = []
+    for block_idx in range(num_blocks[block_index]):
+        block_dpr = drop_path_rate * (block_idx + sum(num_blocks[:block_index])) / (sum(num_blocks) - 1)
+        if token_mixer_type == "repmixer":
+            blocks.append(
+                RepMixerBlock(
+                    dim,
+                    kernel_size=kernel_size,
+                    mlp_ratio=mlp_ratio,
+                    act_layer=act_layer,
+                    drop=drop_rate,
+                    drop_path=block_dpr,
+                    use_layer_scale=use_layer_scale,
+                    layer_scale_init_value=layer_scale_init_value,
+                    inference_mode=inference_mode,
+                )
+            )
+        elif token_mixer_type == "attention":
+            blocks.append(
+                AttentionBlock(
+                    dim,
+                    mlp_ratio=mlp_ratio,
+                    act_layer=act_layer,
+                    norm_layer=norm_layer,
+                    drop=drop_rate,
+                    drop_path=block_dpr,
+                    use_layer_scale=use_layer_scale,
+                    layer_scale_init_value=layer_scale_init_value,
+                )
+            )
+        else:
+            raise ValueError(f"Token mixer type: {token_mixer_type} not supported")
+    blocks = nn.Sequential(*blocks)
+
+    return blocks
+
+
+class FastViT(nn.Module):
+    """This class implements `FastViT architecture <https://arxiv.org/pdf/2303.14189.pdf>`_."""
+
+    def __init__(
+        self,
+        layers,
+        token_mixers: tuple[str, ...],
+        embed_dims=None,
+        mlp_ratios=None,
+        downsamples=None,
+        se_downsamples=None,
+        repmixer_kernel_size=3,
+        norm_layer: nn.Module = nn.BatchNorm2d,
+        act_layer: nn.Module = nn.GELU,
+        num_classes=1000,
+        pos_embs=None,
+        down_patch_size=7,
+        down_stride=2,
+        drop_rate=0.0,
+        drop_path_rate=0.0,
+        use_layer_scale=True,
+        layer_scale_init_value=1e-5,
+        init_cfg=None,
+        pretrained=None,
+        cls_ratio=2.0,
+        inference_mode=False,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+
+        self.num_classes = num_classes
+        if pos_embs is None:
+            pos_embs = [None] * len(layers)
+
+        if se_downsamples is None:
+            se_downsamples = [False] * len(layers)
+
+        # Convolutional stem
+        self.patch_embed = convolutional_stem(3, embed_dims[0], inference_mode)
+
+        # Build the main stages of the network architecture
+        network = []
+        for i in range(len(layers)):
+            # Add position embeddings if requested
+            if pos_embs[i] is not None:
+                network.append(pos_embs[i](embed_dims[i], embed_dims[i], inference_mode=inference_mode))
+            stage = basic_blocks(
+                embed_dims[i],
+                i,
+                layers,
+                token_mixer_type=token_mixers[i],
+                kernel_size=repmixer_kernel_size,
+                mlp_ratio=mlp_ratios[i],
+                act_layer=act_layer,
+                norm_layer=norm_layer,
+                drop_rate=drop_rate,
+                drop_path_rate=drop_path_rate,
+                use_layer_scale=use_layer_scale,
+                layer_scale_init_value=layer_scale_init_value,
+                inference_mode=inference_mode,
+            )
+            network.append(stage)
+            if i >= len(layers) - 1:
+                break
+
+            # Patch merging/downsampling between stages.
+            if downsamples[i] or embed_dims[i] != embed_dims[i + 1]:
+                network.append(
+                    PatchEmbed(
+                        patch_size=down_patch_size,
+                        stride=down_stride,
+                        in_channels=embed_dims[i],
+                        embed_dim=embed_dims[i + 1],
+                        inference_mode=inference_mode,
+                        use_se=se_downsamples[i + 1],
+                    )
+                )
+        self.network = nn.ModuleList(network)
+
+        # Classifier head
+        self.conv_exp = MobileOneBlock(
+            in_channels=embed_dims[-1],
+            out_channels=int(embed_dims[-1] * cls_ratio),
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=embed_dims[-1],
+            inference_mode=inference_mode,
+            use_se=True,
+            num_conv_branches=1,
+        )
+        self.head = nn.Linear(int(embed_dims[-1] * cls_ratio), num_classes) if num_classes > 0 else nn.Identity()
+        self.apply(self.cls_init_weights)
+        self.init_cfg = copy.deepcopy(init_cfg)
+
+    def cls_init_weights(self, m: nn.Module) -> None:
+        """Init.
+
+        for classification.
+        """
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward_embeddings(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        return x
+
+    def forward_tokens(self, x: torch.Tensor) -> torch.Tensor:
+        for idx, block in enumerate(self.network):
+            x = block(x)
+        # output only the features of last layer for image classification
+        return x
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # input embedding
+        x = self.forward_embeddings(x)
+        # through backbone
+        x = self.forward_tokens(x)
+        # for image classification
+        x = self.conv_exp(x)
+        cls_out = self.head(x)
+        return cls_out
+
+
+@register_model
+def mci0(pretrained=False, **kwargs):
+    """Instantiate MCi0 model variant."""
+    layers = [2, 6, 10, 2]
+    embed_dims = [64, 128, 256, 512]
+    mlp_ratios = [3, 3, 3, 3]
+    downsamples = [True, True, True, True]
+    se_downsamples = [False, False, True, True]
+    pos_embs = [None, None, None, partial(RepCPE, spatial_shape=(7, 7))]
+    token_mixers = ("repmixer", "repmixer", "repmixer", "attention")
+    model = FastViT(
+        layers,
+        token_mixers=token_mixers,
+        embed_dims=embed_dims,
+        pos_embs=pos_embs,
+        mlp_ratios=mlp_ratios,
+        downsamples=downsamples,
+        se_downsamples=se_downsamples,
+        **kwargs,
+    )
+    model.default_cfg = default_cfgs["fastvit_s"]
+    if pretrained:
+        raise ValueError("Functionality not implemented.")
+    return model
+
+
+@register_model
+def mci1(pretrained=False, **kwargs):
+    """Instantiate MCi1 model variant."""
+    layers = [4, 12, 20, 4]
+    embed_dims = [64, 128, 256, 512]
+    mlp_ratios = [3, 3, 3, 3]
+    downsamples = [True, True, True, True]
+    se_downsamples = [False, False, True, True]
+    pos_embs = [None, None, None, partial(RepCPE, spatial_shape=(7, 7))]
+    token_mixers = ("repmixer", "repmixer", "repmixer", "attention")
+    model = FastViT(
+        layers,
+        token_mixers=token_mixers,
+        embed_dims=embed_dims,
+        pos_embs=pos_embs,
+        mlp_ratios=mlp_ratios,
+        downsamples=downsamples,
+        se_downsamples=se_downsamples,
+        **kwargs,
+    )
+    model.default_cfg = default_cfgs["fastvit_s"]
+    if pretrained:
+        raise ValueError("Functionality not implemented.")
+    return model
+
+
+@register_model
+def mci2(pretrained=False, **kwargs):
+    """Instantiate MCi2 model variant."""
+    layers = [4, 12, 24, 4]
+    embed_dims = [80, 160, 320, 640]
+    mlp_ratios = [3, 3, 3, 3]
+    downsamples = [True, True, True, True]
+    se_downsamples = [False, False, True, True]
+    pos_embs = [None, None, None, partial(RepCPE, spatial_shape=(7, 7))]
+    token_mixers = ("repmixer", "repmixer", "repmixer", "attention")
+    model = FastViT(
+        layers,
+        token_mixers=token_mixers,
+        embed_dims=embed_dims,
+        pos_embs=pos_embs,
+        mlp_ratios=mlp_ratios,
+        downsamples=downsamples,
+        se_downsamples=se_downsamples,
+        **kwargs,
+    )
+    model.default_cfg = default_cfgs["fastvit_m"]
+    if pretrained:
+        raise ValueError("Functionality not implemented.")
+    return model

mobileclip/models/vit.py

@@ -0,0 +1,389 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+"""
+Implementation of the following modules is borrowed from ml-cvnets repo:
+https://github.com/apple/ml-cvnets/blob/main/cvnets/models/classification/vit.py.
+
+Please see ACKNOWLEDGMENTS for license details.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import torch
+from timm.models import register_model
+from torch import Tensor, nn
+
+from mobileclip import logger
+from mobileclip.modules.common.transformer import (
+    PositionalEmbedding,
+    TransformerEncoder,
+    get_normalization_layer,
+)
+from mobileclip.modules.image.image_projection import SimpleImageProjectionHead
+
+
+class ConvNormAct(nn.Module):
+    """Applies an N-dimensional convolution over an input.
+
+    Args:
+        cfg: Model configuration.
+        in_channels: :math:`C_{out}` from an expected output of size :math:`(bs, C_{in}, X_{1}, ..., X_{N})`.
+        out_channels: :math:`C_{out}` from an expected output of size :math:`(bs, C_{out}, Y_{1}, ..., Y_{N})`.
+        kernel_size: Kernel size for convolution. An integer, or tuple of length ``N``.
+        stride: Stride for convolution. An integer, or tuple of length ``N``. Default: 1.
+        dilation: Dilation rate for convolution. An integer, or tuple of length ``N``. Default: ``1``.
+        padding: Padding for convolution. An integer, or tuple of length ``N``. If not specified, padding is
+            automatically computed based on kernel size and dilation range. Default : ``None`` (equivalent to ``[
+            int((kernel_size[i] - 1) / 2) * dilation[i] for i in range(N)]``).
+        groups: Number of groups in convolution. Default: ``1``.
+        bias: Use bias. Default: ``False``.
+        padding_mode: Padding mode ('zeros', 'reflect', 'replicate' or 'circular'). Default: ``zeros``.
+        use_norm: Use normalization layer after convolution. Default: ``True``.
+        use_act: Use activation layer after convolution (or convolution and normalization). Default: ``True``.
+        norm_layer: If not None, the provided normalization layer object will be used. Otherwise, a normalization object
+            will be created based on config ``model.normalization.*`` opts.
+        act_layer: If not None, the provided activation function will be used. Otherwise, an activation function will be
+            created based on config ``model.activation.*`` opts.
+
+    Notes:
+        - Input: :math:`(bs, C_{in}, X_{1}, ..., X_{N})`.
+        - Output: :math:`(bs, C_{out}, Y_{1}, ..., Y_{N})`.
+        - For depth-wise convolution, `groups=C_{in}=C_{out}`.
+    """
+
+    def __init__(
+        self,
+        cfg: dict,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int | tuple[int, ...],
+        stride: int | tuple[int, ...] = 1,
+        dilation: int | tuple[int, ...] = 1,
+        padding: int | tuple[int, ...] | None = None,
+        groups: int = 1,
+        bias: bool = False,
+        padding_mode: str = "zeros",
+        use_norm: bool = True,
+        use_act: bool = True,
+        norm_layer: nn.Module | None = None,
+        act_layer: nn.Module | None = None,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.ndim = 2
+
+        if norm_layer is None and use_norm:
+            norm_type = cfg.get("normalization", "batch_norm")
+            if norm_type == "batch_norm":
+                norm_layer = nn.BatchNorm2d(
+                    num_features=out_channels,
+                    momentum=cfg.get("momentum", 0.1),
+                )
+            else:
+                norm_layer = get_normalization_layer(num_features=out_channels, norm_type=norm_type)
+        elif norm_layer is not None and use_norm:
+            logger.error(f"When use_norm is False, norm_layer should be None, but norm_layer={norm_layer} is provided.")
+
+        if act_layer is None and use_act:
+            act_layer = nn.GELU()  # Default to GELU
+        elif act_layer is not None and use_act:
+            logger.error(f"When use_act is False, act_layer should be None, but act_layer={act_layer} is provided.")
+
+        if use_norm and any(param[0] == "bias" for param in norm_layer.named_parameters()) and bias:
+            assert not bias, "Do not use bias when using normalization layers with bias."
+
+        if isinstance(kernel_size, int):
+            kernel_size = (kernel_size,) * self.ndim
+
+        if isinstance(stride, int):
+            stride = (stride,) * self.ndim
+
+        if isinstance(dilation, int):
+            dilation = (dilation,) * self.ndim
+
+        assert isinstance(kernel_size, tuple)
+        assert isinstance(stride, tuple)
+        assert isinstance(dilation, tuple)
+
+        if padding is None:
+            padding = (int((kernel_size[i] - 1) / 2) * dilation[i] for i in range(self.ndim))
+
+        if in_channels % groups != 0:
+            logger.error(f"Input channels are not divisible by groups. {in_channels}%{groups} != 0 ")
+        if out_channels % groups != 0:
+            logger.error(f"Output channels are not divisible by groups. {out_channels}%{groups} != 0 ")
+
+        block = nn.Sequential()
+
+        conv_layer = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,  # type: ignore
+            stride=stride,  # type: ignore
+            padding=padding,
+            dilation=dilation,  # type: ignore
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+        )
+
+        block.add_module(name="conv", module=conv_layer)
+
+        self.norm_name = None
+        if use_norm:
+            block.add_module(name="norm", module=norm_layer)
+            self.norm_name = norm_layer.__class__.__name__
+
+        self.act_name = None
+        if use_act:
+            block.add_module(name="act", module=act_layer)
+            self.act_name = act_layer.__class__.__name__
+
+        self.block = block
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.stride = stride
+        self.groups = groups
+        self.kernel_size = conv_layer.kernel_size
+        self.bias = bias
+        self.dilation = dilation
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.block(x)
+
+
+class VisionTransformer(nn.Module):
+    """This class defines the `Vision Transformer architecture <https://arxiv.org/abs/2010.11929>`_. Our model
+    implementation is inspired from `Early Convolutions Help Transformers See
+    Better <https://arxiv.org/abs/2106.14881>`_.
+
+    .. note::
+        Our implementation is different from the original implementation in two ways:
+        1. Kernel size is odd.
+        2. Our positional encoding implementation allows us to use ViT with any multiple input scales
+        3. We do not use StochasticDepth
+        4. We do not add positional encoding to class token (if enabled), as suggested in `DeiT-3 paper <https://arxiv.org/abs/2204.07118>`_
+    """
+
+    def __init__(self, cfg, *args, **kwargs) -> None:
+        super().__init__()
+        image_channels = 3
+        num_classes = cfg.get("n_classes", 1000)
+
+        self.projection_dim = None
+        if "projection_dim" in kwargs:
+            self.projection_dim = kwargs.get("projection_dim")
+
+        kernel_sizes_conv_stem = [4, 2, 2]
+        strides_conv_stem = [4, 2, 2]
+
+        # Typically, in the ImageNet dataset, we use 224x224 as a resolution.
+        # For out ViT implementation, patch size is 16 (16 = 4 * 2 * 2)
+        # Therefore, total number of embeddings along width and height are (224 / 16)^2
+        num_embeddings = (224 // 16) ** 2
+
+        embed_dim = cfg["embed_dim"]
+        ffn_dim = cfg["embed_dim"] * 4
+        pos_emb_drop_p = cfg.get("pos_emb_drop_p", 0.0)
+        n_transformer_layers = cfg["n_transformer_layers"]
+        num_heads = cfg["n_attn_heads"]
+        attn_dropout = cfg.get("attn_dropout", 0.0)
+        dropout = cfg.get("dropout", 0.0)
+        ffn_dropout = cfg.get("ffn_dropout", 0.0)
+        norm_layer = cfg.get("norm_layer", "layer_norm")
+
+        conv_stem_proj_dim = max(32, embed_dim // 4)
+        patch_emb = [
+            ConvNormAct(
+                cfg=cfg,
+                in_channels=image_channels,
+                out_channels=conv_stem_proj_dim,
+                kernel_size=kernel_sizes_conv_stem[0],
+                stride=strides_conv_stem[0],
+                bias=False,
+                use_norm=True,
+                use_act=True,
+            ),
+            ConvNormAct(
+                cfg=cfg,
+                in_channels=conv_stem_proj_dim,
+                out_channels=conv_stem_proj_dim,
+                kernel_size=kernel_sizes_conv_stem[1],
+                stride=strides_conv_stem[1],
+                bias=False,
+                use_norm=True,
+                use_act=True,
+            ),
+            ConvNormAct(
+                cfg=cfg,
+                in_channels=conv_stem_proj_dim,
+                out_channels=embed_dim,
+                kernel_size=kernel_sizes_conv_stem[2],
+                stride=strides_conv_stem[2],
+                bias=True,
+                use_norm=False,
+                use_act=False,
+            ),
+        ]
+
+        self.patch_emb = nn.Sequential(*patch_emb)
+
+        use_cls_token = not cfg.get("no_cls_token", False)
+        stochastic_dropout = cfg.get("stochastic_dropout", 0.0)
+        per_layer_stochastic_drop_rate = [round(x, 3) for x in np.linspace(0, stochastic_dropout, n_transformer_layers)]
+        transformer_blocks = [
+            TransformerEncoder(
+                embed_dim=embed_dim,
+                ffn_latent_dim=ffn_dim,
+                num_heads=num_heads,
+                attn_dropout=attn_dropout,
+                dropout=dropout,
+                ffn_dropout=ffn_dropout,
+                transformer_norm_layer=norm_layer,
+                stochastic_dropout=per_layer_stochastic_drop_rate[layer_idx],
+            )
+            for layer_idx in range(n_transformer_layers)
+        ]
+
+        self.post_transformer_norm = get_normalization_layer(num_features=embed_dim, norm_type=norm_layer)
+
+        self.transformer = nn.Sequential(*transformer_blocks)
+
+        if self.projection_dim is None:
+            self.classifier = nn.Linear(embed_dim, num_classes)
+        else:
+            self.classifier = SimpleImageProjectionHead(embed_dim, self.projection_dim)
+
+        if use_cls_token:
+            self.cls_token = nn.Parameter(torch.zeros(size=(1, 1, embed_dim)))
+            torch.nn.init.trunc_normal_(self.cls_token, std=0.02)
+        else:
+            self.cls_token = None
+
+        self.pos_embed = PositionalEmbedding(
+            num_embeddings=num_embeddings,
+            embedding_dim=embed_dim,
+            padding_idx=None,
+            interpolation_mode="bilinear",
+        )
+        self.emb_dropout = nn.Dropout(p=pos_emb_drop_p)
+
+    def extract_patch_embeddings(self, x: Tensor) -> tuple[Tensor, tuple[int, int]]:
+        # input is of shape [Batch, in_channels, height, width]. in_channels is mostly 3 (for RGB images)
+        batch_size = x.shape[0]
+
+        # [Batch, in_channels, height, width] --> [Batch, emb_dim, num_patches_height, num_patches_width]
+        patch_emb = self.patch_emb(x)
+        n_h, n_w = patch_emb.shape[-2:]
+
+        # [Batch, emb_dim, num_patches_height, num_patches_width] --> [Batch, emb_dim, num_patches]
+        patch_emb = patch_emb.flatten(2)
+        # [Batch, emb_dim, num_patches] --> [Batch, num_patches, emb_dim]
+        patch_emb = patch_emb.transpose(1, 2).contiguous()
+
+        n_patches = patch_emb.shape[1]
+        # we resize the positional encodings dynamically.
+        pos_emb = self.pos_embed(n_patches).to(patch_emb.dtype)
+
+        # add positional encodings
+        patch_emb = pos_emb + patch_emb
+
+        # add classification token
+        if self.cls_token is not None:
+            # [1, 1, emb_dim] --> [Batch, 1, emb_dim]
+            cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+            # Concat([Batch, 1, emb_dim], [Batch, num_patches, emb_dim]) --> [Batch, num_patches + 1, emb_dim]
+            patch_emb = torch.cat((cls_tokens, patch_emb), dim=1)
+
+        # dropout
+        patch_emb = self.emb_dropout(patch_emb)
+        return patch_emb, (n_h, n_w)
+
+    def _features_from_transformer(self, x: Tensor, *args, **kwargs) -> tuple[Tensor, tuple[int, int]]:
+        # this function extract patch embeddings and then apply transformer module to learn
+        # inter-patch representations
+
+        # [B, N, C] --> [N, B, embed_dim], where B is batch size, N is number of tokens,
+        # and embed_dim is feature dim
+        x, (n_h, n_w) = self.extract_patch_embeddings(x)
+
+        for layer in self.transformer:
+            x = layer(x)
+        x = self.post_transformer_norm(x)
+
+        return x, (n_h, n_w)
+
+    def extract_features(self, x: Tensor, *args, **kwargs) -> tuple[Tensor, Tensor | None]:
+        # The extract_features function for ViT returns two outputs: (1) embedding corresponding to CLS token
+        # and (2) image embeddings of the shape [B, C, h//o, w//o], where the value of o is typically 16.
+        return_image_embeddings = kwargs.get("return_image_embeddings", False)
+
+        # [B, C, H, W] --> [B, N + 1, embed_dim] or [B, N, embed_dim]
+        # here, B is batch size, C is input channels
+        # H and W are input height and width
+        # N is the number of pixels (or tokens) after processing input with conv stem and reshaping
+        # We add +1 for cls token (if applicable)
+        # embed_dim --> embedding dimension
+        x, (n_h, n_w) = self._features_from_transformer(x, *args, **kwargs)
+
+        if self.cls_token is not None:
+            # [B, N + 1, embed_dim] --> [B, embed_dim], [B, N, embed_dim]
+            cls_embedding, image_embedding = torch.split(x, split_size_or_sections=[1, x.shape[1] - 1], dim=1)
+            cls_embedding = cls_embedding.squeeze(1)
+        else:
+            # [B, N, embed_dim] -> [B, embed_dim]
+            cls_embedding = torch.mean(x, dim=1)
+            # [B, N, embed_dim]
+            image_embedding = x
+
+        if return_image_embeddings:
+            # reshape image embedding to 4-D tensor
+            # [B, N, C] --> [B, C, N]
+            image_embedding = image_embedding.transpose(1, 2).contiguous()
+            image_embedding = image_embedding.reshape(image_embedding.shape[0], -1, n_h, n_w)
+
+            return cls_embedding, image_embedding
+        else:
+            return cls_embedding, None
+
+    def forward_classifier(self, x: Tensor, *args, **kwargs) -> tuple[Tensor, Tensor]:
+        cls_embedding, image_embedding = self.extract_features(x, *args, **kwargs)
+        # classify based on CLS token
+        cls_embedding = self.classifier(cls_embedding)
+        return cls_embedding, image_embedding
+
+    def forward(self, x: Tensor, *args, **kwargs) -> Tensor | dict[str, Tensor]:
+        # In ViT model, we can return either classifier embeddings (logits) or image embeddings or both.
+        # To return the image embeddings, we need to set keyword argument (return_image_embeddings) as True.
+        if kwargs.get("return_image_embeddings", False):
+            out_dict = dict()
+            prediction, image_embedding = self.forward_classifier(x, *args, **kwargs)
+            out_dict.update({"logits": prediction})
+            if image_embedding is not None:
+                out_dict.update({"image_embeddings": image_embedding})
+            return out_dict
+        else:
+            prediction, _ = self.forward_classifier(x, *args, **kwargs)
+            return prediction
+
+
+@register_model
+def vit_b16(pretrained=False, **kwargs):
+    # Vision transformer config
+    cfg = {
+        "norm_layer": "layer_norm_fp32",
+        "act_layer": "gelu",
+        "embed_dim": 768,
+        "n_transformer_layers": 12,
+        "n_attn_heads": 12,
+    }
+    model = VisionTransformer(cfg=cfg, **kwargs)
+    if pretrained:
+        raise ValueError("Functionality not implemented.")
+    return model

mobileclip/modules/__init__.py

@@ -0,0 +1,6 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#

mobileclip/modules/common/__init__.py

@@ -0,0 +1,6 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#

mobileclip/modules/common/mobileone.py

@@ -0,0 +1,330 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import copy
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+__all__ = ["MobileOneBlock", "reparameterize_model"]
+
+
+class SEBlock(nn.Module):
+    """Squeeze and Excite module.
+
+    Pytorch implementation of `Squeeze-and-Excitation Networks` - https://arxiv.org/pdf/1709.01507.pdf
+    """
+
+    def __init__(self, in_channels: int, rd_ratio: float = 0.0625) -> None:
+        """Construct a Squeeze and Excite Module.
+
+        Args:
+            in_channels: Number of input channels.
+            rd_ratio: Input channel reduction ratio.
+        """
+        super().__init__()
+        self.reduce = nn.Conv2d(
+            in_channels=in_channels,
+            out_channels=int(in_channels * rd_ratio),
+            kernel_size=1,
+            stride=1,
+            bias=True,
+        )
+        self.expand = nn.Conv2d(
+            in_channels=int(in_channels * rd_ratio),
+            out_channels=in_channels,
+            kernel_size=1,
+            stride=1,
+            bias=True,
+        )
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        _b, c, h, w = inputs.size()
+        x = F.avg_pool2d(inputs, kernel_size=[h, w])
+        x = self.reduce(x)
+        x = F.relu(x)
+        x = self.expand(x)
+        x = torch.sigmoid(x)
+        x = x.view(-1, c, 1, 1)
+        return inputs * x
+
+
+class MobileOneBlock(nn.Module):
+    """MobileOne building block.
+
+    This block has a multi-branched architecture at train-time and plain-CNN style architecture at inference time For
+    more details, please refer to our paper: `An Improved One millisecond Mobile Backbone` -
+    https://arxiv.org/pdf/2206.04040.pdf
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: int = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        inference_mode: bool = False,
+        use_se: bool = False,
+        use_act: bool = True,
+        use_scale_branch: bool = True,
+        num_conv_branches: int = 1,
+        activation: nn.Module = nn.GELU(),
+    ) -> None:
+        """Construct a MobileOneBlock module.
+
+        Args:
+            in_channels: Number of channels in the input.
+            out_channels: Number of channels produced by the block.
+            kernel_size: Size of the convolution kernel.
+            stride: Stride size.
+            padding: Zero-padding size.
+            dilation: Kernel dilation factor.
+            groups: Group number.
+            inference_mode: If True, instantiates model in inference mode.
+            use_se: Whether to use SE-ReLU activations.
+            use_act: Whether to use activation. Default: ``True``
+            use_scale_branch: Whether to use scale branch. Default: ``True``
+            num_conv_branches: Number of linear conv branches.
+        """
+        super().__init__()
+        self.inference_mode = inference_mode
+        self.groups = groups
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.kernel_size = kernel_size
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_conv_branches = num_conv_branches
+
+        # Check if SE-ReLU is requested
+        if use_se:
+            self.se = SEBlock(out_channels)
+        else:
+            self.se = nn.Identity()
+
+        if use_act:
+            self.activation = activation
+        else:
+            self.activation = nn.Identity()
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=groups,
+                bias=True,
+            )
+        else:
+            # Re-parameterizable skip connection
+            self.rbr_skip = (
+                nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None
+            )
+
+            # Re-parameterizable conv branches
+            if num_conv_branches > 0:
+                rbr_conv = list()
+                for _ in range(self.num_conv_branches):
+                    rbr_conv.append(self._conv_bn(kernel_size=kernel_size, padding=padding))
+                self.rbr_conv = nn.ModuleList(rbr_conv)
+            else:
+                self.rbr_conv = None
+
+            # Re-parameterizable scale branch
+            self.rbr_scale = None
+            if not isinstance(kernel_size, int):
+                kernel_size = kernel_size[0]
+            if (kernel_size > 1) and use_scale_branch:
+                self.rbr_scale = self._conv_bn(kernel_size=1, padding=0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        # Inference mode forward pass.
+        if self.inference_mode:
+            return self.activation(self.se(self.reparam_conv(x)))
+
+        # Multi-branched train-time forward pass.
+        # Skip branch output
+        identity_out = 0
+        if self.rbr_skip is not None:
+            identity_out = self.rbr_skip(x)
+
+        # Scale branch output
+        scale_out = 0
+        if self.rbr_scale is not None:
+            scale_out = self.rbr_scale(x)
+
+        # Other branches
+        out = scale_out + identity_out
+        if self.rbr_conv is not None:
+            for ix in range(self.num_conv_branches):
+                out += self.rbr_conv[ix](x)
+
+        return self.activation(self.se(out))
+
+    def reparameterize(self):
+        """Following works like `RepVGG: Making VGG-style ConvNets Great Again` - https://arxiv.org/pdf/2101.03697.pdf.
+        We re-parameterize multi-branched architecture used at training time to obtain a plain CNN-like
+        structure for inference.
+        """
+        if self.inference_mode:
+            return
+        kernel, bias = self._get_kernel_bias()
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            kernel_size=self.kernel_size,
+            stride=self.stride,
+            padding=self.padding,
+            dilation=self.dilation,
+            groups=self.groups,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = kernel
+        self.reparam_conv.bias.data = bias
+
+        # Delete un-used branches
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("rbr_conv")
+        self.__delattr__("rbr_scale")
+        if hasattr(self, "rbr_skip"):
+            self.__delattr__("rbr_skip")
+
+        self.inference_mode = True
+
+    def _get_kernel_bias(self) -> tuple[torch.Tensor, torch.Tensor]:
+        """Method to obtain re-parameterized kernel and bias. Reference:
+        https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83.
+
+        Returns:
+            Tuple of (kernel, bias) after fusing branches.
+        """
+        # get weights and bias of scale branch
+        kernel_scale = 0
+        bias_scale = 0
+        if self.rbr_scale is not None:
+            kernel_scale, bias_scale = self._fuse_bn_tensor(self.rbr_scale)
+            # Pad scale branch kernel to match conv branch kernel size.
+            pad = self.kernel_size // 2
+            kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad])
+
+        # get weights and bias of skip branch
+        kernel_identity = 0
+        bias_identity = 0
+        if self.rbr_skip is not None:
+            kernel_identity, bias_identity = self._fuse_bn_tensor(self.rbr_skip)
+
+        # get weights and bias of conv branches
+        kernel_conv = 0
+        bias_conv = 0
+        if self.rbr_conv is not None:
+            for ix in range(self.num_conv_branches):
+                _kernel, _bias = self._fuse_bn_tensor(self.rbr_conv[ix])
+                kernel_conv += _kernel
+                bias_conv += _bias
+
+        kernel_final = kernel_conv + kernel_scale + kernel_identity
+        bias_final = bias_conv + bias_scale + bias_identity
+        return kernel_final, bias_final
+
+    def _fuse_bn_tensor(self, branch: nn.Sequential | nn.BatchNorm2d) -> tuple[torch.Tensor, torch.Tensor]:
+        """Method to fuse batchnorm layer with preceeding conv layer. Reference:
+        https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95.
+
+        Args:
+            branch: Sequence of ops to be fused.
+
+        Returns:
+            Tuple of (kernel, bias) after fusing batchnorm.
+        """
+        if isinstance(branch, nn.Sequential):
+            kernel = branch.conv.weight
+            running_mean = branch.bn.running_mean
+            running_var = branch.bn.running_var
+            gamma = branch.bn.weight
+            beta = branch.bn.bias
+            eps = branch.bn.eps
+        else:
+            assert isinstance(branch, nn.BatchNorm2d)
+            if not hasattr(self, "id_tensor"):
+                input_dim = self.in_channels // self.groups
+
+                kernel_size = self.kernel_size
+                if isinstance(self.kernel_size, int):
+                    kernel_size = (self.kernel_size, self.kernel_size)
+
+                kernel_value = torch.zeros(
+                    (self.in_channels, input_dim, kernel_size[0], kernel_size[1]),
+                    dtype=branch.weight.dtype,
+                    device=branch.weight.device,
+                )
+                for i in range(self.in_channels):
+                    kernel_value[i, i % input_dim, kernel_size[0] // 2, kernel_size[1] // 2] = 1
+                self.id_tensor = kernel_value
+            kernel = self.id_tensor
+            running_mean = branch.running_mean
+            running_var = branch.running_var
+            gamma = branch.weight
+            beta = branch.bias
+            eps = branch.eps
+        std = (running_var + eps).sqrt()
+        t = (gamma / std).reshape(-1, 1, 1, 1)
+        return kernel * t, beta - running_mean * gamma / std
+
+    def _conv_bn(self, kernel_size: int, padding: int) -> nn.Sequential:
+        """Helper method to construct conv-batchnorm layers.
+
+        Args:
+            kernel_size: Size of the convolution kernel.
+            padding: Zero-padding size.
+
+        Returns:
+            Conv-BN module.
+        """
+        mod_list = nn.Sequential()
+        mod_list.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.out_channels,
+                kernel_size=kernel_size,
+                stride=self.stride,
+                padding=padding,
+                groups=self.groups,
+                bias=False,
+            ),
+        )
+        mod_list.add_module("bn", nn.BatchNorm2d(num_features=self.out_channels))
+        return mod_list
+
+
+def reparameterize_model(model: torch.nn.Module) -> nn.Module:
+    """Method returns a model where a multi-branched structure used in training is re-parameterized into a single branch
+    for inference.
+
+    Args:
+        model: MobileOne model in train mode.
+
+    Returns:
+        MobileOne model in inference mode.
+    """
+    # Avoid editing original graph
+    model = copy.deepcopy(model)
+    for module in model.modules():
+        if hasattr(module, "reparameterize"):
+            module.reparameterize()
+    return model

mobileclip/modules/common/transformer.py

@@ -0,0 +1,410 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+"""
+Implementation of the following modules is borrowed from ml-cvnets repo:
+https://github.com/apple/ml-cvnets/blob/main/cvnets/layers/multi_head_attention.py
+https://github.com/apple/ml-cvnets/blob/main/cvnets/text_encoders/transformer.py.
+
+Please see ACKNOWLEDGMENTS for license details.
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import Size, Tensor, nn
+from torch.nn import functional as F
+from torchvision.ops import StochasticDepth
+
+from mobileclip import logger
+
+
+class LayerNormFP32(nn.LayerNorm):
+    """Applies `Layer Normalization <https://arxiv.org/abs/1607.06450>`_ over a input tensor with FP32 precision."""
+
+    def __init__(
+        self,
+        normalized_shape: int | list[int] | Size,
+        eps: float | None = 1e-5,
+        elementwise_affine: bool | None = True,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(
+            normalized_shape=normalized_shape,
+            eps=eps,
+            elementwise_affine=elementwise_affine,
+            *args,
+            **kwargs,
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        # Convert input from dtype X to FP32 and perform normalization operation.
+        # This may help with underflow/overflow issues that we typically see with normalization layers
+        inp_dtype = x.dtype
+        return super().forward(x.to(torch.float32)).to(inp_dtype)
+
+
+def get_normalization_layer(norm_type, num_features):
+    if norm_type == "layer_norm":
+        return nn.LayerNorm(num_features)
+    elif norm_type == "layer_norm_fp32":
+        return LayerNormFP32(num_features)
+    else:
+        raise NotImplementedError(f"Option: {norm_type} not supported.")
+
+
+class PositionalEmbedding(nn.Module):
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: int | None = None,
+        is_learnable: bool | None = False,
+        interpolation_mode: str | None = "bilinear",
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+        # Add other pos embedding here and logic to choose between them
+        module = LearnablePositionalEmbedding
+
+        self.pos_embed = module(
+            num_embeddings=num_embeddings,
+            embedding_dim=embedding_dim,
+            padding_idx=padding_idx,
+            interpolation_mode=interpolation_mode,
+            *args,
+            **kwargs,
+        )
+
+    def forward(self, seq_len: int, *args, **kwargs) -> Tensor:
+        return self.pos_embed(seq_len, *args, **kwargs)
+
+    def __repr__(self):
+        return self.pos_embed.__repr__()
+
+
+class LearnablePositionalEmbedding(nn.Module):
+    """Learnable Positional embedding."""
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: int | None = None,
+        interpolation_mode: str | None = "bilinear",
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+        self.pos_embed = nn.Parameter(torch.empty(1, 1, num_embeddings, embedding_dim))
+        self.embedding_dim = embedding_dim
+        self.num_embeddings = num_embeddings
+        self.padding_idx = padding_idx
+        self.interpolation_mode = interpolation_mode
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        nn.init.trunc_normal_(self.pos_embed, mean=0, std=self.embedding_dim**-0.5)
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                self.pos_embed[:, :, self.padding_idx, ...] = 0.0
+
+    def forward(self, seq_len: int, *args, **kwargs) -> Tensor:
+        # scale pos embedding
+        pos_embed = self.pos_embed
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                pos_embed[:, :, self.padding_idx, ...] = 0.0
+
+        if seq_len != self.num_embeddings:
+            pos_embed = F.interpolate(
+                pos_embed,
+                size=(seq_len, self.embedding_dim),
+                mode=self.interpolation_mode,
+            )
+
+        # Input is of the form [Batch, Seq_len, Embedding_dim]
+        return pos_embed.reshape(1, seq_len, self.embedding_dim)
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(num_embeddings={self.num_embeddings}, embedding_dim={self.embedding_dim}, padding_idx={self.padding_idx})"
+
+
+class MultiHeadAttention(nn.Module):
+    """This layer applies a multi-head self- or cross-attention as described in `Attention is all you need
+    <https://arxiv.org/abs/1706.03762>`_ paper.
+
+    Args:
+        embed_dim (int): :math:`C_{in}` from an expected input of size :math:`(N, S, C_{in})`
+        num_heads (int): Number of heads in multi-head attention
+        attn_dropout (Optional[float]): Attention dropout. Default: 0.0
+        bias (Optional[bool]): Use bias or not. Default: ``True``
+
+    Notes:
+        - Input:
+           - Query tensor (x_q) :math:`(N, S, C_{in})` where :math:`N` is batch size, :math:`S` is number of source tokens,
+        and: math:`C_{in}` is input embedding dim
+           - Optional Key-Value tensor (x_kv) :math:`(N, T, C_{in})` where :math:`T` is number of target tokens
+        - Output: same shape as the input
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        attn_dropout: float | None = 0.0,
+        bias: bool | None = True,
+        output_dim: int | None = None,
+        *args,
+        **kwargs,
+    ) -> None:
+        if output_dim is None:
+            output_dim = embed_dim
+        super().__init__()
+        if embed_dim % num_heads != 0:
+            logger.error(
+                f"Embedding dim must be divisible by number of heads in {self.__class__.__name__}. Got: embed_dim={embed_dim} and num_heads={num_heads}"
+            )
+
+        self.qkv_proj = nn.Linear(in_features=embed_dim, out_features=3 * embed_dim, bias=bias)
+
+        self.attn_dropout = nn.Dropout(p=attn_dropout)
+        self.out_proj = nn.Linear(in_features=embed_dim, out_features=output_dim, bias=bias)
+
+        self.head_dim = embed_dim // num_heads
+        self.scaling = self.head_dim**-0.5
+        self.softmax = nn.Softmax(dim=-1)
+        self.num_heads = num_heads
+        self.embed_dim = embed_dim
+        self.use_separate_proj_weight = embed_dim != output_dim
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(head_dim={self.head_dim}, num_heads={self.num_heads}, attn_dropout={self.attn_dropout.p})"
+
+    def _forward_impl(
+        self,
+        x_q: Tensor,
+        x_kv: Tensor | None = None,
+        key_padding_mask: Tensor | None = None,
+        attn_mask: Tensor | None = None,
+    ) -> Tensor:
+        # [N, S, C]
+        b_sz, S_len, _in_channels = x_q.shape
+
+        if x_kv is None:
+            # self-attention
+            # [N, S, C] --> [N, S, 3C] --> [N, S, 3, h, c] where C = hc
+            qkv = self.qkv_proj(x_q).reshape(b_sz, S_len, 3, self.num_heads, -1)
+            # [N, S, 3, h, c] --> [N, h, 3, S, C]
+            qkv = qkv.transpose(1, 3).contiguous()
+
+            # [N, h, 3, S, C] --> [N, h, S, C] x 3
+            query, key, value = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
+        else:
+            T_len = x_kv.shape[1]
+
+            # cross-attention
+            # [N, S, C]
+            query = F.linear(
+                x_q,
+                weight=self.qkv_proj.weight[: self.embed_dim, ...],
+                bias=self.qkv_proj.bias[: self.embed_dim] if self.qkv_proj.bias is not None else None,
+            )
+            # [N, S, C] --> [N, S, h, c] --> [N, h, S, c]
+            query = query.reshape(b_sz, S_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+            # [N, T, C] --> [N, T, 2C]
+            kv = F.linear(
+                x_kv,
+                weight=self.qkv_proj.weight[self.embed_dim :, ...],
+                bias=self.qkv_proj.bias[self.embed_dim :] if self.qkv_proj.bias is not None else None,
+            )
+            # [N, T, 2C] --> [N, T, 2, h, c]
+            kv = kv.reshape(b_sz, T_len, 2, self.num_heads, self.head_dim)
+            # [N, T, 2, h, c] --> [N, h, 2, T, c]
+            kv = kv.transpose(1, 3).contiguous()
+            key, value = kv[:, :, 0], kv[:, :, 1]
+
+        query = query * self.scaling
+
+        # [N h, T, c] --> [N, h, c, T]
+        key = key.transpose(-1, -2)
+
+        # QK^T
+        # [N, h, S, c] x [N, h, c, T] --> [N, h, S, T]
+        attn = torch.matmul(query, key)
+
+        batch_size, _num_heads, num_src_tokens, num_tgt_tokens = attn.shape
+        if attn_mask is not None:
+            # attn_mask shape should be the same as attn
+            assert list(attn_mask.shape) == [
+                batch_size,
+                num_src_tokens,
+                num_tgt_tokens,
+            ], (
+                f"Shape of attention mask should be [{batch_size}, {num_src_tokens}, {num_tgt_tokens}]. Got: {attn_mask.shape}"
+            )
+            # [N, S, T] --> [N, 1, S, T]
+            attn_mask = attn_mask.unsqueeze(1)
+            attn = attn + attn_mask
+
+        if key_padding_mask is not None:
+            # Do not attend to padding positions
+            # key padding mask size is [N, T]
+            assert key_padding_mask.dim() == 2 and list(key_padding_mask.shape) == [
+                batch_size,
+                num_tgt_tokens,
+            ], (
+                f"Key_padding_mask should be 2-dimension with shape [{batch_size}, {num_tgt_tokens}]. Got: {key_padding_mask.shape}"
+            )
+            attn = attn.masked_fill(
+                key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),  # [N, T] --> [N, 1, 1, T]
+                float("-inf"),
+            )
+
+        attn_dtype = attn.dtype
+        attn_as_float = self.softmax(attn.float())
+        attn = attn_as_float.to(attn_dtype)
+        attn = self.attn_dropout(attn)
+
+        # weighted sum
+        # [N, h, S, T] x [N, h, T, c] --> [N, h, S, c]
+        out = torch.matmul(attn, value)
+
+        # [N, h, S, c] --> [N, S, h, c] --> [N, S, C]
+        out = out.transpose(1, 2).reshape(b_sz, S_len, -1)
+        out = self.out_proj(out)
+
+        return out
+
+    def forward(
+        self,
+        x_q: Tensor,
+        x_kv: Tensor | None = None,
+        key_padding_mask: Tensor | None = None,
+        attn_mask: Tensor | None = None,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        # [Batch , Sequence, Hidden_dim]
+        return self._forward_impl(
+            x_q=x_q,
+            x_kv=x_kv,
+            key_padding_mask=key_padding_mask,
+            attn_mask=attn_mask,
+        )
+
+
+class TransformerEncoder(nn.Module):
+    """This class defines the pre-norm `Transformer encoder <https://arxiv.org/abs/1706.03762>`_.
+
+    Args:
+        embed_dim: :math:`C_{in}` from an expected input of size :math:`(N, P, C_{in})`.
+        ffn_latent_dim: Inner dimension of the FFN.
+        num_heads: Number of heads in multi-head attention. Default: 8.
+        attn_dropout: Dropout rate for attention in multi-head attention. Default: 0.0
+        dropout: Dropout rate. Default: 0.0.
+        ffn_dropout: Dropout between FFN layers. Default: 0.0.
+        transformer_norm_layer: Normalization layer. Default: layer_norm.
+        stochastic_dropout: Stochastic dropout setting. Default: 0.0.
+
+    Notes:
+        - Input: :math:`(N, P, C_{in})` where :math:`N` is batch size, :math:`P` is number of patches,
+        and: math:`C_{in}` is input embedding dim
+        - Output: same shape as the input
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        ffn_latent_dim: int,
+        num_heads: int | None = 8,
+        attn_dropout: float | None = 0.0,
+        dropout: float | None = 0.0,
+        ffn_dropout: float | None = 0.0,
+        transformer_norm_layer: str | None = "layer_norm",
+        stochastic_dropout: float | None = 0.0,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+
+        # Build attention layer
+        attn_unit = MultiHeadAttention(
+            embed_dim,
+            num_heads,
+            attn_dropout=attn_dropout,
+            bias=True,
+        )
+
+        self.pre_norm_mha = nn.Sequential(
+            get_normalization_layer(norm_type=transformer_norm_layer, num_features=embed_dim),
+            attn_unit,
+            nn.Dropout(p=dropout),
+        )
+
+        act_name = nn.GELU()
+        self.pre_norm_ffn = nn.Sequential(
+            get_normalization_layer(norm_type=transformer_norm_layer, num_features=embed_dim),
+            nn.Linear(in_features=embed_dim, out_features=ffn_latent_dim, bias=True),
+            act_name,
+            nn.Dropout(p=ffn_dropout),
+            nn.Linear(in_features=ffn_latent_dim, out_features=embed_dim, bias=True),
+            nn.Dropout(p=dropout),
+        )
+
+        self.drop_path = nn.Identity()
+        if stochastic_dropout > 0.0:
+            if dropout > 0.0:
+                logger.error(
+                    "Stochastic dropout and dropout are mutually exclusive. "
+                    "Use either of them, but not both."
+                    f"Got: {stochastic_dropout} and {dropout}"
+                )
+            self.drop_path = StochasticDepth(p=stochastic_dropout, mode="row")
+
+        self.embed_dim = embed_dim
+        self.ffn_dim = ffn_latent_dim
+        self.ffn_dropout = ffn_dropout
+        self.stochastic_dropout = stochastic_dropout
+        self.std_dropout = dropout
+        self.attn_fn_name = attn_unit.__class__.__name__
+        self.act_fn_name = act_name.__class__.__name__
+        self.norm_type = transformer_norm_layer
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(embed_dim={self.embed_dim}, ffn_dim={self.ffn_dim}, dropout={self.std_dropout}, ffn_dropout={self.ffn_dropout}, stochastic_dropout={self.stochastic_dropout}, attn_fn={self.attn_fn_name}, act_fn={self.act_fn_name}, norm_fn={self.norm_type})"
+
+    def forward(
+        self,
+        x: Tensor,
+        x_prev: Tensor | None = None,
+        key_padding_mask: Tensor | None = None,
+        attn_mask: Tensor | None = None,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        # Multi-head attention
+        res = x
+        x = self.pre_norm_mha[0](x)  # norm
+        x = self.pre_norm_mha[1](
+            x_q=x,
+            x_kv=x_prev,
+            key_padding_mask=key_padding_mask,
+            attn_mask=attn_mask,
+            *args,
+            **kwargs,
+        )  # mha
+
+        x = self.drop_path(self.pre_norm_mha[2](x))  # applying stochastic depth
+        x = x + res
+
+        # Feed forward network
+        x = x + self.drop_path(self.pre_norm_ffn(x))
+        return x

mobileclip/modules/image/__init__.py

@@ -0,0 +1,6 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#

mobileclip/modules/image/image_projection.py

@@ -0,0 +1,97 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from mobileclip import logger
+
+
+class GlobalPool(nn.Module):
+    """This layers applies global pooling over a 4D or 5D input tensor.
+
+    Args:
+        pool_type (Optional[str]): Pooling type. It can be mean, rms, or abs. Default: `mean`
+        keep_dim (Optional[bool]): Do not squeeze the dimensions of a tensor. Default: `False`
+
+    Notes:
+        - Input: :math:`(N, C, H, W)` or :math:`(N, C, D, H, W)`
+        - Output: :math:`(N, C, 1, 1)` or :math:`(N, C, 1, 1, 1)` if keep_dim else :math:`(N, C)`
+    """
+
+    pool_types = ["mean", "rms", "abs"]
+
+    def __init__(self, pool_type: str | None = "mean", keep_dim: bool | None = False, *args, **kwargs) -> None:
+        super().__init__()
+        if pool_type not in self.pool_types:
+            logger.error(f"Supported pool types are: {self.pool_types}. Got {pool_type}")
+        self.pool_type = pool_type
+        self.keep_dim = keep_dim
+
+    def _global_pool(self, x: Tensor, dims: list):
+        if self.pool_type == "rms":  # root mean square
+            x = x**2
+            x = torch.mean(x, dim=dims, keepdim=self.keep_dim)
+            x = x**-0.5
+        elif self.pool_type == "abs":  # absolute
+            x = torch.mean(torch.abs(x), dim=dims, keepdim=self.keep_dim)
+        else:
+            # default is mean
+            # same as AdaptiveAvgPool
+            x = torch.mean(x, dim=dims, keepdim=self.keep_dim)
+        return x
+
+    def forward(self, x: Tensor) -> Tensor:
+        if x.dim() == 4:
+            dims = [-2, -1]
+        elif x.dim() == 5:
+            dims = [-3, -2, -1]
+        else:
+            raise NotImplementedError("Currently 2D and 3D global pooling supported")
+        return self._global_pool(x, dims=dims)
+
+
+class GlobalPool2D(nn.Module):
+    """This class implements global pooling with linear projection."""
+
+    def __init__(self, in_dim: int, out_dim: int, *args, **kwargs) -> None:
+        super().__init__()
+        scale = in_dim**-0.5
+        self.pool = GlobalPool(pool_type="mean", keep_dim=False)
+        self.proj = nn.Parameter(scale * torch.randn(size=(in_dim, out_dim)))
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+    def forward(self, x: Tensor, *args, **kwargs) -> Tensor:
+        # x is of shape [batch, in_dim]
+        assert x.dim() == 4, f"Input should be 4-dimensional (Batch x in_dim x in_height x in_width). Got: {x.shape}"
+
+        # [batch, in_dim, in_height, in_width] --> [batch, in_dim]
+        x = self.pool(x)
+        # [batch, in_dim]  x [in_dim, out_dim] --> [batch, out_dim]
+        x = x @ self.proj
+        return x
+
+
+class SimpleImageProjectionHead(nn.Module):
+    """This class implements linear projection head."""
+
+    def __init__(self, in_dim: int, out_dim: int) -> None:
+        super().__init__()
+        scale = in_dim**-0.5
+        self.proj = nn.Parameter(scale * torch.randn(size=(in_dim, out_dim)))
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+    def forward(self, x: Tensor, *args, **kwargs) -> Tensor:
+        # x is of shape [batch, in_dim]
+        assert x.dim() == 2, f"Input should be 2-dimensional (Batch x in_dim). Got: {x.shape}"
+
+        # [batch, in_dim] x [in_dim, out_dim] --> [batch, out_dim]
+        x = x @ self.proj
+        return x

mobileclip/modules/image/replknet.py

@@ -0,0 +1,177 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For acknowledgment see accompanying ACKNOWLEDGMENTS file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#
+
+import torch
+import torch.nn as nn
+from timm.models.layers import SqueezeExcite
+
+__all__ = ["ReparamLargeKernelConv"]
+
+
+class ReparamLargeKernelConv(nn.Module):
+    """Building Block of RepLKNet.
+
+    This class defines overparameterized large kernel conv block introduced in `RepLKNet
+    <https://arxiv.org/abs/2203.06717>`_
+
+    Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int,
+        groups: int,
+        small_kernel: int,
+        inference_mode: bool = False,
+        use_se: bool = False,
+        activation: nn.Module = nn.GELU(),
+    ) -> None:
+        """Construct a ReparamLargeKernelConv module.
+
+        Args:
+            in_channels: Number of input channels.
+            out_channels: Number of output channels.
+            kernel_size: Kernel size of the large kernel conv branch.
+            stride: Stride size. Default: 1
+            groups: Group number. Default: 1
+            small_kernel: Kernel size of small kernel conv branch.
+            inference_mode: If True, instantiates model in inference mode. Default: ``False``
+            activation: Activation module. Default: ``nn.GELU``
+        """
+        super().__init__()
+
+        self.stride = stride
+        self.groups = groups
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.activation = activation
+
+        self.kernel_size = kernel_size
+        self.small_kernel = small_kernel
+        self.padding = kernel_size // 2
+
+        # Check if SE is requested
+        if use_se:
+            self.se = SqueezeExcite(out_channels, rd_ratio=0.25)
+        else:
+            self.se = nn.Identity()
+
+        if inference_mode:
+            self.lkb_reparam = nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=self.padding,
+                dilation=1,
+                groups=groups,
+                bias=True,
+            )
+        else:
+            self.lkb_origin = self._conv_bn(kernel_size=kernel_size, padding=self.padding)
+            if small_kernel is not None:
+                assert small_kernel <= kernel_size, (
+                    "The kernel size for re-param cannot be larger than the large kernel!"
+                )
+                self.small_conv = self._conv_bn(kernel_size=small_kernel, padding=small_kernel // 2)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Apply forward pass."""
+        if hasattr(self, "lkb_reparam"):
+            out = self.lkb_reparam(x)
+        else:
+            out = self.lkb_origin(x)
+            if hasattr(self, "small_conv"):
+                out += self.small_conv(x)
+
+        return self.activation(self.se(out))
+
+    def get_kernel_bias(self) -> tuple[torch.Tensor, torch.Tensor]:
+        """Method to obtain re-parameterized kernel and bias. Reference: https://github.com/DingXiaoH/RepLKNet-pytorch.
+
+        Returns:
+            Tuple of (kernel, bias) after fusing branches.
+        """
+        eq_k, eq_b = self._fuse_bn(self.lkb_origin.conv, self.lkb_origin.bn)
+        if hasattr(self, "small_conv"):
+            small_k, small_b = self._fuse_bn(self.small_conv.conv, self.small_conv.bn)
+            eq_b += small_b
+            eq_k += nn.functional.pad(small_k, [(self.kernel_size - self.small_kernel) // 2] * 4)
+        return eq_k, eq_b
+
+    def reparameterize(self) -> None:
+        """Following works like `RepVGG: Making VGG-style ConvNets Great Again` - https://arxiv.org/pdf/2101.03697.pdf.
+        We re-parameterize multi-branched architecture used at training time to obtain a plain CNN-like
+        structure for inference.
+        """
+        eq_k, eq_b = self.get_kernel_bias()
+        self.lkb_reparam = nn.Conv2d(
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            kernel_size=self.kernel_size,
+            stride=self.stride,
+            padding=self.padding,
+            dilation=self.lkb_origin.conv.dilation,
+            groups=self.groups,
+            bias=True,
+        )
+
+        self.lkb_reparam.weight.data = eq_k
+        self.lkb_reparam.bias.data = eq_b
+        self.__delattr__("lkb_origin")
+        if hasattr(self, "small_conv"):
+            self.__delattr__("small_conv")
+
+    @staticmethod
+    def _fuse_bn(conv: torch.Tensor, bn: nn.BatchNorm2d) -> tuple[torch.Tensor, torch.Tensor]:
+        """Method to fuse batchnorm layer with conv layer.
+
+        Args:
+            conv: Convolutional kernel weights.
+            bn: Batchnorm 2d layer.
+
+        Returns:
+            Tuple of (kernel, bias) after fusing batchnorm.
+        """
+        kernel = conv.weight
+        running_mean = bn.running_mean
+        running_var = bn.running_var
+        gamma = bn.weight
+        beta = bn.bias
+        eps = bn.eps
+        std = (running_var + eps).sqrt()
+        t = (gamma / std).reshape(-1, 1, 1, 1)
+        return kernel * t, beta - running_mean * gamma / std
+
+    def _conv_bn(self, kernel_size: int, padding: int = 0) -> nn.Sequential:
+        """Helper method to construct conv-batchnorm layers.
+
+        Args:
+            kernel_size: Size of the convolution kernel.
+            padding: Zero-padding size.
+
+        Returns:
+            A nn.Sequential Conv-BN module.
+        """
+        mod_list = nn.Sequential()
+        mod_list.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=self.in_channels,
+                out_channels=self.out_channels,
+                kernel_size=kernel_size,
+                stride=self.stride,
+                padding=padding,
+                groups=self.groups,
+                bias=False,
+            ),
+        )
+        mod_list.add_module("bn", nn.BatchNorm2d(num_features=self.out_channels))
+        return mod_list

mobileclip/modules/text/__init__.py

@@ -0,0 +1,6 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All rights reserved.
+#

mobileclip/modules/text/repmixer.py

@@ -0,0 +1,265 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+from timm.models.layers import DropPath, trunc_normal_
+
+from mobileclip.modules.common.mobileone import MobileOneBlock
+
+
+class ConvFFN(nn.Module):
+    """Convolutional FFN Module."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        context_size: int,
+        hidden_channels: int | None = None,
+        out_channels: int | None = None,
+        act_layer: nn.Module = nn.GELU,
+        drop: float = 0.0,
+    ) -> None:
+        """Build convolutional FFN module.
+
+        Args:
+            in_channels: Number of input channels.
+            context_size: Context size for 1D signals.
+            hidden_channels: Number of channels after expansion. Default: None
+            out_channels: Number of output channels. Default: None
+            act_layer: Activation layer. Default: ``GELU``
+            drop: Dropout rate. Default: ``0.0``.
+        """
+        super().__init__()
+        out_channels = out_channels or in_channels
+        hidden_channels = hidden_channels or in_channels
+        self.conv = nn.Sequential()
+        self.conv.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=(1, int(context_size)),
+                padding=(0, int(context_size // 2)),
+                groups=in_channels,
+                bias=False,
+            ),
+        )
+        self.conv.add_module("bn", nn.BatchNorm2d(num_features=out_channels))
+        self.fc1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=1)
+        self.act = act_layer()
+        self.fc2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=1)
+        self.drop = nn.Dropout(drop)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m: nn.Module) -> None:
+        if isinstance(m, nn.Conv2d):
+            trunc_normal_(m.weight, std=0.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv(x)
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class RepMixer(nn.Module):
+    """Reparameterizable token mixer.
+
+    For more details, please refer to our paper: `FastViT: A Fast Hybrid Vision Transformer using Structural
+    Reparameterization <https://arxiv.org/pdf/2303.14189.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim,
+        kernel_size=3,
+        use_layer_scale=True,
+        layer_scale_init_value=1e-5,
+        inference_mode: bool = False,
+    ):
+        """Build RepMixer Module.
+
+        Args:
+            dim: Input feature map dimension. :math:`C_{in}` from an expected input of size :math:`(B, C_{in}, H, W)`.
+            kernel_size: Kernel size for spatial mixing. Default: 3
+            use_layer_scale: If True, learnable layer scale is used. Default: ``True``
+            layer_scale_init_value: Initial value for layer scale. Default: 1e-5
+            inference_mode: If True, instantiates model in inference mode. Default: ``False``
+        """
+        super().__init__()
+        self.dim = dim
+        self.kernel_size = kernel_size
+        self.inference_mode = inference_mode
+
+        if inference_mode:
+            self.reparam_conv = nn.Conv2d(
+                in_channels=self.dim,
+                out_channels=self.dim,
+                kernel_size=(1, self.kernel_size),
+                stride=1,
+                padding=(0, self.kernel_size // 2),
+                groups=self.dim,
+                bias=True,
+            )
+        else:
+            self.norm = MobileOneBlock(
+                dim,
+                dim,
+                (1, kernel_size),
+                padding=(0, kernel_size // 2),
+                groups=dim,
+                use_act=False,
+                use_scale_branch=False,
+                num_conv_branches=0,
+            )
+            self.mixer = MobileOneBlock(
+                dim,
+                dim,
+                (1, kernel_size),
+                padding=(0, kernel_size // 2),
+                groups=dim,
+                use_act=False,
+            )
+            self.use_layer_scale = use_layer_scale
+            if use_layer_scale:
+                self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if hasattr(self, "reparam_conv"):
+            x = self.reparam_conv(x)
+            return x
+        else:
+            if self.use_layer_scale:
+                x = x + self.layer_scale * (self.mixer(x) - self.norm(x))
+            else:
+                x = x + self.mixer(x) - self.norm(x)
+            return x
+
+    def reparameterize(self) -> None:
+        """Reparameterize mixer and norm into a single convolutional layer for efficient inference."""
+        if self.inference_mode:
+            return
+
+        self.mixer.reparameterize()
+        self.norm.reparameterize()
+
+        if self.use_layer_scale:
+            w = self.mixer.id_tensor + self.layer_scale.unsqueeze(-1) * (
+                self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
+            )
+            b = torch.squeeze(self.layer_scale) * (self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias)
+        else:
+            w = self.mixer.id_tensor + self.mixer.reparam_conv.weight - self.norm.reparam_conv.weight
+            b = self.mixer.reparam_conv.bias - self.norm.reparam_conv.bias
+
+        self.reparam_conv = nn.Conv2d(
+            in_channels=self.dim,
+            out_channels=self.dim,
+            kernel_size=(1, self.kernel_size),
+            stride=1,
+            padding=(0, self.kernel_size // 2),
+            groups=self.dim,
+            bias=True,
+        )
+        self.reparam_conv.weight.data = w
+        self.reparam_conv.bias.data = b
+
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__("mixer")
+        self.__delattr__("norm")
+        if self.use_layer_scale:
+            self.__delattr__("layer_scale")
+
+
+class RepMixerBlock(nn.Module):
+    """Implementation of Metaformer block with RepMixer as token mixer.
+
+    For more details on Metaformer structure, please refer to: `MetaFormer Is Actually What You Need for Vision
+    <https://arxiv.org/pdf/2111.11418.pdf>`_
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        kernel_size: int = 11,
+        mlp_ratio: float = 4.0,
+        act_layer: nn.Module = nn.GELU,
+        drop: float = 0.0,
+        drop_path: float = 0.0,
+        use_layer_scale: bool = True,
+        layer_scale_init_value: float = 1e-5,
+        inference_mode: bool = False,
+        *args,
+        **kwargs,
+    ):
+        """Build RepMixer Block.
+
+        Args:
+            dim: Number of embedding dimensions.
+            kernel_size: Kernel size for repmixer. Default: 3
+            mlp_ratio: MLP expansion ratio. Default: 4.0
+            act_layer: Activation layer. Default: ``nn.GELU``
+            drop: Dropout rate. Default: 0.0
+            drop_path: Drop path rate. Default: 0.0
+            use_layer_scale: Flag to turn on layer scale. Default: ``True``
+            layer_scale_init_value: Layer scale value at initialization. Default: 1e-5
+            inference_mode: Flag to instantiate block in inference mode. Default: ``False``
+        """
+        super().__init__()
+
+        self.token_mixer = RepMixer(
+            dim,
+            kernel_size=kernel_size,
+            use_layer_scale=use_layer_scale,
+            layer_scale_init_value=layer_scale_init_value,
+            inference_mode=inference_mode,
+        )
+
+        assert mlp_ratio > 0, f"MLP ratio should be greater than 0, found: {mlp_ratio}"
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.convffn = ConvFFN(
+            in_channels=dim,
+            context_size=kernel_size,
+            hidden_channels=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        # Drop Path
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        # Layer Scale
+        self.use_layer_scale = use_layer_scale
+        if use_layer_scale:
+            self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((dim, 1, 1)), requires_grad=True)
+
+    def forward(self, x, *args, **kwargs):
+        if x.dim() == 3:
+            # B, C, D --- where C is the context length
+            # Convert to B, D, C --- to match RepMixer impl.
+            x = x.permute(0, 2, 1)
+            x = torch.unsqueeze(x, dim=2)
+        else:
+            raise ValueError(f"Expected tensor of dim=3, obtained tensor of dim={x.dim()}")
+
+        if self.use_layer_scale:
+            x = self.token_mixer(x)
+            x = x + self.drop_path(self.layer_scale * self.convffn(x))
+        else:
+            x = self.token_mixer(x)
+            x = x + self.drop_path(self.convffn(x))
+
+        # Convert tensors back
+        x = x.squeeze(dim=2).permute(0, 2, 1)
+        return x

mobileclip/modules/text/tokenizer.py

@@ -0,0 +1,39 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+
+import open_clip
+from torch import Tensor, nn
+
+
+class ClipTokenizer(nn.Module):
+    def __init__(self, cfg, *args, **kwargs):
+        super().__init__()
+        self.context_length = cfg["text_cfg"]["context_length"]
+        model_name = getattr(cfg["text_cfg"], "open_clip_tokenizer", "ViT-B-16")
+        self.tokenizer = open_clip.get_tokenizer(model_name)
+
+    def get_vocab_size(self) -> int:
+        return len(self.tokenizer.encoder)
+
+    def get_encodings(self) -> dict[str, int]:
+        return self.tokenizer.encoder
+
+    def get_eot_token(self) -> int:
+        # Tokenizing an empty string returns a list [sot_id, eot_id]
+        return self.tokenizer("")[1]
+
+    def get_sot_token(self) -> int:
+        # Tokenizing an empty string returns a list [sot_id, eot_id]
+        return self.tokenizer("")[0]
+
+    def forward(self, input_sentence: str, *args, **kwargs) -> Tensor:
+        # tokenizer returns indices as a string
+        tokenized_sentence = self.tokenizer(input_sentence, self.context_length)
+        assert tokenized_sentence.shape[-1] == self.context_length, (
+            "Tokenized tensor should be exactly `context_length` long."
+        )
+        return tokenized_sentence

mobileclip/text_encoder.py

@@ -0,0 +1,218 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import math
+from collections.abc import Sequence
+
+import torch
+from torch import Tensor, nn
+
+from mobileclip import logger
+from mobileclip.modules.common.transformer import (
+    PositionalEmbedding,
+    TransformerEncoder,
+    get_normalization_layer,
+)
+from mobileclip.modules.text.repmixer import RepMixerBlock
+
+
+class TextTransformer(nn.Module):
+    def __init__(self, cfg: dict, projection_dim: int, *args, **kwargs) -> None:
+        super().__init__()
+
+        model_dim = cfg["dim"]
+        no_scale_embedding = cfg.get("no_scale_embedding", False)
+        no_pos_embedding = cfg.get("no_pos_embedding", False)
+        embed_dropout = cfg.get("embed_dropout", 0.0)
+        norm_layer = cfg["norm_layer"]
+        variant = cfg["model_name"]
+        self.vocab_size = cfg["vocab_size"]
+        self.projection_dim = projection_dim
+
+        # Token embedding layer
+        self.embedding_layer = nn.Embedding(embedding_dim=model_dim, num_embeddings=self.vocab_size)
+        self.embed_scale = 1.0 if no_scale_embedding else model_dim**-0.5
+
+        # Context length
+        context_length = cfg["context_length"]
+        assert context_length is not None, "Context length can't be None. Please set value accordingly."
+
+        self.positional_embedding = (
+            None if no_pos_embedding else PositionalEmbedding(num_embeddings=context_length, embedding_dim=model_dim)
+        )
+
+        self.embedding_dropout = nn.Dropout(p=embed_dropout)
+
+        # Transformer layer
+        n_transformer_layers = cfg["n_transformer_layers"]
+
+        # FFN multipliers for transformer layer
+        ffn_multipliers = cfg["ffn_multiplier_per_layer"]
+        if isinstance(ffn_multipliers, (float, int)):
+            ffn_multipliers = [ffn_multipliers] * n_transformer_layers
+
+        if not isinstance(ffn_multipliers, Sequence):
+            logger.error(
+                f"{self.__class__.__name__} expects FFN multipliers as a list, whose length is the same as"
+                f" number of transformer layers. Got: {type(ffn_multipliers)}"
+            )
+        elif isinstance(ffn_multipliers, Sequence) and len(ffn_multipliers) != n_transformer_layers:
+            logger.error(
+                f"We need FFN multiplier for each transformer layer. Got {len(ffn_multipliers)} ffn"
+                f" multipliers while number of transformer layers = {n_transformer_layers}"
+            )
+        ffn_dims = [int(math.ceil(model_dim * ffn_mult / 16.0) * 16.0) for ffn_mult in ffn_multipliers]
+
+        # Heads for transformer layers
+        mha_heads = cfg["n_heads_per_layer"]
+        if isinstance(mha_heads, int):
+            mha_heads = [mha_heads] * n_transformer_layers
+
+        if not isinstance(mha_heads, Sequence):
+            logger.error(
+                f"{self.__class__.__name__} expects MHA heads as a list, whose length is the same as number of "
+                f"transformer layers. Got: {type(mha_heads)}"
+            )
+        elif isinstance(mha_heads, Sequence) and len(mha_heads) != n_transformer_layers:
+            logger.error(
+                f"{self.__class__.__name__} needs MHA heads for each transformer layer. Got {len(mha_heads)} mha heads while"
+                f" number of transformer layers = {n_transformer_layers}"
+            )
+
+        if variant == "base":
+            self.transformer = nn.ModuleList(
+                [
+                    TransformerEncoder(
+                        embed_dim=model_dim,
+                        num_heads=mha_heads[layer_idx],
+                        ffn_latent_dim=ffn_dims[layer_idx],
+                        transformer_norm_layer=norm_layer,
+                    )
+                    for layer_idx in range(n_transformer_layers)
+                ]
+            )
+        elif variant == "mct":
+            self.transformer = nn.ModuleList([RepMixerBlock(dim=model_dim)])
+            self.transformer.extend(
+                [
+                    TransformerEncoder(
+                        embed_dim=model_dim,
+                        num_heads=mha_heads[layer_idx],
+                        ffn_latent_dim=ffn_dims[layer_idx],
+                        transformer_norm_layer=norm_layer,
+                    )
+                    for layer_idx in range(n_transformer_layers)
+                ]
+            )
+            self.transformer.extend([RepMixerBlock(dim=model_dim)])
+        else:
+            raise ValueError(f"Unrecognized text encoder variant {variant}")
+
+        self.final_layer_norm = get_normalization_layer(num_features=model_dim, norm_type=norm_layer)
+
+        self.projection_layer = nn.Parameter(torch.empty(model_dim, self.projection_dim))
+        self.model_dim = model_dim
+        self.causal_masking = cfg["causal_masking"]
+
+    def forward_embedding(self, text_tokens: Tensor) -> Tensor:
+        """Return text embedding for all tokens.
+
+        Args:
+            text_tokens: a tensor of token indices. Shape: [batch_size, context_length]
+
+        Returns:
+            A tensor of [batch_size, context_length, hidden_dim].
+        """
+        # [batch_size, context_length] --> [batch_size, context_length, hidden_dim]
+        token_emb = self.embedding_layer(text_tokens)
+        seq_len = token_emb.shape[1]
+        if self.positional_embedding is not None:
+            token_emb = token_emb + self.positional_embedding(seq_len).to(token_emb.dtype)
+        token_emb = self.embedding_dropout(token_emb)
+        return token_emb
+
+    @staticmethod
+    @torch.jit.script  # use scripting to avoid device constant
+    def build_attention_mask(text_tokens: torch.Tensor) -> Tensor:
+        """Build causal attention mask [batch_size, context_length, context_length]."""
+        # Build mask with full attention between the tokens
+        # pytorch uses additive attention mask; fill with -inf
+        batch_size, context_length = text_tokens.shape
+        mask = torch.empty(context_length, context_length, device=text_tokens.device, dtype=torch.float32)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)  # zero out the lower diagonal
+        mask = mask.unsqueeze(0)  # add dummy batch dimension
+        mask = mask.expand(batch_size, -1, -1)
+        return mask
+
+    def encode_text(
+        self,
+        text_tokens: Tensor,
+        key_padding_mask: Tensor | None = None,
+        return_all_tokens: bool = False,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        """Return text token embeddings.
+
+        Args:
+            text_tokens: a tensor of token indices. Shape: [batch_size, context_length]
+            key_padding_mask: a tensor of boolean values as the padding mask of shape [batch_size, context_length]
+            return_all_tokens: a boolean flag to return all tokens, defaults to False to return only EOT token
+                embedding.
+
+        Returns:
+            A tensor of [batch_size, context_length, hidden_dim] if return_all_tokens is
+            True, otherwise a tensor of [batch_size, hidden_dim].
+        """
+        # Discrete tokens to continuous embeddings
+        # [batch_size, context_length] --> [batch_size, context_length, hidden_dim]
+        token_emb = self.forward_embedding(text_tokens)
+
+        # [1, context_length, context_length]
+        attn_mask = None
+        if self.causal_masking:
+            attn_mask = self.build_attention_mask(text_tokens=text_tokens)
+            key_padding_mask = None
+
+        for layer in self.transformer:
+            token_emb = layer(
+                token_emb,
+                key_padding_mask=key_padding_mask,
+                attn_mask=attn_mask,
+            )
+
+        # Apply layer norm
+        token_emb = self.final_layer_norm(token_emb)
+
+        if return_all_tokens:
+            return token_emb
+
+        # Take features from the eot embedding (eot_token is the highest number in each sequence)
+        token_emb = token_emb[torch.arange(text_tokens.shape[0]), text_tokens.argmax(dim=-1)]
+
+        token_emb = token_emb @ self.projection_layer
+        return token_emb
+
+    def forward(
+        self,
+        text_tokens: Tensor,
+        key_padding_mask: Tensor | None = None,
+        return_all_tokens: bool = False,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        # Image-text pair data with single caption
+        # [B, CL] --> [B, d]
+        text_tokens = self.encode_text(
+            text_tokens=text_tokens,
+            key_padding_mask=key_padding_mask,
+            return_all_tokens=return_all_tokens,
+            *args,
+            **kwargs,
+        )
+        return text_tokens