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81 lines
2.9 KiB
81 lines
2.9 KiB
3 years ago
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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# Modified from wekws(https://github.com/wenet-e2e/wekws)
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import paddle
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def fill_mask_elements(condition, value, x):
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assert condition.shape == x.shape
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values = paddle.ones_like(x, dtype=x.dtype) * value
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return paddle.where(condition, values, x)
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def max_pooling_loss(logits: paddle.Tensor,
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target: paddle.Tensor,
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lengths: paddle.Tensor,
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min_duration: int=0):
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mask = padding_mask(lengths)
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num_utts = logits.shape[0]
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num_keywords = logits.shape[2]
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loss = 0.0
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for i in range(num_utts):
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for j in range(num_keywords):
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# Add entropy loss CE = -(t * log(p) + (1 - t) * log(1 - p))
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if target[i] == j:
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# For the keyword, do max-polling
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prob = logits[i, :, j]
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m = mask[i]
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if min_duration > 0:
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m[:min_duration] = True
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prob = fill_mask_elements(m, 0.0, prob)
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prob = paddle.clip(prob, 1e-8, 1.0)
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max_prob = prob.max()
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loss += -paddle.log(max_prob)
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else:
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# For other keywords or filler, do min-polling
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prob = 1 - logits[i, :, j]
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prob = fill_mask_elements(mask[i], 1.0, prob)
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prob = paddle.clip(prob, 1e-8, 1.0)
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min_prob = prob.min()
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loss += -paddle.log(min_prob)
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loss = loss / num_utts
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# Compute accuracy of current batch
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mask = mask.unsqueeze(-1)
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logits = fill_mask_elements(mask, 0.0, logits)
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max_logits = logits.max(1)
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num_correct = 0
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for i in range(num_utts):
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max_p = max_logits[i].max(0).item()
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idx = max_logits[i].argmax(0).item()
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# Predict correct as the i'th keyword
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if max_p > 0.5 and idx == target[i].item():
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num_correct += 1
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# Predict correct as the filler, filler id < 0
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if max_p < 0.5 and target[i].item() < 0:
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num_correct += 1
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acc = num_correct / num_utts
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# acc = 0.0
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return loss, num_correct, acc
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def padding_mask(lengths: paddle.Tensor) -> paddle.Tensor:
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batch_size = lengths.shape[0]
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max_len = int(lengths.max().item())
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seq = paddle.arange(max_len, dtype=paddle.int64)
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seq = seq.expand((batch_size, max_len))
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return seq >= lengths.unsqueeze(1)
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