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executable file
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import torch
import torch.nn
def test_BatchNorm1d(device):
from speechbrain.nnet.normalization import BatchNorm1d
input = torch.randn(100, 10, device=device) + 2.0
norm = BatchNorm1d(input_shape=input.shape).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=0).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=0).mean()
assert torch.abs(1.0 - current_std) < 0.01
input = torch.randn(100, 20, 10, device=device) + 2.0
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=0).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=0).mean()
assert torch.abs(1.0 - current_std) < 0.01
# Test with combined dimensions
input = torch.randn(100, 10, 20, device=device) + 2.0
norm = BatchNorm1d(input_shape=input.shape, combine_batch_time=True).to(
device
)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=0).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=0).mean()
assert torch.abs(1.0 - current_std) < 0.01
input = torch.randn(100, 40, 20, 30, device=device) + 2.0
norm = BatchNorm1d(input_shape=input.shape, combine_batch_time=True).to(
device
)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=0).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=0).mean()
assert torch.abs(1.0 - current_std) < 0.01
assert torch.jit.trace(norm, input)
def test_BatchNorm2d(device):
from speechbrain.nnet.normalization import BatchNorm2d
input = torch.randn(100, 10, 4, 20, device=device) + 2.0
norm = BatchNorm2d(input_shape=input.shape).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=0).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=0).mean()
assert torch.abs(1.0 - current_std) < 0.01
assert torch.jit.trace(norm, input)
def test_LayerNorm(device):
from speechbrain.nnet.normalization import LayerNorm
input = torch.randn(4, 101, 256, device=device) + 2.0
norm = LayerNorm(input_shape=input.shape).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=2).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=2).mean()
assert torch.abs(1.0 - current_std) < 0.01
input = torch.randn(100, 101, 16, 32, device=device) + 2.0
norm = LayerNorm(input_shape=input.shape).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=[2, 3]).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=[2, 3]).mean()
assert torch.abs(1.0 - current_std) < 0.01
assert torch.jit.trace(norm, input)
def test_InstanceNorm1d(device):
from speechbrain.nnet.normalization import InstanceNorm1d
input = torch.randn(100, 10, 128, device=device) + 2.0
norm = InstanceNorm1d(input_shape=input.shape).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=2).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=2).mean()
assert torch.abs(1.0 - current_std) < 0.01
assert torch.jit.trace(norm, input)
def test_InstanceNorm2d(device):
from speechbrain.nnet.normalization import InstanceNorm2d
input = torch.randn(100, 10, 20, 2, device=device) + 2.0
norm = InstanceNorm2d(input_shape=input.shape).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=[2, 3]).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=[2, 3]).mean()
assert torch.abs(1.0 - current_std) < 0.01
assert torch.jit.trace(norm, input)
def test_GroupNorm(device):
from speechbrain.nnet.normalization import GroupNorm
input = torch.randn(4, 101, 256, device=device) + 2.0
norm = GroupNorm(input_shape=input.shape, num_groups=256).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=2).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=2).mean()
assert torch.abs(1.0 - current_std) < 0.01
input = torch.randn(4, 101, 256, device=device) + 2.0
norm = GroupNorm(input_shape=input.shape, num_groups=128).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=2).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=2).mean()
assert torch.abs(1.0 - current_std) < 0.01
input = torch.randn(100, 101, 16, 32, device=device) + 2.0
norm = GroupNorm(input_shape=input.shape, num_groups=32).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=3).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=3).mean()
assert torch.abs(1.0 - current_std) < 0.01
input = torch.randn(100, 101, 16, 32, device=device) + 2.0
norm = GroupNorm(input_shape=input.shape, num_groups=8).to(device)
output = norm(input)
assert input.shape == output.shape
current_mean = output.mean(dim=3).mean()
assert torch.abs(current_mean) < 1e-06
current_std = output.std(dim=3).mean()
assert torch.abs(1.0 - current_std) < 0.01
assert torch.jit.trace(norm, input)