yolo.py

# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
YOLO-specific modules.

Usage:
    $ python models/yolo.py --cfg yolov5s.yaml
"""

import argparse
import contextlib
import math
import os
import platform
import sys
from copy import deepcopy
from pathlib import Path

import torch
import torch.nn as nn

FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
if platform.system() != "Windows":
    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import (
    C3,
    C3SPP,
    C3TR,
    SPP,
    SPPF,
    Bottleneck,
    BottleneckCSP,
    C3Ghost,
    C3x,
    Classify,
    Concat,
    Contract,
    Conv,
    CrossConv,
    DetectMultiBackend,
    DWConv,
    DWConvTranspose2d,
    Expand,
    Focus,
    GhostBottleneck,
    GhostConv,
    Proto,
)
from models.experimental import MixConv2d
from utils.autoanchor import check_anchor_order
from utils.general import LOGGER, check_version, check_yaml, colorstr, make_divisible, print_args
from utils.plots import feature_visualization
from utils.torch_utils import (
    fuse_conv_and_bn,
    initialize_weights,
    model_info,
    profile,
    scale_img,
    select_device,
    time_sync,
)

try:
    import thop  # for FLOPs computation
except ImportError:
    thop = None

class ConvLSTMCell(nn.Module):

    def __init__(self, input_dim, hidden_dim, kernel_size, bias):
        """
        Initialize ConvLSTM cell.

        Parameters
        ----------
        input_dim: int
            Number of channels of input tensor.
        hidden_dim: int
            Number of channels of hidden state.
        kernel_size: (int, int)
            Size of the convolutional kernel.
        bias: bool
            Whether or not to add the bias.
        """

        super(ConvLSTMCell, self).__init__()

        self.input_dim = input_dim
        self.hidden_dim = hidden_dim

        self.kernel_size = kernel_size
        self.padding = kernel_size[0] // 2, kernel_size[1] // 2
        self.bias = bias

        self.conv = nn.Conv2d(in_channels=self.input_dim + self.hidden_dim,
                              out_channels=4 * self.hidden_dim,
                              kernel_size=self.kernel_size,
                              padding=self.padding,
                              bias=self.bias)

    def forward(self, input_tensor, cur_state):
        h_cur, c_cur = cur_state

        combined = torch.cat([input_tensor, h_cur], dim=1)  # concatenate along channel axis

        combined_conv = self.conv(combined)
        cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1)
        i = torch.sigmoid(cc_i)
        f = torch.sigmoid(cc_f)
        o = torch.sigmoid(cc_o)
        g = torch.tanh(cc_g)

        c_next = f * c_cur + i * g
        h_next = o * torch.tanh(c_next)

        return h_next, c_next

    def init_hidden(self, batch_size, image_size):
        height, width = image_size
        dtype = self.conv.weight.dtype  # Get the data type of the convolutional weights
        device = self.conv.weight.device
        return (
            torch.zeros(batch_size, self.hidden_dim, height, width, device=device, dtype=dtype),
            torch.zeros(batch_size, self.hidden_dim, height, width, device=device, dtype=dtype)
        )


class ConvLSTM(nn.Module):

    """

    Parameters:
        input_dim: Number of channels in input
        hidden_dim: Number of hidden channels
        kernel_size: Size of kernel in convolutions
        num_layers: Number of LSTM layers stacked on each other
        batch_first: Whether or not dimension 0 is the batch or not
        bias: Bias or no bias in Convolution
        return_all_layers: Return the list of computations for all layers
        Note: Will do same padding.

    Input:
        A tensor of size B, T, C, H, W or T, B, C, H, W
    Output:
        A tuple of two lists of length num_layers (or length 1 if return_all_layers is False).
            0 - layer_output_list is the list of lists of length T of each output
            1 - last_state_list is the list of last states
                    each element of the list is a tuple (h, c) for hidden state and memory
    Example:
        >> x = torch.rand((32, 10, 64, 128, 128))
        >> convlstm = ConvLSTM(64, 16, 3, 1, True, True, False)
        >> _, last_states = convlstm(x)
        >> h = last_states[0][0]  # 0 for layer index, 0 for h index
    """

    def __init__(self, input_dim, hidden_dim, kernel_size, num_layers,
                 batch_first=False, bias=True, return_all_layers=False):
        super(ConvLSTM, self).__init__()

        self._check_kernel_size_consistency(kernel_size)

        # Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers
        kernel_size = self._extend_for_multilayer(kernel_size, num_layers)
        hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers)
        if not len(kernel_size) == len(hidden_dim) == num_layers:
            raise ValueError('Inconsistent list length.')

        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.kernel_size = kernel_size
        self.num_layers = num_layers
        self.batch_first = batch_first
        self.bias = bias
        self.return_all_layers = return_all_layers

        cell_list = []
        for i in range(0, self.num_layers):
            cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i - 1]

            cell_list.append(ConvLSTMCell(input_dim=cur_input_dim,
                                          hidden_dim=self.hidden_dim[i],
                                          kernel_size=self.kernel_size[i],
                                          bias=self.bias))

        self.cell_list = nn.ModuleList(cell_list)

    def forward(self, input_tensor, hidden_state=None):
        """

        Parameters
        ----------
        input_tensor: todo
            5-D Tensor either of shape (t, b, c, h, w) or (b, t, c, h, w)
        hidden_state: todo
            None. todo implement stateful

        Returns
        -------
        last_state_list, layer_output
        """
        if not self.batch_first:
            # (t, b, c, h, w) -> (b, t, c, h, w)
            input_tensor = input_tensor.permute(1, 0, 2, 3, 4)

        b, _, _, h, w = input_tensor.size()

        # Implement stateful ConvLSTM
        if hidden_state is not None:
            # Ensure hidden_state has the correct format
            if len(hidden_state) != self.num_layers:
                raise ValueError("Length of hidden_state does not match num_layers")
        else:
            # Since the init is done in forward. Can send image size here
            hidden_state = self._init_hidden(batch_size=b,
                                             image_size=(h, w))

        layer_output_list = []
        last_state_list = []

        seq_len = input_tensor.size(1)
        cur_layer_input = input_tensor

        for layer_idx in range(self.num_layers):

            h, c = hidden_state[layer_idx]
            output_inner = []
            for t in range(seq_len):
                h, c = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :, :, :],
                                                 cur_state=[h, c])
                output_inner.append(h)

            layer_output = torch.stack(output_inner, dim=1)
            cur_layer_input = layer_output

            layer_output_list.append(layer_output)
            last_state_list.append([h, c])

        if not self.return_all_layers:
            layer_output_list = layer_output_list[-1:]
            last_state_list = last_state_list[-1:]

        return layer_output_list, last_state_list

    def _init_hidden(self, batch_size, image_size):
        init_states = []
        for i in range(self.num_layers):
            init_states.append(self.cell_list[i].init_hidden(batch_size, image_size))
        return init_states

    @staticmethod
    def _check_kernel_size_consistency(kernel_size):
        if not (isinstance(kernel_size, tuple) or
                (isinstance(kernel_size, list) and all([isinstance(elem, tuple) for elem in kernel_size]))):
            raise ValueError('`kernel_size` must be tuple or list of tuples')

    @staticmethod
    def _extend_for_multilayer(param, num_layers):
        if not isinstance(param, list):
            param = [param] * num_layers
        return param


class Detect(nn.Module):
    """YOLOv5 Detect head for processing input tensors and generating detection outputs in object detection models."""

    stride = None  # strides computed during build
    dynamic = False  # force grid reconstruction
    export = False  # export mode

    def __init__(self, nc=80, anchors=(), ch=(), sl=2, inplace=True):
        """Initializes YOLOv5 detection layer with specified classes, anchors, channels, and inplace operations."""
        super().__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.empty(0) for _ in range(self.nl)]  # init grid
        self.anchor_grid = [torch.empty(0) for _ in range(self.nl)]  # init anchor grid
        self.register_buffer("anchors", torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)
        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
        self.sl = sl
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv

        self.convlstms = nn.ModuleList([
            ConvLSTM(input_dim=ch[i],
                     hidden_dim=ch[i],
                     kernel_size=(3, 3),
                     num_layers=1,
                     batch_first=True,
                     bias=True,
                     return_all_layers=True)
            for i in range(self.nl)
        ])

    def forward(self, x, h=None):
        """Processes input through YOLOv5 layers, altering shape for detection: `x(bs, 3, ny, nx, 85)`."""
        z = []  # inference output
        h_new = []
        if h is None:
            h = [None] * self.nl

        for i in range(self.nl):

            if self.training:
                cx = x[i].view(-1, self.sl, x[i].shape[1], x[i].shape[2], x[i].shape[3])
            else:
                cx = x[i].view(-1, 1, x[i].shape[1], x[i].shape[2], x[i].shape[3])

            if h[i] is None:
                xo, xhc = self.convlstms[i](cx)
            else:
                xo, xhc = self.convlstms[i](cx, hidden_state=[h[i]])  # Wrap h[i] in a list
            
            h_new.append(tuple(xhc[0]))
            x[i] = xo[0].view(-1, xo[0].shape[2], xo[0].shape[3], xo[0].shape[4])
            #cx = xo[0].view(-1, x[i].shape[1], x[i].shape[2], x[i].shape[3])

            #bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            #x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            if not self.training:  # inference
                if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)

                if isinstance(self, Segment):  # (boxes + masks)
                    xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4)
                    xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i]  # xy
                    wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)
                else:  # Detect (boxes only)
                    xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
                    xy = (xy * 2 + self.grid[i]) * self.stride[i]  # xy
                    wh = (wh * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, conf), 4)
                z.append(y.view(bs, self.na * nx * ny, self.no))

        h_new = tuple(h_new)
        
        if self.training:
            return x, h_new
        else:
            if self.export:
                return (torch.cat(z, 1), h_new)
            else:
                return (torch.cat(z, 1), x, h_new)

    def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, "1.10.0")):
        """Generates a mesh grid for anchor boxes with optional compatibility for torch versions < 1.10."""
        d = self.anchors[i].device
        t = self.anchors[i].dtype
        shape = 1, self.na, ny, nx, 2  # grid shape
        y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
        yv, xv = torch.meshgrid(y, x, indexing="ij") if torch_1_10 else torch.meshgrid(y, x)  # torch>=0.7 compatibility
        grid = torch.stack((xv, yv), 2).expand(shape) - 0.5  # add grid offset, i.e. y = 2.0 * x - 0.5
        anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
        return grid, anchor_grid


class Segment(Detect):
    """YOLOv5 Segment head for segmentation models, extending Detect with mask and prototype layers."""

    def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
        """Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments."""
        super().__init__(nc, anchors, ch, inplace)
        self.nm = nm  # number of masks
        self.npr = npr  # number of protos
        self.no = 5 + nc + self.nm  # number of outputs per anchor
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
        self.detect = Detect.forward

    def forward(self, x):
        """Processes input through the network, returning detections and prototypes; adjusts output based on
        training/export mode.
        """
        p = self.proto(x[0])
        x = self.detect(self, x)
        return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1])


class BaseModel(nn.Module):
    """YOLOv5 base model."""

    def forward(self, x, h=None, profile=False, visualize=False):
        """Executes a single-scale inference or training pass on the YOLOv5 base model, with options for profiling and
        visualization.
        """
        return self._forward_once(x, h, profile, visualize)  # single-scale inference, train

    def _forward_once(self, x, h=None, profile=False, visualize=False):
        y, dt = [], []  # outputs
        for m in self.model:
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            # Check if the current module is the Detect layer
            if isinstance(m, Detect):
                x = m(x, h)  # pass h to Detect
            else:
                x = m(x)  # run without h
            y.append(x if m.i in self.save else None)  # save output
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
        return x

    def _profile_one_layer(self, m, x, dt):
        """Profiles a single layer's performance by computing GFLOPs, execution time, and parameters."""
        c = m == self.model[-1]  # is final layer, copy input as inplace fix
        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1e9 * 2 if thop else 0  # FLOPs
        t = time_sync()
        for _ in range(10):
            m(x.copy() if c else x)
        dt.append((time_sync() - t) * 100)
        if m == self.model[0]:
            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
        LOGGER.info(f"{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}")
        if c:
            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")

    def fuse(self):
        """Fuses Conv2d() and BatchNorm2d() layers in the model to improve inference speed."""
        LOGGER.info("Fusing layers... ")
        for m in self.model.modules():
            if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"):
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                delattr(m, "bn")  # remove batchnorm
                m.forward = m.forward_fuse  # update forward
        self.info()
        return self

    def info(self, verbose=False, img_size=640):
        """Prints model information given verbosity and image size, e.g., `info(verbose=True, img_size=640)`."""
        model_info(self, verbose, img_size)

    def _apply(self, fn):
        """Applies transformations like to(), cpu(), cuda(), half() to model tensors excluding parameters or registered
        buffers.
        """
        self = super()._apply(fn)
        m = self.model[-1]  # Detect()
        if isinstance(m, (Detect, Segment)):
            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


class DetectionModel(BaseModel):
    """YOLOv5 detection model class for object detection tasks, supporting custom configurations and anchors."""

    def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None, sl=2):
        """Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors."""
        super().__init__()
        self.sl = sl  

        if isinstance(cfg, dict):
            self.yaml = cfg  # model dict
        else:  # is *.yaml
            import yaml  # for torch hub

            self.yaml_file = Path(cfg).name
            with open(cfg, encoding="ascii", errors="ignore") as f:
                self.yaml = yaml.safe_load(f)  # model dict

        # Define model
        ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
        if nc and nc != self.yaml["nc"]:
            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml["nc"] = nc  # override yaml value
        if anchors:
            LOGGER.info(f"Overriding model.yaml anchors with anchors={anchors}")
            self.yaml["anchors"] = round(anchors)  # override yaml value
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch], sl=self.sl)  # model, savelist
        self.names = [str(i) for i in range(self.yaml["nc"])]  # default names
        self.inplace = self.yaml.get("inplace", True)

        # Build strides, anchors
        m = self.model[-1]  # Detect()
        if isinstance(m, (Detect, Segment)):

            def _forward(x):
                outputs, _ = self.forward(x)
                if isinstance(outputs, list):
                    return outputs
                else:
                    return [outputs]

            s = 256  # 2x min stride
            m.inplace = self.inplace
            x_input = torch.zeros(sl, ch, s, s)
            x_output = _forward(x_input)
            m.stride = torch.tensor([s / x.shape[-2] for x in x_output])  # forward
            check_anchor_order(m)
            m.anchors /= m.stride.view(-1, 1, 1)
            self.stride = m.stride
            self._initialize_biases()  # only run once

        # Init weights, biases
        initialize_weights(self)
        self.info()
        LOGGER.info("")

    def forward(self, x, h=None, augment=False, profile=False, visualize=False):
        """Performs single-scale or augmented inference and may include profiling or visualization."""
        if augment:
            return self._forward_augment(x)  # augmented inference, None
        return self._forward_once(x, h, profile, visualize)  # single-scale inference, train

    def _forward_augment(self, x):
        """Performs augmented inference across different scales and flips, returning combined detections."""
        img_size = x.shape[-2:]  # height, width
        s = [1, 0.83, 0.67]  # scales
        f = [None, 3, None]  # flips (2-ud, 3-lr)
        y = []  # outputs
        for si, fi in zip(s, f):
            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
            yi = self._forward_once(xi)[0]  # forward
            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
            yi = self._descale_pred(yi, fi, si, img_size)
            y.append(yi)
        y = self._clip_augmented(y)  # clip augmented tails
        return torch.cat(y, 1), None  # augmented inference, train

    def _descale_pred(self, p, flips, scale, img_size):
        """De-scales predictions from augmented inference, adjusting for flips and image size."""
        if self.inplace:
            p[..., :4] /= scale  # de-scale
            if flips == 2:
                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
            elif flips == 3:
                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
        else:
            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
            if flips == 2:
                y = img_size[0] - y  # de-flip ud
            elif flips == 3:
                x = img_size[1] - x  # de-flip lr
            p = torch.cat((x, y, wh, p[..., 4:]), -1)
        return p

    def _clip_augmented(self, y):
        """Clips augmented inference tails for YOLOv5 models, affecting first and last tensors based on grid points and
        layer counts.
        """
        nl = self.model[-1].nl  # number of detection layers (P3-P5)
        g = sum(4**x for x in range(nl))  # grid points
        e = 1  # exclude layer count
        i = (y[0].shape[1] // g) * sum(4**x for x in range(e))  # indices
        y[0] = y[0][:, :-i]  # large
        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
        y[-1] = y[-1][:, i:]  # small
        return y

    def _initialize_biases(self, cf=None):
        """
        Initializes biases for the ConvLSTM layers in YOLOv5's Detect() module, optionally using class frequencies (cf).

        For details see https://arxiv.org/abs/1708.02002 section 3.3.
        """
        m = self.model[-1]  # Detect() module
        # for convlstm_layer, s in zip(m.convlstms, m.stride):  # Iterate over ConvLSTM layers and strides
        #     for layer_idx in range(convlstm_layer.num_layers):
        #         convlstm_cell = convlstm_layer.cell_list[layer_idx]
        #         conv = convlstm_cell.conv  # nn.Conv2d

        #         num_gates = 4  # input gate, forget gate, cell gate, output gate
        #         hidden_dim = convlstm_cell.hidden_dim
        #         if isinstance(hidden_dim, list):
        #             hidden_dim = hidden_dim[0]  # Assuming single hidden_dim per layer

        #         # Clone and detach the bias to avoid in-place operations on a leaf variable
        #         bias = conv.bias.clone().detach()  # Shape: (num_gates * hidden_dim,)

        #         # Reshape bias to (num_gates, hidden_dim)
        #         bias = bias.view(num_gates, hidden_dim)

        #         # Initialize the biases for the output gate (gate index 3)
        #         b = torch.zeros(m.na, m.no, device=bias.device, dtype=bias.dtype)

        #         # Initialize b as in the original YOLOv5 code
        #         b[:, 4] += math.log(8 / (640 / s) ** 2)  # Objectness bias
        #         if cf is None:
        #             b[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999))  # Class bias
        #         else:
        #             b[:, 5:5 + m.nc] += torch.log(cf / cf.sum())

        #         # Flatten b to match hidden_dim
        #         b_flat = b.view(-1)  # Shape: (m.na * m.no,)

        #         if b_flat.shape[0] != hidden_dim:
        #             raise ValueError(f"Mismatch in hidden_dim ({hidden_dim}) and bias size ({b_flat.shape[0]}).")

        #         # Assign b_flat to the biases of the output gate
        #         bias[3] = b_flat  # Safe in-place operation on cloned tensor

        #         # Reshape bias back to (num_gates * hidden_dim,)
        #         bias = bias.view(-1)

        #         # Assign the modified bias back to conv.bias as a Parameter
        #         conv.bias = nn.Parameter(bias, requires_grad=True)


Model = DetectionModel  # retain YOLOv5 'Model' class for backwards compatibility


class SegmentationModel(DetectionModel):
    """YOLOv5 segmentation model for object detection and segmentation tasks with configurable parameters."""

    def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
        """Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for channels, nc (int) for num classes, anchors (list)."""
        super().__init__(cfg, ch, nc, anchors)


class ClassificationModel(BaseModel):
    """YOLOv5 classification model for image classification tasks, initialized with a config file or detection model."""

    def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):
        """Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff`
        index.
        """
        super().__init__()
        self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)

    def _from_detection_model(self, model, nc=1000, cutoff=10):
        """Creates a classification model from a YOLOv5 detection model, slicing at `cutoff` and adding a classification
        layer.
        """
        if isinstance(model, DetectMultiBackend):
            model = model.model  # unwrap DetectMultiBackend
        model.model = model.model[:cutoff]  # backbone
        m = model.model[-1]  # last layer
        ch = m.conv.in_channels if hasattr(m, "conv") else m.cv1.conv.in_channels  # ch into module
        c = Classify(ch, nc)  # Classify()
        c.i, c.f, c.type = m.i, m.f, "models.common.Classify"  # index, from, type
        model.model[-1] = c  # replace
        self.model = model.model
        self.stride = model.stride
        self.save = []
        self.nc = nc

    def _from_yaml(self, cfg):
        """Creates a YOLOv5 classification model from a specified *.yaml configuration file."""
        self.model = None

def parse_model(d, ch, sl=2):
    """Parses a YOLOv5 model from a dict `d`, configuring layers based on input channels `ch` and model architecture."""
    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
    anchors, nc, gd, gw, act, ch_mul = (
        d["anchors"],
        d["nc"],
        d["depth_multiple"],
        d["width_multiple"],
        d.get("activation"),
        d.get("channel_multiple"),
    )

    if act:
        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
        LOGGER.info(f"{colorstr('activation:')} {act}")  # print
    if not ch_mul:
        ch_mul = 8
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)

    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
        m = eval(m) if isinstance(m, str) else m  # eval strings
        for j, a in enumerate(args):
            with contextlib.suppress(NameError):
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings

        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
        if m in {
            Conv,
            GhostConv,
            Bottleneck,
            GhostBottleneck,
            SPP,
            SPPF,
            DWConv,
            MixConv2d,
            Focus,
            CrossConv,
            BottleneckCSP,
            C3,
            C3TR,
            C3SPP,
            C3Ghost,
            nn.ConvTranspose2d,
            DWConvTranspose2d,
            C3x,
        }:
            c1, c2 = ch[f], args[0]
            if c2 != no:  # if not output
                c2 = make_divisible(c2 * gw, ch_mul)

            args = [c1, c2, *args[1:]]
            if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
                args.insert(2, n)  # number of repeats
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        # TODO: channel, gw, gd
        elif m in {Detect, Segment}:
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  # number of anchors
                args[1] = [list(range(args[1] * 2))] * len(f)
            if m is Segment:
                args[3] = make_divisible(args[3] * gw, ch_mul)
            args.append(sl)  # Append sl to args for Detect module
        elif m is Contract:
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:
            c2 = ch[f] // args[0] ** 2
        else:
            c2 = ch[f]

        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace("__main__.", "")  # module type
        np = sum(x.numel() for x in m_.parameters())  # number params
        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
        LOGGER.info(f"{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}")  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--cfg", type=str, default="yolov5s.yaml", help="model.yaml")
    parser.add_argument("--batch-size", type=int, default=1, help="total batch size for all GPUs")
    parser.add_argument("--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
    parser.add_argument("--profile", action="store_true", help="profile model speed")
    parser.add_argument("--line-profile", action="store_true", help="profile model speed layer by layer")
    parser.add_argument("--test", action="store_true", help="test all yolo*.yaml")
    opt = parser.parse_args()
    opt.cfg = check_yaml(opt.cfg)  # check YAML
    print_args(vars(opt))
    device = select_device(opt.device)

    # Create model
    im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
    model = Model(opt.cfg).to(device)

    # Options
    if opt.line_profile:  # profile layer by layer
        model(im, profile=True)

    elif opt.profile:  # profile forward-backward
        results = profile(input=im, ops=[model], n=3)

    elif opt.test:  # test all models
        for cfg in Path(ROOT / "models").rglob("yolo*.yaml"):
            try:
                _ = Model(cfg)
            except Exception as e:
                print(f"Error in {cfg}: {e}")

    else:  # report fused model summary
        #model.fuse()
        output0, h0 = model(im, None, profile=False)
        output1, h1 = model(im, h0, profile=False)
        output2, h2 = model(im, h1, profile=False)