def test_recognize_digits_conv(self):
program = Program()
with program_guard(program, startup_program=Program()):
images = layers.data(
name='pixel', shape=[1, 28, 28], dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int32')
conv_pool_1 = nets.simple_img_conv_pool(
input=images,
filter_size=5,
num_filters=2,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=4,
pool_size=2,
pool_stride=2,
act="relu")
predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(cost)
print(str(program))
def test_detection_output(self):
program = Program()
with program_guard(program):
pb = layers.data(
name='prior_box',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
pbv = layers.data(
name='prior_box_var',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
loc = layers.data(
name='target_box',
shape=[2, 10, 4],
append_batch_size=False,
dtype='float32')
scores = layers.data(
name='scores',
shape=[2, 10, 20],
append_batch_size=False,
dtype='float32')
out = layers.detection_output(
scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv)
self.assertIsNotNone(out)
self.assertEqual(out.shape[-1], 6)
print(str(program))
def test_detection_api(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[4], dtype='float32')
y = layers.data(name='y', shape=[4], dtype='float32')
z = layers.data(name='z', shape=[4], dtype='float32', lod_level=1)
iou = layers.iou_similarity(x=x, y=y)
bcoder = layers.box_coder(
prior_box=x,
prior_box_var=y,
target_box=z,
code_type='encode_center_size')
self.assertIsNotNone(iou)
self.assertIsNotNone(bcoder)
matched_indices, matched_dist = layers.bipartite_match(iou)
self.assertIsNotNone(matched_indices)
self.assertIsNotNone(matched_dist)
gt = layers.data(
name='gt', shape=[1, 1], dtype='int32', lod_level=1)
trg, trg_weight = layers.target_assign(
gt, matched_indices, mismatch_value=0)
self.assertIsNotNone(trg)
self.assertIsNotNone(trg_weight)
gt2 = layers.data(
name='gt2', shape=[10, 4], dtype='float32', lod_level=1)
trg, trg_weight = layers.target_assign(
gt2, matched_indices, mismatch_value=0)
self.assertIsNotNone(trg)
self.assertIsNotNone(trg_weight)
print(str(program))
def create_rnn_op(self):
x = layers.data(
shape=[self.sent_len, self.batch_size, self.input_dim],
dtype='float32',
name='x',
append_batch_size=False,
**self.p_info)
x.stop_gradient = False
h_boot = layers.data(
shape=[self.input_dim],
dtype='float32',
name='h_boot',
**self.p_info)
h_boot.stop_gradient = False
rnn = layers.StaticRNN(main_program=self.main_program)
with rnn.step():
h_pre = rnn.memory(init=h_boot)
x_t = rnn.step_input(x)
h = layers.scale(
x=layers.elementwise_add(
x=h_pre, y=x_t, **self.p_info),
scale=self.py_rnn.scale,
**self.p_info)
rnn.update_memory(h_pre, h)
rnn.output(h)
return rnn()
def test_softmax_with_cross_entropy(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[16], dtype='float32')
y = layers.data(name='label', shape=[1], dtype='int64')
loss = layers.softmax_with_cross_entropy(x, y)
self.assertIsNotNone(loss)
print(str(program))
def test_smooth_l1(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[4], dtype='float32')
y = layers.data(name='label', shape=[4], dtype='float32')
loss = layers.smooth_l1(x, y)
self.assertIsNotNone(loss)
print(str(program))
def test_sequence_expand(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[10], dtype='float32')
y = layers.data(
name='y', shape=[10, 20], dtype='float32', lod_level=2)
self.assertIsNotNone(layers.sequence_expand(x=x, y=y, ref_level=1))
print(str(program))
def test_lod_reset(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[10], dtype='float32')
y = layers.data(
name='y', shape=[10, 20], dtype='float32', lod_level=2)
print(layers.lod_reset(x=x, y=y))
print(str(program))
def test_multiplex(self):
program = Program()
with program_guard(program):
x1 = layers.data(name='x1', shape=[4], dtype='float32')
x2 = layers.data(name='x2', shape=[4], dtype='float32')
index = layers.data(name='index', shape=[1], dtype='int32')
out = layers.multiplex(inputs=[x1, x2], index=index)
self.assertIsNotNone(out)
print(str(program))
def test_sigmoid_cross_entropy(self):
program = Program()
with program_guard(program):
dat = layers.data(name='data', shape=[10], dtype='float32')
lbl = layers.data(name='label', shape=[10], dtype='float32')
self.assertIsNotNone(
layers.sigmoid_cross_entropy_with_logits(
x=dat, label=lbl))
print(str(program))
def test_roi_pool(self):
program = Program()
with program_guard(program):
x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
rois = layers.data(
name="rois", shape=[4], dtype="float32", lod_level=1)
output = layers.roi_pool(x, rois, 7, 7, 0.6)
self.assertIsNotNone(output)
print(str(program))
def get_usr_combined_features():
# FIXME(dzh) : old API integer_value(10) may have range check.
# currently we don't have user configurated check.
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
param_attr='user_table',
is_sparse=IS_SPARSE)
usr_fc = layers.fc(input=usr_emb, size=32)
USR_GENDER_DICT_SIZE = 2
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
is_sparse=IS_SPARSE)
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
param_attr='age_table')
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
is_sparse=IS_SPARSE)
usr_job_fc = layers.fc(input=usr_job_emb, size=16)
concat_embed = layers.concat(
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)
usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return usr_combined_features
def test_fit_line_inference_model(self):
MODEL_DIR = "./tmp/inference_model"
init_program = Program()
program = Program()
with program_guard(program, init_program):
x = layers.data(name='x', shape=[2], dtype='float32')
y = layers.data(name='y', shape=[1], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(cost)
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost, init_program)
place = core.CPUPlace()
exe = executor.Executor(place)
exe.run(init_program, feed={}, fetch_list=[])
for i in xrange(100):
tensor_x = np.array(
[[1, 1], [1, 2], [3, 4], [5, 2]]).astype("float32")
tensor_y = np.array([[-2], [-3], [-7], [-7]]).astype("float32")
exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
expected = exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])[0]
reload(executor) # reload to build a new scope
exe = executor.Executor(place)
[infer_prog, feed_var_names, fetch_vars] = load_inference_model(
MODEL_DIR, exe)
outs = exe.run(
infer_prog,
feed={feed_var_names[0]: tensor_x,
feed_var_names[1]: tensor_y},
fetch_list=fetch_vars)
actual = outs[0]
self.assertEqual(feed_var_names, ["x", "y"])
self.assertEqual(len(fetch_vars), 1)
self.assertEqual(str(fetch_vars[0]), str(avg_cost))
self.assertEqual(expected, actual)
def test_ifelse(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_batch_size_like(
input=label, dtype='int64', shape=[1], value=5.0)
cond = layers.less_than(x=label, y=limit)
ie = layers.IfElse(cond)
with ie.true_block():
true_image = ie.input(image)
hidden = layers.fc(input=true_image, size=100, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
with ie.false_block():
false_image = ie.input(image)
hidden = layers.fc(input=false_image, size=200, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
prob = ie()
loss = layers.cross_entropy(input=prob[0], 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=200)
place = core.CPUPlace()
exe = Executor(place)
exe.run(kwargs['startup_program'])
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape((y_data.shape[0], 1))
outs = exe.run(kwargs['main_program'],
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_fit_a_line(self):
program = Program()
with program_guard(program, startup_program=Program()):
x = layers.data(name='x', shape=[13], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
y = layers.data(name='y', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(cost)
self.assertIsNotNone(avg_cost)
print(str(program))
def test_simple_forward(self):
d0 = layers.data(
"d0", shape=[10], append_batch_size=False, dtype='float32')
d1 = layers.data(
"d1", shape=[10], append_batch_size=False, dtype='float32')
d2 = layers.data(
"d2", shape=[10], append_batch_size=False, dtype='float32')
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = True
init = layers.zeros(shape=[10], dtype='float32')
mem_array = layers.array_write(x=init, i=i)
data_array = layers.array_write(x=d0, i=i)
i = layers.increment(i)
layers.array_write(d1, i, array=data_array)
i = layers.increment(i)
layers.array_write(d2, i, array=data_array)
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = True
array_len = layers.fill_constant(shape=[1], dtype='int64', value=3)
array_len.stop_gradient = True
cond = layers.less_than(x=i, y=array_len)
while_op = layers.While(cond=cond)
with while_op.block():
d = layers.array_read(array=data_array, i=i)
prev = layers.array_read(array=mem_array, i=i)
result = layers.sums(input=[d, prev])
i = layers.increment(x=i, in_place=True)
layers.array_write(result, i=i, array=mem_array)
layers.less_than(x=i, y=array_len, cond=cond)
sum_result = layers.array_read(array=mem_array, i=i)
loss = layers.mean(sum_result)
append_backward(loss)
cpu = core.CPUPlace()
exe = Executor(cpu)
d = []
for i in xrange(3):
d.append(numpy.random.random(size=[10]).astype('float32'))
outs = exe.run(feed={'d0': d[0],
'd1': d[1],
'd2': d[2]},
fetch_list=[sum_result])
self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)
def setUp(self):
program = Program()
with program_guard(program, startup_program=Program()):
x = layers.data(name='x', shape=[13], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
y = layers.data(name='y', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(cost)
opt = optimizer.SGD(learning_rate=0.001)
opt = opt.minimize(avg_cost)
self.program = program
def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
param_attr='movie_table',
is_sparse=IS_SPARSE)
mov_fc = layers.fc(input=mov_emb, size=32)
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = layers.data(
name='category_id', shape=[1], dtype='int64', lod_level=1)
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
input=mov_categories_emb, pool_type="sum")
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = layers.data(
name='movie_title', shape=[1], dtype='int64', lod_level=1)
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
input=mov_title_emb,
num_filters=32,
filter_size=3,
act="tanh",
pool_type="sum")
concat_embed = layers.concat(
input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)
# FIXME(dzh) : need tanh operator
mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return mov_combined_features
def test_lstm_unit(self):
program = Program()
with program_guard(program):
x_t_data = layers.data(
name='x_t_data', shape=[10, 10], dtype='float32')
x_t = layers.fc(input=x_t_data, size=10)
prev_hidden_data = layers.data(
name='prev_hidden_data', shape=[10, 30], dtype='float32')
prev_hidden = layers.fc(input=prev_hidden_data, size=30)
prev_cell_data = layers.data(
name='prev_cell', shape=[10, 30], dtype='float32')
prev_cell = layers.fc(input=prev_cell_data, size=30)
self.assertIsNotNone(
layers.lstm_unit(
x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
print(str(program))
def test_forward(self):
data = layers.data(name='X', shape=[1], dtype='float32')
data.stop_gradient = False
cond = layers.ConditionalBlock(inputs=[data])
out = layers.create_tensor(dtype='float32')
with cond.block():
hidden = layers.fc(input=data, size=10)
layers.assign(hidden, out)
cpu = core.CPUPlace()
exe = Executor(cpu)
exe.run(default_startup_program())
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = exe.run(feed={'X': x}, fetch_list=[out])[0]
print outs
loss = layers.mean(out)
append_backward(loss=loss)
outs = exe.run(
feed={'X': x},
fetch_list=[
default_main_program().block(0).var(data.name + "@GRAD")
])[0]
print outs
def test_softmax(self):
program = Program()
with program_guard(program):
data = layers.data(name='data', shape=[10], dtype='float32')
hid = layers.fc(input=data, size=20)
self.assertIsNotNone(layers.softmax(hid))
print(str(program))
def test_sequence_reshape(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[8], dtype='float32', lod_level=1)
out = layers.sequence_reshape(input=x, new_dim=16)
self.assertIsNotNone(out)
print(str(program))
def test_simple_conv2d(self):
program = Program()
with program_guard(program, startup_program=Program()):
images = layers.data(name='pixel', shape=[3, 48, 48], dtype='int32')
layers.conv2d(input=images, num_filters=3, filter_size=[4, 4])
print(str(program))
def test_nce(self):
window_size = 5
words = []
for i in xrange(window_size):
words.append(
layers.data(
name='word_{0}'.format(i), shape=[1], dtype='int64'))
dict_size = 10000
label_word = int(window_size / 2) + 1
embs = []
for i in xrange(window_size):
if i == label_word:
continue
emb = layers.embedding(
input=words[i],
size=[dict_size, 32],
param_attr='emb.w',
is_sparse=True)
embs.append(emb)
embs = layers.concat(input=embs, axis=1)
loss = layers.nce(input=embs,
label=words[label_word],
num_total_classes=dict_size,
param_attr='nce.w',
bias_attr='nce.b')
avg_loss = layers.mean(loss)
self.assertIsNotNone(avg_loss)
print(str(default_main_program()))
def test_row_conv(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[16], dtype='float32', lod_level=1)
out = layers.row_conv(input=x, future_context_size=2)
self.assertIsNotNone(out)
print(str(program))
def train_decoder(context, is_sparse):
# decoder
trg_language_word = pd.data(
name="target_language_word", shape=[1], dtype='int64', lod_level=1)
trg_embedding = pd.embedding(
input=trg_language_word,
size=[dict_size, word_dim],
dtype='float32',
is_sparse=is_sparse,
param_attr=fluid.ParamAttr(name='vemb'))
rnn = pd.DynamicRNN()
with rnn.block():
current_word = rnn.step_input(trg_embedding)
pre_state = rnn.memory(init=context)
current_state = pd.fc(input=[current_word, pre_state],
size=decoder_size,
act='tanh')
current_score = pd.fc(input=current_state,
size=target_dict_dim,
act='softmax')
rnn.update_memory(pre_state, current_state)
rnn.output(current_score)
return rnn()
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)
table = layers.lod_rank_table(x, level=0)
array = layers.lod_tensor_to_array(x, table)
result = layers.array_to_lod_tensor(array, table)
mean = layers.mean(result)
append_backward(mean)
tensor = core.LoDTensor()
tensor.set(numpy.arange(10).reshape(10, 1).astype('float32'), place)
tensor.set_lod([[0, 3, 9, 10]])
g_vars = program.global_block().var(x.name + "@GRAD")
exe = Executor(place)
g_out = [
numpy.array(item).sum()
for item in exe.run(program,
feed={'x': tensor},
fetch_list=[g_vars],
return_numpy=False)
]
g_out_sum = numpy.array(g_out).sum()
self.assertAlmostEqual(1.0, g_out_sum, delta=0.1)
def test_mul(self):
a = data(name='a', shape=[784], dtype='float32')
b = data(
name='b',
shape=[784, 100],
dtype='float32',
append_batch_size=False)
out = mul(x=a, y=b)
place = core.CPUPlace()
a_np = numpy.random.random((100, 784)).astype('float32')
b_np = numpy.random.random((784, 100)).astype('float32')
exe = Executor(place)
outs = exe.run(feed={'a': a_np, 'b': b_np}, fetch_list=[out])
out = outs[0]
self.assertEqual((100, 100), out.shape)
self.assertTrue(numpy.allclose(out, numpy.dot(a_np, b_np)))
def main(self, tensor, expect_array, expect_lod, expect_max_len, level=0):
place = self.place()
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[10])
x.persistable = True
table = layers.lod_rank_table(x, level=level)
max_len = layers.max_sequence_len(table)
max_len.persistable = True
array = layers.lod_tensor_to_array(x, table)
array.persistable = True
result = layers.array_to_lod_tensor(array, table)
result.persistable = True
exe = Executor(place)
scope = core.Scope()
exe.run(program, feed={'x': tensor}, scope=scope)
var = scope.find_var(array.name)
array = var.get_lod_tensor_array()
if expect_array is not None and expect_lod is not None:
self.check_array_same(array, expect_array, expect_lod)
self.check_tensor_same(scope.find_var(result.name).get_tensor(), tensor)
self.assertEqual(
numpy.array(scope.find_var(max_len.name).get_tensor())[0],
expect_max_len)
def build_network(self, only_forward, **kargs):
x = layers.data('x', shape=[3], dtype='float32', lod_level=1)
x.stop_gradient = False
printed = layers.Print(input=x, **kargs)
if only_forward: return printed
loss = layers.mean(printed)
append_backward(loss=loss)
return loss
def train_program(is_sparse):
context = encoder(is_sparse)
rnn_out = train_decoder(context, is_sparse)
label = pd.data(name="target_language_next_word",
shape=[1],
dtype='int64',
lod_level=1)
cost = pd.cross_entropy(input=rnn_out, label=label)
avg_cost = pd.mean(cost)
return avg_cost
def test_sequence_softmax(self):
program = Program()
with program_guard(program):
seq_data = layers.data(name='seq_data',
shape=[10, 10],
dtype='float32',
lod_level=1)
seq = layers.fc(input=seq_data, size=20)
self.assertIsNotNone(layers.sequence_softmax(seq))
print(str(program))
def test_im2sequence(self):
print("test_im2sequence")
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
output = layers.im2sequence(input=x,
stride=[1, 1],
filter_size=[2, 2])
self.assertIsNotNone(output)
print(str(program))
def test_label_smooth(self):
program = Program()
with program_guard(program):
label = layers.data(name="label", shape=[1], dtype="float32")
one_hot_label = layers.one_hot(input=label, depth=10)
smooth_label = layers.label_smooth(label=one_hot_label,
epsilon=0.1,
dtype="float32")
self.assertIsNotNone(smooth_label)
print(str(program))
def test_yolov3_loss_with_scale(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
gt_box = layers.data(name='gt_box', shape=[10, 4], dtype='float32')
gt_label = layers.data(name='gt_label', shape=[10], dtype='int32')
gt_score = layers.data(name='gt_score', shape=[10], dtype='float32')
loss = layers.yolov3_loss(
x,
gt_box,
gt_label, [10, 13, 30, 13], [0, 1],
10,
0.7,
32,
gt_score=gt_score,
use_label_smooth=False,
scale_x_y=1.2)
self.assertIsNotNone(loss)
def func(self, place):
# the shape of input variable should be clearly specified, not inlcude -1.
shape = [2, 3, 7, 9]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.data('y', shape[:-1], False, dtype)
x.persistable = True
y.persistable = True
out = layers.elementwise_add(x, y, axis=0)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
y_arr = np.random.uniform(-1, 1, shape[:-1]).astype(dtype)
gradient_checker.double_grad_check([x, y],
out,
x_init=[x_arr, y_arr],
place=place,
eps=eps)
def encoder(input_name):
input_ids = layers.data(
name=input_name, shape=[1], dtype='int64', lod_level=1)
input_embedding = layers.embedding(
input=input_ids,
size=[source_dict_dim, embedding_dim],
dtype='float32',
is_sparse=True)
encoder_out = bi_lstm_encoder(input_seq=input_embedding, gate_size=embedding_dim)
return encoder_out
def test_serialize_program_and_persistables(self):
init_program = fluid.default_startup_program()
program = fluid.default_main_program()
# fake program without feed/fetch
with program_guard(program, init_program):
x = layers.data(name='x', shape=[2], dtype='float32')
y = layers.data(name='y', shape=[1], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(cost)
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost, init_program)
place = core.CPUPlace()
exe = executor.Executor(place)
exe.run(init_program, feed={}, fetch_list=[])
tensor_x = np.array([[1, 1], [1, 2], [5, 2]]).astype("float32")
tensor_y = np.array([[-2], [-3], [-7]]).astype("float32")
for i in six.moves.xrange(3):
exe.run(program,
feed={
'x': tensor_x,
'y': tensor_y
},
fetch_list=[avg_cost])
# test if return type of serialize_program is bytes
res1 = paddle.static.io.serialize_program([x, y], [avg_cost])
self.assertTrue(isinstance(res1, bytes))
# test if return type of serialize_persistables is bytes
res2 = paddle.static.io.serialize_persistables([x, y], [avg_cost], exe)
self.assertTrue(isinstance(res2, bytes))
# test if variables in program is empty
res = paddle.static.io._serialize_persistables(Program(), None)
self.assertEqual(res, None)
self.assertRaises(TypeError, paddle.static.io.deserialize_persistables,
None, None, None)
def __init__(self, config, dataset, place):
logging.info(['model is: ', self.__class__.__name__])
self.config = config
self.graph = dataset.graph
self.placce = place
self.edge_types = sorted(self.graph.edge_types_info())
logging.info('edge_types in model: %s' % str(self.edge_types))
neg_num = dataset.config['neg_num']
# hyper parameters
self.num_nodes = self.graph.num_nodes
self.embedding_size = self.config['dimensions']
self.embedding_u_size = self.config['edge_dim']
self.dim_a = self.config['att_dim']
self.att_head = self.config['att_head']
self.edge_type_count = len(self.edge_types)
self.u_num = self.edge_type_count
self.gw = heter_graph_wrapper.HeterGraphWrapper(
name="heter_graph",
place=place,
edge_types=self.graph.edge_types_info(),
node_feat=self.graph.node_feat_info(),
edge_feat=self.graph.edge_feat_info())
self.train_inputs = fl.data('train_inputs',
shape=[None],
dtype='int64')
self.train_labels = fl.data('train_labels',
shape=[None, 1, 1],
dtype='int64')
self.train_types = fl.data('train_types',
shape=[None, 1],
dtype='int64')
self.train_negs = fl.data('train_negs',
shape=[None, neg_num, 1],
dtype='int64')
self.forward()
def test_word_embedding(self):
program = Program()
with program_guard(program, startup_program=Program()):
dict_size = 10000
embed_size = 32
first_word = layers.data(name='firstw', shape=[1], dtype='int64')
second_word = layers.data(name='secondw', shape=[1], dtype='int64')
third_word = layers.data(name='thirdw', shape=[1], dtype='int64')
forth_word = layers.data(name='forthw', shape=[1], dtype='int64')
next_word = layers.data(name='nextw', shape=[1], dtype='int64')
embed_first = layers.embedding(input=first_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w')
embed_second = layers.embedding(input=second_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w')
embed_third = layers.embedding(input=third_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w')
embed_forth = layers.embedding(input=forth_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w')
concat_embed = layers.concat(
input=[embed_first, embed_second, embed_third, embed_forth],
axis=1)
hidden1 = layers.fc(input=concat_embed, size=256, act='sigmoid')
predict_word = layers.fc(input=hidden1,
size=dict_size,
act='softmax')
cost = layers.cross_entropy(input=predict_word, label=next_word)
avg_cost = layers.mean(cost)
self.assertIsNotNone(avg_cost)
print(str(program))
def test_dynamic_lstmp(self):
program = Program()
with program_guard(program):
hidden_dim, proj_dim = 16, 8
seq_data = layers.data(
name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
fc_out = layers.fc(input=seq_data, size=4 * hidden_dim)
self.assertIsNotNone(
layers.dynamic_lstmp(
input=fc_out, size=4 * hidden_dim, proj_size=proj_dim))
print(str(program))
def func(self, place):
x_shape = [2, 3, 4, 5]
pad = [1, 0, 1, 0, 1, 0, 1, 0]
dtype = np.float64
x = layers.data('x', x_shape, False, dtype)
x.persistable = True
out = paddle.nn.functional.pad(x, pad)
x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place)
def run_local(self, place):
main = fluid.Program()
with fluid.program_guard(main):
x = layers.data(shape=[32, 32],
dtype='float32',
name='X',
append_batch_size=False)
fluid.initializer.Constant(value=2.3)(x, main.global_block())
o = layers.scale(x=x, scale=10.0)
exe = fluid.Executor(place)
self.local_out = exe.run(main, fetch_list=[o])
def get_model(self, main_prog, startup_program):
ring_id = 0
nranks = 2
with fluid.program_guard(main_prog, startup_program):
tindata = layers.data(name="tindata",
shape=[10, 1000],
dtype='float32')
toutdata = fluid.layers.collective._c_reducescatter(
tindata, nranks)
toutdata = fluid.layers.collective._c_sync_comm_stream(toutdata, 0)
return toutdata
def test_spectral_norm(self):
program = Program()
with program_guard(program):
weight = layers.data(name='weight',
shape=[2, 3, 32, 32],
dtype="float32",
append_batch_size=False)
out = layers.spectral_norm(weight, dim=1, power_iters=1)
self.assertIsNotNone(out)
print(str(program))
def func(self, place):
# the shape of input variable should be clearly specified, not inlcude -1.
x_shape = [7, 11]
y_shape = [11, 9]
eps = 0.005
dtype = np.float64
x = layers.data('x', x_shape, False, dtype)
x.persistable = True
y = layers.data('y', y_shape, False, dtype)
y.persistable = True
out = layers.mul(x, y)
x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
y_arr = np.random.uniform(-1, 1, y_shape).astype(dtype)
gradient_checker.double_grad_check([x, y],
out,
x_init=[x_arr, y_arr],
place=place,
eps=eps)
def test_multiclass_nms2(self):
program = Program()
with program_guard(program):
bboxes = layers.data(name='bboxes',
shape=[-1, 10, 4],
dtype='float32')
scores = layers.data(name='scores',
shape=[-1, 10],
dtype='float32')
output = layers.multiclass_nms2(bboxes, scores, 0.3, 400, 200, 0.7)
output2, index = layers.multiclass_nms2(bboxes,
scores,
0.3,
400,
200,
0.7,
return_index=True)
self.assertIsNotNone(output)
self.assertIsNotNone(output2)
self.assertIsNotNone(index)
def func(self, place):
shape = [2, 3, 7, 9]
eps = 0.0005
dtype = np.float64
x = layers.data('x', shape, False, dtype=dtype)
x.persistable = True
y = layers.sigmoid(x)
x_arr = np.random.random(shape).astype(dtype)
x_arr[np.abs(x_arr) < 0.005] = 0.002
gradient_checker.triple_grad_check(
[x], y, x_init=x_arr, place=place, eps=eps)
def __init__(self, args, gw, num_class):
self.args = args
self.num_layers = self.args.num_layers
self.hidden_size = self.args.hidden_size
self.train_eps = self.args.train_eps
self.pool_type = self.args.pool_type
self.dropout_prob = self.args.dropout_prob
self.num_class = num_class
self.gw = gw
self.labels = fl.data(name="labels", shape=[None, 1], dtype="int64")
def func(self, place):
x_shape = [2, 3, 4, 5]
pad = [1, 1, 1, 1]
dtype = np.float64
x1 = layers.data('x', x_shape, False, dtype)
x2 = layers.data('x', x_shape, False, dtype)
x1.persistable = True
x2.persistable = True
out = paddle.concat([x1, x2], axis=0)
x2_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
x1_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
gradient_checker.double_grad_check(
[x1, x2], out, x_init=[x1_arr, x2_arr], place=place)
gradient_checker.double_grad_check_for_dygraph(
self.concat_wrapper, [x1, x2],
out,
x_init=[x1_arr, x2_arr],
place=place)
def test_slice(self):
starts = [1, 0, 2]
ends = [3, 3, 4]
axes = [0, 1, 2]
program = Program()
with program_guard(program):
input = layers.data(
name="input", shape=[3, 4, 5, 6], dtype='float32')
out = layers.slice(input, axes=axes, starts=starts, ends=ends)
def func(self, place):
x_shape = [2, 3, 4, 5]
perm = [0, 2, 3, 1]
dtype = np.float64
x = layers.data('x', x_shape, False, dtype)
x.persistable = True
out = paddle.transpose(x, perm)
x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place)
def test_gaussian_random_batch_size_like(self):
program = Program()
with program_guard(program):
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.gaussian_random_batch_size_like(input,
shape=[-1, 11],
mean=1.0,
std=2.0)
self.assertIsNotNone(out)
print(str(program))
def test_save_inference_model(self):
MODEL_DIR = "./tmp/inference_model2"
init_program = Program()
program = Program()
# fake program without feed/fetch
with program_guard(program, init_program):
x = layers.data(name='x', shape=[2], dtype='float32')
y = layers.data(name='y', shape=[1], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(cost)
place = core.CPUPlace()
exe = executor.Executor(place)
exe.run(init_program, feed={}, fetch_list=[])
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
def test_sampling_id(self):
program = Program()
with program_guard(program):
x = layers.data(name="X",
shape=[13, 11],
dtype='float32',
append_batch_size=False)
out = layers.sampling_id(x)
self.assertIsNotNone(out)
print(str(program))
def test_sequence_scatter(self):
program = Program()
with program_guard(program):
x = layers.data(name='x',
shape=[3, 6],
append_batch_size=False,
dtype='float32')
idx = layers.data(name='idx',
shape=[12, 1],
append_batch_size=False,
dtype='int32',
lod_level=1)
updates = layers.data(name='updates',
shape=[12, 1],
append_batch_size=False,
dtype='float32',
lod_level=1)
out = layers.sequence_scatter(input=x, index=idx, updates=updates)
self.assertIsNotNone(out)
print(str(program))
def test_lstm_unit(self):
program = Program()
with program_guard(program):
x_t_data = layers.data(name='x_t_data',
shape=[10, 10],
dtype='float32')
x_t = layers.fc(input=x_t_data, size=10)
prev_hidden_data = layers.data(name='prev_hidden_data',
shape=[10, 30],
dtype='float32')
prev_hidden = layers.fc(input=prev_hidden_data, size=30)
prev_cell_data = layers.data(name='prev_cell',
shape=[10, 30],
dtype='float32')
prev_cell = layers.fc(input=prev_cell_data, size=30)
self.assertIsNotNone(
layers.lstm_unit(x_t=x_t,
hidden_t_prev=prev_hidden,
cell_t_prev=prev_cell))
print(str(program))
def test_linear_chain_crf(self):
program = Program()
with program_guard(program, startup_program=Program()):
label_dict_len = 10
images = layers.data(name='pixel', shape=[784], dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int32')
hidden = layers.fc(input=images, size=128)
crf = layers.linear_chain_crf(input=hidden,
label=label,
param_attr=ParamAttr(name="crfw"))
crf_decode = layers.crf_decoding(input=hidden,
param_attr=ParamAttr(name="crfw"))
layers.chunk_eval(input=crf_decode,
label=label,
chunk_scheme="IOB",
num_chunk_types=(label_dict_len - 1) // 2)
self.assertFalse(crf is None)
self.assertFalse(crf_decode is None)
print(str(program))
def test_categorical_distribution(self,
batch_size=2,
dims=3,
tolerance=1e-6):
test_program = fluid.Program()
logits_np = np.random.randn(batch_size, dims).astype('float32')
other_logits_np = np.random.randn(batch_size, dims).astype('float32')
with fluid.program_guard(test_program):
logits = layers.data(name='logits', shape=[dims], dtype='float32')
other_logits = layers.data(name='other_logits',
shape=[dims],
dtype='float32')
categorical_np = Categorical(logits_np)
other_categorical_np = Categorical(other_logits_np)
entropy_np = categorical_np.entropy()
kl_np = categorical_np.kl_divergence(other_categorical_np)
self.executor.run(fluid.default_main_program())
np_categorical = CategoricalNumpy(logits_np)
np_other_categorical = CategoricalNumpy(other_logits_np)
gt_entropy_np = np_categorical.entropy()
gt_kl_np = np_categorical.kl_divergence(np_other_categorical)
# result calculated by paddle
[output_entropy_np,
output_kl_np] = self.executor.run(program=test_program,
feed={'logits': logits_np},
fetch_list=[entropy_np, kl_np])
np.testing.assert_allclose(output_entropy_np,
gt_entropy_np,
rtol=tolerance,
atol=tolerance)
np.testing.assert_allclose(output_kl_np,
gt_kl_np,
rtol=tolerance,
atol=tolerance)
def __init__(self, args, dataset):
self.args = args
self.dataset = dataset
self.hidden_size = self.args.hidden_size
self.embed_dim = self.args.embed_dim
self.dropout_prob = self.args.dropout_rate
self.pool_type = self.args.pool_type
self._init_vars = []
graph_data = []
g, label = self.dataset[0]
graph_data.append(g)
g, label = self.dataset[1]
graph_data.append(g)
batch_graph = pgl.graph.MultiGraph(graph_data)
graph_data = batch_graph
graph_data.edge_feat["feat"] = graph_data.edge_feat["feat"].astype(
"int64")
graph_data.node_feat["feat"] = graph_data.node_feat["feat"].astype(
"int64")
self.graph_wrapper = GraphWrapper(
name="graph",
place=F.CPUPlace(),
node_feat=graph_data.node_feat_info(),
edge_feat=graph_data.edge_feat_info())
self.atom_encoder = AtomEncoder(name="atom", emb_dim=self.embed_dim)
self.bond_encoder = BondEncoder(name="bond", emb_dim=self.embed_dim)
self.labels = L.data("labels",
shape=[None, self.args.num_class],
dtype="float32",
append_batch_size=False)
self.unmask = L.data("unmask",
shape=[None, self.args.num_class],
dtype="float32",
append_batch_size=False)
self.build_model()