未验证 提交 4a00bea7 编写于 作者: X Xin Pan 提交者: GitHub

Merge pull request #15789 from panyx0718/imperative2

polish parameter names
......@@ -17,7 +17,7 @@ import contextlib
import sys
import numpy as np
import collections
from .. import unique_name
from paddle.fluid import core
from paddle.fluid import framework
from paddle.fluid.imperative import base
......@@ -26,14 +26,33 @@ __all__ = ['Layer', 'PyLayer']
class Layer(core.Layer):
"""Layers composed of operators."""
def __init__(self, dtype=core.VarDesc.VarType.FP32, name=None):
"""Layers composed of operators.
Args:
name_scope: prefix name used by the layer to name parameters.
If prefix is "my_model/layer_1", parameter name in MyLayer
can be "my_model/layer_1/MyLayer/w_n", where w is the parameter
base name and n is an unique suffix auto-generated.
dtype: data type for the variables in the layer.
"""
def __init__(self, name_scope, dtype=core.VarDesc.VarType.FP32):
self._full_name = unique_name.generate(name_scope + "/" +
self.__class__.__name__)
self._built = False
self._dtype = dtype
self._parameters = collections.OrderedDict()
self._sub_layers = collections.OrderedDict()
def full_name(self):
"""Full name for this layers.
Full name is composed by name_scope + "/" + MyLayer.__class__.__name__
Returns full name of this name.
"""
return self._full_name
def parameters(self, include_sublayers=True):
"""Returns a list of Parameters from current and sub-layers.
......
......@@ -27,6 +27,7 @@ __all__ = ['Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding']
class Conv2D(layers.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
......@@ -38,19 +39,17 @@ class Conv2D(layers.Layer):
act=None,
param_attr=None,
bias_attr=None,
name=None,
dtype=core.VarDesc.VarType.FP32):
assert param_attr is not False, "param_attr should not be False here."
super(Conv2D, self).__init__(name=name, dtype=dtype)
super(Conv2D, self).__init__(name_scope, dtype=dtype)
# TODO(minqiyang): Move this to the top.
from ..layer_helper import LayerHelper
self._helper = LayerHelper(
type(self).__name__,
self.full_name(),
param_attr=param_attr,
bias_attr=bias_attr,
dtype=dtype,
name=name,
act=act)
self._groups = groups
......@@ -143,6 +142,7 @@ class Conv2D(layers.Layer):
class Pool2D(layers.Layer):
def __init__(self,
name_scope,
pool_size=-1,
pool_type="max",
pool_stride=1,
......@@ -151,7 +151,6 @@ class Pool2D(layers.Layer):
use_cudnn=True,
ceil_mode=False,
exclusive=True,
name=None,
dtype=core.VarDesc.VarType.FP32):
if pool_type not in ["max", "avg"]:
raise ValueError(
......@@ -166,10 +165,10 @@ class Pool2D(layers.Layer):
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
super(Pool2D, self).__init__(name=name, dtype=dtype)
super(Pool2D, self).__init__(name_scope, dtype=dtype)
from ..layer_helper import LayerHelper
self._helper = LayerHelper(type(self).__name__, dtype=dtype, name=name)
self._helper = LayerHelper(self.full_name(), dtype=dtype)
self._pool_type = pool_type
self._pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
......@@ -205,25 +204,24 @@ class Pool2D(layers.Layer):
class FC(layers.Layer):
def __init__(self,
name_scope,
size,
param_attr=None,
bias_attr=None,
num_flatten_dims=1,
dtype=core.VarDesc.VarType.FP32,
act=None,
name=None):
super(FC, self).__init__()
act=None):
super(FC, self).__init__(name_scope)
self._size = size
self._num_flatten_dims = num_flatten_dims
self._dtype = dtype
from ..layer_helper import LayerHelper
self._helper = LayerHelper(
'FC',
self.full_name(),
param_attr=param_attr,
bias_attr=bias_attr,
act=act,
name=name)
act=act)
def _build_once(self, input):
input_shape = input.shape
......@@ -282,6 +280,7 @@ class FC(layers.Layer):
class BatchNorm(layers.Layer):
def __init__(self,
name_scope,
num_channels,
act=None,
is_test=False,
......@@ -292,22 +291,20 @@ class BatchNorm(layers.Layer):
dtype=core.VarDesc.VarType.FP32,
data_layout='NCHW',
in_place=False,
name=None,
moving_mean_name=None,
moving_variance_name=None,
do_model_average_for_mean_and_var=False,
fuse_with_relu=False,
use_global_stats=False):
super(BatchNorm, self).__init__()
super(BatchNorm, self).__init__(name_scope)
assert bias_attr is not False, "bias_attr should not be False in batch_norm."
from ..layer_helper import LayerHelper
self._helper = LayerHelper(
'batch_norm',
self.full_name(),
param_attr=param_attr,
bias_attr=bias_attr,
name=name,
act=act)
if dtype == core.VarDesc.VarType.FP16:
......@@ -419,6 +416,7 @@ class Embedding(layers.Layer):
constructor.
Args:
name_scope: See base class.
size(tuple|list): The shape of the look up table parameter. It should
have two elements which indicate the size of the dictionary of
embeddings and the size of each embedding vector respectively.
......@@ -446,6 +444,7 @@ class Embedding(layers.Layer):
"""
def __init__(self,
name_scope,
size,
is_sparse=False,
is_distributed=False,
......@@ -453,7 +452,7 @@ class Embedding(layers.Layer):
param_attr=None,
dtype='float32'):
super(Embedding, self).__init__()
super(Embedding, self).__init__(name_scope)
self._size = size
self._is_sparse = is_sparse
self._is_distributed = is_distributed
......@@ -468,7 +467,7 @@ class Embedding(layers.Layer):
assert self._is_sparse is True and self._is_distributed is False
from ..layer_helper import LayerHelper
self._helper = LayerHelper('embedding', param_attr=param_attr)
self._helper = LayerHelper(self.full_name(), param_attr=param_attr)
self._w = self._helper.create_parameter(
attr=self._param_attr,
shape=self._size,
......
......@@ -34,6 +34,9 @@ class LayerHelper(object):
self.kwargs = kwargs
self.layer_type = layer_type
name = self.kwargs.get('name', None)
# TODO(panyx0718, minqiyang): imperative mode
# can not use both `layer_type` and `name`. Deprecate LayerHelper
# and write a Helper for imperative mode.
if name is None:
self.kwargs['name'] = unique_name.generate(self.layer_type)
......
......@@ -20,10 +20,10 @@ from paddle.fluid.layer_helper import LayerHelper
class L1(fluid.imperative.Layer):
def __init__(self):
super(L1, self).__init__()
def __init__(self, prefix):
super(L1, self).__init__(prefix)
self._helper = LayerHelper(
'MyLayer',
self.full_name(),
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
......@@ -43,20 +43,20 @@ class L1(fluid.imperative.Layer):
class L2(fluid.imperative.Layer):
def __init__(self):
super(L2, self).__init__()
self.layer1 = L1()
self.layer2 = L1()
def __init__(self, prefix):
super(L2, self).__init__(prefix)
self.layer1 = L1(self.full_name())
self.layer2 = L1(self.full_name())
def forward(self):
return self.layer1() + self.layer2()
class L3(fluid.imperative.Layer):
def __init__(self):
super(L3, self).__init__()
self.layer1 = L2()
self.layer2 = L2()
def __init__(self, prefix):
super(L3, self).__init__(prefix)
self.layer1 = L2(self.full_name())
self.layer2 = L2(self.full_name())
def forward(self):
return self.layer1() + self.layer2()
......@@ -65,16 +65,23 @@ class L3(fluid.imperative.Layer):
class TestBaseLayer(unittest.TestCase):
def test_one_level(self):
with fluid.imperative.guard():
l = L1()
l = L1('test_one_level')
ret = l()
self.assertEqual(l.w1.name, "MyLayer_0.w_0")
self.assertEqual(l.w2.name, "MyLayer_0.w_1")
self.assertEqual(l.w1.name, "test_one_level/L1_0_0.w_0")
self.assertEqual(l.w2.name, "test_one_level/L1_0_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2])))
def test_three_level(self):
with fluid.imperative.guard():
l = L3()
l = L3('test_three_level')
names = [p.name for p in l.parameters()]
ret = l()
self.assertEqual(names[0], "test_three_level/L3_0/L2_0/L1_0_0.w_0")
self.assertEqual(names[1], "test_three_level/L3_0/L2_0/L1_0_0.w_1")
self.assertEqual(names[2], "test_three_level/L3_0/L2_0/L1_1_0.w_0")
self.assertEqual(names[3], "test_three_level/L3_0/L2_0/L1_1_0.w_1")
self.assertEqual(names[4], "test_three_level/L3_0/L2_1/L1_0_0.w_0")
self.assertEqual(names[5], "test_three_level/L3_0/L2_1/L1_0_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.8 * np.ones([2, 2])))
......
......@@ -15,7 +15,6 @@
import contextlib
import unittest
import numpy as np
import sys
import paddle.fluid as fluid
from paddle.fluid import core
......@@ -24,8 +23,8 @@ from test_imperative_base import new_program_scope
class MyLayer(fluid.imperative.Layer):
def __init__(self):
super(MyLayer, self).__init__()
def __init__(self, name_scope):
super(MyLayer, self).__init__(name_scope)
def forward(self, inputs):
x = fluid.layers.relu(inputs)
......@@ -50,12 +49,14 @@ class MyPyLayer(fluid.imperative.PyLayer):
class MLP(fluid.imperative.Layer):
def __init__(self):
super(MLP, self).__init__()
self._fc1 = FC(3,
def __init__(self, name_scope):
super(MLP, self).__init__(name_scope)
self._fc1 = FC(self.full_name(),
3,
fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
self._fc2 = FC(4,
self._fc2 = FC(self.full_name(),
4,
fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
......@@ -67,8 +68,9 @@ class MLP(fluid.imperative.Layer):
class SimpleRNNCell(fluid.imperative.Layer):
def __init__(self, step_input_size, hidden_size, output_size, param_attr):
super(SimpleRNNCell, self).__init__()
def __init__(self, name_scope, step_input_size, hidden_size, output_size,
param_attr):
super(SimpleRNNCell, self).__init__(name_scope)
self.step_input_size = step_input_size
self.hidden_size = hidden_size
self.output_size = output_size
......@@ -158,10 +160,11 @@ class SimpleRNNCell(fluid.imperative.Layer):
class SimpleRNN(fluid.imperative.Layer):
def __init__(self):
super(SimpleRNN, self).__init__()
def __init__(self, name_scope):
super(SimpleRNN, self).__init__(name_scope)
self.seq_len = 4
self._cell = SimpleRNNCell(
self.full_name(),
3,
3,
3,
......@@ -205,7 +208,7 @@ class TestImperative(unittest.TestCase):
with fluid.imperative.guard():
cl = core.Layer()
cl.forward([])
l = fluid.imperative.Layer()
l = fluid.imperative.Layer("l")
self.assertRaises(NotImplementedError, l.forward, [])
def test_pylayer_func_id(self):
......@@ -281,7 +284,7 @@ class TestImperative(unittest.TestCase):
np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
l = MyLayer()
l = MyLayer("my_layer")
x = l(var_inp)[0]
self.assertIsNotNone(x)
dy_out = x._numpy()
......@@ -291,7 +294,7 @@ class TestImperative(unittest.TestCase):
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[3], append_batch_size=False)
l = MyLayer()
l = MyLayer("my_layer")
x = l(inp)[0]
param_grads = fluid.backward.append_backward(
x, parameter_list=[l._x_for_debug.name])[0]
......@@ -309,7 +312,7 @@ class TestImperative(unittest.TestCase):
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
mlp = MLP()
mlp = MLP("mlp")
out = mlp(var_inp)
dy_out = out._numpy()
out._backward()
......@@ -318,7 +321,7 @@ class TestImperative(unittest.TestCase):
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
mlp = MLP()
mlp = MLP("mlp")
out = mlp(inp)
param_grads = fluid.backward.append_backward(
out, parameter_list=[mlp._fc1._w.name])[0]
......@@ -334,10 +337,10 @@ class TestImperative(unittest.TestCase):
self.assertTrue(np.allclose(dy_grad, static_grad))
params = mlp.parameters(True)
self.assertEqual("FC_0.w_0", params[0].name)
self.assertEqual("FC_0.b_0", params[1].name)
self.assertEqual("FC_1.w_0", params[2].name)
self.assertEqual("FC_1.b_0", params[3].name)
self.assertEqual("mlp/MLP_0/FC_0_0.w_0", params[0].name)
self.assertEqual("mlp/MLP_0/FC_0_0.b_0", params[1].name)
self.assertEqual("mlp/MLP_0/FC_1_0.w_0", params[2].name)
self.assertEqual("mlp/MLP_0/FC_1_0.b_0", params[3].name)
self.assertEqual(len(params), 4)
sublayers = mlp.sublayers(True)
......@@ -353,7 +356,7 @@ class TestImperative(unittest.TestCase):
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
simple_rnn = SimpleRNN()
simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn.forward(var_inp)
dy_out = outs[3]._numpy()
outs[3]._backward()
......@@ -364,7 +367,7 @@ class TestImperative(unittest.TestCase):
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[1, 4, 3], append_batch_size=False)
simple_rnn = SimpleRNN()
simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn(inp)
param_grads = fluid.backward.append_backward(outs[3])
exe = fluid.Executor(fluid.CPUPlace())
......
......@@ -28,10 +28,10 @@ from paddle.fluid.imperative.base import to_variable
class Discriminator(fluid.imperative.Layer):
def __init__(self):
super(Discriminator, self).__init__()
self._fc1 = FC(size=32, act='elu', name="d_fc1")
self._fc2 = FC(size=1, name="d_fc2")
def __init__(self, name_scope):
super(Discriminator, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), size=32, act='elu')
self._fc2 = FC(self.full_name(), size=1)
def forward(self, inputs):
x = self._fc1(inputs)
......@@ -39,11 +39,11 @@ class Discriminator(fluid.imperative.Layer):
class Generator(fluid.imperative.Layer):
def __init__(self):
super(Generator, self).__init__()
self._fc1 = FC(size=64, act='elu', name="g_fc1")
self._fc2 = FC(size=64, act='elu', name="g_fc2")
self._fc3 = FC(size=1, name="g_fc3")
def __init__(self, name_scope):
super(Generator, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), size=64, act='elu')
self._fc2 = FC(self.full_name(), size=64, act='elu')
self._fc3 = FC(self.full_name(), size=1)
def forward(self, inputs):
x = self._fc1(inputs)
......@@ -65,8 +65,8 @@ class TestImperativeMnist(unittest.TestCase):
scope = fluid.core.Scope()
with new_program_scope(
main=discriminate_p, startup=startup, scope=scope):
discriminator = Discriminator()
generator = Generator()
discriminator = Discriminator("d")
generator = Generator("g")
img = fluid.layers.data(
name="img", shape=[2, 1], append_batch_size=False)
......@@ -93,8 +93,8 @@ class TestImperativeMnist(unittest.TestCase):
sgd.minimize(d_loss)
with new_program_scope(main=generate_p, startup=startup, scope=scope):
discriminator = Discriminator()
generator = Generator()
discriminator = Discriminator("d")
generator = Generator("g")
noise = fluid.layers.data(
name="noise", shape=[2, 2], append_batch_size=False)
......@@ -134,8 +134,8 @@ class TestImperativeMnist(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
discriminator = Discriminator()
generator = Generator()
discriminator = Discriminator("d")
generator = Generator("g")
sgd = SGDOptimizer(learning_rate=1e-3)
d_real = discriminator(to_variable(np.ones([2, 1], np.float32)))
......
......@@ -28,6 +28,7 @@ from test_imperative_base import new_program_scope
class SimpleImgConvPool(fluid.imperative.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
......@@ -44,9 +45,10 @@ class SimpleImgConvPool(fluid.imperative.Layer):
use_cudnn=False,
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__()
super(SimpleImgConvPool, self).__init__(name_scope)
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
......@@ -59,6 +61,7 @@ class SimpleImgConvPool(fluid.imperative.Layer):
use_cudnn=use_cudnn)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
......@@ -73,19 +76,20 @@ class SimpleImgConvPool(fluid.imperative.Layer):
class MNIST(fluid.imperative.Layer):
def __init__(self, param_attr=None, bias_attr=None):
super(MNIST, self).__init__()
def __init__(self, name_scope, param_attr=None, bias_attr=None):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
1, 20, 5, 2, 2, act="relu")
self.full_name(), 1, 20, 5, 2, 2, act="relu")
self._simple_img_conv_pool_2 = SimpleImgConvPool(
20, 50, 5, 2, 2, act="relu")
self.full_name(), 20, 50, 5, 2, 2, act="relu")
pool_2_shape = 50 * 4 * 4
SIZE = 10
scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
self._fc = FC(10,
self._fc = FC(self.full_name(),
10,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=scale)),
......@@ -106,7 +110,7 @@ class TestImperativeMnist(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
mnist = MNIST()
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
......@@ -150,7 +154,7 @@ class TestImperativeMnist(unittest.TestCase):
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
mnist = MNIST()
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
......
......@@ -28,12 +28,13 @@ from paddle.fluid.backward import append_backward
class SimpleLSTMRNN(fluid.imperative.Layer):
def __init__(self,
name_scope,
hidden_size,
num_steps,
num_layers=2,
init_scale=0.1,
dropout=None):
super(SimpleLSTMRNN, self).__init__()
super(SimpleLSTMRNN, self).__init__(name_scope)
self._hidden_size = hidden_size
self._num_layers = num_layers
self._init_scale = init_scale
......@@ -130,13 +131,14 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
class PtbModel(fluid.imperative.Layer):
def __init__(self,
name_scope,
hidden_size,
vocab_size,
num_layers=2,
num_steps=20,
init_scale=0.1,
dropout=None):
super(PtbModel, self).__init__()
super(PtbModel, self).__init__(name_scope)
self.hidden_size = hidden_size
self.vocab_size = vocab_size
self.init_scale = init_scale
......@@ -146,12 +148,14 @@ class PtbModel(fluid.imperative.Layer):
from paddle.fluid.layer_helper import LayerHelper
self._helper = LayerHelper('PtbModel', act="tanh")
self.simple_lstm_rnn = SimpleLSTMRNN(
self.full_name(),
hidden_size,
num_steps,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
self.embedding = Embedding(
self.full_name(),
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False,
......@@ -226,6 +230,7 @@ class TestImperativePtbRnn(unittest.TestCase):
fluid.default_main_program().random_seed = seed
# TODO: marsyang1993 Change seed to
ptb_model = PtbModel(
"ptb_model",
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
......@@ -265,6 +270,7 @@ class TestImperativePtbRnn(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
ptb_model = PtbModel(
"ptb_model",
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
......
......@@ -70,15 +70,17 @@ def optimizer_setting(params):
class ConvBNLayer(fluid.imperative.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None):
super(ConvBNLayer, self).__init__()
super(ConvBNLayer, self).__init__(name_scope)
self._conv = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
......@@ -88,7 +90,7 @@ class ConvBNLayer(fluid.imperative.Layer):
act=None,
bias_attr=None)
self._batch_norm = BatchNorm(num_filters, act=act)
self._batch_norm = BatchNorm(self.full_name(), num_filters, act=act)
def forward(self, inputs):
y = self._conv(inputs)
......@@ -98,21 +100,29 @@ class ConvBNLayer(fluid.imperative.Layer):
class BottleneckBlock(fluid.imperative.Layer):
def __init__(self, num_channels, num_filters, stride, shortcut=True):
super(BottleneckBlock, self).__init__()
def __init__(self,
name_scope,
num_channels,
num_filters,
stride,
shortcut=True):
super(BottleneckBlock, self).__init__(name_scope)
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act='relu')
self.conv1 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu')
self.conv2 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
......@@ -120,6 +130,7 @@ class BottleneckBlock(fluid.imperative.Layer):
if not shortcut:
self.short = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
......@@ -141,13 +152,13 @@ class BottleneckBlock(fluid.imperative.Layer):
y = fluid.layers.elementwise_add(x=short, y=conv2)
layer_helper = LayerHelper('elementwise_add_activation', act='relu')
layer_helper = LayerHelper(self.full_name(), act='relu')
return layer_helper.append_activation(y)
class ResNet(fluid.imperative.Layer):
def __init__(self, layers=50, class_dim=102):
super(ResNet, self).__init__()
def __init__(self, name_scope, layers=50, class_dim=102):
super(ResNet, self).__init__(name_scope)
self.layers = layers
supported_layers = [50, 101, 152]
......@@ -163,9 +174,18 @@ class ResNet(fluid.imperative.Layer):
num_filters = [64, 128, 256, 512]
self.conv = ConvBNLayer(
num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
self.full_name(),
num_channels=3,
num_filters=64,
filter_size=7,
stride=2,
act='relu')
self.pool2d_max = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
self.bottleneck_block_list = []
num_channels = 64
......@@ -175,6 +195,7 @@ class ResNet(fluid.imperative.Layer):
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
......@@ -184,12 +205,13 @@ class ResNet(fluid.imperative.Layer):
shortcut = True
self.pool2d_avg = Pool2D(
pool_size=7, pool_type='avg', global_pooling=True)
self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)
import math
stdv = 1.0 / math.sqrt(2048 * 1.0)
self.out = FC(size=class_dim,
self.out = FC(self.full_name(),
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)))
......@@ -214,7 +236,7 @@ class TestImperativeResnet(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
resnet = ResNet()
resnet = ResNet("resnet")
optimizer = optimizer_setting(train_parameters)
np.random.seed(seed)
import random
......@@ -275,7 +297,7 @@ class TestImperativeResnet(unittest.TestCase):
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
resnet = ResNet()
resnet = ResNet("resnet")
optimizer = optimizer_setting(train_parameters)
np.random.seed(seed)
......
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