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Hanzhang Ma
2024-06-08 21:35:35 +02:00
parent 2c00828630
commit 4f8945ca07
6 changed files with 72 additions and 3 deletions
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4
graph_dit/datasets/abstract_dataset.py
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@@ -124,3 +124,7 @@ class AbstractDatasetInfos:
self.output_dims = {'X': example_batch_x.size(1),
'E': example_batch_edge_attr.size(1),
'y': example_batch['y'].size(1)}
print('input dims')
print(self.input_dims)
print('output dims')
print(self.output_dims)

33
graph_dit/datasets/dataset.py
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@@ -28,19 +28,38 @@ class DataModule(AbstractDataModule):
def __init__(self, cfg):
self.datadir = cfg.dataset.datadir
self.task = cfg.dataset.task_name
print("DataModule")
print("task", self.task)
print("datadir`",self.datadir)
super().__init__(cfg)
def prepare_data(self) -> None:
target = getattr(self.cfg.dataset, 'guidance_target', None)
print("target", target)
base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]
root_path = os.path.join(base_path, self.datadir)
self.root_path = root_path
batch_size = self.cfg.train.batch_size
num_workers = self.cfg.train.num_workers
pin_memory = self.cfg.dataset.pin_memory
# Load the dataset to the memory
# Dataset has target property, root path, and transform
dataset = Dataset(source=self.task, root=root_path, target_prop=target, transform=None)
print("len dataset", len(dataset))
def print_data(dataset):
print("dataset", dataset)
print("dataset keys", dataset.keys)
print("dataset x", dataset.x)
print("dataset edge_index", dataset.edge_index)
print("dataset edge_attr", dataset.edge_attr)
print("dataset y", dataset.y)
print("")
print_data(dataset=dataset[0])
print_data(dataset=dataset[1])
if len(self.task.split('-')) == 2:
train_index, val_index, test_index, unlabeled_index = self.fixed_split(dataset)
@@ -54,7 +73,11 @@ class DataModule(AbstractDataModule):
train_dataset, val_dataset, test_dataset = dataset[train_index], dataset[val_index], dataset[test_index]
self.train_dataset = train_dataset
print('train len', len(train_dataset), 'val len', len(val_dataset), 'test len', len(test_dataset))
print('train len', len(train_index), 'val len', len(val_index), 'test len', len(test_index))
print('dataset len', len(dataset) , 'train len', len(train_dataset), 'val len', len(val_dataset), 'test len', len(test_dataset))
self.train_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True, pin_memory=pin_memory)
self.val_loader = DataLoader(val_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False, pin_memory=False)
self.test_loader = DataLoader(test_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False, pin_memory=False)
@@ -253,6 +276,9 @@ class DataInfos(AbstractDatasetInfos):
def compute_meta(root, source_name, train_index, test_index):
# initialize the periodic table
# 118 elements + 1 for *
# Initializes arrays to count the number of atoms per molecule, bond types, valencies, and transition probabilities between atom types.
pt = Chem.GetPeriodicTable()
atom_name_list = []
atom_count_list = []
@@ -267,11 +293,13 @@ def compute_meta(root, source_name, train_index, test_index):
valencies = [0] * 500
tansition_E = np.zeros((118, 118, 5))
# Load the data from the source file
filename = f'{source_name}.csv.gz'
df = pd.read_csv(f'{root}/{filename}')
all_index = list(range(len(df)))
non_test_index = list(set(all_index) - set(test_index))
df = df.iloc[non_test_index]
# extract the smiles from the dataframe
tot_smiles = df['smiles'].tolist()
n_atom_list = []
@@ -323,6 +351,11 @@ def compute_meta(root, source_name, train_index, test_index):
bond_index = bond_type_to_index[bond_type]
bond_count_list[bond_index] += 2
# Update the transition matrix
# The transition matrix is symmetric, so we update both directions
# We also update the temporary transition matrix to check for errors
# in the atom count
tansition_E[start_index, end_index, bond_index] += 2
tansition_E[end_index, start_index, bond_index] += 2
tansition_E_temp[start_index, end_index, bond_index] += 2

4
graph_dit/diffusion_model.py
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@@ -76,12 +76,16 @@ class Graph_DiT(pl.LightningModule):
timesteps=cfg.model.diffusion_steps)
print("__init__")
print("dataset_info.node_types", self.dataset_info.node_types)
# dataset_info.node_types tensor([7.4826e-01, 2.6870e-02, 9.3930e-02, 4.4959e-02, 5.2982e-03, 7.5689e-04, 5.3739e-03, 1.5138e-03, 7.5689e-05, 4.3143e-03, 6.8650e-02])
x_marginals = self.dataset_info.node_types.float() / torch.sum(self.dataset_info.node_types.float())
e_marginals = self.dataset_info.edge_types.float() / torch.sum(self.dataset_info.edge_types.float())
x_marginals = x_marginals / (x_marginals ).sum()
e_marginals = e_marginals / (e_marginals ).sum()
# transition e is the probability of transitioning from x1 to x2 with e
xe_conditions = self.dataset_info.transition_E.float()
xe_conditions = xe_conditions[self.active_index][:, self.active_index]

1
graph_dit/main.py
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@@ -82,6 +82,7 @@ def main(cfg: DictConfig):
dataset_infos = dataset.DataInfos(datamodule=datamodule, cfg=cfg)
train_smiles, reference_smiles = datamodule.get_train_smiles()
# get input output dimensions
dataset_infos.compute_input_output_dims(datamodule=datamodule)
train_metrics = TrainMolecularMetricsDiscrete(dataset_infos)

2
graph_dit/metrics/molecular_metrics_train.py
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@@ -84,7 +84,7 @@ class BondMetricsCE(MetricCollection):
ce_TR = TripleCE(3)
super().__init__([ce_no_bond, ce_SI, ce_DO, ce_TR])
#
class TrainMolecularMetricsDiscrete(nn.Module):
def __init__(self, dataset_infos):
super().__init__()

27
graph_dit/models/transformer.py
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@@ -75,28 +75,55 @@ class Denoiser(nn.Module):
_constant_init(block.adaLN_modulation[0], 0)
_constant_init(self.out_layer.adaLN_modulation[0], 0)
"""
Input Parameters:
x: Node features.
e: Edge features.
node_mask: Mask indicating valid nodes.
y: Condition features.
t: Current timestep in the diffusion process.
unconditioned: Boolean flag indicating whether to ignore conditions.
"""
def forward(self, x, e, node_mask, y, t, unconditioned):
print("Denoiser Forward")
print(x.shape, e.shape, y.shape, t.shape, unconditioned)
force_drop_id = torch.zeros_like(y.sum(-1))
# drop the nan values
force_drop_id[torch.isnan(y.sum(-1))] = 1
if unconditioned:
force_drop_id = torch.ones_like(y[:, 0])
x_in, e_in, y_in = x, e, y
# bs = batch size, n = number of nodes
bs, n, _ = x.size()
x = torch.cat([x, e.reshape(bs, n, -1)], dim=-1)
print("X after concat with E")
print(x.shape)
# self.x_embedder = nn.Linear(Xdim + max_n_nodes * Edim, hidden_size, bias=False)
x = self.x_embedder(x)
print("X after x_embedder")
print(x.shape)
# self.t_embedder = TimestepEmbedder(hidden_size)
c1 = self.t_embedder(t)
print("C1 after t_embedder")
print(c1.shape)
for i in range(1, self.ydim):
if i == 1:
c2 = self.y_embedding_list[i-1](y[:, :2], self.training, force_drop_id, t)
else:
c2 = c2 + self.y_embedding_list[i-1](y[:, i:i+1], self.training, force_drop_id, t)
print("C2 after y_embedding_list")
print(c2.shape)
print("C1 + C2")
c = c1 + c2
print(c.shape)
for i, block in enumerate(self.encoders):
x = block(x, c, node_mask)
print("X after block")
print(x.shape)
# X: B * N * dx, E: B * N * N * de
X, E, y = self.out_layer(x, x_in, e_in, c, t, node_mask)