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ValueError: Dimension 1 in both shapes must be equal, but are 13 and 14. Shapes are [8,13,13] and [8,14,14]. for '{{node yolo/concat/concat}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](yolo/conv_39/mul, yolo/upsample/resize/ResizeNearestNeighbor, yolo/concat/concat/axis)' with input shapes: [8,13,13,128], [8,14,14,128], [] and with computed input tensors: input[2] = <-1>.
The text was updated successfully, but these errors were encountered:
❔Question
用自己的数据集训练时,不同尺寸的图片维度不同拼接会报错,请问下这个代码里面的马赛克增强代码是正确的吗?
Additional context
ValueError: Dimension 1 in both shapes must be equal, but are 13 and 14. Shapes are [8,13,13] and [8,14,14]. for '{{node yolo/concat/concat}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](yolo/conv_39/mul, yolo/upsample/resize/ResizeNearestNeighbor, yolo/concat/concat/axis)' with input shapes: [8,13,13,128], [8,14,14,128], [] and with computed input tensors: input[2] = <-1>.
The text was updated successfully, but these errors were encountered: