def test_y_range():
"""Tests whether setting a y range works correctly"""
for _ in range(100):
val1 = random.random() - 3.0*random.random()
val2 = random.random() + 2.0*random.random()
lower_bound = min(val1, val2)
upper_bound = max(val1, val2)
CNN_instance = CNN(layers_info=[["conv", 2, 2, 1, 2], ["adaptivemaxpool", 2, 2], ["linear", 5]],
hidden_activations="relu", y_range=(lower_bound, upper_bound),
initialiser="xavier", input_dim=(1, 20, 20))
random_data = torch.randn((10, 1, 20, 20))
out = CNN_instance.forward(random_data)
assert torch.sum(out > lower_bound).item() == 10*5, "lower {} vs. {} ".format(lower_bound, out)
assert torch.sum(out < upper_bound).item() == 10*5, "upper {} vs. {} ".format(upper_bound, out)
def test_check_input_data_into_forward_once():
"""Tests that check_input_data_into_forward_once method only runs once"""
CNN_instance = CNN(layers_info=[["conv", 2, 2, 1, 2], ["adaptivemaxpool", 2, 2], ["linear", 6]],
hidden_activations="relu", input_dim=(4, 2, 5),
output_activation="relu", initialiser="xavier")
data_not_to_throw_error = torch.randn((1, 4, 2, 5))
data_to_throw_error = torch.randn((1, 2, 20, 20))
with pytest.raises(AssertionError):
CNN_instance.forward(data_to_throw_error)
with pytest.raises(RuntimeError):
CNN_instance.forward(data_not_to_throw_error)
CNN_instance.forward(data_to_throw_error)
def test_output_activation():
"""Tests whether network outputs data that has gone through correct activation function"""
RANDOM_ITERATIONS = 20
input_dim = (5, 100, 100)
for _ in range(RANDOM_ITERATIONS):
data = torch.randn((1, *input_dim))
CNN_instance = CNN(layers_info=[["conv", 2, 2, 1, 2], ["adaptivemaxpool", 2, 2], ["linear", 50]],
hidden_activations="relu", input_dim=input_dim,
output_activation="relu", initialiser="xavier")
out = CNN_instance.forward(data)
assert all(out.squeeze() >= 0)
CNN_instance = CNN(layers_info=[["conv", 2, 20, 1, 0], ["linear", 5]],
hidden_activations="relu", input_dim=input_dim,
output_activation="relu", initialiser="xavier")
out = CNN_instance.forward(data)
assert all(out.squeeze() >= 0)
CNN_instance = CNN(layers_info=[["conv", 5, 20, 1, 0], ["linear", 5]],
hidden_activations="relu", input_dim=input_dim,
output_activation="relu", initialiser="xavier")
out = CNN_instance.forward(data)
assert all(out.squeeze() >= 0)
CNN_instance = CNN(layers_info=[["conv", 5, 20, 1, 0], ["linear", 22]],
hidden_activations="relu", input_dim=input_dim,
output_activation="sigmoid", initialiser="xavier")
out = CNN_instance.forward(data)
assert all(out.squeeze() >= 0)
assert all(out.squeeze() <= 1)
assert round(torch.sum(out.squeeze()).item(), 3) != 1.0
CNN_instance = CNN(layers_info=[["conv", 2, 2, 1, 2], ["adaptivemaxpool", 2, 2], ["linear", 5]],
hidden_activations="relu", input_dim=input_dim,
output_activation="softmax", initialiser="xavier")
out = CNN_instance.forward(data)
assert all(out.squeeze() >= 0)
assert all(out.squeeze() <= 1)
assert round(torch.sum(out.squeeze()).item(), 3) == 1.0
CNN_instance = CNN(layers_info=[["conv", 2, 2, 1, 2], ["adaptivemaxpool", 2, 2], ["linear", 5]],
hidden_activations="relu", input_dim=input_dim,
initialiser="xavier")
out = CNN_instance.forward(data)
assert not all(out.squeeze() >= 0)
assert not round(torch.sum(out.squeeze()).item(), 3) == 1.0