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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import unittest
import numpy as np
import torch
from pytorchvideo.models.head import (
create_res_basic_head,
create_res_roi_pooling_head,
create_vit_basic_head,
ResNetBasicHead,
ResNetRoIHead,
SequencePool,
)
from torch import nn
from torchvision.ops import RoIAlign
class TestHeadHelper(unittest.TestCase):
def setUp(self):
super().setUp()
torch.set_rng_state(torch.manual_seed(42).get_state())
def test_build_simple_head(self):
"""
Test simple ResNetBasicHead (without dropout and activation layers).
"""
for input_dim, output_dim in itertools.product((4, 8), (4, 8, 16)):
model = ResNetBasicHead(
proj=nn.Linear(input_dim, output_dim),
pool=nn.AdaptiveAvgPool3d(1),
output_pool=nn.AdaptiveAvgPool3d(1),
)
# Test forwarding.
for input_tensor in TestHeadHelper._get_inputs(input_dim=input_dim):
if input_tensor.shape[1] != input_dim:
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
output_shape_gt = (input_shape[0], output_dim)
self.assertEqual(
output_shape,
output_shape_gt,
"Output shape {} is different from expected shape {}".format(
output_shape, output_shape_gt
),
)
def test_build_complex_head(self):
"""
Test complex ResNetBasicHead.
"""
for input_dim, output_dim in itertools.product((4, 8), (4, 8, 16)):
model = ResNetBasicHead(
proj=nn.Linear(input_dim, output_dim),
activation=nn.Softmax(),
pool=nn.AdaptiveAvgPool3d(1),
dropout=nn.Dropout(0.5),
output_pool=nn.AdaptiveAvgPool3d(1),
)
# Test forwarding.
for input_tensor in TestHeadHelper._get_inputs(input_dim=input_dim):
if input_tensor.shape[1] != input_dim:
with self.assertRaises(Exception):
output_tensor = model(input_tensor)
continue
output_tensor = model(input_tensor)
input_shape = input_tensor.shape
output_shape = output_tensor.shape
output_shape_gt = (input_shape[0], output_dim)
self.assertEqual(
output_shape,
output_shape_gt,
"Output shape {} is different from expected shape {}".format(
output_shape, output_shape_gt
),
)
def test_build_head_with_callable(self):
"""
Test builder `create_res_basic_head`.
"""
for (pool, activation) in itertools.product(
(nn.AvgPool3d, nn.MaxPool3d, nn.AdaptiveAvgPool3d, None),
(nn.ReLU, nn.Softmax, nn.Sigmoid, None),
):
if activation is None:
activation_model = None
elif activation == nn.Softmax:
activation_model = activation(dim=1)
else:
activation_model = activation()
if pool is None:
pool_model = None
elif pool == nn.AdaptiveAvgPool3d:
pool_model = pool(1)
else:
pool_model = pool(kernel_size=[5, 7, 7], stride=[1, 1, 1])
model = create_res_basic_head(
in_features=16,
out_features=32,
pool=pool,
pool_kernel_size=(5, 7, 7),
output_size=(1, 1, 1),
dropout_rate=0.0,
activation=activation,
output_with_global_average=True,
)
model_gt = ResNetBasicHead(
proj=nn.Linear(16, 32),
activation=activation_model,
pool=pool_model,
dropout=None,
output_pool=nn.AdaptiveAvgPool3d(1),
)
model.load_state_dict(
model_gt.state_dict(), strict=True
) # explicitly use strict mode.
# Test forwarding.
for input_tensor in TestHeadHelper._get_inputs(input_dim=16):
with torch.no_grad():
if input_tensor.shape[1] != 16:
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)
class TestRoIHeadHelper(unittest.TestCase):
def setUp(self):
super().setUp()
torch.set_rng_state(torch.manual_seed(42).get_state())
def test_build_simple_head(self):
"""
Test simple ResNetRoIHead
(without pool_spatial, roi, dropout and activation layers).
"""
for input_dim, output_dim in itertools.product((4, 8), (4, 8, 16)):
model = ResNetRoIHead(
proj=nn.Linear(input_dim, output_dim),
pool=nn.AdaptiveAvgPool3d(1),
output_pool=nn.AdaptiveAvgPool3d(1),
)
bboxes = None
# Test forwarding.
for input_tensor in TestHeadHelper._get_inputs(input_dim=input_dim):
if input_tensor.shape[1] != input_dim:
with self.assertRaises(RuntimeError):
output_tensor = model(input_tensor, bboxes)
continue
else:
output_tensor = model(input_tensor, bboxes)
input_shape = input_tensor.shape
output_shape = output_tensor.shape
output_shape_gt = (input_shape[0], output_dim)
self.assertEqual(
output_shape,
output_shape_gt,
"Output shape {} is different from expected shape {}".format(
output_shape, output_shape_gt
),
)
def test_create_vit_basic_head(self):
batch_size = 8
seq_len = 10
input_dim = 10
out_dim = 20
head = create_vit_basic_head(
in_features=input_dim,
out_features=out_dim,
)
fake_input = torch.rand(batch_size, seq_len, input_dim)
output = head(fake_input)
gt_shape = (batch_size, out_dim)
self.assertEqual(tuple(output.shape), gt_shape)
def test_sequence_pool(self):
model = SequencePool("cls")
fake_input = torch.rand(8, 10, 10)
output = model(fake_input)
self.assertTrue(torch.equal(output, fake_input[:, 0]))
model = SequencePool("mean")
output = model(fake_input)
self.assertTrue(torch.equal(output, fake_input.mean(1)))
def test_build_complex_head(self):
"""
Test complex ResNetRoIHead.
"""
# ROI layer configs
resolution = (10, 15)
spatial_scale = 1.0 / 5.0
sampling_ratio = 0
roi_layer = RoIAlign(
resolution, spatial_scale=spatial_scale, sampling_ratio=sampling_ratio
)
for input_dim, output_dim in itertools.product((4, 8), (4, 8, 16)):
model = ResNetRoIHead(
proj=nn.Linear(input_dim, output_dim),
activation=nn.Softmax(),
pool=nn.AdaptiveAvgPool3d(1),
pool_spatial=nn.MaxPool2d(resolution, stride=1),
roi_layer=roi_layer,
dropout=nn.Dropout(0.5),
output_pool=nn.AdaptiveAvgPool3d(1),
)
# Test forwarding.
for (input_tensor, bboxes) in TestRoIHeadHelper._get_inputs(
input_dim=input_dim
):
if input_tensor.shape[1] != input_dim:
with self.assertRaises(Exception):
output_tensor = model(input_tensor, bboxes)
continue
output_tensor = model(input_tensor, bboxes)
bboxes_shape = bboxes.shape
output_shape = output_tensor.shape
output_shape_gt = (bboxes_shape[0], output_dim)
self.assertEqual(
output_shape,
output_shape_gt,
"Output shape {} is different from expected shape {}".format(
output_shape, output_shape_gt
),
)
def test_build_head_with_callable(self):
"""
Test builder `create_res_roi_pooling_head`.
"""
# ROI layer configs
resolution = (10, 15)
spatial_scale = 1.0 / 5.0
sampling_ratio = 0
roi_layer = RoIAlign(
resolution, spatial_scale=spatial_scale, sampling_ratio=sampling_ratio
)
for (pool, activation) in itertools.product(
(nn.AvgPool3d, nn.MaxPool3d, nn.AdaptiveAvgPool3d, None),
(nn.ReLU, nn.Softmax, nn.Sigmoid, None),
):
if activation is None:
activation_model = None
elif activation == nn.Softmax:
activation_model = activation(dim=1)
else:
activation_model = activation()
if pool is None:
pool_model = None
elif pool == nn.AdaptiveAvgPool3d:
pool_model = pool(1)
else:
pool_model = pool(kernel_size=[5, 1, 1], stride=[1, 1, 1])
model = create_res_roi_pooling_head(
in_features=16,
out_features=32,
resolution=resolution,
spatial_scale=spatial_scale,
sampling_ratio=sampling_ratio,
roi=RoIAlign,
pool=pool,
pool_spatial=nn.MaxPool2d,
pool_kernel_size=(5, 1, 1),
output_size=(1, 1, 1),
dropout_rate=0.0,
activation=activation,
output_with_global_average=True,
)
model_gt = ResNetRoIHead(
proj=nn.Linear(16, 32),
activation=activation_model,
pool=pool_model,
pool_spatial=nn.MaxPool2d(resolution, stride=1),
roi_layer=roi_layer,
dropout=None,
output_pool=nn.AdaptiveAvgPool3d(1),
)
model.load_state_dict(
model_gt.state_dict(), strict=True
) # explicitly use strict mode.
# Test forwarding.
for (input_tensor, bboxes) in TestRoIHeadHelper._get_inputs(input_dim=16):
with torch.no_grad():
if (
input_tensor.shape[1] != 16
or (pool is None)
or (
input_tensor.shape[-3] != 5 and pool != nn.AdaptiveAvgPool3d
)
):
with self.assertRaises(Exception):
output_tensor = model(input_tensor, bboxes)
continue
else:
output_tensor = model(input_tensor, bboxes)
output_tensor_gt = model_gt(input_tensor, bboxes)
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.
(torch.tensor): tensor as test case bboxes.
"""
# 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:
input_tensor = torch.rand(shape)
bboxes = [[i, 1, 2, 3, 4] for i in range(input_tensor.shape[0])]
bboxes = torch.Tensor(bboxes)
yield (input_tensor, bboxes)
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