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Fix top-down inference on Apple Silicon #1547

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Fix top-down inference on Apple Silicon #1547

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78 changes: 54 additions & 24 deletions sleap/nn/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -408,12 +408,13 @@ def process_batch(ex):
ex["frame_ind"] = ex["frame_ind"].numpy().flatten()

# Adjust for potential SizeMatcher scaling.
offset_x = ex.get("offset_x", 0)
offset_y = ex.get("offset_y", 0)
ex["instance_peaks"] -= np.reshape([offset_x, offset_y], [-1, 1, 1, 2])
ex["instance_peaks"] /= np.expand_dims(
np.expand_dims(ex["scale"], axis=1), axis=1
)
if ex["instance_peaks"].size > 0:
offset_x = ex.get("offset_x", 0)
offset_y = ex.get("offset_y", 0)
ex["instance_peaks"] -= np.reshape([offset_x, offset_y], [-1, 1, 1, 2])
ex["instance_peaks"] /= np.expand_dims(
np.expand_dims(ex["scale"], axis=1), axis=1
)

return ex

Expand Down Expand Up @@ -795,6 +796,7 @@ def call(self, example_gt: Dict[str, tf.Tensor]) -> Dict[str, tf.Tensor]:
crop_offsets=crop_offsets,
centroids=example_gt["centroids"],
centroid_vals=centroid_vals,
n_peaks=n_peaks,
)


Expand Down Expand Up @@ -1907,7 +1909,9 @@ def call(self, inputs):
centroid_vals, crop_sample_inds, nrows=samples
)

outputs = dict(centroids=centroids, centroid_vals=centroid_vals)
outputs = dict(
centroids=centroids, centroid_vals=centroid_vals, n_peaks=n_peaks
)
if self.return_confmaps:
# Return confidence maps with outputs.
cms = tf.RaggedTensor.from_value_rowids(
Expand Down Expand Up @@ -2081,6 +2085,15 @@ def call(
samples = tf.shape(crops)[0]
crop_sample_inds = tf.range(samples, dtype=tf.int32)

outputs = {}

if "centroids" in inputs:
outputs["centroids"] = inputs["centroids"]
if "centroid_vals" in inputs:
outputs["centroid_vals"] = inputs["centroid_vals"]
if "centroid_confmaps" in inputs:
outputs["centroid_confmaps"] = inputs["centroid_confmaps"]

# Preprocess inputs (scaling, padding, colorspace, int to float).
crops = self.preprocess(crops)

Expand Down Expand Up @@ -2140,13 +2153,8 @@ def call(
)

# Build outputs.
outputs = {"instance_peaks": peaks, "instance_peak_vals": peak_vals}
if "centroids" in inputs:
outputs["centroids"] = inputs["centroids"]
if "centroid_vals" in inputs:
outputs["centroid_vals"] = inputs["centroid_vals"]
if "centroid_confmaps" in inputs:
outputs["centroid_confmaps"] = inputs["centroid_confmaps"]
outputs["instance_peaks"] = peaks
outputs["instance_peak_vals"] = peak_vals
if self.return_confmaps:
cms = tf.RaggedTensor.from_value_rowids(
cms, crop_sample_inds, nrows=samples
Expand Down Expand Up @@ -2253,17 +2261,39 @@ def call(

crop_output = self.centroid_crop(example)

if isinstance(self.instance_peaks, FindInstancePeaksGroundTruth):
if "instances" in example:
peaks_output = self.instance_peaks(example, crop_output)
else:
raise ValueError(
"Ground truth data was not detected... "
"Please load both models when predicting on non-ground-truth data."
)
if crop_output["n_peaks"] == 0:
samples = tf.shape(example["image"])[0]
output = {
"centroids": crop_output["centroids"],
"centroid_vals": crop_output["centroid_vals"],
"instance_peak_vals": tf.RaggedTensor.from_value_rowids(
tf.zeros(shape=(0,), dtype=tf.float32),
tf.zeros(shape=(0,), dtype=tf.int32),
nrows=samples,
),
"instance_peaks": tf.RaggedTensor.from_value_rowids(
tf.zeros(shape=(0, 2), dtype=tf.float32),
tf.zeros(shape=(0,), dtype=tf.int32),
nrows=samples,
),
}

if self.instance_peaks.return_confmaps:
output["instance_confmaps"] = tf.zeros((0, 0, 0, 0), dtype=tf.float32)

return output
else:
peaks_output = self.instance_peaks(crop_output)
return peaks_output
if isinstance(self.instance_peaks, FindInstancePeaksGroundTruth):
if "instances" in example:
peaks_output = self.instance_peaks(example, crop_output)
else:
raise ValueError(
"Ground truth data was not detected... "
"Please load both models when predicting on non-ground-truth data."
)
else:
peaks_output = self.instance_peaks(crop_output)
return peaks_output


@attr.s(auto_attribs=True)
Expand Down