def run(self):
"""Run with current dialog settings."""
pipeline_form_data = self.pipeline_form_widget.get_form_data()
items_for_inference = self.get_items_for_inference(pipeline_form_data)
config_info_list = self.get_every_head_config_data(pipeline_form_data)
# Close the dialog now that we have the data from it
self.accept()
# Run training/learning pipeline using the TrainingJobs
new_counts = runners.run_learning_pipeline(
labels_filename=self.labels_filename,
labels=self.labels,
config_info_list=config_info_list,
inference_params=pipeline_form_data,
items_for_inference=items_for_inference,
)
self._handle_learning_finished.emit(new_counts)
# count < 0 means there was an error and we didn't get any results.
if new_counts >= 0:
total_count = items_for_inference.total_frame_count
no_result_count = total_count - new_counts
message = f"Inference ran on {total_count} frames."
message += f"\n\nInstances were predicted on {new_counts} frames "
message += f"({no_result_count} frame{'s' if no_result_count != 1 else ''} with no instances found)."
win = QtWidgets.QMessageBox(text=message)
win.setWindowTitle("Inference Results")
win.exec_()
def run(self):
"""Run with current dialog settings."""
pipeline_form_data = self.pipeline_form_widget.get_form_data()
items_for_inference = self.get_items_for_inference(pipeline_form_data)
config_info_list = self.get_every_head_config_data(pipeline_form_data)
# Close the dialog now that we have the data from it
self.accept()
# Run training/learning pipeline using the TrainingJobs
new_counts = runners.run_learning_pipeline(
labels_filename=self.labels_filename,
labels=self.labels,
config_info_list=config_info_list,
inference_params=pipeline_form_data,
items_for_inference=items_for_inference,
)
self.learningFinished.emit(new_counts)
if new_counts >= 0:
QtWidgets.QMessageBox(
text=f"Inference has finished. Instances were predicted on {new_counts} frames."
).exec_()