updates
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@@ -64,3 +64,16 @@ def fuse_conv_and_bn(conv, bn):
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fusedconv.bias.copy_(b_conv + b_bn)
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return fusedconv
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def model_info(model, report='summary'):
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# Plots a line-by-line description of a PyTorch model
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n_p = sum(x.numel() for x in model.parameters()) # number parameters
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n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
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if report is 'full':
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print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
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for i, (name, p) in enumerate(model.named_parameters()):
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name = name.replace('module_list.', '')
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print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
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(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
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print('Model Summary: %g layers, %g parameters, %g gradients' % (len(list(model.parameters())), n_p, n_g))
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@@ -41,19 +41,6 @@ def load_classes(path):
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return list(filter(None, names)) # filter removes empty strings (such as last line)
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def model_info(model, report='summary'):
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# Plots a line-by-line description of a PyTorch model
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n_p = sum(x.numel() for x in model.parameters()) # number parameters
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n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
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if report is 'full':
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print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
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for i, (name, p) in enumerate(model.named_parameters()):
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name = name.replace('module_list.', '')
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print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
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(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
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print('Model Summary: %g layers, %g parameters, %g gradients' % (len(list(model.parameters())), n_p, n_g))
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def labels_to_class_weights(labels, nc=80):
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# Get class weights (inverse frequency) from training labels
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ni = len(labels) # number of images
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