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film.py
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film.py
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import os.path as osp
import torch
import torch.nn.functional as F
from sklearn.metrics import f1_score
from torch.nn import BatchNorm1d
from torch_geometric.datasets import PPI
from torch_geometric.loader import DataLoader
from torch_geometric.nn import FiLMConv
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'PPI')
train_dataset = PPI(path, split='train')
val_dataset = PPI(path, split='val')
test_dataset = PPI(path, split='test')
train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=2, shuffle=False)
class Net(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
dropout=0.0):
super().__init__()