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f6ce135608
* Various fixes to support torch script. * Add tests to ensure that the model is torch scriptable. * Update tests.
84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List
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import torch
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import torch.nn as nn
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class Joiner(nn.Module):
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def __init__(self, input_dim: int, output_dim: int):
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super().__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.output_linear = nn.Linear(input_dim, output_dim)
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def forward(
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self,
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encoder_out: torch.Tensor,
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decoder_out: torch.Tensor,
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encoder_out_len: torch.Tensor,
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decoder_out_len: torch.Tensor,
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) -> torch.Tensor:
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"""
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Args:
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encoder_out:
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Output from the encoder. Its shape is (N, T, self.input_dim).
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decoder_out:
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Output from the decoder. Its shape is (N, U, self.input_dim).
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encoder_out_len:
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A 1-D tensor of shape (N,) containing valid number of frames
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before padding in `encoder_out`.
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decoder_out_len:
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A 1-D tensor of shape (N,) containing valid number of frames
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before padding in `decoder_out`.
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Returns:
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Return a tensor of shape (sum_all_TU, self.output_dim).
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"""
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assert encoder_out.ndim == decoder_out.ndim == 3
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assert encoder_out.size(0) == decoder_out.size(0)
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assert encoder_out.size(2) == self.input_dim
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assert decoder_out.size(2) == self.input_dim
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N = encoder_out.size(0)
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encoder_out_len: List[int] = encoder_out_len.tolist()
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decoder_out_len: List[int] = decoder_out_len.tolist()
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encoder_out_list = [
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encoder_out[i, : encoder_out_len[i], :] for i in range(N)
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]
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decoder_out_list = [
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decoder_out[i, : decoder_out_len[i], :] for i in range(N)
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]
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x = [
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e.unsqueeze(1) + d.unsqueeze(0)
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for e, d in zip(encoder_out_list, decoder_out_list)
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]
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x = [p.reshape(-1, self.input_dim) for p in x]
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x = torch.cat(x)
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activations = torch.tanh(x)
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logits = self.output_linear(activations)
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return logits
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