def make_viz_pipeline(self, data_provider: Provider,
keras_model: tf.keras.Model) -> Pipeline:
"""Create visualization pipeline.
Args:
data_provider: A `Provider` that generates data examples, typically a
`LabelsReader` instance.
keras_model: A `tf.keras.Model` that can be used for inference.
Returns:
A `Pipeline` instance configured to fetch data and run inference to generate
predictions useful for visualization during training.
"""
pipeline = self.make_base_pipeline(data_provider=data_provider)
pipeline += Prefetcher()
pipeline += Repeater()
pipeline += KerasModelPredictor(
keras_model=keras_model,
model_input_keys="image",
model_output_keys="predicted_confidence_maps",
)
pipeline += GlobalPeakFinder(
confmaps_key="predicted_confidence_maps",
peaks_key="predicted_points",
peak_vals_key="predicted_confidences",
confmaps_stride=self.single_instance_confmap_head.output_stride,
peak_threshold=0.2,
)
return pipeline
def make_viz_pipeline(self, data_provider: Provider,
keras_model: tf.keras.Model) -> Pipeline:
"""Create visualization pipeline.
Args:
data_provider: A `Provider` that generates data examples, typically a
`LabelsReader` instance.
keras_model: A `tf.keras.Model` that can be used for inference.
Returns:
A `Pipeline` instance configured to fetch data and run inference to generate
predictions useful for visualization during training.
"""
pipeline = self.make_base_pipeline(data_provider=data_provider)
pipeline += Prefetcher()
pipeline += Repeater()
if self.optimization_config.augmentation_config.random_crop:
pipeline += RandomCropper(
crop_height=self.optimization_config.augmentation_config.
random_crop_height,
crop_width=self.optimization_config.augmentation_config.
random_crop_width,
)
pipeline += KerasModelPredictor(
keras_model=keras_model,
model_input_keys="image",
model_output_keys="predicted_centroid_confidence_maps",
)
pipeline += LocalPeakFinder(
confmaps_stride=self.centroid_confmap_head.output_stride,
peak_threshold=0.2,
confmaps_key="predicted_centroid_confidence_maps",
peaks_key="predicted_centroids",
peak_vals_key="predicted_centroid_confidences",
peak_sample_inds_key="predicted_centroid_sample_inds",
peak_channel_inds_key="predicted_centroid_channel_inds",
)
return pipeline