class ShuffleSelfAttentionModel(Model):
def __init__(self,
save_path,
log_path,
n_depth,
d_features,
d_classifier,
d_output,
threshold=None,
stack='ShuffleSelfAttention',
expansion_layer='ChannelWiseConvExpansion',
mode='1d',
optimizer=None,
**kwargs):
'''*args: n_layers, n_head, n_channel, n_vchannel, dropout, use_bottleneck, d_bottleneck'''
'''
Arguments:
mode: 1d: 1d output
2d: 2d output
residual: residual output
dense: dense net
'''
super().__init__(save_path, log_path)
self.d_output = d_output
self.threshold = threshold
# ----------------------------- Model ------------------------------ #
stack_dict = {
'ReactionAttention': ReactionAttentionStack,
'SelfAttention': SelfAttentionStack,
'Alternate': AlternateStack,
'Parallel': ParallelStack,
'ShuffleSelfAttention': ShuffleSelfAttentionStack,
'ShuffleSelfAttentionStackV2': ShuffleSelfAttentionStackV2,
}
expansion_dict = {
'LinearExpansion': LinearExpansion,
'ReduceParamLinearExpansion': ReduceParamLinearExpansion,
'ConvExpansion': ConvExpansion,
'LinearConvExpansion': LinearConvExpansion,
'ShuffleConvExpansion': ShuffleConvExpansion,
'ChannelWiseConvExpansion': ChannelWiseConvExpansion,
}
self.model = stack_dict[stack](expansion_dict[expansion_layer],
n_depth=n_depth,
d_features=d_features,
mode=mode,
**kwargs)
# --------------------------- Classifier --------------------------- #
if mode == '1d':
self.classifier = LinearClassifier(d_features, d_classifier,
d_output)
elif mode == '2d':
self.classifier = LinearClassifier(n_depth * d_features,
d_classifier, d_output)
else:
self.classifier = None
# ------------------------------ CUDA ------------------------------ #
# If GPU available, move the graph to GPU(s)
self.CUDA_AVAILABLE = self.check_cuda()
if self.CUDA_AVAILABLE:
# self.model.cuda()
# self.classifier.cuda()
device_ids = list(range(torch.cuda.device_count()))
self.model = nn.DataParallel(self.model, device_ids)
self.classifier = nn.DataParallel(self.classifier, device_ids)
self.model.to('cuda')
self.classifier.to('cuda')
assert (next(self.model.parameters()).is_cuda)
assert (next(self.classifier.parameters()).is_cuda)
pass
else:
print('CUDA not found or not enabled, use CPU instead')
# ---------------------------- Optimizer --------------------------- #
self.parameters = list(self.model.parameters()) + list(
self.classifier.parameters())
if optimizer == None:
self.optimizer = AdamW(self.parameters,
lr=0.002,
betas=(0.9, 0.999),
weight_decay=0.001)
# ------------------------ training control ------------------------ #
self.controller = TrainingControl(max_step=100000,
evaluate_every_nstep=100,
print_every_nstep=10)
self.early_stopping = EarlyStopping(patience=50)
# --------------------- logging and tensorboard -------------------- #
self.set_logger()
self.set_summary_writer()
# ---------------------------- END INIT ---------------------------- #
def checkpoint(self, step):
checkpoint = {
'model_state_dict': self.model.state_dict(),
'classifier_state_dict': self.classifier.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'global_step': step
}
return checkpoint
def train_epoch(self, train_dataloader, eval_dataloader, device, smothing,
earlystop):
''' Epoch operation in training phase'''
if device == 'cuda':
assert self.CUDA_AVAILABLE
# Set model and classifier training mode
self.model.train()
self.classifier.train()
total_loss = 0
batch_counter = 0
# update param per batch
for batch in tqdm(train_dataloader,
mininterval=1,
desc=' - (Training) ',
leave=False): # training_data should be a iterable
# get data from dataloader
feature_1, feature_2, y = parse_data(batch, device)
batch_size = len(feature_1)
# forward
self.optimizer.zero_grad()
logits, attn = self.model(feature_1, feature_2)
logits = logits.view(batch_size, -1)
logits = self.classifier(logits)
# Judge if it's a regression problem
if self.d_output == 1:
pred = logits.sigmoid()
loss = mse_loss(pred, y)
else:
pred = logits
loss = cross_entropy_loss(pred, y, smoothing=smothing)
# calculate gradients
loss.backward()
# update parameters
self.optimizer.step()
# get metrics for logging
acc = accuracy(pred, y, threshold=self.threshold)
precision, recall, precision_avg, recall_avg = precision_recall(
pred, y, self.d_output, threshold=self.threshold)
total_loss += loss.item()
batch_counter += 1
# training control
state_dict = self.controller(batch_counter)
if state_dict['step_to_print']:
self.train_logger.info(
'[TRAINING] - step: %5d, loss: %3.4f, acc: %1.4f, pre: %1.4f, rec: %1.4f'
% (state_dict['step'], loss, acc, precision[1], recall[1]))
self.summary_writer.add_scalar('loss/train', loss,
state_dict['step'])
self.summary_writer.add_scalar('acc/train', acc,
state_dict['step'])
self.summary_writer.add_scalar('precision/train', precision[1],
state_dict['step'])
self.summary_writer.add_scalar('recall/train', recall[1],
state_dict['step'])
if state_dict['step_to_evaluate']:
stop = self.val_epoch(eval_dataloader, device,
state_dict['step'])
state_dict['step_to_stop'] = stop
if earlystop & stop:
break
if self.controller.current_step == self.controller.max_step:
state_dict['step_to_stop'] = True
break
return state_dict
def val_epoch(self, dataloader, device, step=0, plot=False):
''' Epoch operation in evaluation phase '''
if device == 'cuda':
assert self.CUDA_AVAILABLE
# Set model and classifier training mode
self.model.eval()
self.classifier.eval()
# use evaluator to calculate the average performance
evaluator = Evaluator()
pred_list = []
real_list = []
with torch.no_grad():
for batch in tqdm(
dataloader,
mininterval=5,
desc=' - (Evaluation) ',
leave=False): # training_data should be a iterable
# get data from dataloader
feature_1, feature_2, y = parse_data(batch, device)
batch_size = len(feature_1)
# get logits
logits, attn = self.model(feature_1, feature_2)
logits = logits.view(batch_size, -1)
logits = self.classifier(logits)
if self.d_output == 1:
pred = logits.sigmoid()
loss = mse_loss(pred, y)
else:
pred = logits
loss = cross_entropy_loss(pred, y, smoothing=False)
acc = accuracy(pred, y, threshold=self.threshold)
precision, recall, _, _ = precision_recall(
pred, y, self.d_output, threshold=self.threshold)
# feed the metrics in the evaluator
evaluator(loss.item(), acc.item(), precision[1].item(),
recall[1].item())
'''append the results to the predict / real list for drawing ROC or PR curve.'''
if plot:
pred_list += pred.tolist()
real_list += y.tolist()
if plot:
area, precisions, recalls, thresholds = pr(
pred_list, real_list)
plot_pr_curve(recalls, precisions, auc=area)
# get evaluation results from the evaluator
loss_avg, acc_avg, pre_avg, rec_avg = evaluator.avg_results()
self.eval_logger.info(
'[EVALUATION] - step: %5d, loss: %3.4f, acc: %1.4f, pre: %1.4f, rec: %1.4f'
% (step, loss_avg, acc_avg, pre_avg, rec_avg))
self.summary_writer.add_scalar('loss/eval', loss_avg, step)
self.summary_writer.add_scalar('acc/eval', acc_avg, step)
self.summary_writer.add_scalar('precision/eval', pre_avg, step)
self.summary_writer.add_scalar('recall/eval', rec_avg, step)
state_dict = self.early_stopping(loss_avg)
if state_dict['save']:
checkpoint = self.checkpoint(step)
self.save_model(
checkpoint,
self.save_path + '-step-%d_loss-%.5f' % (step, loss_avg))
return state_dict['break']
def train(self,
max_epoch,
train_dataloader,
eval_dataloader,
device,
smoothing=False,
earlystop=False,
save_mode='best'):
assert save_mode in ['all', 'best']
# train for n epoch
for epoch_i in range(max_epoch):
print('[ Epoch', epoch_i, ']')
# set current epoch
self.controller.set_epoch(epoch_i + 1)
# train for on epoch
state_dict = self.train_epoch(train_dataloader, eval_dataloader,
device, smoothing, earlystop)
# if state_dict['step_to_stop']:
# break
checkpoint = self.checkpoint(state_dict['step'])
self.save_model(checkpoint,
self.save_path + '-step-%d' % state_dict['step'])
self.train_logger.info(
'[INFO]: Finish Training, ends with %d epoch(s) and %d batches, in total %d training steps.'
%
(state_dict['epoch'] - 1, state_dict['batch'], state_dict['step']))
def get_predictions(self,
data_loader,
device,
max_batches=None,
activation=None):
pred_list = []
real_list = []
self.model.eval()
self.classifier.eval()
batch_counter = 0
with torch.no_grad():
for batch in tqdm(data_loader,
desc=' - (Testing) ',
leave=False):
feature_1, feature_2, y = parse_data(batch, device)
# get logits
logits, attn = self.model(feature_1, feature_2)
logits = logits.view(logits.shape[0], -1)
logits = self.classifier(logits)
# Whether to apply activation function
if activation != None:
pred = activation(logits)
else:
pred = logits.softmax(dim=-1)
pred_list += pred.tolist()
real_list += y.tolist()
if max_batches != None:
batch_counter += 1
if batch_counter >= max_batches:
break
return pred_list, real_list
class TransformingAutoEncoderController(nn.Module, ControllableModel):
'''
Transforming Auto-Encoder.
References:
- Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
- Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681-694.
- Miller, A., Fisch, A., Dodge, J., Karimi, A. H., Bordes, A., & Weston, J. (2016). Key-value memory networks for directly reading documents. arXiv preprint arXiv:1606.03126.
- Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018) Improving Language Understanding by Generative Pre-Training. OpenAI (URL: https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
- Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention is all you need. arXiv preprint arXiv:1706.03762.
'''
__loaded_filename = None
__loaded_ctx = None
# `bool` that means initialization in this class will be deferred or not.
__init_deferred_flag = False
def __init__(
self,
computable_loss=None,
optimizer_f=None,
encoder=None,
decoder=None,
reconstructor=None,
layer_n=3,
head_n=3,
seq_len=5,
depth_dim=100,
hidden_dim=100,
self_attention_activation_list=[],
multi_head_attention_activation_list=[],
fc_activation_list=[],
learning_rate=1e-05,
weight_decay=0.01,
dropout_rate=0.5,
ctx="cpu",
regularizatable_data_list=[],
not_init_flag=False,
):
'''
Init.
Args:
computable_loss: is-a `ComputableLoss` or `gluon.loss`.
encoder: is-a `TransformerModel`.
decoder: is-a `TransformerModel`.
reconstructor: is-a `TransformerModel`.
layer_n: `int` of the number of layers.
head_n: `int` of the number of heads for multi-head attention model.
seq_len: `int` of the length of sequences.
depth_dim: `int` of dimension of dense layer.
hidden_dim: `int` of dimension of hidden(encoder) layer.
self_attention_activation_list: `list` of `str` of activation function for self-attention model.
multi_head_attention_activation_list: `list` of `str` of activation function for multi-head attention model.
fc_activation_list: `list` of `str` of activation function in fully-connected layers.
learning_rate: `float` of learning rate.
learning_attenuate_rate: `float` of attenuate the `learning_rate` by a factor of this value every `attenuate_epoch`.
attenuate_epoch: `int` of attenuate the `learning_rate` by a factor of `learning_attenuate_rate` every `attenuate_epoch`.
optimizer_name: `str` of name of optimizer.
hybridize_flag: Call `mxnet.gluon.HybridBlock.hybridize()` or not.
scale: `float` of scaling factor for initial parameters.
ctx: `mx.cpu()` or `mx.gpu()`.
initializer: is-a `mxnet.initializer` for parameters of model. If `None`, it is drawing from the Xavier distribution.
'''
super(TransformingAutoEncoderController, self).__init__()
if computable_loss is None:
computable_loss = nn.CrossEntropyLoss()
self.__computable_loss = computable_loss
if encoder is None:
if hidden_dim is None or hidden_dim == depth_dim:
encoder = TransformerEncoder(
depth_dim=depth_dim,
layer_n=layer_n,
head_n=head_n,
self_attention_activation_list=
self_attention_activation_list,
fc_activation_list=fc_activation_list,
computable_loss=computable_loss,
not_init_flag=not_init_flag,
dropout_rate=dropout_rate,
ctx=ctx)
else:
encoder = TransformerEncoder(
depth_dim=hidden_dim,
layer_n=layer_n,
head_n=head_n,
self_attention_activation_list=
self_attention_activation_list,
fc_activation_list=fc_activation_list,
computable_loss=computable_loss,
not_init_flag=not_init_flag,
dropout_rate=dropout_rate,
ctx=ctx)
encoder.embedding_flag = False
else:
if isinstance(encoder, TransformerModel) is False:
raise TypeError(
"The type of `encoder` must be `TransformerModel`.")
if decoder is None:
if hidden_dim is None or hidden_dim == depth_dim:
decoder = TransformerDecoder(
head_n=head_n,
depth_dim=depth_dim,
layer_n=layer_n,
self_attention_activation_list=
self_attention_activation_list,
multi_head_attention_activation_list=
multi_head_attention_activation_list,
fc_activation_list=fc_activation_list,
computable_loss=computable_loss,
not_init_flag=not_init_flag,
ctx=ctx)
else:
decoder = TransformerDecoder(
head_n=head_n,
depth_dim=hidden_dim,
output_dim=hidden_dim,
layer_n=layer_n,
self_attention_activation_list=
self_attention_activation_list,
multi_head_attention_activation_list=
multi_head_attention_activation_list,
fc_activation_list=fc_activation_list,
computable_loss=computable_loss,
not_init_flag=not_init_flag,
ctx=ctx)
decoder.embedding_flag = False
else:
if isinstance(decoder, TransformerModel) is False:
raise TypeError(
"The type of `decoder` must be `TransformerModel`.")
if reconstructor is None:
if hidden_dim is None or hidden_dim == depth_dim:
reconstructor = TransformerReconstructor(
head_n=head_n,
depth_dim=depth_dim,
layer_n=layer_n,
self_attention_activation_list=
self_attention_activation_list,
fc_activation_list=fc_activation_list,
computable_loss=computable_loss,
not_init_flag=not_init_flag,
ctx=ctx,
)
else:
reconstructor = TransformerReconstructor(
head_n=head_n,
depth_dim=hidden_dim,
output_dim=depth_dim,
layer_n=layer_n,
self_attention_activation_list=
self_attention_activation_list,
fc_activation_list=fc_activation_list,
computable_loss=computable_loss,
not_init_flag=not_init_flag,
ctx=ctx,
)
reconstructor.embedding_flag = False
else:
if isinstance(reconstructor, TransformerModel) is False:
raise TypeError(
"The type of `reconstructor` must be `TransformerModel`.")
logger = getLogger("accelbrainbase")
self.logger = logger
self.encoder = encoder
self.decoder = decoder
self.reconstructor = reconstructor
if hidden_dim is not None and hidden_dim != depth_dim:
self.encoder_hidden_fc = nn.Linear(
depth_dim,
hidden_dim,
bias=True,
)
self.decoder_hidden_fc = nn.Linear(
depth_dim,
hidden_dim,
bias=True,
)
init_flag = True
else:
self.encoder_hidden_fc = None
self.decoder_hidden_fc = None
init_flag = False
self.__ctx = ctx
self.to(self.__ctx)
if self.init_deferred_flag is False:
if not_init_flag is False:
if optimizer_f is not None:
if init_flag is True:
self.optimizer = optimizer_f(self.parameters(), )
else:
if init_flag is True:
self.optimizer = AdamW(self.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
for v in regularizatable_data_list:
if isinstance(v, RegularizatableData) is False:
raise TypeError(
"The type of values of `regularizatable_data_list` must be `RegularizatableData`."
)
self.__regularizatable_data_list = regularizatable_data_list
self.__learning_rate = learning_rate
self.__weight_decay = weight_decay
self.seq_len = seq_len
self.epoch = 0
def learn(self, iteratable_data):
'''
Learn samples drawn by `IteratableData.generate_learned_samples()`.
Args:
iteratable_data: is-a `TransformerIterator`.
'''
if isinstance(iteratable_data, TransformerIterator) is False:
raise TypeError(
"The type of `iteratable_data` must be `TransformerIterator`.")
self.__loss_list = []
learning_rate = self.__learning_rate
try:
epoch = self.epoch
iter_n = 0
for encoded_observed_arr, decoded_observed_arr, encoded_mask_arr, decoded_mask_arr, test_encoded_observed_arr, test_decoded_observed_arr, test_encoded_mask_arr, test_decoded_mask_arr, training_target_arr, test_target_arr in iteratable_data.generate_learned_samples(
):
self.epoch = epoch
if self.encoder.optimizer is not None and self.decoder.optimizer is not None:
optimizer_setup_flag = True
self.encoder.optimizer.zero_grad()
self.decoder.optimizer.zero_grad()
self.optimizer.zero_grad()
else:
optimizer_setup_flag = False
pred_arr = self.inference(encoded_observed_arr,
decoded_observed_arr,
encoded_mask_arr, decoded_mask_arr)
loss = self.compute_loss(pred_arr, training_target_arr)
if optimizer_setup_flag is False:
self.encoder.optimizer.zero_grad()
self.decoder.optimizer.zero_grad()
self.optimizer.zero_grad()
pred_arr = self.inference(encoded_observed_arr,
decoded_observed_arr,
encoded_mask_arr,
decoded_mask_arr)
loss = self.compute_loss(pred_arr, training_target_arr)
loss.backward()
self.optimizer.step()
self.decoder.optimizer.step()
self.encoder.optimizer.step()
self.regularize()
self.decoder.regularize()
self.encoder.regularize()
if (iter_n + 1) % int(
iteratable_data.iter_n / iteratable_data.epochs) == 0:
if torch.inference_mode():
test_pred_arr = self.inference(
test_encoded_observed_arr,
test_decoded_observed_arr, test_encoded_mask_arr,
test_decoded_mask_arr)
test_loss = self.compute_loss(test_pred_arr,
test_target_arr)
_loss = loss.to('cpu').detach().numpy().copy()
_test_loss = test_loss.to('cpu').detach().numpy().copy()
self.__loss_list.append((_loss, _test_loss))
self.logger.debug("Epochs: " + str(epoch + 1) +
" Train loss: " + str(_loss) +
" Test loss: " + str(_test_loss))
epoch += 1
iter_n += 1
except KeyboardInterrupt:
self.logger.debug("Interrupt.")
self.logger.debug("end. ")
self.epoch = epoch
def inference(
self,
encoded_observed_arr,
decoded_observed_arr,
encoded_mask_arr=None,
decoded_mask_arr=None,
):
'''
Inference samples drawn by `IteratableData.generate_inferenced_samples()`.
Args:
encoded_observed_arr: rank-3 Array like or sparse matrix as the observed data points.
The shape is: (batch size, the length of sequence, feature points)
decoded_observed_arr: rank-3 Array like or sparse matrix as the observed data points.
The shape is: (batch size, the length of sequence, feature points)
encoded_mask_arr: rank-3 Array like or sparse matrix as the observed data points.
The shape is: (batch size, the length of sequence, feature points)
decoded_mask_arr: rank-3 Array like or sparse matrix as the observed data points.
The shape is: (batch size, the length of sequence, feature points)
Returns:
`mxnet.ndarray` of inferenced feature points.
'''
return self(
encoded_observed_arr,
decoded_observed_arr,
encoded_mask_arr,
decoded_mask_arr,
)
def compute_loss(self, pred_arr, labeled_arr):
'''
Compute loss.
Args:
pred_arr: `mxnet.ndarray` or `mxnet.symbol`.
labeled_arr: `mxnet.ndarray` or `mxnet.symbol`.
Returns:
loss.
'''
return self.__computable_loss(pred_arr, labeled_arr)
def forward(
self,
encoded_observed_arr,
decoded_observed_arr,
encoded_mask_arr=None,
decoded_mask_arr=None,
):
'''
Hybrid forward with Gluon API.
Args:
F: `mxnet.ndarray` or `mxnet.symbol`.
encoded_observed_arr: rank-3 Array like or sparse matrix as the observed data points.
The shape is: (batch size, the length of sequence, feature points)
decoded_observed_arr: rank-3 Array like or sparse matrix as the observed data points.
The shape is: (batch size, the length of sequence, feature points)
encoded_mask_arr: rank-3 Array like or sparse matrix as the observed data points.
The shape is: (batch size, the length of sequence, feature points)
decoded_mask_arr: rank-3 Array like or sparse matrix as the observed data points.
The shape is: (batch size, the length of sequence, feature points)
Returns:
`mxnet.ndarray` or `mxnet.symbol` of inferenced feature points.
'''
if self.__loaded_filename is not None:
loaded_filename = self.__loaded_filename
self.__loaded_filename = None
init_encoded_observed_arr = encoded_observed_arr.detach()
init_decoded_observed_arr = decoded_observed_arr.detach()
if encoded_mask_arr is not None:
init_encoded_mask_arr = encoded_mask_arr.detach()
else:
init_encoded_mask_arr = None
if decoded_mask_arr is not None:
init_decoded_mask_arr = decoded_mask_arr.detach()
else:
init_decoded_mask_arr = decoded_mask_arr
_ = self.forward(
init_encoded_observed_arr,
init_decoded_observed_arr,
init_encoded_mask_arr,
init_decoded_mask_arr,
)
self.load_parameters(loaded_filename, ctx=self.__loaded_ctx)
self.__loaded_ctx = None
if encoded_mask_arr is None:
encoded_mask_arr = torch.ones(
(encoded_observed_arr.shape[0], 1, 1, 1), )
encoded_mask_arr = encoded_mask_arr.to(encoded_observed_arr.device)
if decoded_mask_arr is None:
decoded_mask_arr = torch.ones(
(decoded_observed_arr.shape[0], 1, 1, 1), )
decoded_mask_arr = decoded_mask_arr.to(decoded_observed_arr.device)
if self.encoder_hidden_fc is not None:
encoded_observed_arr = self.encoder_hidden_fc(encoded_observed_arr)
if self.decoder_hidden_fc is not None:
decoded_observed_arr = self.decoder_hidden_fc(decoded_observed_arr)
encoded_arr = self.encoder(encoded_observed_arr, encoded_mask_arr)
decoded_arr = self.decoder(
decoded_observed_arr,
encoded_arr,
decoded_mask_arr,
encoded_mask_arr,
)
self.feature_points_arr = decoded_arr
reconstructed_arr = self.reconstructor(decoded_arr, None)
return reconstructed_arr
def extract_learned_dict(self):
'''
Extract (pre-) learned parameters.
Returns:
`dict` of the parameters.
'''
params_dict = {}
for k in self.state_dict().keys():
params_dict.setdefault(k, self.state_dict()[k])
return params_dict
def regularize(self):
'''
Regularization.
'''
if len(self.__regularizatable_data_list) > 0:
params_dict = self.extract_learned_dict()
for regularizatable in self.__regularizatable_data_list:
params_dict = regularizatable.regularize(params_dict)
for k, params in params_dict.items():
self.load_state_dict({k: params}, strict=False)
def __rename_file(self, filename):
filename_list = filename.split(".")
_format = filename_list[-1]
encoder_filename = filename.replace("." + _format,
"_encoder." + _format)
decoder_filename = filename.replace("." + _format,
"_decoder." + _format)
reconstructor_filename = filename.replace("." + _format,
"_reconstructor." + _format)
return encoder_filename, decoder_filename, reconstructor_filename
def save_parameters(self, filename):
'''
Save parameters to files.
Args:
filename: File name.
'''
encoder_filename, decoder_filename, reconstructor_filename = self.__rename_file(
filename)
self.encoder.epoch = self.epoch
self.encoder.loss_arr = self.loss_arr
self.encoder.save_parameters(encoder_filename)
self.decoder.epoch = self.epoch
self.decoder.loss_arr = self.loss_arr
self.decoder.save_parameters(decoder_filename)
self.reconstructor.epoch = self.epoch
self.reconstructor.loss_arr = self.loss_arr
self.reconstructor.save_parameters(decoder_filename)
torch.save(
{
'model_state_dict': self.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'epoch': self.epoch,
'loss': self.loss_arr,
}, filename)
def load_parameters(self, filename, ctx=None, strict=True):
'''
Load parameters to files.
Args:
filename: File name.
ctx: Context-manager that changes the selected device.
strict: Whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: `True`.
'''
try:
encoder_filename, decoder_filename, reconstructor_filename = self.__rename_file(
filename)
self.encoder.load_parameters(encoder_filename,
ctx=ctx,
strict=strict)
self.decoder.load_parameters(decoder_filename,
ctx=ctx,
strict=strict)
self.reconstructor.load_parameters(reconstructor_filename,
ctx=ctx,
strict=strict)
checkpoint = torch.load(filename)
self.load_state_dict(checkpoint['model_state_dict'], strict=strict)
self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
self.epoch = checkpoint['epoch']
self.__loss_list = checkpoint['loss'].tolist()
except RuntimeError:
self.__loaded_filename = filename
self.__loaded_ctx = ctx
if ctx is not None:
self.to(ctx)
self.encoder.to(ctx)
self.decoder.to(ctx)
self.reconstructor.to(ctx)
self.__ctx = ctx
def set_readonly(self, value):
''' setter '''
raise TypeError("This property must be read-only.")
def get_init_deferred_flag(self):
''' getter for `bool` that means initialization in this class will be deferred or not.'''
return self.__init_deferred_flag
def set_init_deferred_flag(self, value):
''' setter for `bool` that means initialization in this class will be deferred or not.'''
self.__init_deferred_flag = value
init_deferred_flag = property(get_init_deferred_flag,
set_init_deferred_flag)
__loss_list = []
def get_loss_arr(self):
''' getter '''
return np.array(self.__loss_list)
def set_loss_arr(self, value):
''' setter '''
raise TypeError("This property must be read-only.")
loss_arr = property(get_loss_arr, set_loss_arr)
def train():
global writer
# For parsing commandline arguments
parser = argparse.ArgumentParser()
parser.add_argument(
"--dataset_root",
type=str,
required=True,
help='path to dataset folder containing train-test-validation folders')
parser.add_argument("--checkpoint_dir",
type=str,
required=True,
help='path to folder for saving checkpoints')
parser.add_argument("--checkpoint",
type=str,
help='path of checkpoint for pretrained model')
parser.add_argument(
"--train_continue",
type=bool,
default=False,
help=
'If resuming from checkpoint, set to True and set `checkpoint` path. Default: False.'
)
parser.add_argument("--epochs",
type=int,
default=200,
help='number of epochs to train. Default: 200.')
parser.add_argument("--train_batch_size",
type=int,
default=3,
help='batch size for training. Default: 6.')
parser.add_argument("--validation_batch_size",
type=int,
default=6,
help='batch size for validation. Default: 10.')
parser.add_argument("--init_learning_rate",
type=float,
default=0.0001,
help='set initial learning rate. Default: 0.0001.')
parser.add_argument(
"--milestones",
type=list,
default=[25, 50],
help=
'UNUSED NOW: Set to epoch values where you want to decrease learning rate by a factor of 0.1. Default: [100, 150]'
)
parser.add_argument(
"--progress_iter",
type=int,
default=200,
help=
'frequency of reporting progress and validation. N: after every N iterations. Default: 100.'
)
parser.add_argument(
"--checkpoint_epoch",
type=int,
default=5,
help=
'checkpoint saving frequency. N: after every N epochs. Each checkpoint is roughly of size 151 MB.Default: 5.'
)
args = parser.parse_args()
##[TensorboardX](https://github.com/lanpa/tensorboardX)
### For visualizing loss and interpolated frames
###Initialize flow computation and arbitrary-time flow interpolation CNNs.
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
flowComp = model.UNet(6, 4)
flowComp.to(device)
ArbTimeFlowIntrp = model.UNet(20, 5)
ArbTimeFlowIntrp.to(device)
###Initialze backward warpers for train and validation datasets
train_W_dim = 352
train_H_dim = 352
trainFlowBackWarp = model.backWarp(train_W_dim, train_H_dim, device)
trainFlowBackWarp = trainFlowBackWarp.to(device)
validationFlowBackWarp = model.backWarp(train_W_dim * 2, train_H_dim,
device)
validationFlowBackWarp = validationFlowBackWarp.to(device)
###Load Datasets
# Channel wise mean calculated on custom training dataset
# mean = [0.43702903766008444, 0.43715053433990597, 0.40436416782660994]
mean = [0.5] * 3
std = [1, 1, 1]
normalize = transforms.Normalize(mean=mean, std=std)
transform = transforms.Compose([transforms.ToTensor(), normalize])
trainset = dataloader.SuperSloMo(root=args.dataset_root + '/train',
randomCropSize=(train_W_dim, train_H_dim),
transform=transform,
train=True)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=args.train_batch_size,
shuffle=True,
num_workers=2,
pin_memory=True)
validationset = dataloader.SuperSloMo(
root=args.dataset_root + '/validation',
transform=transform,
randomCropSize=(2 * train_W_dim, train_H_dim),
train=False)
validationloader = torch.utils.data.DataLoader(
validationset,
batch_size=args.validation_batch_size,
shuffle=False,
num_workers=2,
pin_memory=True)
print(trainset, validationset)
###Create transform to display image from tensor
negmean = [x * -1 for x in mean]
revNormalize = transforms.Normalize(mean=negmean, std=std)
TP = transforms.Compose([revNormalize, transforms.ToPILImage()])
###Utils
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
###Loss and Optimizer
L1_lossFn = nn.L1Loss()
MSE_LossFn = nn.MSELoss()
if args.train_continue:
dict1 = torch.load(args.checkpoint)
last_epoch = dict1['epoch'] * len(trainloader)
else:
last_epoch = -1
params = list(ArbTimeFlowIntrp.parameters()) + list(flowComp.parameters())
optimizer = AdamW(params, lr=args.init_learning_rate, amsgrad=True)
# optimizer = optim.SGD(params, lr=args.init_learning_rate, momentum=0.9, nesterov=True)
# scheduler to decrease learning rate by a factor of 10 at milestones.
# Patience suggested value:
# patience = number of item in train dataset / train_batch_size * (Number of epochs patience)
# It does say epoch, but in this case, the number of progress iterations is what's really being worked with.
# As such, each epoch will be given by the above formula (roughly, if using a rough dataset count)
# If the model seems to equalize fast, reduce the number of epochs accordingly.
# scheduler = optim.lr_scheduler.CyclicLR(optimizer,
# base_lr=1e-8,
# max_lr=9.0e-3,
# step_size_up=3500,
# mode='triangular2',
# cycle_momentum=False,
# last_epoch=last_epoch)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode='min',
factor=0.1,
patience=len(trainloader) * 3,
cooldown=len(trainloader) * 2,
verbose=True,
min_lr=1e-8)
# Changed to use this to ensure a more adaptive model.
# The changed model used here seems to converge or plateau faster with more rapid swings over time.
# As such letting the model deal with stagnation more proactively than at a set stage seems more useful.
###Initializing VGG16 model for perceptual loss
vgg16 = torchvision.models.vgg16(pretrained=True)
vgg16_conv_4_3 = nn.Sequential(*list(vgg16.children())[0][:22])
vgg16_conv_4_3.to(device)
for param in vgg16_conv_4_3.parameters():
param.requires_grad = False
# Validation function
def validate():
# For details see training.
psnr = 0
tloss = 0
flag = 1
with torch.no_grad():
for validationIndex, (validationData,
validationFrameIndex) in enumerate(
validationloader, 0):
frame0, frameT, frame1 = validationData
I0 = frame0.to(device)
I1 = frame1.to(device)
IFrame = frameT.to(device)
torch.cuda.empty_cache()
flowOut = flowComp(torch.cat((I0, I1), dim=1))
F_0_1 = flowOut[:, :2, :, :]
F_1_0 = flowOut[:, 2:, :, :]
fCoeff = model.getFlowCoeff(validationFrameIndex, device)
torch.cuda.empty_cache()
F_t_0 = fCoeff[0] * F_0_1 + fCoeff[1] * F_1_0
F_t_1 = fCoeff[2] * F_0_1 + fCoeff[3] * F_1_0
g_I0_F_t_0 = validationFlowBackWarp(I0, F_t_0)
g_I1_F_t_1 = validationFlowBackWarp(I1, F_t_1)
torch.cuda.empty_cache()
intrpOut = ArbTimeFlowIntrp(
torch.cat((I0, I1, F_0_1, F_1_0, F_t_1, F_t_0, g_I1_F_t_1,
g_I0_F_t_0),
dim=1))
F_t_0_f = intrpOut[:, :2, :, :] + F_t_0
F_t_1_f = intrpOut[:, 2:4, :, :] + F_t_1
V_t_0 = torch.sigmoid(intrpOut[:, 4:5, :, :])
V_t_1 = 1 - V_t_0
# torch.cuda.empty_cache()
g_I0_F_t_0_f = validationFlowBackWarp(I0, F_t_0_f)
g_I1_F_t_1_f = validationFlowBackWarp(I1, F_t_1_f)
wCoeff = model.getWarpCoeff(validationFrameIndex, device)
torch.cuda.empty_cache()
Ft_p = (wCoeff[0] * V_t_0 * g_I0_F_t_0_f + wCoeff[1] * V_t_1 *
g_I1_F_t_1_f) / (wCoeff[0] * V_t_0 + wCoeff[1] * V_t_1)
# For tensorboard
if (flag):
retImg = torchvision.utils.make_grid([
revNormalize(frame0[0]),
revNormalize(frameT[0]),
revNormalize(Ft_p.cpu()[0]),
revNormalize(frame1[0])
],
padding=10)
flag = 0
# loss
recnLoss = L1_lossFn(Ft_p, IFrame)
# torch.cuda.empty_cache()
prcpLoss = MSE_LossFn(vgg16_conv_4_3(Ft_p),
vgg16_conv_4_3(IFrame))
warpLoss = L1_lossFn(g_I0_F_t_0, IFrame) + L1_lossFn(
g_I1_F_t_1, IFrame) + L1_lossFn(
validationFlowBackWarp(I0, F_1_0), I1) + L1_lossFn(
validationFlowBackWarp(I1, F_0_1), I0)
torch.cuda.empty_cache()
loss_smooth_1_0 = torch.mean(
torch.abs(F_1_0[:, :, :, :-1] -
F_1_0[:, :, :, 1:])) + torch.mean(
torch.abs(F_1_0[:, :, :-1, :] -
F_1_0[:, :, 1:, :]))
loss_smooth_0_1 = torch.mean(
torch.abs(F_0_1[:, :, :, :-1] -
F_0_1[:, :, :, 1:])) + torch.mean(
torch.abs(F_0_1[:, :, :-1, :] -
F_0_1[:, :, 1:, :]))
loss_smooth = loss_smooth_1_0 + loss_smooth_0_1
# torch.cuda.empty_cache()
loss = 204 * recnLoss + 102 * warpLoss + 0.005 * prcpLoss + loss_smooth
tloss += loss.item()
# psnr
MSE_val = MSE_LossFn(Ft_p, IFrame)
psnr += (10 * log10(1 / MSE_val.item()))
torch.cuda.empty_cache()
return (psnr / len(validationloader)), (tloss /
len(validationloader)), retImg
### Initialization
if args.train_continue:
ArbTimeFlowIntrp.load_state_dict(dict1['state_dictAT'])
flowComp.load_state_dict(dict1['state_dictFC'])
optimizer.load_state_dict(dict1.get('state_optimizer', {}))
scheduler.load_state_dict(dict1.get('state_scheduler', {}))
for param_group in optimizer.param_groups:
param_group['lr'] = dict1.get('learningRate',
args.init_learning_rate)
else:
dict1 = {'loss': [], 'valLoss': [], 'valPSNR': [], 'epoch': -1}
### Training
import time
start = time.time()
cLoss = dict1['loss']
valLoss = dict1['valLoss']
valPSNR = dict1['valPSNR']
checkpoint_counter = 0
### Main training loop
optimizer.step()
for epoch in range(dict1['epoch'] + 1, args.epochs):
print("Epoch: ", epoch)
# Append and reset
cLoss.append([])
valLoss.append([])
valPSNR.append([])
iLoss = 0
for trainIndex, (trainData,
trainFrameIndex) in enumerate(trainloader, 0):
## Getting the input and the target from the training set
frame0, frameT, frame1 = trainData
I0 = frame0.to(device)
I1 = frame1.to(device)
IFrame = frameT.to(device)
optimizer.zero_grad()
# torch.cuda.empty_cache()
# Calculate flow between reference frames I0 and I1
flowOut = flowComp(torch.cat((I0, I1), dim=1))
# Extracting flows between I0 and I1 - F_0_1 and F_1_0
F_0_1 = flowOut[:, :2, :, :]
F_1_0 = flowOut[:, 2:, :, :]
fCoeff = model.getFlowCoeff(trainFrameIndex, device)
# Calculate intermediate flows
F_t_0 = fCoeff[0] * F_0_1 + fCoeff[1] * F_1_0
F_t_1 = fCoeff[2] * F_0_1 + fCoeff[3] * F_1_0
# Get intermediate frames from the intermediate flows
g_I0_F_t_0 = trainFlowBackWarp(I0, F_t_0)
g_I1_F_t_1 = trainFlowBackWarp(I1, F_t_1)
torch.cuda.empty_cache()
# Calculate optical flow residuals and visibility maps
intrpOut = ArbTimeFlowIntrp(
torch.cat((I0, I1, F_0_1, F_1_0, F_t_1, F_t_0, g_I1_F_t_1,
g_I0_F_t_0),
dim=1))
# Extract optical flow residuals and visibility maps
F_t_0_f = intrpOut[:, :2, :, :] + F_t_0
F_t_1_f = intrpOut[:, 2:4, :, :] + F_t_1
V_t_0 = torch.sigmoid(intrpOut[:, 4:5, :, :])
V_t_1 = 1 - V_t_0
# torch.cuda.empty_cache()
# Get intermediate frames from the intermediate flows
g_I0_F_t_0_f = trainFlowBackWarp(I0, F_t_0_f)
g_I1_F_t_1_f = trainFlowBackWarp(I1, F_t_1_f)
# torch.cuda.empty_cache()
wCoeff = model.getWarpCoeff(trainFrameIndex, device)
torch.cuda.empty_cache()
# Calculate final intermediate frame
Ft_p = (wCoeff[0] * V_t_0 * g_I0_F_t_0_f + wCoeff[1] * V_t_1 *
g_I1_F_t_1_f) / (wCoeff[0] * V_t_0 + wCoeff[1] * V_t_1)
# Loss
recnLoss = L1_lossFn(Ft_p, IFrame)
# torch.cuda.empty_cache()
prcpLoss = MSE_LossFn(vgg16_conv_4_3(Ft_p), vgg16_conv_4_3(IFrame))
# torch.cuda.empty_cache()
warpLoss = L1_lossFn(g_I0_F_t_0, IFrame) + L1_lossFn(
g_I1_F_t_1, IFrame) + L1_lossFn(
trainFlowBackWarp(I0, F_1_0), I1) + L1_lossFn(
trainFlowBackWarp(I1, F_0_1), I0)
loss_smooth_1_0 = torch.mean(
torch.abs(F_1_0[:, :, :, :-1] - F_1_0[:, :, :, 1:])
) + torch.mean(torch.abs(F_1_0[:, :, :-1, :] - F_1_0[:, :, 1:, :]))
loss_smooth_0_1 = torch.mean(
torch.abs(F_0_1[:, :, :, :-1] - F_0_1[:, :, :, 1:])
) + torch.mean(torch.abs(F_0_1[:, :, :-1, :] - F_0_1[:, :, 1:, :]))
loss_smooth = loss_smooth_1_0 + loss_smooth_0_1
# torch.cuda.empty_cache()
# Total Loss - Coefficients 204 and 102 are used instead of 0.8 and 0.4
# since the loss in paper is calculated for input pixels in range 0-255
# and the input to our network is in range 0-1
loss = 204 * recnLoss + 102 * warpLoss + 0.005 * prcpLoss + loss_smooth
# Backpropagate
loss.backward()
optimizer.step()
scheduler.step(loss.item())
iLoss += loss.item()
torch.cuda.empty_cache()
# Validation and progress every `args.progress_iter` iterations
if ((trainIndex % args.progress_iter) == args.progress_iter - 1):
# Increment scheduler count
scheduler.step(iLoss / args.progress_iter)
end = time.time()
psnr, vLoss, valImg = validate()
optimizer.zero_grad()
# torch.cuda.empty_cache()
valPSNR[epoch].append(psnr)
valLoss[epoch].append(vLoss)
# Tensorboard
itr = trainIndex + epoch * (len(trainloader))
writer.add_scalars(
'Loss', {
'trainLoss': iLoss / args.progress_iter,
'validationLoss': vLoss
}, itr)
writer.add_scalar('PSNR', psnr, itr)
writer.add_image('Validation', valImg, itr)
#####
endVal = time.time()
print(
" Loss: %0.6f Iterations: %4d/%4d TrainExecTime: %0.1f ValLoss:%0.6f ValPSNR: %0.4f ValEvalTime: %0.2f LearningRate: %.1e"
% (iLoss / args.progress_iter, trainIndex,
len(trainloader), end - start, vLoss, psnr,
endVal - end, get_lr(optimizer)))
# torch.cuda.empty_cache()
cLoss[epoch].append(iLoss / args.progress_iter)
iLoss = 0
start = time.time()
# Create checkpoint after every `args.checkpoint_epoch` epochs
if (epoch % args.checkpoint_epoch) == args.checkpoint_epoch - 1:
dict1 = {
'Detail': "End to end Super SloMo.",
'epoch': epoch,
'timestamp': datetime.datetime.now(),
'trainBatchSz': args.train_batch_size,
'validationBatchSz': args.validation_batch_size,
'learningRate': get_lr(optimizer),
'loss': cLoss,
'valLoss': valLoss,
'valPSNR': valPSNR,
'state_dictFC': flowComp.state_dict(),
'state_dictAT': ArbTimeFlowIntrp.state_dict(),
'state_optimizer': optimizer.state_dict(),
'state_scheduler': scheduler.state_dict()
}
torch.save(
dict1, args.checkpoint_dir + "/SuperSloMo" +
str(checkpoint_counter) + ".ckpt")
checkpoint_counter += 1
class CienaTransformerModel(Model):
def __init__(
self, save_path, log_path, d_features, d_meta, max_length, d_classifier, n_classes, threshold=None,
optimizer=None, **kwargs):
'''**kwargs: n_layers, n_head, dropout, use_bottleneck, d_bottleneck'''
'''
Arguments:
save_path -- model file path
log_path -- log file path
d_features -- how many PMs
d_meta -- how many facility types
max_length -- input sequence length
d_classifier -- classifier hidden unit
n_classes -- output dim
threshold -- if not None, n_classes should be 1 (regression).
'''
super().__init__(save_path, log_path)
self.d_output = n_classes
self.threshold = threshold
self.max_length = max_length
# ----------------------------- Model ------------------------------ #
self.model = Encoder(TimeFacilityEncoding, d_features=d_features, max_seq_length=max_length,
d_meta=d_meta, **kwargs)
# --------------------------- Classifier --------------------------- #
self.classifier = LinearClassifier(d_features * max_length, d_classifier, n_classes)
# ------------------------------ CUDA ------------------------------ #
# If GPU available, move the graph to GPU(s)
self.CUDA_AVAILABLE = self.check_cuda()
if self.CUDA_AVAILABLE:
device_ids = list(range(torch.cuda.device_count()))
self.model = nn.DataParallel(self.model, device_ids)
self.classifier = nn.DataParallel(self.classifier, device_ids)
self.model.to('cuda')
self.classifier.to('cuda')
assert (next(self.model.parameters()).is_cuda)
assert (next(self.classifier.parameters()).is_cuda)
pass
else:
print('CUDA not found or not enabled, use CPU instead')
# ---------------------------- Optimizer --------------------------- #
self.parameters = list(self.model.parameters()) + list(self.classifier.parameters())
if optimizer == None:
self.optimizer = AdamW(self.parameters, lr=0.001, betas=(0.9, 0.999), weight_decay=0.001)
# ------------------------ training control ------------------------ #
self.controller = TrainingControl(max_step=100000, evaluate_every_nstep=100, print_every_nstep=10)
self.early_stopping = EarlyStopping(patience=50)
# --------------------- logging and tensorboard -------------------- #
self.count_parameters()
self.set_logger()
self.set_summary_writer()
# ---------------------------- END INIT ---------------------------- #
def checkpoint(self, step):
checkpoint = {
'model_state_dict': self.model.state_dict(),
'classifier_state_dict': self.classifier.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'global_step': step}
return checkpoint
def train_epoch(self, train_dataloader, eval_dataloader, device, smothing, earlystop):
''' Epoch operation in training phase'''
if device == 'cuda':
assert self.CUDA_AVAILABLE
# Set model and classifier training mode
self.model.train()
self.classifier.train()
total_loss = 0
batch_counter = 0
# update param per batch
for batch in tqdm(
train_dataloader, mininterval=1,
desc=' - (Training) ', leave=False): # training_data should be a iterable
# get data from dataloader
input_feature_sequence, padding, time_facility, y = map(lambda x: x.to(device), batch)
batch_size = len(y)
non_pad_mask, slf_attn_mask = get_attn_mask(padding)
# forward
self.optimizer.zero_grad()
logits, attn = self.model(input_feature_sequence, time_facility, non_pad_mask, slf_attn_mask)
logits = logits.view(batch_size, -1)
logits = self.classifier(logits)
# Judge if it's a regression problem
if self.d_output == 1:
pred = logits.sigmoid()
loss = mse_loss(pred, y)
else:
pred = logits
loss = cross_entropy_loss(pred, y, smoothing=smothing)
# calculate gradients
loss.backward()
# update parameters
self.optimizer.step()
# get metrics for logging
acc = accuracy(pred, y, threshold=self.threshold)
precision, recall, precision_avg, recall_avg = precision_recall(pred, y, self.d_output,
threshold=self.threshold)
total_loss += loss.item()
batch_counter += 1
# training control
state_dict = self.controller(batch_counter)
if state_dict['step_to_print']:
self.train_logger.info(
'[TRAINING] - step: %5d, loss: %3.4f, acc: %1.4f, pre: %1.4f, rec: %1.4f' % (
state_dict['step'], loss, acc, precision[1], recall[1]))
self.summary_writer.add_scalar('loss/train', loss, state_dict['step'])
self.summary_writer.add_scalar('acc/train', acc, state_dict['step'])
self.summary_writer.add_scalar('precision/train', precision[1], state_dict['step'])
self.summary_writer.add_scalar('recall/train', recall[1], state_dict['step'])
if state_dict['step_to_evaluate']:
stop = self.val_epoch(eval_dataloader, device, state_dict['step'])
state_dict['step_to_stop'] = stop
if earlystop & stop:
break
if self.controller.current_step == self.controller.max_step:
state_dict['step_to_stop'] = True
break
return state_dict
def val_epoch(self, dataloader, device, step=0, plot=False):
''' Epoch operation in evaluation phase '''
if device == 'cuda':
assert self.CUDA_AVAILABLE
# Set model and classifier training mode
self.model.eval()
self.classifier.eval()
# use evaluator to calculate the average performance
evaluator = Evaluator()
pred_list = []
real_list = []
with torch.no_grad():
for batch in tqdm(
dataloader, mininterval=5,
desc=' - (Evaluation) ', leave=False): # training_data should be a iterable
input_feature_sequence, padding, time_facility, y = map(lambda x: x.to(device), batch)
batch_size = len(y)
non_pad_mask, slf_attn_mask = get_attn_mask(padding)
# get logits
logits, attn = self.model(input_feature_sequence, time_facility, non_pad_mask, slf_attn_mask)
logits = logits.view(batch_size, -1)
logits = self.classifier(logits)
if self.d_output == 1:
pred = logits.sigmoid()
loss = mse_loss(pred, y)
else:
pred = logits
loss = cross_entropy_loss(pred, y, smoothing=False)
acc = accuracy(pred, y, threshold=self.threshold)
precision, recall, _, _ = precision_recall(pred, y, self.d_output, threshold=self.threshold)
# feed the metrics in the evaluator
evaluator(loss.item(), acc.item(), precision[1].item(), recall[1].item())
'''append the results to the predict / real list for drawing ROC or PR curve.'''
# if plot:
# pred_list += pred.tolist()
# real_list += y.tolist()
#
# if plot:
# area, precisions, recalls, thresholds = pr(pred_list, real_list)
# plot_pr_curve(recalls, precisions, auc=area)
# get evaluation results from the evaluator
loss_avg, acc_avg, pre_avg, rec_avg = evaluator.avg_results()
self.eval_logger.info(
'[EVALUATION] - step: %5d, loss: %3.4f, acc: %1.4f, pre: %1.4f, rec: %1.4f' % (
step, loss_avg, acc_avg, pre_avg, rec_avg))
self.summary_writer.add_scalar('loss/eval', loss_avg, step)
self.summary_writer.add_scalar('acc/eval', acc_avg, step)
self.summary_writer.add_scalar('precision/eval', pre_avg, step)
self.summary_writer.add_scalar('recall/eval', rec_avg, step)
state_dict = self.early_stopping(loss_avg)
if state_dict['save']:
checkpoint = self.checkpoint(step)
self.save_model(checkpoint, self.save_path + '-step-%d_loss-%.5f' % (step, loss_avg))
return state_dict['break']
def train(self, max_epoch, train_dataloader, eval_dataloader, device,
smoothing=False, earlystop=False, save_mode='best'):
assert save_mode in ['all', 'best']
# train for n epoch
for epoch_i in range(max_epoch):
print('[ Epoch', epoch_i, ']')
# set current epoch
self.controller.set_epoch(epoch_i + 1)
# train for on epoch
state_dict = self.train_epoch(train_dataloader, eval_dataloader, device, smoothing, earlystop)
# if state_dict['step_to_stop']:
# break
checkpoint = self.checkpoint(state_dict['step'])
self.save_model(checkpoint, self.save_path + '-step-%d' % state_dict['step'])
self.train_logger.info(
'[INFO]: Finish Training, ends with %d epoch(s) and %d batches, in total %d training steps.' % (
state_dict['epoch'] - 1, state_dict['batch'], state_dict['step']))
def get_predictions(self, data_loader, device, max_batches=None, activation=None):
pred_list = []
real_list = []
self.model.eval()
self.classifier.eval()
batch_counter = 0
with torch.no_grad():
for batch in tqdm(
data_loader,
desc=' - (Testing) ', leave=False):
input_feature_sequence, padding, time_facility, y = map(lambda x: x.to(device), batch)
non_pad_mask, slf_attn_mask = get_attn_mask(padding)
# get logits
logits, attn = self.model(input_feature_sequence, time_facility, non_pad_mask, slf_attn_mask)
logits = logits.view(logits.shape[0], -1)
logits = self.classifier(logits)
# Whether to apply activation function
if activation != None:
pred = activation(logits)
else:
pred = logits.softmax(dim=-1)
pred_list += pred.tolist()
real_list += y.tolist()
if max_batches != None:
batch_counter += 1
if batch_counter >= max_batches:
break
return pred_list, real_list
