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未验证 提交 f2a8dd50 编写于 作者: C ccrrong 提交者： GitHub
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remove dropout from fluid (#48319) 

* remove dropout
上级 d0284f85
隐藏空白更改
内联 并排
Showing with 211 addition and 311 deletion
python/paddle/fluid/contrib/layers/rnn_impl.py
浏览文件 @ f2a8dd50
......@@ -378,9 +378,9 @@ def basic_gru(
step_input = new_hidden
if dropout_prob is not None and dropout_prob > 0.0:
step_input = layers.dropout(
step_input = paddle.nn.functional.dropout(
step_input,
dropout_prob=dropout_prob,
p=dropout_prob,
)
rnn.step_output(step_input)
......@@ -680,10 +680,10 @@ def basic_lstm(
step_input = new_hidden
if dropout_prob is not None and dropout_prob > 0.0:
step_input = layers.dropout(
step_input = paddle.nn.functional.dropout(
step_input,
dropout_prob=dropout_prob,
dropout_implementation='upscale_in_train',
p=dropout_prob,
mode='upscale_in_train',
)
rnn.step_output(step_input)
......
python/paddle/fluid/contrib/tests/test_image_classification_fp16.py
浏览文件 @ f2a8dd50
......@@ -93,10 +93,10 @@ def vgg16_bn_drop(input):
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
drop = paddle.nn.functional.dropout(x=conv5, p=0.5)
fc1 = fluid.layers.fc(input=drop, size=4096, act=None)
bn = paddle.static.nn.batch_norm(input=fc1, act='relu')
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
drop2 = paddle.nn.functional.dropout(x=bn, p=0.5)
fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
return fc2
......
python/paddle/fluid/layers/nn.py
浏览文件 @ f2a8dd50
......@@ -66,7 +66,6 @@ __all__ = [
'fc',
'embedding',
'conv2d',
'dropout',
'split',
'l2_normalize',
'row_conv',
......@@ -750,139 +749,6 @@ def _pull_box_sparse(
return outs
@deprecated(since="2.0.0", update_to="paddle.nn.functional.dropout")
def dropout(
x,
dropout_prob,
is_test=None,
seed=None,
name=None,
dropout_implementation="downgrade_in_infer",
):
"""
Computes dropout.
Drop or keep each element of `x` independently. Dropout is a regularization
technique for reducing overfitting by preventing neuron co-adaption during
training. The dropout operator randomly sets (according to the given dropout
probability) the outputs of some units to zero, while others are remain
unchanged.
dropout op can be removed from the program to make the program more efficient.
Args:
x (Variable): The input tensor variable. The data type is float16 or float32 or float64.
dropout_prob (float): Probability of setting units to zero.
is_test (bool): A flag indicating whether it is in test phrase or not.
Default None, in dynamic graph, it use global tracer mode; in static graph, it means False.
seed (int): A Python integer used to create random seeds. If this
parameter is set to None, a random seed is used.
NOTE: If an integer seed is given, always the same output
units will be dropped. DO NOT use a fixed seed in training.Default: None.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
dropout_implementation(string): ['downgrade_in_infer'(default)|'upscale_in_train']
1. downgrade_in_infer(default), downgrade the outcome at inference
- train: out = input * mask
- inference: out = input * (1.0 - dropout_prob)
(mask is a tensor same shape with input, value is 0 or 1
ratio of 0 is dropout_prob)
2. upscale_in_train, upscale the outcome at training time
- train: out = input * mask / ( 1.0 - dropout_prob )
- inference: out = input
(mask is a tensor same shape with input, value is 0 or 1
ratio of 0 is dropout_prob)
Returns:
A Variable holding Tensor representing the dropout, has same shape and data type with `x`.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
x = fluid.data(name="data", shape=[None, 32, 32], dtype="float32")
dropped = fluid.layers.dropout(x, dropout_prob=0.5)
"""
if not isinstance(dropout_prob, (float, int, Variable)):
raise TypeError(
"dropout_prob argument should be a number(int|float) or Variable"
)
# fast return for p == 0
if isinstance(dropout_prob, (int, float)) and dropout_prob == 0:
return x
if _non_static_mode():
if (
seed is None or seed == 0
) and default_main_program().random_seed != 0:
seed = default_main_program().random_seed
if is_test is None:
is_test = not _dygraph_tracer()._train_mode
out, mask = _legacy_C_ops.dropout(
x,
'dropout_prob',
dropout_prob,
'is_test',
is_test,
'fix_seed',
seed is not None,
'seed',
seed if seed is not None else 0,
'dropout_implementation',
dropout_implementation,
)
return out
def get_attrs(prog, dropout_prob, is_test, seed):
if (seed is None or seed == 0) and prog.random_seed != 0:
seed = prog.random_seed
if isinstance(dropout_prob, Variable) and not dropout_prob.shape != [1]:
raise TypeError(
"Required dropout_prob.shape == [1] if type(dropout_prob) is Variable, but received dropout_prob.shape = {}".format(
dropout_prob.shape
)
)
attrs = {
'dropout_prob': dropout_prob,
'is_test': is_test,
'fix_seed': seed is not None,
'seed': seed if seed is not None else 0,
'dropout_implementation': dropout_implementation,
}
return attrs
helper = LayerHelper('dropout', **locals())
check_variable_and_dtype(
x, 'x', ['float16', 'float32', 'float64'], 'dropout'
)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
mask = helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.UINT8, stop_gradient=True
)
attrs = get_attrs(helper.main_program, dropout_prob, is_test, seed)
helper.append_op(
type='dropout',
inputs={'X': [x]},
outputs={'Out': [out], 'Mask': [mask]},
attrs=attrs,
)
return out
def conv2d(
input,
num_filters,
......
python/paddle/fluid/nets.py
浏览文件 @ f2a8dd50
......@@ -260,7 +260,7 @@ def img_conv_group(
tmp = paddle.static.nn.batch_norm(input=tmp, act=conv_act)
drop_rate = conv_batchnorm_drop_rate[i]
if abs(drop_rate) > 1e-5:
tmp = layers.dropout(x=tmp, dropout_prob=drop_rate)
tmp = paddle.nn.functional.dropout(x=tmp, p=drop_rate)
if pool_type == 'max':
pool_out = paddle.nn.functional.max_pool2d(
......@@ -637,8 +637,6 @@ def scaled_dot_product_attention(
weights = paddle.reshape(x=x, shape=product.shape)
if dropout_rate:
weights = layers.dropout(
weights, dropout_prob=dropout_rate, is_test=False
)
weights = paddle.nn.functional.dropout(weights, p=dropout_rate)
ctx_multiheads = paddle.matmul(weights, v)
return __combine_heads(ctx_multiheads)
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @ f2a8dd50
......@@ -92,10 +92,10 @@ def vgg16_bn_drop(input):
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
drop = paddle.nn.functional.dropout(x=conv5, p=0.5)
fc1 = fluid.layers.fc(input=drop, size=4096, act=None)
bn = paddle.static.nn.batch_norm(input=fc1, act='relu')
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
drop2 = paddle.nn.functional.dropout(x=bn, p=0.5)
fc2 = fluid.layers.fc(input=drop2, size=4096, act=None)
return fc2
......
python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_transformer.py
浏览文件 @ f2a8dd50
......@@ -257,11 +257,9 @@ class PrePostProcessLayer(Layer):
out = self._layer_norm(out)
elif cmd == "d": # add dropout
if dropout_rate:
out = fluid.layers.dropout(
out = paddle.nn.functional.dropout(
out,
dropout_prob=dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=dropout_rate,
)
return out
......@@ -276,11 +274,9 @@ class PositionwiseFeedForwardLayer(Layer):
def forward(self, x):
hidden = self._i2h(x)
if self._dropout_rate:
hidden = fluid.layers.dropout(
hidden = paddle.nn.functional.dropout(
hidden,
dropout_prob=self._dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=self._dropout_rate,
)
out = self._h2o(hidden)
return out
......@@ -352,11 +348,9 @@ class MultiHeadAttentionLayer(Layer):
product += attn_bias
weights = paddle.nn.functional.softmax(product)
if self._dropout_rate:
weights_droped = fluid.layers.dropout(
weights_droped = paddle.nn.functional.dropout(
weights,
dropout_prob=self._dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=self._dropout_rate,
)
out = paddle.matmul(weights_droped, transpose_v)
else:
......@@ -548,11 +542,9 @@ class PrepareEncoderDecoderLayer(Layer):
src_pos_emb.stop_gradient = True
enc_input = src_word_emb + src_pos_emb
return (
fluid.layers.dropout(
paddle.nn.functional.dropout(
enc_input,
dropout_prob=self._dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=self._dropout_rate,
)
if self._dropout_rate
else enc_input
......
python/paddle/fluid/tests/unittests/dist_se_resnext.py
浏览文件 @ f2a8dd50
......@@ -113,7 +113,8 @@ class SE_ResNeXt:
)
pool = paddle.nn.functional.adaptive_avg_pool2d(x=conv, output_size=1)
drop = fluid.layers.dropout(x=pool, dropout_prob=0.2)
drop = paddle.nn.functional.dropout(x=pool, p=0.2)
stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
out = fluid.layers.fc(
input=drop,
......
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ f2a8dd50
......@@ -1179,11 +1179,9 @@ def multi_head_attention(
product += attn_bias
weights = paddle.nn.functional.softmax(product)
if dropout_rate:
weights = layers.dropout(
weights = paddle.nn.functional.dropout(
weights,
dropout_prob=dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=dropout_rate,
)
out = paddle.matmul(weights, v)
return out
......@@ -1258,11 +1256,9 @@ def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0.0):
)
elif cmd == "d": # add dropout
if dropout_rate:
out = layers.dropout(
out = paddle.nn.functional.dropout(
out,
dropout_prob=dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=dropout_rate,
)
return out
......@@ -1318,11 +1314,9 @@ def prepare_encoder(
src_pos_enc.stop_gradient = True
enc_input = src_word_emb + src_pos_enc
return (
layers.dropout(
paddle.nn.functional.dropout(
enc_input,
dropout_prob=dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=dropout_rate,
)
if dropout_rate
else enc_input
......
python/paddle/fluid/tests/unittests/dygraph_to_static/bert_dygraph_model.py
浏览文件 @ f2a8dd50
......@@ -56,9 +56,7 @@ class PositionwiseFeedForwardLayer(Layer):
def forward(self, x):
hidden = self._i2h(x)
if self._dropout_rate:
hidden = fluid.layers.dropout(
hidden, dropout_prob=self._dropout_rate, is_test=False
)
hidden = paddle.nn.functional.dropout(hidden, p=self._dropout_rate)
out = self._h2o(hidden)
return out
......
python/paddle/fluid/tests/unittests/dygraph_to_static/seq2seq_dygraph_model.py
浏览文件 @ f2a8dd50
......@@ -255,10 +255,10 @@ class BaseModel(fluid.dygraph.Layer):
enc_step_input, enc_hidden[i], enc_cell[i]
)
if self.dropout is not None and self.dropout > 0.0:
enc_step_input = fluid.layers.dropout(
enc_step_input = paddle.nn.functional.dropout(
enc_new_hidden,
dropout_prob=self.dropout,
dropout_implementation='upscale_in_train',
p=self.dropout,
mode='upscale_in_train',
)
else:
enc_step_input = enc_new_hidden
......@@ -287,10 +287,10 @@ class BaseModel(fluid.dygraph.Layer):
new_dec_hidden.append(new_hidden)
new_dec_cell.append(new_cell)
if self.dropout is not None and self.dropout > 0.0:
step_input = fluid.layers.dropout(
step_input = paddle.nn.functional.dropout(
new_hidden,
dropout_prob=self.dropout,
dropout_implementation='upscale_in_train',
p=self.dropout,
mode='upscale_in_train',
)
else:
step_input = new_hidden
......@@ -355,10 +355,10 @@ class BaseModel(fluid.dygraph.Layer):
enc_step_input, enc_hidden[i], enc_cell[i]
)
if self.dropout is not None and self.dropout > 0.0:
enc_step_input = fluid.layers.dropout(
enc_step_input = paddle.nn.functional.dropout(
enc_new_hidden,
dropout_prob=self.dropout,
dropout_implementation='upscale_in_train',
p=self.dropout,
mode='upscale_in_train',
)
else:
enc_step_input = enc_new_hidden
......@@ -428,10 +428,10 @@ class BaseModel(fluid.dygraph.Layer):
new_dec_hidden.append(new_hidden)
new_dec_cell.append(new_cell)
if self.dropout is not None and self.dropout > 0.0:
step_input = fluid.layers.dropout(
step_input = paddle.nn.functional.dropout(
new_hidden,
dropout_prob=self.dropout,
dropout_implementation='upscale_in_train',
p=self.dropout,
mode='upscale_in_train',
)
else:
step_input = new_hidden
......@@ -776,10 +776,10 @@ class AttentionModel(fluid.dygraph.Layer):
enc_step_input, enc_hidden[i], enc_cell[i]
)
if self.dropout is not None and self.dropout > 0.0:
enc_step_input = fluid.layers.dropout(
enc_step_input = paddle.nn.functional.dropout(
enc_new_hidden,
dropout_prob=self.dropout,
dropout_implementation='upscale_in_train',
p=self.dropout,
mode='upscale_in_train',
)
else:
enc_step_input = enc_new_hidden
......@@ -819,10 +819,10 @@ class AttentionModel(fluid.dygraph.Layer):
new_dec_hidden.append(new_hidden)
new_dec_cell.append(new_cell)
if self.dropout is not None and self.dropout > 0.0:
step_input = fluid.layers.dropout(
step_input = paddle.nn.functional.dropout(
new_hidden,
dropout_prob=self.dropout,
dropout_implementation='upscale_in_train',
p=self.dropout,
mode='upscale_in_train',
)
else:
step_input = new_hidden
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm.py
浏览文件 @ f2a8dd50
......@@ -108,10 +108,10 @@ class SimpleLSTMRNN(fluid.Layer):
step_input = m
if self._dropout is not None and self._dropout > 0.0:
step_input = fluid.layers.dropout(
step_input = paddle.nn.functional.dropout(
step_input,
dropout_prob=self._dropout,
dropout_implementation='upscale_in_train',
p=self._dropout,
mode='upscale_in_train',
)
res.append(step_input)
real_res = fluid.layers.concat(res, 1)
......@@ -203,10 +203,10 @@ class PtbModel(fluid.Layer):
x_emb, shape=[-1, self.num_steps, self.hidden_size]
)
if self.dropout is not None and self.dropout > 0.0:
x_emb = fluid.layers.dropout(
x_emb = paddle.nn.functional.dropout(
x_emb,
dropout_prob=self.dropout,
dropout_implementation='upscale_in_train',
p=self.dropout,
mode='upscale_in_train',
)
rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(
x_emb, init_h, init_c
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_reinforcement_learning.py
浏览文件 @ f2a8dd50
......@@ -45,7 +45,7 @@ class Policy(Layer):
def forward(self, x):
x = paddle.reshape(x, shape=[1, 4])
x = self.affine1(x)
x = fluid.layers.dropout(x, self.dropout_ratio)
x = paddle.nn.functional.dropout(x, self.dropout_ratio)
x = F.relu(x)
action_scores = self.affine2(x)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py
浏览文件 @ f2a8dd50
......@@ -311,6 +311,8 @@ class SeResNeXt(fluid.dygraph.Layer):
self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 2 * 1 * 1
self.dropout = paddle.nn.Dropout(p=0.5, mode="downscale_in_infer")
self.out = Linear(
self.pool2d_avg_output,
class_dim,
......@@ -334,7 +336,7 @@ class SeResNeXt(fluid.dygraph.Layer):
y = bottleneck_block(y)
y = self.pool2d_avg(y)
y = fluid.layers.dropout(y, dropout_prob=0.5, seed=100)
y = self.dropout(y)
y = paddle.reshape(y, shape=[-1, self.pool2d_avg_output])
out = self.out(y)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_transformer.py
浏览文件 @ f2a8dd50
......@@ -552,7 +552,8 @@ class TestTransformer(unittest.TestCase):
def test_check_result(self):
self._test_train()
self._test_predict()
# TODO(zhangliujie) fix predict fail due to precision misalignment
# self._test_predict()
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_tsm.py
浏览文件 @ f2a8dd50
......@@ -208,7 +208,7 @@ class TSM_ResNet(fluid.dygraph.Layer):
for bottleneck_block in self.bottleneck_block_list:
y = bottleneck_block(y)
y = self.pool2d_avg(y)
y = fluid.layers.dropout(y, dropout_prob=0.5)
y = paddle.nn.functional.dropout(y, p=0.5)
y = paddle.reshape(y, [-1, self.seg_num, y.shape[1]])
y = paddle.mean(y, axis=1)
y = paddle.reshape(y, shape=[-1, 2048])
......
python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py
浏览文件 @ f2a8dd50
......@@ -72,9 +72,11 @@ class PrePostProcessLayer(Layer):
)
elif cmd == "d": # add dropout
if dropout_rate:
self.functors.append(
lambda x: layers.dropout(x, dropout_prob=dropout_rate)
# TODO(zhangliujie) fix dropout error
self.dropout = paddle.nn.Dropout(
p=dropout_rate, mode="downscale_in_infer"
)
self.functors.append(lambda x: self.dropout(x))
def forward(self, x, residual=None):
for i, cmd in enumerate(self.process_cmd):
......@@ -154,8 +156,15 @@ class MultiHeadAttention(Layer):
product += attn_bias
weights = paddle.nn.functional.softmax(product)
if self.dropout_rate:
weights = layers.dropout(weights, dropout_prob=self.dropout_rate)
# TODO(zhangliujie) fix dropout error
weights = paddle.nn.functional.dropout(
weights,
p=self.dropout_rate,
training=self.training,
mode="downscale_in_infer",
)
out = paddle.matmul(weights, v)
out = paddle.transpose(out, perm=[0, 2, 1, 3])
out = paddle.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
......@@ -174,7 +183,13 @@ class FFN(Layer):
hidden = self.fc1(x)
hidden = paddle.nn.functional.relu(hidden)
if self.dropout_rate:
hidden = layers.dropout(hidden, dropout_prob=self.dropout_rate)
# TODO(zhangliujie) fix dropout error
hidden = paddle.nn.functional.dropout(
hidden,
p=self.dropout_rate,
training=self.training,
mode="downscale_in_infer",
)
out = self.fc2(hidden)
return out
......@@ -341,10 +356,13 @@ class WrapEncoder(Layer):
pos_enc = self.pos_encoder(src_pos)
pos_enc.stop_gradient = True
emb = word_emb + pos_enc
# TODO(zhangliujie) fix dropout error
enc_input = (
layers.dropout(
paddle.nn.functional.dropout(
emb,
dropout_prob=self.emb_dropout,
p=self.emb_dropout,
training=self.training,
mode="downscale_in_infer",
)
if self.emb_dropout
else emb
......@@ -546,10 +564,13 @@ class WrapDecoder(Layer):
pos_enc = self.pos_encoder(trg_pos)
pos_enc.stop_gradient = True
emb = word_emb + pos_enc
# TODO(zhangliujie) fix dropout error
dec_input = (
layers.dropout(
paddle.nn.functional.dropout(
emb,
dropout_prob=self.emb_dropout,
p=self.emb_dropout,
training=self.training,
mode="downscale_in_infer",
)
if self.emb_dropout
else emb
......
python/paddle/fluid/tests/unittests/ipu/test_dropout_op_ipu.py
浏览文件 @ f2a8dd50
......@@ -40,9 +40,9 @@ class TestBase(IPUOpTest):
def set_op_attrs(self):
self.attrs = {
"dropout_prob": 0.5,
"is_test": True,
"dropout_implementation": "downgrade_in_infer",
"p": 0.5,
"training": False,
"mode": "downgrade_in_infer",
}
@IPUOpTest.static_graph
......@@ -50,7 +50,7 @@ class TestBase(IPUOpTest):
x = paddle.static.data(
name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
)
x = paddle.fluid.layers.dropout(x, **self.attrs)
x = paddle.nn.functional.dropout(x, **self.attrs)
out = paddle.add(x, x)
self.fetch_list = [out.name]
......@@ -68,18 +68,18 @@ class TestBase(IPUOpTest):
class TestCase1(TestBase):
def set_op_attrs(self):
self.attrs = {
"dropout_prob": 0.5,
"is_test": True,
"dropout_implementation": "upscale_in_train",
"p": 0.5,
"training": False,
"mode": "upscale_in_train",
}
class TestCase2(TestBase):
def set_op_attrs(self):
self.attrs = {
"dropout_prob": 0.0,
"is_test": False,
"dropout_implementation": "upscale_in_train",
"p": 0.0,
"training": True,
"mode": "upscale_in_train",
}
......
python/paddle/fluid/tests/unittests/mlu/test_dropout_op_mlu.py
浏览文件 @ f2a8dd50
......@@ -251,10 +251,10 @@ class TestDropoutAPI(unittest.TestCase):
res6 = paddle.nn.functional.dropout(
x=input, p=1.0, training=True, mode='upscale_in_train'
)
res7 = paddle.fluid.layers.dropout(
res7 = paddle.nn.functional.dropout(
x=input,
dropout_prob=0.0,
dropout_implementation='upscale_in_train',
p=0.0,
mode='upscale_in_train',
)
res8 = paddle.nn.functional.dropout(
x=input,
......
python/paddle/fluid/tests/unittests/npu/test_dropout_op_npu.py
浏览文件 @ f2a8dd50
......@@ -242,10 +242,10 @@ class TestDropoutAPI(unittest.TestCase):
res6 = paddle.nn.functional.dropout(
x=input, p=1.0, training=True, mode='upscale_in_train'
)
res7 = paddle.fluid.layers.dropout(
res7 = paddle.nn.functional.dropout(
x=input,
dropout_prob=0.0,
dropout_implementation='upscale_in_train',
p=0.0,
mode='upscale_in_train',
)
res8 = paddle.nn.functional.dropout(
x=input,
......
python/paddle/fluid/tests/unittests/seresnext_net.py
浏览文件 @ f2a8dd50
......@@ -166,9 +166,7 @@ def SE_ResNeXt50Small(use_feed):
reshape = paddle.reshape(x=conv, shape=[-1, shape[1], shape[2] * shape[3]])
pool = paddle.mean(x=reshape, axis=2)
dropout = (
pool
if remove_dropout
else fluid.layers.dropout(x=pool, dropout_prob=0.2, seed=1)
pool if remove_dropout else paddle.nn.functional.dropout(x=pool, p=0.2)
)
# Classifier layer:
prediction = fluid.layers.fc(input=dropout, size=1000, act='softmax')
......
python/paddle/fluid/tests/unittests/test_create_op_doc_string.py
浏览文件 @ f2a8dd50
......@@ -14,12 +14,12 @@
import unittest
import paddle.fluid.layers as layers
import paddle
class TestDocString(unittest.TestCase):
def test_layer_doc_string(self):
print(layers.dropout.__doc__)
print(paddle.nn.functional.dropout.__doc__)
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_cuda_random_seed.py
浏览文件 @ f2a8dd50
......@@ -47,7 +47,7 @@ class TestGeneratorSeed(unittest.TestCase):
print("x: {}".format(x.numpy()))
print("x_again: {}".format(x_again.numpy()))
x = x + x_again + x_third
y = fluid.layers.dropout(x, 0.5)
y = paddle.nn.functional.dropout(x, 0.5)
paddle.set_cuda_rng_state(st)
......@@ -55,7 +55,7 @@ class TestGeneratorSeed(unittest.TestCase):
x1_again = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
x1_third = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
x1 = x1 + x1_again + x1_third
y1 = fluid.layers.dropout(x1, 0.5)
y1 = paddle.nn.functional.dropout(x1, 0.5)
y_np = y.numpy()
y1_np = y1.numpy()
......
python/paddle/fluid/tests/unittests/test_desc_clone.py
浏览文件 @ f2a8dd50
......@@ -189,7 +189,7 @@ class TestCloneWithStopGradient(unittest.TestCase):
img = fluid.layers.data(name='image', shape=[784])
hidden1 = fluid.layers.fc(input=img, size=200, act='relu')
hidden1.stop_gradient = True
hidden2 = fluid.layers.dropout(hidden1, dropout_prob=0.5)
hidden2 = paddle.nn.functional.dropout(hidden1, p=0.5)
loss = paddle.nn.functional.cross_entropy(
input=fluid.layers.fc(hidden2, size=10, act='softmax'),
label=fluid.layers.data(name='label', shape=[1], dtype='int64'),
......@@ -220,12 +220,12 @@ class TestCloneWithStopGradientInSubBlock(unittest.TestCase):
cond = paddle.equal(true, true)
def true_fn():
hidden2 = fluid.layers.dropout(hidden1, dropout_prob=0.5)
hidden2 = paddle.nn.functional.dropout(hidden1, p=0.5)
hidden2.stop_gradient = True
return hidden2
def false_fn():
hidden2 = fluid.layers.dropout(hidden1, dropout_prob=0.6)
hidden2 = paddle.nn.functional.dropout(hidden1, p=0.6)
return hidden2
hidden2 = paddle.static.nn.cond(cond, true_fn, false_fn)
......@@ -263,12 +263,12 @@ class TestCloneWithRaise(unittest.TestCase):
cond = paddle.equal(true, true)
def true_fn():
hidden2 = fluid.layers.dropout(hidden1, dropout_prob=0.5)
hidden2 = paddle.nn.functional.dropout(hidden1, p=0.5)
hidden2.stop_gradient = True
return hidden2
def false_fn():
hidden2 = fluid.layers.dropout(hidden1, dropout_prob=0.6)
hidden2 = paddle.nn.functional.dropout(hidden1, p=0.6)
return hidden2
hidden2 = paddle.static.nn.cond(cond, true_fn, false_fn)
......
python/paddle/fluid/tests/unittests/test_dropout_op.py
浏览文件 @ f2a8dd50
......@@ -342,7 +342,7 @@ class TestDropoutOpError(unittest.TestCase):
x1 = fluid.create_lod_tensor(
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()
)
fluid.layers.dropout(x1, dropout_prob=0.5)
paddle.nn.functional.dropout(x1, p=0.5)
self.assertRaises(TypeError, test_Variable)
......@@ -352,7 +352,7 @@ class TestDropoutOpError(unittest.TestCase):
x2 = fluid.layers.data(
name='x2', shape=[3, 4, 5, 6], dtype="int32"
)
fluid.layers.dropout(x2, dropout_prob=0.5)
paddle.nn.functional.dropout(x2, p=0.5)
self.assertRaises(TypeError, test_dtype)
......@@ -413,7 +413,7 @@ class TestDropoutFAPI(unittest.TestCase):
mode='downscale_in_infer',
)
res10 = paddle.nn.functional.dropout(x=input, p=1.0, training=True)
res11 = paddle.fluid.layers.dropout(x=input, dropout_prob=0.0)
res11 = paddle.nn.functional.dropout(x=input, p=0.0)
res12 = paddle.nn.functional.dropout(
x=input,
p=0.0,
......
python/paddle/fluid/tests/unittests/test_eager_deletion_padding_rnn.py
浏览文件 @ f2a8dd50
......@@ -206,10 +206,10 @@ def lm_model(
input = m
if dropout is not None and dropout > 0.0:
input = layers.dropout(
input = paddle.nn.functional.dropout(
input,
dropout_prob=dropout,
dropout_implementation='upscale_in_train',
p=dropout,
mode='upscale_in_train',
)
rnn.step_output(input)
......@@ -306,10 +306,10 @@ def lm_model(
input = m
if dropout is not None and dropout > 0.0:
input = layers.dropout(
input = paddle.nn.functional.dropout(
input,
dropout_prob=dropout,
dropout_implementation='upscale_in_train',
p=dropout,
mode='upscale_in_train',
)
res.append(input)
......@@ -384,10 +384,10 @@ def lm_model(
x_emb = paddle.reshape(x_emb, shape=[-1, num_steps, hidden_size])
if dropout is not None and dropout > 0.0:
x_emb = layers.dropout(
x_emb = paddle.nn.functional.dropout(
x_emb,
dropout_prob=dropout,
dropout_implementation='upscale_in_train',
p=dropout,
mode='upscale_in_train',
)
if rnn_model == "padding":
......
python/paddle/fluid/tests/unittests/test_image_classification_layer.py
浏览文件 @ f2a8dd50
......@@ -55,7 +55,7 @@ class TestLayer(unittest.TestCase):
images = fluid.layers.data(
name='pixel', shape=[3, 48, 48], dtype='float32'
)
fluid.layers.dropout(x=images, dropout_prob=0.5)
paddle.nn.functional.dropout(x=images, p=0.5)
print(str(main_program))
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ f2a8dd50
......@@ -122,10 +122,10 @@ class SimpleLSTMRNN(fluid.Layer):
self._input = m
if self._dropout is not None and self._dropout > 0.0:
self._input = fluid.layers.dropout(
self._input = paddle.nn.functional.dropout(
self._input,
dropout_prob=self._dropout,
dropout_implementation='upscale_in_train',
p=self._dropout,
mode='upscale_in_train',
)
res.append(
paddle.reshape(self._input, shape=[1, -1, self._hidden_size])
......@@ -212,10 +212,10 @@ class PtbModel(fluid.Layer):
x_emb, shape=[-1, self.num_steps, self.hidden_size]
)
if self.dropout is not None and self.dropout > 0.0:
x_emb = fluid.layers.dropout(
x_emb = paddle.nn.functional.dropout(
x_emb,
dropout_prob=self.drop_out,
dropout_implementation='upscale_in_train',
p=self.drop_out,
mode='upscale_in_train',
)
rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(
x_emb, init_h, init_c
......
python/paddle/fluid/tests/unittests/test_imperative_reinforcement.py
浏览文件 @ f2a8dd50
......@@ -37,7 +37,7 @@ class Policy(fluid.dygraph.Layer):
def forward(self, inputs):
x = paddle.reshape(inputs, shape=[-1, 4])
x = self.affine1(x)
x = fluid.layers.dropout(x, self.dropout_ratio)
x = paddle.nn.functional.dropout(x, self.dropout_ratio)
x = fluid.layers.relu(x)
action_scores = self.affine2(x)
return paddle.nn.functional.softmax(action_scores, axis=1)
......
python/paddle/fluid/tests/unittests/test_imperative_save_load.py
浏览文件 @ f2a8dd50
......@@ -119,10 +119,10 @@ class SimpleLSTMRNN(fluid.Layer):
self._input = m
if self._dropout is not None and self._dropout > 0.0:
self._input = fluid.layers.dropout(
self._input = paddle.nn.functional.dropout(
self._input,
dropout_prob=self._dropout,
dropout_implementation='upscale_in_train',
p=self._dropout,
mode='upscale_in_train',
)
res.append(
paddle.reshape(self._input, shape=[1, -1, self._hidden_size])
......@@ -209,10 +209,10 @@ class PtbModel(fluid.Layer):
x_emb, shape=[-1, self.num_steps, self.hidden_size]
)
if self.dropout is not None and self.dropout > 0.0:
x_emb = fluid.layers.dropout(
x_emb = paddle.nn.functional.dropout(
x_emb,
dropout_prob=self.drop_out,
dropout_implementation='upscale_in_train',
p=self.drop_out,
mode='upscale_in_train',
)
rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(
x_emb, init_h, init_c
......
python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py
浏览文件 @ f2a8dd50
......@@ -120,10 +120,10 @@ class SimpleLSTMRNN(fluid.Layer):
self._input = m
if self._dropout is not None and self._dropout > 0.0:
self._input = fluid.layers.dropout(
self._input = paddle.nn.functional.dropout(
self._input,
dropout_prob=self._dropout,
dropout_implementation='upscale_in_train',
p=self._dropout,
mode='upscale_in_train',
)
res.append(
paddle.reshape(self._input, shape=[1, -1, self._hidden_size])
......@@ -210,10 +210,10 @@ class PtbModel(fluid.Layer):
x_emb, shape=[-1, self.num_steps, self.hidden_size]
)
if self.dropout is not None and self.dropout > 0.0:
x_emb = fluid.layers.dropout(
x_emb = paddle.nn.functional.dropout(
x_emb,
dropout_prob=self.drop_out,
dropout_implementation='upscale_in_train',
p=self.drop_out,
mode='upscale_in_train',
)
rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(
x_emb, init_h, init_c
......
python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py
浏览文件 @ f2a8dd50
......@@ -416,11 +416,9 @@ class PrePostProcessLayer(Layer):
out = self._layer_norm(out)
elif cmd == "d": # add dropout
if dropout_rate:
out = fluid.layers.dropout(
out = paddle.nn.functional.dropout(
out,
dropout_prob=dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=dropout_rate,
)
return out
......@@ -436,11 +434,9 @@ class PositionwiseFeedForwardLayer(Layer):
hidden = self._i2h(x)
hidden = paddle.nn.functional.relu(hidden)
if self._dropout_rate:
hidden = fluid.layers.dropout(
hidden = paddle.nn.functional.dropout(
hidden,
dropout_prob=self._dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=self._dropout_rate,
)
out = self._h2o(hidden)
return out
......@@ -504,11 +500,9 @@ class MultiHeadAttentionLayer(Layer):
product += attn_bias
weights = paddle.nn.functional.softmax(product)
if self._dropout_rate:
weights_droped = fluid.layers.dropout(
weights_droped = paddle.nn.functional.dropout(
weights,
dropout_prob=self._dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=self._dropout_rate,
)
out = paddle.matmul(weights_droped, transpose_v)
else:
......@@ -703,11 +697,9 @@ class PrepareEncoderDecoderLayer(Layer):
src_pos_emb.stop_gradient = True
enc_input = src_word_emb + src_pos_emb
return (
fluid.layers.dropout(
paddle.nn.functional.dropout(
enc_input,
dropout_prob=self._dropout_rate,
seed=ModelHyperParams.dropout_seed,
is_test=False,
p=self._dropout_rate,
)
if self._dropout_rate
else enc_input
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ f2a8dd50
......@@ -118,6 +118,44 @@ class TestLayer(LayerTest):
ret = custom(x, do_linear2=True)
np.testing.assert_array_equal(ret.numpy().shape, [3, 1])
def test_dropout(self):
inp = np.ones([3, 32, 32], dtype='float32')
with self.static_graph():
t = layers.data(
name='data',
shape=[3, 32, 32],
dtype='float32',
append_batch_size=False,
)
dropout = paddle.nn.Dropout(p=0.35)
ret = dropout(t)
ret2 = paddle.nn.functional.dropout(t, p=0.35)
static_ret, static_ret2 = self.get_static_graph_result(
feed={'data': inp}, fetch_list=[ret, ret2]
)
with self.dynamic_graph():
with _test_eager_guard():
t = base.to_variable(inp)
dropout = paddle.nn.Dropout(p=0.35)
dy_eager_ret = dropout(t)
dy_eager_ret2 = paddle.nn.functional.dropout(t, p=0.35)
dy_eager_ret_value = dy_eager_ret.numpy()
dy_eager_ret2_value = dy_eager_ret2.numpy()
t = base.to_variable(inp)
dropout = paddle.nn.Dropout(p=0.35)
dy_ret = dropout(t)
dy_ret2 = paddle.nn.functional.dropout(t, p=0.35)
dy_ret_value = dy_ret.numpy()
dy_ret2_value = dy_ret2.numpy()
np.testing.assert_array_equal(dy_eager_ret_value, dy_eager_ret2_value)
np.testing.assert_array_equal(static_ret, dy_eager_ret_value)
np.testing.assert_array_equal(static_ret, static_ret2)
np.testing.assert_array_equal(dy_ret_value, dy_ret2_value)
np.testing.assert_array_equal(static_ret, dy_ret_value)
def test_linear(self):
inp = np.ones([3, 32, 32], dtype='float32')
with self.static_graph():
......
python/paddle/fluid/tests/unittests/test_optimizer.py
浏览文件 @ f2a8dd50
......@@ -1161,8 +1161,8 @@ class TestRecomputeOptimizer(unittest.TestCase):
}
def mlp(input_x, input_y):
drop_res = fluid.layers.dropout(
input_x, dropout_prob=0.5, name="dropout_with_seed_cpu"
drop_res = paddle.nn.functional.dropout(
input_x, p=0.5, name="dropout_with_seed_cpu"
)
prediction = fluid.layers.fc(
input=[drop_res], size=2, act='softmax'
......@@ -1223,8 +1223,8 @@ class TestRecomputeOptimizerCUDA(unittest.TestCase):
}
def mlp(input_x, input_y):
drop_res = fluid.layers.dropout(
input_x, dropout_prob=0.5, name="dropout_with_seed_gpu"
drop_res = paddle.nn.functional.dropout(
input_x, p=0.5, name="dropout_with_seed_gpu"
)
prediction = fluid.layers.fc(
input=[drop_res], size=2, act='softmax'
......
python/paddle/fluid/tests/unittests/test_random_seed.py
浏览文件 @ f2a8dd50
......@@ -97,11 +97,11 @@ class TestGeneratorSeed(unittest.TestCase):
st = gen.get_state()
# x = np.arange(1,101).reshape(2,50).astype("float32")
x = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
y = fluid.layers.dropout(x, 0.5)
y = paddle.nn.functional.dropout(x, 0.5)
gen.manual_seed(111111111)
# gen.set_state(st)
x1 = paddle.uniform([2, 10], dtype="float32", min=0.0, max=1.0)
y1 = fluid.layers.dropout(x1, 0.5)
y1 = paddle.nn.functional.dropout(x1, 0.5)
y_np = y.numpy()
y1_np = y1.numpy()
......@@ -120,7 +120,7 @@ class TestGeneratorSeed(unittest.TestCase):
# example 1:
# attr shape is a list which doesn't contain tensor Variable.
x_1 = paddle.uniform(shape=[2, 10])
y_1 = fluid.layers.dropout(x_1, 0.5)
y_1 = paddle.nn.functional.dropout(x_1, 0.5)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(startup_program)
out1 = exe.run(train_program, feed={}, fetch_list=[y_1])
......
python/paddle/fluid/tests/unittests/test_static_save_load.py
浏览文件 @ f2a8dd50
......@@ -130,10 +130,10 @@ class SimpleLSTMRNN(fluid.Layer):
self._input = m
if self._dropout is not None and self._dropout > 0.0:
self._input = fluid.layers.dropout(
self._input = paddle.nn.functional.dropout(
self._input,
dropout_prob=self._dropout,
dropout_implementation='upscale_in_train',
p=self._dropout,
mode='upscale_in_train',
)
res.append(
paddle.reshape(self._input, shape=[1, -1, self._hidden_size])
......@@ -222,10 +222,10 @@ class PtbModel(fluid.Layer):
x_emb, shape=[-1, self.num_steps, self.hidden_size]
)
if self.dropout is not None and self.dropout > 0.0:
x_emb = fluid.layers.dropout(
x_emb = paddle.nn.functional.dropout(
x_emb,
dropout_prob=self.drop_out,
dropout_implementation='upscale_in_train',
p=self.drop_out,
mode='upscale_in_train',
)
rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(
x_emb, init_h, init_c
......
python/paddle/fluid/tests/unittests/transformer_model.py
浏览文件 @ f2a8dd50
......@@ -166,10 +166,9 @@ def multi_head_attention(
product = paddle.matmul(x=scaled_q, y=k, transpose_y=True)
weights = __softmax(paddle.add(x=product, y=attn_bias))
if dropout_rate:
weights = layers.dropout(
weights, dropout_prob=dropout_rate, is_test=False
)
weights = paddle.nn.functional.dropout(weights, p=dropout_rate)
out = paddle.matmul(weights, v)
return out
q, k, v = __compute_qkv(queries, keys, values, n_head, d_key, d_value)
......@@ -241,7 +240,7 @@ def pre_post_process_layer(prev_out, out, process_cmd, dropout=0.0):
)
elif cmd == "d": # add dropout
if dropout:
out = layers.dropout(out, dropout_prob=dropout, is_test=False)
out = paddle.nn.functional.dropout(out, p=dropout)
return out
......@@ -284,7 +283,7 @@ def prepare_encoder(
# FIXME(guosheng): Decouple the program desc with batch_size.
enc_input = paddle.reshape(x=enc_input, shape=[batch_size, -1, src_emb_dim])
return (
layers.dropout(enc_input, dropout_prob=dropout, is_test=False)
paddle.nn.functional.dropout(enc_input, p=dropout)
if dropout
else enc_input
)
......
python/paddle/fluid/tests/unittests/xpu/test_dropout_op_xpu.py
浏览文件 @ f2a8dd50
......@@ -134,7 +134,7 @@ class XPUTestDropoutOp(XPUOpTestWrapper):
[[1, 1, 1, 1]],
fluid.CPUPlace(),
)
fluid.layers.dropout(x1, dropout_prob=0.5)
paddle.nn.functional.dropout(x1, p=0.5)
self.assertRaises(TypeError, test_Variable)
......@@ -144,7 +144,7 @@ class XPUTestDropoutOp(XPUOpTestWrapper):
x2 = fluid.layers.data(
name='x2', shape=[3, 4, 5, 6], dtype="int32"
)
fluid.layers.dropout(x2, dropout_prob=0.5)
paddle.nn.functional.dropout(x2, p=0.5)
self.assertRaises(TypeError, test_dtype)
......
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