Evaluate the parameter amount and speed of the modified YOLOv8 model

 YOLOv8 announced the speed and parameter amount of each model

So if we have made some modifications to YOLOv8, how can we write the code and count the parameters and speed of the modified model?

In fact, to evaluate these things, in most cases, we don't need to write a function from scratch to evaluate

In general, as long as the authors publish these scores. The author will put the implementation code of these scores in its code. All you need is to find the location of this code, and then directly call the author's code to calculate.

The diam required for the above calculation is placed in the following position (this is the version of ultralytircs8.0.40, other versions may be different)

\ultralytics\yolo\utils\torch_utils.py line 165

Functions that calculate the amount of arguments

def model_info(model, verbose=False, imgsz=640):
    # Model information. imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]
    n_p = get_num_params(model)
    n_g = get_num_gradients(model)  # number gradients
    if verbose:
        print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
        for i, (name, p) in enumerate(model.named_parameters()):
            name = name.replace('module_list.', '')
            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))

    flops = get_flops(model, imgsz)
    fused = ' (fused)' if model.is_fused() else ''
    fs = f', {flops:.1f} GFLOPs' if flops else ''
    m = Path(getattr(model, 'yaml_file', '') or model.yaml.get('yaml_file', '')).stem.replace('yolo', 'YOLO') or 'Model'
    LOGGER.info(f"{m} summary{fused}: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")


def get_num_params(model):
    return sum(x.numel() for x in model.parameters())


def get_num_gradients(model):
    return sum(x.numel() for x in model.parameters() if x.requires_grad)


def get_flops(model, imgsz=640):
    try:
        model = de_parallel(model)
        p = next(model.parameters())
        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32  # max stride
        im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
        flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2  # stride GFLOPs
        imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
        flops = flops * imgsz[0] / stride * imgsz[1] / stride  # 640x640 GFLOPs
        return flops
    except Exception:
        return 0

function to calculate velocity

line354

def profile(input, ops, n=10, device=None):
    """ YOLOv8 speed/memory/FLOPs profiler
    Usage:
        input = torch.randn(16, 3, 640, 640)
        m1 = lambda x: x * torch.sigmoid(x)
        m2 = nn.SiLU()
        profile(input, [m1, m2], n=100)  # profile over 100 iterations
    """
    results = []
    if not isinstance(device, torch.device):
        device = select_device(device)
    print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
          f"{'input':>24s}{'output':>24s}")

    for x in input if isinstance(input, list) else [input]:
        x = x.to(device)
        x.requires_grad = True
        for m in ops if isinstance(ops, list) else [ops]:
            m = m.to(device) if hasattr(m, 'to') else m  # device
            m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
            tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
            try:
                flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
            except Exception:
                flops = 0

            try:
                for _ in range(n):
                    t[0] = time_sync()
                    y = m(x)
                    t[1] = time_sync()
                    try:
                        _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
                        t[2] = time_sync()
                    except Exception:  # no backward method
                        # print(e)  # for debug
                        t[2] = float('nan')
                    tf += (t[1] - t[0]) * 1000 / n  # ms per op forward
                    tb += (t[2] - t[1]) * 1000 / n  # ms per op backward
                mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0  # (GB)
                s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y))  # shapes
                p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0  # parameters
                print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
                results.append([p, flops, mem, tf, tb, s_in, s_out])
            except Exception as e:
                print(e)
                results.append(None)
            torch.cuda.empty_cache()
    return results