Hi,
I'm trying using the .load_state() function, but the result is:
TypeError Traceback (most recent call last)
in
1 model_t = dm.MatchingModel(attr_summarizer="hybrid")
----> 2 model_t.load_state("model_1.pth")
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/deepmatcher/models/core.py in load_state(self, path, map_location)
479 MatchingDataset.finalize_metadata(train_info)
480
--> 481 self.initialize(train_info, self.state_meta.init_batch)
482
483 self.load_state_dict(state['model'])
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/deepmatcher/models/core.py in initialize(self, train_dataset, init_batch)
353 sort_in_buckets=False)
354 init_batch = next(run_iter.iter())
--> 355 self.forward(init_batch)
356
357 # Keep this init_batch for future initializations.
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/deepmatcher/models/core.py in forward(self, input)
417 for name in self.meta.all_text_fields:
418 attr_input = getattr(input, name)
--> 419 embeddings[name] = self.embedname
420
421 attr_comparisons = []
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/deepmatcher/models/modules.py in forward(self, *args)
185 module_args.append(arg.data if isinstance(arg, AttrTensor) else arg)
186
--> 187 results = self.module(*module_args)
188
189 if not isinstance(args[0], AttrTensor):
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/torch/nn/modules/sparse.py in forward(self, input)
143
144 def forward(self, input: Tensor) -> Tensor:
--> 145 return F.embedding(
146 input, self.weight, self.padding_idx, self.max_norm,
147 self.norm_type, self.scale_grad_by_freq, self.sparse)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
1911 # remove once script supports set_grad_enabled
1912 no_grad_embedding_renorm(weight, input, max_norm, norm_type)
-> 1913 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
1914
1915
TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not AttrTensor
Hi,
I'm trying using the .load_state() function, but the result is:
TypeError Traceback (most recent call last)
in
1 model_t = dm.MatchingModel(attr_summarizer="hybrid")
----> 2 model_t.load_state("model_1.pth")
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/deepmatcher/models/core.py in load_state(self, path, map_location)
479 MatchingDataset.finalize_metadata(train_info)
480
--> 481 self.initialize(train_info, self.state_meta.init_batch)
482
483 self.load_state_dict(state['model'])
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/deepmatcher/models/core.py in initialize(self, train_dataset, init_batch)
353 sort_in_buckets=False)
354 init_batch = next(run_iter.iter())
--> 355 self.forward(init_batch)
356
357 # Keep this init_batch for future initializations.
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/deepmatcher/models/core.py in forward(self, input)
417 for name in self.meta.all_text_fields:
418 attr_input = getattr(input, name)
--> 419 embeddings[name] = self.embedname
420
421 attr_comparisons = []
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/deepmatcher/models/modules.py in forward(self, *args)
185 module_args.append(arg.data if isinstance(arg, AttrTensor) else arg)
186
--> 187 results = self.module(*module_args)
188
189 if not isinstance(args[0], AttrTensor):
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/torch/nn/modules/sparse.py in forward(self, input)
143
144 def forward(self, input: Tensor) -> Tensor:
--> 145 return F.embedding(
146 input, self.weight, self.padding_idx, self.max_norm,
147 self.norm_type, self.scale_grad_by_freq, self.sparse)
/local_disk0/.ephemeral_nfs/envs/pythonEnv-33cf32f5-60db-4ff5-acdf-2c87c014459e/lib/python3.8/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
1911 # remove once script supports set_grad_enabled
1912 no_grad_embedding_renorm(weight, input, max_norm, norm_type)
-> 1913 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
1914
1915
TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not AttrTensor