61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
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import torch
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import torch.nn.functional as F
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def drop_path(x, drop_prob):
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if drop_prob > 0.:
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keep_prob = 1. - drop_prob
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mask = x.new_zeros(x.size(0), 1, 1, 1)
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mask = mask.bernoulli_(keep_prob)
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x = torch.div(x, keep_prob)
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x.mul_(mask)
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return x
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def return_alphas_str(basemodel):
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if hasattr(basemodel, 'alphas_normal'):
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string = 'normal [{:}] : \n-->>{:}'.format(basemodel.alphas_normal.size(), F.softmax(basemodel.alphas_normal, dim=-1) )
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else: string = ''
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if hasattr(basemodel, 'alphas_reduce'):
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string = string + '\nreduce : {:}'.format( F.softmax(basemodel.alphas_reduce, dim=-1) )
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if hasattr(basemodel, 'get_adjacency'):
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adjacency = basemodel.get_adjacency()
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for i in range( len(adjacency) ):
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weight = F.softmax( basemodel.connect_normal[str(i)], dim=-1 )
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adj = torch.mm(weight, adjacency[i]).view(-1)
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adj = ['{:3.3f}'.format(x) for x in adj.cpu().tolist()]
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string = string + '\nnormal--{:}-->{:}'.format(i, ', '.join(adj))
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for i in range( len(adjacency) ):
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weight = F.softmax( basemodel.connect_reduce[str(i)], dim=-1 )
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adj = torch.mm(weight, adjacency[i]).view(-1)
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adj = ['{:3.3f}'.format(x) for x in adj.cpu().tolist()]
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string = string + '\nreduce--{:}-->{:}'.format(i, ', '.join(adj))
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if hasattr(basemodel, 'alphas_connect'):
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weight = F.softmax(basemodel.alphas_connect, dim=-1).cpu()
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ZERO = ['{:.3f}'.format(x) for x in weight[:,0].tolist()]
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IDEN = ['{:.3f}'.format(x) for x in weight[:,1].tolist()]
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string = string + '\nconnect [{:}] : \n ->{:}\n ->{:}'.format( list(basemodel.alphas_connect.size()), ZERO, IDEN )
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else:
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string = string + '\nconnect = None'
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if hasattr(basemodel, 'get_gcn_out'):
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outputs = basemodel.get_gcn_out(True)
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for i, output in enumerate(outputs):
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string = string + '\nnormal:[{:}] : {:}'.format(i, F.softmax(output, dim=-1) )
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return string
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def remove_duplicate_archs(all_archs):
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archs = []
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str_archs = ['{:}'.format(x) for x in all_archs]
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for i, arch_x in enumerate(str_archs):
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choose = True
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for j in range(i):
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if arch_x == str_archs[j]:
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choose = False; break
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if choose: archs.append(all_archs[i])
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return archs
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