def test_layer_normalize(node, use_gpu):
node = Variable(node * 50)
assert_cuda_active(use_gpu)
layer = LayerNormalize()
layer2 = Dense(4)
layer3 = Conv2d(channel=3)
def func(node):
ret = layer(node)
if len(ret.shape) > 2:
return sum(layer3(ret))
else:
return sum(layer2(ret))
a = 1e-5
r = 1e-3
if use_gpu:
a = 1e-2
r = 1e-3
for trial in range(3):
try:
compare(func, node, node, atol=a, rtol=r)
compare(func, layer.params["gain"], node)
compare(func, layer.params["bias"], node)
return
except:
node = Variable(rand(node.shape))
assert False
def test_max_unpoolnd(node, use_gpu):
assert_cuda_active(use_gpu)
node = Variable(node)
l0 = MaxPoolNd(kernel=2, padding=1, stride=1)
l1 = MaxUnPoolNd()
l2 = Dense(2)
np.set_printoptions(suppress=True)
def func(node):
ret = node
reta = l0(node)
ret = l1(reta, reta)
ret = l2(ret.reshape(ret.shape[0], -1))
ret = sum(ret)
return ret
for trial in range(3):
try:
compare(func, node, node)
return
except AssertionError as e:
print(e)
node = Variable(rand(node.shape))
raise AssertionError("Failed all attempts.")
def test_dense(node, use_gpu):
node = Variable(node)
set_cuda_active(use_gpu)
layer = Dense(output_size=2)
def func(node):
return sum(layer(node))
compare(func, node, node)
compare(func, layer.params["w"], node)
compare(func, layer.params["b"], node)
def test_weight_normalize(node, use_gpu):
node = Variable(node)
assert_cuda_active(use_gpu)
layer = WeightNormalize(4)
layer2 = Dense(3) # This is important to ensure that dy is properly transferred backwards
def func(node):
return sum(layer2(layer(node)))
compare(func, node, node)
compare(func, layer.params["gain"], node)
compare(func, layer.params["w"], node, atol=1e-4)
compare(func, layer.params["bias"], node)
def test_dense(node, use_gpu, ignore_bias):
node = Variable(node)
assert_cuda_active(use_gpu)
layer = Dense(output_size=2, ignore_bias=ignore_bias)
def func(node):
return sum(layer(node))
compare(func, node, node)
compare(func, layer.params["w"], node)
try:
compare(func, layer.params["b"], node)
except Exception:
assert ignore_bias
def test_average_unpoolnd(node, use_gpu):
assert_cuda_active(use_gpu)
node = Variable(node)
l0 = AveragePoolNd(kernel=2)
l1 = AverageUnPoolNd()
l2 = Dense(2)
np.set_printoptions(suppress=True)
def func(node):
ret = node
reta = l0(node)
ret = l1(reta, reta)
ret = l2(ret.reshape(ret.shape[0], -1))
return sum(ret)
compare(func, node, node)
def test_average_unpool2d(node, use_gpu):
assert_cuda_active(use_gpu)
node = Variable(node)
l0 = AveragePool2d(filter=2)
l1 = AverageUnPool2d()
l2 = Dense(2)
np.set_printoptions(suppress=True)
def func(node):
ret = l0(node)
ret = l1(ret)
ret = l2(ret.reshape(ret.shape[0], -1))
return sum(ret)
for trial in range(1):
try:
compare(func, node, node)
return
except AssertionError as e:
print(e)
node = Variable(rand(node.shape))
raise AssertionError("Failed all three attempts.")