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ultralytics 8.0.53 DDP AMP and Edge TPU fixes (#1362)

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Co-authored-by: Richard Aljaste <richardaljasteabramson@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Vuong Kha Sieu <75152429+hotfur@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-03-12 02:08:13 +01:00
committed by GitHub
parent 177a68b39f
commit f921e1ac21
46 changed files with 1045 additions and 384 deletions
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11
ultralytics/nn/autobackend.py
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@ -332,13 +332,6 @@ class AutoBackend(nn.Module):
y = [self.bindings[x].data for x in sorted(self.output_names)]
elif self.coreml: # CoreML
im = im[0].cpu().numpy()
if self.task == 'classify':
from ultralytics.yolo.data.utils import IMAGENET_MEAN, IMAGENET_STD
# im_pil = Image.fromarray(((im / 6 + 0.5) * 255).astype('uint8'))
for i in range(3):
im[..., i] *= IMAGENET_STD[i]
im[..., i] += IMAGENET_MEAN[i]
im_pil = Image.fromarray((im * 255).astype('uint8'))
# im = im.resize((192, 320), Image.ANTIALIAS)
y = self.model.predict({'image': im_pil}) # coordinates are xywh normalized
@ -371,10 +364,10 @@ class AutoBackend(nn.Module):
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.uint8 # is TFLite quantized uint8 model
int8 = input['dtype'] == np.int8 # is TFLite quantized int8 model
if int8:
scale, zero_point = input['quantization']
im = (im / scale + zero_point).astype(np.uint8) # de-scale
im = (im / scale + zero_point).astype(np.int8) # de-scale
self.interpreter.set_tensor(input['index'], im)
self.interpreter.invoke()
y = []