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add expanuser for preprocess, updater backward testing for pwg

This commit is contained in:
chenfeiyu 2021-06-25 01:01:30 +08:00
parent 542bbf6a81
commit 7e049a7744
2 changed files with 40 additions and 18 deletions
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4
examples/parallelwave_gan/baker/preprocess.py
View File

@ -232,8 +232,8 @@ def main():
print(vars(args))
print(C)
root_dir = Path(args.rootdir)
dumpdir = Path(args.dumpdir)
root_dir = Path(args.rootdir).expanduser()
dumpdir = Path(args.dumpdir).expanduser()
dumpdir.mkdir(parents=True, exist_ok=True)
wav_files = sorted(list((root_dir / "Wave").rglob("*.wav")))

54
tests/test_pwg.py
View File

@ -71,8 +71,8 @@ def test_convin_upsample_net():
print(out2.data.cpu().numpy()[0])
print("backward check")
print(net.conv_in.weight.numpy()[0])
print(net2.conv_in.weight.data.cpu().numpy()[0])
print(net.conv_in.weight.grad.numpy()[0])
print(net2.conv_in.weight.grad.data.cpu().numpy()[0])
def test_residual_block():
@ -87,23 +87,40 @@ def test_residual_block():
c = paddle.randn([4, 80, 180])
res, skip = net(x, c)
res2, skip2 = net2(torch.as_tensor(x.numpy()), torch.as_tensor(c.numpy()))
print("forward:")
print(res.numpy()[0])
print(res2.data.cpu().numpy()[0])
print(skip.numpy()[0])
print(skip2.data.cpu().numpy()[0])
(res.sum() + skip.sum()).backward()
(res2.sum() + skip2.sum()).backward()
print("backward:")
print(net.conv.weight.grad.numpy().squeeze()[0])
print(net2.conv.weight.grad.data.cpu().numpy().squeeze()[0])
def test_pwg_generator():
net = PWGGenerator(
layers=9,
stacks=3,
upsample_scales=[4, 4, 4, 4],
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2})
net2 = pwgan.ParallelWaveGANGenerator(upsample_params={
"upsample_scales": [4, 4, 4, 4],
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {
"negative_slope": 0.2
}
}).to(device)
nonlinear_activation_params={"negative_slope": 0.5},
use_weight_norm=True)
net2 = pwgan.ParallelWaveGANGenerator(
layers=9,
stacks=3,
upsample_params={
"upsample_scales": [4, 4, 4, 4],
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {
"negative_slope": 0.5
}
},
use_weight_norm=True).to(device)
summary(net)
summary(net2)
for k, v in net2.named_parameters():
@ -112,8 +129,8 @@ def test_pwg_generator():
p.set_value(v.data.cpu().numpy().reshape([-1]))
else:
p.set_value(v.data.cpu().numpy())
x = paddle.randn([4, 1, 180 * 256])
c = paddle.randn([4, 80, 180 + 4])
x = paddle.randn([4, 1, 80 * 256])
c = paddle.randn([4, 80, 80 + 4])
synchronize()
with timer(unit='s') as t:
@ -147,8 +164,13 @@ def test_pwg_generator():
print(out2.data.cpu().numpy()[0])
print("test backward:")
print(net.first_conv.weight.numpy()[0])
print(net2.first_conv.weight.data.cpu().numpy()[0])
print("wv")
print(net.first_conv.weight_v.grad.numpy().squeeze())
print(net2.first_conv.weight_v.grad.data.cpu().numpy().squeeze())
print("wg")
print(net.first_conv.weight_g.grad.numpy().squeeze())
print(net2.first_conv.weight_g.grad.data.cpu().numpy().squeeze())
# print(out.shape)
@ -195,8 +217,8 @@ def test_pwg_discriminator():
print(y2.data.cpu().numpy()[0])
print("test backward:")
print(net.conv_layers[0].weight.numpy()[0])
print(net2.conv_layers[0].weight.data.cpu().numpy()[0])
print(net.conv_layers[0].weight_v.grad.numpy().squeeze())
print(net2.conv_layers[0].weight_v.grad.data.cpu().numpy().squeeze())
def test_residual_pwg_discriminator():