mmpretrain/tests/test_backbones/test_embed.py

57 lines
2.0 KiB
Python

import pytest
import torch
from mmcls.models.utils import PatchMerging
def cal_unfold_dim(dim, kernel_size, stride, padding=0, dilation=1):
return (dim + 2 * padding - dilation * (kernel_size - 1) - 1) // stride + 1
def test_patch_merging():
settings = dict(
input_resolution=(56, 56), in_channels=16, expansion_ratio=2)
downsample = PatchMerging(**settings)
# test forward with wrong dims
with pytest.raises(AssertionError):
inputs = torch.rand((1, 16, 56 * 56))
downsample(inputs)
# test patch merging forward
inputs = torch.rand((1, 56 * 56, 16))
out = downsample(inputs)
assert downsample.output_resolution == (28, 28)
assert out.shape == (1, 28 * 28, 32)
# test different kernel_size in each direction
downsample = PatchMerging(kernel_size=(2, 3), **settings)
out = downsample(inputs)
expected_dim = cal_unfold_dim(56, 2, 2) * cal_unfold_dim(56, 3, 3)
assert downsample.sampler.kernel_size == (2, 3)
assert downsample.output_resolution == (cal_unfold_dim(56, 2, 2),
cal_unfold_dim(56, 3, 3))
assert out.shape == (1, expected_dim, 32)
# test default stride
downsample = PatchMerging(kernel_size=6, **settings)
assert downsample.sampler.stride == (6, 6)
# test stride=3
downsample = PatchMerging(kernel_size=6, stride=3, **settings)
out = downsample(inputs)
assert downsample.sampler.stride == (3, 3)
assert out.shape == (1, cal_unfold_dim(56, 6, stride=3)**2, 32)
# test padding
downsample = PatchMerging(kernel_size=6, padding=2, **settings)
out = downsample(inputs)
assert downsample.sampler.padding == (2, 2)
assert out.shape == (1, cal_unfold_dim(56, 6, 6, padding=2)**2, 32)
# test dilation
downsample = PatchMerging(kernel_size=6, dilation=2, **settings)
out = downsample(inputs)
assert downsample.sampler.dilation == (2, 2)
assert out.shape == (1, cal_unfold_dim(56, 6, 6, dilation=2)**2, 32)