Error process with input png file

[blinkbink]

results.txt

🐛 Bug

A clear and concise description of what the bug is.
try to detect object with image png, some case png working as well but some case got error :

RuntimeError: Given groups=1, weight of size [32, 12, 3, 3], expected input[1, 16, 224, 320] to have 12 channels, but got 16 channels instead

To Reproduce (REQUIRED)

make a little change for detect under API with flask, from file detect.py
the function of detect from main file :
Input:

def detect(id, base64):
    try:
        out, source, view_img, save_txt, imgsz = \
            'datapredict', '', False, True, 640
        webcam = source.isnumeric() or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
        # Initialize
        set_logging()

        # if os.path.exists(out):
        #     shutil.rmtree(out)  # delete output folder
        # os.makedirs(out)  # make new output folder
        half = device.type != 'cpu'  # half precision only supported on CUDA

        imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
        if half:
            model.half()  # to FP16

        # Second-stage classifier
        classify = False
        if classify:
            modelc = load_classifier(name='resnet101', n=2)  # initialize
            modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
            modelc.to(device).eval()

        save_img = True
        dataset = LoadImages(id, base64, source, img_size=imgsz)

        # Get names and colors
        names = model.module.names if hasattr(model, 'module') else model.names
        colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]

        # Run inference
        t0 = time.time()
        img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
        _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once

        for path, img, im0s in dataset:

            img = torch.from_numpy(img).to(device)
            img = img.half() if half else img.float()  # uint8 to fp16/32
            img /= 255.0  # 0 - 255 to 0.0 - 1.0
            if img.ndimension() == 3:
                img = img.unsqueeze(0)

            # Inference
            t1 = time_synchronized()
            pred = model(img, augment=False)[0]

            # Apply NMS
            pred = non_max_suppression(pred, 0.60, 0.7, classes=0, agnostic=False)

            if pred == [None] * len(pred):
                logger.info(id + " : Not E-KTP ")
                return jsonify(object="Not E-KTP", score=0.0, result='16')
            t2 = time_synchronized()

            # Apply Classifier
            if classify:
                pred = apply_classifier(pred, modelc, img, im0s)

            # Process detections
            for i, det in enumerate(pred):  # detections per image
                if webcam:  # batch_size >= 1
                    p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
                else:
                    p, s, im0 = path, '', im0s

                save_path = str(Path(out) / Path(p).name)
                txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
                s += '%gx%g ' % img.shape[2:]  # print string
                gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
                if det is not None and len(det):
                    # Rescale boxes from img_size to im0 size
                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                    # Print results
                    for c in det[:, -1].unique():
                        n = (det[:, -1] == c).sum()  # detections per class
                        s += '%g %ss, ' % (n, names[int(c)])  # add to string

                    # Write results
                    for *xyxy, conf, cls in reversed(det):
                        # if save_txt:  # Write to file
                        #     xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        #     with open(txt_path + '.txt', 'a') as f:
                        #         f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format

                        if save_img or view_img:  # Add bbox to image
                            label = '%s %.2f' % (names[int(cls)], conf)
                            object = names[int(cls)]

                            score = '%.5f' % (conf)
                            plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)

                # Print time (inference + NMS)
                logger.info(id + ' : %sDone. (%.3fs)' % (s, t2 - t1))

                is_completed, buf = cv2.imencode(".jpg", im0)
                byte_buffer = io.BytesIO(buf.tostring())

                # Stream results
                if view_img:
                    cv2.imshow(p, im0)
                    if cv2.waitKey(1) == ord('q'):  # q to quit
                        raise StopIteration

                # Save results (image with detections)
                cv2.imwrite(save_path, im0)

        # if save_txt or save_img:
        #     if platform.system() == 'Darwin' and not opt.update:  # MacOS
        #         os.system('open ' + save_path)

        logger.info(id + ' : Done. (%.3fs)' % (time.time() - t0))
        logger.info(id + ' : Prediction is E-' + object + " Score : " + score)

        return jsonify(object=str(object), score=str(score), result='15')

        # logger.info(id + " : Send file as image buffer")
        # return send_file(byte_buffer,"image/jpeg",as_attachment=True,attachment_filename="output.jpg")
    except Exception as error:
        logger.error(error)
        traceback.print_exc()
        return jsonify(info='Cant process image', result='88')

then from utils make change on function LoadImages:
`class LoadImages: # for inference
def init(self, id, base64data, path, img_size=640):

    self.img_size = img_size
    self.id = id
    self.mode = 'images'
    self.encodedbase64 = base64data


def __iter__(self):
    self.count = 0
    return self

def __next__(self):
    if self.count == 1:
        raise StopIteration
    path = "/var/www/html/classifier/object_detection/yolov5/datapredict/"+str(self.id)+".jpg"

    self.count += 1
    decoded_data = base64.b64decode(self.encodedbase64)
    np_data = np.fromstring(decoded_data, np.uint8)

    img0 = cv2.imdecode(np_data, cv2.IMREAD_UNCHANGED)

    # img0 = cv2.imread(path)  # BGR
    assert img0 is not None, 'Image Not Found ' + path
    # print('image %g/%g %s: ' % (self.count, self.nf, path), end='')

    # Padded resize
    img = letterbox(img0, new_shape=self.img_size)[0]

    # Convert
    img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
    img = np.ascontiguousarray(img)

    return path, img, img0

`

Output:

Traceback (most recent call last):
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/flask/app.py", line 1950, in full_dispatch_request
    rv = self.dispatch_request()
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/flask/app.py", line 1936, in dispatch_request
    return self.view_functions[rule.endpoint](**req.view_args)
  File "/var/www/html/classifier/object_detection/yolov5/apidetection.py", line 69, in detectionKTP
    result = detect(id, base64.b64encode(ktp.read()))
  File "/var/www/html/classifier/object_detection/yolov5/apidetection.py", line 118, in detect
    pred = model(img, augment=False)[0]
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/var/www/html/classifier/object_detection/yolov5/models/yolo.py", line 113, in forward
    return self.forward_once(x, profile)  # single-scale inference, train
  File "/var/www/html/classifier/object_detection/yolov5/models/yolo.py", line 133, in forward_once
    x = m(x)  # run
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/var/www/html/classifier/object_detection/yolov5/models/common.py", line 88, in forward
    return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/var/www/html/classifier/object_detection/yolov5/models/common.py", line 32, in fuseforward
    return self.act(self.conv(x))
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 419, in forward
    return self._conv_forward(input, self.weight)
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 416, in _conv_forward
    self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [32, 12, 3, 3], expected input[1, 16, 224, 320] to have 12 channels, but got 16 channels instead

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/flask/app.py", line 2464, in __call__
    return self.wsgi_app(environ, start_response)
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/flask/app.py", line 2450, in wsgi_app
    response = self.handle_exception(e)
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/flask/app.py", line 1867, in handle_exception
    reraise(exc_type, exc_value, tb)
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/flask/_compat.py", line 39, in reraise
    raise value
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/flask/app.py", line 2447, in wsgi_app
    response = self.full_dispatch_request()
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/flask/app.py", line 1952, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "/var/www/html/classifier/object_detection/yolov5/yoloenvi/lib/python3.6/site-packages/flask_api/app.py", line 96, in handle_user_exception
    app_handlers = self.error_handler_spec[None].get(None, ())
KeyError: None

Expected behavior

can perform with png extension because can't perform detect in some case if the file extension with .png

Environment

• OS: Ubuntu 18.04
• GPU Tesla V100 32Gb

Additional context

[Aktcob]

it seemed that the input shape of img is [4, H, W], not [3, H, W]. U can check out this by simply printing the shape of input tensor.

[blinkbink]

the input just allowed [3, H, W] ?

[glenn-jocher]

@blinkbink image input dimensions are (1,3,H,W) for batch size 1.

[Aktcob]

@blinkbink if the input size is [4, H, W], simply change like that:

class Focus(nn.Module):
# Focus wh information into c-space
def init(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Focus, self).init()
self.conv = Conv(16, c2, k, s, p, g, act). #16 = 4*4

def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
    return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))

[blinkbink]

@blinkbink image input dimensions are (1,3,H,W) for batch size 1.

@glenn-jocher the batch size determine on train ? i use --batch-size 64

[github-actions]

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