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Add random.seed() #419

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merged 3 commits into from
Jul 13, 2023
Merged

Add random.seed() #419

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4a6add6
Add random.seed
yygf123 Jul 13, 2023
c4dff14
update tests
yygf123 Jul 13, 2023
ab74681
Merge branch 'master' of https://github.com/brainpy/BrainPy
yygf123 Jul 13, 2023
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4 changes: 2 additions & 2 deletions .github/workflows/CI.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -169,8 +169,8 @@ jobs:
python -m pip install --upgrade pip
python -m pip install flake8 pytest
python -m pip install numpy>=1.21.0
python -m pip install "jaxlib==0.4.10" -f https://whls.blob.core.windows.net/unstable/index.html --use-deprecated legacy-resolver
python -m pip install jax==0.4.10
python -m pip install "jaxlib==0.4.11" -f https://whls.blob.core.windows.net/unstable/index.html --use-deprecated legacy-resolver
python -m pip install jax==0.4.11
python -m pip install -r requirements-dev.txt
python -m pip install tqdm brainpylib
pip uninstall brainpy -y
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27 changes: 27 additions & 0 deletions brainpy/_src/dnn/tests/test_activation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@ def test_Threshold(self, inplace):
inplace=[True, False]
)
def test_ReLU(self, inplace):
bm.random.seed()
ReLU_layer = bp.dnn.ReLU(inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -33,6 +34,7 @@ def test_ReLU(self, inplace):
inplace=[True, False]
)
def test_RReLU(self, inplace):
bm.random.seed()
RReLU_layer = bp.dnn.RReLU(lower=0, upper=1, inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -44,6 +46,7 @@ def test_RReLU(self, inplace):
inplace=[True, False]
)
def test_Hardtanh(self, inplace):
bm.random.seed()
Hardtanh_layer = bp.dnn.Hardtanh(min_val=0, max_val=1, inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -55,6 +58,7 @@ def test_Hardtanh(self, inplace):
inplace=[True, False]
)
def test_ReLU6(self, inplace):
bm.random.seed()
ReLU6_layer = bp.dnn.ReLU6(inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -63,6 +67,7 @@ def test_ReLU6(self, inplace):
output = ReLU6_layer(input)

def test_Sigmoid(self):
bm.random.seed()
Sigmoid_layer = bp.dnn.Sigmoid()
input = bm.random.randn(2)
output = Sigmoid_layer(input)
Expand All @@ -71,6 +76,7 @@ def test_Sigmoid(self):
inplace=[True, False]
)
def test_Hardsigmoid(self, inplace):
bm.random.seed()
Hardsigmoid_layer = bp.dnn.Hardsigmoid(inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -79,6 +85,7 @@ def test_Hardsigmoid(self, inplace):
output = Hardsigmoid_layer(input)

def test_Tanh(self):
bm.random.seed()
Tanh_layer = bp.dnn.Tanh()
input = bm.random.randn(2)
output = Tanh_layer(input)
Expand All @@ -87,6 +94,7 @@ def test_Tanh(self):
inplace=[True, False]
)
def test_SiLU(self, inplace):
bm.random.seed()
SiLU_layer = bp.dnn.SiLU(inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -98,6 +106,7 @@ def test_SiLU(self, inplace):
inplace=[True, False]
)
def test_Mish(self, inplace):
bm.random.seed()
Mish_layer = bp.dnn.Mish(inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -109,6 +118,7 @@ def test_Mish(self, inplace):
inplace=[True, False]
)
def test_Hardswish(self, inplace):
bm.random.seed()
Hardswish_layer = bp.dnn.Hardswish(inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -120,6 +130,7 @@ def test_Hardswish(self, inplace):
inplace=[True, False]
)
def test_ELU(self, inplace):
bm.random.seed()
ELU_layer = bp.dnn.ELU(alpha=0.5, inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -131,6 +142,7 @@ def test_ELU(self, inplace):
inplace=[True, False]
)
def test_CELU(self, inplace):
bm.random.seed()
CELU_layer = bp.dnn.CELU(alpha=0.5, inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -142,6 +154,7 @@ def test_CELU(self, inplace):
inplace=[True, False]
)
def test_SELU(self, inplace):
bm.random.seed()
SELU_layer = bp.dnn.SELU(inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -150,6 +163,7 @@ def test_SELU(self, inplace):
output = SELU_layer(input)

def test_GLU(self):
bm.random.seed()
GLU_layer = bp.dnn.GLU()
input = bm.random.randn(4, 2)
output = GLU_layer(input)
Expand All @@ -158,11 +172,13 @@ def test_GLU(self):
approximate=['tanh', 'none']
)
def test_GELU(self, approximate):
bm.random.seed()
GELU_layer = bp.dnn.GELU()
input = bm.random.randn(2)
output = GELU_layer(input)

def test_Hardshrink(self):
bm.random.seed()
Hardshrink_layer = bp.dnn.Hardshrink(lambd=1)
input = bm.random.randn(2)
output = Hardshrink_layer(input)
Expand All @@ -171,6 +187,7 @@ def test_Hardshrink(self):
inplace=[True, False]
)
def test_LeakyReLU(self, inplace):
bm.random.seed()
LeakyReLU_layer = bp.dnn.LeakyReLU(inplace=inplace)
input = bm.random.randn(2)
if inplace == True:
Expand All @@ -179,6 +196,7 @@ def test_LeakyReLU(self, inplace):
output = LeakyReLU_layer(input)

def test_LogSigmoid(self):
bm.random.seed()
LogSigmoid_layer = bp.dnn.LogSigmoid()
input = bm.random.randn(2)
output = LogSigmoid_layer(input)
Expand All @@ -188,46 +206,55 @@ def test_LogSigmoid(self):
threshold=[20, 21, 22]
)
def test_Softplus(self, beta, threshold):
bm.random.seed()
Softplus_layer = bp.dnn.Softplus(beta=beta, threshold=threshold)
input = bm.random.randn(2)
output = Softplus_layer(input)

def test_Softshrink(self):
bm.random.seed()
Softshrink_layer = bp.dnn.Softshrink(lambd=1)
input = bm.random.randn(2)
output = Softshrink_layer(input)

def test_PReLU(self):
bm.random.seed()
PReLU_layer = bp.dnn.PReLU(num_parameters=2, init=0.5)
input = bm.random.randn(2)
output = PReLU_layer(input)

def test_Softsign(self):
bm.random.seed()
Softsign_layer = bp.dnn.Softsign()
input = bm.random.randn(2)
output = Softsign_layer(input)

def test_Tanhshrink(self):
bm.random.seed()
Tanhshrink_layer = bp.dnn.Tanhshrink()
input = bm.random.randn(2)
output = Tanhshrink_layer(input)

def test_Softmin(self):
bm.random.seed()
Softmin_layer = bp.dnn.Softmin(dim=2)
input = bm.random.randn(2, 3, 4)
output = Softmin_layer(input)

def test_Softmax(self):
bm.random.seed()
Softmax_layer = bp.dnn.Softmax(dim=2)
input = bm.random.randn(2, 3, 4)
output = Softmax_layer(input)

def test_Softmax2d(self):
bm.random.seed()
Softmax2d_layer = bp.dnn.Softmax2d()
input = bm.random.randn(2, 3, 12, 13)
output = Softmax2d_layer(input)

def test_LogSoftmax(self):
bm.random.seed()
LogSoftmax_layer = bp.dnn.LogSoftmax(dim=2)
input = bm.random.randn(2, 3, 4)
output = LogSoftmax_layer(input)
Expand Down
23 changes: 18 additions & 5 deletions brainpy/_src/dnn/tests/test_conv_layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,12 +4,13 @@
from absl.testing import absltest
import jax.numpy as jnp
import brainpy.math as bm

from absl.testing import parameterized
import brainpy as bp


class TestConv(bp.testing.UnitTestCase):
class TestConv(parameterized.TestCase):
def test_Conv2D_img(self):
bm.random.seed()
img = jnp.zeros((2, 200, 198, 4))
for k in range(4):
x = 30 + 60 * k
Expand All @@ -28,6 +29,7 @@ def test_Conv2D_img(self):
# plt.show()

def test_conv1D(self):
bm.random.seed()
with bp.math.training_environment():
model = bp.layers.Conv1d(in_channels=3, out_channels=32, kernel_size=(3,))

Expand All @@ -41,6 +43,7 @@ def test_conv1D(self):
# plt.show()

def test_conv2D(self):
bm.random.seed()
with bp.math.training_environment():
model = bp.layers.Conv2d(in_channels=3, out_channels=32, kernel_size=(3, 3))

Expand All @@ -54,15 +57,17 @@ def test_conv2D(self):
# plt.show()

def test_conv3D(self):
bm.random.seed()
with bp.math.training_environment():
model = bp.layers.Conv3d(in_channels=3, out_channels=32, kernel_size=(3, 3, 3))
input = bp.math.ones((2, 5, 5, 5, 3))
out = model(input)
print("out shape: ", out.shape)


class TestConvTranspose1d(bp.testing.UnitTestCase):
class TestConvTranspose1d(parameterized.TestCase):
def test_conv_transpose(self):
bm.random.seed()
x = bm.ones((1, 8, 3))
for use_bias in [True, False]:
conv_transpose_module = bp.layers.ConvTranspose1d(
Expand Down Expand Up @@ -92,6 +97,7 @@ def test_conv_transpose(self):
self.assertTrue(bm.allclose(y, correct_ans))

def test_single_input_masked_conv_transpose(self):
bm.random.seed()
x = jnp.ones((1, 8, 3))
m = jnp.tril(jnp.ones((3, 3, 4)))
conv_transpose_module = bp.layers.ConvTranspose1d(
Expand Down Expand Up @@ -120,6 +126,7 @@ def test_single_input_masked_conv_transpose(self):
self.assertTrue(bm.allclose(y, correct_ans))

def test_computation_padding_same(self):
bm.random.seed()
data = jnp.ones([1, 3, 1])
for use_bias in [True, False]:
net = bp.layers.ConvTranspose1d(
Expand All @@ -141,8 +148,9 @@ def test_computation_padding_same(self):
self.assertTrue(bm.allclose(out, expected_out, rtol=1e-5))


class TestConvTranspose2d(bp.testing.UnitTestCase):
class TestConvTranspose2d(parameterized.TestCase):
def test_conv_transpose(self):
bm.random.seed()
x = bm.ones((1, 8, 8, 3))
for use_bias in [True, False]:
conv_transpose_module = bp.layers.ConvTranspose2d(
Expand All @@ -159,6 +167,7 @@ def test_conv_transpose(self):
print(y.shape)

def test_single_input_masked_conv_transpose(self):
bm.random.seed()
x = jnp.ones((1, 8, 8, 3))
m = jnp.tril(jnp.ones((3, 3, 3, 4)))
conv_transpose_module = bp.layers.ConvTranspose2d(
Expand All @@ -174,6 +183,7 @@ def test_single_input_masked_conv_transpose(self):
print(y.shape)

def test_computation_padding_same(self):
bm.random.seed()
x = bm.ones((1, 8, 8, 3))
for use_bias in [True, False]:
conv_transpose_module = bp.layers.ConvTranspose2d(
Expand All @@ -191,8 +201,9 @@ def test_computation_padding_same(self):
print(y.shape)


class TestConvTranspose3d(bp.testing.UnitTestCase):
class TestConvTranspose3d(parameterized.TestCase):
def test_conv_transpose(self):
bm.random.seed()
x = bm.ones((1, 8, 8, 8, 3))
for use_bias in [True, False]:
conv_transpose_module = bp.layers.ConvTranspose3d(
Expand All @@ -208,6 +219,7 @@ def test_conv_transpose(self):
print(y.shape)

def test_single_input_masked_conv_transpose(self):
bm.random.seed()
x = jnp.ones((1, 8, 8, 8, 3))
m = jnp.tril(jnp.ones((3, 3, 3, 3, 4)))
conv_transpose_module = bp.layers.ConvTranspose3d(
Expand All @@ -223,6 +235,7 @@ def test_single_input_masked_conv_transpose(self):
print(y.shape)

def test_computation_padding_same(self):
bm.random.seed()
x = bm.ones((1, 8, 8, 8, 3))
for use_bias in [True, False]:
conv_transpose_module = bp.layers.ConvTranspose3d(
Expand Down
5 changes: 4 additions & 1 deletion brainpy/_src/dnn/tests/test_function.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,12 +5,14 @@
import jax.numpy as jnp
import brainpy.math as bm
from absl.testing import absltest
from absl.testing import parameterized
import brainpy as bp


class TestFunction(bp.testing.UnitTestCase):
class TestFunction(parameterized.TestCase):

def test_flatten_batching_mode(self):
bm.random.seed()
layer = bp.dnn.Flatten(mode=bm.BatchingMode())
input = bm.random.randn(20, 10, 10, 6)

Expand All @@ -20,6 +22,7 @@ def test_flatten_batching_mode(self):
self.assertEqual(output.shape, expected_shape)

def test_flatten_non_batching_mode(self):
bm.random.seed()
layer = bp.dnn.Flatten(mode=bm.NonBatchingMode())
input = bm.random.randn(10, 10, 6)

Expand Down
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