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218
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/encoders/rnn_encoder.py
python
RNNEncoder._bridge
(self, hidden)
return outs
Forward hidden state through bridge
Forward hidden state through bridge
[ "Forward", "hidden", "state", "through", "bridge" ]
def _bridge(self, hidden): """ Forward hidden state through bridge """ def bottle_hidden(linear, states): """ Transform from 3D to 2D, apply linear and return initial size """ size = states.size() result = linear(states.view(-1, self.total_hidden_dim)) return F.relu(result).view(size) if isinstance(hidden, tuple): # LSTM outs = tuple([bottle_hidden(layer, hidden[ix]) for ix, layer in enumerate(self.bridge)]) else: outs = bottle_hidden(self.bridge[0], hidden) return outs
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/encoders/rnn_encoder.py#L90-L107
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/encoders/image_encoder.py
python
ImageEncoder.load_pretrained_vectors
(self, opt)
Pass in needed options only when modify function definition.
Pass in needed options only when modify function definition.
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def load_pretrained_vectors(self, opt): """ Pass in needed options only when modify function definition.""" pass
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/encoders/image_encoder.py#L50-L52
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/encoders/image_encoder.py
python
ImageEncoder.forward
(self, src, lengths=None)
return hidden_t, out
See :obj:`onmt.encoders.encoder.EncoderBase.forward()`
See :obj:`onmt.encoders.encoder.EncoderBase.forward()`
[ "See", ":", "obj", ":", "onmt", ".", "encoders", ".", "encoder", ".", "EncoderBase", ".", "forward", "()" ]
def forward(self, src, lengths=None): "See :obj:`onmt.encoders.encoder.EncoderBase.forward()`" batch_size = src.size(0) # (batch_size, 64, imgH, imgW) # layer 1 src = F.relu(self.layer1(src[:, :, :, :] - 0.5), True) # (batch_size, 64, imgH/2, imgW/2) src = F.max_pool2d(src, kernel_size=(2, 2), stride=(2, 2)) # (batch_size, 128, imgH/2, imgW/2) # layer 2 src = F.relu(self.layer2(src), True) # (batch_size, 128, imgH/2/2, imgW/2/2) src = F.max_pool2d(src, kernel_size=(2, 2), stride=(2, 2)) # (batch_size, 256, imgH/2/2, imgW/2/2) # layer 3 # batch norm 1 src = F.relu(self.batch_norm1(self.layer3(src)), True) # (batch_size, 256, imgH/2/2, imgW/2/2) # layer4 src = F.relu(self.layer4(src), True) # (batch_size, 256, imgH/2/2/2, imgW/2/2) src = F.max_pool2d(src, kernel_size=(1, 2), stride=(1, 2)) # (batch_size, 512, imgH/2/2/2, imgW/2/2) # layer 5 # batch norm 2 src = F.relu(self.batch_norm2(self.layer5(src)), True) # (batch_size, 512, imgH/2/2/2, imgW/2/2/2) src = F.max_pool2d(src, kernel_size=(2, 1), stride=(2, 1)) # (batch_size, 512, imgH/2/2/2, imgW/2/2/2) src = F.relu(self.batch_norm3(self.layer6(src)), True) # # (batch_size, 512, H, W) all_outputs = [] for row in range(src.size(2)): inp = src[:, :, row, :].transpose(0, 2) \ .transpose(1, 2) row_vec = torch.Tensor(batch_size).type_as(inp.data) \ .long().fill_(row) pos_emb = self.pos_lut(row_vec) with_pos = torch.cat( (pos_emb.view(1, pos_emb.size(0), pos_emb.size(1)), inp), 0) outputs, hidden_t = self.rnn(with_pos) all_outputs.append(outputs) out = torch.cat(all_outputs, 0) return hidden_t, out
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/encoders/image_encoder.py#L54-L109
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/encoders/cnn_encoder.py
python
CNNEncoder.forward
(self, input, lengths=None, hidden=None)
return emb_remap.squeeze(3).transpose(0, 1).contiguous(), \ out.squeeze(3).transpose(0, 1).contiguous()
See :obj:`onmt.modules.EncoderBase.forward()`
See :obj:`onmt.modules.EncoderBase.forward()`
[ "See", ":", "obj", ":", "onmt", ".", "modules", ".", "EncoderBase", ".", "forward", "()" ]
def forward(self, input, lengths=None, hidden=None): """ See :obj:`onmt.modules.EncoderBase.forward()`""" self._check_args(input, lengths, hidden) emb = self.embeddings(input) # s_len, batch, emb_dim = emb.size() emb = emb.transpose(0, 1).contiguous() emb_reshape = emb.view(emb.size(0) * emb.size(1), -1) emb_remap = self.linear(emb_reshape) emb_remap = emb_remap.view(emb.size(0), emb.size(1), -1) emb_remap = shape_transform(emb_remap) out = self.cnn(emb_remap) return emb_remap.squeeze(3).transpose(0, 1).contiguous(), \ out.squeeze(3).transpose(0, 1).contiguous()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/encoders/cnn_encoder.py#L28-L43
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/models/sru.py
python
check_sru_requirement
(abort=False)
return True
Return True if check pass; if check fails and abort is True, raise an Exception, othereise return False.
Return True if check pass; if check fails and abort is True, raise an Exception, othereise return False.
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def check_sru_requirement(abort=False): """ Return True if check pass; if check fails and abort is True, raise an Exception, othereise return False. """ # Check 1. try: if platform.system() == 'Windows': subprocess.check_output('pip freeze | findstr cupy', shell=True) subprocess.check_output('pip freeze | findstr pynvrtc', shell=True) else: # Unix-like systems subprocess.check_output('pip freeze | grep -w cupy', shell=True) subprocess.check_output('pip freeze | grep -w pynvrtc', shell=True) except subprocess.CalledProcessError: if not abort: return False raise AssertionError("Using SRU requires 'cupy' and 'pynvrtc' " "python packages installed.") # Check 2. if torch.cuda.is_available() is False: if not abort: return False raise AssertionError("Using SRU requires pytorch built with cuda.") # Check 3. pattern = re.compile(".*cuda/lib.*") ld_path = os.getenv('LD_LIBRARY_PATH', "") if re.match(pattern, ld_path) is None: if not abort: return False raise AssertionError("Using SRU requires setting cuda lib path, e.g. " "export LD_LIBRARY_PATH=/usr/local/cuda/lib64.") return True
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/models/sru.py#L32-L69
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/models/model.py
python
NMTModel.forward
(self, src, tgt, lengths, dec_state=None)
return decoder_outputs, attns, dec_state
Forward propagate a `src` and `tgt` pair for training. Possible initialized with a beginning decoder state. Args: src (:obj:`Tensor`): a source sequence passed to encoder. typically for inputs this will be a padded :obj:`LongTensor` of size `[len x batch x features]`. however, may be an image or other generic input depending on encoder. tgt (:obj:`LongTensor`): a target sequence of size `[tgt_len x batch]`. lengths(:obj:`LongTensor`): the src lengths, pre-padding `[batch]`. dec_state (:obj:`DecoderState`, optional): initial decoder state Returns: (:obj:`FloatTensor`, `dict`, :obj:`onmt.Models.DecoderState`): * decoder output `[tgt_len x batch x hidden]` * dictionary attention dists of `[tgt_len x batch x src_len]` * final decoder state
Forward propagate a `src` and `tgt` pair for training. Possible initialized with a beginning decoder state.
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def forward(self, src, tgt, lengths, dec_state=None): """Forward propagate a `src` and `tgt` pair for training. Possible initialized with a beginning decoder state. Args: src (:obj:`Tensor`): a source sequence passed to encoder. typically for inputs this will be a padded :obj:`LongTensor` of size `[len x batch x features]`. however, may be an image or other generic input depending on encoder. tgt (:obj:`LongTensor`): a target sequence of size `[tgt_len x batch]`. lengths(:obj:`LongTensor`): the src lengths, pre-padding `[batch]`. dec_state (:obj:`DecoderState`, optional): initial decoder state Returns: (:obj:`FloatTensor`, `dict`, :obj:`onmt.Models.DecoderState`): * decoder output `[tgt_len x batch x hidden]` * dictionary attention dists of `[tgt_len x batch x src_len]` * final decoder state """ tgt = tgt[:-1] # exclude last target from inputs enc_final, memory_bank = self.encoder(src, lengths) enc_state = \ self.decoder.init_decoder_state(src, memory_bank, enc_final) decoder_outputs, dec_state, attns = \ self.decoder(tgt, memory_bank, enc_state if dec_state is None else dec_state, memory_lengths=lengths) if self.multigpu: # Not yet supported on multi-gpu dec_state = None attns = None return decoder_outputs, attns, dec_state
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/models/model.py#L22-L57
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/models/model_saver.py
python
ModelSaverBase.maybe_save
(self, step)
Main entry point for model saver It wraps the `_save` method with checks and apply `keep_checkpoint` related logic
Main entry point for model saver It wraps the `_save` method with checks and apply `keep_checkpoint` related logic
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def maybe_save(self, step): """ Main entry point for model saver It wraps the `_save` method with checks and apply `keep_checkpoint` related logic """ if self.keep_checkpoint == 0: return if step % self.save_checkpoint_steps != 0: return chkpt, chkpt_name = self._save(step) if self.keep_checkpoint > 0: if len(self.checkpoint_queue) == self.checkpoint_queue.maxlen: todel = self.checkpoint_queue.popleft() self._rm_checkpoint(todel) self.checkpoint_queue.append(chkpt_name)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/models/model_saver.py#L43-L61
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/models/model_saver.py
python
ModelSaverBase._save
(self, step)
Save a resumable checkpoint. Args: step (int): step number Returns: checkpoint: the saved object checkpoint_name: name (or path) of the saved checkpoint
Save a resumable checkpoint.
[ "Save", "a", "resumable", "checkpoint", "." ]
def _save(self, step): """ Save a resumable checkpoint. Args: step (int): step number Returns: checkpoint: the saved object checkpoint_name: name (or path) of the saved checkpoint """ raise NotImplementedError()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/models/model_saver.py#L63-L73
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/models/model_saver.py
python
ModelSaverBase._rm_checkpoint
(self, name)
Remove a checkpoint Args: name(str): name that indentifies the checkpoint (it may be a filepath)
Remove a checkpoint
[ "Remove", "a", "checkpoint" ]
def _rm_checkpoint(self, name): """ Remove a checkpoint Args: name(str): name that indentifies the checkpoint (it may be a filepath) """ raise NotImplementedError()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/models/model_saver.py#L75-L83
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/cnn_factory.py
python
shape_transform
(x)
return torch.unsqueeze(torch.transpose(x, 1, 2), 3)
Tranform the size of the tensors to fit for conv input.
Tranform the size of the tensors to fit for conv input.
[ "Tranform", "the", "size", "of", "the", "tensors", "to", "fit", "for", "conv", "input", "." ]
def shape_transform(x): """ Tranform the size of the tensors to fit for conv input. """ return torch.unsqueeze(torch.transpose(x, 1, 2), 3)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/cnn_factory.py#L14-L16
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/optimizers.py
python
build_optim
(model, opt, checkpoint)
return optim
Build optimizer
Build optimizer
[ "Build", "optimizer" ]
def build_optim(model, opt, checkpoint): """ Build optimizer """ saved_optimizer_state_dict = None if opt.train_from: optim = checkpoint['optim'] # We need to save a copy of optim.optimizer.state_dict() for setting # the, optimizer state later on in Stage 2 in this method, since # the method optim.set_parameters(model.parameters()) will overwrite # optim.optimizer, and with ith the values stored in # optim.optimizer.state_dict() saved_optimizer_state_dict = optim.optimizer.state_dict() else: optim = Optimizer( opt.optim, opt.learning_rate, opt.max_grad_norm, lr_decay=opt.learning_rate_decay, start_decay_steps=opt.start_decay_steps, decay_steps=opt.decay_steps, beta1=opt.adam_beta1, beta2=opt.adam_beta2, adagrad_accum=opt.adagrad_accumulator_init, decay_method=opt.decay_method, warmup_steps=opt.warmup_steps, model_size=opt.rnn_size) # Stage 1: # Essentially optim.set_parameters (re-)creates and optimizer using # model.paramters() as parameters that will be stored in the # optim.optimizer.param_groups field of the torch optimizer class. # Importantly, this method does not yet load the optimizer state, as # essentially it builds a new optimizer with empty optimizer state and # parameters from the model. optim.set_parameters(model.named_parameters()) if opt.train_from: # Stage 2: In this stage, which is only performed when loading an # optimizer from a checkpoint, we load the saved_optimizer_state_dict # into the re-created optimizer, to set the optim.optimizer.state # field, which was previously empty. For this, we use the optimizer # state saved in the "saved_optimizer_state_dict" variable for # this purpose. # See also: https://github.com/pytorch/pytorch/issues/2830 optim.optimizer.load_state_dict(saved_optimizer_state_dict) # Convert back the state values to cuda type if applicable if use_gpu(opt): for state in optim.optimizer.state.values(): for k, v in state.items(): if torch.is_tensor(v): state[k] = v.cuda() # We want to make sure that indeed we have a non-empty optimizer state # when we loaded an existing model. This should be at least the case # for Adam, which saves "exp_avg" and "exp_avg_sq" state # (Exponential moving average of gradient and squared gradient values) if (optim.method == 'adam') and (len(optim.optimizer.state) < 1): raise RuntimeError( "Error: loaded Adam optimizer from existing model" + " but optimizer state is empty") return optim
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/optimizers.py#L9-L68
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/optimizers.py
python
MultipleOptimizer.__init__
(self, op)
?
?
[ "?" ]
def __init__(self, op): """ ? """ self.optimizers = op
[ "def", "__init__", "(", "self", ",", "op", ")", ":", "self", ".", "optimizers", "=", "op" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/optimizers.py#L74-L76
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/optimizers.py
python
MultipleOptimizer.zero_grad
(self)
?
?
[ "?" ]
def zero_grad(self): """ ? """ for op in self.optimizers: op.zero_grad()
[ "def", "zero_grad", "(", "self", ")", ":", "for", "op", "in", "self", ".", "optimizers", ":", "op", ".", "zero_grad", "(", ")" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/optimizers.py#L78-L81
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/optimizers.py
python
MultipleOptimizer.step
(self)
?
?
[ "?" ]
def step(self): """ ? """ for op in self.optimizers: op.step()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/optimizers.py#L83-L86
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/optimizers.py
python
MultipleOptimizer.state
(self)
return {k: v for op in self.optimizers for k, v in op.state.items()}
?
?
[ "?" ]
def state(self): """ ? """ return {k: v for op in self.optimizers for k, v in op.state.items()}
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/optimizers.py#L89-L91
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/optimizers.py
python
MultipleOptimizer.state_dict
(self)
return [op.state_dict() for op in self.optimizers]
?
?
[ "?" ]
def state_dict(self): """ ? """ return [op.state_dict() for op in self.optimizers]
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/optimizers.py#L93-L95
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/optimizers.py
python
MultipleOptimizer.load_state_dict
(self, state_dicts)
?
?
[ "?" ]
def load_state_dict(self, state_dicts): """ ? """ assert len(state_dicts) == len(self.optimizers) for i in range(len(state_dicts)): self.optimizers[i].load_state_dict(state_dicts[i])
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/optimizers.py#L97-L101
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/optimizers.py
python
Optimizer.set_parameters
(self, params)
?
?
[ "?" ]
def set_parameters(self, params): """ ? """ self.params = [] self.sparse_params = [] for k, p in params: if p.requires_grad: if self.method != 'sparseadam' or "embed" not in k: self.params.append(p) else: self.sparse_params.append(p) if self.method == 'sgd': self.optimizer = optim.SGD(self.params, lr=self.learning_rate) elif self.method == 'adagrad': self.optimizer = optim.Adagrad(self.params, lr=self.learning_rate) for group in self.optimizer.param_groups: for p in group['params']: self.optimizer.state[p]['sum'] = self.optimizer\ .state[p]['sum'].fill_(self.adagrad_accum) elif self.method == 'adadelta': self.optimizer = optim.Adadelta(self.params, lr=self.learning_rate) elif self.method == 'adam': self.optimizer = optim.Adam(self.params, lr=self.learning_rate, betas=self.betas, eps=1e-9) elif self.method == 'sparseadam': self.optimizer = MultipleOptimizer( [optim.Adam(self.params, lr=self.learning_rate, betas=self.betas, eps=1e-8), optim.SparseAdam(self.sparse_params, lr=self.learning_rate, betas=self.betas, eps=1e-8)]) else: raise RuntimeError("Invalid optim method: " + self.method)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/optimizers.py#L158-L188
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/optimizers.py
python
Optimizer.step
(self)
Update the model parameters based on current gradients. Optionally, will employ gradient modification or update learning rate.
Update the model parameters based on current gradients.
[ "Update", "the", "model", "parameters", "based", "on", "current", "gradients", "." ]
def step(self): """Update the model parameters based on current gradients. Optionally, will employ gradient modification or update learning rate. """ self._step += 1 # Decay method used in tensor2tensor. if self.decay_method == "noam": self._set_rate( self.original_lr * (self.model_size ** (-0.5) * min(self._step ** (-0.5), self._step * self.warmup_steps**(-1.5)))) # Decay based on start_decay_steps every decay_steps else: if ((self.start_decay_steps is not None) and ( self._step >= self.start_decay_steps)): self.start_decay = True if self.start_decay: if ((self._step - self.start_decay_steps) % self.decay_steps == 0): self.learning_rate = self.learning_rate * self.lr_decay if self.method != 'sparseadam': self.optimizer.param_groups[0]['lr'] = self.learning_rate if self.max_grad_norm: clip_grad_norm_(self.params, self.max_grad_norm) self.optimizer.step()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/optimizers.py#L198-L228
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/statistics.py
python
Statistics.all_gather_stats
(stat, max_size=4096)
return stats[0]
Gather a `Statistics` object accross multiple process/nodes Args: stat(:obj:Statistics): the statistics object to gather accross all processes/nodes max_size(int): max buffer size to use Returns: `Statistics`, the update stats object
Gather a `Statistics` object accross multiple process/nodes
[ "Gather", "a", "Statistics", "object", "accross", "multiple", "process", "/", "nodes" ]
def all_gather_stats(stat, max_size=4096): """ Gather a `Statistics` object accross multiple process/nodes Args: stat(:obj:Statistics): the statistics object to gather accross all processes/nodes max_size(int): max buffer size to use Returns: `Statistics`, the update stats object """ stats = Statistics.all_gather_stats_list([stat], max_size=max_size) return stats[0]
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/statistics.py#L30-L43
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/statistics.py
python
Statistics.all_gather_stats_list
(stat_list, max_size=4096)
return our_stats
Gather a `Statistics` list accross all processes/nodes Args: stat_list(list([`Statistics`])): list of statistics objects to gather accross all processes/nodes max_size(int): max buffer size to use Returns: our_stats(list([`Statistics`])): list of updated stats
Gather a `Statistics` list accross all processes/nodes
[ "Gather", "a", "Statistics", "list", "accross", "all", "processes", "/", "nodes" ]
def all_gather_stats_list(stat_list, max_size=4096): """ Gather a `Statistics` list accross all processes/nodes Args: stat_list(list([`Statistics`])): list of statistics objects to gather accross all processes/nodes max_size(int): max buffer size to use Returns: our_stats(list([`Statistics`])): list of updated stats """ # Get a list of world_size lists with len(stat_list) Statistics objects all_stats = all_gather_list(stat_list, max_size=max_size) our_rank = get_rank() our_stats = all_stats[our_rank] for other_rank, stats in enumerate(all_stats): if other_rank == our_rank: continue for i, stat in enumerate(stats): our_stats[i].update(stat, update_n_src_words=True) return our_stats
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/statistics.py#L46-L68
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/statistics.py
python
Statistics.update
(self, stat, update_n_src_words=False)
Update statistics by suming values with another `Statistics` object Args: stat: another statistic object update_n_src_words(bool): whether to update (sum) `n_src_words` or not
Update statistics by suming values with another `Statistics` object
[ "Update", "statistics", "by", "suming", "values", "with", "another", "Statistics", "object" ]
def update(self, stat, update_n_src_words=False): """ Update statistics by suming values with another `Statistics` object Args: stat: another statistic object update_n_src_words(bool): whether to update (sum) `n_src_words` or not """ self.loss += stat.loss self.n_words += stat.n_words self.n_correct += stat.n_correct if update_n_src_words: self.n_src_words += stat.n_src_words
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/statistics.py#L70-L85
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/statistics.py
python
Statistics.accuracy
(self)
return 100 * (self.n_correct / self.n_words)
compute accuracy
compute accuracy
[ "compute", "accuracy" ]
def accuracy(self): """ compute accuracy """ return 100 * (self.n_correct / self.n_words)
[ "def", "accuracy", "(", "self", ")", ":", "return", "100", "*", "(", "self", ".", "n_correct", "/", "self", ".", "n_words", ")" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/statistics.py#L87-L89
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/statistics.py
python
Statistics.xent
(self)
return self.loss / self.n_words
compute cross entropy
compute cross entropy
[ "compute", "cross", "entropy" ]
def xent(self): """ compute cross entropy """ return self.loss / self.n_words
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/statistics.py#L91-L93
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/statistics.py
python
Statistics.ppl
(self)
return math.exp(min(self.loss / self.n_words, 100))
compute perplexity
compute perplexity
[ "compute", "perplexity" ]
def ppl(self): """ compute perplexity """ return math.exp(min(self.loss / self.n_words, 100))
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/statistics.py#L95-L97
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/statistics.py
python
Statistics.elapsed_time
(self)
return time.time() - self.start_time
compute elapsed time
compute elapsed time
[ "compute", "elapsed", "time" ]
def elapsed_time(self): """ compute elapsed time """ return time.time() - self.start_time
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/statistics.py#L99-L101
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/statistics.py
python
Statistics.output
(self, step, num_steps, learning_rate, start)
Write out statistics to stdout. Args: step (int): current step n_batch (int): total batches start (int): start time of step.
Write out statistics to stdout.
[ "Write", "out", "statistics", "to", "stdout", "." ]
def output(self, step, num_steps, learning_rate, start): """Write out statistics to stdout. Args: step (int): current step n_batch (int): total batches start (int): start time of step. """ t = self.elapsed_time() logger.info( ("Step %2d/%5d; acc: %6.2f; ppl: %5.2f; xent: %4.2f; " + "lr: %7.5f; %3.0f/%3.0f tok/s; %6.0f sec") % (step, num_steps, self.accuracy(), self.ppl(), self.xent(), learning_rate, self.n_src_words / (t + 1e-5), self.n_words / (t + 1e-5), time.time() - start)) sys.stdout.flush()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/statistics.py#L103-L123
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/statistics.py
python
Statistics.log_tensorboard
(self, prefix, writer, learning_rate, step)
display statistics to tensorboard
display statistics to tensorboard
[ "display", "statistics", "to", "tensorboard" ]
def log_tensorboard(self, prefix, writer, learning_rate, step): """ display statistics to tensorboard """ t = self.elapsed_time() writer.add_scalar(prefix + "/xent", self.xent(), step) writer.add_scalar(prefix + "/ppl", self.ppl(), step) writer.add_scalar(prefix + "/accuracy", self.accuracy(), step) writer.add_scalar(prefix + "/tgtper", self.n_words / t, step) writer.add_scalar(prefix + "/lr", learning_rate, step)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/statistics.py#L125-L132
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/rnn_factory.py
python
rnn_factory
(rnn_type, **kwargs)
return rnn, no_pack_padded_seq
rnn factory, Use pytorch version when available.
rnn factory, Use pytorch version when available.
[ "rnn", "factory", "Use", "pytorch", "version", "when", "available", "." ]
def rnn_factory(rnn_type, **kwargs): """ rnn factory, Use pytorch version when available. """ no_pack_padded_seq = False if rnn_type == "SRU": # SRU doesn't support PackedSequence. no_pack_padded_seq = True rnn = onmt.models.sru.SRU(**kwargs) else: rnn = getattr(nn, rnn_type)(**kwargs) return rnn, no_pack_padded_seq
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/rnn_factory.py#L10-L19
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/distributed.py
python
all_reduce_and_rescale_tensors
(tensors, rescale_denom, buffer_size=10485760)
All-reduce and rescale tensors in chunks of the specified size. Args: tensors: list of Tensors to all-reduce rescale_denom: denominator for rescaling summed Tensors buffer_size: all-reduce chunk size in bytes
All-reduce and rescale tensors in chunks of the specified size.
[ "All", "-", "reduce", "and", "rescale", "tensors", "in", "chunks", "of", "the", "specified", "size", "." ]
def all_reduce_and_rescale_tensors(tensors, rescale_denom, buffer_size=10485760): """All-reduce and rescale tensors in chunks of the specified size. Args: tensors: list of Tensors to all-reduce rescale_denom: denominator for rescaling summed Tensors buffer_size: all-reduce chunk size in bytes """ # buffer size in bytes, determine equiv. # of elements based on data type buffer_t = tensors[0].new( math.ceil(buffer_size / tensors[0].element_size())).zero_() buffer = [] def all_reduce_buffer(): # copy tensors into buffer_t offset = 0 for t in buffer: numel = t.numel() buffer_t[offset:offset+numel].copy_(t.view(-1)) offset += numel # all-reduce and rescale torch.distributed.all_reduce(buffer_t[:offset]) buffer_t.div_(rescale_denom) # copy all-reduced buffer back into tensors offset = 0 for t in buffer: numel = t.numel() t.view(-1).copy_(buffer_t[offset:offset+numel]) offset += numel filled = 0 for t in tensors: sz = t.numel() * t.element_size() if sz > buffer_size: # tensor is bigger than buffer, all-reduce and rescale directly torch.distributed.all_reduce(t) t.div_(rescale_denom) elif filled + sz > buffer_size: # buffer is full, all-reduce and replace buffer with grad all_reduce_buffer() buffer = [t] filled = sz else: # add tensor to buffer buffer.append(t) filled += sz if len(buffer) > 0: all_reduce_buffer()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/distributed.py#L35-L86
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/distributed.py
python
all_gather_list
(data, max_size=4096)
return results
Gathers arbitrary data from all nodes into a list.
Gathers arbitrary data from all nodes into a list.
[ "Gathers", "arbitrary", "data", "from", "all", "nodes", "into", "a", "list", "." ]
def all_gather_list(data, max_size=4096): """Gathers arbitrary data from all nodes into a list.""" world_size = torch.distributed.get_world_size() if not hasattr(all_gather_list, '_in_buffer') or \ max_size != all_gather_list._in_buffer.size(): all_gather_list._in_buffer = torch.cuda.ByteTensor(max_size) all_gather_list._out_buffers = [ torch.cuda.ByteTensor(max_size) for i in range(world_size) ] in_buffer = all_gather_list._in_buffer out_buffers = all_gather_list._out_buffers enc = pickle.dumps(data) enc_size = len(enc) if enc_size + 2 > max_size: raise ValueError( 'encoded data exceeds max_size: {}'.format(enc_size + 2)) assert max_size < 255*256 in_buffer[0] = enc_size // 255 # this encoding works for max_size < 65k in_buffer[1] = enc_size % 255 in_buffer[2:enc_size+2] = torch.ByteTensor(list(enc)) torch.distributed.all_gather(out_buffers, in_buffer.cuda()) results = [] for i in range(world_size): out_buffer = out_buffers[i] size = (255 * out_buffer[0].item()) + out_buffer[1].item() bytes_list = bytes(out_buffer[2:size+2].tolist()) result = pickle.loads(bytes_list) results.append(result) return results
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/distributed.py#L89-L122
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/loss.py
python
build_loss_compute
(model, tgt_vocab, opt, train=True)
return compute
This returns user-defined LossCompute object, which is used to compute loss in train/validate process. You can implement your own *LossCompute class, by subclassing LossComputeBase.
This returns user-defined LossCompute object, which is used to compute loss in train/validate process. You can implement your own *LossCompute class, by subclassing LossComputeBase.
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def build_loss_compute(model, tgt_vocab, opt, train=True): """ This returns user-defined LossCompute object, which is used to compute loss in train/validate process. You can implement your own *LossCompute class, by subclassing LossComputeBase. """ device = torch.device("cuda" if onmt.utils.misc.use_gpu(opt) else "cpu") if opt.copy_attn: compute = onmt.modules.CopyGeneratorLossCompute( model.generator, tgt_vocab, opt.copy_attn_force, opt.copy_loss_by_seqlength) else: compute = NMTLossCompute( model.generator, tgt_vocab, label_smoothing=opt.label_smoothing if train else 0.0) compute.to(device) return compute
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/loss.py#L17-L35
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/loss.py
python
filter_shard_state
(state, shard_size=None)
?
?
[ "?" ]
def filter_shard_state(state, shard_size=None): """ ? """ for k, v in state.items(): if shard_size is None: yield k, v if v is not None: v_split = [] if isinstance(v, torch.Tensor): for v_chunk in torch.split(v, shard_size): v_chunk = v_chunk.data.clone() v_chunk.requires_grad = v.requires_grad v_split.append(v_chunk) yield k, (v, v_split)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/loss.py#L252-L265
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/loss.py
python
shards
(state, shard_size, eval_only=False)
Args: state: A dictionary which corresponds to the output of *LossCompute._make_shard_state(). The values for those keys are Tensor-like or None. shard_size: The maximum size of the shards yielded by the model. eval_only: If True, only yield the state, nothing else. Otherwise, yield shards. Yields: Each yielded shard is a dict. Side effect: After the last shard, this function does back-propagation.
Args: state: A dictionary which corresponds to the output of *LossCompute._make_shard_state(). The values for those keys are Tensor-like or None. shard_size: The maximum size of the shards yielded by the model. eval_only: If True, only yield the state, nothing else. Otherwise, yield shards.
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def shards(state, shard_size, eval_only=False): """ Args: state: A dictionary which corresponds to the output of *LossCompute._make_shard_state(). The values for those keys are Tensor-like or None. shard_size: The maximum size of the shards yielded by the model. eval_only: If True, only yield the state, nothing else. Otherwise, yield shards. Yields: Each yielded shard is a dict. Side effect: After the last shard, this function does back-propagation. """ if eval_only: yield filter_shard_state(state) else: # non_none: the subdict of the state dictionary where the values # are not None. non_none = dict(filter_shard_state(state, shard_size)) # Now, the iteration: # state is a dictionary of sequences of tensor-like but we # want a sequence of dictionaries of tensors. # First, unzip the dictionary into a sequence of keys and a # sequence of tensor-like sequences. keys, values = zip(*((k, [v_chunk for v_chunk in v_split]) for k, (_, v_split) in non_none.items())) # Now, yield a dictionary for each shard. The keys are always # the same. values is a sequence of length #keys where each # element is a sequence of length #shards. We want to iterate # over the shards, not over the keys: therefore, the values need # to be re-zipped by shard and then each shard can be paired # with the keys. for shard_tensors in zip(*values): yield dict(zip(keys, shard_tensors)) # Assumed backprop'd variables = [] for k, (v, v_split) in non_none.items(): if isinstance(v, torch.Tensor) and state[k].requires_grad: variables.extend(zip(torch.split(state[k], shard_size), [v_chunk.grad for v_chunk in v_split])) inputs, grads = zip(*variables) torch.autograd.backward(inputs, grads)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/loss.py#L268-L315
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/loss.py
python
LossComputeBase._make_shard_state
(self, batch, output, range_, attns=None)
return NotImplementedError
Make shard state dictionary for shards() to return iterable shards for efficient loss computation. Subclass must define this method to match its own _compute_loss() interface. Args: batch: the current batch. output: the predict output from the model. range_: the range of examples for computing, the whole batch or a trunc of it? attns: the attns dictionary returned from the model.
Make shard state dictionary for shards() to return iterable shards for efficient loss computation. Subclass must define this method to match its own _compute_loss() interface. Args: batch: the current batch. output: the predict output from the model. range_: the range of examples for computing, the whole batch or a trunc of it? attns: the attns dictionary returned from the model.
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def _make_shard_state(self, batch, output, range_, attns=None): """ Make shard state dictionary for shards() to return iterable shards for efficient loss computation. Subclass must define this method to match its own _compute_loss() interface. Args: batch: the current batch. output: the predict output from the model. range_: the range of examples for computing, the whole batch or a trunc of it? attns: the attns dictionary returned from the model. """ return NotImplementedError
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/loss.py#L64-L76
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/loss.py
python
LossComputeBase._compute_loss
(self, batch, output, target, **kwargs)
return NotImplementedError
Compute the loss. Subclass must define this method. Args: batch: the current batch. output: the predict output from the model. target: the validate target to compare output with. **kwargs(optional): additional info for computing loss.
Compute the loss. Subclass must define this method.
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def _compute_loss(self, batch, output, target, **kwargs): """ Compute the loss. Subclass must define this method. Args: batch: the current batch. output: the predict output from the model. target: the validate target to compare output with. **kwargs(optional): additional info for computing loss. """ return NotImplementedError
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/loss.py#L78-L89
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/loss.py
python
LossComputeBase.monolithic_compute_loss
(self, batch, output, attns)
return batch_stats
Compute the forward loss for the batch. Args: batch (batch): batch of labeled examples output (:obj:`FloatTensor`): output of decoder model `[tgt_len x batch x hidden]` attns (dict of :obj:`FloatTensor`) : dictionary of attention distributions `[tgt_len x batch x src_len]` Returns: :obj:`onmt.utils.Statistics`: loss statistics
Compute the forward loss for the batch.
[ "Compute", "the", "forward", "loss", "for", "the", "batch", "." ]
def monolithic_compute_loss(self, batch, output, attns): """ Compute the forward loss for the batch. Args: batch (batch): batch of labeled examples output (:obj:`FloatTensor`): output of decoder model `[tgt_len x batch x hidden]` attns (dict of :obj:`FloatTensor`) : dictionary of attention distributions `[tgt_len x batch x src_len]` Returns: :obj:`onmt.utils.Statistics`: loss statistics """ range_ = (0, batch.tgt.size(0)) shard_state = self._make_shard_state(batch, output, range_, attns) _, batch_stats = self._compute_loss(batch, **shard_state) return batch_stats
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/loss.py#L91-L109
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/loss.py
python
LossComputeBase.sharded_compute_loss
(self, batch, output, attns, cur_trunc, trunc_size, shard_size, normalization)
return batch_stats
Compute the forward loss and backpropagate. Computation is done with shards and optionally truncation for memory efficiency. Also supports truncated BPTT for long sequences by taking a range in the decoder output sequence to back propagate in. Range is from `(cur_trunc, cur_trunc + trunc_size)`. Note sharding is an exact efficiency trick to relieve memory required for the generation buffers. Truncation is an approximate efficiency trick to relieve the memory required in the RNN buffers. Args: batch (batch) : batch of labeled examples output (:obj:`FloatTensor`) : output of decoder model `[tgt_len x batch x hidden]` attns (dict) : dictionary of attention distributions `[tgt_len x batch x src_len]` cur_trunc (int) : starting position of truncation window trunc_size (int) : length of truncation window shard_size (int) : maximum number of examples in a shard normalization (int) : Loss is divided by this number Returns: :obj:`onmt.utils.Statistics`: validation loss statistics
Compute the forward loss and backpropagate. Computation is done with shards and optionally truncation for memory efficiency.
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def sharded_compute_loss(self, batch, output, attns, cur_trunc, trunc_size, shard_size, normalization): """Compute the forward loss and backpropagate. Computation is done with shards and optionally truncation for memory efficiency. Also supports truncated BPTT for long sequences by taking a range in the decoder output sequence to back propagate in. Range is from `(cur_trunc, cur_trunc + trunc_size)`. Note sharding is an exact efficiency trick to relieve memory required for the generation buffers. Truncation is an approximate efficiency trick to relieve the memory required in the RNN buffers. Args: batch (batch) : batch of labeled examples output (:obj:`FloatTensor`) : output of decoder model `[tgt_len x batch x hidden]` attns (dict) : dictionary of attention distributions `[tgt_len x batch x src_len]` cur_trunc (int) : starting position of truncation window trunc_size (int) : length of truncation window shard_size (int) : maximum number of examples in a shard normalization (int) : Loss is divided by this number Returns: :obj:`onmt.utils.Statistics`: validation loss statistics """ batch_stats = onmt.utils.Statistics() range_ = (cur_trunc, cur_trunc + trunc_size) shard_state = self._make_shard_state(batch, output, range_, attns) for shard in shards(shard_state, shard_size): loss, stats = self._compute_loss(batch, **shard) loss.div(float(normalization)).backward() batch_stats.update(stats) return batch_stats
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/loss.py#L111-L149
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/loss.py
python
LossComputeBase._stats
(self, loss, scores, target)
return onmt.utils.Statistics(loss.item(), num_non_padding, num_correct)
Args: loss (:obj:`FloatTensor`): the loss computed by the loss criterion. scores (:obj:`FloatTensor`): a score for each possible output target (:obj:`FloatTensor`): true targets Returns: :obj:`onmt.utils.Statistics` : statistics for this batch.
Args: loss (:obj:`FloatTensor`): the loss computed by the loss criterion. scores (:obj:`FloatTensor`): a score for each possible output target (:obj:`FloatTensor`): true targets
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def _stats(self, loss, scores, target): """ Args: loss (:obj:`FloatTensor`): the loss computed by the loss criterion. scores (:obj:`FloatTensor`): a score for each possible output target (:obj:`FloatTensor`): true targets Returns: :obj:`onmt.utils.Statistics` : statistics for this batch. """ pred = scores.max(1)[1] non_padding = target.ne(self.padding_idx) num_correct = pred.eq(target) \ .masked_select(non_padding) \ .sum() \ .item() num_non_padding = non_padding.sum().item() return onmt.utils.Statistics(loss.item(), num_non_padding, num_correct)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/loss.py#L151-L168
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/loss.py
python
LabelSmoothingLoss.forward
(self, output, target)
return F.kl_div(output, model_prob, reduction='sum')
output (FloatTensor): batch_size x n_classes target (LongTensor): batch_size
output (FloatTensor): batch_size x n_classes target (LongTensor): batch_size
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def forward(self, output, target): """ output (FloatTensor): batch_size x n_classes target (LongTensor): batch_size """ model_prob = self.one_hot.repeat(target.size(0), 1) model_prob.scatter_(1, target.unsqueeze(1), self.confidence) model_prob.masked_fill_((target == self.padding_idx).unsqueeze(1), 0) return F.kl_div(output, model_prob, reduction='sum')
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/loss.py#L195-L204
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/misc.py
python
aeq
(*args)
Assert all arguments have the same value
Assert all arguments have the same value
[ "Assert", "all", "arguments", "have", "the", "same", "value" ]
def aeq(*args): """ Assert all arguments have the same value """ arguments = (arg for arg in args) first = next(arguments) assert all(arg == first for arg in arguments), \ "Not all arguments have the same value: " + str(args)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/misc.py#L6-L13
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/misc.py
python
sequence_mask
(lengths, max_len=None)
return (torch.arange(0, max_len) .type_as(lengths) .repeat(batch_size, 1) .lt(lengths.unsqueeze(1)))
Creates a boolean mask from sequence lengths.
Creates a boolean mask from sequence lengths.
[ "Creates", "a", "boolean", "mask", "from", "sequence", "lengths", "." ]
def sequence_mask(lengths, max_len=None): """ Creates a boolean mask from sequence lengths. """ batch_size = lengths.numel() max_len = max_len or lengths.max() return (torch.arange(0, max_len) .type_as(lengths) .repeat(batch_size, 1) .lt(lengths.unsqueeze(1)))
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/misc.py#L16-L25
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/misc.py
python
tile
(x, count, dim=0)
return x
Tiles x on dimension dim count times.
Tiles x on dimension dim count times.
[ "Tiles", "x", "on", "dimension", "dim", "count", "times", "." ]
def tile(x, count, dim=0): """ Tiles x on dimension dim count times. """ perm = list(range(len(x.size()))) if dim != 0: perm[0], perm[dim] = perm[dim], perm[0] x = x.permute(perm).contiguous() out_size = list(x.size()) out_size[0] *= count batch = x.size(0) x = x.view(batch, -1) \ .transpose(0, 1) \ .repeat(count, 1) \ .transpose(0, 1) \ .contiguous() \ .view(*out_size) if dim != 0: x = x.permute(perm).contiguous() return x
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/misc.py#L28-L47
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/misc.py
python
use_gpu
(opt)
return (hasattr(opt, 'gpu_ranks') and len(opt.gpu_ranks) > 0) or \ (hasattr(opt, 'gpu') and opt.gpu > -1)
Creates a boolean if gpu used
Creates a boolean if gpu used
[ "Creates", "a", "boolean", "if", "gpu", "used" ]
def use_gpu(opt): """ Creates a boolean if gpu used """ return (hasattr(opt, 'gpu_ranks') and len(opt.gpu_ranks) > 0) or \ (hasattr(opt, 'gpu') and opt.gpu > -1)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/misc.py#L50-L55
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/report_manager.py
python
ReportMgrBase.__init__
(self, report_every, start_time=-1.)
Args: report_every(int): Report status every this many sentences start_time(float): manually set report start time. Negative values means that you will need to set it later or use `start()`
Args: report_every(int): Report status every this many sentences start_time(float): manually set report start time. Negative values means that you will need to set it later or use `start()`
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def __init__(self, report_every, start_time=-1.): """ Args: report_every(int): Report status every this many sentences start_time(float): manually set report start time. Negative values means that you will need to set it later or use `start()` """ self.report_every = report_every self.progress_step = 0 self.start_time = start_time
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/report_manager.py#L33-L42
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/report_manager.py
python
ReportMgrBase.report_training
(self, step, num_steps, learning_rate, report_stats, multigpu=False)
return onmt.utils.Statistics()
This is the user-defined batch-level traing progress report function. Args: step(int): current step count. num_steps(int): total number of batches. learning_rate(float): current learning rate. report_stats(Statistics): old Statistics instance. Returns: report_stats(Statistics): updated Statistics instance.
This is the user-defined batch-level traing progress report function.
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def report_training(self, step, num_steps, learning_rate, report_stats, multigpu=False): """ This is the user-defined batch-level traing progress report function. Args: step(int): current step count. num_steps(int): total number of batches. learning_rate(float): current learning rate. report_stats(Statistics): old Statistics instance. Returns: report_stats(Statistics): updated Statistics instance. """ if self.start_time < 0: raise ValueError("""ReportMgr needs to be started (set 'start_time' or use 'start()'""") if multigpu: report_stats = onmt.utils.Statistics.all_gather_stats(report_stats) if step % self.report_every == 0: self._report_training( step, num_steps, learning_rate, report_stats) self.progress_step += 1 return onmt.utils.Statistics()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/report_manager.py#L50-L75
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/report_manager.py
python
ReportMgrBase._report_training
(self, *args, **kwargs)
To be overridden
To be overridden
[ "To", "be", "overridden" ]
def _report_training(self, *args, **kwargs): """ To be overridden """ raise NotImplementedError()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/report_manager.py#L77-L79
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/report_manager.py
python
ReportMgrBase.report_step
(self, lr, step, train_stats=None, valid_stats=None)
Report stats of a step Args: train_stats(Statistics): training stats valid_stats(Statistics): validation stats lr(float): current learning rate
Report stats of a step
[ "Report", "stats", "of", "a", "step" ]
def report_step(self, lr, step, train_stats=None, valid_stats=None): """ Report stats of a step Args: train_stats(Statistics): training stats valid_stats(Statistics): validation stats lr(float): current learning rate """ self._report_step( lr, step, train_stats=train_stats, valid_stats=valid_stats)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/report_manager.py#L81-L91
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/report_manager.py
python
ReportMgr.__init__
(self, report_every, start_time=-1., tensorboard_writer=None)
A report manager that writes statistics on standard output as well as (optionally) TensorBoard Args: report_every(int): Report status every this many sentences tensorboard_writer(:obj:`tensorboard.SummaryWriter`): The TensorBoard Summary writer to use or None
A report manager that writes statistics on standard output as well as (optionally) TensorBoard
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def __init__(self, report_every, start_time=-1., tensorboard_writer=None): """ A report manager that writes statistics on standard output as well as (optionally) TensorBoard Args: report_every(int): Report status every this many sentences tensorboard_writer(:obj:`tensorboard.SummaryWriter`): The TensorBoard Summary writer to use or None """ super(ReportMgr, self).__init__(report_every, start_time) self.tensorboard_writer = tensorboard_writer
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/report_manager.py#L98-L109
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/report_manager.py
python
ReportMgr._report_training
(self, step, num_steps, learning_rate, report_stats)
return report_stats
See base class method `ReportMgrBase.report_training`.
See base class method `ReportMgrBase.report_training`.
[ "See", "base", "class", "method", "ReportMgrBase", ".", "report_training", "." ]
def _report_training(self, step, num_steps, learning_rate, report_stats): """ See base class method `ReportMgrBase.report_training`. """ report_stats.output(step, num_steps, learning_rate, self.start_time) # Log the progress using the number of batches on the x-axis. self.maybe_log_tensorboard(report_stats, "progress", learning_rate, self.progress_step) report_stats = onmt.utils.Statistics() return report_stats
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/report_manager.py#L116-L131
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/utils/report_manager.py
python
ReportMgr._report_step
(self, lr, step, train_stats=None, valid_stats=None)
See base class method `ReportMgrBase.report_step`.
See base class method `ReportMgrBase.report_step`.
[ "See", "base", "class", "method", "ReportMgrBase", ".", "report_step", "." ]
def _report_step(self, lr, step, train_stats=None, valid_stats=None): """ See base class method `ReportMgrBase.report_step`. """ if train_stats is not None: self.log('Train perplexity: %g' % train_stats.ppl()) self.log('Train accuracy: %g' % train_stats.accuracy()) self.maybe_log_tensorboard(train_stats, "train", lr, step) if valid_stats is not None: self.log('Validation perplexity: %g' % valid_stats.ppl()) self.log('Validation accuracy: %g' % valid_stats.accuracy()) self.maybe_log_tensorboard(valid_stats, "valid", lr, step)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/utils/report_manager.py#L133-L153
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/multi_headed_attn.py
python
MultiHeadedAttention.forward
(self, key, value, query, mask=None, layer_cache=None, type=None)
return output, top_attn
Compute the context vector and the attention vectors. Args: key (`FloatTensor`): set of `key_len` key vectors `[batch, key_len, dim]` value (`FloatTensor`): set of `key_len` value vectors `[batch, key_len, dim]` query (`FloatTensor`): set of `query_len` query vectors `[batch, query_len, dim]` mask: binary mask indicating which keys have non-zero attention `[batch, query_len, key_len]` Returns: (`FloatTensor`, `FloatTensor`) : * output context vectors `[batch, query_len, dim]` * one of the attention vectors `[batch, query_len, key_len]`
Compute the context vector and the attention vectors.
[ "Compute", "the", "context", "vector", "and", "the", "attention", "vectors", "." ]
def forward(self, key, value, query, mask=None, layer_cache=None, type=None): """ Compute the context vector and the attention vectors. Args: key (`FloatTensor`): set of `key_len` key vectors `[batch, key_len, dim]` value (`FloatTensor`): set of `key_len` value vectors `[batch, key_len, dim]` query (`FloatTensor`): set of `query_len` query vectors `[batch, query_len, dim]` mask: binary mask indicating which keys have non-zero attention `[batch, query_len, key_len]` Returns: (`FloatTensor`, `FloatTensor`) : * output context vectors `[batch, query_len, dim]` * one of the attention vectors `[batch, query_len, key_len]` """ # CHECKS # batch, k_len, d = key.size() # batch_, k_len_, d_ = value.size() # aeq(batch, batch_) # aeq(k_len, k_len_) # aeq(d, d_) # batch_, q_len, d_ = query.size() # aeq(batch, batch_) # aeq(d, d_) # aeq(self.model_dim % 8, 0) # if mask is not None: # batch_, q_len_, k_len_ = mask.size() # aeq(batch_, batch) # aeq(k_len_, k_len) # aeq(q_len_ == q_len) # END CHECKS batch_size = key.size(0) dim_per_head = self.dim_per_head head_count = self.head_count key_len = key.size(1) query_len = query.size(1) def shape(x): """ projection """ return x.view(batch_size, -1, head_count, dim_per_head) \ .transpose(1, 2) def unshape(x): """ compute context """ return x.transpose(1, 2).contiguous() \ .view(batch_size, -1, head_count * dim_per_head) # 1) Project key, value, and query. if layer_cache is not None: if type == "self": query, key, value = self.linear_query(query),\ self.linear_keys(query),\ self.linear_values(query) key = shape(key) value = shape(value) if layer_cache is not None: device = key.device if layer_cache["self_keys"] is not None: key = torch.cat( (layer_cache["self_keys"].to(device), key), dim=2) if layer_cache["self_values"] is not None: value = torch.cat( (layer_cache["self_values"].to(device), value), dim=2) layer_cache["self_keys"] = key layer_cache["self_values"] = value elif type == "context": query = self.linear_query(query) if layer_cache is not None: if layer_cache["memory_keys"] is None: key, value = self.linear_keys(key),\ self.linear_values(value) key = shape(key) value = shape(value) else: key, value = layer_cache["memory_keys"],\ layer_cache["memory_values"] layer_cache["memory_keys"] = key layer_cache["memory_values"] = value else: key, value = self.linear_keys(key),\ self.linear_values(value) key = shape(key) value = shape(value) else: key = self.linear_keys(key) value = self.linear_values(value) query = self.linear_query(query) key = shape(key) value = shape(value) query = shape(query) key_len = key.size(2) query_len = query.size(2) # 2) Calculate and scale scores. query = query / math.sqrt(dim_per_head) scores = torch.matmul(query, key.transpose(2, 3)) if mask is not None: mask = mask.unsqueeze(1).expand_as(scores) scores = scores.masked_fill(mask, -1e18) # 3) Apply attention dropout and compute context vectors. attn = self.softmax(scores) drop_attn = self.dropout(attn) context = unshape(torch.matmul(drop_attn, value)) output = self.final_linear(context) # CHECK # batch_, q_len_, d_ = output.size() # aeq(q_len, q_len_) # aeq(batch, batch_) # aeq(d, d_) # Return one attn top_attn = attn \ .view(batch_size, head_count, query_len, key_len)[:, 0, :, :] \ .contiguous() return output, top_attn
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/multi_headed_attn.py#L69-L201
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/position_ffn.py
python
PositionwiseFeedForward.forward
(self, x)
return output + x
Layer definition. Args: input: [ batch_size, input_len, model_dim ] Returns: output: [ batch_size, input_len, model_dim ]
Layer definition.
[ "Layer", "definition", "." ]
def forward(self, x): """ Layer definition. Args: input: [ batch_size, input_len, model_dim ] Returns: output: [ batch_size, input_len, model_dim ] """ inter = self.dropout_1(self.relu(self.w_1(self.layer_norm(x)))) output = self.dropout_2(self.w_2(inter)) return output + x
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/position_ffn.py#L29-L42
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/sparse_activations.py
python
threshold_and_support
(z, dim=0)
return tau_z, k_z
z: any dimension dim: dimension along which to apply the sparsemax
z: any dimension dim: dimension along which to apply the sparsemax
[ "z", ":", "any", "dimension", "dim", ":", "dimension", "along", "which", "to", "apply", "the", "sparsemax" ]
def threshold_and_support(z, dim=0): """ z: any dimension dim: dimension along which to apply the sparsemax """ sorted_z, _ = torch.sort(z, descending=True, dim=dim) z_sum = sorted_z.cumsum(dim) - 1 # sort of a misnomer k = torch.arange(1, sorted_z.size(dim) + 1, device=z.device).float().view( torch.Size([-1] + [1] * (z.dim() - 1)) ).transpose(0, dim) support = k * sorted_z > z_sum k_z_indices = support.sum(dim=dim).unsqueeze(dim) k_z = k_z_indices.float() tau_z = z_sum.gather(dim, k_z_indices - 1) / k_z return tau_z, k_z
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/sparse_activations.py#L11-L26
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/sparse_activations.py
python
SparsemaxFunction.forward
(ctx, input, dim=0)
return output
input (FloatTensor): any shape returns (FloatTensor): same shape with sparsemax computed on given dim
input (FloatTensor): any shape returns (FloatTensor): same shape with sparsemax computed on given dim
[ "input", "(", "FloatTensor", ")", ":", "any", "shape", "returns", "(", "FloatTensor", ")", ":", "same", "shape", "with", "sparsemax", "computed", "on", "given", "dim" ]
def forward(ctx, input, dim=0): """ input (FloatTensor): any shape returns (FloatTensor): same shape with sparsemax computed on given dim """ ctx.dim = dim tau_z, k_z = threshold_and_support(input, dim=dim) output = torch.clamp(input - tau_z, min=0) ctx.save_for_backward(k_z, output) return output
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/sparse_activations.py#L32-L41
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/global_attention.py
python
GlobalAttention.score
(self, h_t, h_s)
Args: h_t (`FloatTensor`): sequence of queries `[batch x tgt_len x dim]` h_s (`FloatTensor`): sequence of sources `[batch x src_len x dim]` Returns: :obj:`FloatTensor`: raw attention scores (unnormalized) for each src index `[batch x tgt_len x src_len]`
Args: h_t (`FloatTensor`): sequence of queries `[batch x tgt_len x dim]` h_s (`FloatTensor`): sequence of sources `[batch x src_len x dim]`
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def score(self, h_t, h_s): """ Args: h_t (`FloatTensor`): sequence of queries `[batch x tgt_len x dim]` h_s (`FloatTensor`): sequence of sources `[batch x src_len x dim]` Returns: :obj:`FloatTensor`: raw attention scores (unnormalized) for each src index `[batch x tgt_len x src_len]` """ # Check input sizes src_batch, src_len, src_dim = h_s.size() tgt_batch, tgt_len, tgt_dim = h_t.size() aeq(src_batch, tgt_batch) aeq(src_dim, tgt_dim) aeq(self.dim, src_dim) if self.attn_type in ["general", "dot"]: if self.attn_type == "general": h_t_ = h_t.view(tgt_batch * tgt_len, tgt_dim) h_t_ = self.linear_in(h_t_) h_t = h_t_.view(tgt_batch, tgt_len, tgt_dim) h_s_ = h_s.transpose(1, 2) # (batch, t_len, d) x (batch, d, s_len) --> (batch, t_len, s_len) return torch.bmm(h_t, h_s_) else: dim = self.dim wq = self.linear_query(h_t.view(-1, dim)) wq = wq.view(tgt_batch, tgt_len, 1, dim) wq = wq.expand(tgt_batch, tgt_len, src_len, dim) uh = self.linear_context(h_s.contiguous().view(-1, dim)) uh = uh.view(src_batch, 1, src_len, dim) uh = uh.expand(src_batch, tgt_len, src_len, dim) # (batch, t_len, s_len, d) wquh = torch.tanh(wq + uh) return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/global_attention.py#L95-L136
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/global_attention.py
python
GlobalAttention.forward
(self, source, memory_bank, memory_lengths=None, coverage=None)
return attn_h, align_vectors
Args: source (`FloatTensor`): query vectors `[batch x tgt_len x dim]` memory_bank (`FloatTensor`): source vectors `[batch x src_len x dim]` memory_lengths (`LongTensor`): the source context lengths `[batch]` coverage (`FloatTensor`): None (not supported yet) Returns: (`FloatTensor`, `FloatTensor`): * Computed vector `[tgt_len x batch x dim]` * Attention distribtutions for each query `[tgt_len x batch x src_len]`
[]
def forward(self, source, memory_bank, memory_lengths=None, coverage=None): """ Args: source (`FloatTensor`): query vectors `[batch x tgt_len x dim]` memory_bank (`FloatTensor`): source vectors `[batch x src_len x dim]` memory_lengths (`LongTensor`): the source context lengths `[batch]` coverage (`FloatTensor`): None (not supported yet) Returns: (`FloatTensor`, `FloatTensor`): * Computed vector `[tgt_len x batch x dim]` * Attention distribtutions for each query `[tgt_len x batch x src_len]` """ # one step input if source.dim() == 2: one_step = True source = source.unsqueeze(1) else: one_step = False batch, source_l, dim = memory_bank.size() batch_, target_l, dim_ = source.size() aeq(batch, batch_) aeq(dim, dim_) aeq(self.dim, dim) if coverage is not None: batch_, source_l_ = coverage.size() aeq(batch, batch_) aeq(source_l, source_l_) if coverage is not None: cover = coverage.view(-1).unsqueeze(1) memory_bank += self.linear_cover(cover).view_as(memory_bank) memory_bank = torch.tanh(memory_bank) # compute attention scores, as in Luong et al. align = self.score(source, memory_bank) if memory_lengths is not None: mask = sequence_mask(memory_lengths, max_len=align.size(-1)) mask = mask.unsqueeze(1) # Make it broadcastable. align.masked_fill_(1 - mask, -float('inf')) # Softmax or sparsemax to normalize attention weights if self.attn_func == "softmax": align_vectors = F.softmax(align.view(batch*target_l, source_l), -1) else: align_vectors = sparsemax(align.view(batch*target_l, source_l), -1) align_vectors = align_vectors.view(batch, target_l, source_l) # each context vector c_t is the weighted average # over all the source hidden states c = torch.bmm(align_vectors, memory_bank) # concatenate concat_c = torch.cat([c, source], 2).view(batch*target_l, dim*2) attn_h = self.linear_out(concat_c).view(batch, target_l, dim) if self.attn_type in ["general", "dot"]: attn_h = torch.tanh(attn_h) if one_step: attn_h = attn_h.squeeze(1) align_vectors = align_vectors.squeeze(1) # Check output sizes batch_, dim_ = attn_h.size() aeq(batch, batch_) aeq(dim, dim_) batch_, source_l_ = align_vectors.size() aeq(batch, batch_) aeq(source_l, source_l_) else: attn_h = attn_h.transpose(0, 1).contiguous() align_vectors = align_vectors.transpose(0, 1).contiguous() # Check output sizes target_l_, batch_, dim_ = attn_h.size() aeq(target_l, target_l_) aeq(batch, batch_) aeq(dim, dim_) target_l_, batch_, source_l_ = align_vectors.size() aeq(target_l, target_l_) aeq(batch, batch_) aeq(source_l, source_l_) return attn_h, align_vectors
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/global_attention.py#L138-L227
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/gate.py
python
context_gate_factory
(gate_type, embeddings_size, decoder_size, attention_size, output_size)
return gate_types[gate_type](embeddings_size, decoder_size, attention_size, output_size)
Returns the correct ContextGate class
Returns the correct ContextGate class
[ "Returns", "the", "correct", "ContextGate", "class" ]
def context_gate_factory(gate_type, embeddings_size, decoder_size, attention_size, output_size): """Returns the correct ContextGate class""" gate_types = {'source': SourceContextGate, 'target': TargetContextGate, 'both': BothContextGate} assert gate_type in gate_types, "Not valid ContextGate type: {0}".format( gate_type) return gate_types[gate_type](embeddings_size, decoder_size, attention_size, output_size)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/gate.py#L6-L17
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/average_attn.py
python
AverageAttention.cumulative_average_mask
(self, batch_size, inputs_len)
return mask.unsqueeze(0).expand(batch_size, inputs_len, inputs_len)
Builds the mask to compute the cumulative average as described in https://arxiv.org/abs/1805.00631 -- Figure 3 Args: batch_size (int): batch size inputs_len (int): length of the inputs Returns: (`FloatTensor`): * A Tensor of shape `[batch_size x input_len x input_len]`
Builds the mask to compute the cumulative average as described in https://arxiv.org/abs/1805.00631 -- Figure 3
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def cumulative_average_mask(self, batch_size, inputs_len): """ Builds the mask to compute the cumulative average as described in https://arxiv.org/abs/1805.00631 -- Figure 3 Args: batch_size (int): batch size inputs_len (int): length of the inputs Returns: (`FloatTensor`): * A Tensor of shape `[batch_size x input_len x input_len]` """ triangle = torch.tril(torch.ones(inputs_len, inputs_len)) weights = torch.ones(1, inputs_len) / torch.arange( 1, inputs_len + 1, dtype=torch.float) mask = triangle * weights.transpose(0, 1) return mask.unsqueeze(0).expand(batch_size, inputs_len, inputs_len)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/average_attn.py#L31-L51
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/average_attn.py
python
AverageAttention.cumulative_average
(self, inputs, mask_or_step, layer_cache=None, step=None)
Computes the cumulative average as described in https://arxiv.org/abs/1805.00631 -- Equations (1) (5) (6) Args: inputs (`FloatTensor`): sequence to average `[batch_size x input_len x dimension]` mask_or_step: if cache is set, this is assumed to be the current step of the dynamic decoding. Otherwise, it is the mask matrix used to compute the cumulative average. cache: a dictionary containing the cumulative average of the previous step.
Computes the cumulative average as described in https://arxiv.org/abs/1805.00631 -- Equations (1) (5) (6)
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def cumulative_average(self, inputs, mask_or_step, layer_cache=None, step=None): """ Computes the cumulative average as described in https://arxiv.org/abs/1805.00631 -- Equations (1) (5) (6) Args: inputs (`FloatTensor`): sequence to average `[batch_size x input_len x dimension]` mask_or_step: if cache is set, this is assumed to be the current step of the dynamic decoding. Otherwise, it is the mask matrix used to compute the cumulative average. cache: a dictionary containing the cumulative average of the previous step. """ if layer_cache is not None: step = mask_or_step device = inputs.device average_attention = (inputs + step * layer_cache["prev_g"].to(device)) / (step + 1) layer_cache["prev_g"] = average_attention return average_attention else: mask = mask_or_step return torch.matmul(mask, inputs)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/average_attn.py#L53-L78
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/average_attn.py
python
AverageAttention.forward
(self, inputs, mask=None, layer_cache=None, step=None)
return gating_outputs, average_outputs
Args: inputs (`FloatTensor`): `[batch_size x input_len x model_dim]` Returns: (`FloatTensor`, `FloatTensor`): * gating_outputs `[batch_size x 1 x model_dim]` * average_outputs average attention `[batch_size x 1 x model_dim]`
Args: inputs (`FloatTensor`): `[batch_size x input_len x model_dim]`
[ "Args", ":", "inputs", "(", "FloatTensor", ")", ":", "[", "batch_size", "x", "input_len", "x", "model_dim", "]" ]
def forward(self, inputs, mask=None, layer_cache=None, step=None): """ Args: inputs (`FloatTensor`): `[batch_size x input_len x model_dim]` Returns: (`FloatTensor`, `FloatTensor`): * gating_outputs `[batch_size x 1 x model_dim]` * average_outputs average attention `[batch_size x 1 x model_dim]` """ batch_size = inputs.size(0) inputs_len = inputs.size(1) device = inputs.device average_outputs = self.cumulative_average( inputs, self.cumulative_average_mask(batch_size, inputs_len).to(device).float() if layer_cache is None else step, layer_cache=layer_cache) average_outputs = self.average_layer(average_outputs) gating_outputs = self.gating_layer(torch.cat((inputs, average_outputs), -1)) input_gate, forget_gate = torch.chunk(gating_outputs, 2, dim=2) gating_outputs = torch.sigmoid(input_gate) * inputs + \ torch.sigmoid(forget_gate) * average_outputs return gating_outputs, average_outputs
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/average_attn.py#L80-L106
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/copy_generator.py
python
CopyGenerator.forward
(self, hidden, attn, src_map)
return torch.cat([out_prob, copy_prob], 1)
Compute a distribution over the target dictionary extended by the dynamic dictionary implied by compying source words. Args: hidden (`FloatTensor`): hidden outputs `[batch*tlen, input_size]` attn (`FloatTensor`): attn for each `[batch*tlen, input_size]` src_map (`FloatTensor`): A sparse indicator matrix mapping each source word to its index in the "extended" vocab containing. `[src_len, batch, extra_words]`
Compute a distribution over the target dictionary extended by the dynamic dictionary implied by compying source words.
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def forward(self, hidden, attn, src_map): """ Compute a distribution over the target dictionary extended by the dynamic dictionary implied by compying source words. Args: hidden (`FloatTensor`): hidden outputs `[batch*tlen, input_size]` attn (`FloatTensor`): attn for each `[batch*tlen, input_size]` src_map (`FloatTensor`): A sparse indicator matrix mapping each source word to its index in the "extended" vocab containing. `[src_len, batch, extra_words]` """ # CHECKS batch_by_tlen, _ = hidden.size() batch_by_tlen_, slen = attn.size() slen_, batch, cvocab = src_map.size() aeq(batch_by_tlen, batch_by_tlen_) aeq(slen, slen_) # Original probabilities. logits = self.linear(hidden) logits[:, self.tgt_dict.stoi[inputters.PAD_WORD]] = -float('inf') prob = self.softmax(logits) # Probability of copying p(z=1) batch. p_copy = self.sigmoid(self.linear_copy(hidden)) # Probibility of not copying: p_{word}(w) * (1 - p(z)) out_prob = torch.mul(prob, 1 - p_copy.expand_as(prob)) mul_attn = torch.mul(attn, p_copy.expand_as(attn)) copy_prob = torch.bmm(mul_attn.view(-1, batch, slen) .transpose(0, 1), src_map.transpose(0, 1)).transpose(0, 1) copy_prob = copy_prob.contiguous().view(-1, cvocab) return torch.cat([out_prob, copy_prob], 1)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/copy_generator.py#L71-L106
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/copy_generator.py
python
CopyGeneratorLossCompute._make_shard_state
(self, batch, output, range_, attns)
return { "output": output, "target": batch.tgt[range_[0] + 1: range_[1]], "copy_attn": attns.get("copy"), "align": batch.alignment[range_[0] + 1: range_[1]] }
See base class for args description.
See base class for args description.
[ "See", "base", "class", "for", "args", "description", "." ]
def _make_shard_state(self, batch, output, range_, attns): """ See base class for args description. """ if getattr(batch, "alignment", None) is None: raise AssertionError("using -copy_attn you need to pass in " "-dynamic_dict during preprocess stage.") return { "output": output, "target": batch.tgt[range_[0] + 1: range_[1]], "copy_attn": attns.get("copy"), "align": batch.alignment[range_[0] + 1: range_[1]] }
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/copy_generator.py#L163-L174
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/copy_generator.py
python
CopyGeneratorLossCompute._compute_loss
(self, batch, output, target, copy_attn, align)
return loss, stats
Compute the loss. The args must match self._make_shard_state(). Args: batch: the current batch. output: the predict output from the model. target: the validate target to compare output with. copy_attn: the copy attention value. align: the align info.
Compute the loss. The args must match self._make_shard_state(). Args: batch: the current batch. output: the predict output from the model. target: the validate target to compare output with. copy_attn: the copy attention value. align: the align info.
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def _compute_loss(self, batch, output, target, copy_attn, align): """ Compute the loss. The args must match self._make_shard_state(). Args: batch: the current batch. output: the predict output from the model. target: the validate target to compare output with. copy_attn: the copy attention value. align: the align info. """ target = target.view(-1) align = align.view(-1) scores = self.generator(self._bottle(output), self._bottle(copy_attn), batch.src_map) loss = self.criterion(scores, align, target) scores_data = scores.data.clone() scores_data = inputters.TextDataset.collapse_copy_scores( self._unbottle(scores_data, batch.batch_size), batch, self.tgt_vocab, batch.dataset.src_vocabs) scores_data = self._bottle(scores_data) # Correct target copy token instead of <unk> # tgt[i] = align[i] + len(tgt_vocab) # for i such that tgt[i] == 0 and align[i] != 0 target_data = target.data.clone() correct_mask = target_data.eq(0) * align.data.ne(0) correct_copy = (align.data + len(self.tgt_vocab)) * correct_mask.long() target_data = target_data + correct_copy # Compute sum of perplexities for stats loss_data = loss.sum().data.clone() stats = self._stats(loss_data, scores_data, target_data) if self.normalize_by_length: # Compute Loss as NLL divided by seq length # Compute Sequence Lengths pad_ix = batch.dataset.fields['tgt'].vocab.stoi[inputters.PAD_WORD] tgt_lens = batch.tgt.ne(pad_ix).float().sum(0) # Compute Total Loss per sequence in batch loss = loss.view(-1, batch.batch_size).sum(0) # Divide by length of each sequence and sum loss = torch.div(loss, tgt_lens).sum() else: loss = loss.sum() return loss, stats
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/copy_generator.py#L176-L222
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/embeddings.py
python
Embeddings.word_lut
(self)
return self.make_embedding[0][0]
word look-up table
word look-up table
[ "word", "look", "-", "up", "table" ]
def word_lut(self): """ word look-up table """ return self.make_embedding[0][0]
[ "def", "word_lut", "(", "self", ")", ":", "return", "self", ".", "make_embedding", "[", "0", "]", "[", "0", "]" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/embeddings.py#L160-L162
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/embeddings.py
python
Embeddings.emb_luts
(self)
return self.make_embedding[0]
embedding look-up table
embedding look-up table
[ "embedding", "look", "-", "up", "table" ]
def emb_luts(self): """ embedding look-up table """ return self.make_embedding[0]
[ "def", "emb_luts", "(", "self", ")", ":", "return", "self", ".", "make_embedding", "[", "0", "]" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/embeddings.py#L165-L167
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/embeddings.py
python
Embeddings.load_pretrained_vectors
(self, emb_file, fixed)
Load in pretrained embeddings. Args: emb_file (str) : path to torch serialized embeddings fixed (bool) : if true, embeddings are not updated
Load in pretrained embeddings.
[ "Load", "in", "pretrained", "embeddings", "." ]
def load_pretrained_vectors(self, emb_file, fixed): """Load in pretrained embeddings. Args: emb_file (str) : path to torch serialized embeddings fixed (bool) : if true, embeddings are not updated """ if emb_file: pretrained = torch.load(emb_file) pretrained_vec_size = pretrained.size(1) if self.word_vec_size > pretrained_vec_size: self.word_lut.weight.data[:, :pretrained_vec_size] = pretrained elif self.word_vec_size < pretrained_vec_size: self.word_lut.weight.data \ .copy_(pretrained[:, :self.word_vec_size]) else: self.word_lut.weight.data.copy_(pretrained) if fixed: self.word_lut.weight.requires_grad = False
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/embeddings.py#L169-L187
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/embeddings.py
python
Embeddings.forward
(self, source, step=None)
return source
Computes the embeddings for words and features. Args: source (`LongTensor`): index tensor `[len x batch x nfeat]` Return: `FloatTensor`: word embeddings `[len x batch x embedding_size]`
Computes the embeddings for words and features.
[ "Computes", "the", "embeddings", "for", "words", "and", "features", "." ]
def forward(self, source, step=None): """ Computes the embeddings for words and features. Args: source (`LongTensor`): index tensor `[len x batch x nfeat]` Return: `FloatTensor`: word embeddings `[len x batch x embedding_size]` """ if self.position_encoding: for i, module in enumerate(self.make_embedding._modules.values()): if i == len(self.make_embedding._modules.values()) - 1: source = module(source, step=step) else: source = module(source) else: source = self.make_embedding(source) return source
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/embeddings.py#L189-L207
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/sparse_losses.py
python
SparsemaxLossFunction.forward
(ctx, input, target)
return torch.clamp(x / 2 - z_k + 0.5, min=0.0)
input (FloatTensor): n x num_classes target (LongTensor): n, the indices of the target classes
input (FloatTensor): n x num_classes target (LongTensor): n, the indices of the target classes
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def forward(ctx, input, target): """ input (FloatTensor): n x num_classes target (LongTensor): n, the indices of the target classes """ input_batch, classes = input.size() target_batch = target.size(0) aeq(input_batch, target_batch) z_k = input.gather(1, target.unsqueeze(1)).squeeze() tau_z, support_size = threshold_and_support(input, dim=1) support = input > tau_z x = torch.where( support, input**2 - tau_z**2, torch.tensor(0.0, device=input.device) ).sum(dim=1) ctx.save_for_backward(input, target, tau_z) # clamping necessary because of numerical errors: loss should be lower # bounded by zero, but negative values near zero are possible without # the clamp return torch.clamp(x / 2 - z_k + 0.5, min=0.0)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/sparse_losses.py#L11-L31
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/conv_multi_step_attention.py
python
seq_linear
(linear, x)
return torch.transpose(h.view(batch, length, hidden_size, 1), 1, 2)
linear transform for 3-d tensor
linear transform for 3-d tensor
[ "linear", "transform", "for", "3", "-", "d", "tensor" ]
def seq_linear(linear, x): """ linear transform for 3-d tensor """ batch, hidden_size, length, _ = x.size() h = linear(torch.transpose(x, 1, 2).contiguous().view( batch * length, hidden_size)) return torch.transpose(h.view(batch, length, hidden_size, 1), 1, 2)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/conv_multi_step_attention.py#L11-L16
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/conv_multi_step_attention.py
python
ConvMultiStepAttention.apply_mask
(self, mask)
Apply mask
Apply mask
[ "Apply", "mask" ]
def apply_mask(self, mask): """ Apply mask """ self.mask = mask
[ "def", "apply_mask", "(", "self", ",", "mask", ")", ":", "self", ".", "mask", "=", "mask" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/conv_multi_step_attention.py#L34-L36
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/conv_multi_step_attention.py
python
ConvMultiStepAttention.forward
(self, base_target_emb, input_from_dec, encoder_out_top, encoder_out_combine)
return context_output, attn
Args: base_target_emb: target emb tensor input: output of decode conv encoder_out_t: the key matrix for calculation of attetion weight, which is the top output of encode conv encoder_out_combine: the value matrix for the attention-weighted sum, which is the combination of base emb and top output of encode
Args: base_target_emb: target emb tensor input: output of decode conv encoder_out_t: the key matrix for calculation of attetion weight, which is the top output of encode conv encoder_out_combine: the value matrix for the attention-weighted sum, which is the combination of base emb and top output of encode
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def forward(self, base_target_emb, input_from_dec, encoder_out_top, encoder_out_combine): """ Args: base_target_emb: target emb tensor input: output of decode conv encoder_out_t: the key matrix for calculation of attetion weight, which is the top output of encode conv encoder_out_combine: the value matrix for the attention-weighted sum, which is the combination of base emb and top output of encode """ # checks # batch, channel, height, width = base_target_emb.size() batch, _, height, _ = base_target_emb.size() # batch_, channel_, height_, width_ = input_from_dec.size() batch_, _, height_, _ = input_from_dec.size() aeq(batch, batch_) aeq(height, height_) # enc_batch, enc_channel, enc_height = encoder_out_top.size() enc_batch, _, enc_height = encoder_out_top.size() # enc_batch_, enc_channel_, enc_height_ = encoder_out_combine.size() enc_batch_, _, enc_height_ = encoder_out_combine.size() aeq(enc_batch, enc_batch_) aeq(enc_height, enc_height_) preatt = seq_linear(self.linear_in, input_from_dec) target = (base_target_emb + preatt) * SCALE_WEIGHT target = torch.squeeze(target, 3) target = torch.transpose(target, 1, 2) pre_attn = torch.bmm(target, encoder_out_top) if self.mask is not None: pre_attn.data.masked_fill_(self.mask, -float('inf')) pre_attn = pre_attn.transpose(0, 2) attn = F.softmax(pre_attn, dim=-1) attn = attn.transpose(0, 2).contiguous() context_output = torch.bmm( attn, torch.transpose(encoder_out_combine, 1, 2)) context_output = torch.transpose( torch.unsqueeze(context_output, 3), 1, 2) return context_output, attn
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/conv_multi_step_attention.py#L38-L83
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/weight_norm.py
python
get_var_maybe_avg
(namespace, var_name, training, polyak_decay)
utility for retrieving polyak averaged params Update average
utility for retrieving polyak averaged params Update average
[ "utility", "for", "retrieving", "polyak", "averaged", "params", "Update", "average" ]
def get_var_maybe_avg(namespace, var_name, training, polyak_decay): """ utility for retrieving polyak averaged params Update average """ v = getattr(namespace, var_name) v_avg = getattr(namespace, var_name + '_avg') v_avg -= (1 - polyak_decay) * (v_avg - v.data) if training: return v else: return v_avg
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/weight_norm.py#L8-L19
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/modules/weight_norm.py
python
get_vars_maybe_avg
(namespace, var_names, training, polyak_decay)
return vars
utility for retrieving polyak averaged params
utility for retrieving polyak averaged params
[ "utility", "for", "retrieving", "polyak", "averaged", "params" ]
def get_vars_maybe_avg(namespace, var_names, training, polyak_decay): """ utility for retrieving polyak averaged params """ vars = [] for vn in var_names: vars.append(get_var_maybe_avg( namespace, vn, training, polyak_decay)) return vars
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/modules/weight_norm.py#L22-L28
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py
python
DatasetBase.__reduce_ex__
(self, proto)
return super(DatasetBase, self).__reduce_ex__()
This is a hack. Something is broken with torch pickle.
This is a hack. Something is broken with torch pickle.
[ "This", "is", "a", "hack", ".", "Something", "is", "broken", "with", "torch", "pickle", "." ]
def __reduce_ex__(self, proto): "This is a hack. Something is broken with torch pickle." return super(DatasetBase, self).__reduce_ex__()
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py#L37-L39
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py
python
DatasetBase.load_fields
(self, vocab_dict)
Load fields from vocab.pt, and set the `fields` attribute. Args: vocab_dict (dict): a dict of loaded vocab from vocab.pt file.
Load fields from vocab.pt, and set the `fields` attribute.
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def load_fields(self, vocab_dict): """ Load fields from vocab.pt, and set the `fields` attribute. Args: vocab_dict (dict): a dict of loaded vocab from vocab.pt file. """ fields = onmt.inputters.inputter.load_fields_from_vocab( vocab_dict.items(), self.data_type) self.fields = dict([(k, f) for (k, f) in fields.items() if k in self.examples[0].__dict__])
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py#L41-L50
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py
python
DatasetBase.extract_text_features
(tokens)
return tuple(words), features, n_feats - 1
Args: tokens: A list of tokens, where each token consists of a word, optionally followed by u"│"-delimited features. Returns: A sequence of words, a sequence of features, and num of features.
Args: tokens: A list of tokens, where each token consists of a word, optionally followed by u"│"-delimited features. Returns: A sequence of words, a sequence of features, and num of features.
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def extract_text_features(tokens): """ Args: tokens: A list of tokens, where each token consists of a word, optionally followed by u"│"-delimited features. Returns: A sequence of words, a sequence of features, and num of features. """ if not tokens: return [], [], -1 specials = [PAD_WORD, UNK_WORD, BOS_WORD, EOS_WORD] words = [] features = [] n_feats = None for token in tokens: split_token = token.split(u"│") assert all([special != split_token[0] for special in specials]), \ "Dataset cannot contain Special Tokens" if split_token[0]: words += [split_token[0]] features += [split_token[1:]] if n_feats is None: n_feats = len(split_token) else: assert len(split_token) == n_feats, \ "all words must have the same number of features" features = list(zip(*features)) return tuple(words), features, n_feats - 1
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py#L53-L83
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py
python
DatasetBase._join_dicts
(self, *args)
return dict(chain(*[d.items() for d in args]))
Args: dictionaries with disjoint keys. Returns: a single dictionary that has the union of these keys.
Args: dictionaries with disjoint keys.
[ "Args", ":", "dictionaries", "with", "disjoint", "keys", "." ]
def _join_dicts(self, *args): """ Args: dictionaries with disjoint keys. Returns: a single dictionary that has the union of these keys. """ return dict(chain(*[d.items() for d in args]))
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py#L87-L95
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py
python
DatasetBase._peek
(self, seq)
return first, chain([first], seq)
Args: seq: an iterator. Returns: the first thing returned by calling next() on the iterator and an iterator created by re-chaining that value to the beginning of the iterator.
Args: seq: an iterator.
[ "Args", ":", "seq", ":", "an", "iterator", "." ]
def _peek(self, seq): """ Args: seq: an iterator. Returns: the first thing returned by calling next() on the iterator and an iterator created by re-chaining that value to the beginning of the iterator. """ first = next(seq) return first, chain([first], seq)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py#L97-L108
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py
python
DatasetBase._construct_example_fromlist
(self, data, fields)
return ex
Args: data: the data to be set as the value of the attributes of the to-be-created `Example`, associating with respective `Field` objects with same key. fields: a dict of `torchtext.data.Field` objects. The keys are attributes of the to-be-created `Example`. Returns: the created `Example` object.
Args: data: the data to be set as the value of the attributes of the to-be-created `Example`, associating with respective `Field` objects with same key. fields: a dict of `torchtext.data.Field` objects. The keys are attributes of the to-be-created `Example`.
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def _construct_example_fromlist(self, data, fields): """ Args: data: the data to be set as the value of the attributes of the to-be-created `Example`, associating with respective `Field` objects with same key. fields: a dict of `torchtext.data.Field` objects. The keys are attributes of the to-be-created `Example`. Returns: the created `Example` object. """ ex = torchtext.data.Example() for (name, field), val in zip(fields, data): if field is not None: setattr(ex, name, field.preprocess(val)) else: setattr(ex, name, val) return ex
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/dataset_base.py#L110-L128
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py
python
TextDataset.sort_key
(self, ex)
return len(ex.src)
Sort using length of source sentences.
Sort using length of source sentences.
[ "Sort", "using", "length", "of", "source", "sentences", "." ]
def sort_key(self, ex): """ Sort using length of source sentences. """ # Default to a balanced sort, prioritizing tgt len match. # TODO: make this configurable. if hasattr(ex, "tgt"): return len(ex.src), len(ex.tgt) return len(ex.src)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py#L97-L103
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py
python
TextDataset.collapse_copy_scores
(scores, batch, tgt_vocab, src_vocabs)
return scores
Given scores from an expanded dictionary corresponeding to a batch, sums together copies, with a dictionary word when it is ambigious.
Given scores from an expanded dictionary corresponeding to a batch, sums together copies, with a dictionary word when it is ambigious.
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def collapse_copy_scores(scores, batch, tgt_vocab, src_vocabs): """ Given scores from an expanded dictionary corresponeding to a batch, sums together copies, with a dictionary word when it is ambigious. """ offset = len(tgt_vocab) for b in range(batch.batch_size): blank = [] fill = [] index = batch.indices.data[b] src_vocab = src_vocabs[index] for i in range(1, len(src_vocab)): sw = src_vocab.itos[i] ti = tgt_vocab.stoi[sw] if ti != 0: blank.append(offset + i) fill.append(ti) if blank: blank = torch.Tensor(blank).type_as(batch.indices.data) fill = torch.Tensor(fill).type_as(batch.indices.data) scores[:, b].index_add_(1, fill, scores[:, b].index_select(1, blank)) scores[:, b].index_fill_(1, blank, 1e-10) return scores
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py#L106-L130
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py
python
TextDataset.make_text_examples_nfeats_tpl
(text_iter, text_path, truncate, side)
return (examples_iter, num_feats)
Args: text_iter(iterator): an iterator (or None) that we can loop over to read examples. It may be an openned file, a string list etc... text_path(str): path to file or None path (str): location of a src or tgt file. truncate (int): maximum sequence length (0 for unlimited). side (str): "src" or "tgt". Returns: (example_dict iterator, num_feats) tuple.
Args: text_iter(iterator): an iterator (or None) that we can loop over to read examples. It may be an openned file, a string list etc... text_path(str): path to file or None path (str): location of a src or tgt file. truncate (int): maximum sequence length (0 for unlimited). side (str): "src" or "tgt".
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def make_text_examples_nfeats_tpl(text_iter, text_path, truncate, side): """ Args: text_iter(iterator): an iterator (or None) that we can loop over to read examples. It may be an openned file, a string list etc... text_path(str): path to file or None path (str): location of a src or tgt file. truncate (int): maximum sequence length (0 for unlimited). side (str): "src" or "tgt". Returns: (example_dict iterator, num_feats) tuple. """ assert side in ['src', 'tgt'] if text_iter is None: if text_path is not None: text_iter = TextDataset.make_text_iterator_from_file(text_path) else: return (None, 0) # All examples have same number of features, so we peek first one # to get the num_feats. examples_nfeats_iter = \ TextDataset.make_examples(text_iter, truncate, side) first_ex = next(examples_nfeats_iter) num_feats = first_ex[1] # Chain back the first element - we only want to peek it. examples_nfeats_iter = chain([first_ex], examples_nfeats_iter) examples_iter = (ex for ex, nfeats in examples_nfeats_iter) return (examples_iter, num_feats)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py#L133-L167
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py
python
TextDataset.make_examples
(text_iter, truncate, side)
Args: text_iter (iterator): iterator of text sequences truncate (int): maximum sequence length (0 for unlimited). side (str): "src" or "tgt". Yields: (word, features, nfeat) triples for each line.
Args: text_iter (iterator): iterator of text sequences truncate (int): maximum sequence length (0 for unlimited). side (str): "src" or "tgt".
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def make_examples(text_iter, truncate, side): """ Args: text_iter (iterator): iterator of text sequences truncate (int): maximum sequence length (0 for unlimited). side (str): "src" or "tgt". Yields: (word, features, nfeat) triples for each line. """ for i, line in enumerate(text_iter): line = line.strip().split() if truncate: line = line[:truncate] words, feats, n_feats = \ TextDataset.extract_text_features(line) example_dict = {side: words, "indices": i} if feats: prefix = side + "_feat_" example_dict.update((prefix + str(j), f) for j, f in enumerate(feats)) yield example_dict, n_feats
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py#L170-L193
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py
python
TextDataset.get_fields
(n_src_features, n_tgt_features)
return fields
Args: n_src_features (int): the number of source features to create `torchtext.data.Field` for. n_tgt_features (int): the number of target features to create `torchtext.data.Field` for. Returns: A dictionary whose keys are strings and whose values are the corresponding Field objects.
Args: n_src_features (int): the number of source features to create `torchtext.data.Field` for. n_tgt_features (int): the number of target features to create `torchtext.data.Field` for.
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def get_fields(n_src_features, n_tgt_features): """ Args: n_src_features (int): the number of source features to create `torchtext.data.Field` for. n_tgt_features (int): the number of target features to create `torchtext.data.Field` for. Returns: A dictionary whose keys are strings and whose values are the corresponding Field objects. """ fields = {} fields["src"] = torchtext.data.Field( pad_token=PAD_WORD, include_lengths=True) for j in range(n_src_features): fields["src_feat_" + str(j)] = \ torchtext.data.Field(pad_token=PAD_WORD) fields["tgt"] = torchtext.data.Field( init_token=BOS_WORD, eos_token=EOS_WORD, pad_token=PAD_WORD) for j in range(n_tgt_features): fields["tgt_feat_" + str(j)] = \ torchtext.data.Field(init_token=BOS_WORD, eos_token=EOS_WORD, pad_token=PAD_WORD) def make_src(data, vocab): """ ? """ #pdb.set_trace() src_size = max([t.size(0) for t in data]) src_vocab_size = int(max([t.max() for t in data])) + 1 try: alignment = torch.zeros(src_size, len(data), src_vocab_size) except: print(src_size) print(len(data)) print(src_vocab_size) for i, sent in enumerate(data): for j, t in enumerate(sent): alignment[j, i, t] = 1 return alignment fields["src_map"] = torchtext.data.Field( use_vocab=False, dtype=torch.float, postprocessing=make_src, sequential=False) def make_tgt(data, vocab): """ ? """ #pdb.set_trace() tgt_size = max([t.size(0) for t in data]) alignment = torch.zeros(tgt_size, len(data)).long() for i, sent in enumerate(data): alignment[:sent.size(0), i] = sent return alignment fields["alignment"] = torchtext.data.Field( use_vocab=False, dtype=torch.long, postprocessing=make_tgt, sequential=False) fields["indices"] = torchtext.data.Field( use_vocab=False, dtype=torch.long, sequential=False) return fields
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py#L202-L274
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py
python
TextDataset.get_num_features
(corpus_file, side)
return num_feats
Peek one line and get number of features of it. (All lines must have same number of features). For text corpus, both sides are in text form, thus it works the same. Args: corpus_file (str): file path to get the features. side (str): 'src' or 'tgt'. Returns: number of features on `side`.
Peek one line and get number of features of it. (All lines must have same number of features). For text corpus, both sides are in text form, thus it works the same.
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def get_num_features(corpus_file, side): """ Peek one line and get number of features of it. (All lines must have same number of features). For text corpus, both sides are in text form, thus it works the same. Args: corpus_file (str): file path to get the features. side (str): 'src' or 'tgt'. Returns: number of features on `side`. """ with codecs.open(corpus_file, "r", "utf-8") as cf: f_line = cf.readline().strip().split() _, _, num_feats = TextDataset.extract_text_features(f_line) return num_feats
[ "def", "get_num_features", "(", "corpus_file", ",", "side", ")", ":", "with", "codecs", ".", "open", "(", "corpus_file", ",", "\"r\"", ",", "\"utf-8\"", ")", "as", "cf", ":", "f_line", "=", "cf", ".", "readline", "(", ")", ".", "strip", "(", ")", ".", "split", "(", ")", "_", ",", "_", ",", "num_feats", "=", "TextDataset", ".", "extract_text_features", "(", "f_line", ")", "return", "num_feats" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py#L277-L295
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py
python
ShardedTextCorpusIterator.__init__
(self, corpus_path, line_truncate, side, shard_size, assoc_iter=None)
Args: corpus_path: the corpus file path. line_truncate: the maximum length of a line to read. 0 for unlimited. side: "src" or "tgt". shard_size: the shard size, 0 means not sharding the file. assoc_iter: if not None, it is the associate iterator that this iterator should align its step with.
Args: corpus_path: the corpus file path. line_truncate: the maximum length of a line to read. 0 for unlimited. side: "src" or "tgt". shard_size: the shard size, 0 means not sharding the file. assoc_iter: if not None, it is the associate iterator that this iterator should align its step with.
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def __init__(self, corpus_path, line_truncate, side, shard_size, assoc_iter=None): """ Args: corpus_path: the corpus file path. line_truncate: the maximum length of a line to read. 0 for unlimited. side: "src" or "tgt". shard_size: the shard size, 0 means not sharding the file. assoc_iter: if not None, it is the associate iterator that this iterator should align its step with. """ try: # The codecs module seems to have bugs with seek()/tell(), # so we use io.open(). self.corpus = io.open(corpus_path, "r", encoding="utf-8") except IOError: sys.stderr.write("Failed to open corpus file: %s" % corpus_path) sys.exit(1) self.line_truncate = line_truncate self.side = side self.shard_size = shard_size self.assoc_iter = assoc_iter self.last_pos = 0 self.line_index = -1 self.eof = False
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py#L326-L352
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py
python
ShardedTextCorpusIterator.__iter__
(self)
Iterator of (example_dict, nfeats). On each call, it iterates over as many (example_dict, nfeats) tuples until this shard's size equals to or approximates `self.shard_size`.
Iterator of (example_dict, nfeats). On each call, it iterates over as many (example_dict, nfeats) tuples until this shard's size equals to or approximates `self.shard_size`.
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def __iter__(self): """ Iterator of (example_dict, nfeats). On each call, it iterates over as many (example_dict, nfeats) tuples until this shard's size equals to or approximates `self.shard_size`. """ iteration_index = -1 if self.assoc_iter is not None: # We have associate iterator, just yields tuples # util we run parallel with it. while self.line_index < self.assoc_iter.line_index: line = self.corpus.readline() if line == '': raise AssertionError( "Two corpuses must have same number of lines!") self.line_index += 1 iteration_index += 1 yield self._example_dict_iter(line, iteration_index) if self.assoc_iter.eof: self.eof = True self.corpus.close() else: # Yield tuples util this shard's size reaches the threshold. self.corpus.seek(self.last_pos) while True: if self.shard_size != 0 and self.line_index % 64 == 0: # This part of check is time consuming on Py2 (but # it is quite fast on Py3, weird!). So we don't bother # to check for very line. Instead we chekc every 64 # lines. Thus we are not dividing exactly per # `shard_size`, but it is not too much difference. cur_pos = self.corpus.tell() if cur_pos >= self.last_pos + self.shard_size: self.last_pos = cur_pos return line = self.corpus.readline() if line == '': self.eof = True self.corpus.close() return self.line_index += 1 iteration_index += 1 yield self._example_dict_iter(line, iteration_index)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py#L354-L400
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py
python
ShardedTextCorpusIterator.hit_end
(self)
return self.eof
?
?
[ "?" ]
def hit_end(self): """ ? """ return self.eof
[ "def", "hit_end", "(", "self", ")", ":", "return", "self", ".", "eof" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py#L402-L404
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py
python
ShardedTextCorpusIterator.num_feats
(self)
return self.n_feats
We peek the first line and seek back to the beginning of the file.
We peek the first line and seek back to the beginning of the file.
[ "We", "peek", "the", "first", "line", "and", "seek", "back", "to", "the", "beginning", "of", "the", "file", "." ]
def num_feats(self): """ We peek the first line and seek back to the beginning of the file. """ saved_pos = self.corpus.tell() line = self.corpus.readline().split() if self.line_truncate: line = line[:self.line_truncate] _, _, self.n_feats = TextDataset.extract_text_features(line) self.corpus.seek(saved_pos) return self.n_feats
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/text_dataset.py#L407-L421
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py
python
ImageDataset.sort_key
(self, ex)
return (ex.src.size(2), ex.src.size(1))
Sort using the size of the image: (width, height).
Sort using the size of the image: (width, height).
[ "Sort", "using", "the", "size", "of", "the", "image", ":", "(", "width", "height", ")", "." ]
def sort_key(self, ex): """ Sort using the size of the image: (width, height).""" return (ex.src.size(2), ex.src.size(1))
[ "def", "sort_key", "(", "self", ",", "ex", ")", ":", "return", "(", "ex", ".", "src", ".", "size", "(", "2", ")", ",", "ex", ".", "src", ".", "size", "(", "1", ")", ")" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py#L80-L82
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py
python
ImageDataset.make_image_examples_nfeats_tpl
(img_iter, img_path, img_dir, image_channel_size=3)
return (examples_iter, num_feats)
Note: one of img_iter and img_path must be not None Args: img_iter(iterator): an iterator that yields pairs (img, filename) (or None) img_path(str): location of a src file containing image paths (or None) src_dir (str): location of source images Returns: (example_dict iterator, num_feats) tuple
Note: one of img_iter and img_path must be not None Args: img_iter(iterator): an iterator that yields pairs (img, filename) (or None) img_path(str): location of a src file containing image paths (or None) src_dir (str): location of source images
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def make_image_examples_nfeats_tpl(img_iter, img_path, img_dir, image_channel_size=3): """ Note: one of img_iter and img_path must be not None Args: img_iter(iterator): an iterator that yields pairs (img, filename) (or None) img_path(str): location of a src file containing image paths (or None) src_dir (str): location of source images Returns: (example_dict iterator, num_feats) tuple """ if img_iter is None: if img_path is not None: img_iter = ImageDataset. \ make_img_iterator_from_file(img_path, img_dir, image_channel_size) else: raise ValueError("""One of 'img_iter' and 'img_path' must be not None""") examples_iter = ImageDataset.make_examples(img_iter, img_dir, 'src') num_feats = 0 # Source side(img) has no features. return (examples_iter, num_feats)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py#L85-L111
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py
python
ImageDataset.make_examples
(img_iter, src_dir, side, truncate=None)
Args: path (str): location of a src file containing image paths src_dir (str): location of source images side (str): 'src' or 'tgt' truncate: maximum img size ((0,0) or None for unlimited) Yields: a dictionary containing image data, path and index for each line.
Args: path (str): location of a src file containing image paths src_dir (str): location of source images side (str): 'src' or 'tgt' truncate: maximum img size ((0,0) or None for unlimited)
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def make_examples(img_iter, src_dir, side, truncate=None): """ Args: path (str): location of a src file containing image paths src_dir (str): location of source images side (str): 'src' or 'tgt' truncate: maximum img size ((0,0) or None for unlimited) Yields: a dictionary containing image data, path and index for each line. """ assert (src_dir is not None) and os.path.exists(src_dir), \ 'src_dir must be a valid directory if data_type is img' for index, (img, filename) in enumerate(img_iter): if truncate and truncate != (0, 0): if not (img.size(1) <= truncate[0] and img.size(2) <= truncate[1]): continue example_dict = {side: img, side + '_path': filename, 'indices': index} yield example_dict
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py#L114-L137
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py
python
ImageDataset.make_img_iterator_from_file
(path, src_dir, image_channel_size=3)
Args: path(str): src_dir(str): Yields: img: and image tensor filename(str): the image filename
Args: path(str): src_dir(str):
[ "Args", ":", "path", "(", "str", ")", ":", "src_dir", "(", "str", ")", ":" ]
def make_img_iterator_from_file(path, src_dir, image_channel_size=3): """ Args: path(str): src_dir(str): Yields: img: and image tensor filename(str): the image filename """ from PIL import Image from torchvision import transforms with codecs.open(path, "r", "utf-8") as corpus_file: for line in corpus_file: filename = line.strip() img_path = os.path.join(src_dir, filename) if not os.path.exists(img_path): img_path = line assert os.path.exists(img_path), \ 'img path %s not found' % (line.strip()) if (image_channel_size == 1): img = transforms.ToTensor()( Image.fromarray(cv2.imread(img_path, 0))) else: img = transforms.ToTensor()(Image.open(img_path)) yield img, filename
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py#L140-L169
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py
python
ImageDataset.get_fields
(n_src_features, n_tgt_features)
return fields
Args: n_src_features: the number of source features to create `torchtext.data.Field` for. n_tgt_features: the number of target features to create `torchtext.data.Field` for. Returns: A dictionary whose keys are strings and whose values are the corresponding Field objects.
Args: n_src_features: the number of source features to create `torchtext.data.Field` for. n_tgt_features: the number of target features to create `torchtext.data.Field` for.
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def get_fields(n_src_features, n_tgt_features): """ Args: n_src_features: the number of source features to create `torchtext.data.Field` for. n_tgt_features: the number of target features to create `torchtext.data.Field` for. Returns: A dictionary whose keys are strings and whose values are the corresponding Field objects. """ fields = {} def make_img(data, vocab): """ ? """ c = data[0].size(0) h = max([t.size(1) for t in data]) w = max([t.size(2) for t in data]) imgs = torch.zeros(len(data), c, h, w).fill_(1) for i, img in enumerate(data): imgs[i, :, 0:img.size(1), 0:img.size(2)] = img return imgs fields["src"] = torchtext.data.Field( use_vocab=False, dtype=torch.float, postprocessing=make_img, sequential=False) for j in range(n_src_features): fields["src_feat_" + str(j)] = \ torchtext.data.Field(pad_token=PAD_WORD) fields["tgt"] = torchtext.data.Field( init_token=BOS_WORD, eos_token=EOS_WORD, pad_token=PAD_WORD) for j in range(n_tgt_features): fields["tgt_feat_" + str(j)] = \ torchtext.data.Field(init_token=BOS_WORD, eos_token=EOS_WORD, pad_token=PAD_WORD) def make_src(data, vocab): """ ? """ src_size = max([t.size(0) for t in data]) src_vocab_size = max([t.max() for t in data]) + 1 alignment = torch.zeros(src_size, len(data), src_vocab_size) for i, sent in enumerate(data): for j, t in enumerate(sent): alignment[j, i, t] = 1 return alignment fields["src_map"] = torchtext.data.Field( use_vocab=False, dtype=torch.float, postprocessing=make_src, sequential=False) def make_tgt(data, vocab): """ ? """ tgt_size = max([t.size(0) for t in data]) alignment = torch.zeros(tgt_size, len(data)).long() for i, sent in enumerate(data): alignment[:sent.size(0), i] = sent return alignment fields["alignment"] = torchtext.data.Field( use_vocab=False, dtype=torch.long, postprocessing=make_tgt, sequential=False) fields["indices"] = torchtext.data.Field( use_vocab=False, dtype=torch.long, sequential=False) return fields
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py#L172-L243
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py
python
ImageDataset.get_num_features
(corpus_file, side)
return num_feats
For image corpus, source side is in form of image, thus no feature; while target side is in form of text, thus we can extract its text features. Args: corpus_file (str): file path to get the features. side (str): 'src' or 'tgt'. Returns: number of features on `side`.
For image corpus, source side is in form of image, thus no feature; while target side is in form of text, thus we can extract its text features.
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def get_num_features(corpus_file, side): """ For image corpus, source side is in form of image, thus no feature; while target side is in form of text, thus we can extract its text features. Args: corpus_file (str): file path to get the features. side (str): 'src' or 'tgt'. Returns: number of features on `side`. """ if side == 'src': num_feats = 0 else: with codecs.open(corpus_file, "r", "utf-8") as cf: f_line = cf.readline().strip().split() _, _, num_feats = ImageDataset.extract_text_features(f_line) return num_feats
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/image_dataset.py#L246-L266
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/audio_dataset.py
python
AudioDataset.sort_key
(self, ex)
return ex.src.size(1)
Sort using duration time of the sound spectrogram.
Sort using duration time of the sound spectrogram.
[ "Sort", "using", "duration", "time", "of", "the", "sound", "spectrogram", "." ]
def sort_key(self, ex): """ Sort using duration time of the sound spectrogram. """ return ex.src.size(1)
[ "def", "sort_key", "(", "self", ",", "ex", ")", ":", "return", "ex", ".", "src", ".", "size", "(", "1", ")" ]
https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/audio_dataset.py#L90-L92
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/audio_dataset.py
python
AudioDataset.make_audio_examples_nfeats_tpl
(path, audio_dir, sample_rate, window_size, window_stride, window, normalize_audio, truncate=None)
return (examples_iter, num_feats)
Args: path (str): location of a src file containing audio paths. audio_dir (str): location of source audio files. sample_rate (int): sample_rate. window_size (float) : window size for spectrogram in seconds. window_stride (float): window stride for spectrogram in seconds. window (str): window type for spectrogram generation. normalize_audio (bool): subtract spectrogram by mean and divide by std or not. truncate (int): maximum audio length (0 or None for unlimited). Returns: (example_dict iterator, num_feats) tuple
Args: path (str): location of a src file containing audio paths. audio_dir (str): location of source audio files. sample_rate (int): sample_rate. window_size (float) : window size for spectrogram in seconds. window_stride (float): window stride for spectrogram in seconds. window (str): window type for spectrogram generation. normalize_audio (bool): subtract spectrogram by mean and divide by std or not. truncate (int): maximum audio length (0 or None for unlimited).
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def make_audio_examples_nfeats_tpl(path, audio_dir, sample_rate, window_size, window_stride, window, normalize_audio, truncate=None): """ Args: path (str): location of a src file containing audio paths. audio_dir (str): location of source audio files. sample_rate (int): sample_rate. window_size (float) : window size for spectrogram in seconds. window_stride (float): window stride for spectrogram in seconds. window (str): window type for spectrogram generation. normalize_audio (bool): subtract spectrogram by mean and divide by std or not. truncate (int): maximum audio length (0 or None for unlimited). Returns: (example_dict iterator, num_feats) tuple """ examples_iter = AudioDataset.read_audio_file( path, audio_dir, "src", sample_rate, window_size, window_stride, window, normalize_audio, truncate) num_feats = 0 # Source side(audio) has no features. return (examples_iter, num_feats)
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/audio_dataset.py#L95-L120
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/audio_dataset.py
python
AudioDataset.read_audio_file
(path, src_dir, side, sample_rate, window_size, window_stride, window, normalize_audio, truncate=None)
Args: path (str): location of a src file containing audio paths. src_dir (str): location of source audio files. side (str): 'src' or 'tgt'. sample_rate (int): sample_rate. window_size (float) : window size for spectrogram in seconds. window_stride (float): window stride for spectrogram in seconds. window (str): window type for spectrogram generation. normalize_audio (bool): subtract spectrogram by mean and divide by std or not. truncate (int): maximum audio length (0 or None for unlimited). Yields: a dictionary containing audio data for each line.
Args: path (str): location of a src file containing audio paths. src_dir (str): location of source audio files. side (str): 'src' or 'tgt'. sample_rate (int): sample_rate. window_size (float) : window size for spectrogram in seconds. window_stride (float): window stride for spectrogram in seconds. window (str): window type for spectrogram generation. normalize_audio (bool): subtract spectrogram by mean and divide by std or not. truncate (int): maximum audio length (0 or None for unlimited).
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def read_audio_file(path, src_dir, side, sample_rate, window_size, window_stride, window, normalize_audio, truncate=None): """ Args: path (str): location of a src file containing audio paths. src_dir (str): location of source audio files. side (str): 'src' or 'tgt'. sample_rate (int): sample_rate. window_size (float) : window size for spectrogram in seconds. window_stride (float): window stride for spectrogram in seconds. window (str): window type for spectrogram generation. normalize_audio (bool): subtract spectrogram by mean and divide by std or not. truncate (int): maximum audio length (0 or None for unlimited). Yields: a dictionary containing audio data for each line. """ assert (src_dir is not None) and os.path.exists(src_dir),\ "src_dir must be a valid directory if data_type is audio" import torchaudio import librosa import numpy as np with codecs.open(path, "r", "utf-8") as corpus_file: index = 0 for line in corpus_file: audio_path = os.path.join(src_dir, line.strip()) if not os.path.exists(audio_path): audio_path = line assert os.path.exists(audio_path), \ 'audio path %s not found' % (line.strip()) sound, sample_rate = torchaudio.load(audio_path) if truncate and truncate > 0: if sound.size(0) > truncate: continue assert sample_rate == sample_rate, \ 'Sample rate of %s != -sample_rate (%d vs %d)' \ % (audio_path, sample_rate, sample_rate) sound = sound.numpy() if len(sound.shape) > 1: if sound.shape[1] == 1: sound = sound.squeeze() else: sound = sound.mean(axis=1) # average multiple channels n_fft = int(sample_rate * window_size) win_length = n_fft hop_length = int(sample_rate * window_stride) # STFT d = librosa.stft(sound, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window) spect, _ = librosa.magphase(d) spect = np.log1p(spect) spect = torch.FloatTensor(spect) if normalize_audio: mean = spect.mean() std = spect.std() spect.add_(-mean) spect.div_(std) example_dict = {side: spect, side + '_path': line.strip(), 'indices': index} index += 1 yield example_dict
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/audio_dataset.py#L123-L195
Alex-Fabbri/Multi-News
f6476d1f114662eb93db32e9b704b7c4fe047217
code/OpenNMT-py-baselines/onmt/inputters/audio_dataset.py
python
AudioDataset.get_fields
(n_src_features, n_tgt_features)
return fields
Args: n_src_features: the number of source features to create `torchtext.data.Field` for. n_tgt_features: the number of target features to create `torchtext.data.Field` for. Returns: A dictionary whose keys are strings and whose values are the corresponding Field objects.
Args: n_src_features: the number of source features to create `torchtext.data.Field` for. n_tgt_features: the number of target features to create `torchtext.data.Field` for.
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def get_fields(n_src_features, n_tgt_features): """ Args: n_src_features: the number of source features to create `torchtext.data.Field` for. n_tgt_features: the number of target features to create `torchtext.data.Field` for. Returns: A dictionary whose keys are strings and whose values are the corresponding Field objects. """ fields = {} def make_audio(data, vocab): """ ? """ nfft = data[0].size(0) t = max([t.size(1) for t in data]) sounds = torch.zeros(len(data), 1, nfft, t) for i, spect in enumerate(data): sounds[i, :, :, 0:spect.size(1)] = spect return sounds fields["src"] = torchtext.data.Field( use_vocab=False, dtype=torch.float, postprocessing=make_audio, sequential=False) for j in range(n_src_features): fields["src_feat_" + str(j)] = \ torchtext.data.Field(pad_token=PAD_WORD) fields["tgt"] = torchtext.data.Field( init_token=BOS_WORD, eos_token=EOS_WORD, pad_token=PAD_WORD) for j in range(n_tgt_features): fields["tgt_feat_" + str(j)] = \ torchtext.data.Field(init_token=BOS_WORD, eos_token=EOS_WORD, pad_token=PAD_WORD) def make_src(data, vocab): """ ? """ src_size = max([t.size(0) for t in data]) src_vocab_size = max([t.max() for t in data]) + 1 alignment = torch.zeros(src_size, len(data), src_vocab_size) for i, sent in enumerate(data): for j, t in enumerate(sent): alignment[j, i, t] = 1 return alignment fields["src_map"] = torchtext.data.Field( use_vocab=False, dtype=torch.float, postprocessing=make_src, sequential=False) def make_tgt(data, vocab): """ ? """ tgt_size = max([t.size(0) for t in data]) alignment = torch.zeros(tgt_size, len(data)).long() for i, sent in enumerate(data): alignment[:sent.size(0), i] = sent return alignment fields["alignment"] = torchtext.data.Field( use_vocab=False, dtype=torch.long, postprocessing=make_tgt, sequential=False) fields["indices"] = torchtext.data.Field( use_vocab=False, dtype=torch.long, sequential=False) return fields
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https://github.com/Alex-Fabbri/Multi-News/blob/f6476d1f114662eb93db32e9b704b7c4fe047217/code/OpenNMT-py-baselines/onmt/inputters/audio_dataset.py#L198-L268