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244 lines
7.5 KiB
244 lines
7.5 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import torch
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from paddle.device.cuda import synchronize
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from parallel_wavegan.layers import residual_block
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from parallel_wavegan.layers import upsample
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from parallel_wavegan.models import parallel_wavegan as pwgan
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from timer import timer
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from paddlespeech.t2s.models.parallel_wavegan import ConvInUpsampleNet
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from paddlespeech.t2s.models.parallel_wavegan import PWGDiscriminator
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from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
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from paddlespeech.t2s.models.parallel_wavegan import ResidualBlock
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from paddlespeech.t2s.models.parallel_wavegan import ResidualPWGDiscriminator
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from paddlespeech.t2s.utils.layer_tools import summary
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paddle.set_device("gpu:0")
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device = torch.device("cuda:0")
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def test_convin_upsample_net():
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net = ConvInUpsampleNet(
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[4, 4, 4, 4],
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"LeakyReLU", {"negative_slope": 0.2},
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freq_axis_kernel_size=3,
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aux_context_window=0)
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net2 = upsample.ConvInUpsampleNetwork(
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[4, 4, 4, 4],
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nonlinear_activation="LeakyReLU",
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nonlinear_activation_params={"negative_slope": 0.2},
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freq_axis_kernel_size=3,
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aux_context_window=0).to(device)
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summary(net)
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for k, v in net2.named_parameters():
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print(k, v.shape)
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net.state_dict()[k].set_value(v.data.cpu().numpy())
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c = paddle.randn([4, 80, 180])
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synchronize()
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with timer(unit='s') as t:
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out = net(c)
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synchronize()
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print(f"paddle conv_in_upsample_net forward takes {t.elapse}s.")
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with timer(unit='s') as t:
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out.sum().backward()
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synchronize()
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print(f"paddle conv_in_upsample_net backward takes {t.elapse}s.")
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c_torch = torch.as_tensor(c.numpy()).to(device)
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torch.cuda.synchronize()
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with timer(unit='s') as t:
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out2 = net2(c_torch)
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print(f"torch conv_in_upsample_net forward takes {t.elapse}s.")
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with timer(unit='s') as t:
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out2.sum().backward()
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print(f"torch conv_in_upsample_net backward takes {t.elapse}s.")
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print("forward check")
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print(out.numpy()[0])
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print(out2.data.cpu().numpy()[0])
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print("backward check")
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print(net.conv_in.weight.grad.numpy()[0])
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print(net2.conv_in.weight.grad.data.cpu().numpy()[0])
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def test_residual_block():
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net = ResidualBlock(dilation=9)
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net2 = residual_block.ResidualBlock(dilation=9)
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summary(net)
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summary(net2)
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for k, v in net2.named_parameters():
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net.state_dict()[k].set_value(v.data.cpu().numpy())
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x = paddle.randn([4, 64, 180])
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c = paddle.randn([4, 80, 180])
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res, skip = net(x, c)
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res2, skip2 = net2(torch.as_tensor(x.numpy()), torch.as_tensor(c.numpy()))
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print("forward:")
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print(res.numpy()[0])
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print(res2.data.cpu().numpy()[0])
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print(skip.numpy()[0])
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print(skip2.data.cpu().numpy()[0])
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(res.sum() + skip.sum()).backward()
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(res2.sum() + skip2.sum()).backward()
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print("backward:")
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print(net.conv.weight.grad.numpy().squeeze()[0])
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print(net2.conv.weight.grad.data.cpu().numpy().squeeze()[0])
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def test_pwg_generator():
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net = PWGGenerator(
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layers=9,
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stacks=3,
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upsample_scales=[4, 4, 4, 4],
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nonlinear_activation="LeakyReLU",
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nonlinear_activation_params={"negative_slope": 0.5},
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use_weight_norm=True)
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net2 = pwgan.ParallelWaveGANGenerator(
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layers=9,
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stacks=3,
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upsample_params={
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"upsample_scales": [4, 4, 4, 4],
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"nonlinear_activation": "LeakyReLU",
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"nonlinear_activation_params": {
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"negative_slope": 0.5
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}
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},
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use_weight_norm=True).to(device)
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summary(net)
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summary(net2)
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for k, v in net2.named_parameters():
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p = net.state_dict()[k]
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if k.endswith("_g"):
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p.set_value(v.data.cpu().numpy().reshape([-1]))
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else:
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p.set_value(v.data.cpu().numpy())
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x = paddle.randn([4, 1, 80 * 256])
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c = paddle.randn([4, 80, 80 + 4])
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synchronize()
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with timer(unit='s') as t:
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out = net(x, c)
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synchronize()
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print(f"paddle generator forward takes {t.elapse}s.")
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synchronize()
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with timer(unit='s') as t:
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out.sum().backward()
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synchronize()
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print(f"paddle generator backward takes {t.elapse}s.")
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x_torch = torch.as_tensor(x.numpy()).to(device)
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c_torch = torch.as_tensor(c.numpy()).to(device)
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torch.cuda.synchronize()
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with timer(unit='s') as t:
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out2 = net2(x_torch, c_torch)
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torch.cuda.synchronize()
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print(f"torch generator forward takes {t.elapse}s.")
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torch.cuda.synchronize()
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with timer(unit='s') as t:
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out2.sum().backward()
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torch.cuda.synchronize()
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print(f"torch generator backward takes {t.elapse}s.")
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print("test forward:")
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print(out.numpy()[0])
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print(out2.data.cpu().numpy()[0])
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print("test backward:")
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print("wv")
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print(net.first_conv.weight_v.grad.numpy().squeeze())
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print(net2.first_conv.weight_v.grad.data.cpu().numpy().squeeze())
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print("wg")
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print(net.first_conv.weight_g.grad.numpy().squeeze())
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print(net2.first_conv.weight_g.grad.data.cpu().numpy().squeeze())
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# print(out.shape)
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def test_pwg_discriminator():
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net = PWGDiscriminator()
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net2 = pwgan.ParallelWaveGANDiscriminator().to(device)
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summary(net)
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summary(net2)
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for k, v in net2.named_parameters():
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p = net.state_dict()[k]
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if k.endswith("_g"):
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p.set_value(v.data.cpu().numpy().reshape([-1]))
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else:
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p.set_value(v.data.cpu().numpy())
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x = paddle.randn([4, 1, 180 * 256])
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synchronize()
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with timer() as t:
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y = net(x)
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synchronize()
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print(f"forward takes {t.elapse}s.")
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synchronize()
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with timer() as t:
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y.sum().backward()
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synchronize()
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print(f"backward takes {t.elapse}s.")
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x_torch = torch.as_tensor(x.numpy()).to(device)
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torch.cuda.synchronize()
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with timer() as t:
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y2 = net2(x_torch)
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torch.cuda.synchronize()
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print(f"forward takes {t.elapse}s.")
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torch.cuda.synchronize()
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with timer() as t:
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y2.sum().backward()
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torch.cuda.synchronize()
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print(f"backward takes {t.elapse}s.")
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print("test forward:")
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print(y.numpy()[0])
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print(y2.data.cpu().numpy()[0])
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print("test backward:")
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print(net.conv_layers[0].weight_v.grad.numpy().squeeze())
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print(net2.conv_layers[0].weight_v.grad.data.cpu().numpy().squeeze())
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def test_residual_pwg_discriminator():
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net = ResidualPWGDiscriminator()
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net2 = pwgan.ResidualParallelWaveGANDiscriminator()
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summary(net)
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summary(net2)
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for k, v in net2.named_parameters():
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p = net.state_dict()[k]
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if k.endswith("_g"):
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p.set_value(v.data.cpu().numpy().reshape([-1]))
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else:
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p.set_value(v.data.cpu().numpy())
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x = paddle.randn([4, 1, 180 * 256])
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y = net(x)
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y2 = net2(torch.as_tensor(x.numpy()))
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print(y.numpy()[0])
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print(y2.data.cpu().numpy()[0])
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print(y.shape)
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