prior_network = pd.read_csv('./network_mouse.csv')
data = cf.data_preparation(adata, prior_network)
[0] - Data loading and preprocessing...
Consider the input data with 1 lineages:
Lineage - all:
337 extra edges (Spearman correlation > 0.6) are added into the prior gene interaction network.
Total number of edges: 3537148.
n_genes × n_cells = 12335 × 1822
CUDA = '0'
%%time
cefcon_results_dict = {}
for li, data_li in data.items():
# We suggest setting up multiple repeats to minimize the randomness of the computation.
cefcon_GRN_model = cf.NetModel(epochs=350, repeats=3, seed=-1,cuda=CUDA)
cefcon_GRN_model.run(data_li)
cefcon_results = cefcon_GRN_model.get_cefcon_results(edge_threshold_avgDegree=8)
cefcon_results_dict[li] = cefcon_results
[1] - Constructing cell-lineage-specific GRN...
Lineage - all:
Warning: Auxiliary gene scores (e.g., differential expression level) are not considered!
0%| | 0/350 [00:00<?, ?it/s]
---------------------------------------------------------------------------
OutOfMemoryError Traceback (most recent call last)
File <timed exec>:5
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/cefcon/cell_lineage_GRN.py:370, in NetModel.run(self, adata, showProgressBar)
368 with trange(self.epochs, ncols=100) as t:
369 for epoch in t:
--> 370 loss = self.__train(data, DGI_model, optimizer)
371 t.set_description(' Iter: {}/{}'.format(rep + 1, self.repeats))
372 if epoch < self.epochs - 1:
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/cefcon/cell_lineage_GRN.py:326, in NetModel.__train(data, model, optimizer)
324 model.train()
325 optimizer.zero_grad()
--> 326 pos_z, neg_z, summary = model(data)
327 loss = model.loss(pos_z, neg_z, summary)
328 loss.backward()
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
1190 # If we don't have any hooks, we want to skip the rest of the logic in
1191 # this function, and just call forward.
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 full_backward_hooks, non_full_backward_hooks = [], []
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/torch_geometric/nn/models/deep_graph_infomax.py:52, in DeepGraphInfomax.forward(self, *args, **kwargs)
49 def forward(self, *args, **kwargs) -> Tuple[Tensor, Tensor, Tensor]:
50 """Returns the latent space for the input arguments, their
51 corruptions and their summary representation."""
---> 52 pos_z = self.encoder(*args, **kwargs)
54 cor = self.corruption(*args, **kwargs)
55 cor = cor if isinstance(cor, tuple) else (cor, )
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
1190 # If we don't have any hooks, we want to skip the rest of the logic in
1191 # this function, and just call forward.
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 full_backward_hooks, non_full_backward_hooks = [], []
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/cefcon/cell_lineage_GRN.py:222, in GRN_Encoder.forward(self, data)
220 for norm, attn_in, attn_out, ffn in self.layers:
221 x = norm(x)
--> 222 x_in, att_weights_in_ = attn_in(x, edge_index, x_auxiliary, return_attention_weights=True)
223 x_out, att_weights_out_ = attn_out(x, edge_index, x_auxiliary, return_attention_weights=True)
224 x = ffn(torch.cat((self.act(x_in), self.act(x_out)), 1))
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
1190 # If we don't have any hooks, we want to skip the rest of the logic in
1191 # this function, and just call forward.
1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1193 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194 return forward_call(*input, **kwargs)
1195 # Do not call functions when jit is used
1196 full_backward_hooks, non_full_backward_hooks = [], []
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/cefcon/cell_lineage_GRN.py:111, in GraphAttention_layer.forward(self, x, edge_index, x_auxiliary, return_attention_weights)
109 x_norm_l = F.normalize(x_l, p=2., dim=-1)
110 x_norm_r = F.normalize(x_r, p=2., dim=-1)
--> 111 out = self.propagate(edge_index, x=(x_l, x_r), x_norm=(x_norm_l, x_norm_r),
112 x_auxiliary=x_auxiliary, size=None)
113 else: # SD
114 out = self.propagate(edge_index, x=(x_l, x_r), x_norm=None,
115 x_auxiliary=x_auxiliary, size=None)
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py:455, in MessagePassing.propagate(self, edge_index, size, **kwargs)
452 for arg in decomp_args:
453 kwargs[arg] = decomp_kwargs[arg][i]
--> 455 coll_dict = self._collect(self._user_args, edge_index, size,
456 kwargs)
458 msg_kwargs = self.inspector.distribute('message', coll_dict)
459 for hook in self._message_forward_pre_hooks.values():
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py:329, in MessagePassing._collect(self, args, edge_index, size, kwargs)
327 if isinstance(data, Tensor):
328 self._set_size(size, dim, data)
--> 329 data = self._lift(data, edge_index, dim)
331 out[arg] = data
333 if is_torch_sparse_tensor(edge_index):
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py:276, in MessagePassing._lift(self, src, edge_index, dim)
269 raise IndexError(
270 f"Encountered an index error. Please ensure that all "
271 f"indices in 'edge_index' point to valid indices in "
272 f"the interval [0, {src.size(self.node_dim) - 1}] "
273 f"(got interval "
274 f"[{int(index.min())}, {int(index.max())}])")
275 else:
--> 276 raise e
278 if index.numel() > 0 and index.min() < 0:
279 raise ValueError(
280 f"Found negative indices in 'edge_index' (got "
281 f"{index.min().item()}). Please ensure that all "
282 f"indices in 'edge_index' point to valid indices "
283 f"in the interval [0, {src.size(self.node_dim)}) in "
284 f"your node feature matrix and try again.")
File ~/run/miniconda3/envs/CEFCON/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py:266, in MessagePassing._lift(self, src, edge_index, dim)
264 try:
265 index = edge_index[dim]
--> 266 return src.index_select(self.node_dim, index)
267 except (IndexError, RuntimeError) as e:
268 if index.min() < 0 or index.max() >= src.size(self.node_dim):
OutOfMemoryError: CUDA out of memory. Tried to allocate 6.77 GiB (GPU 0; 23.65 GiB total capacity; 20.62 GiB already allocated; 2.49 GiB free; 20.64 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Hi ,great work!
when I run
get error like this