def not_test_raw_api(self):
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
image = layers.data(name='x', shape=[784], dtype='float32')
label = layers.data(name='y', shape=[1], dtype='int64')
limit = layers.fill_constant(shape=[1], dtype='int64', value=5)
cond = layers.less_than(x=label, y=limit)
true_image, false_image = split_lod_tensor(input=image, mask=cond)
true_out = layers.create_tensor(dtype='float32')
true_cond = ConditionalBlock([cond])
with true_cond.block():
hidden = layers.fc(input=true_image, size=100, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=true_out)
false_out = layers.create_tensor(dtype='float32')
false_cond = ConditionalBlock([cond])
with false_cond.block():
hidden = layers.fc(input=false_image, size=200, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
layers.assign(input=prob, output=false_out)
prob = merge_lod_tensor(
in_true=true_out, in_false=false_out, mask=cond, x=image)
loss = layers.cross_entropy(input=prob, label=label)
avg_loss = layers.mean(loss)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(avg_loss, startup_prog)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=10)
place = core.CPUPlace()
exe = Executor(place)
exe.run(startup_prog)
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array([x[0] for x in data]).astype("float32")
y_data = np.array([x[1] for x in data]).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
outs = exe.run(prog,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print(outs[0])
if outs[0] < 1.0:
return
self.assertFalse(True)
def test_xfalse():
out = merge_lod_tensor(
int_true=x_true,
in_false=set(),
x=input_data,
mask=y,
level=level)
def test_x():
out = merge_lod_tensor(
int_true=x_true,
in_false=x_false,
x=set(),
mask=y,
level=level)
def test_grad(self):
place = core.CPUPlace()
program = Program()
with program_guard(program):
x = layers.data(name='x',
shape=[1],
dtype='float32',
stop_gradient=False)
y = layers.data(name='y',
shape=[1],
dtype='bool',
stop_gradient=False)
level = 0
out_true, out_false = split_lod_tensor(input=x,
mask=y,
level=level)
out = merge_lod_tensor(in_true=out_true,
in_false=out_false,
mask=y,
x=x,
level=level)
mean = layers.mean(out)
append_backward(mean)
tensor = core.LoDTensor()
tensor.set(np.arange(10).reshape(10, 1).astype('float32'), place)
tensor.set_recursive_sequence_lengths([[3, 6, 1]])
mask_np = np.array([0, 1, 0]).astype('bool')
mask_np = np.expand_dims(mask_np, axis=1)
mask = core.LoDTensor()
mask.set(mask_np, place)
exe = Executor(place)
scope = core.Scope()
g_vars = program.global_block().var(x.name + "@GRAD")
g_out = [
item.sum() for item in map(
np.array,
exe.run(program,
feed={
'x': tensor,
'y': mask
},
fetch_list=[g_vars],
scope=scope,
return_numpy=False))
]
g_out_sum = np.array(g_out).sum()
self.assertAlmostEqual(1.0, g_out_sum, delta=0.1)
def main(self,
tensor,
mask,
expect_true,
expect_false,
expect_out,
level=0):
place = self.place()
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[1])
x.persistable = True
y = layers.data(name='y', shape=[1])
y.persistable = True
out_true, out_false = split_lod_tensor(input=x,
mask=y,
level=level)
out_true.persistable = True
out_false.persistable = True
out = merge_lod_tensor(in_true=out_true,
in_false=out_false,
mask=y,
x=x,
level=level)
out.persistable = True
exe = Executor(place)
scope = core.Scope()
exe.run(program,
feed={
'x': tensor,
'y': mask
},
scope=scope,
return_numpy=False)
var_true = scope.find_var(out_true.name).get_tensor()
var_false = scope.find_var(out_false.name).get_tensor()
var_out = scope.find_var(out.name).get_tensor()
self.check_tensor_same(var_true, expect_true)
self.check_tensor_same(var_false, expect_false)
self.check_tensor_same(var_out, expect_out)
def main(self,
tensor,
mask,
expect_true,
expect_false,
expect_out,
level=0,
use_merge_lod_infer=False):
place = self.place()
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[1])
x.persistable = True
y = layers.data(name='y', shape=[1])
y.persistable = True
out_true, out_false = split_lod_tensor(input=x, mask=y, level=level)
out_true.persistable = True
out_false.persistable = True
if use_merge_lod_infer:
input_dict = {
'X': x,
'Mask': mask,
'InTrue': out_true,
'InFalse': out_false,
'level': level
}
helper = LayerHelper('merge_lod_tensor_infer')
out = helper.create_variable_for_type_inference(
dtype=out_true.dtype)
helper.append_op(
type='merge_lod_tensor_infer',
inputs={
'X': x,
'Mask': y,
'InTrue': out_true,
'InFalse': out_false
},
outputs={'Out': out},
attrs={'level': level})
out.persistable = True
else:
out = merge_lod_tensor(
in_true=out_true,
in_false=out_false,
mask=y,
x=x,
level=level)
out.persistable = True
exe = Executor(place)
scope = core.Scope()
exe.run(program,
feed={'x': tensor,
'y': mask},
scope=scope,
return_numpy=False)
var_true = scope.find_var(out_true.name).get_tensor()
var_false = scope.find_var(out_false.name).get_tensor()
var_out = scope.find_var(out.name).get_tensor()
if not use_merge_lod_infer:
self.check_tensor_same(var_true, expect_true)
self.check_tensor_same(var_false, expect_false)
self.check_tensor_same(var_out, expect_out)