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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 math | |
| import random | |
| import pytest | |
| import torch | |
| from torch import nn | |
| from audiocraft.modules import ( | |
| NormConv1d, | |
| NormConvTranspose1d, | |
| StreamableConv1d, | |
| StreamableConvTranspose1d, | |
| pad1d, | |
| unpad1d, | |
| ) | |
| def test_get_extra_padding_for_conv1d(): | |
| # TODO: Implement me! | |
| pass | |
| def test_pad1d_zeros(): | |
| x = torch.randn(1, 1, 20) | |
| xp1 = pad1d(x, (0, 5), mode='constant', value=0.) | |
| assert xp1.shape[-1] == 25 | |
| xp2 = pad1d(x, (5, 5), mode='constant', value=0.) | |
| assert xp2.shape[-1] == 30 | |
| xp3 = pad1d(x, (0, 0), mode='constant', value=0.) | |
| assert xp3.shape[-1] == 20 | |
| xp4 = pad1d(x, (10, 30), mode='constant', value=0.) | |
| assert xp4.shape[-1] == 60 | |
| with pytest.raises(AssertionError): | |
| pad1d(x, (-1, 0), mode='constant', value=0.) | |
| with pytest.raises(AssertionError): | |
| pad1d(x, (0, -1), mode='constant', value=0.) | |
| with pytest.raises(AssertionError): | |
| pad1d(x, (-1, -1), mode='constant', value=0.) | |
| def test_pad1d_reflect(): | |
| x = torch.randn(1, 1, 20) | |
| xp1 = pad1d(x, (0, 5), mode='reflect', value=0.) | |
| assert xp1.shape[-1] == 25 | |
| xp2 = pad1d(x, (5, 5), mode='reflect', value=0.) | |
| assert xp2.shape[-1] == 30 | |
| xp3 = pad1d(x, (0, 0), mode='reflect', value=0.) | |
| assert xp3.shape[-1] == 20 | |
| xp4 = pad1d(x, (10, 30), mode='reflect', value=0.) | |
| assert xp4.shape[-1] == 60 | |
| with pytest.raises(AssertionError): | |
| pad1d(x, (-1, 0), mode='reflect', value=0.) | |
| with pytest.raises(AssertionError): | |
| pad1d(x, (0, -1), mode='reflect', value=0.) | |
| with pytest.raises(AssertionError): | |
| pad1d(x, (-1, -1), mode='reflect', value=0.) | |
| def test_unpad1d(): | |
| x = torch.randn(1, 1, 20) | |
| u1 = unpad1d(x, (5, 5)) | |
| assert u1.shape[-1] == 10 | |
| u2 = unpad1d(x, (0, 5)) | |
| assert u2.shape[-1] == 15 | |
| u3 = unpad1d(x, (5, 0)) | |
| assert u3.shape[-1] == 15 | |
| u4 = unpad1d(x, (0, 0)) | |
| assert u4.shape[-1] == x.shape[-1] | |
| with pytest.raises(AssertionError): | |
| unpad1d(x, (-1, 0)) | |
| with pytest.raises(AssertionError): | |
| unpad1d(x, (0, -1)) | |
| with pytest.raises(AssertionError): | |
| unpad1d(x, (-1, -1)) | |
| class TestNormConv1d: | |
| def test_norm_conv1d_modules(self): | |
| N, C, T = 2, 2, random.randrange(1, 100_000) | |
| t0 = torch.randn(N, C, T) | |
| C_out, kernel_size, stride = 1, 4, 1 | |
| expected_out_length = int((T - kernel_size) / stride + 1) | |
| wn_conv = NormConv1d(C, 1, kernel_size=4, norm='weight_norm') | |
| gn_conv = NormConv1d(C, 1, kernel_size=4, norm='time_group_norm') | |
| nn_conv = NormConv1d(C, 1, kernel_size=4, norm='none') | |
| assert isinstance(wn_conv.norm, nn.Identity) | |
| assert isinstance(wn_conv.conv, nn.Conv1d) | |
| assert isinstance(gn_conv.norm, nn.GroupNorm) | |
| assert isinstance(gn_conv.conv, nn.Conv1d) | |
| assert isinstance(nn_conv.norm, nn.Identity) | |
| assert isinstance(nn_conv.conv, nn.Conv1d) | |
| for conv_layer in [wn_conv, gn_conv, nn_conv]: | |
| out = conv_layer(t0) | |
| assert isinstance(out, torch.Tensor) | |
| assert list(out.shape) == [N, C_out, expected_out_length] | |
| class TestNormConvTranspose1d: | |
| def test_normalizations(self): | |
| N, C, T = 2, 2, random.randrange(1, 100_000) | |
| t0 = torch.randn(N, C, T) | |
| C_out, kernel_size, stride = 1, 4, 1 | |
| expected_out_length = (T - 1) * stride + (kernel_size - 1) + 1 | |
| wn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='weight_norm') | |
| gn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='time_group_norm') | |
| nn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='none') | |
| assert isinstance(wn_convtr.norm, nn.Identity) | |
| assert isinstance(wn_convtr.convtr, nn.ConvTranspose1d) | |
| assert isinstance(gn_convtr.norm, nn.GroupNorm) | |
| assert isinstance(gn_convtr.convtr, nn.ConvTranspose1d) | |
| assert isinstance(nn_convtr.norm, nn.Identity) | |
| assert isinstance(nn_convtr.convtr, nn.ConvTranspose1d) | |
| for convtr_layer in [wn_convtr, gn_convtr, nn_convtr]: | |
| out = convtr_layer(t0) | |
| assert isinstance(out, torch.Tensor) | |
| assert list(out.shape) == [N, C_out, expected_out_length] | |
| class TestStreamableConv1d: | |
| def get_streamable_conv1d_output_length(self, length, kernel_size, stride, dilation): | |
| # StreamableConv1d internally pads to make sure that the last window is full | |
| padding_total = (kernel_size - 1) * dilation - (stride - 1) | |
| n_frames = (length - kernel_size + padding_total) / stride + 1 | |
| ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total) | |
| return ideal_length // stride | |
| def test_streamable_conv1d(self): | |
| N, C, T = 2, 2, random.randrange(1, 100_000) | |
| t0 = torch.randn(N, C, T) | |
| C_out = 1 | |
| # conv params are [(kernel_size, stride, dilation)] | |
| conv_params = [(4, 1, 1), (4, 2, 1), (3, 1, 3), (10, 5, 1), (3, 2, 3)] | |
| for causal, (kernel_size, stride, dilation) in product([False, True], conv_params): | |
| expected_out_length = self.get_streamable_conv1d_output_length(T, kernel_size, stride, dilation) | |
| sconv = StreamableConv1d(C, C_out, kernel_size=kernel_size, stride=stride, dilation=dilation, causal=causal) | |
| out = sconv(t0) | |
| assert isinstance(out, torch.Tensor) | |
| print(list(out.shape), [N, C_out, expected_out_length]) | |
| assert list(out.shape) == [N, C_out, expected_out_length] | |
| class TestStreamableConvTranspose1d: | |
| def get_streamable_convtr1d_output_length(self, length, kernel_size, stride): | |
| padding_total = (kernel_size - stride) | |
| return (length - 1) * stride - padding_total + (kernel_size - 1) + 1 | |
| def test_streamable_convtr1d(self): | |
| N, C, T = 2, 2, random.randrange(1, 100_000) | |
| t0 = torch.randn(N, C, T) | |
| C_out = 1 | |
| with pytest.raises(AssertionError): | |
| StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=False, trim_right_ratio=0.5) | |
| StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=-1.) | |
| StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=2) | |
| # causal params are [(causal, trim_right)] | |
| causal_params = [(False, 1.0), (True, 1.0), (True, 0.5), (True, 0.0)] | |
| # conv params are [(kernel_size, stride)] | |
| conv_params = [(4, 1), (4, 2), (3, 1), (10, 5)] | |
| for ((causal, trim_right_ratio), (kernel_size, stride)) in product(causal_params, conv_params): | |
| expected_out_length = self.get_streamable_convtr1d_output_length(T, kernel_size, stride) | |
| sconvtr = StreamableConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, | |
| causal=causal, trim_right_ratio=trim_right_ratio) | |
| out = sconvtr(t0) | |
| assert isinstance(out, torch.Tensor) | |
| assert list(out.shape) == [N, C_out, expected_out_length] | |