"mxnet.gluon.parameter.DeferredInitializationError: Parameter 'seq2seq_encoder_g0_in_mlp0_dense0_weight' has not been initialized yet because initialization was deferred. Actual initialization happens during the first forward pass. Please pass one batch of data through the network before accessing Parameters. You can also avoid deferred initialization by specifying in_units, num_features, etc., for network layers."

When running the codes, I met the above initialization problem. How can I debug it?

Hi, thank you for sharing your code!
When I run the cmd line:
python train.py --file model_setting/st-metanet.yaml --gpus 0 --epochs 200
The Traceback message likes:

/home/ypy/rec_sys/env_sb/lib/python3.6/site-packages/dgl/heterograph.py:72: DGLWarning: Recommend creating graphs by dgl.graph(data) instead of dgl.DGLGraph(data).
dgl_warning('Recommend creating graphs by dgl.graph(data)'
Traceback (most recent call last):
File "train.py", line 172, in
main(args)
File "train.py", line 150, in main
metrics = [MAE(scaler), RMSE(scaler), IndexMAE(scaler, [0,1,2]), IndexRMSE(scaler, [0,1,2])],
File "train.py", line 72, in fit
self.process_data(epoch, train, metrics)
File "train.py", line 53, in process_data
outputs = [self.net(*x, is_training) for x in zip(*inputs)]
File "train.py", line 53, in
outputs = [self.net(*x, is_training) for x in zip(*inputs)]
File "/home/ypy/rec_sys/env_sb/lib/python3.6/site-packages/mxnet/gluon/block.py", line 540, in call
out = self.forward(*args)
File "/home/ypy/rec_sys/ST-MetaNet/traffic-prediction/src/model/seq2seq.py", line 233, in forward
states = self.encoder(feature, data)
File "/home/ypy/rec_sys/env_sb/lib/python3.6/site-packages/mxnet/gluon/block.py", line 540, in call
out = self.forward(*args)
File "/home/ypy/rec_sys/ST-MetaNet/traffic-prediction/src/model/seq2seq.py", line 52, in forward
_data = _data + g(data, feature)
File "/home/ypy/rec_sys/env_sb/lib/python3.6/site-packages/mxnet/gluon/block.py", line 540, in call
out = self.forward(*args)
File "/home/ypy/rec_sys/ST-MetaNet/traffic-prediction/src/model/graph.py", line 70, in forward
g = self.get_graph_on_ctx(state.context)
File "/home/ypy/rec_sys/ST-MetaNet/traffic-prediction/src/model/graph.py", line 66, in get_graph_on_ctx
self.build_graph_on_ctx(ctx)
File "/home/ypy/rec_sys/ST-MetaNet/traffic-prediction/src/model/graph.py", line 60, in build_graph_on_ctx
g.edata['dist'] = self.dist.as_in_context(ctx)
File "/home/ypy/rec_sys/env_sb/lib/python3.6/site-packages/dgl/view.py", line 227, in setitem
self._graph._set_e_repr(self._etid, self._edges, {key: val})
File "/home/ypy/rec_sys/env_sb/lib/python3.6/site-packages/dgl/heterograph.py", line 4135, in _set_e_repr
if F.context(val) != self.device:
File "/home/ypy/rec_sys/env_sb/lib/python3.6/site-packages/dgl/backend/pytorch/tensor.py", line 99, in context
return input.device
AttributeError: 'NDArray' object has no attribute 'device'

Could you please help me fix this~
Waiting for your reply. Thanks a lot~

Hello, I'm having a question reading the input of Seq2Seq model. In the paper you mentioned that "The features of nodes are added as the additional inputs." From my understanding on the last dimension the input should have a size of: 9 (node features) + 8 (neighbors) * 2 (x,y coordinates). I searched everywhere in the MyGRUCell and I can not find such shape, furthermore I can see that the features are not used in the cell.

Thank you in advance and looking forward to hearing from you.

作者您好，请问如何装此版本？pip命令报错找不到此版本。期待您的回复，提前感谢！

您好，很感谢您分享代码。
在运行示范命令
python train.py --file model_setting/st-metanet.yaml --gpus 0 --epochs 200
遇到报错信息如下：
Traceback (most recent call last):
File "train.py", line 7, in
import mxnet as mx
File "/home/cll/anaconda3/envs/MetaNet/lib/python3.5/site-packages/mxnet/init.py", line 24, in
from .context import Context, current_context, cpu, gpu, cpu_pinned
File "/home/cll/anaconda3/envs/MetaNet/lib/python3.5/site-packages/mxnet/context.py", line 24, in
from .base import classproperty, with_metaclass, _MXClassPropertyMetaClass
File "/home/cll/anaconda3/envs/MetaNet/lib/python3.5/site-packages/mxnet/base.py", line 213, in
_LIB = _load_lib()
File "/home/cll/anaconda3/envs/MetaNet/lib/python3.5/site-packages/mxnet/base.py", line 204, in _load_lib
lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL)
File "/home/cll/anaconda3/envs/MetaNet/lib/python3.5/ctypes/init.py", line 347, in init
self._handle = _dlopen(self._name, mode)
OSError: libcudart.so.9.0: cannot open shared object file: No such file or directory
环境安装，除了mxnet_cu90，剩下的lib都是严格对照requirements.txt。mxnet_cu90从https://dist.mxnet.io/python/cu90网站下载临近的两个whl文件，mxnet_cu90-1.5.0b20190106-py2.py3-none-manylinux1_x86_64.whl 和mxnet_cu90-1.5.0b20190113-py2.py3-none-manylinux1_x86_64.whl。分别测试后， 都会出现上述报错，请问该如何Debug～
谢谢！

I have read the source code and the idea of EMK/NMK learner is interesting.

But I can't find them in the source data. It seems that the training process of both two learners is not provided in the source, and the training results have been saved in the "FEATURE.h5" file.

Would you provide the training strategy of the embedding vectors with edge/node meta knowledge if it is convenient for you?

Hello, I have a question about the original data. In the grid feature data 'BJ_FEATURE.h5', each dimension of the feature value of 989 dimensions corresponds to what features, and 989 is not equal to 940 ‘BJ_POI’ + 47 ‘BJ_ROAD’, why?

Or if you can, could you share the 'BJ_FEATURE.h5' file before it was embedded?

Thank you very much and look forward to your reply!

Hello, thank you for sharing your work.

I wonder where do you load the above two files(BJ_POI.h5 and BJ_ROAD.h5) as extra meta data for the model.

Thanks

Hello, thank you very much for sharing the code. I'm receiving an error in metric.py line 53: TypeError: unsupported operand type(s) for +: 'NoneType' and 'float').

Hi,

Thank you for sharing the code.

In the article, you said that the new state h(i) of MetaGAT is calculated by a linear combination of the previous hidden state and the new state calculated with the attention mechanism. In other words:

However, in the code, I can only find the right side of the equation in the function msg_reduce. Is this enough or maybe a msg_update function should be added to apply the previous formula?

Thank you in advance,
Best regards.

I am deploying your model on Colab with several adaptations, whose link is here: https://colab.research.google.com/drive/1BZ9PFWz-61KiKB91ET93iJkm35lklCr-?usp=sharing

The model ran smoothly with Mxnet 1.4.0 and 1.5.1, but end up with a sudden cancellation (possibly due to the RAM surge).
Successfully loading the model st-metanet [epoch: 131] seq2seq_ ( Parameter seq2seq_encoder_c0_gru0_i2h_weight (shape=(192, 3), dtype=<class 'numpy.float32'>) Parameter seq2seq_encoder_c0_gru0_h2h_weight (shape=(192, 64), dtype=<class 'numpy.float32'>) Parameter seq2seq_encoder_c0_gru0_i2h_bias (shape=(192,), dtype=<class 'numpy.float32'>) Parameter seq2seq_encoder_c0_gru0_h2h_bias (shape=(192,), dtype=<class 'numpy.float32'>) Parameter seq2seq_encoder_c1_dense_z_w_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_w_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_w_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_w_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_w_dense2_weight (shape=(8192, 2), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_w_dense2_bias (shape=(8192,), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_b_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_b_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_b_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_b_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_b_dense2_weight (shape=(1, 2), dtype=float32) Parameter seq2seq_encoder_c1_dense_z_b_dense2_bias (shape=(1,), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_w_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_w_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_w_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_w_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_w_dense2_weight (shape=(8192, 2), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_w_dense2_bias (shape=(8192,), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_b_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_b_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_b_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_b_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_b_dense2_weight (shape=(1, 2), dtype=float32) Parameter seq2seq_encoder_c1_dense_r_b_dense2_bias (shape=(1,), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_w_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_w_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_w_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_w_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_w_dense2_weight (shape=(4096, 2), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_w_dense2_bias (shape=(4096,), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_b_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_b_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_b_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_b_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_b_dense2_weight (shape=(1, 2), dtype=float32) Parameter seq2seq_encoder_c1_dense_i2h_b_dense2_bias (shape=(1,), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_w_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_w_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_w_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_w_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_w_dense2_weight (shape=(4096, 2), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_w_dense2_bias (shape=(4096,), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_b_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_b_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_b_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_b_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_b_dense2_weight (shape=(1, 2), dtype=float32) Parameter seq2seq_encoder_c1_dense_h2h_b_dense2_bias (shape=(1,), dtype=float32) Parameter seq2seq_encoder_g0_graph_weight (shape=(1, 1), dtype=<class 'numpy.float32'>) Parameter seq2seq_encoder_g0_graph_mlp0_dense0_weight (shape=(16, 96), dtype=float32) Parameter seq2seq_encoder_g0_graph_mlp0_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_encoder_g0_graph_mlp0_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_encoder_g0_graph_mlp0_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_encoder_g0_graph_mlp0_dense2_weight (shape=(8192, 2), dtype=float32) Parameter seq2seq_encoder_g0_graph_mlp0_dense2_bias (shape=(8192,), dtype=float32) Parameter seq2seq_decoder_c0_gru0_i2h_weight (shape=(192, 3), dtype=<class 'numpy.float32'>) Parameter seq2seq_decoder_c0_gru0_h2h_weight (shape=(192, 64), dtype=<class 'numpy.float32'>) Parameter seq2seq_decoder_c0_gru0_i2h_bias (shape=(192,), dtype=<class 'numpy.float32'>) Parameter seq2seq_decoder_c0_gru0_h2h_bias (shape=(192,), dtype=<class 'numpy.float32'>) Parameter seq2seq_decoder_c1_dense_z_w_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_w_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_w_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_w_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_w_dense2_weight (shape=(8192, 2), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_w_dense2_bias (shape=(8192,), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_b_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_b_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_b_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_b_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_b_dense2_weight (shape=(1, 2), dtype=float32) Parameter seq2seq_decoder_c1_dense_z_b_dense2_bias (shape=(1,), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_w_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_w_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_w_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_w_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_w_dense2_weight (shape=(8192, 2), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_w_dense2_bias (shape=(8192,), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_b_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_b_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_b_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_b_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_b_dense2_weight (shape=(1, 2), dtype=float32) Parameter seq2seq_decoder_c1_dense_r_b_dense2_bias (shape=(1,), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_w_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_w_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_w_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_w_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_w_dense2_weight (shape=(4096, 2), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_w_dense2_bias (shape=(4096,), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_b_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_b_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_b_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_b_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_b_dense2_weight (shape=(1, 2), dtype=float32) Parameter seq2seq_decoder_c1_dense_i2h_b_dense2_bias (shape=(1,), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_w_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_w_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_w_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_w_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_w_dense2_weight (shape=(4096, 2), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_w_dense2_bias (shape=(4096,), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_b_dense0_weight (shape=(16, 32), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_b_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_b_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_b_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_b_dense2_weight (shape=(1, 2), dtype=float32) Parameter seq2seq_decoder_c1_dense_h2h_b_dense2_bias (shape=(1,), dtype=float32) Parameter seq2seq_decoder_g0_graph_weight (shape=(1, 1), dtype=<class 'numpy.float32'>) Parameter seq2seq_decoder_g0_graph_mlp0_dense0_weight (shape=(16, 96), dtype=float32) Parameter seq2seq_decoder_g0_graph_mlp0_dense0_bias (shape=(16,), dtype=float32) Parameter seq2seq_decoder_g0_graph_mlp0_dense1_weight (shape=(2, 16), dtype=float32) Parameter seq2seq_decoder_g0_graph_mlp0_dense1_bias (shape=(2,), dtype=float32) Parameter seq2seq_decoder_g0_graph_mlp0_dense2_weight (shape=(8192, 2), dtype=float32) Parameter seq2seq_decoder_g0_graph_mlp0_dense2_bias (shape=(8192,), dtype=float32) Parameter decoder0_proj_weight (shape=(2, 96), dtype=float32) Parameter decoder0_proj_bias (shape=(2,), dtype=float32) Parameter geo_encoder_dense0_weight (shape=(32, 989), dtype=float32) Parameter geo_encoder_dense0_bias (shape=(32,), dtype=float32) Parameter geo_encoder_dense1_weight (shape=(32, 32), dtype=float32) Parameter geo_encoder_dense1_bias (shape=(32,), dtype=float32) ) NUMBER OF PARAMS: 268224 INFO:root:Processing 1000 timestamps INFO:root:Processing 2000 timestamps tcmalloc: large alloc 9179439104 bytes == 0x55fd96a46000 @ 0x7f54362f31e7 0x7f5433000cf1 0x7f5433065768 0x7f5433065883 0x7f5433105b5e 0x7f54331063c4 0x7f5433106512 0x55fcee9530a4 0x55fcee952da0 0x55fcee9c7868 0x55fcee9c2235 0x55fcee95473a 0x55fcee9c6f40 0x55fcee9c1c35 0x55fcee95473a 0x55fcee9c2b0e 0x55fcee95465a 0x55fcee9c2b0e 0x55fcee95465a 0x55fcee9c2b0e 0x55fcee9c1c35 0x55fcee9c1933 0x55fceea8b402 0x55fceea8b77d 0x55fceea8b626 0x55fceea63313 0x55fceea62fbc 0x7f54350ddbf7 0x55fceea62e9a tcmalloc: large alloc 9179439104 bytes == 0x55ffba478000 @ 0x7f54362d5b6b 0x7f54362f5379 0x7f54064bde75 0x7f54064bdf0d 0x7f54064c622e 0x7f5405cdaa26 0x7f5405cdb298 0x7f5433825dae 0x7f543382571f 0x7f5433a395ac 0x7f5433a389e3 0x55fcee9537b2 0x55fcee9c76f2 0x55fcee9c1c35 0x55fcee95473a 0x55fcee9c2b0e 0x55fcee9c1c35 0x55fcee95473a 0x55fcee9c2b0e 0x55fcee9c1c35 0x55fcee95473a 0x55fcee9c393b 0x55fcee9c1c35 0x55fcee95473a 0x55fcee9c6f40 0x55fcee9c1c35 0x55fcee95473a 0x55fcee9c2b0e 0x55fcee95465a 0x55fcee9c2b0e 0x55fcee95465a ^C

Hi,in your paper,the Taxi flow dataset is range from Feb 1st 2015 to Jun. 2nd 2015，The number of days in this period is 122. less than 3600 timeslot.
but the BJ_FLOW.h5, shape is (150,24,32,32,2),which has 150 days?

Traceback (most recent call last):
File "train.py", line 172, in
main(args)
File "train.py", line 143, in main
train, eval, test, scaler = getattr(data.dataloader, dataset_setting['dataloader'])(settings)
File "/home/xinyilian/ST-MetaNet/flow-prediction/src/data/dataloader.py", line 109, in dataloader_all_sensors_seq2seq
return dataiter_all_sensors_seq2seq(train, scaler, setting),
File "/home/xinyilian/ST-MetaNet/flow-prediction/src/data/dataloader.py", line 90, in dataiter_all_sensors_seq2seq
feature = mx.nd.array(np.stack(feature)) # [B, N, D]
File "/home/xinyilian/.conda/envs/xyl/lib/python3.6/site-packages/mxnet/ndarray/utils.py", line 146, in array
return _array(source_array, ctx=ctx, dtype=dtype)
File "/home/xinyilian/.conda/envs/xyl/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 2505, in array
arr[:] = source_array
File "/home/xinyilian/.conda/envs/xyl/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 449, in setitem
self._set_nd_basic_indexing(key, value)
File "/home/xinyilian/.conda/envs/xyl/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 715, in _set_nd_basic_indexing
self._sync_copyfrom(value)
File "/home/xinyilian/.conda/envs/xyl/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 881, in _sync_copyfrom
ctypes.c_size_t(source_array.size)))
File "/home/xinyilian/.conda/envs/xyl/lib/python3.6/site-packages/mxnet/base.py", line 253, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [19:21:13] src/ndarray/ndarray_function.cc:51: Check failed: size == to->Size() (-2000107520 vs. 2294859776) : copying size mismatch, from: 18446744065709121536 bytes, to: 9179439104 bytes.
Stack trace:
[bt] (0) /home/xinyilian/.conda/envs/xyl/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x4b04cb) [0x7feb9aa234cb]
[bt] (1) /home/xinyilian/.conda/envs/xyl/lib/python3.6/site-packages/mxnet/libmxnet.so(+0x281c85b) [0x7feb9cd8f85b]
[bt] (2) /home/xinyilian/.conda/envs/xyl/lib/python3.6/site-packages/mxnet/libmxnet.so(mxnet::NDArray::SyncCopyFromCPU(void const*, unsigned long) const+0x27c) [0x7feb9cd1b59c]
[bt] (3) /home/xinyilian/.conda/envs/xyl/lib/python3.6/site-packages/mxnet/libmxnet.so(MXNDArraySyncCopyFromCPU+0x2b) [0x7feb9ca9790b]
[bt] (4) /home/xinyilian/.conda/envs/xyl/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c) [0x7fec1d551ec0]
[bt] (5) /home/xinyilian/.conda/envs/xyl/lib/python3.6/lib-dynload/../../libffi.so.6(ffi_call+0x22d) [0x7fec1d55187d]
[bt] (6) /home/xinyilian/.conda/envs/xyl/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2ce) [0x7fec1d767ede]
[bt] (7) /home/xinyilian/.conda/envs/xyl/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(+0x13915) [0x7fec1d768915]
[bt] (8) python(_PyObject_FastCallDict+0x8b) [0x560bdc88ae3b]

Hi,

When i run this code I got the following error:

File "/user/cs.aau.dk/seany/SourceCode/traffic-prediction/src/train.py", line 179, in
main(args)
File "/user/cs.aau.dk/seany/SourceCode/traffic-prediction/src/train.py", line 157, in main
metrics = [MAE(scaler), RMSE(scaler), IndexMAE(scaler, [0,1,2]), IndexRMSE(scaler, [0,1,2])],
File "/user/cs.aau.dk/seany/SourceCode/traffic-prediction/src/train.py", line 79, in fit
self.process_data(epoch, train, metrics)
File "/user/cs.aau.dk/seany/SourceCode/traffic-prediction/src/train.py", line 60, in process_data
outputs = [self.net(*x, is_training) for x in zip(*inputs)]
File "/user/cs.aau.dk/seany/SourceCode/traffic-prediction/src/train.py", line 60, in
outputs = [self.net(*x, is_training) for x in zip(*inputs)]
File "/opt/mxnet/python/mxnet/gluon/block.py", line 360, in call
return self.forward(*args)
File "/user/cs.aau.dk/seany/SourceCode/traffic-prediction/src/model/seq2seq.py", line 233, in forward
states = self.encoder(feature, data)
File "/opt/mxnet/python/mxnet/gluon/block.py", line 360, in call
return self.forward(*args)
File "/user/cs.aau.dk/seany/SourceCode/traffic-prediction/src/model/seq2seq.py", line 52, in forward
_data = _data + g(data, feature)
File "/opt/mxnet/python/mxnet/gluon/block.py", line 360, in call
return self.forward(*args)
File "/user/cs.aau.dk/seany/SourceCode/traffic-prediction/src/model/graph.py", line 71, in forward
g = self.get_graph_on_ctx(state.context)
File "/user/cs.aau.dk/seany/SourceCode/traffic-prediction/src/model/graph.py", line 67, in get_graph_on_ctx
self.build_graph_on_ctx(ctx)
File "/user/cs.aau.dk/seany/SourceCode/traffic-prediction/src/model/graph.py", line 57, in build_graph_on_ctx
g = DGLGraph()
File "/usr/local/lib/python3.5/dist-packages/dgl/graph.py", line 907, in init
self._msg_index = utils.zero_index(size=self.number_of_edges())
File "/usr/local/lib/python3.5/dist-packages/dgl/utils.py", line 249, in zero_index
return Index(F.zeros((size,), dtype=F.int64, ctx=F.cpu()))
File "/usr/local/lib/python3.5/dist-packages/dgl/backend/mxnet/tensor.py", line 151, in zeros
return nd.zeros(shape, dtype=dtype, ctx=ctx)
File "/opt/mxnet/python/mxnet/ndarray/utils.py", line 67, in zeros
return _zeros_ndarray(shape, ctx, dtype, **kwargs)
File "/opt/mxnet/python/mxnet/ndarray/ndarray.py", line 3387, in zeros
return _internal._zeros(shape=shape, ctx=ctx, dtype=dtype, **kwargs)
File "", line 34, in _zeros
File "/opt/mxnet/python/mxnet/_ctypes/ndarray.py", line 92, in _imperative_invoke
ctypes.byref(out_stypes)))
File "/opt/mxnet/python/mxnet/base.py", line 148, in check_call
raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: Invalid Input: 'int64', valid values are: {'float16', 'float32', 'float64', 'int32', 'uint8'}, in operator _zeros(name="", dtype="int64", ctx="cpu(0)", shape="(0,)")
srun: error: nv-ai-03.srv.aau.dk: task 0: Exited with exit code 1

So could you please give me some suggestions? Thanks in advance.