hannesstark / 3dinfomax Goto Github PK

File "/home/ubuntu/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/torch/nn/functional.py", line 1753, in linear
return torch._C._nn.linear(input, weight, bias)
RuntimeError: CUDA out of memory. Tried to allocate 410.00 MiB (GPU 0; 11.17 GiB total capacity; 9.92 GiB already allocated; 336.44 MiB free; 10.30 GiB reserved in total by PyTorch)
Any idea?

Also would it be possible for you to put up trained models for both QM9 and Geom-Drugs

Hello!

Hope you are well! When I run inference on the included example, I get the following error:

ImportError: cannot import name 'DglPCQM4MDataset' from 'ogb.lsc' 

obg is installed and is version 1.3.3. Any thoughts as to why this error would occur?
Thanks.

Could you provide another .yml file with respect to classification problem such as bbbp or tox21.

Hi,
After installing all the required packages, I follow the Step 2 in Readme.md to run the following code:

python train.py --config=configs_clean/pre-train_QM9.yml

However, I got the error as follow:
Traceback (most recent call last):
File "train.py", line 699, in
train(args)
File "train.py", line 270, in train
return train_qm9(args, device, metrics_dict)
File "train.py", line 562, in train_qm9
dist_embedding=args.dist_embedding, num_radial=args.num_radial)
File "/data2/3DInfomax/datasets/qm9_dataset.py", line 187, in init
self.dist_embedder = dist_emb(num_radial=6).to(device)
File "/data2/3DInfomax/commons/spherical_encoding.py", line 183, in init
self.reset_parameters()
File "/data2/3DInfomax/commons/spherical_encoding.py", line 186, in reset_parameters
torch.arange(1, self.freq.numel() + 1, out=self.freq).mul_(PI)
RuntimeError: a leaf Variable that requires grad is being used in an in-place operation.

I didn't modify the code. Any idea for aforementioned error?

hello everyone! I need help. When I repretrain the model with the commend
python train.py --config=configs_clean/pre-train_QM9.yml

there is negative value starting in the second epoch, this is the loss in epoch 200 and the reult:
[Epoch 199] NTXent: -1.781640 val loss: -1.781640 [Epoch 200; Iter 2/ 100] train: loss: -2.2522569 [Epoch 200; Iter 4/ 100] train: loss: -2.2169321 [Epoch 200; Iter 6/ 100] train: loss: -2.2570577 [Epoch 200; Iter 8/ 100] train: loss: -2.2113991 [Epoch 200; Iter 10/ 100] train: loss: -2.2871132 [Epoch 200; Iter 12/ 100] train: loss: -2.1956975 [Epoch 200; Iter 14/ 100] train: loss: -2.2575712 [Epoch 200; Iter 16/ 100] train: loss: -2.2456131 [Epoch 200; Iter 18/ 100] train: loss: -2.2454598 [Epoch 200; Iter 20/ 100] train: loss: -2.2255766 [Epoch 200; Iter 22/ 100] train: loss: -2.2309322 [Epoch 200; Iter 24/ 100] train: loss: -2.2573645 [Epoch 200; Iter 26/ 100] train: loss: -2.2338004 [Epoch 200; Iter 28/ 100] train: loss: -2.2719038 [Epoch 200; Iter 30/ 100] train: loss: -2.1639729 [Epoch 200; Iter 32/ 100] train: loss: -2.1668520 [Epoch 200; Iter 34/ 100] train: loss: -2.2000210 [Epoch 200; Iter 36/ 100] train: loss: -2.2143204 [Epoch 200; Iter 38/ 100] train: loss: -2.1709681 [Epoch 200; Iter 40/ 100] train: loss: -2.1966450 [Epoch 200; Iter 42/ 100] train: loss: -2.2163792 [Epoch 200; Iter 44/ 100] train: loss: -2.2385902 [Epoch 200; Iter 46/ 100] train: loss: -2.2735734 [Epoch 200; Iter 48/ 100] train: loss: -2.2653208 [Epoch 200; Iter 50/ 100] train: loss: -2.2500036 [Epoch 200; Iter 52/ 100] train: loss: -2.2324305 [Epoch 200; Iter 54/ 100] train: loss: -2.2250788 [Epoch 200; Iter 56/ 100] train: loss: -2.2158983 [Epoch 200; Iter 58/ 100] train: loss: -2.2318683 [Epoch 200; Iter 60/ 100] train: loss: -2.2518740 [Epoch 200; Iter 62/ 100] train: loss: -2.2250538 [Epoch 200; Iter 64/ 100] train: loss: -2.2269733 [Epoch 200; Iter 66/ 100] train: loss: -2.2310219 [Epoch 200; Iter 68/ 100] train: loss: -2.2161174 [Epoch 200; Iter 70/ 100] train: loss: -2.2205598 [Epoch 200; Iter 72/ 100] train: loss: -2.2224882 [Epoch 200; Iter 74/ 100] train: loss: -2.2406216 [Epoch 200; Iter 76/ 100] train: loss: -2.1987047 [Epoch 200; Iter 78/ 100] train: loss: -2.2273459 [Epoch 200; Iter 80/ 100] train: loss: -2.2188897 [Epoch 200; Iter 82/ 100] train: loss: -2.2317674 [Epoch 200; Iter 84/ 100] train: loss: -2.2398126 [Epoch 200; Iter 86/ 100] train: loss: -2.2111089 [Epoch 200; Iter 88/ 100] train: loss: -2.2469833 [Epoch 200; Iter 90/ 100] train: loss: -2.2543628 [Epoch 200; Iter 92/ 100] train: loss: -2.2504985 [Epoch 200; Iter 94/ 100] train: loss: -2.1923199 [Epoch 200; Iter 96/ 100] train: loss: -2.2006516 [Epoch 200; Iter 98/ 100] train: loss: -2.2204037 [Epoch 200; Iter 100/ 100] train: loss: -2.2411747 [Epoch 200] NTXent: -1.799788 val loss: -1.799788 Early stopping criterion based on -NTXent- that should be min reached after 200 epochs. Best model checkpoint was in epoch 165. Statistics on val_best_checkpoint positive_similarity: 0.9688039703501595 negative_similarity: 0.49956806831889683 contrastive_accuracy: 0.759679623776012 true_negative_rate: 0.5193592525190778 true_positive_rate: 1.0 uniformity: -4.5691987540986805 alignment: 2.764178216457367 batch_variance: 0.11491788489123185 dimension_covariance: 0.0008367771305428403 NTXent: -1.797559466626909 mean_pred: -0.0002646076123306153 std_pred: 0.10940126019219558 mean_targets: 1.0975488838956457e-05 std_targets: 0.006032964382838044 Statistics on test positive_similarity: 0.9683920939763387 negative_similarity: 0.499461951079192 contrastive_accuracy: 0.7599385954715587 true_negative_rate: 0.519951272893835 true_positive_rate: 0.9999259268796002 uniformity: -4.5703361829121905 alignment: 2.7638528523621737 batch_variance: 0.11491100241740544 dimension_covariance: 0.0008471731475933835 NTXent: -1.8462339418905753 mean_pred: -0.0003244010140743167 std_pred: 0.10939329227915516 mean_targets: 5.363229907591934e-06 std_targets: 0.006017989599732337
I just clone the code from the repitory without modification.
can someone tell me where the code mistake is?

Hello, I am trying to fine-tune the pre-trained model you provided here runs/PNA_qmugs_NTXentMultiplePositives_620000_123_25-08_09-19-52/best_checkpoint_35epochs.pt with the tox21 dataset. I change the parameters in configs_clean/tune_freesolv.yml that set the target-dim as 12, which is the classes in tox21. However, the model loss is nan.

[Epoch 1; Iter   150/  209] train: loss: nan
[Epoch 1; Iter   180/  209] train: loss: nan
[Epoch 1; Iter   180/  209] train: loss: nan
[Epoch 1; Iter   180/  209] train: loss: nan
[Epoch 1; Iter   180/  209] train: loss: nan
[Epoch 1; Iter   180/  209] train: loss: nan
...
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

1. Could you provide another .yml file with respect to classification problem such as tox21 or sider.
2. Could you provide the evaluation merics using for classification tasks such as aucroc or f1-score.

Thank you!

Hello,
After reading the paper and part of the code (might be difficult for me to understand), I understand that you have a 3d model but not pre-trained, right?
If I misunderstood, could you tell me how I should use the 3d pre-trained model.
Thanks for your help!

Hello, when using Anaconda in Windows 10:
I met the following question:
Resolve Package Not Found:

• dgl-cuda 10.2
• torchaudio
• pytorch-geometry
• torchvision

I wonder whether it means I really need a computer with an NVIDIA card so that I can install cuda and cudnn, and then I can install packages above?
Great thanks.

Hello, I am trying to fine-tune the pre-trained model you provided here runs/PNA_qmugs_NTXentMultiplePositives_620000_123_25-08_09-19-52/best_checkpoint_35epochs.pt with a csv of the bace dataset. When I use the BACEGeomol class to load and process the data, I get the following error when trying to collate the data with torch_geometric.

Traceback (most recent call last):
  File "train.py", line 702, in <module>
    train(args)
  File "train.py", line 286, in train
    return train_geomol(args, device, metrics_dict)
  File "train.py", line 313, in train_geomol
    train = dataset(split='train', device=device)
  File "/home/ubuntu/code/3DInfomax/datasets/bace_geomol_feat.py", line 59, in __init__
    super(BACEGeomol, self).__init__(root, transform, pre_transform)
  File "/home/ubuntu/anaconda3/envs/3DInfomax/lib/python3.7/site-packages/torch_geometric/data/in_memory_dataset.py", line 57, in __init__
    super().__init__(root, transform, pre_transform, pre_filter)
  File "/home/ubuntu/anaconda3/envs/3DInfomax/lib/python3.7/site-packages/torch_geometric/data/dataset.py", line 88, in __init__
    self._process()
  File "/home/ubuntu/anaconda3/envs/3DInfomax/lib/python3.7/site-packages/torch_geometric/data/dataset.py", line 171, in _process
    self.process()
  File "/home/ubuntu/code/3DInfomax/datasets/bace_geomol_feat.py", line 127, in process
    data, slices = self.collate(data_list)
  File "/home/ubuntu/anaconda3/envs/3DInfomax/lib/python3.7/site-packages/torch_geometric/data/in_memory_dataset.py", line 116, in collate
    add_batch=False,
  File "/home/ubuntu/anaconda3/envs/3DInfomax/lib/python3.7/site-packages/torch_geometric/data/collate.py", line 85, in collate
    increment)
  File "/home/ubuntu/anaconda3/envs/3DInfomax/lib/python3.7/site-packages/torch_geometric/data/collate.py", line 179, in _collate
    key, [v[key] for v in values], data_list, stores, increment)
  File "/home/ubuntu/anaconda3/envs/3DInfomax/lib/python3.7/site-packages/torch_geometric/data/collate.py", line 179, in <listcomp>
    key, [v[key] for v in values], data_list, stores, increment)
KeyError: 0

My questions are:

1. I see there are a lot of different dataset classes. Can I use the BACEGeomol dataset class to fine-tune that model, or should I be using a different dataset class? I'm also not sure because I see different functions to featurize molecules in the different classes.
2. Have you seen this error before, and do you know what might be causing the bug?

Thank you!

Can someone provide the correct environment information, including CUDA version, RDKIT version, OGB version, Python version

What model is gin.py? Graph isomorphic neural network? Are there references?

In this line,

Hi, sorry to bother you.
Could you please to teach me how to deal with the problem in screenshot?

Dear professor，I have some questions about the 3DInfomax.
I want to get the evaluation metrics such as Precision，so I use the Function which you provided in your metric.py such as TruePositiveRate() and TrueNegativeRate() to get this metric. But I tried all OGB datasets and found that those metrics such as Precision，Accuracy and Recall were not ideal. I hope you can reply to me as soon as possible. Thank you, professor.

Here is the HIV dataset's metric:
Precision: 0.008995866402983665
Accuracy: 0.9988852739334106
Recall: 0.002496626228094101
F1_score: 0.003908519633114338
ROC_AUC: 0.7427065372467041
PR_AUC: 0.2141391634941101
ogbg-molhiv: 0.742706502636204
BCEWithLogitsLoss: 0.17792926660992883

Here is the BBBP dataset's metric:
Precision: 0.44607841968536377
Accuracy: 0.6127931475639343
Recall: 0.005654983688145876
F1_score: 0.011168383993208408
ROC_AUC: 0.6745756268501282
PR_AUC: 0.6546612977981567
ogbg-molbbbp: 0.6745756172839505
BCEWithLogitsLoss: 1.1453146849359785

Here is my metric code:
class Precision(nn.Module):
def init(self, threshold=0.5) -> None:
super(Precision, self).init()
self.threshold = threshold

def forward(self, x1: Tensor, x2: Tensor, pos_mask: Tensor = None) -> Tensor:
    batch_size, _ = x1.size()
    if x1.shape != x2.shape and pos_mask == None: 
        x2 = x2[:batch_size]
    sim_matrix = torch.einsum('ik,jk->ij', x1, x2)

    x1_abs = x1.norm(dim=1)
    x2_abs = x2.norm(dim=1)
    sim_matrix = sim_matrix / torch.einsum('i,j->ij', x1_abs, x2_abs)

    preds: Tensor = (sim_matrix + 1) / 2 > self.threshold
    if pos_mask == None:  # if we are comparing global with global
        pos_mask = torch.eye(batch_size, device=x1.device) 
        neg_mask = 1 - pos_mask 

    num_positives = len(x1)
    num_negatives = len(x1) * (len(x2) - 1)

    false_positives = ((preds.long() - pos_mask) * pos_mask).count_nonzero()
    true_positives = num_positives - ((preds.long() - pos_mask) * pos_mask).count_nonzero()

    false_negatives = (((~preds).long() - neg_mask) * neg_mask).count_nonzero()
    true_negatives = num_negatives - (((~preds).long() - neg_mask) * neg_mask).count_nonzero()

    pre = true_positives /(true_positives + false_positives)
    return pre

Hi, this doesn't concern the repo, but the linked talk (youtube video) freezes after ~22 min. Is that the complete recording, or is there a fix? Thanks.

I found the dataset of "BBBP.csv". Where is the "bbbpscaffold123.pkl"? And what is the "bbbpscaffold123.pkl"? Thank you very much for helping me.

Hi,
I find the code a bit difficult to read, is there an easy way to generate the embedding as a fingerprint ?