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# Copyright (c) Meta Platforms, Inc. and affiliates. | |
# All rights reserved. | |
# | |
# This source code is licensed under the license found in the | |
# LICENSE file in the root directory of this source tree. | |
from itertools import product | |
import pytest | |
import torch | |
from audiocraft.modules.seanet import SEANetEncoder, SEANetDecoder, SEANetResnetBlock | |
from audiocraft.modules import StreamableConv1d, StreamableConvTranspose1d | |
class TestSEANetModel: | |
def test_base(self): | |
encoder = SEANetEncoder() | |
decoder = SEANetDecoder() | |
x = torch.randn(1, 1, 24000) | |
z = encoder(x) | |
assert list(z.shape) == [1, 128, 75], z.shape | |
y = decoder(z) | |
assert y.shape == x.shape, (x.shape, y.shape) | |
def test_causal(self): | |
encoder = SEANetEncoder(causal=True) | |
decoder = SEANetDecoder(causal=True) | |
x = torch.randn(1, 1, 24000) | |
z = encoder(x) | |
assert list(z.shape) == [1, 128, 75], z.shape | |
y = decoder(z) | |
assert y.shape == x.shape, (x.shape, y.shape) | |
def test_conv_skip_connection(self): | |
encoder = SEANetEncoder(true_skip=False) | |
decoder = SEANetDecoder(true_skip=False) | |
x = torch.randn(1, 1, 24000) | |
z = encoder(x) | |
assert list(z.shape) == [1, 128, 75], z.shape | |
y = decoder(z) | |
assert y.shape == x.shape, (x.shape, y.shape) | |
def test_seanet_encoder_decoder_final_act(self): | |
encoder = SEANetEncoder(true_skip=False) | |
decoder = SEANetDecoder(true_skip=False, final_activation='Tanh') | |
x = torch.randn(1, 1, 24000) | |
z = encoder(x) | |
assert list(z.shape) == [1, 128, 75], z.shape | |
y = decoder(z) | |
assert y.shape == x.shape, (x.shape, y.shape) | |
def _check_encoder_blocks_norm(self, encoder: SEANetEncoder, n_disable_blocks: int, norm: str): | |
n_blocks = 0 | |
for layer in encoder.model: | |
if isinstance(layer, StreamableConv1d): | |
n_blocks += 1 | |
assert layer.conv.norm_type == 'none' if n_blocks <= n_disable_blocks else norm | |
elif isinstance(layer, SEANetResnetBlock): | |
for resnet_layer in layer.block: | |
if isinstance(resnet_layer, StreamableConv1d): | |
# here we add + 1 to n_blocks as we increment n_blocks just after the block | |
assert resnet_layer.conv.norm_type == 'none' if (n_blocks + 1) <= n_disable_blocks else norm | |
def test_encoder_disable_norm(self): | |
n_residuals = [0, 1, 3] | |
disable_blocks = [0, 1, 2, 3, 4, 5, 6] | |
norms = ['weight_norm', 'none'] | |
for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms): | |
encoder = SEANetEncoder(n_residual_layers=n_res, norm=norm, | |
disable_norm_outer_blocks=disable_blocks) | |
self._check_encoder_blocks_norm(encoder, disable_blocks, norm) | |
def _check_decoder_blocks_norm(self, decoder: SEANetDecoder, n_disable_blocks: int, norm: str): | |
n_blocks = 0 | |
for layer in decoder.model: | |
if isinstance(layer, StreamableConv1d): | |
n_blocks += 1 | |
assert layer.conv.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm | |
elif isinstance(layer, StreamableConvTranspose1d): | |
n_blocks += 1 | |
assert layer.convtr.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm | |
elif isinstance(layer, SEANetResnetBlock): | |
for resnet_layer in layer.block: | |
if isinstance(resnet_layer, StreamableConv1d): | |
assert resnet_layer.conv.norm_type == 'none' \ | |
if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm | |
def test_decoder_disable_norm(self): | |
n_residuals = [0, 1, 3] | |
disable_blocks = [0, 1, 2, 3, 4, 5, 6] | |
norms = ['weight_norm', 'none'] | |
for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms): | |
decoder = SEANetDecoder(n_residual_layers=n_res, norm=norm, | |
disable_norm_outer_blocks=disable_blocks) | |
self._check_decoder_blocks_norm(decoder, disable_blocks, norm) | |
def test_disable_norm_raises_exception(self): | |
# Invalid disable_norm_outer_blocks values raise exceptions | |
with pytest.raises(AssertionError): | |
SEANetEncoder(disable_norm_outer_blocks=-1) | |
with pytest.raises(AssertionError): | |
SEANetEncoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7) | |
with pytest.raises(AssertionError): | |
SEANetDecoder(disable_norm_outer_blocks=-1) | |
with pytest.raises(AssertionError): | |
SEANetDecoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7) | |