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Replace an easy way to construct a ncnn.Mat (#3638)

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triple Mu
2023-07-11 22:09:32 +08:00
committed by GitHub
parent 495edc261f
commit 82920ef7ec
1 changed files with 1 additions and 4 deletions
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5
ultralytics/nn/autobackend.py
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@ -372,10 +372,7 @@ class AutoBackend(nn.Module):
self.predictor.run() self.predictor.run()
y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
elif self.ncnn: # ncnn elif self.ncnn: # ncnn
im = (im[0] * 255.).cpu().numpy().astype(np.uint8) mat_in = self.pyncnn.Mat(im[0].cpu().numpy())
im = np.ascontiguousarray(im.transpose(1, 2, 0))
mat_in = self.pyncnn.Mat.from_pixels(im, self.pyncnn.Mat.PixelType.PIXEL_RGB, *im.shape[:2])
mat_in.substract_mean_normalize([], [1 / 255.0, 1 / 255.0, 1 / 255.0])
ex = self.net.create_extractor() ex = self.net.create_extractor()
input_names, output_names = self.net.input_names(), self.net.output_names() input_names, output_names = self.net.input_names(), self.net.output_names()
ex.input(input_names[0], mat_in) ex.input(input_names[0], mat_in)