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Please rebase commit to resolve conflicts. |
l-bat
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Aug 17, 2020
l-bat
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Aug 17, 2020
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| class MatmulWithTwoInputs(nn.Module): | ||
| def __init__(self, in_dim = 2, const_dim=2, interm_dim = 2): | ||
| super(MatmulWithTwoInputs, self).__init__() | ||
| self.in_dim = in_dim | ||
| self.const_dim = const_dim | ||
| self.interm_dim = interm_dim | ||
| self.linear_for_const = nn.Linear(const_dim, interm_dim) | ||
| self.first_linear = nn.Linear(in_dim, interm_dim) | ||
| self.second_linear = nn.Linear(interm_dim, 1) | ||
| def forward(self, x): | ||
| x = x.reshape(-1, self.in_dim) | ||
| x_projected = self.first_linear(x) | ||
| const = torch.zeros(1, self.interm_dim) | ||
| const_projected = self.linear_for_const(const) | ||
| const_projected = const_projected.expand(2, self.interm_dim) | ||
| sum_tanh = torch.tanh(const_projected + x_projected) | ||
| sum_tanh = sum_tanh.reshape(-1, self.interm_dim) | ||
| sum_tanh_projected = self.second_linear(sum_tanh) | ||
| sum_tanh_projected = sum_tanh_projected.reshape(1, 2) | ||
| after_softmax = F.softmax(sum_tanh_projected, dim=1) | ||
| return torch.matmul(after_softmax, x) |
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Do we really need such a large subgraph? Maybe you can cover your changes in the onnx importer using just one matrix multiplication?
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Unfortunately, it does not reproduce that way
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Please do not use merge commits in PRs to resolve conflicts. There are several artifacts during PR merge which requires extra changes: |
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Data for PR opencv/opencv#17978