Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Additional MXNet Convolution and Deconvolution tests #4026

Merged
merged 1 commit into from
Sep 28, 2019
Merged

Additional MXNet Convolution and Deconvolution tests #4026

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 14 additions & 2 deletions tests/python/frontend/mxnet/test_forward.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -833,7 +833,7 @@ def verify(data_shape, out_shape, begin, end):

def test_forward_convolution():
def verify(data_shape, kernel_size, stride, pad, num_filter):
weight_shape=(num_filter,1,) + kernel_size
weight_shape=(num_filter, data_shape[1],) + kernel_size
x = np.random.uniform(size=data_shape).astype("float32")
weight = np.random.uniform(size=weight_shape).astype("float32")
bias = np.random.uniform(size=num_filter).astype("float32")
Expand All @@ -852,11 +852,17 @@ def verify(data_shape, kernel_size, stride, pad, num_filter):
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-3)

verify(data_shape=(1,1,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
verify(data_shape=(20,1,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
verify(data_shape=(1,8,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
verify(data_shape=(20,8,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
verify(data_shape=(1, 1, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
verify(data_shape=(20, 1, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
verify(data_shape=(1, 8, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
verify(data_shape=(20, 8, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)

def test_forward_deconvolution():
def verify(data_shape, kernel_size, stride, pad, num_filter):
weight_shape=(1, num_filter) + kernel_size
weight_shape=(data_shape[1], num_filter) + kernel_size
x = np.random.uniform(size=data_shape).astype("float32")
weight = np.random.uniform(size=weight_shape).astype("float32")
bias = np.random.uniform(size=num_filter).astype("float32")
Expand All @@ -875,7 +881,13 @@ def verify(data_shape, kernel_size, stride, pad, num_filter):
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-3)

verify(data_shape=(1,1,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
verify(data_shape=(20,1,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
verify(data_shape=(1,8,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
verify(data_shape=(20,8,1024*16), kernel_size=(17,), stride=(2,), pad=(8,), num_filter=4)
verify(data_shape=(1, 1, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
verify(data_shape=(20, 1, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
verify(data_shape=(1, 8, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)
verify(data_shape=(20, 8, 32, 32), kernel_size=(3, 3), stride=(1, 1), pad=(1, 1), num_filter=2)


if __name__ == '__main__':
Expand Down