yolov9/models/experimental.py

import math

import numpy as np
import torch
import torch.nn as nn

from utils.downloads import attempt_download


class Sum(nn.Module):
    # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
    def __init__(self, n, weight=False):  # n: number of inputs
        super().__init__()
        self.weight = weight  # apply weights boolean
        self.iter = range(n - 1)  # iter object
        if weight:
            self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True)  # layer weights

    def forward(self, x):
        y = x[0]  # no weight
        if self.weight:
            w = torch.sigmoid(self.w) * 2
            for i in self.iter:
                y = y + x[i + 1] * w[i]
        else:
            for i in self.iter:
                y = y + x[i + 1]
        return y


class MixConv2d(nn.Module):
    # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):  # ch_in, ch_out, kernel, stride, ch_strategy
        super().__init__()
        n = len(k)  # number of convolutions
        if equal_ch:  # equal c_ per group
            i = torch.linspace(0, n - 1E-6, c2).floor()  # c2 indices
            c_ = [(i == g).sum() for g in range(n)]  # intermediate channels
        else:  # equal weight.numel() per group
            b = [c2] + [0] * n
            a = np.eye(n + 1, n, k=-1)
            a -= np.roll(a, 1, axis=1)
            a *= np.array(k) ** 2
            a[0] = 1
            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b

        self.m = nn.ModuleList([
            nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU()

    def forward(self, x):
        return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))


class Ensemble(nn.ModuleList):
    # Ensemble of models
    def __init__(self):
        super().__init__()

    def forward(self, x, augment=False, profile=False, visualize=False):
        y = [module(x, augment, profile, visualize)[0] for module in self]
        # y = torch.stack(y).max(0)[0]  # max ensemble
        # y = torch.stack(y).mean(0)  # mean ensemble
        y = torch.cat(y, 1)  # nms ensemble
        return y, None  # inference, train output


class ORT_NMS(torch.autograd.Function):
    '''ONNX-Runtime NMS operation'''
    @staticmethod
    def forward(ctx,
                boxes,
                scores,
                max_output_boxes_per_class=torch.tensor([100]),
                iou_threshold=torch.tensor([0.45]),
                score_threshold=torch.tensor([0.25])):
        device = boxes.device
        batch = scores.shape[0]
        num_det = random.randint(0, 100)
        batches = torch.randint(0, batch, (num_det,)).sort()[0].to(device)
        idxs = torch.arange(100, 100 + num_det).to(device)
        zeros = torch.zeros((num_det,), dtype=torch.int64).to(device)
        selected_indices = torch.cat([batches[None], zeros[None], idxs[None]], 0).T.contiguous()
        selected_indices = selected_indices.to(torch.int64)
        return selected_indices

    @staticmethod
    def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold):
        return g.op("NonMaxSuppression", boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold)


class TRT_NMS(torch.autograd.Function):
    '''TensorRT NMS operation'''
    @staticmethod
    def forward(
        ctx,
        boxes,
        scores,
        background_class=-1,
        box_coding=1,
        iou_threshold=0.45,
        max_output_boxes=100,
        plugin_version="1",
        score_activation=0,
        score_threshold=0.25,
    ):

        batch_size, num_boxes, num_classes = scores.shape
        num_det = torch.randint(0, max_output_boxes, (batch_size, 1), dtype=torch.int32)
        det_boxes = torch.randn(batch_size, max_output_boxes, 4)
        det_scores = torch.randn(batch_size, max_output_boxes)
        det_classes = torch.randint(0, num_classes, (batch_size, max_output_boxes), dtype=torch.int32)
        return num_det, det_boxes, det_scores, det_classes

    @staticmethod
    def symbolic(g,
                 boxes,
                 scores,
                 background_class=-1,
                 box_coding=1,
                 iou_threshold=0.45,
                 max_output_boxes=100,
                 plugin_version="1",
                 score_activation=0,
                 score_threshold=0.25):
        out = g.op("TRT::EfficientNMS_TRT",
                   boxes,
                   scores,
                   background_class_i=background_class,
                   box_coding_i=box_coding,
                   iou_threshold_f=iou_threshold,
                   max_output_boxes_i=max_output_boxes,
                   plugin_version_s=plugin_version,
                   score_activation_i=score_activation,
                   score_threshold_f=score_threshold,
                   outputs=4)
        nums, boxes, scores, classes = out
        return nums, boxes, scores, classes


class ONNX_ORT(nn.Module):
    '''onnx module with ONNX-Runtime NMS operation.'''
    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=640, device=None, n_classes=80):
        super().__init__()
        self.device = device if device else torch.device("cpu")
        self.max_obj = torch.tensor([max_obj]).to(device)
        self.iou_threshold = torch.tensor([iou_thres]).to(device)
        self.score_threshold = torch.tensor([score_thres]).to(device)
        self.max_wh = max_wh # if max_wh != 0 : non-agnostic else : agnostic
        self.convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],
                                           dtype=torch.float32,
                                           device=self.device)
        self.n_classes=n_classes

    def forward(self, x):
        ## https://github.com/thaitc-hust/yolov9-tensorrt/blob/main/torch2onnx.py
        ## thanks https://github.com/thaitc-hust
        if isinstance(x, list):  ## yolov9-c.pt and yolov9-e.pt return list
            x = x[1]
        x = x.permute(0, 2, 1)
        bboxes_x = x[..., 0:1]
        bboxes_y = x[..., 1:2]
        bboxes_w = x[..., 2:3]
        bboxes_h = x[..., 3:4]
        bboxes = torch.cat([bboxes_x, bboxes_y, bboxes_w, bboxes_h], dim = -1)
        bboxes = bboxes.unsqueeze(2) # [n_batch, n_bboxes, 4] -> [n_batch, n_bboxes, 1, 4]
        obj_conf = x[..., 4:]
        scores = obj_conf
        bboxes @= self.convert_matrix
        max_score, category_id = scores.max(2, keepdim=True)
        dis = category_id.float() * self.max_wh
        nmsbox = bboxes + dis
        max_score_tp = max_score.transpose(1, 2).contiguous()
        selected_indices = ORT_NMS.apply(nmsbox, max_score_tp, self.max_obj, self.iou_threshold, self.score_threshold)
        X, Y = selected_indices[:, 0], selected_indices[:, 2]
        selected_boxes = bboxes[X, Y, :]
        selected_categories = category_id[X, Y, :].float()
        selected_scores = max_score[X, Y, :]
        X = X.unsqueeze(1).float()
        return torch.cat([X, selected_boxes, selected_categories, selected_scores], 1)


class ONNX_TRT(nn.Module):
    '''onnx module with TensorRT NMS operation.'''
    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None ,device=None, n_classes=80):
        super().__init__()
        assert max_wh is None
        self.device = device if device else torch.device('cpu')
        self.background_class = -1,
        self.box_coding = 1,
        self.iou_threshold = iou_thres
        self.max_obj = max_obj
        self.plugin_version = '1'
        self.score_activation = 0
        self.score_threshold = score_thres
        self.n_classes=n_classes

    def forward(self, x):
        ## https://github.com/thaitc-hust/yolov9-tensorrt/blob/main/torch2onnx.py
        ## thanks https://github.com/thaitc-hust
        if isinstance(x, list):  ## yolov9-c.pt and yolov9-e.pt return list
            x = x[1]
        x = x.permute(0, 2, 1)
        bboxes_x = x[..., 0:1]
        bboxes_y = x[..., 1:2]
        bboxes_w = x[..., 2:3]
        bboxes_h = x[..., 3:4]
        bboxes = torch.cat([bboxes_x, bboxes_y, bboxes_w, bboxes_h], dim = -1)
        bboxes = bboxes.unsqueeze(2) # [n_batch, n_bboxes, 4] -> [n_batch, n_bboxes, 1, 4]
        obj_conf = x[..., 4:]
        scores = obj_conf
        num_det, det_boxes, det_scores, det_classes = TRT_NMS.apply(bboxes, scores, self.background_class, self.box_coding,
                                                                    self.iou_threshold, self.max_obj,
                                                                    self.plugin_version, self.score_activation,
                                                                    self.score_threshold)
        return num_det, det_boxes, det_scores, det_classes

class End2End(nn.Module):
    '''export onnx or tensorrt model with NMS operation.'''
    def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None, device=None, n_classes=80):
        super().__init__()
        device = device if device else torch.device('cpu')
        assert isinstance(max_wh,(int)) or max_wh is None
        self.model = model.to(device)
        self.model.model[-1].end2end = True
        self.patch_model = ONNX_TRT if max_wh is None else ONNX_ORT
        self.end2end = self.patch_model(max_obj, iou_thres, score_thres, max_wh, device, n_classes)
        self.end2end.eval()

    def forward(self, x):
        x = self.model(x)
        x = self.end2end(x)
        return x


def attempt_load(weights, device=None, inplace=True, fuse=True):
    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
    from models.yolo import Detect, Model

    model = Ensemble()
    for w in weights if isinstance(weights, list) else [weights]:
        ckpt = torch.load(attempt_download(w), map_location='cpu')  # load
        ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model

        # Model compatibility updates
        if not hasattr(ckpt, 'stride'):
            ckpt.stride = torch.tensor([32.])
        if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
            ckpt.names = dict(enumerate(ckpt.names))  # convert to dict

        model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval())  # model in eval mode

    # Module compatibility updates
    for m in model.modules():
        t = type(m)
        if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
            m.inplace = inplace  # torch 1.7.0 compatibility
            # if t is Detect and not isinstance(m.anchor_grid, list):
            #    delattr(m, 'anchor_grid')
            #    setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
        elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
            m.recompute_scale_factor = None  # torch 1.11.0 compatibility

    # Return model
    if len(model) == 1:
        return model[-1]

    # Return detection ensemble
    print(f'Ensemble created with {weights}\n')
    for k in 'names', 'nc', 'yaml':
        setattr(model, k, getattr(model[0], k))
    model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stride
    assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
    return model