'block': PReLUNet(),
'desc_inputs': [Tensor(np.ones([1, 3, 4, 4], np.float32))],
}),
('PReLUGradNet', {
'block': PReLUGradNet(),
'desc_inputs': [Tensor(np.ones([1, 3, 4, 4], np.float32)),
Tensor(np.ones([1, 3, 4, 4], np.float32)),
Tensor(np.ones(3, np.float32))],
}),
('MatrixDiag', {
'block': nn.MatrixDiag(),
'desc_inputs': [Tensor(np.array([1, 2, 3]).astype(np.float32))],
'skip': ['backward']
}),
('MatrixDiagPart', {
'block': nn.MatrixDiagPart(),
'desc_inputs': [Tensor(np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32))],
'skip': ['backward']
}),
('MatrixSetDiag', {
'block': nn.MatrixSetDiag(),
'desc_inputs': [Tensor(np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)),
Tensor(np.array([1, 2]).astype(np.float32))],
'skip': ['backward']
}),
('LRNNet', {
'block': LRNNet(),
'desc_inputs': [Tensor(np.ones([1, 5, 4, 4], np.float32))],
}),
('LRNGradNet', {
'block': LRNGradNet(),
'block':
PReLUGradNet(),
'desc_inputs': [
Tensor(np.ones([1, 3, 4, 4], np.float32)),
Tensor(np.ones([1, 3, 4, 4], np.float32)),
Tensor(np.ones(3, np.float32))
],
}),
('MatrixDiag', {
'block': nn.MatrixDiag(),
'desc_inputs': [Tensor(np.array([1, 2, 3]).astype(np.float32))],
'skip': ['backward']
}),
('MatrixDiagPart', {
'block':
nn.MatrixDiagPart(),
'desc_inputs':
[Tensor(np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32))],
'skip': ['backward']
}),
('MatrixSetDiag', {
'block':
nn.MatrixSetDiag(),
'desc_inputs': [
Tensor(np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)),
Tensor(np.array([1, 2]).astype(np.float32))
],
'skip': ['backward']
}),
('LRNNet', {
'block': LRNNet(),