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import torch
from tqdm import tqdm
from torch_geometric.nn import GraphSAGE, to_hetero
class HeteroGraphSAGE(torch.nn.Module):
def __init__(self, metadata, hidden_channels, num_layers, output_channels):
super().__init__()
self.model = to_hetero(
GraphSAGE((-1, -1), hidden_channels, num_layers, output_channels),
metadata)
def forward(self, x_dict, edge_index_dict):
return self.model(x_dict, edge_index_dict)
@torch.no_grad()
def inference(self, loader, device, progress_bar=False, **kwargs):
self.model.eval()
if progress_bar:
loader = tqdm(loader, desc="Inference")
for batch in loader:
batch = batch.to(device)
if 'adj_t' in batch:
self.model(batch.x_dict, batch.adj_t_dict)
else:
self.model(batch.x_dict, batch.edge_index_dict)
@torch.no_grad()
def test(self, loader, device, progress_bar=False):
self.model.eval()
total_examples = total_correct = 0
if progress_bar:
loader = tqdm(loader, desc="Evaluate")
for batch in loader:
batch = batch.to(device)
if 'adj_t' in batch:
out = self.model(batch.x_dict, batch.adj_t_dict)
else:
out = self.model(batch.x_dict, batch.edge_index_dict)
batch_size = batch['paper'].batch_size
out = out['paper'][:batch_size]
pred = out.argmax(dim=-1)
total_examples += batch_size
total_correct += int((pred == batch['paper'].y[:batch_size]).sum())
return total_correct / total_examples
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