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fix formatting

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This commit is contained in:
Dan Brown 2025-12-02 21:02:01 +01:00
parent 6ee36d6ffc
commit f5f84a3c53
1 changed files with 6 additions and 16 deletions
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20
frigate/detectors/plugins/edgetpu_tfl.py
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@ -114,9 +114,7 @@ class EdgeTpuTfl(DetectionApi):
# to differentiate from (not used) max score tensor
output_classes_index = i
if output_boxes_index is None or output_classes_index is None:
logger.warning(
"Unrecognized model output, unexpected tensor shapes."
)
logger.warning("Unrecognized model output, unexpected tensor shapes.")
output_classes_index = (
0
if (output_boxes_index is None or output_classes_index == 1)
@ -127,17 +125,13 @@ class EdgeTpuTfl(DetectionApi):
scores_details = self.tensor_output_details[output_classes_index]
classes_count = scores_details["shape"][2]
self.scores_tensor_index = scores_details["index"]
self.scores_scale, self.scores_zero_point = scores_details[
"quantization"
]
self.scores_scale, self.scores_zero_point = scores_details["quantization"]
# calculate the quantized version of the min_score
self.min_score_quantized = int(
(self.min_logit_value / self.scores_scale) + self.scores_zero_point
)
self.logit_shift_to_positive_values = (
max(
0, math.ceil((128 + self.scores_zero_point) * self.scores_scale)
)
max(0, math.ceil((128 + self.scores_zero_point) * self.scores_scale))
+ 1
) # round up
@ -245,9 +239,7 @@ class EdgeTpuTfl(DetectionApi):
scores_output_quantized = self.interpreter.get_tensor(
self.scores_tensor_index
)[0] # (2100, NC)
max_scores_quantized = np.max(
scores_output_quantized, axis=1
) # (2100,)
max_scores_quantized = np.max(scores_output_quantized, axis=1) # (2100,)
mask = max_scores_quantized >= self.min_score_quantized # (2100,)
if not np.any(mask):
@ -277,9 +269,7 @@ class EdgeTpuTfl(DetectionApi):
# Softmax over the 16 bins
dfl_max = np.max(dfl_distributions, axis=2, keepdims=True)
dfl_exp = np.exp(dfl_distributions - dfl_max)
dfl_probs = dfl_exp / np.sum(
dfl_exp, axis=2, keepdims=True
) # (N, 4, 16)
dfl_probs = dfl_exp / np.sum(dfl_exp, axis=2, keepdims=True) # (N, 4, 16)
# Weighted sum: (N, 4, 16) * (16,) -> (N, 4)
distances = np.einsum("pcr,r->pc", dfl_probs, self.project)