Source code for rastervision.pytorch_learner.classification_learner
import warnings
import logging
import torch.distributed as dist
from rastervision.pytorch_learner.learner import Learner
from rastervision.pytorch_learner.utils import (
compute_conf_mat_metrics, compute_conf_mat, aggregate_metrics)
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):
metrics = aggregate_metrics(outputs, exclude_keys={'conf_mat'})
conf_mat = sum([o['conf_mat'] for o in outputs])
if self.is_ddp_process:
metrics = self.reduce_distributed_metrics(metrics)
dist.reduce(conf_mat, dst=0, op=dist.ReduceOp.SUM)
if not self.is_ddp_master:
return metrics
ignored_idx = self.cfg.solver.ignore_class_index
if ignored_idx is not None and ignored_idx < 0:
ignored_idx += self.cfg.data.num_classes
class_names = self.cfg.data.class_names
conf_mat_metrics = compute_conf_mat_metrics(
conf_mat, class_names, ignore_idx=ignored_idx)
metrics.update(conf_mat_metrics)
return metrics