Spaces:
Sleeping
Sleeping
| import pytorch_lightning as pl | |
| import torch | |
| from src.const import ZINC_TRAIN_LINKER_ID2SIZE, ZINC_TRAIN_LINKER_SIZE2ID | |
| from src.linker_size import SizeGNN | |
| from src.egnn import coord2diff | |
| from src.datasets import ZincDataset, get_dataloader, collate_with_fragment_edges | |
| from typing import Dict, List, Optional | |
| from torch.nn.functional import cross_entropy, mse_loss, sigmoid | |
| from pdb import set_trace | |
| class SizeClassifier(pl.LightningModule): | |
| train_dataset = None | |
| val_dataset = None | |
| test_dataset = None | |
| metrics: Dict[str, List[float]] = {} | |
| def __init__( | |
| self, data_path, train_data_prefix, val_data_prefix, | |
| in_node_nf, hidden_nf, out_node_nf, n_layers, batch_size, lr, torch_device, | |
| normalization=None, | |
| loss_weights=None, | |
| min_linker_size=None, | |
| linker_size2id=ZINC_TRAIN_LINKER_SIZE2ID, | |
| linker_id2size=ZINC_TRAIN_LINKER_ID2SIZE, | |
| task='classification', | |
| ): | |
| super(SizeClassifier, self).__init__() | |
| self.save_hyperparameters() | |
| self.data_path = data_path | |
| self.train_data_prefix = train_data_prefix | |
| self.val_data_prefix = val_data_prefix | |
| self.min_linker_size = min_linker_size | |
| self.linker_size2id = linker_size2id | |
| self.linker_id2size = linker_id2size | |
| self.batch_size = batch_size | |
| self.lr = lr | |
| self.torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| self.loss_weights = None if loss_weights is None else torch.tensor(loss_weights, device=self.torch_device) | |
| self.gnn = SizeGNN( | |
| in_node_nf=in_node_nf, | |
| hidden_nf=hidden_nf, | |
| out_node_nf=out_node_nf, | |
| n_layers=n_layers, | |
| device=self.torch_device, | |
| normalization=normalization, | |
| ) | |
| def setup(self, stage: Optional[str] = None): | |
| if stage == 'fit': | |
| self.train_dataset = ZincDataset( | |
| data_path=self.data_path, | |
| prefix=self.train_data_prefix, | |
| device=self.torch_device | |
| ) | |
| self.val_dataset = ZincDataset( | |
| data_path=self.data_path, | |
| prefix=self.val_data_prefix, | |
| device=self.torch_device | |
| ) | |
| elif stage == 'val': | |
| self.val_dataset = ZincDataset( | |
| data_path=self.data_path, | |
| prefix=self.val_data_prefix, | |
| device=self.torch_device | |
| ) | |
| else: | |
| raise NotImplementedError | |
| def train_dataloader(self): | |
| return get_dataloader(self.train_dataset, self.batch_size, collate_fn=collate_with_fragment_edges, shuffle=True) | |
| def val_dataloader(self): | |
| return get_dataloader(self.val_dataset, self.batch_size, collate_fn=collate_with_fragment_edges) | |
| def test_dataloader(self): | |
| return get_dataloader(self.test_dataset, self.batch_size, collate_fn=collate_with_fragment_edges) | |
| def forward(self, data, return_loss=True): | |
| h = data['one_hot'] | |
| x = data['positions'] | |
| fragment_mask = data['fragment_mask'] | |
| linker_mask = data['linker_mask'] | |
| edge_mask = data['edge_mask'] | |
| edges = data['edges'] | |
| # Considering only fragments | |
| x = x * fragment_mask | |
| h = h * fragment_mask | |
| # Reshaping | |
| bs, n_nodes = x.shape[0], x.shape[1] | |
| fragment_mask = fragment_mask.view(bs * n_nodes, 1) | |
| x = x.view(bs * n_nodes, -1) | |
| h = h.view(bs * n_nodes, -1) | |
| # Prediction | |
| distances, _ = coord2diff(x, edges) | |
| distance_edge_mask = (edge_mask.bool() & (distances < 6)).long() | |
| output = self.gnn.forward(h, edges, distances, fragment_mask, distance_edge_mask) | |
| output = output.view(bs, n_nodes, -1).mean(1) | |
| if return_loss: | |
| true = self.get_true_labels(linker_mask) | |
| loss = cross_entropy(output, true, weight=self.loss_weights) | |
| else: | |
| loss = None | |
| return output, loss | |
| def get_true_labels(self, linker_mask): | |
| labels = [] | |
| sizes = linker_mask.squeeze().sum(-1).long().detach().cpu().numpy() | |
| for size in sizes: | |
| label = self.linker_size2id.get(size) | |
| if label is None: | |
| label = self.linker_size2id[max(self.linker_id2size)] | |
| labels.append(label) | |
| labels = torch.tensor(labels, device=linker_mask.device, dtype=torch.long) | |
| return labels | |
| def training_step(self, data, *args): | |
| _, loss = self.forward(data) | |
| return {'loss': loss} | |
| def validation_step(self, data, *args): | |
| _, loss = self.forward(data) | |
| return {'loss': loss} | |
| def test_step(self, data, *args): | |
| loss = self.forward(data) | |
| return {'loss': loss} | |
| def training_epoch_end(self, training_step_outputs): | |
| for metric in training_step_outputs[0].keys(): | |
| avg_metric = self.aggregate_metric(training_step_outputs, metric) | |
| self.metrics.setdefault(f'{metric}/train', []).append(avg_metric) | |
| self.log(f'{metric}/train', avg_metric, prog_bar=True) | |
| def validation_epoch_end(self, validation_step_outputs): | |
| for metric in validation_step_outputs[0].keys(): | |
| avg_metric = self.aggregate_metric(validation_step_outputs, metric) | |
| self.metrics.setdefault(f'{metric}/val', []).append(avg_metric) | |
| self.log(f'{metric}/val', avg_metric, prog_bar=True) | |
| correct = 0 | |
| total = 0 | |
| for data in self.val_dataloader(): | |
| output, _ = self.forward(data) | |
| pred = output.argmax(dim=-1) | |
| true = self.get_true_labels(data['linker_mask']) | |
| correct += (pred == true).sum() | |
| total += len(pred) | |
| accuracy = correct / total | |
| self.metrics.setdefault(f'accuracy/val', []).append(accuracy) | |
| self.log(f'accuracy/val', accuracy, prog_bar=True) | |
| def configure_optimizers(self): | |
| return torch.optim.AdamW(self.gnn.parameters(), lr=self.lr, amsgrad=True, weight_decay=1e-12) | |
| def aggregate_metric(step_outputs, metric): | |
| return torch.tensor([out[metric] for out in step_outputs]).mean() | |
| class SizeOrdinalClassifier(pl.LightningModule): | |
| train_dataset = None | |
| val_dataset = None | |
| test_dataset = None | |
| metrics: Dict[str, List[float]] = {} | |
| def __init__( | |
| self, data_path, train_data_prefix, val_data_prefix, | |
| in_node_nf, hidden_nf, out_node_nf, n_layers, batch_size, lr, torch_device, | |
| normalization=None, | |
| min_linker_size=None, | |
| linker_size2id=ZINC_TRAIN_LINKER_SIZE2ID, | |
| linker_id2size=ZINC_TRAIN_LINKER_ID2SIZE, | |
| task='ordinal', | |
| ): | |
| super(SizeOrdinalClassifier, self).__init__() | |
| self.save_hyperparameters() | |
| self.data_path = data_path | |
| self.train_data_prefix = train_data_prefix | |
| self.val_data_prefix = val_data_prefix | |
| self.min_linker_size = min_linker_size | |
| self.batch_size = batch_size | |
| self.lr = lr | |
| self.torch_device = torch_device | |
| self.linker_size2id = linker_size2id | |
| self.linker_id2size = linker_id2size | |
| self.gnn = SizeGNN( | |
| in_node_nf=in_node_nf, | |
| hidden_nf=hidden_nf, | |
| out_node_nf=out_node_nf, | |
| n_layers=n_layers, | |
| device=torch_device, | |
| normalization=normalization, | |
| ) | |
| def setup(self, stage: Optional[str] = None): | |
| if stage == 'fit': | |
| self.train_dataset = ZincDataset( | |
| data_path=self.data_path, | |
| prefix=self.train_data_prefix, | |
| device=self.torch_device | |
| ) | |
| self.val_dataset = ZincDataset( | |
| data_path=self.data_path, | |
| prefix=self.val_data_prefix, | |
| device=self.torch_device | |
| ) | |
| elif stage == 'val': | |
| self.val_dataset = ZincDataset( | |
| data_path=self.data_path, | |
| prefix=self.val_data_prefix, | |
| device=self.torch_device | |
| ) | |
| else: | |
| raise NotImplementedError | |
| def train_dataloader(self): | |
| return get_dataloader(self.train_dataset, self.batch_size, collate_fn=collate_with_fragment_edges, shuffle=True) | |
| def val_dataloader(self): | |
| return get_dataloader(self.val_dataset, self.batch_size, collate_fn=collate_with_fragment_edges) | |
| def test_dataloader(self): | |
| return get_dataloader(self.test_dataset, self.batch_size, collate_fn=collate_with_fragment_edges) | |
| def forward(self, data): | |
| h = data['one_hot'] | |
| x = data['positions'] | |
| fragment_mask = data['fragment_mask'] | |
| linker_mask = data['linker_mask'] | |
| edge_mask = data['edge_mask'] | |
| edges = data['edges'] | |
| # Considering only fragments | |
| x = x * fragment_mask | |
| h = h * fragment_mask | |
| # Reshaping | |
| bs, n_nodes = x.shape[0], x.shape[1] | |
| fragment_mask = fragment_mask.view(bs * n_nodes, 1) | |
| x = x.view(bs * n_nodes, -1) | |
| h = h.view(bs * n_nodes, -1) | |
| # Prediction | |
| distances, _ = coord2diff(x, edges) | |
| distance_edge_mask = (edge_mask.bool() & (distances < 6)).long() | |
| output = self.gnn.forward(h, edges, distances, fragment_mask, distance_edge_mask) | |
| output = output.view(bs, n_nodes, -1).mean(1) | |
| output = sigmoid(output) | |
| true = self.get_true_labels(linker_mask) | |
| loss = self.ordinal_loss(output, true) | |
| return output, loss | |
| def ordinal_loss(self, pred, true): | |
| target = torch.zeros_like(pred, device=self.torch_device) | |
| for i, label in enumerate(true): | |
| target[i, 0:label + 1] = 1 | |
| return mse_loss(pred, target, reduction='none').sum(1).mean() | |
| def get_true_labels(self, linker_mask): | |
| labels = [] | |
| sizes = linker_mask.squeeze().sum(-1).long().detach().cpu().numpy() | |
| for size in sizes: | |
| label = self.linker_size2id.get(size) | |
| if label is None: | |
| label = self.linker_size2id[max(self.linker_id2size)] | |
| labels.append(label) | |
| labels = torch.tensor(labels, device=linker_mask.device, dtype=torch.long) | |
| return labels | |
| def prediction2label(pred): | |
| return torch.cumprod(pred > 0.5, dim=1).sum(dim=1) - 1 | |
| def training_step(self, data, *args): | |
| _, loss = self.forward(data) | |
| return {'loss': loss} | |
| def validation_step(self, data, *args): | |
| _, loss = self.forward(data) | |
| return {'loss': loss} | |
| def test_step(self, data, *args): | |
| loss = self.forward(data) | |
| return {'loss': loss} | |
| def training_epoch_end(self, training_step_outputs): | |
| for metric in training_step_outputs[0].keys(): | |
| avg_metric = self.aggregate_metric(training_step_outputs, metric) | |
| self.metrics.setdefault(f'{metric}/train', []).append(avg_metric) | |
| self.log(f'{metric}/train', avg_metric, prog_bar=True) | |
| def validation_epoch_end(self, validation_step_outputs): | |
| for metric in validation_step_outputs[0].keys(): | |
| avg_metric = self.aggregate_metric(validation_step_outputs, metric) | |
| self.metrics.setdefault(f'{metric}/val', []).append(avg_metric) | |
| self.log(f'{metric}/val', avg_metric, prog_bar=True) | |
| correct = 0 | |
| total = 0 | |
| for data in self.val_dataloader(): | |
| output, _ = self.forward(data) | |
| pred = self.prediction2label(output) | |
| true = self.get_true_labels(data['linker_mask']) | |
| correct += (pred == true).sum() | |
| total += len(pred) | |
| accuracy = correct / total | |
| self.metrics.setdefault(f'accuracy/val', []).append(accuracy) | |
| self.log(f'accuracy/val', accuracy, prog_bar=True) | |
| def configure_optimizers(self): | |
| return torch.optim.AdamW(self.gnn.parameters(), lr=self.lr, amsgrad=True, weight_decay=1e-12) | |
| def aggregate_metric(step_outputs, metric): | |
| return torch.tensor([out[metric] for out in step_outputs]).mean() | |
| class SizeRegressor(pl.LightningModule): | |
| train_dataset = None | |
| val_dataset = None | |
| test_dataset = None | |
| metrics: Dict[str, List[float]] = {} | |
| def __init__( | |
| self, data_path, train_data_prefix, val_data_prefix, | |
| in_node_nf, hidden_nf, n_layers, batch_size, lr, torch_device, | |
| normalization=None, task='regression', | |
| ): | |
| super(SizeRegressor, self).__init__() | |
| self.save_hyperparameters() | |
| self.data_path = data_path | |
| self.train_data_prefix = train_data_prefix | |
| self.val_data_prefix = val_data_prefix | |
| self.batch_size = batch_size | |
| self.lr = lr | |
| self.torch_device = torch_device | |
| self.gnn = SizeGNN( | |
| in_node_nf=in_node_nf, | |
| hidden_nf=hidden_nf, | |
| out_node_nf=1, | |
| n_layers=n_layers, | |
| device=torch_device, | |
| normalization=normalization, | |
| ) | |
| def setup(self, stage: Optional[str] = None): | |
| if stage == 'fit': | |
| self.train_dataset = ZincDataset( | |
| data_path=self.data_path, | |
| prefix=self.train_data_prefix, | |
| device=self.torch_device | |
| ) | |
| self.val_dataset = ZincDataset( | |
| data_path=self.data_path, | |
| prefix=self.val_data_prefix, | |
| device=self.torch_device | |
| ) | |
| elif stage == 'val': | |
| self.val_dataset = ZincDataset( | |
| data_path=self.data_path, | |
| prefix=self.val_data_prefix, | |
| device=self.torch_device | |
| ) | |
| else: | |
| raise NotImplementedError | |
| def train_dataloader(self): | |
| return get_dataloader(self.train_dataset, self.batch_size, collate_fn=collate_with_fragment_edges, shuffle=True) | |
| def val_dataloader(self): | |
| return get_dataloader(self.val_dataset, self.batch_size, collate_fn=collate_with_fragment_edges) | |
| def test_dataloader(self): | |
| return get_dataloader(self.test_dataset, self.batch_size, collate_fn=collate_with_fragment_edges) | |
| def forward(self, data): | |
| h = data['one_hot'] | |
| x = data['positions'] | |
| fragment_mask = data['fragment_mask'] | |
| linker_mask = data['linker_mask'] | |
| edge_mask = data['edge_mask'] | |
| edges = data['edges'] | |
| # Considering only fragments | |
| x = x * fragment_mask | |
| h = h * fragment_mask | |
| # Reshaping | |
| bs, n_nodes = x.shape[0], x.shape[1] | |
| fragment_mask = fragment_mask.view(bs * n_nodes, 1) | |
| x = x.view(bs * n_nodes, -1) | |
| h = h.view(bs * n_nodes, -1) | |
| # Prediction | |
| distances, _ = coord2diff(x, edges) | |
| distance_edge_mask = (edge_mask.bool() & (distances < 6)).long() | |
| output = self.gnn.forward(h, edges, distances, fragment_mask, distance_edge_mask) | |
| output = output.view(bs, n_nodes, -1).mean(1).squeeze() | |
| true = linker_mask.squeeze().sum(-1).float() | |
| loss = mse_loss(output, true) | |
| return output, loss | |
| def training_step(self, data, *args): | |
| _, loss = self.forward(data) | |
| return {'loss': loss} | |
| def validation_step(self, data, *args): | |
| _, loss = self.forward(data) | |
| return {'loss': loss} | |
| def test_step(self, data, *args): | |
| loss = self.forward(data) | |
| return {'loss': loss} | |
| def training_epoch_end(self, training_step_outputs): | |
| for metric in training_step_outputs[0].keys(): | |
| avg_metric = self.aggregate_metric(training_step_outputs, metric) | |
| self.metrics.setdefault(f'{metric}/train', []).append(avg_metric) | |
| self.log(f'{metric}/train', avg_metric, prog_bar=True) | |
| def validation_epoch_end(self, validation_step_outputs): | |
| for metric in validation_step_outputs[0].keys(): | |
| avg_metric = self.aggregate_metric(validation_step_outputs, metric) | |
| self.metrics.setdefault(f'{metric}/val', []).append(avg_metric) | |
| self.log(f'{metric}/val', avg_metric, prog_bar=True) | |
| correct = 0 | |
| total = 0 | |
| for data in self.val_dataloader(): | |
| output, _ = self.forward(data) | |
| pred = torch.round(output).long() | |
| true = data['linker_mask'].squeeze().sum(-1).long() | |
| correct += (pred == true).sum() | |
| total += len(pred) | |
| accuracy = correct / total | |
| self.metrics.setdefault(f'accuracy/val', []).append(accuracy) | |
| self.log(f'accuracy/val', accuracy, prog_bar=True) | |
| def configure_optimizers(self): | |
| return torch.optim.AdamW(self.gnn.parameters(), lr=self.lr, amsgrad=True, weight_decay=1e-12) | |
| def aggregate_metric(step_outputs, metric): | |
| return torch.tensor([out[metric] for out in step_outputs]).mean() | |