yolov5/models/common.py

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Common modules."""

import ast
import contextlib
import json
import math
import platform
import warnings
import zipfile
from collections import OrderedDict, namedtuple
from copy import copy
from pathlib import Path
from urllib.parse import urlparse

import cv2
import numpy as np
import pandas as pd
import requests
import torch
import torch.nn as nn
from PIL import Image
from torch.cuda import amp

# Import 'ultralytics' package or install if missing
try:
    import ultralytics

    assert hasattr(ultralytics, "__version__")  # verify package is not directory
except (ImportError, AssertionError):
    import os

    os.system("pip install -U ultralytics")
    import ultralytics

from ultralytics.utils.plotting import Annotator, colors, save_one_box

from utils import TryExcept
from utils.dataloaders import exif_transpose, letterbox
from utils.general import (
    LOGGER,
    ROOT,
    Profile,
    check_requirements,
    check_suffix,
    check_version,
    colorstr,
    increment_path,
    is_jupyter,
    make_divisible,
    non_max_suppression,
    scale_boxes,
    xywh2xyxy,
    xyxy2xywh,
    yaml_load,
)
from utils.torch_utils import copy_attr, smart_inference_mode


def autopad(k, p=None, d=1):
    """
    Pads kernel to 'same' output shape, adjusting for optional dilation; returns padding size.

    `k`: kernel, `p`: padding, `d`: dilation.
    """
    if d > 1:
        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
    return p


class Conv(nn.Module):
    """Applies a convolution, batch normalization, and activation function to an input tensor in a neural network."""

    default_act = nn.SiLU()  # default activation

    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
        """Initializes a standard convolution layer with optional batch normalization and activation."""
        super().__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()

    def forward(self, x):
        """Applies a convolution followed by batch normalization and an activation function to the input tensor `x`."""
        return self.act(self.bn(self.conv(x)))

    def forward_fuse(self, x):
        """Applies a fused convolution and activation function to the input tensor `x`."""
        return self.act(self.conv(x))


class DWConv(Conv):
    """Implements a depth-wise convolution layer with optional activation for efficient spatial filtering."""

    def __init__(self, c1, c2, k=1, s=1, d=1, act=True):
        """Initializes a depth-wise convolution layer with optional activation; args: input channels (c1), output
        channels (c2), kernel size (k), stride (s), dilation (d), and activation flag (act).
        """
        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)


class DWConvTranspose2d(nn.ConvTranspose2d):
    """A depth-wise transpose convolutional layer for upsampling in neural networks, particularly in YOLOv5 models."""

    def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):
        """Initializes a depth-wise transpose convolutional layer for YOLOv5; args: input channels (c1), output channels
        (c2), kernel size (k), stride (s), input padding (p1), output padding (p2).
        """
        super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))


class TransformerLayer(nn.Module):
    """Transformer layer with multihead attention and linear layers, optimized by removing LayerNorm."""

    def __init__(self, c, num_heads):
        """
        Initializes a transformer layer, sans LayerNorm for performance, with multihead attention and linear layers.

        See  as described in https://arxiv.org/abs/2010.11929.
        """
        super().__init__()
        self.q = nn.Linear(c, c, bias=False)
        self.k = nn.Linear(c, c, bias=False)
        self.v = nn.Linear(c, c, bias=False)
        self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
        self.fc1 = nn.Linear(c, c, bias=False)
        self.fc2 = nn.Linear(c, c, bias=False)

    def forward(self, x):
        """Performs forward pass using MultiheadAttention and two linear transformations with residual connections."""
        x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
        x = self.fc2(self.fc1(x)) + x
        return x


class TransformerBlock(nn.Module):
    """A Transformer block for vision tasks with convolution, position embeddings, and Transformer layers."""

    def __init__(self, c1, c2, num_heads, num_layers):
        """Initializes a Transformer block for vision tasks, adapting dimensions if necessary and stacking specified
        layers.
        """
        super().__init__()
        self.conv = None
        if c1 != c2:
            self.conv = Conv(c1, c2)
        self.linear = nn.Linear(c2, c2)  # learnable position embedding
        self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
        self.c2 = c2

    def forward(self, x):
        """Processes input through an optional convolution, followed by Transformer layers and position embeddings for
        object detection.
        """
        if self.conv is not None:
            x = self.conv(x)
        b, _, w, h = x.shape
        p = x.flatten(2).permute(2, 0, 1)
        return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)


class Bottleneck(nn.Module):
    """A bottleneck layer with optional shortcut and group convolution for efficient feature extraction."""

    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
        """Initializes a standard bottleneck layer with optional shortcut and group convolution, supporting channel
        expansion.
        """
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2

    def forward(self, x):
        """Processes input through two convolutions, optionally adds shortcut if channel dimensions match; input is a
        tensor.
        """
        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))


class BottleneckCSP(nn.Module):
    """CSP bottleneck layer for feature extraction with cross-stage partial connections and optional shortcuts."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initializes CSP bottleneck with optional shortcuts; args: ch_in, ch_out, number of repeats, shortcut bool,
        groups, expansion.
        """
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
        self.cv4 = Conv(2 * c_, c2, 1, 1)
        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
        self.act = nn.SiLU()
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

    def forward(self, x):
        """Performs forward pass by applying layers, activation, and concatenation on input x, returning feature-
        enhanced output.
        """
        y1 = self.cv3(self.m(self.cv1(x)))
        y2 = self.cv2(x)
        return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))


class CrossConv(nn.Module):
    """Implements a cross convolution layer with downsampling, expansion, and optional shortcut."""

    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
        """
        Initializes CrossConv with downsampling, expanding, and optionally shortcutting; `c1` input, `c2` output
        channels.

        Inputs are ch_in, ch_out, kernel, stride, groups, expansion, shortcut.
        """
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, (1, k), (1, s))
        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
        self.add = shortcut and c1 == c2

    def forward(self, x):
        """Performs feature sampling, expanding, and applies shortcut if channels match; expects `x` input tensor."""
        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))


class C3(nn.Module):
    """Implements a CSP Bottleneck module with three convolutions for enhanced feature extraction in neural networks."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initializes C3 module with options for channel count, bottleneck repetition, shortcut usage, group
        convolutions, and expansion.
        """
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

    def forward(self, x):
        """Performs forward propagation using concatenated outputs from two convolutions and a Bottleneck sequence."""
        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))


class C3x(C3):
    """Extends the C3 module with cross-convolutions for enhanced feature extraction in neural networks."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initializes C3x module with cross-convolutions, extending C3 with customizable channel dimensions, groups,
        and expansion.
        """
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)
        self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))


class C3TR(C3):
    """C3 module with TransformerBlock for enhanced feature extraction in object detection models."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initializes C3 module with TransformerBlock for enhanced feature extraction, accepts channel sizes, shortcut
        config, group, and expansion.
        """
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)
        self.m = TransformerBlock(c_, c_, 4, n)


class C3SPP(C3):
    """Extends the C3 module with an SPP layer for enhanced spatial feature extraction and customizable channels."""

    def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
        """Initializes a C3 module with SPP layer for advanced spatial feature extraction, given channel sizes, kernel
        sizes, shortcut, group, and expansion ratio.
        """
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)
        self.m = SPP(c_, c_, k)


class C3Ghost(C3):
    """Implements a C3 module with Ghost Bottlenecks for efficient feature extraction in YOLOv5."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initializes YOLOv5's C3 module with Ghost Bottlenecks for efficient feature extraction."""
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
        self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))


class SPP(nn.Module):
    """Implements Spatial Pyramid Pooling (SPP) for feature extraction, ref: https://arxiv.org/abs/1406.4729."""

    def __init__(self, c1, c2, k=(5, 9, 13)):
        """Initializes SPP layer with Spatial Pyramid Pooling, ref: https://arxiv.org/abs/1406.4729, args: c1 (input channels), c2 (output channels), k (kernel sizes)."""
        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])

    def forward(self, x):
        """Applies convolution and max pooling layers to the input tensor `x`, concatenates results, and returns output
        tensor.
        """
        x = self.cv1(x)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")  # suppress torch 1.9.0 max_pool2d() warning
            return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))


class SPPF(nn.Module):
    """Implements a fast Spatial Pyramid Pooling (SPPF) layer for efficient feature extraction in YOLOv5 models."""

    def __init__(self, c1, c2, k=5):
        """
        Initializes YOLOv5 SPPF layer with given channels and kernel size for YOLOv5 model, combining convolution and
        max pooling.

        Equivalent to SPP(k=(5, 9, 13)).
        """
        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_ * 4, c2, 1, 1)
        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)

    def forward(self, x):
        """Processes input through a series of convolutions and max pooling operations for feature extraction."""
        x = self.cv1(x)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")  # suppress torch 1.9.0 max_pool2d() warning
            y1 = self.m(x)
            y2 = self.m(y1)
            return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))


class Focus(nn.Module):
    """Focuses spatial information into channel space using slicing and convolution for efficient feature extraction."""

    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
        """Initializes Focus module to concentrate width-height info into channel space with configurable convolution
        parameters.
        """
        super().__init__()
        self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
        # self.contract = Contract(gain=2)

    def forward(self, x):
        """Processes input through Focus mechanism, reshaping (b,c,w,h) to (b,4c,w/2,h/2) then applies convolution."""
        return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
        # return self.conv(self.contract(x))


class GhostConv(nn.Module):
    """Implements Ghost Convolution for efficient feature extraction, see https://github.com/huawei-noah/ghostnet."""

    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
        """Initializes GhostConv with in/out channels, kernel size, stride, groups, and activation; halves out channels
        for efficiency.
        """
        super().__init__()
        c_ = c2 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)

    def forward(self, x):
        """Performs forward pass, concatenating outputs of two convolutions on input `x`: shape (B,C,H,W)."""
        y = self.cv1(x)
        return torch.cat((y, self.cv2(y)), 1)


class GhostBottleneck(nn.Module):
    """Efficient bottleneck layer using Ghost Convolutions, see https://github.com/huawei-noah/ghostnet."""

    def __init__(self, c1, c2, k=3, s=1):
        """Initializes GhostBottleneck with ch_in `c1`, ch_out `c2`, kernel size `k`, stride `s`; see https://github.com/huawei-noah/ghostnet."""
        super().__init__()
        c_ = c2 // 2
        self.conv = nn.Sequential(
            GhostConv(c1, c_, 1, 1),  # pw
            DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
            GhostConv(c_, c2, 1, 1, act=False),
        )  # pw-linear
        self.shortcut = (
            nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
        )

    def forward(self, x):
        """Processes input through conv and shortcut layers, returning their summed output."""
        return self.conv(x) + self.shortcut(x)


class Contract(nn.Module):
    """Contracts spatial dimensions into channel dimensions for efficient processing in neural networks."""

    def __init__(self, gain=2):
        """Initializes a layer to contract spatial dimensions (width-height) into channels, e.g., input shape
        (1,64,80,80) to (1,256,40,40).
        """
        super().__init__()
        self.gain = gain

    def forward(self, x):
        """Processes input tensor to expand channel dimensions by contracting spatial dimensions, yielding output shape
        `(b, c*s*s, h//s, w//s)`.
        """
        b, c, h, w = x.size()  # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
        s = self.gain
        x = x.view(b, c, h // s, s, w // s, s)  # x(1,64,40,2,40,2)
        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
        return x.view(b, c * s * s, h // s, w // s)  # x(1,256,40,40)


class Expand(nn.Module):
    """Expands spatial dimensions by redistributing channels, e.g., from (1,64,80,80) to (1,16,160,160)."""

    def __init__(self, gain=2):
        """
        Initializes the Expand module to increase spatial dimensions by redistributing channels, with an optional gain
        factor.

        Example: x(1,64,80,80) to x(1,16,160,160).
        """
        super().__init__()
        self.gain = gain

    def forward(self, x):
        """Processes input tensor x to expand spatial dimensions by redistributing channels, requiring C / gain^2 ==
        0.
        """
        b, c, h, w = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
        s = self.gain
        x = x.view(b, s, s, c // s**2, h, w)  # x(1,2,2,16,80,80)
        x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
        return x.view(b, c // s**2, h * s, w * s)  # x(1,16,160,160)


class Concat(nn.Module):
    """Concatenates tensors along a specified dimension for efficient tensor manipulation in neural networks."""

    def __init__(self, dimension=1):
        """Initializes a Concat module to concatenate tensors along a specified dimension."""
        super().__init__()
        self.d = dimension

    def forward(self, x):
        """Concatenates a list of tensors along a specified dimension; `x` is a list of tensors, `dimension` is an
        int.
        """
        return torch.cat(x, self.d)


class DetectMultiBackend(nn.Module):
    """YOLOv5 MultiBackend class for inference on various backends including PyTorch, ONNX, TensorRT, and more."""

    def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), dnn=False, data=None, fp16=False, fuse=True):
        """Initializes DetectMultiBackend with support for various inference backends, including PyTorch and ONNX."""
        #   PyTorch:              weights = *.pt
        #   TorchScript:                    *.torchscript
        #   ONNX Runtime:                   *.onnx
        #   ONNX OpenCV DNN:                *.onnx --dnn
        #   OpenVINO:                       *_openvino_model
        #   CoreML:                         *.mlpackage
        #   TensorRT:                       *.engine
        #   TensorFlow SavedModel:          *_saved_model
        #   TensorFlow GraphDef:            *.pb
        #   TensorFlow Lite:                *.tflite
        #   TensorFlow Edge TPU:            *_edgetpu.tflite
        #   PaddlePaddle:                   *_paddle_model
        from models.experimental import attempt_download, attempt_load  # scoped to avoid circular import

        super().__init__()
        w = str(weights[0] if isinstance(weights, list) else weights)
        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
        fp16 &= pt or jit or onnx or engine or triton  # FP16
        nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
        stride = 32  # default stride
        cuda = torch.cuda.is_available() and device.type != "cpu"  # use CUDA
        if not (pt or triton):
            w = attempt_download(w)  # download if not local

        if pt:  # PyTorch
            model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
            stride = max(int(model.stride.max()), 32)  # model stride
            names = model.module.names if hasattr(model, "module") else model.names  # get class names
            model.half() if fp16 else model.float()
            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
        elif jit:  # TorchScript
            LOGGER.info(f"Loading {w} for TorchScript inference...")
            extra_files = {"config.txt": ""}  # model metadata
            model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
            model.half() if fp16 else model.float()
            if extra_files["config.txt"]:  # load metadata dict
                d = json.loads(
                    extra_files["config.txt"],
                    object_hook=lambda d: {int(k) if k.isdigit() else k: v for k, v in d.items()},
                )
                stride, names = int(d["stride"]), d["names"]
        elif dnn:  # ONNX OpenCV DNN
            LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...")
            check_requirements("opencv-python>=4.5.4")
            net = cv2.dnn.readNetFromONNX(w)
        elif onnx:  # ONNX Runtime
            LOGGER.info(f"Loading {w} for ONNX Runtime inference...")
            check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime"))
            import onnxruntime

            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] if cuda else ["CPUExecutionProvider"]
            session = onnxruntime.InferenceSession(w, providers=providers)
            output_names = [x.name for x in session.get_outputs()]
            meta = session.get_modelmeta().custom_metadata_map  # metadata
            if "stride" in meta:
                stride, names = int(meta["stride"]), eval(meta["names"])
        elif xml:  # OpenVINO
            LOGGER.info(f"Loading {w} for OpenVINO inference...")
            check_requirements("openvino>=2023.0")  # requires openvino-dev: https://pypi.org/project/openvino-dev/
            from openvino.runtime import Core, Layout, get_batch

            core = Core()
            if not Path(w).is_file():  # if not *.xml
                w = next(Path(w).glob("*.xml"))  # get *.xml file from *_openvino_model dir
            ov_model = core.read_model(model=w, weights=Path(w).with_suffix(".bin"))
            if ov_model.get_parameters()[0].get_layout().empty:
                ov_model.get_parameters()[0].set_layout(Layout("NCHW"))
            batch_dim = get_batch(ov_model)
            if batch_dim.is_static:
                batch_size = batch_dim.get_length()
            ov_compiled_model = core.compile_model(ov_model, device_name="AUTO")  # AUTO selects best available device
            stride, names = self._load_metadata(Path(w).with_suffix(".yaml"))  # load metadata
        elif engine:  # TensorRT
            LOGGER.info(f"Loading {w} for TensorRT inference...")
            import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download

            check_version(trt.__version__, "7.0.0", hard=True)  # require tensorrt>=7.0.0
            if device.type == "cpu":
                device = torch.device("cuda:0")
            Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr"))
            logger = trt.Logger(trt.Logger.INFO)
            with open(w, "rb") as f, trt.Runtime(logger) as runtime:
                model = runtime.deserialize_cuda_engine(f.read())
            context = model.create_execution_context()
            bindings = OrderedDict()
            output_names = []
            fp16 = False  # default updated below
            dynamic = False
            is_trt10 = not hasattr(model, "num_bindings")
            num = range(model.num_io_tensors) if is_trt10 else range(model.num_bindings)
            for i in num:
                if is_trt10:
                    name = model.get_tensor_name(i)
                    dtype = trt.nptype(model.get_tensor_dtype(name))
                    is_input = model.get_tensor_mode(name) == trt.TensorIOMode.INPUT
                    if is_input:
                        if -1 in tuple(model.get_tensor_shape(name)):  # dynamic
                            dynamic = True
                            context.set_input_shape(name, tuple(model.get_profile_shape(name, 0)[2]))
                        if dtype == np.float16:
                            fp16 = True
                    else:  # output
                        output_names.append(name)
                    shape = tuple(context.get_tensor_shape(name))
                else:
                    name = model.get_binding_name(i)
                    dtype = trt.nptype(model.get_binding_dtype(i))
                    if model.binding_is_input(i):
                        if -1 in tuple(model.get_binding_shape(i)):  # dynamic
                            dynamic = True
                            context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
                        if dtype == np.float16:
                            fp16 = True
                    else:  # output
                        output_names.append(name)
                    shape = tuple(context.get_binding_shape(i))
                im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
                bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
            batch_size = bindings["images"].shape[0]  # if dynamic, this is instead max batch size
        elif coreml:  # CoreML
            LOGGER.info(f"Loading {w} for CoreML inference...")
            import coremltools as ct

            model = ct.models.MLModel(w)
        elif saved_model:  # TF SavedModel
            LOGGER.info(f"Loading {w} for TensorFlow SavedModel inference...")
            import tensorflow as tf

            keras = False  # assume TF1 saved_model
            model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
        elif pb:  # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
            LOGGER.info(f"Loading {w} for TensorFlow GraphDef inference...")
            import tensorflow as tf

            def wrap_frozen_graph(gd, inputs, outputs):
                """Wraps a TensorFlow GraphDef for inference, returning a pruned function."""
                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped
                ge = x.graph.as_graph_element
                return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))

            def gd_outputs(gd):
                """Generates a sorted list of graph outputs excluding NoOp nodes and inputs, formatted as '<name>:0'."""
                name_list, input_list = [], []
                for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
                    name_list.append(node.name)
                    input_list.extend(node.input)
                return sorted(f"{x}:0" for x in list(set(name_list) - set(input_list)) if not x.startswith("NoOp"))

            gd = tf.Graph().as_graph_def()  # TF GraphDef
            with open(w, "rb") as f:
                gd.ParseFromString(f.read())
            frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
        elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
            try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
                from tflite_runtime.interpreter import Interpreter, load_delegate
            except ImportError:
                import tensorflow as tf

                Interpreter, load_delegate = (
                    tf.lite.Interpreter,
                    tf.lite.experimental.load_delegate,
                )
            if edgetpu:  # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
                LOGGER.info(f"Loading {w} for TensorFlow Lite Edge TPU inference...")
                delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[
                    platform.system()
                ]
                interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
            else:  # TFLite
                LOGGER.info(f"Loading {w} for TensorFlow Lite inference...")
                interpreter = Interpreter(model_path=w)  # load TFLite model
            interpreter.allocate_tensors()  # allocate
            input_details = interpreter.get_input_details()  # inputs
            output_details = interpreter.get_output_details()  # outputs
            # load metadata
            with contextlib.suppress(zipfile.BadZipFile):
                with zipfile.ZipFile(w, "r") as model:
                    meta_file = model.namelist()[0]
                    meta = ast.literal_eval(model.read(meta_file).decode("utf-8"))
                    stride, names = int(meta["stride"]), meta["names"]
        elif tfjs:  # TF.js
            raise NotImplementedError("ERROR: YOLOv5 TF.js inference is not supported")
        # PaddlePaddle
        elif paddle:
            LOGGER.info(f"Loading {w} for PaddlePaddle inference...")
            check_requirements("paddlepaddle-gpu" if cuda else "paddlepaddle>=3.0.0")
            import paddle.inference as pdi

            w = Path(w)
            if w.is_dir():
                model_file = next(w.rglob("*.json"), None)
                params_file = next(w.rglob("*.pdiparams"), None)
            elif w.suffix == ".pdiparams":
                model_file = w.with_name("model.json")
                params_file = w
            else:
                raise ValueError(f"Invalid model path {w}. Provide model directory or a .pdiparams file.")

            if not (model_file and params_file and model_file.is_file() and params_file.is_file()):
                raise FileNotFoundError(f"Model files not found in {w}. Both .json and .pdiparams files are required.")

            config = pdi.Config(str(model_file), str(params_file))
            if cuda:
                config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
            predictor = pdi.create_predictor(config)
            input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
            output_names = predictor.get_output_names()

        elif triton:  # NVIDIA Triton Inference Server
            LOGGER.info(f"Using {w} as Triton Inference Server...")
            check_requirements("tritonclient[all]")
            from utils.triton import TritonRemoteModel

            model = TritonRemoteModel(url=w)
            nhwc = model.runtime.startswith("tensorflow")
        else:
            raise NotImplementedError(f"ERROR: {w} is not a supported format")

        # class names
        if "names" not in locals():
            names = yaml_load(data)["names"] if data else {i: f"class{i}" for i in range(999)}
        if names[0] == "n01440764" and len(names) == 1000:  # ImageNet
            names = yaml_load(ROOT / "data/ImageNet.yaml")["names"]  # human-readable names

        self.__dict__.update(locals())  # assign all variables to self

    def forward(self, im, augment=False, visualize=False):
        """Performs YOLOv5 inference on input images with options for augmentation and visualization."""
        b, ch, h, w = im.shape  # batch, channel, height, width
        if self.fp16 and im.dtype != torch.float16:
            im = im.half()  # to FP16
        if self.nhwc:
            im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)

        if self.pt:  # PyTorch
            y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
        elif self.jit:  # TorchScript
            y = self.model(im)
        elif self.dnn:  # ONNX OpenCV DNN
            im = im.cpu().numpy()  # torch to numpy
            self.net.setInput(im)
            y = self.net.forward()
        elif self.onnx:  # ONNX Runtime
            im = im.cpu().numpy()  # torch to numpy
            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
        elif self.xml:  # OpenVINO
            im = im.cpu().numpy()  # FP32
            y = list(self.ov_compiled_model(im).values())
        elif self.engine:  # TensorRT
            if self.dynamic and im.shape != self.bindings["images"].shape:
                i = self.model.get_binding_index("images")
                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
                self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
                for name in self.output_names:
                    i = self.model.get_binding_index(name)
                    self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
            s = self.bindings["images"].shape
            assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
            self.binding_addrs["images"] = int(im.data_ptr())
            self.context.execute_v2(list(self.binding_addrs.values()))
            y = [self.bindings[x].data for x in sorted(self.output_names)]
        elif self.coreml:  # CoreML
            im = im.cpu().numpy()
            im = Image.fromarray((im[0] * 255).astype("uint8"))
            # im = im.resize((192, 320), Image.BILINEAR)
            y = self.model.predict({"image": im})  # coordinates are xywh normalized
            if "confidence" in y:
                box = xywh2xyxy(y["coordinates"] * [[w, h, w, h]])  # xyxy pixels
                conf, cls = y["confidence"].max(1), y["confidence"].argmax(1).astype(np.float)
                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
            else:
                y = list(reversed(y.values()))  # reversed for segmentation models (pred, proto)
        elif self.paddle:  # PaddlePaddle
            im = im.cpu().numpy().astype(np.float32)
            self.input_handle.copy_from_cpu(im)
            self.predictor.run()
            y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
        elif self.triton:  # NVIDIA Triton Inference Server
            y = self.model(im)
        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
            im = im.cpu().numpy()
            if self.saved_model:  # SavedModel
                y = self.model(im, training=False) if self.keras else self.model(im)
            elif self.pb:  # GraphDef
                y = self.frozen_func(x=self.tf.constant(im))
            else:  # Lite or Edge TPU
                input = self.input_details[0]
                int8 = input["dtype"] == np.uint8  # is TFLite quantized uint8 model
                if int8:
                    scale, zero_point = input["quantization"]
                    im = (im / scale + zero_point).astype(np.uint8)  # de-scale
                self.interpreter.set_tensor(input["index"], im)
                self.interpreter.invoke()
                y = []
                for output in self.output_details:
                    x = self.interpreter.get_tensor(output["index"])
                    if int8:
                        scale, zero_point = output["quantization"]
                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
                    y.append(x)
            if len(y) == 2 and len(y[1].shape) != 4:
                y = list(reversed(y))
            y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
            y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels

        if isinstance(y, (list, tuple)):
            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
        else:
            return self.from_numpy(y)

    def from_numpy(self, x):
        """Converts a NumPy array to a torch tensor, maintaining device compatibility."""
        return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x

    def warmup(self, imgsz=(1, 3, 640, 640)):
        """Performs a single inference warmup to initialize model weights, accepting an `imgsz` tuple for image size."""
        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
        if any(warmup_types) and (self.device.type != "cpu" or self.triton):
            im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
            for _ in range(2 if self.jit else 1):  #
                self.forward(im)  # warmup

    @staticmethod
    def _model_type(p="path/to/model.pt"):
        """
        Determines model type from file path or URL, supporting various export formats.

        Example: path='path/to/model.onnx' -> type=onnx
        """
        # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
        from export import export_formats
        from utils.downloads import is_url

        sf = list(export_formats().Suffix)  # export suffixes
        if not is_url(p, check=False):
            check_suffix(p, sf)  # checks
        url = urlparse(p)  # if url may be Triton inference server
        types = [s in Path(p).name for s in sf]
        types[8] &= not types[9]  # tflite &= not edgetpu
        triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc])
        return types + [triton]

    @staticmethod
    def _load_metadata(f=Path("path/to/meta.yaml")):
        """Loads metadata from a YAML file, returning strides and names if the file exists, otherwise `None`."""
        if f.exists():
            d = yaml_load(f)
            return d["stride"], d["names"]  # assign stride, names
        return None, None


class AutoShape(nn.Module):
    """AutoShape class for robust YOLOv5 inference with preprocessing, NMS, and support for various input formats."""

    conf = 0.25  # NMS confidence threshold
    iou = 0.45  # NMS IoU threshold
    agnostic = False  # NMS class-agnostic
    multi_label = False  # NMS multiple labels per box
    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
    max_det = 1000  # maximum number of detections per image
    amp = False  # Automatic Mixed Precision (AMP) inference

    def __init__(self, model, verbose=True):
        """Initializes YOLOv5 model for inference, setting up attributes and preparing model for evaluation."""
        super().__init__()
        if verbose:
            LOGGER.info("Adding AutoShape... ")
        copy_attr(self, model, include=("yaml", "nc", "hyp", "names", "stride", "abc"), exclude=())  # copy attributes
        self.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instance
        self.pt = not self.dmb or model.pt  # PyTorch model
        self.model = model.eval()
        if self.pt:
            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
            m.inplace = False  # Detect.inplace=False for safe multithread inference
            m.export = True  # do not output loss values

    def _apply(self, fn):
        """
        Applies to(), cpu(), cuda(), half() etc.

        to model tensors excluding parameters or registered buffers.
        """
        self = super()._apply(fn)
        if self.pt:
            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
            m.stride = fn(m.stride)
            m.grid = list(map(fn, m.grid))
            if isinstance(m.anchor_grid, list):
                m.anchor_grid = list(map(fn, m.anchor_grid))
        return self

    @smart_inference_mode()
    def forward(self, ims, size=640, augment=False, profile=False):
        """
        Performs inference on inputs with optional augment & profiling.

        Supports various formats including file, URI, OpenCV, PIL, numpy, torch.
        """
        # For size(height=640, width=1280), RGB images example inputs are:
        #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
        #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
        #   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # HWC x(640,1280,3)
        #   numpy:           = np.zeros((640,1280,3))  # HWC
        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images

        dt = (Profile(), Profile(), Profile())
        with dt[0]:
            if isinstance(size, int):  # expand
                size = (size, size)
            p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
            autocast = self.amp and (p.device.type != "cpu")  # Automatic Mixed Precision (AMP) inference
            if isinstance(ims, torch.Tensor):  # torch
                with amp.autocast(autocast):
                    return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference

            # Pre-process
            n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
            shape0, shape1, files = [], [], []  # image and inference shapes, filenames
            for i, im in enumerate(ims):
                f = f"image{i}"  # filename
                if isinstance(im, (str, Path)):  # filename or uri
                    im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im), im
                    im = np.asarray(exif_transpose(im))
                elif isinstance(im, Image.Image):  # PIL Image
                    im, f = np.asarray(exif_transpose(im)), getattr(im, "filename", f) or f
                files.append(Path(f).with_suffix(".jpg").name)
                if im.shape[0] < 5:  # image in CHW
                    im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
                im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
                s = im.shape[:2]  # HWC
                shape0.append(s)  # image shape
                g = max(size) / max(s)  # gain
                shape1.append([int(y * g) for y in s])
                ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)]  # inf shape
            x = [letterbox(im, shape1, auto=False)[0] for im in ims]  # pad
            x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
            x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32

        with amp.autocast(autocast):
            # Inference
            with dt[1]:
                y = self.model(x, augment=augment)  # forward

            # Post-process
            with dt[2]:
                y = non_max_suppression(
                    y if self.dmb else y[0],
                    self.conf,
                    self.iou,
                    self.classes,
                    self.agnostic,
                    self.multi_label,
                    max_det=self.max_det,
                )  # NMS
                for i in range(n):
                    scale_boxes(shape1, y[i][:, :4], shape0[i])

            return Detections(ims, y, files, dt, self.names, x.shape)


class Detections:
    """Manages YOLOv5 detection results with methods for visualization, saving, cropping, and exporting detections."""

    def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
        """Initializes the YOLOv5 Detections class with image info, predictions, filenames, timing and normalization."""
        super().__init__()
        d = pred[0].device  # device
        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims]  # normalizations
        self.ims = ims  # list of images as numpy arrays
        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
        self.names = names  # class names
        self.files = files  # image filenames
        self.times = times  # profiling times
        self.xyxy = pred  # xyxy pixels
        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
        self.n = len(self.pred)  # number of images (batch size)
        self.t = tuple(x.t / self.n * 1e3 for x in times)  # timestamps (ms)
        self.s = tuple(shape)  # inference BCHW shape

    def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path("")):
        """Executes model predictions, displaying and/or saving outputs with optional crops and labels."""
        s, crops = "", []
        for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
            s += f"\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} "  # string
            if pred.shape[0]:
                for c in pred[:, -1].unique():
                    n = (pred[:, -1] == c).sum()  # detections per class
                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
                s = s.rstrip(", ")
                if show or save or render or crop:
                    annotator = Annotator(im, example=str(self.names))
                    for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
                        label = f"{self.names[int(cls)]} {conf:.2f}"
                        if crop:
                            file = save_dir / "crops" / self.names[int(cls)] / self.files[i] if save else None
                            crops.append(
                                {
                                    "box": box,
                                    "conf": conf,
                                    "cls": cls,
                                    "label": label,
                                    "im": save_one_box(box, im, file=file, save=save),
                                }
                            )
                        else:  # all others
                            annotator.box_label(box, label if labels else "", color=colors(cls))
                    im = annotator.im
            else:
                s += "(no detections)"

            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
            if show:
                if is_jupyter():
                    from IPython.display import display

                    display(im)
                else:
                    im.show(self.files[i])
            if save:
                f = self.files[i]
                im.save(save_dir / f)  # save
                if i == self.n - 1:
                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
            if render:
                self.ims[i] = np.asarray(im)
        if pprint:
            s = s.lstrip("\n")
            return f"{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}" % self.t
        if crop:
            if save:
                LOGGER.info(f"Saved results to {save_dir}\n")
            return crops

    @TryExcept("Showing images is not supported in this environment")
    def show(self, labels=True):
        """
        Displays detection results with optional labels.

        Usage: show(labels=True)
        """
        self._run(show=True, labels=labels)  # show results

    def save(self, labels=True, save_dir="runs/detect/exp", exist_ok=False):
        """
        Saves detection results with optional labels to a specified directory.

        Usage: save(labels=True, save_dir='runs/detect/exp', exist_ok=False)
        """
        save_dir = increment_path(save_dir, exist_ok, mkdir=True)  # increment save_dir
        self._run(save=True, labels=labels, save_dir=save_dir)  # save results

    def crop(self, save=True, save_dir="runs/detect/exp", exist_ok=False):
        """
        Crops detection results, optionally saves them to a directory.

        Args: save (bool), save_dir (str), exist_ok (bool).
        """
        save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
        return self._run(crop=True, save=save, save_dir=save_dir)  # crop results

    def render(self, labels=True):
        """Renders detection results with optional labels on images; args: labels (bool) indicating label inclusion."""
        self._run(render=True, labels=labels)  # render results
        return self.ims

    def pandas(self):
        """
        Returns detections as pandas DataFrames for various box formats (xyxy, xyxyn, xywh, xywhn).

        Example: print(results.pandas().xyxy[0]).
        """
        new = copy(self)  # return copy
        ca = "xmin", "ymin", "xmax", "ymax", "confidence", "class", "name"  # xyxy columns
        cb = "xcenter", "ycenter", "width", "height", "confidence", "class", "name"  # xywh columns
        for k, c in zip(["xyxy", "xyxyn", "xywh", "xywhn"], [ca, ca, cb, cb]):
            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
        return new

    def tolist(self):
        """
        Converts a Detections object into a list of individual detection results for iteration.

        Example: for result in results.tolist():
        """
        r = range(self.n)  # iterable
        return [
            Detections(
                [self.ims[i]],
                [self.pred[i]],
                [self.files[i]],
                self.times,
                self.names,
                self.s,
            )
            for i in r
        ]

    def print(self):
        """Logs the string representation of the current object's state via the LOGGER."""
        LOGGER.info(self.__str__())

    def __len__(self):
        """Returns the number of results stored, overrides the default len(results)."""
        return self.n

    def __str__(self):
        """Returns a string representation of the model's results, suitable for printing, overrides default
        print(results).
        """
        return self._run(pprint=True)  # print results

    def __repr__(self):
        """Returns a string representation of the YOLOv5 object, including its class and formatted results."""
        return f"YOLOv5 {self.__class__} instance\n" + self.__str__()


class Proto(nn.Module):
    """YOLOv5 mask Proto module for segmentation models, performing convolutions and upsampling on input tensors."""

    def __init__(self, c1, c_=256, c2=32):
        """Initializes YOLOv5 Proto module for segmentation with input, proto, and mask channels configuration."""
        super().__init__()
        self.cv1 = Conv(c1, c_, k=3)
        self.upsample = nn.Upsample(scale_factor=2, mode="nearest")
        self.cv2 = Conv(c_, c_, k=3)
        self.cv3 = Conv(c_, c2)

    def forward(self, x):
        """Performs a forward pass using convolutional layers and upsampling on input tensor `x`."""
        return self.cv3(self.cv2(self.upsample(self.cv1(x))))


class Classify(nn.Module):
    """YOLOv5 classification head with convolution, pooling, and dropout layers for channel transformation."""

    def __init__(
        self, c1, c2, k=1, s=1, p=None, g=1, dropout_p=0.0
    ):  # ch_in, ch_out, kernel, stride, padding, groups, dropout probability
        """Initializes YOLOv5 classification head with convolution, pooling, and dropout layers for input to output
        channel transformation.
        """
        super().__init__()
        c_ = 1280  # efficientnet_b0 size
        self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
        self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)
        self.drop = nn.Dropout(p=dropout_p, inplace=True)
        self.linear = nn.Linear(c_, c2)  # to x(b,c2)

    def forward(self, x):
        """Processes input through conv, pool, drop, and linear layers; supports list concatenation input."""
        if isinstance(x, list):
            x = torch.cat(x, 1)
        return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))