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Source code for flash.graph.classification.model

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

from torch import nn, Tensor
from torch.nn import functional as F
from torch.nn import Linear

from flash.core.classification import ClassificationTask
from flash.core.data.io.input import DataKeys
from flash.core.registry import FlashRegistry
from flash.core.utilities.imports import _GRAPH_AVAILABLE
from flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
from flash.graph.backbones import GRAPH_BACKBONES
from flash.graph.collate import _pyg_collate

if _GRAPH_AVAILABLE:
    from torch_geometric.nn import global_add_pool, global_max_pool, global_mean_pool

    POOLING_FUNCTIONS = {"mean": global_mean_pool, "add": global_add_pool, "max": global_max_pool}
else:
    POOLING_FUNCTIONS = {}


[docs]class GraphClassifier(ClassificationTask): """The ``GraphClassifier`` is a :class:`~flash.Task` for classifying graphs. For more details, see :ref:`graph_classification`. Args: num_features (int): The number of features in the input. num_classes (int): Number of classes to classify. backbone: Name of the backbone to use. backbone_kwargs: Dictionary dependent on the backbone, containing for example in_channels, out_channels, hidden_channels or depth (number of layers). pooling_fn: The global pooling operation to use (one of: "max", "max", "add" or a callable). head: The head to use. loss_fn: Loss function for training, defaults to cross entropy. learning_rate: Learning rate to use for training. optimizer: Optimizer to use for training. lr_scheduler: The LR scheduler to use during training. metrics: Metrics to compute for training and evaluation. """ backbones: FlashRegistry = GRAPH_BACKBONES required_extras: str = "graph" def __init__( self, num_features: int, num_classes: Optional[int] = None, labels: Optional[List[str]] = None, backbone: Union[str, Tuple[nn.Module, int]] = "GCN", backbone_kwargs: Optional[Dict] = {}, pooling_fn: Optional[Union[str, Callable]] = "mean", head: Optional[Union[Callable, nn.Module]] = None, loss_fn: LOSS_FN_TYPE = F.cross_entropy, learning_rate: Optional[float] = None, optimizer: OPTIMIZER_TYPE = "Adam", lr_scheduler: LR_SCHEDULER_TYPE = None, metrics: METRICS_TYPE = None, ): self.save_hyperparameters() if labels is not None and num_classes is None: num_classes = len(labels) super().__init__( loss_fn=loss_fn, optimizer=optimizer, lr_scheduler=lr_scheduler, metrics=metrics, learning_rate=learning_rate, num_classes=num_classes, labels=labels, ) self.save_hyperparameters() if isinstance(backbone, tuple): self.backbone, num_out_features = backbone else: self.backbone = self.backbones.get(backbone)(in_channels=num_features, **backbone_kwargs) num_out_features = self.backbone.hidden_channels self.pooling_fn = POOLING_FUNCTIONS[pooling_fn] if isinstance(pooling_fn, str) else pooling_fn if head is not None: self.head = head else: self.head = DefaultGraphHead(num_out_features, num_classes) self.collate_fn = _pyg_collate def training_step(self, batch: Any, batch_idx: int) -> Any: batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET]) return super().training_step(batch, batch_idx) def validation_step(self, batch: Any, batch_idx: int) -> Any: batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET]) return super().validation_step(batch, batch_idx) def test_step(self, batch: Any, batch_idx: int) -> Any: batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET]) return super().test_step(batch, batch_idx) def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any: return super().predict_step(batch[DataKeys.INPUT], batch_idx, dataloader_idx=dataloader_idx) def forward(self, data) -> Tensor: x = self.backbone(data.x, data.edge_index) x = self.pooling_fn(x, data.batch) return self.head(x)
class DefaultGraphHead(nn.Module): def __init__(self, hidden_channels, num_classes, dropout=0.5): super().__init__() self.lin1 = Linear(hidden_channels, hidden_channels) self.lin2 = Linear(hidden_channels, num_classes) self.dropout = dropout def reset_parameters(self): self.lin1.reset_parameters() self.lin2.reset_parameters() def forward(self, x): x = F.relu(self.lin1(x)) x = F.dropout(x, p=self.dropout, training=self.training) return self.lin2(x)

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