Gertting different output sizes when using exported torchscript

[dagap]

I am running some dummy data through the model as follows:

import torch

image = torch.zeros(1, 3, 640, 640).to('cuda:0')
model = torch.load('yolov5.pt', map_location=map_location)['model'].float().fuse().eval())
pred = model(image)[0]

The shape of the output prediction is [1, 192, 80, 80]

I have exported the model to torchscript using the export.py script and the usage is as follows:

import torch
model = torch.jit.load('yolov5m.torchscript.pt', map_location='cuda:0').eval()
pred = m(img)[0]

The output of this is of shape [1, 3, 80, 80, 85].

So the model definitely gets exported and I can use it for inference but really no clue why the output shape is different. Maybe this is due to laye fusion, so I tried to do something like to replicate this fusion:

model = torch.jit.load('yolov5m.torchscript.pt', map_location='cuda:0').float().fuse().eval()

but this results in RecursiveScriptModule' object has no attribute 'fuse'

I was wondering if there is something else one needs to do to make the two approaches consistent.

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[smartinellimarco]

Try changing:
model.model[-1].export = True
to False.
That should add an extra output of the same shape than the model in eager mode.
You'll need to do NMS over the new one (borrow it from predict.py).

I recommend you reading these issues entirely:

• https://docs.ultralytics.com/yolov5/tutorials/model_export
• Use export.py to generate yolov5s.onnx will get a negative number. #343 (comment)

[dagap]

@MarcoCBA Thank you for the reply. So, the code in detect.py is doing NMS after the call to pred = model(image)[0]. I see the difference already before the call by simply making the forward pass over the exported model.

If I set it to False, I get this error:

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!

If I do everything on the CPU, the output for an input like image = torch.zeros(1, 3, 640, 640) is a tuple. The first element is a tensor of shape ([1, 25200, 85]).

The second element is a list of size 3 with tensors of shape:

torch.Size([1, 3, 80, 80, 85]
torch.Size([1, 3, 40, 40, 85])
torch.Size([1, 3, 20, 20, 85])

Do you reckon I need to replicate the detection layer?

[smartinellimarco]

@MarcoCBA Thank you for the reply. So, the code in detect.py is doing NMS after the call to pred = model(image)[0]. I see the difference already before the call by simply making the forward pass over the exported model.

If I set it to False, I get this error:

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!

If I do everything on the CPU, the output for an input like image = torch.zeros(1, 3, 640, 640) is a tuple. The first element is a tensor of shape ([1, 25200, 85]).

The second element is a list of size 3 with tensors of shape:

torch.Size([1, 3, 80, 80, 85]
torch.Size([1, 3, 40, 40, 85])
torch.Size([1, 3, 20, 20, 85])

Do you reckon I need to replicate the detection layer?

The problem is that some constants are loading on a specific device. You could either move everything to CUDA (but the model will work only in cuda) or, you could modify the code inside the traced model (unzip it) in order to make it device agnostic.
I do not know why, but the guy that posted how to do that, deleted his comment.
Luckily I quoted him, so you can still read it:

• https://docs.ultralytics.com/yolov5/tutorials/model_export#issuecomment-733903053

The shapes are exactly what you are expecting. Im not sure what those three tensors even are, but If you do NMS over the first one of shape ([1, 25200, 85]), you will get boxes, scores and labels.

Here is the code for NMS:

def non_max_suppression(prediction, conf_thres=0.5, iou_thres=0.6, classes=None, agnostic=False, labels=()):
    """
    Performs Non-Maximum Suppression (NMS) on inference results
    Returns:
         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
    """

    # Number of classes.
    nc = prediction[0].shape[1] - 5
    
    # Candidates.
    xc = prediction[..., 4] > conf_thres

    # Settings:
    # Minimum and maximum box width and height in pixels.
    min_wh, max_wh = 2, 4096

    # Maximum number of detections per image.
    max_det = 300
    
    # Timeout.
    time_limit = 10.0  
    
    # Require redundant detections.
    redundant = True
    
    # Multiple labels per box (adds 0.5ms/img).
    multi_label = nc > 1
    
    # Use Merge-NMS.
    merge = False

    t = time.time()
    output = [torch.zeros(0, 6)] * prediction.shape[0]
    for xi, x in enumerate(prediction):  # image index, image inference
        
        # Apply constraints:
        # Confidence.
        x = x[xc[xi]]

        # Cat apriori labels if autolabelling.
        if labels and len(labels[xi]):
            l = labels[xi]
            v = torch.zeros((len(l), nc + 5), device=x.device)
            v[:, :4] = l[:, 1:5]  # box
            v[:, 4] = 1.0  # conf
            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
            x = torch.cat((x, v), 0)

        # If none remain process next image.
        if not x.shape[0]:
            continue

        # Compute conf.
        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf

        # Box (center x, center y, width, height) to (x1, y1, x2, y2).
        box = xywh2xyxy(x[:, :4])

        # Detections matrix nx6 (xyxy, conf, cls).
        if multi_label:
            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
        else:

            # Best class only.
            conf, j = x[:, 5:].max(1, keepdim=True)
            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]

        # Filter by class.
        if classes:
            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]

        # If none remain process next image.
        # Number of boxes.
        n = x.shape[0]
        if not n:
            continue

        # Batched NMS:
        # Classes.
        c = x[:, 5:6] * (0 if agnostic else max_wh)
        
        # Boxes (offset by class), scores.
        boxes, scores = x[:, :4] + c, x[:, 4]
        
        # NMS.
        i = torchvision.ops.nms(boxes, scores, iou_thres)
        
        # Limit detections.
        if i.shape[0] > max_det:  # limit detections
            i = i[:max_det]
        if merge and (1 < n < 3E3):
            
            # Merge NMS (boxes merged using weighted mean).
            # Update boxes as boxes(i,4) = weights(i,n) * boxes(n,4).
            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
            weights = iou * scores[None]  # box weights
            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
            if redundant:
                i = i[iou.sum(1) > 1]  # require redundancy

        output[xi] = x[i]
        if (time.time() - t) > time_limit:
            break  # time limit exceeded

    return output


def xywh2xyxy(x):
    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
    y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
    return y

just do

out = non_max_suppression(out, conf_thres=0.7)[0] # BATCH SIZE is 1.

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[sourabhyadav]

@MarcoCBA Thank you for the reply. So, the code in detect.py is doing NMS after the call to pred = model(image)[0]. I see the difference already before the call by simply making the forward pass over the exported model.

If I set it to False, I get this error:

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!

If I do everything on the CPU, the output for an input like image = torch.zeros(1, 3, 640, 640) is a tuple. The first element is a tensor of shape ([1, 25200, 85]).

The second element is a list of size 3 with tensors of shape:

torch.Size([1, 3, 80, 80, 85]
torch.Size([1, 3, 40, 40, 85])
torch.Size([1, 3, 20, 20, 85])

Do you reckon I need to replicate the detection layer?

@dagap I am also facing the same issue:

At normal inference, the output is a torch tensor and the shape is consistent wrt to batch size:

Input shape:
imgs size: torch.Size([2, 3, 384, 640])

Output shape:
dtype=torch.float16) shape: torch.Size([2, 15120, 85])

However, in the torchscript output is a list and the length of 3 even when the input batch size is 1 or 2.

Input Shape:
imgs size: torch.Size([1, 3, 384, 640])

Output Shape:
inf_out[0] : torch.Size([1, 3, 48, 80, 85])
inf_out[1] : torch.Size([1, 3, 24, 40, 85])
inf_out[2] : torch.Size([1, 3, 12, 20, 85])

The doubts I am having is:
Although the overall output size matches but I am not sure how to pack these properly for further processing like nms and box filtering etc.

The main doubt is unlike in your case you were getting the 1st tensor as correct tensor but I am not getting the same?