Improved yolo.py profiling (#2940)
* Improved yolo.py profiling Improved column order and labelling. * Update yolo.py
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@ -134,7 +134,9 @@ class Model(nn.Module):
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for _ in range(10):
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_ = m(x)
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dt.append((time_synchronized() - t) * 100)
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logger.info('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
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if m == self.model[0]:
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logger.info(f"{'time (ms)':>10s} {'GFLOPS':>10s} {'params':>10s} {'module'}")
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logger.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
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x = m(x) # run
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y.append(x if m.i in self.save else None) # save output
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@ -157,7 +159,8 @@ class Model(nn.Module):
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m = self.model[-1] # Detect() module
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for mi in m.m: # from
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b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
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logger.info(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
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logger.info(
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('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
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# def _print_weights(self):
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# for m in self.model.modules():
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