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def forward(self, x): |
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# do patching |
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n_vars = x.shape[1] |
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glb = self.glb_token.repeat((x.shape[0], 1, 1, 1)) |
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|
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x = x.unfold(dimension=-1, size=self.patch_len, step=self.patch_len) |
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x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) |
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# Input encoding |
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x = self.value_embedding(x) + self.position_embedding(x) |
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x = torch.reshape(x, (-1, n_vars, x.shape[-2], x.shape[-1])) |
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x = torch.cat([x, glb], dim=2) |
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x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) |
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return self.dropout(x), n_vars |
NaN occurs atx = self.value_embedding(x) + self.position_embedding(x) . The value_embedding and position_embedding layers are likely not robust to input values with larger magnitudes?