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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import unittest
import torch
from pytorchvideo.models.accelerator.mobile_cpu.efficient_x3d import create_x3d
class TestEfficientX3d(unittest.TestCase):
def setUp(self):
super().setUp()
torch.set_rng_state(torch.manual_seed(42).get_state())
def test_create_x3d(self):
"""
To test different versions, set the (expansion, clip_length, crop_size) to:
X3D-XS: ("XS", 4, 160)
X3D-S: ("S", 13, 160)
X3D-M: ("M", 16, 224)
X3D-L: ("L", 16, 312)
"""
for (expansion, input_clip_length, input_crop_size,) in [
("XS", 4, 160),
]:
model = create_x3d(expansion=expansion)
# Test forwarding.
for tensor in TestEfficientX3d._get_inputs(
input_clip_length, input_crop_size
):
if tensor.shape[1] != 3:
with self.assertRaises(RuntimeError):
out = model(tensor)
continue
out = model(tensor)
output_shape = out.shape
output_shape_gt = (tensor.shape[0], 400)
self.assertEqual(
output_shape,
output_shape_gt,
"Output shape {} is different from expected shape {}".format(
output_shape, output_shape_gt
),
)
def test_load_hubconf(self):
path = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"..",
)
for (input_clip_length, input_crop_size, model_name) in [
(4, 160, "efficient_x3d_xs"),
(13, 160, "efficient_x3d_s"),
]:
model = torch.hub.load(
repo_or_dir=path,
source="local",
model=model_name,
pretrained=False,
)
self.assertIsNotNone(model)
# Test forwarding.
for tensor in TestEfficientX3d._get_inputs(
input_clip_length, input_crop_size
):
if tensor.shape[1] != 3:
with self.assertRaises(RuntimeError):
out = model(tensor)
continue
out = model(tensor)
output_shape = out.shape
output_shape_gt = (tensor.shape[0], 400)
self.assertEqual(
output_shape,
output_shape_gt,
"Output shape {} is different from expected shape {}".format(
output_shape, output_shape_gt
),
)
@staticmethod
def _get_inputs(clip_length: int = 4, crop_size: int = 160) -> torch.tensor:
"""
Provide different tensors as test cases.
Yield:
(torch.tensor): tensor as test case input.
"""
# Prepare random inputs as test cases.
shapes = (
(1, 3, clip_length, crop_size, crop_size),
(2, 3, clip_length, crop_size, crop_size),
)
for shape in shapes:
yield torch.rand(shape)
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