Exemplo n.º 1
0
 def on_epoch_end(self, data: Data) -> None:
     if self.system.mode == 'train' and self.histogram_freq.freq and not self.histogram_freq.is_step and \
             self.system.epoch_idx % self.histogram_freq.freq == 0:
         self.writer.write_weights(mode=self.system.mode,
                                   models=self.system.network.models,
                                   step=self.system.global_step,
                                   visualize=self.paint_weights)
     if self.update_freq.freq and (self.update_freq.is_step
                                   or self.system.epoch_idx %
                                   self.update_freq.freq == 0):
         self.writer.write_scalars(mode=self.system.mode,
                                   step=self.system.global_step,
                                   scalars=filter(lambda x: is_number(x[1]),
                                                  data.items()))
         self.writer.write_images(mode=self.system.mode,
                                  step=self.system.global_step,
                                  images=filter(
                                      lambda x: x[1] is not None,
                                      map(lambda y: (y, data.get(y)),
                                          self.write_images)))
         self.writer.write_embeddings(
             mode=self.system.mode,
             step=self.system.global_step,
             embeddings=filter(
                 lambda x: x[1] is not None,
                 map(
                     lambda t:
                     (t[0], data.get(t[0]), data.get(t[1]), data.get(t[2])),
                     self.write_embeddings)))
Exemplo n.º 2
0
 def on_batch_end(self, data: Data) -> None:
     if self.write_graph and self.system.network.epoch_models.symmetric_difference(
             self.painted_graphs):
         self.writer.write_epoch_models(mode=self.system.mode)
         self.painted_graphs = self.system.network.epoch_models
     if self.system.mode != 'train':
         return
     if self.histogram_freq.freq and self.histogram_freq.is_step and \
             self.system.global_step % self.histogram_freq.freq == 0:
         self.writer.write_weights(mode=self.system.mode,
                                   models=self.system.network.models,
                                   step=self.system.global_step,
                                   visualize=self.paint_weights)
     if self.update_freq.freq and self.update_freq.is_step and self.system.global_step % self.update_freq.freq == 0:
         self.writer.write_scalars(mode=self.system.mode,
                                   step=self.system.global_step,
                                   scalars=filter(lambda x: is_number(x[1]),
                                                  data.items()))
         self.writer.write_images(mode=self.system.mode,
                                  step=self.system.global_step,
                                  images=filter(
                                      lambda x: x[1] is not None,
                                      map(lambda y: (y, data.get(y)),
                                          self.write_images)))
         self.writer.write_embeddings(
             mode=self.system.mode,
             step=self.system.global_step,
             embeddings=filter(
                 lambda x: x[1] is not None,
                 map(
                     lambda t:
                     (t[0], data.get(t[0]), data.get(t[1]), data.get(t[2])),
                     self.write_embeddings)))
Exemplo n.º 3
0
 def on_epoch_end(self, data: Data) -> None:
     if self.system.mode == 'train' and self.histogram_freq.freq and not self.histogram_freq.is_step and \
             self.system.epoch_idx % self.histogram_freq.freq == 0:
         self.writer.write_weights(mode=self.system.mode,
                                   models=self.system.network.models,
                                   step=self.system.global_step,
                                   visualize=self.paint_weights)
     # Write out any embeddings which were aggregated over batches
     for name, val_list in self.collected_embeddings.items():
         embeddings = None if any(
             x[0] is None
             for x in val_list) else concat([x[0] for x in val_list])
         labels = None if any(
             x[1] is None
             for x in val_list) else concat([x[1] for x in val_list])
         imgs = None if any(
             x[2] is None
             for x in val_list) else concat([x[2] for x in val_list])
         self.writer.write_embeddings(mode=self.system.mode,
                                      step=self.system.global_step,
                                      embeddings=[(name, embeddings, labels,
                                                   imgs)])
     self.collected_embeddings.clear()
     # Get any embeddings which were generated externally on epoch end
     if self.embedding_freq.freq and (self.embedding_freq.is_step
                                      or self.system.epoch_idx %
                                      self.embedding_freq.freq == 0):
         self.writer.write_embeddings(
             mode=self.system.mode,
             step=self.system.global_step,
             embeddings=filter(
                 lambda x: x[1] is not None,
                 map(
                     lambda t:
                     (t[0], data.get(t[0]), data.get(t[1]), data.get(t[2])),
                     self.write_embeddings)))
     if self.update_freq.freq and (self.update_freq.is_step
                                   or self.system.epoch_idx %
                                   self.update_freq.freq == 0):
         self.writer.write_scalars(mode=self.system.mode,
                                   step=self.system.global_step,
                                   scalars=filter(lambda x: is_number(x[1]),
                                                  data.items()))
         self.writer.write_images(mode=self.system.mode,
                                  step=self.system.global_step,
                                  images=filter(
                                      lambda x: x[1] is not None,
                                      map(lambda y: (y, data.get(y)),
                                          self.write_images)))
Exemplo n.º 4
0
 def on_batch_end(self, data: Data) -> None:
     if self.write_graph and self.system.network.epoch_models.symmetric_difference(
             self.painted_graphs):
         self.writer.write_epoch_models(mode=self.system.mode,
                                        epoch=self.system.epoch_idx)
         self.painted_graphs = self.system.network.epoch_models
     # Collect embeddings if present in batch but viewing per epoch. Don't aggregate during training though
     if self.system.mode != 'train' and self.embedding_freq.freq and not self.embedding_freq.is_step and self.system.epoch_idx % self.embedding_freq.freq == 0:
         for elem in self.write_embeddings:
             name, lbl, img = elem
             if name in data:
                 self.collected_embeddings[name].append(
                     (data.get(name), data.get(lbl), data.get(img)))
     # Handle embeddings if viewing per step
     if self.embedding_freq.freq and self.embedding_freq.is_step and self.system.global_step % self.embedding_freq.freq == 0:
         self.writer.write_embeddings(
             mode=self.system.mode,
             step=self.system.global_step,
             embeddings=filter(
                 lambda x: x[1] is not None,
                 map(
                     lambda t:
                     (t[0], data.get(t[0]), data.get(t[1]), data.get(t[2])),
                     self.write_embeddings)))
     if self.system.mode != 'train':
         return
     if self.histogram_freq.freq and self.histogram_freq.is_step and \
             self.system.global_step % self.histogram_freq.freq == 0:
         self.writer.write_weights(mode=self.system.mode,
                                   models=self.system.network.models,
                                   step=self.system.global_step,
                                   visualize=self.paint_weights)
     if self.update_freq.freq and self.update_freq.is_step and self.system.global_step % self.update_freq.freq == 0:
         self.writer.write_scalars(mode=self.system.mode,
                                   step=self.system.global_step,
                                   scalars=filter(lambda x: is_number(x[1]),
                                                  data.items()))
         self.writer.write_images(mode=self.system.mode,
                                  step=self.system.global_step,
                                  images=filter(
                                      lambda x: x[1] is not None,
                                      map(lambda y: (y, data.get(y)),
                                          self.write_images)))