updates
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@ -633,7 +633,7 @@ def get_yolo_layers(model):
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def print_model_biases(model):
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def print_model_biases(model):
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# prints the bias neurons preceding each yolo layer
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# prints the bias neurons preceding each yolo layer
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print('\nModel Bias Summary:')
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print('\nModel Bias Summary: %8s%18s%18s%18s' % ('layer', 'regression', 'objectness', 'classification'))
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multi_gpu = type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
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multi_gpu = type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
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for l in model.yolo_layers: # print pretrained biases
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for l in model.yolo_layers: # print pretrained biases
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if multi_gpu:
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if multi_gpu:
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@ -642,9 +642,9 @@ def print_model_biases(model):
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else:
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else:
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na = model.module_list[l].na
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na = model.module_list[l].na
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b = model.module_list[l - 1][0].bias.view(na, -1) # bias 3x85
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b = model.module_list[l - 1][0].bias.view(na, -1) # bias 3x85
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print('layer %3g regression: %5.2f+/-%-5.2f ' % (l, b[:, :4].mean(), b[:, :4].std()),
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print(' ' * 20 + '%8g %18s%18s%18s' % (l, '%5.2f+/-%-5.2f' % (b[:, :4].mean(), b[:, :4].std()),
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'objectness: %5.2f+/-%-5.2f ' % (b[:, 4].mean(), b[:, 4].std()),
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'%5.2f+/-%-5.2f' % (b[:, 4].mean(), b[:, 4].std()),
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'classification: %5.2f+/-%-5.2f' % (b[:, 5:].mean(), b[:, 5:].std()))
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'%5.2f+/-%-5.2f' % (b[:, 5:].mean(), b[:, 5:].std())))
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def strip_optimizer(f='weights/last.pt'): # from utils.utils import *; strip_optimizer()
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def strip_optimizer(f='weights/last.pt'): # from utils.utils import *; strip_optimizer()
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