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inference.py
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inference.py
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"""Transformers for performing inference."""
import tensorflow as tf
import numpy as np
import attr
from typing import List, Text, Optional, Tuple
from sleap.nn.data.utils import expand_to_rank, ensure_list
from sleap.nn.system import best_logical_device_name
from sleap.nn.peak_finding import (
find_local_peaks,
find_global_peaks,
find_local_peaks_integral,
find_global_peaks_integral,
)
@attr.s(auto_attribs=True)
class KerasModelPredictor:
"""Transformer for performing tf.keras model inference."""
keras_model: tf.keras.Model
model_input_keys: Text = attr.ib(default="instance_image", converter=ensure_list)
model_output_keys: Text = attr.ib(
default="predicted_instance_confidence_maps", converter=ensure_list
)
device_name: Optional[Text] = None
@property
def input_keys(self) -> List[Text]:
return self.model_input_keys
@property
def output_keys(self) -> List[Text]:
return self.input_keys + self.model_output_keys
def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset:
test_ex = next(iter(input_ds))
input_shapes = [test_ex[k].shape for k in self.model_input_keys]
input_layers = [tf.keras.layers.Input(shape) for shape in input_shapes]
keras_model = tf.keras.Model(input_layers, self.keras_model(input_layers))
device_name = self.device_name
if device_name is None:
device_name = best_logical_device_name()
def predict(example):
with tf.device(device_name):
X = []
for input_key in self.model_input_keys:
input_rank = tf.rank(example[input_key])
X.append(
expand_to_rank(example[input_key], target_rank=4, prepend=True)
)
Y = keras_model(X)
if not isinstance(Y, list):
Y = [Y]
for output_key, y in zip(self.model_output_keys, Y):
if isinstance(y, list):
y = y[0]
if input_rank < tf.rank(y):
y = tf.squeeze(y, axis=0)
example[output_key] = y
return example
output_ds = input_ds.map(
predict, num_parallel_calls=tf.data.experimental.AUTOTUNE
)
return output_ds
@attr.s(auto_attribs=True)
class GlobalPeakFinder:
"""Global peak finding transformer."""
confmaps_key: Text = "predicted_instance_confidence_maps"
confmaps_stride: int = 1
peak_threshold: float = 0.2
peaks_key: Text = "predicted_center_instance_points"
peak_vals_key: Text = "predicted_center_instance_confidences"
keep_confmaps: bool = True
device_name: Optional[Text] = None
integral: bool = True
integral_patch_size: int = 5
@property
def input_keys(self) -> List[Text]:
return [self.confmaps_key]
@property
def output_keys(self) -> List[Text]:
output_keys = [self.peaks_key, self.peak_vals_key]
if self.keep_confmaps:
output_keys.append(self.confmaps_key)
return output_keys
def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset:
device_name = self.device_name
if device_name is None:
device_name = best_logical_device_name()
def find_peaks(example):
with tf.device(device_name):
confmaps = example[self.confmaps_key]
confmaps = expand_to_rank(confmaps, target_rank=4, prepend=True)
if self.integral:
# Find peaks via integral regression.
peaks, peak_vals = find_global_peaks_integral(
confmaps,
threshold=self.peak_threshold,
crop_size=self.integral_patch_size,
)
peaks *= tf.cast(self.confmaps_stride, tf.float32)
else:
# Find peaks via standard grid aligned global argmax.
peaks, peak_vals = find_global_peaks(
confmaps, threshold=self.peak_threshold
)
peaks *= tf.cast(self.confmaps_stride, tf.float32)
if tf.rank(example[self.confmaps_key]) == 3:
peaks = tf.squeeze(peaks, axis=0)
peak_vals = tf.squeeze(peak_vals, axis=0)
example[self.peaks_key] = peaks
example[self.peak_vals_key] = peak_vals
if not self.keep_confmaps:
example.pop(self.confmaps_key)
return example
output_ds = input_ds.map(
find_peaks, num_parallel_calls=tf.data.experimental.AUTOTUNE
)
return output_ds
@attr.s(auto_attribs=True)
class MockGlobalPeakFinder:
"""Transformer that mimics `GlobalPeakFinder` but passes ground truth data."""
all_peaks_in_key: Text = "instances"
peaks_out_key: Text = "predicted_center_instance_points"
peak_vals_key: Text = "predicted_center_instance_confidences"
keep_confmaps: bool = True
confmaps_in_key: Text = "instance_confidence_maps"
confmaps_out_key: Text = "predicted_instance_confidence_maps"
@property
def input_keys(self) -> List[Text]:
input_keys = [self.all_peaks_in_key, "centroid", "bbox", "scale"]
if self.keep_confmaps:
input_keys.append(self.confmaps_in_key)
return input_keys
@property
def output_keys(self) -> List[Text]:
output_keys = [self.peaks_out_key, self.peak_vals_key]
if self.keep_confmaps:
output_keys.append(self.confmaps_out_key)
return output_keys
def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset:
def find_peaks(example):
# Match example centroid to the instance with the closest node.
centroid = example["centroid"] / example["scale"]
all_peaks = example[self.all_peaks_in_key] # (n_instances, n_nodes, 2)
dists = tf.reduce_min(
tf.norm(all_peaks - tf.reshape(centroid, [1, 1, 2]), axis=-1),
axis=1,
) # (n_instances,)
instance_ind = tf.argmin(dists)
center_instance = tf.gather(all_peaks, instance_ind)
# Adjust to coordinates relative to bounding box.
center_instance -= tf.reshape(tf.gather(example["bbox"], [1, 0]), [1, 2])
# Fill in mock data.
example[self.peaks_out_key] = center_instance
example[self.peak_vals_key] = tf.ones(
[tf.shape(center_instance)[0]], dtype=tf.float32
)
example.pop(self.all_peaks_in_key)
if self.keep_confmaps:
example[self.confmaps_out_key] = example[self.confmaps_in_key]
example.pop(self.confmaps_in_key)
return example
output_ds = input_ds.map(
find_peaks, num_parallel_calls=tf.data.experimental.AUTOTUNE
)
return output_ds
@attr.s(auto_attribs=True)
class LocalPeakFinder:
"""Local peak finding transformer."""
confmaps_key: Text = "centroid_confidence_maps"
confmaps_stride: int = 1
peak_threshold: float = 0.2
peaks_key: Text = "predicted_centroids"
peak_vals_key: Text = "predicted_centroid_confidences"
peak_sample_inds_key: Text = "predicted_centroid_sample_inds"
peak_channel_inds_key: Text = "predicted_centroid_channel_inds"
keep_confmaps: bool = True
device_name: Optional[Text] = None
integral: bool = True
@property
def input_keys(self) -> List[Text]:
return [self.confmaps_key]
@property
def output_keys(self) -> List[Text]:
output_keys = [
self.peaks_key,
self.peak_vals_key,
self.peak_sample_inds_key,
self.peak_channel_inds_key,
]
if self.keep_confmaps:
output_keys.append(self.confmaps_key)
return output_keys
def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset:
device_name = self.device_name
if device_name is None:
device_name = best_logical_device_name()
def find_peaks(example):
with tf.device(device_name):
confmaps = example[self.confmaps_key]
confmaps = expand_to_rank(confmaps, target_rank=4, prepend=True)
if self.integral:
# Find local peaks with local NMS + integral refinement.
(
peaks,
peak_vals,
peak_sample_inds,
peak_channel_inds,
) = find_local_peaks_integral(
confmaps, threshold=self.peak_threshold
)
else:
# Find local peaks with grid-aligned NMS.
(
peaks,
peak_vals,
peak_sample_inds,
peak_channel_inds,
) = find_local_peaks(confmaps, threshold=self.peak_threshold)
# Adjust for confidence map stride.
peaks *= tf.cast(self.confmaps_stride, tf.float32)
example[self.peaks_key] = peaks
example[self.peak_vals_key] = peak_vals
example[self.peak_sample_inds_key] = peak_sample_inds
example[self.peak_channel_inds_key] = peak_channel_inds
if not self.keep_confmaps:
example.pop(self.confmaps_key)
return example
output_ds = input_ds.map(
find_peaks, num_parallel_calls=tf.data.experimental.AUTOTUNE
)
return output_ds
@attr.s(auto_attribs=True)
class PredictedCenterInstanceNormalizer:
"""Transformer for adjusting centered instance coordinates."""
centroid_key: Text = "centroid"
centroid_confidence_key: Text = "centroid_confidence"
peaks_key: Text = "predicted_center_instance_points"
peak_confidences_key: Text = "predicted_center_instance_confidences"
new_centroid_key: Text = "predicted_centroid"
new_centroid_confidence_key: Text = "predicted_centroid_confidence"
new_peaks_key: Text = "predicted_instance"
new_peak_confidences_key: Text = "predicted_instance_confidences"
@property
def input_keys(self) -> List[Text]:
"""Return the keys that incoming elements are expected to have."""
return [
self.centroid_key,
self.centroid_confidence_key,
self.peaks_key,
self.peak_confidences_key,
"scale",
"bbox",
]
@property
def output_keys(self) -> List[Text]:
"""Return the keys that outgoing elements will have."""
output_keys = [
self.new_centroid_key,
self.new_centroid_confidence_key,
self.new_peaks_key,
self.new_peak_confidences_key,
]
return output_keys
def transform_dataset(self, input_ds: tf.data.Dataset) -> tf.data.Dataset:
"""Create a dataset that contains instance cropped data."""
def norm_instance(example):
"""Local processing function for dataset mapping."""
centroids = example[self.centroid_key] / example["scale"]
bboxes = example["bbox"]
bboxes = expand_to_rank(bboxes, 2)
bboxes_x1y1 = tf.gather(bboxes, [1, 0], axis=1)
pts = example[self.peaks_key]
pts += bboxes_x1y1
pts /= example["scale"]
example[self.new_centroid_key] = centroids
example[self.new_centroid_confidence_key] = example[
self.centroid_confidence_key
]
example[self.new_peaks_key] = pts
example[self.new_peak_confidences_key] = example[self.peak_confidences_key]
return example
# Map the main processing function to each example.
output_ds = input_ds.map(
norm_instance, num_parallel_calls=tf.data.experimental.AUTOTUNE
)
return output_ds