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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import unittest
from typing import Iterable
import numpy as np
import torch
from pytorchvideo.layers.nonlocal_net import create_nonlocal, NonLocal
from torch import nn
class TestNonlocal(unittest.TestCase):
def setUp(self):
super().setUp()
torch.set_rng_state(torch.manual_seed(42).get_state())
def test_build_nonlocal(self):
"""
Test Nonlocal model builder.
"""
for dim_in, dim_inner, pool, norm, instantiation in itertools.product(
(4, 8),
(2, 4),
(None, nn.MaxPool3d(2)),
(None, nn.BatchNorm3d),
("dot_product", "softmax"),
):
model = NonLocal(
conv_theta=nn.Conv3d(
dim_in, dim_inner, kernel_size=1, stride=1, padding=0
),
conv_phi=nn.Conv3d(
dim_in, dim_inner, kernel_size=1, stride=1, padding=0
),
conv_g=nn.Conv3d(dim_in, dim_inner, kernel_size=1, stride=1, padding=0),
conv_out=nn.Conv3d(
dim_inner, dim_in, kernel_size=1, stride=1, padding=0
),
pool=pool,
norm=norm(dim_in) if norm is not None else None,
instantiation=instantiation,
)
# Test forwarding.
for input_tensor in TestNonlocal._get_inputs(input_dim=dim_in):
if input_tensor.shape[1] != dim_in:
with self.assertRaises(RuntimeError):
output_tensor = model(input_tensor)
continue
else:
output_tensor = model(input_tensor)
input_shape = input_tensor.shape
output_shape = output_tensor.shape
self.assertEqual(
input_shape,
output_shape,
"Input shape {} is different from output shape {}".format(
input_shape, output_shape
),
)
def test_nonlocal_builder(self):
"""
Test builder `create_nonlocal`.
"""
for dim_in, dim_inner, pool_size, norm, instantiation in itertools.product(
(4, 8),
(2, 4),
((1, 1, 1), (2, 2, 2)),
(None, nn.BatchNorm3d),
("dot_product", "softmax"),
):
conv_theta = nn.Conv3d(
dim_in, dim_inner, kernel_size=1, stride=1, padding=0
)
conv_phi = nn.Conv3d(dim_in, dim_inner, kernel_size=1, stride=1, padding=0)
conv_g = nn.Conv3d(dim_in, dim_inner, kernel_size=1, stride=1, padding=0)
conv_out = nn.Conv3d(dim_inner, dim_in, kernel_size=1, stride=1, padding=0)
if norm is None:
norm_model = None
else:
norm_model = norm(num_features=dim_in)
if isinstance(pool_size, Iterable) and any(size > 1 for size in pool_size):
pool_model = nn.MaxPool3d(
kernel_size=pool_size, stride=pool_size, padding=[0, 0, 0]
)
else:
pool_model = None
model = create_nonlocal(
dim_in=dim_in,
dim_inner=dim_inner,
pool_size=pool_size,
instantiation=instantiation,
norm=norm,
)
model_gt = NonLocal(
conv_theta=conv_theta,
conv_phi=conv_phi,
conv_g=conv_g,
conv_out=conv_out,
pool=pool_model,
norm=norm_model,
instantiation=instantiation,
)
model.load_state_dict(
model_gt.state_dict(), strict=True
) # explicitly use strict mode.
# Test forwarding.
for input_tensor in TestNonlocal._get_inputs(input_dim=dim_in):
with torch.no_grad():
if input_tensor.shape[1] != dim_in:
with self.assertRaises(RuntimeError):
output_tensor = model(input_tensor)
continue
else:
output_tensor = model(input_tensor)
output_tensor_gt = model_gt(input_tensor)
self.assertEqual(
output_tensor.shape,
output_tensor_gt.shape,
"Output shape {} is different from expected shape {}".format(
output_tensor.shape, output_tensor_gt.shape
),
)
self.assertTrue(
np.allclose(output_tensor.numpy(), output_tensor_gt.numpy())
)
@staticmethod
def _get_inputs(input_dim: int = 8) -> torch.tensor:
"""
Provide different tensors as test cases.
Yield:
(torch.tensor): tensor as test case input.
"""
# Prepare random tensor as test cases.
shapes = (
# Forward succeeded.
(1, input_dim, 5, 7, 7),
(2, input_dim, 5, 7, 7),
(4, input_dim, 5, 7, 7),
(4, input_dim, 5, 7, 7),
(4, input_dim, 7, 7, 7),
(4, input_dim, 7, 7, 14),
(4, input_dim, 7, 14, 7),
(4, input_dim, 7, 14, 14),
# Forward failed.
(8, input_dim * 2, 3, 7, 7),
(8, input_dim * 4, 5, 7, 7),
)
for shape in shapes:
yield torch.rand(shape)
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