93 lines
3.5 KiB
Python
93 lines
3.5 KiB
Python
# Copyright (c) 2021 Binbin Zhang
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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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import logging
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import torch
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from torch.nn.utils import clip_grad_norm_
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from kws.model.loss import criterion
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class Executor:
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def __init__(self):
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self.step = 0
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def train(self, model, optimizer, data_loader, device, writer, args):
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''' Train one epoch
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'''
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model.train()
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clip = args.get('grad_clip', 50.0)
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log_interval = args.get('log_interval', 10)
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epoch = args.get('epoch', 0)
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min_duration = args.get('min_duration', 0)
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num_total_batch = 0
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total_loss = 0.0
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for batch_idx, batch in enumerate(data_loader):
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key, feats, target, feats_lengths = batch
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feats = feats.to(device)
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target = target.to(device)
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feats_lengths = feats_lengths.to(device)
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num_utts = feats_lengths.size(0)
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if num_utts == 0:
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continue
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logits = model(feats)
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loss_type = args.get('criterion', 'max_pooling')
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loss, acc = criterion(loss_type, logits, target, feats_lengths)
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loss.backward()
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grad_norm = clip_grad_norm_(model.parameters(), clip)
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if torch.isfinite(grad_norm):
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optimizer.step()
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if batch_idx % log_interval == 0:
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logging.debug(
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'TRAIN Batch {}/{} loss {:.8f} acc {:.8f}'.format(
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epoch, batch_idx, loss.item(), acc))
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def cv(self, model, data_loader, device, args):
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''' Cross validation on
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'''
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model.eval()
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log_interval = args.get('log_interval', 10)
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epoch = args.get('epoch', 0)
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# in order to avoid division by 0
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num_seen_utts = 1
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total_loss = 0.0
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total_acc = 0.0
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with torch.no_grad():
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for batch_idx, batch in enumerate(data_loader):
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key, feats, target, feats_lengths = batch
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feats = feats.to(device)
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target = target.to(device)
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feats_lengths = feats_lengths.to(device)
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num_utts = feats_lengths.size(0)
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if num_utts == 0:
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continue
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logits = model(feats)
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loss, acc = criterion(args.get('criterion', 'max_pooling'),
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logits, target, feats_lengths)
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if torch.isfinite(loss):
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num_seen_utts += num_utts
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total_loss += loss.item() * num_utts
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total_acc += acc * num_utts
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if batch_idx % log_interval == 0:
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logging.debug(
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'CV Batch {}/{} loss {:.8f} acc {:.8f} history loss {:.8f}'
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.format(epoch, batch_idx, loss.item(), acc,
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total_loss / num_seen_utts))
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return total_loss / num_seen_utts, total_acc / num_seen_utts
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def test(self, model, data_loader, device, args):
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return self.cv(model, data_loader, device, args)
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