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0efc562 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmpose.models.utils.transformer import GAUEncoder, SinePositionalEncoding
class TestSinePositionalEncoding(TestCase):
def test_init(self):
spe = SinePositionalEncoding(out_channels=128)
self.assertTrue(hasattr(spe, 'dim_t'))
self.assertFalse(spe.dim_t.requires_grad)
self.assertEqual(spe.dim_t.size(0), 128 // 2)
spe = SinePositionalEncoding(out_channels=128, learnable=True)
self.assertTrue(spe.dim_t.requires_grad)
spe = SinePositionalEncoding(out_channels=128, eval_size=10)
self.assertTrue(hasattr(spe, 'pos_enc_10'))
self.assertEqual(spe.pos_enc_10.size(-1), 128)
spe = SinePositionalEncoding(
out_channels=128, eval_size=(2, 3), spatial_dim=2)
self.assertTrue(hasattr(spe, 'pos_enc_(2, 3)'))
self.assertSequenceEqual(
getattr(spe, 'pos_enc_(2, 3)').shape[-2:], (128, 2))
def test_generate_speoding(self):
# spatial_dim = 1
spe = SinePositionalEncoding(out_channels=128)
pos_enc = spe.generate_pos_encoding(size=10)
self.assertSequenceEqual(pos_enc.shape, (10, 128))
position = torch.arange(8)
pos_enc = spe.generate_pos_encoding(position=position)
self.assertSequenceEqual(pos_enc.shape, (8, 128))
with self.assertRaises(AssertionError):
pos_enc = spe.generate_pos_encoding(size=10, position=position)
# spatial_dim = 2
spe = SinePositionalEncoding(out_channels=128, spatial_dim=2)
pos_enc = spe.generate_pos_encoding(size=10)
self.assertSequenceEqual(pos_enc.shape, (100, 128, 2))
pos_enc = spe.generate_pos_encoding(size=(5, 6))
self.assertSequenceEqual(pos_enc.shape, (30, 128, 2))
position = torch.arange(8).unsqueeze(1).repeat(1, 2)
pos_enc = spe.generate_pos_encoding(position=position)
self.assertSequenceEqual(pos_enc.shape, (8, 128, 2))
with self.assertRaises(AssertionError):
pos_enc = spe.generate_pos_encoding(size=10, position=position)
with self.assertRaises(ValueError):
pos_enc = spe.generate_pos_encoding(size=position)
def test_apply_additional_pos_enc(self):
# spatial_dim = 1
spe = SinePositionalEncoding(out_channels=128)
pos_enc = spe.generate_pos_encoding(size=10)
feature = torch.randn(2, 3, 10, 128)
out_feature = spe.apply_additional_pos_enc(feature, pos_enc,
spe.spatial_dim)
self.assertSequenceEqual(feature.shape, out_feature.shape)
# spatial_dim = 2
spe = SinePositionalEncoding(out_channels=128 // 2, spatial_dim=2)
pos_enc = spe.generate_pos_encoding(size=(2, 5))
feature = torch.randn(2, 3, 10, 128)
out_feature = spe.apply_additional_pos_enc(feature, pos_enc,
spe.spatial_dim)
self.assertSequenceEqual(feature.shape, out_feature.shape)
def test_apply_rotary_pos_enc(self):
# spatial_dim = 1
spe = SinePositionalEncoding(out_channels=128)
pos_enc = spe.generate_pos_encoding(size=10)
feature = torch.randn(2, 3, 10, 128)
out_feature = spe.apply_rotary_pos_enc(feature, pos_enc,
spe.spatial_dim)
self.assertSequenceEqual(feature.shape, out_feature.shape)
# spatial_dim = 2
spe = SinePositionalEncoding(out_channels=128, spatial_dim=2)
pos_enc = spe.generate_pos_encoding(size=(2, 5))
feature = torch.randn(2, 3, 10, 128)
out_feature = spe.apply_rotary_pos_enc(feature, pos_enc,
spe.spatial_dim)
self.assertSequenceEqual(feature.shape, out_feature.shape)
class TestGAUEncoder(TestCase):
def test_init(self):
gau = GAUEncoder(in_token_dims=64, out_token_dims=64)
self.assertTrue(gau.shortcut)
gau = GAUEncoder(in_token_dims=64, out_token_dims=64, dropout_rate=0.5)
self.assertTrue(hasattr(gau, 'dropout'))
def test_forward(self):
gau = GAUEncoder(in_token_dims=64, out_token_dims=64)
# compatibility with various dimension input
feat = torch.randn(2, 3, 64)
with torch.no_grad():
out_feat = gau.forward(feat)
self.assertSequenceEqual(feat.shape, out_feat.shape)
feat = torch.randn(1, 2, 3, 64)
with torch.no_grad():
out_feat = gau.forward(feat)
self.assertSequenceEqual(feat.shape, out_feat.shape)
feat = torch.randn(1, 2, 3, 4, 64)
with torch.no_grad():
out_feat = gau.forward(feat)
self.assertSequenceEqual(feat.shape, out_feat.shape)
# positional encoding
gau = GAUEncoder(
s=32, in_token_dims=64, out_token_dims=64, pos_enc=True)
feat = torch.randn(2, 3, 64)
spe = SinePositionalEncoding(out_channels=32)
pos_enc = spe.generate_pos_encoding(size=3)
with torch.no_grad():
out_feat = gau.forward(feat, pos_enc=pos_enc)
self.assertSequenceEqual(feat.shape, out_feat.shape)
gau = GAUEncoder(
s=32,
in_token_dims=64,
out_token_dims=64,
pos_enc=True,
spatial_dim=2)
feat = torch.randn(1, 2, 6, 64)
spe = SinePositionalEncoding(out_channels=32, spatial_dim=2)
pos_enc = spe.generate_pos_encoding(size=(2, 3))
with torch.no_grad():
out_feat = gau.forward(feat, pos_enc=pos_enc)
self.assertSequenceEqual(feat.shape, out_feat.shape)
# mask
gau = GAUEncoder(in_token_dims=64, out_token_dims=64)
# compatibility with various dimension input
feat = torch.randn(2, 3, 64)
mask = torch.rand(2, 3, 3)
with torch.no_grad():
out_feat = gau.forward(feat, mask=mask)
self.assertSequenceEqual(feat.shape, out_feat.shape)
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