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from collections import OrderedDict
import torch
import torch.fx
from torch.nn import Dropout, Linear, ReLU
import torch_geometric.typing
from torch_geometric.nn import (
GCNConv,
JumpingKnowledge,
MessagePassing,
SAGEConv,
Sequential,
global_mean_pool,
to_hetero,
)
from torch_geometric.typing import SparseTensor
def test_sequential_basic():
x = torch.randn(4, 16)
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
batch = torch.zeros(4, dtype=torch.long)
model = Sequential('x, edge_index', [
(GCNConv(16, 64), 'x, edge_index -> x'),
ReLU(inplace=True),
(GCNConv(64, 64), 'x, edge_index -> x'),
ReLU(inplace=True),
Linear(64, 7),
]).cpu()
model.reset_parameters()
assert len(model) == 5
assert str(model) == (
'Sequential(\n'
' (0) - GCNConv(16, 64): x, edge_index -> x\n'
' (1) - ReLU(inplace=True): x -> x\n'
' (2) - GCNConv(64, 64): x, edge_index -> x\n'
' (3) - ReLU(inplace=True): x -> x\n'
' (4) - Linear(in_features=64, out_features=7, bias=True): x -> x\n'
')')
assert isinstance(model[0], GCNConv)
assert isinstance(model[1], ReLU)
assert isinstance(model[2], GCNConv)
assert isinstance(model[3], ReLU)
assert isinstance(model[4], Linear)
out = model(x, edge_index)
assert out.size() == (4, 7)
model = Sequential('x, edge_index, batch', [
(Dropout(p=0.5), 'x -> x'),
(GCNConv(16, 64), 'x, edge_index -> x1'),
ReLU(inplace=True),
(GCNConv(64, 64), 'x1, edge_index -> x2'),
ReLU(inplace=True),
(lambda x1, x2: [x1, x2], 'x1, x2 -> xs'),
(JumpingKnowledge('cat', 64, num_layers=2), 'xs -> x'),
(global_mean_pool, 'x, batch -> x'),
Linear(2 * 64, 7),
])
model.reset_parameters()
out = model(x, edge_index, batch)
assert out.size() == (1, 7)
def test_sequential_jit():
x = torch.randn(4, 16)
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
model = Sequential('x: Tensor, edge_index: Tensor', [
(GCNConv(16, 64), 'x, edge_index -> x'),
ReLU(inplace=True),
(GCNConv(64, 64), 'x, edge_index -> x'),
ReLU(inplace=True),
Linear(64, 7),
])
torch.jit.script(model)(x, edge_index)
if torch_geometric.typing.WITH_TORCH_SPARSE:
adj_t = SparseTensor.from_edge_index(edge_index).t()
model = Sequential('x: Tensor, edge_index: SparseTensor', [
(GCNConv(16, 64), 'x, edge_index -> x'),
ReLU(inplace=True),
(GCNConv(64, 64), 'x, edge_index -> x'),
ReLU(inplace=True),
Linear(64, 7),
])
torch.jit.script(model)(x, adj_t)
def symbolic_trace(module):
class Tracer(torch.fx.Tracer):
def is_leaf_module(self, module, *args, **kwargs) -> bool:
return (isinstance(module, MessagePassing)
or super().is_leaf_module(module, *args, **kwargs))
return torch.fx.GraphModule(module, Tracer().trace(module))
def test_sequential_tracable():
model = Sequential('x, edge_index', [
(GCNConv(16, 64), 'x, edge_index -> x1'),
ReLU(inplace=True),
(GCNConv(64, 64), 'x1, edge_index -> x2'),
ReLU(inplace=True),
(lambda x1, x2: x1 + x2, 'x1, x2 -> x'),
Linear(64, 7),
])
symbolic_trace(model)
def test_sequential_with_multiple_return_values():
x = torch.randn(4, 16)
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
model = Sequential('x, edge_index', [
(GCNConv(16, 32), 'x, edge_index -> x1'),
(GCNConv(32, 64), 'x1, edge_index -> x2'),
(lambda x1, x2: (x1, x2), 'x1, x2 -> x1, x2'),
])
x1, x2 = model(x, edge_index)
assert x1.size() == (4, 32)
assert x2.size() == (4, 64)
def test_sequential_with_ordered_dict():
x = torch.randn(4, 16)
edge_index = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
model = Sequential(
'x, edge_index', modules=OrderedDict([
('conv1', (GCNConv(16, 32), 'x, edge_index -> x')),
('conv2', (GCNConv(32, 64), 'x, edge_index -> x')),
]))
assert isinstance(model.conv1, GCNConv)
assert isinstance(model.conv2, GCNConv)
x = model(x, edge_index)
assert x.size() == (4, 64)
def test_sequential_to_hetero():
model = Sequential('x, edge_index', [
(SAGEConv((-1, -1), 32), 'x, edge_index -> x1'),
ReLU(),
(SAGEConv((-1, -1), 64), 'x1, edge_index -> x2'),
ReLU(),
])
x_dict = {
'paper': torch.randn(100, 16),
'author': torch.randn(100, 16),
}
edge_index_dict = {
('paper', 'cites', 'paper'):
torch.randint(100, (2, 200), dtype=torch.long),
('paper', 'written_by', 'author'):
torch.randint(100, (2, 200), dtype=torch.long),
('author', 'writes', 'paper'):
torch.randint(100, (2, 200), dtype=torch.long),
}
metadata = list(x_dict.keys()), list(edge_index_dict.keys())
model = to_hetero(model, metadata, debug=False)
out_dict = model(x_dict, edge_index_dict)
assert isinstance(out_dict, dict) and len(out_dict) == 2
assert out_dict['paper'].size() == (100, 64)
assert out_dict['author'].size() == (100, 64)
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