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Copy pathtest_activation.py
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executable file
·46 lines (42 loc) · 1.74 KB
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import pytest
import torch
from ding.torch_utils import build_activation
@pytest.mark.unittest
class TestActivation:
def test(self):
act_type = 'relu'
act = build_activation(act_type, inplace=True)
act_type = 'prelu'
act = build_activation(act_type)
with pytest.raises(AssertionError):
act = build_activation(act_type, inplace=True)
with pytest.raises(KeyError):
act = build_activation('xxxlu')
act_type = 'glu'
input_dim = 50
output_dim = 150
context_dim = 200
act = build_activation(act_type
)(input_dim=input_dim, output_dim=output_dim, context_dim=context_dim, input_type='fc')
batch_size = 10
inputs = torch.rand(batch_size, input_dim).requires_grad_(True)
context = torch.rand(batch_size, context_dim).requires_grad_(True)
output = act(inputs, context)
assert output.shape == (batch_size, output_dim)
assert act.layer1.weight.grad is None
loss = output.mean()
loss.backward()
assert isinstance(inputs.grad, torch.Tensor)
assert isinstance(act.layer1.weight.grad, torch.Tensor)
act = build_activation(act_type)(
input_dim=input_dim, output_dim=output_dim, context_dim=context_dim, input_type='conv2d'
)
size = 16
inputs = torch.rand(batch_size, input_dim, size, size)
context = torch.rand(batch_size, context_dim, size, size)
output = act(inputs, context)
assert output.shape == (batch_size, output_dim, size, size)
assert act.layer1.weight.grad is None
loss = output.mean()
loss.backward()
assert isinstance(act.layer1.weight.grad, torch.Tensor)