问题描述：
# RuntimeError:
Error(s) in loading state_dict for Fusion_Generator: size mismatch for fg_decoder.0.weight: copying a param with shape torch.Size([4096, 1024])，g_decoder.0.weight: copying a param with shape torch.Size([4096, 1024]...
出现两个参数的不匹配。
具体内容如下：
model = GAN(opt)
loaded = torch.load(model_path)
assert (opt.epoch == loaded['epoch'])
model.load_state_dict(loaded['model'], strict=False) # 这里爆出上述Error，定位到下面的函数
def load_state_dict(self, pretrained_dict, strict=False):
for k in pretrained_dict:
if k ...
...
...
elif k == "generator":
self.generator.load_state_dict(pretrained_dict[k], strict=strict) # 这里虽然strict传入的是False，忽略不匹配参数，仍有上述问题
elif k ...
...
在参考 这里 后，如果只是pop()掉fg_decoder.0.weight 和bg_decoder.0.weight后，会有新的问题出现(一般问题通过pop掉能解决问题)，即
KeyError: 'fg_decoder.0.weight，bg_decoder.0.weight'
即不能识别上述两个键值，这时可以通过打印模型参数具体内容查看：
def load_state_dict(self, pretrained_dict, strict=False):
for k in pretrained_dict:
if k ...
...
...
elif k == "fusion_generator":
for u in pretrained_dict[k].keys():
print(u," ",pretrained_dict[k][u])
self.fusion_generator.load_state_dict(pretrained_dict[k], strict=strict) #
elif k ...
...
打印结果
fg_decoder.0.weight xxxxxx tensor([0., 0., 0., ..., 0., 0., 0.], device='cuda:0')
fg_decoder.0.bias xxxxxx tensor([0., 0., 0., ..., 0., 0., 0.], device='cuda:0') fg_decoder.1.weight xxxxxx tensor([1.0362, 0.9969, 0.9892, ..., 0.9939, 1.0122, 1.0190], device='cuda:0') fg_decoder.1.bias xxxxxx tensor([0., 0., 0., ..., 0., 0., 0.], device='cuda:0') fg_decoder.1.running_mean xxxxxx tensor([ 0.1915, -0.5510, 0.5370, ..., -0.1265, 0.8344, 1.4391], device='cuda:0') fg_decoder.1.running_var xxxxxx tensor([0.9402, 0.7382, 0.0167, ..., 0.3988, 0.1081, 0.4470], device='cuda:0') fg_decoder.1.num_batches_tracked xxxxxx tensor(3880, device='cuda:0') fg_decoder.3.weight xxxxxx tensor([[ 0.0211, -0.0072, 0.0030, ..., 0.0090, 0.0120, 0.0043], [ 0.0221, -0.0320, -0.0050, ..., 0.0239, 0.0035, 0.0438], [ 0.0246, -0.0091, 0.0146, ..., -0.0003, 0.0257, -0.0025], ..., [ 0.0077, -0.0209, -0.0017, ..., 0.0135, 0.0418, 0.0052], [ 0.0109, 0.0066, -0.0093, ..., 0.0048, -0.0019, -0.0381], [ 0.0145, -0.0165, 0.0095, ..., 0.0252, -0.0184, 0.0178]], device='cuda:0')
....
bg_decoder.0.weight xxxxxx tensor([0., 0., 0., ..., 0., 0., 0.], device='cuda:0')
....
可以发现fg_decoder.0.weight和bg_decoder.0.weight都在里面，并且对应为pretrained_dict[k][u]
所以！！！在有序字典中将对应报错内容删除后，就能解决size mismatch问题
def load_state_dict(self, pretrained_dict, strict=False):
for k in pretrained_dict:
if k ...
...
...
elif k == "fusion_generator":
for u in list(pretrained_dict[k].keys()):# (小坑)加list防止同时读写报错
if u == "fg_decoder.0.weight" or u == "bg_decoder.0.weight":
pretrained_dict[k].pop(u)
self.fusion_generator.load_state_dict(pretrained_dict[k], strict=strict) #
elif k ...
...
成功解决问题~
标签：...,decoder.0,False,weight,mismatch,strict,dict,pretrained From： https://www.cnblogs.com/ygsworld/p/17656637.html