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Fix catastrophic accuracy degradation of TFLite static quantized integer models (#1695)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Motoki Kimura
2023-07-27 02:12:49 +09:00
committed by GitHub
parent 9627323d7c
commit 3c787eb080
3 changed files with 42 additions and 11 deletions
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16
ultralytics/nn/autobackend.py
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@ -400,21 +400,21 @@ class AutoBackend(nn.Module):
nc = y[ib].shape[1] - y[ip].shape[3] - 4 # y = (1, 160, 160, 32), (1, 116, 8400)
self.names = {i: f'class{i}' for i in range(nc)}
else: # Lite or Edge TPU
input = self.input_details[0]
int8 = input['dtype'] == np.int8 # is TFLite quantized int8 model
if int8:
scale, zero_point = input['quantization']
im = (im / scale + zero_point).astype(np.int8) # de-scale
self.interpreter.set_tensor(input['index'], im)
details = self.input_details[0]
integer = details['dtype'] in (np.int8, np.int16) # is TFLite quantized int8 or int16 model
if integer:
scale, zero_point = details['quantization']
im = (im / scale + zero_point).astype(details['dtype']) # de-scale
self.interpreter.set_tensor(details['index'], im)
self.interpreter.invoke()
y = []
for output in self.output_details:
x = self.interpreter.get_tensor(output['index'])
if int8:
if integer:
scale, zero_point = output['quantization']
x = (x.astype(np.float32) - zero_point) * scale # re-scale
if x.ndim > 2: # if task is not classification
# Unnormalize xywh with input image size
# Denormalize xywh with input image size
# xywh are normalized in TFLite/EdgeTPU to mitigate quantization error of integer models
# See this PR for details: https://github.com/ultralytics/ultralytics/pull/1695
x[:, 0] *= w