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Deprecate nn.glob package #5039

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merged 16 commits into from
Jul 26, 2022
Merged

Deprecate nn.glob package #5039

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814bb12
add only needed
Padarn Jul 20, 2022
b281587
delete test
Padarn Jul 20, 2022
eada40a
delete test
Padarn Jul 20, 2022
d8b0218
revert name change
Padarn Jul 23, 2022
72edcab
update changelog
Padarn Jul 23, 2022
f1c453a
remove doc references
Padarn Jul 23, 2022
01a6482
Update torch_geometric/nn/pool/glob.py
Padarn Jul 24, 2022
9fd15d4
Update torch_geometric/nn/pool/glob.py
Padarn Jul 24, 2022
99b9d5e
Update CHANGELOG.md
Padarn Jul 24, 2022
4e04bcd
readd docstring
Padarn Jul 24, 2022
44fb56e
change depreciaton notice
Padarn Jul 24, 2022
fdf7379
add back dense methods
Padarn Jul 24, 2022
108abff
formatting and naming
Padarn Jul 26, 2022
f8bc2d7
Merge branch 'master' into padarn/depreciate-glob
Padarn Jul 26, 2022
702c00e
fix import order
Padarn Jul 26, 2022
0357f32
fix implementation of max
Padarn Jul 26, 2022
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2 changes: 1 addition & 1 deletion CHANGELOG.md
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Expand Up @@ -37,7 +37,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
- Added the `bias` vector to the `GCN` model definition in the "Create Message Passing Networks" tutorial ([#4755](https://github.com/pyg-team/pytorch_geometric/pull/4755))
- Added `transforms.RootedSubgraph` interface with two implementations: `RootedEgoNets` and `RootedRWSubgraph` ([#3926](https://github.com/pyg-team/pytorch_geometric/pull/3926))
- Added `ptr` vectors for `follow_batch` attributes within `Batch.from_data_list` ([#4723](https://github.com/pyg-team/pytorch_geometric/pull/4723))
- Added `torch_geometric.nn.aggr` package ([#4687](https://github.com/pyg-team/pytorch_geometric/pull/4687), [#4721](https://github.com/pyg-team/pytorch_geometric/pull/4721), [#4731](https://github.com/pyg-team/pytorch_geometric/pull/4731), [#4762](https://github.com/pyg-team/pytorch_geometric/pull/4762), [#4749](https://github.com/pyg-team/pytorch_geometric/pull/4749), [#4779](https://github.com/pyg-team/pytorch_geometric/pull/4779), [#4863](https://github.com/pyg-team/pytorch_geometric/pull/4863), [#4864](https://github.com/pyg-team/pytorch_geometric/pull/4864), [#4865](https://github.com/pyg-team/pytorch_geometric/pull/4865), [#4866](https://github.com/pyg-team/pytorch_geometric/pull/4866), [#4872](https://github.com/pyg-team/pytorch_geometric/pull/4872), [#4934](https://github.com/pyg-team/pytorch_geometric/pull/4934), [#4935](https://github.com/pyg-team/pytorch_geometric/pull/4935), [#4957](https://github.com/pyg-team/pytorch_geometric/pull/4957), [#4973](https://github.com/pyg-team/pytorch_geometric/pull/4973),[#4973](https://github.com/pyg-team/pytorch_geometric/pull/4973), [4986](https://github.com/pyg-team/pytorch_geometric/pull/4986), [#4995](https://github.com/pyg-team/pytorch_geometric/pull/4995), [5000](https://github.com/pyg-team/pytorch_geometric/pull/5000))
- Added `torch_geometric.nn.aggr` package ([#4687](https://github.com/pyg-team/pytorch_geometric/pull/4687), [#4721](https://github.com/pyg-team/pytorch_geometric/pull/4721), [#4731](https://github.com/pyg-team/pytorch_geometric/pull/4731), [#4762](https://github.com/pyg-team/pytorch_geometric/pull/4762), [#4749](https://github.com/pyg-team/pytorch_geometric/pull/4749), [#4779](https://github.com/pyg-team/pytorch_geometric/pull/4779), [#4863](https://github.com/pyg-team/pytorch_geometric/pull/4863), [#4864](https://github.com/pyg-team/pytorch_geometric/pull/4864), [#4865](https://github.com/pyg-team/pytorch_geometric/pull/4865), [#4866](https://github.com/pyg-team/pytorch_geometric/pull/4866), [#4872](https://github.com/pyg-team/pytorch_geometric/pull/4872), [#4934](https://github.com/pyg-team/pytorch_geometric/pull/4934), [#4935](https://github.com/pyg-team/pytorch_geometric/pull/4935), [#4957](https://github.com/pyg-team/pytorch_geometric/pull/4957), [#4973](https://github.com/pyg-team/pytorch_geometric/pull/4973),[#4973](https://github.com/pyg-team/pytorch_geometric/pull/4973), [4986](https://github.com/pyg-team/pytorch_geometric/pull/4986), [#4995](https://github.com/pyg-team/pytorch_geometric/pull/4995), [#5000](https://github.com/pyg-team/pytorch_geometric/pull/5000),[#5039](https://github.com/pyg-team/pytorch_geometric/issues/5039)
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- Added the `DimeNet++` model ([#4432](https://github.com/pyg-team/pytorch_geometric/pull/4432), [#4699](https://github.com/pyg-team/pytorch_geometric/pull/4699), [#4700](https://github.com/pyg-team/pytorch_geometric/pull/4700), [#4800](https://github.com/pyg-team/pytorch_geometric/pull/4800))
- Added an example of using PyG with PyTorch Ignite ([#4487](https://github.com/pyg-team/pytorch_geometric/pull/4487))
- Added `GroupAddRev` module with support for reducing training GPU memory ([#4671](https://github.com/pyg-team/pytorch_geometric/pull/4671), [#4701](https://github.com/pyg-team/pytorch_geometric/pull/4701), [#4715](https://github.com/pyg-team/pytorch_geometric/pull/4715), [#4730](https://github.com/pyg-team/pytorch_geometric/pull/4730))
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15 changes: 0 additions & 15 deletions docs/source/modules/nn.rst
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Expand Up @@ -71,21 +71,6 @@ Normalization Layers
:undoc-members:
:exclude-members: training

Global Pooling Layers
---------------------

.. currentmodule:: torch_geometric.nn.glob
.. autosummary::
:nosignatures:
{% for cls in torch_geometric.nn.glob.classes %}
{{ cls }}
{% endfor %}

.. automodule:: torch_geometric.nn.glob
:members:
:undoc-members:
:exclude-members: training

Pooling Layers
--------------

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78 changes: 0 additions & 78 deletions test/nn/aggr/test_sort.py
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@@ -1,78 +0,0 @@
import torch
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Could you please clarify why the tests are removed?

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Mistake, added back.


from torch_geometric.nn.aggr import SortAggr


def test_global_sort_pool():
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N_1, N_2 = 4, 6
x = torch.randn(N_1 + N_2, 4)
index = torch.tensor([0 for _ in range(N_1)] + [1 for _ in range(N_2)])

aggr = SortAggr(k=5)
assert str(aggr) == 'SortAggr(k=5)'

out = aggr(x, index)
assert out.size() == (2, 5 * 4)

out_dim = out = aggr(x, index, dim=0)
assert torch.allclose(out_dim, out)

out = out.view(2, 5, 4)

# First graph output has been filled up with zeros.
assert out[0, -1].tolist() == [0, 0, 0, 0]

# Nodes are sorted.
expected = 3 - torch.arange(4)
assert out[0, :4, -1].argsort().tolist() == expected.tolist()

expected = 4 - torch.arange(5)
assert out[1, :, -1].argsort().tolist() == expected.tolist()


def test_global_sort_pool_smaller_than_k():
N_1, N_2 = 4, 6
x = torch.randn(N_1 + N_2, 4)
index = torch.tensor([0 for _ in range(N_1)] + [1 for _ in range(N_2)])

# Set k which is bigger than both N_1=4 and N_2=6.
aggr = SortAggr(k=10)
assert str(aggr) == 'SortAggr(k=10)'

out = aggr(x, index)
assert out.size() == (2, 10 * 4)

out_dim = out = aggr(x, index, dim=0)
assert torch.allclose(out_dim, out)

out = out.view(2, 10, 4)

# Both graph outputs have been filled up with zeros.
assert out[0, -1].tolist() == [0, 0, 0, 0]
assert out[1, -1].tolist() == [0, 0, 0, 0]

# Nodes are sorted.
expected = 3 - torch.arange(4)
assert out[0, :4, -1].argsort().tolist() == expected.tolist()

expected = 5 - torch.arange(6)
assert out[1, :6, -1].argsort().tolist() == expected.tolist()


def test_global_sort_pool_dim_size():
N_1, N_2 = 4, 6
x = torch.randn(N_1 + N_2, 4)
index = torch.tensor([0 for _ in range(N_1)] + [1 for _ in range(N_2)])

aggr = SortAggr(k=5)
assert str(aggr) == 'SortAggr(k=5)'

# expand batch output by 1
out = aggr(x, index, dim_size=3)
assert out.size() == (3, 5 * 4)

out = out.view(3, 5, 4)

# Both first and last graph outputs have been filled up with zeros.
assert out[0, -1].tolist() == [0, 0, 0, 0]
assert out[2, -1].tolist() == [0, 0, 0, 0]
72 changes: 0 additions & 72 deletions test/nn/glob/test_glob.py
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1 change: 1 addition & 0 deletions test/nn/models/test_gnn_explainer.py
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Expand Up @@ -139,6 +139,7 @@ def test_gnn_explainer_with_existing_self_loops(model, return_type):
[0, 1, 0, 2, 1, 3, 2, 4, 3, 5, 4, 6, 5, 7, 6]])

node_feat_mask, edge_mask = explainer.explain_node(2, x, edge_index)

assert node_feat_mask.size() == (x.size(1), )
assert node_feat_mask.min() >= 0 and node_feat_mask.max() <= 1
assert edge_mask.size() == (edge_index.size(1), )
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1 change: 0 additions & 1 deletion torch_geometric/nn/__init__.py
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Expand Up @@ -7,7 +7,6 @@
from .aggr import * # noqa
from .conv import * # noqa
from .norm import * # noqa
from .glob import * # noqa
from .pool import * # noqa
from .unpool import * # noqa
from .dense import * # noqa
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49 changes: 0 additions & 49 deletions torch_geometric/nn/glob/__init__.py
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117 changes: 0 additions & 117 deletions torch_geometric/nn/glob/glob.py
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8 changes: 8 additions & 0 deletions torch_geometric/nn/pool/__init__.py
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Expand Up @@ -11,6 +11,8 @@
from .asap import ASAPooling
from .pan_pool import PANPooling
from .mem_pool import MemPooling
from .glob import (global_max_pool, global_mean_pool, global_add_pool,
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Ah sorry, this is not what I meant. I meant:

  • Move global_add_pool, global_mean_pool, and global_max_pool to glob/glob.py to pool/glob.py`.
  • Move glob/__init__.py to nn/glob.py and keep the deprecations in there.

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The idea here is that we keep the implementations of global_*_pool as they were (since they are so heavily used).

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hmm sorry I don't fully understand the distinction between these. In both cases you can

from torch_geometric.nn import global_max_pool

is it that you want to keep this?

from torch_geometric.nn.glob import global_max_pool

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Or you just want to clearly separate the deprecations?

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Yes, we only need to
deprecate the import of torch_geometric.nn.glob.

GraphMultisetTransformer, Set2Set, GlobalAttention)

try:
import torch_cluster
Expand Down Expand Up @@ -263,6 +265,12 @@ def nearest(x: Tensor, y: Tensor, batch_x: OptTensor = None,
'radius',
'radius_graph',
'nearest',
'global_max_pool',
'global_add_pool',
'global_mean_pool',
'GraphMultisetTransformer',
'Set2Set',
'GlobalAttention',
]

classes = __all__
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