Update (15eaed00) · Commits · 钟慕尧 / deepchem

deepchem/feat/graph_data.py

+3 −3

Original line number	Diff line number	Diff line
		@@ -150,8 +150,8 @@ class GraphData:
		src = np.concatenate([src, np.arange(self.num_nodes)])
		dst = np.concatenate([dst, np.arange(self.num_nodes)])

		g = dgl.graph((torch.from_numpy(src).long(),
		torch.from_numpy(dst).long()),
		g = dgl.graph(
		(torch.from_numpy(src).long(), torch.from_numpy(dst).long()),
		num_nodes=self.num_nodes)
		g.ndata['x'] = torch.from_numpy(self.node_features).float()

deepchem/models/tests/test_gcn.py

+76 −69

Original line number	Diff line number	Diff line
		@@ -16,6 +16,7 @@ try:
		except:
		has_torch_and_dgl = False


		@unittest.skipIf(not has_torch_and_dgl,
		'PyTorch, DGL, or DGL-LifeSci are not installed')
		def test_gcn_regression():
		@@ -26,13 +27,18 @@ def test_gcn_regression():

		# initialize models
		n_tasks = len(tasks)
		model = GCNModel(mode='regression', n_tasks=n_tasks, number_atom_features=30, batch_size=10)
		model = GCNModel(
		mode='regression',
		n_tasks=n_tasks,
		number_atom_features=30,
		batch_size=10)

		# overfit test
		model.fit(dataset, nb_epoch=100)
		scores = model.evaluate(dataset, [metric], transformers)
		assert scores['mean_absolute_error'] < 0.5


		@unittest.skipIf(not has_torch_and_dgl,
		'PyTorch, DGL, or DGL-LifeSci are not installed')
		def test_gcn_classification():
		@@ -55,6 +61,7 @@ def test_gcn_classification():
		scores = model.evaluate(dataset, [metric], transformers)
		assert scores['mean-roc_auc_score'] >= 0.85


		@unittest.skipIf(not has_torch_and_dgl,
		'PyTorch, DGL, or DGL-LifeSci are not installed')
		def test_gcn_reload():

deepchem/models/torch_models/gcn.py

+134 −125

Original line number	Diff line number	Diff line
		@@ -7,6 +7,7 @@ import torch.nn.functional as F
		from deepchem.models.losses import Loss, L2Loss, SparseSoftmaxCrossEntropy
		from deepchem.models.torch_models.torch_model import TorchModel


		class GCN(nn.Module):
		"""Model for Graph Property Prediction Based on Graph Convolution Networks (GCN).

		@@ -65,6 +66,7 @@ class GCN(nn.Module):
		* There are various minor differences in using dropout, skip connection and batch
		normalization.
		"""

		def __init__(self,
		n_tasks: int,
		graph_conv_layers: list = None,
		@@ -143,7 +145,8 @@ class GCN(nn.Module):
		if activation is not None:
		activation = [activation] * num_gnn_layers

		self.model = DGLGCNPredictor(in_feats=number_atom_features,
		self.model = DGLGCNPredictor(
		in_feats=number_atom_features,
		hidden_feats=graph_conv_layers,
		activation=activation,
		residual=[residual] * num_gnn_layers,
		@@ -192,6 +195,7 @@ class GCN(nn.Module):
		else:
		return out


		class GCNModel(TorchModel):
		"""Model for Graph Property Prediction Based on Graph Convolution Networks (GCN).

		@@ -243,6 +247,7 @@ class GCNModel(TorchModel):
		* There are various minor differences in using dropout, skip connection and batch
		normalization.
		"""

		def __init__(self,
		n_tasks: int,
		graph_conv_layers: list = None,
		@@ -296,7 +301,8 @@ class GCNModel(TorchModel):
		kwargs
		This can include any keyword argument of TorchModel.
		"""
		model = GCN(graph_conv_layers=graph_conv_layers,
		model = GCN(
		graph_conv_layers=graph_conv_layers,
		activation=activation,
		residual=residual,
		batchnorm=batchnorm,
		@@ -345,7 +351,10 @@ class GCNModel(TorchModel):
		raise ImportError('This class requires dgl.')

		inputs, labels, weights = batch
		dgl_graphs = [graph.to_dgl_graph(self_loop=self._self_loop) for graph in inputs[0]]
		dgl_graphs = [
		graph.to_dgl_graph(self_loop=self._self_loop) for graph in inputs[0]
		]
		inputs = dgl.batch(dgl_graphs).to(self.device)
		_, labels, weights = super(GCNModel, self)._prepare_batch(([], labels, weights))
		_, labels, weights = super(GCNModel, self)._prepare_batch(([], labels,
		weights))
		return inputs, labels, weights

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