Link to the classroom item you are referring to: https://www.coursera.org/learn/convolutional-neural-networks/programming/3VCFG/car-detection-with-yolo
Description
Error at section 3.5 - Run the YOLO on an Image at :
out_scores, out_boxes, out_classes = predict("test.jpg")
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-17-fb6b7cad9cd5> in <module>
----> 1 out_scores, out_boxes, out_classes = predict("car_image.jpeg")
<ipython-input-15-85846e4b09d5> in predict(image_file)
20 yolo_outputs = yolo_head(yolo_model_outputs, anchors, len(class_names))
21
---> 22 out_scores, out_boxes, out_classes = yolo_eval(yolo_outputs, [image.size[1], image.size[0]], 10, 0.3, 0.5)
23
24 # Print predictions info
<ipython-input-10-b275b60b3937> in yolo_eval(yolo_outputs, image_shape, max_boxes, score_threshold, iou_threshold)
38 # Use one of the functions you've implemented to perform Non-max suppression with
39 # maximum number of boxes set to max_boxes and a threshold of iou_threshold (≈1 line)
---> 40 scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes, max_boxes = max_boxes, iou_threshold = 1.0)
41 ### END CODE HERE
42
<ipython-input-7-0ac3ebaa382d> in yolo_non_max_suppression(scores, boxes, classes, max_boxes, iou_threshold)
61 # Flatten the list of indices and concatenate
62 # Use tf.concat() with 'nms_indices' and `axis=0`
---> 63 nms_indices = tf.concat( nms_indices, 0)
64
65 # Use tf.gather() to select only nms_indices from scores, boxes and classes
/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
199 """Call target, and fall back on dispatchers if there is a TypeError."""
200 try:
--> 201 return target(*args, **kwargs)
202 except (TypeError, ValueError):
203 # Note: convert_to_eager_tensor currently raises a ValueError, not a
/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py in concat(values, axis, name)
1652 dtype=dtypes.int32).get_shape().assert_has_rank(0)
1653 return identity(values[0], name=name)
-> 1654 return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
1655
1656
/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/gen_array_ops.py in concat_v2(values, axis, name)
1205 return _result
1206 except _core._NotOkStatusException as e:
-> 1207 _ops.raise_from_not_ok_status(e, name)
1208 except _core._FallbackException:
1209 pass
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e, name)
6841 message = e.message + (" name: " + name if name is not None else "")
6842 # pylint: disable=protected-access
-> 6843 six.raise_from(core._status_to_exception(e.code, message), None)
6844 # pylint: enable=protected-access
6845
/opt/conda/lib/python3.7/site-packages/six.py in raise_from(value, from_value)
InvalidArgumentError: OpKernel 'ConcatV2' has constraint on attr 'T' not in NodeDef '[N=0, Tidx=DT_INT32]', KernelDef: 'op: "ConcatV2" device_type: "CPU" constraint { name: "T" allowed_values { list { type: DT_UINT64 } } } host_memory_arg: "axis"' [Op:ConcatV2] name: concat
I did receive 100% on submission, so i am assuming code is correct.
Am I missing something here?
Thanks.