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When I test the above case, there are some errors with
tests/hw4/test_conv.py::test_op_conv[backward-needle.backend_ndarray.ndarray_backend_cuda-Z_shape16-W_shape16-1-0]
- ValueError: operands could not be broadcast together with shapes (1,16,16,1) (1,17,17,1)
This function may not have considered the situation of a large convolution kernel during implementation, especially when $(H - 2p + k) \pmod s \ne 0$.
May I ask if it‘s possible to check such case and carry out improvement/perfection? Thanks for your reply.
The text was updated successfully, but these errors were encountered:
When I test the above case, there are some errors with
tests/hw4/test_conv.py::test_op_conv[backward-needle.backend_ndarray.ndarray_backend_cuda-Z_shape16-W_shape16-1-0]
- ValueError: operands could not be broadcast together with shapes (1,16,16,1) (1,17,17,1)
This function may not have considered the situation of a large convolution kernel during implementation, especially when (H−2p+k)(mods)≠0.
May I ask if it‘s possible to check such case and carry out improvement/perfection? Thanks for your reply.
Maybe there is a mistake in the conv's gradient function with the following place
CMU10-714/homework/hw4/python/needle/ops.py
Line 613 in 99180c4
I have a test case with :
When I test the above case, there are some errors with
This function may not have considered the situation of a large convolution kernel during implementation, especially when$(H - 2p + k) \pmod s \ne 0$ .
May I ask if it‘s possible to check such case and carry out improvement/perfection? Thanks for your reply.
The text was updated successfully, but these errors were encountered: