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add models and layers backward test

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This commit is contained in:
cyberguli
2024-02-19 23:09:10 +01:00
committed by Nicola Demo
parent cbb43a5392
commit eb1af0b50e
10 changed files with 308 additions and 1 deletions
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33
tests/test_layers/test_conv.py
View File

@@ -106,6 +106,39 @@ def test_forward():
conv(x)
def test_backward():
model = MLP
x = torch.rand(dim_input)
x = make_grid(x)
x.requires_grad = True
# simple backward
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model)
conv(x)
l=torch.mean(conv(x))
l.backward()
assert x._grad.shape == torch.Size([2, 2, 20, 3])
x = torch.rand(dim_input)
x = make_grid(x)
x.requires_grad = True
# simple backward with optimization
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model,
optimize=True)
conv(x)
l=torch.mean(conv(x))
l.backward()
assert x._grad.shape == torch.Size([2, 2, 20, 3])
def test_transpose():
model = MLP

36
tests/test_layers/test_fourier.py
View File

@@ -20,6 +20,18 @@ def test_forward_1d():
sconv(x)
def test_backward_1d():
sconv = FourierBlock1D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=4)
x = torch.rand(batch, input_numb_fields, 10)
x.requires_grad = True
sconv(x)
l = torch.mean(sconv(x))
l.backward()
assert x._grad.shape == torch.Size([5, 3, 10])
def test_constructor_2d():
FourierBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
@@ -34,6 +46,18 @@ def test_forward_2d():
sconv(x)
def test_backward_2d():
sconv = FourierBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4])
x = torch.rand(batch, input_numb_fields, 10, 10)
x.requires_grad = True
sconv(x)
l = torch.mean(sconv(x))
l.backward()
assert x._grad.shape == torch.Size([5, 3, 10, 10])
def test_constructor_3d():
FourierBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
@@ -46,3 +70,15 @@ def test_forward_3d():
n_modes=[5, 4, 4])
x = torch.rand(batch, input_numb_fields, 10, 10, 10)
sconv(x)
def test_backward_3d():
sconv = FourierBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4])
x = torch.rand(batch, input_numb_fields, 10, 10, 10)
x.requires_grad = True
sconv(x)
l = torch.mean(sconv(x))
l.backward()
assert x._grad.shape == torch.Size([5, 3, 10, 10, 10])

35
tests/test_layers/test_residual.py
View File

@@ -22,6 +22,17 @@ def test_forward_residual_block():
assert y.shape[1] == 3
assert y.shape[0] == x.shape[0]
def test_backward_residual_block():
res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4)
x = torch.rand(size=(80, 10))
x.requires_grad = True
y = res_block(x)
l = torch.mean(y)
l.backward()
assert x._grad.shape == torch.Size([80,10])
def test_constructor_no_activation_no_dropout():
linear_layer = nn.Linear(10, 20)
enhanced_linear = EnhancedLinear(linear_layer)
@@ -59,6 +70,17 @@ def test_forward_enhanced_linear_no_dropout():
assert y.shape[1] == 3
assert y.shape[0] == x.shape[0]
def test_backward_enhanced_linear_no_dropout():
enhanced_linear = EnhancedLinear(nn.Linear(10, 3))
x = torch.rand(size=(80, 10))
x.requires_grad = True
y = enhanced_linear(x)
l = torch.mean(y)
l.backward()
assert x._grad.shape == torch.Size([80, 10])
def test_forward_enhanced_linear_dropout():
enhanced_linear = EnhancedLinear(nn.Linear(10, 3), dropout=0.5)
@@ -66,4 +88,15 @@ def test_forward_enhanced_linear_dropout():
x = torch.rand(size=(80, 10))
y = enhanced_linear(x)
assert y.shape[1] == 3
assert y.shape[0] == x.shape[0]
assert y.shape[0] == x.shape[0]
def test_backward_enhanced_linear_dropout():
enhanced_linear = EnhancedLinear(nn.Linear(10, 3), dropout=0.5)
x = torch.rand(size=(80, 10))
x.requires_grad = True
y = enhanced_linear(x)
l = torch.mean(y)
l.backward()
assert x._grad.shape == torch.Size([80, 10])

36
tests/test_layers/test_spectral_conv.py
View File

@@ -20,6 +20,18 @@ def test_forward_1d():
sconv(x)
def test_backward_1d():
sconv = SpectralConvBlock1D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=4)
x = torch.rand(batch, input_numb_fields, 10)
x.requires_grad = True
sconv(x)
l=torch.mean(sconv(x))
l.backward()
assert x._grad.shape == torch.Size([5,3,10])
def test_constructor_2d():
SpectralConvBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
@@ -34,6 +46,18 @@ def test_forward_2d():
sconv(x)
def test_backward_2d():
sconv = SpectralConvBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4])
x = torch.rand(batch, input_numb_fields, 10, 10)
x.requires_grad = True
sconv(x)
l=torch.mean(sconv(x))
l.backward()
assert x._grad.shape == torch.Size([5,3,10,10])
def test_constructor_3d():
SpectralConvBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
@@ -46,3 +70,15 @@ def test_forward_3d():
n_modes=[5, 4, 4])
x = torch.rand(batch, input_numb_fields, 10, 10, 10)
sconv(x)
def test_backward_3d():
sconv = SpectralConvBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4])
x = torch.rand(batch, input_numb_fields, 10, 10, 10)
x.requires_grad = True
sconv(x)
l=torch.mean(sconv(x))
l.backward()
assert x._grad.shape == torch.Size([5,3,10,10,10])