参考于：https://blog.csdn.net/jdzwanghao/article/details/84196239
def model_structure(model): blank = ' ' print('-'*90) print('|'+' '*11+'weight name'+' '*10+'|' \ +' '*15+'weight shape'+' '*15+'|' \ +' '*3+'number'+' '*3+'|') print('-'*90) num_para = 0 type_size = 1 ##如果是浮点数就是4 for index, (key, w_variable) in enumerate(model.named_parameters()): if len(key) <= 30: key = key + (30-len(key)) * blank shape = str(w_variable.shape) if len(shape) <= 40: shape = shape + (40-len(shape)) * blank each_para = 1 for k in w_variable.shape: each_para *= k num_para += each_para str_num = str(each_para) if len(str_num) <= 10: str_num = str_num + (10-len(str_num)) * blank print('| {} | {} | {} |'.format(key, shape, str_num)) print('-'*90) print('The total number of parameters: ' + str(num_para)) print('The parameters of Model {}: {:4f}M'.format(model._get_name(), num_para * type_size / 1000 / 1000)) print('-'*90)结果：
------------------------------------------------------------------------------------------ | weight name | weight shape | number | ------------------------------------------------------------------------------------------ | embed_in.0.weight | torch.Size([28, 1, 1, 5, 5]) | 700 | | embed_in.0.bias | torch.Size([28]) | 28 | | downC.0.block1.0.weight | torch.Size([28, 28, 1, 3, 3]) | 7056 | | downC.0.block1.1.weight | torch.Size([28]) | 28 | | downC.0.block1.1.bias | torch.Size([28]) | 28 | | downC.0.block2.0.weight | torch.Size([28, 28, 3, 3, 3]) | 21168 | | downC.0.block2.1.weight | torch.Size([28]) | 28 | | downC.0.block2.1.bias | torch.Size([28]) | 28 | | downC.0.block2.3.weight | torch.Size([28, 28, 3, 3, 3]) | 21168 | | downC.0.block3.weight | torch.Size([28]) | 28 | | downC.0.block3.bias | torch.Size([28]) | 28 | | downC.1.block1.0.weight | torch.Size([36, 28, 1, 3, 3]) | 9072 | | downC.1.block1.1.weight | torch.Size([36]) | 36 | | downC.1.block1.1.bias | torch.Size([36]) | 36 | | downC.1.block2.0.weight | torch.Size([36, 36, 3, 3, 3]) | 34992 | | downC.1.block2.1.weight | torch.Size([36]) | 36 | | downC.1.block2.1.bias | torch.Size([36]) | 36 | | downC.1.block2.3.weight | torch.Size([36, 36, 3, 3, 3]) | 34992 | | downC.1.block3.weight | torch.Size([36]) | 36 | | downC.1.block3.bias | torch.Size([36]) | 36 | | downC.2.block1.0.weight | torch.Size([48, 36, 1, 3, 3]) | 15552 | | downC.2.block1.1.weight | torch.Size([48]) | 48 | | downC.2.block1.1.bias | torch.Size([48]) | 48 | | downC.2.block2.0.weight | torch.Size([48, 48, 3, 3, 3]) | 62208 | | downC.2.block2.1.weight | torch.Size([48]) | 48 | | downC.2.block2.1.bias | torch.Size([48]) | 48 | | downC.2.block2.3.weight | torch.Size([48, 48, 3, 3, 3]) | 62208 | | downC.2.block3.weight | torch.Size([48]) | 48 | | downC.2.block3.bias | torch.Size([48]) | 48 | | center.block1.0.weight | torch.Size([64, 48, 1, 3, 3]) | 27648 | | center.block1.1.weight | torch.Size([64]) | 64 | | center.block1.1.bias | torch.Size([64]) | 64 | | center.block2.0.weight | torch.Size([64, 64, 3, 3, 3]) | 110592 | | center.block2.1.weight | torch.Size([64]) | 64 | | center.block2.1.bias | torch.Size([64]) | 64 | | center.block2.3.weight | torch.Size([64, 64, 3, 3, 3]) | 110592 | | center.block3.weight | torch.Size([64]) | 64 | | center.block3.bias | torch.Size([64]) | 64 | | upS.0.1.weight | torch.Size([48, 64, 1, 1, 1]) | 3072 | | upS.0.1.bias | torch.Size([48]) | 48 | | upS.1.1.weight | torch.Size([36, 48, 1, 1, 1]) | 1728 | | upS.1.1.bias | torch.Size([36]) | 36 | | upS.2.1.weight | torch.Size([28, 36, 1, 1, 1]) | 1008 | | upS.2.1.bias | torch.Size([28]) | 28 | | upC.0.block1.0.weight | torch.Size([48, 48, 1, 3, 3]) | 20736 | | upC.0.block1.1.weight | torch.Size([48]) | 48 | | upC.0.block1.1.bias | torch.Size([48]) | 48 | | upC.0.block2.0.weight | torch.Size([48, 48, 3, 3, 3]) | 62208 | | upC.0.block2.1.weight | torch.Size([48]) | 48 | | upC.0.block2.1.bias | torch.Size([48]) | 48 | | upC.0.block2.3.weight | torch.Size([48, 48, 3, 3, 3]) | 62208 | | upC.0.block3.weight | torch.Size([48]) | 48 | | upC.0.block3.bias | torch.Size([48]) | 48 | | upC.1.block1.0.weight | torch.Size([36, 36, 1, 3, 3]) | 11664 | | upC.1.block1.1.weight | torch.Size([36]) | 36 | | upC.1.block1.1.bias | torch.Size([36]) | 36 | | upC.1.block2.0.weight | torch.Size([36, 36, 3, 3, 3]) | 34992 | | upC.1.block2.1.weight | torch.Size([36]) | 36 | | upC.1.block2.1.bias | torch.Size([36]) | 36 | | upC.1.block2.3.weight | torch.Size([36, 36, 3, 3, 3]) | 34992 | | upC.1.block3.weight | torch.Size([36]) | 36 | | upC.1.block3.bias | torch.Size([36]) | 36 | | upC.2.block1.0.weight | torch.Size([28, 28, 1, 3, 3]) | 7056 | | upC.2.block1.1.weight | torch.Size([28]) | 28 | | upC.2.block1.1.bias | torch.Size([28]) | 28 | | upC.2.block2.0.weight | torch.Size([28, 28, 3, 3, 3]) | 21168 | | upC.2.block2.1.weight | torch.Size([28]) | 28 | | upC.2.block2.1.bias | torch.Size([28]) | 28 | | upC.2.block2.3.weight | torch.Size([28, 28, 3, 3, 3]) | 21168 | | upC.2.block3.weight | torch.Size([28]) | 28 | | upC.2.block3.bias | torch.Size([28]) | 28 | | embed_out.0.weight | torch.Size([28, 28, 1, 5, 5]) | 19600 | | embed_out.0.bias | torch.Size([28]) | 28 | | out_affs_2.0.weight | torch.Size([3, 28, 1, 1, 1]) | 84 | | out_affs_2.0.bias | torch.Size([3]) | 3 | ------------------------------------------------------------------------------------------ The total number of parameters: 821531 The parameters of Model UNet_PNI: 0.821531M ------------------------------------------------------------------------------------------