Source code for rastervision.pytorch_learner.classification_learner
import warnings
import logging
import torch
from rastervision.pytorch_learner.learner import Learner
from rastervision.pytorch_learner.utils import (compute_conf_mat_metrics,
compute_conf_mat)
from rastervision.pytorch_learner.dataset.visualizer import (
ClassificationVisualizer)
warnings.filterwarnings('ignore')
log = logging.getLogger(__name__)
[docs]class ClassificationLearner(Learner):
[docs] def train_step(self, batch, batch_ind):
x, y = batch
out = self.post_forward(self.model(x))
return {'train_loss': self.loss(out, y)}
[docs] def validate_step(self, batch, batch_ind):
x, y = batch
out = self.post_forward(self.model(x))
val_loss = self.loss(out, y)
num_labels = len(self.cfg.data.class_names)
out = self.prob_to_pred(out)
conf_mat = compute_conf_mat(out, y, num_labels)
return {'val_loss': val_loss, 'conf_mat': conf_mat}
[docs] def validate_end(self, outputs, num_samples):
conf_mat = sum([o['conf_mat'] for o in outputs])
val_loss = torch.stack([o['val_loss']
for o in outputs]).sum() / num_samples
conf_mat_metrics = compute_conf_mat_metrics(conf_mat,
self.cfg.data.class_names)
metrics = {'val_loss': val_loss.item()}
metrics.update(conf_mat_metrics)
return metrics