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d596074 | 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 | #!/usr/bin/env python3
import torch
from scaling import ScheduledFloat
from subsampling import Conv2dSubsampling
def test_conv2d_subsampling():
layer1_channels = 8
layer2_channels = 32
layer3_channels = 128
out_channels = 192
encoder_embed = Conv2dSubsampling(
in_channels=80,
out_channels=out_channels,
layer1_channels=layer1_channels,
layer2_channels=layer2_channels,
layer3_channels=layer3_channels,
dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)),
)
N = 2
T = 200
num_features = 80
x = torch.rand(N, T, num_features)
x_copy = x.clone()
x = x.unsqueeze(1) # (N, 1, T, num_features)
x = encoder_embed.conv[0](x) # conv2d, in 1, out 8, kernel 3, padding (0,1)
assert x.shape == (N, layer1_channels, T - 2, num_features)
# (2, 8, 198, 80)
x = encoder_embed.conv[1](x) # scale grad
x = encoder_embed.conv[2](x) # balancer
x = encoder_embed.conv[3](x) # swooshR
x = encoder_embed.conv[4](x) # conv2d, in 8, out 32, kernel 3, stride 2
assert x.shape == (
N,
layer2_channels,
((T - 2) - 3) // 2 + 1,
(num_features - 3) // 2 + 1,
)
# (2, 32, 98, 39)
x = encoder_embed.conv[5](x) # balancer
x = encoder_embed.conv[6](x) # swooshR
# conv2d:
# in 32, out 128, kernel 3, stride (1, 2)
x = encoder_embed.conv[7](x)
assert x.shape == (
N,
layer3_channels,
(((T - 2) - 3) // 2 + 1) - 2,
(((num_features - 3) // 2 + 1) - 3) // 2 + 1,
)
# (2, 128, 96, 19)
x = encoder_embed.conv[8](x) # balancer
x = encoder_embed.conv[9](x) # swooshR
# (((T - 2) - 3) // 2 + 1) - 2
# = (T - 2) - 3) // 2 + 1 - 2
# = ((T - 2) - 3) // 2 - 1
# = (T - 2 - 3) // 2 - 1
# = (T - 5) // 2 - 1
# = (T - 7) // 2
assert x.shape[2] == (x_copy.shape[1] - 7) // 2
# (((num_features - 3) // 2 + 1) - 3) // 2 + 1,
# = ((num_features - 3) // 2 + 1 - 3) // 2 + 1,
# = ((num_features - 3) // 2 - 2) // 2 + 1,
# = (num_features - 3 - 4) // 2 // 2 + 1,
# = (num_features - 7) // 2 // 2 + 1,
# = (num_features - 7) // 4 + 1,
# = (num_features - 3) // 4
assert x.shape[3] == (x_copy.shape[2] - 3) // 4
assert x.shape == (N, layer3_channels, (T - 7) // 2, (num_features - 3) // 4)
# Input shape to convnext is
#
# (N, layer3_channels, (T-7)//2, (num_features - 3)//4)
# conv2d: in layer3_channels, out layer3_channels, groups layer3_channels
# kernel_size 7, padding 3
x = encoder_embed.convnext.depthwise_conv(x)
assert x.shape == (N, layer3_channels, (T - 7) // 2, (num_features - 3) // 4)
# conv2d: in layer3_channels, out hidden_ratio * layer3_channels, kernel_size 1
x = encoder_embed.convnext.pointwise_conv1(x)
assert x.shape == (N, layer3_channels * 3, (T - 7) // 2, (num_features - 3) // 4)
x = encoder_embed.convnext.hidden_balancer(x) # balancer
x = encoder_embed.convnext.activation(x) # swooshL
# conv2d: in hidden_ratio * layer3_channels, out layer3_channels, kernel 1
x = encoder_embed.convnext.pointwise_conv2(x)
assert x.shape == (N, layer3_channels, (T - 7) // 2, (num_features - 3) // 4)
# bypass and layer drop, omitted here.
x = encoder_embed.convnext.out_balancer(x)
# Note: the input and output shape of ConvNeXt are the same
x = x.transpose(1, 2).reshape(N, (T - 7) // 2, -1)
assert x.shape == (N, (T - 7) // 2, layer3_channels * ((num_features - 3) // 4))
x = encoder_embed.out(x)
assert x.shape == (N, (T - 7) // 2, out_channels)
x = encoder_embed.out_whiten(x)
x = encoder_embed.out_norm(x)
# final layer is dropout
# test streaming forward
subsampling_factor = 2
cached_left_padding = encoder_embed.get_init_states(batch_size=N)
depthwise_conv_kernel_size = 7
pad_size = (depthwise_conv_kernel_size - 1) // 2
assert cached_left_padding.shape == (
N,
layer3_channels,
pad_size,
(num_features - 3) // 4,
)
chunk_size = 16
right_padding = pad_size * subsampling_factor
T = chunk_size * subsampling_factor + 7 + right_padding
x = torch.rand(N, T, num_features)
x_lens = torch.tensor([T] * N)
y, y_lens, next_cached_left_padding = encoder_embed.streaming_forward(
x, x_lens, cached_left_padding
)
assert y.shape == (N, chunk_size, out_channels), y.shape
assert next_cached_left_padding.shape == cached_left_padding.shape
assert y.shape[1] == y_lens[0] == y_lens[1]
def main():
test_conv2d_subsampling()
if __name__ == "__main__":
main()
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