Hi,
I have got a custom torch.nn.Module. I want to deepcopy it for checkpoint (according to this blog post Can I deepcopy a model?)
However when I try:
import copy
model2 = copy.deepcopy(model)
I have the following error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-35-c0373e47f471> in <module>
1 import copy
----> 2 model2 = copy.deepcopy(model)
/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
173
174 # If is its own copy, don't memoize.
/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
268 if state is not None:
269 if deep:
--> 270 state = deepcopy(state, memo)
271 if hasattr(y, '__setstate__'):
272 y.__setstate__(state)
/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
/home/user/miniconda/lib/python3.8/copy.py in _deepcopy_dict(x, memo, deepcopy)
228 memo[id(x)] = y
229 for key, value in x.items():
--> 230 y[deepcopy(key, memo)] = deepcopy(value, memo)
231 return y
232 d[dict] = _deepcopy_dict
/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
173
174 # If is its own copy, don't memoize.
/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
294 for key, value in dictiter:
295 key = deepcopy(key, memo)
--> 296 value = deepcopy(value, memo)
297 y[key] = value
298 else:
/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
173
174 # If is its own copy, don't memoize.
/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
268 if state is not None:
269 if deep:
--> 270 state = deepcopy(state, memo)
271 if hasattr(y, '__setstate__'):
272 y.__setstate__(state)
/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
/home/user/miniconda/lib/python3.8/copy.py in _deepcopy_dict(x, memo, deepcopy)
228 memo[id(x)] = y
229 for key, value in x.items():
--> 230 y[deepcopy(key, memo)] = deepcopy(value, memo)
231 return y
232 d[dict] = _deepcopy_dict
/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
173
174 # If is its own copy, don't memoize.
/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
294 for key, value in dictiter:
295 key = deepcopy(key, memo)
--> 296 value = deepcopy(value, memo)
297 y[key] = value
298 else:
/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
173
174 # If is its own copy, don't memoize.
/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
268 if state is not None:
269 if deep:
--> 270 state = deepcopy(state, memo)
271 if hasattr(y, '__setstate__'):
272 y.__setstate__(state)
/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
144 copier = _deepcopy_dispatch.get(cls)
145 if copier is not None:
--> 146 y = copier(x, memo)
147 else:
148 if issubclass(cls, type):
/home/user/miniconda/lib/python3.8/copy.py in _deepcopy_dict(x, memo, deepcopy)
228 memo[id(x)] = y
229 for key, value in x.items():
--> 230 y[deepcopy(key, memo)] = deepcopy(value, memo)
231 return y
232 d[dict] = _deepcopy_dict
/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
170 y = x
171 else:
--> 172 y = _reconstruct(x, memo, *rv)
173
174 # If is its own copy, don't memoize.
/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
294 for key, value in dictiter:
295 key = deepcopy(key, memo)
--> 296 value = deepcopy(value, memo)
297 y[key] = value
298 else:
/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
151 copier = getattr(x, "__deepcopy__", None)
152 if copier is not None:
--> 153 y = copier(memo)
154 else:
155 reductor = dispatch_table.get(cls)
/home/user/miniconda/lib/python3.8/site-packages/torch/nn/parameter.py in __deepcopy__(self, memo)
30 return memo[id(self)]
31 else:
---> 32 result = type(self)(self.data.clone(memory_format=torch.preserve_format), self.requires_grad)
33 memo[id(self)] = result
34 return result
TypeError: __new__() takes from 1 to 2 positional arguments but 3 were given
I am not sure to understand where the issue is 
my model:
RelationNet(
(embedding): BasicEmbeddingModule(
(conv_blocks): Sequential(
(0): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
(2): ReLU()
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(1): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
(2): ReLU()
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(2): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
(2): ReLU()
)
(3): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
(2): ReLU()
)
)
)
(relation): BasicRelationModule(
(conv1): Sequential(
(0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
(2): ReLU()
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(conv2): Sequential(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
(2): ReLU()
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(linear1): LazyLinear(in_features=0, out_features=8, bias=True)
(linear2): Linear(in_features=8, out_features=1, bias=True)
)
)
pytorch version : 1.8.0
python versions : 3.8.3
Thanks in advance 