def conv_7x1_1x7(x, c_out, stride, affine=True, name=''):
x = fluid.layers.relu(x)
k = (1. / x.shape[1] / 1 / 7)**0.5
x = fluid.layers.conv2d(
x,
c_out, (1, 7),
padding=(0, 3),
param_attr=fluid.ParamAttr(name=name + "_conv_7x1_1x7_1",
initializer=UniformInitializer(low=-k,
high=k)),
bias_attr=False)
k = (1. / x.shape[1] / 7 / 1)**0.5
x = fluid.layers.conv2d(
x,
c_out, (7, 1),
padding=(3, 0),
param_attr=fluid.ParamAttr(name=name + "_conv_7x1_1x7_2",
initializer=UniformInitializer(low=-k,
high=k)),
bias_attr=False)
gama, beta = bn_param_config(name, affine, "conv_7x1_1x7_bn")
x = fluid.layers.batch_norm(
x,
param_attr=gama,
bias_attr=beta,
moving_mean_name=name + "_conv_7x1_1x7_bn_mean",
moving_variance_name=name + "_conv_7x1_1x7_bn_variance")
return x
def _factorized_reduce(self, x, c_out, affine=True, name=''):
assert c_out % 2 == 0
x = fluid.layers.relu(x)
x_sliced = x[:, :, 1:, 1:]
k = (1. / x.shape[1] / 1 / 1)**0.5
conv1 = fluid.layers.conv2d(
x,
c_out // 2,
1,
stride=2,
param_attr=fluid.ParamAttr(
name=name + "/fr_conv1",
initializer=UniformInitializer(
low=-k, high=k)),
bias_attr=False)
k = (1. / x_sliced.shape[1] / 1 / 1)**0.5
conv2 = fluid.layers.conv2d(
x_sliced,
c_out // 2,
1,
stride=2,
param_attr=fluid.ParamAttr(
name=name + "/fr_conv2",
initializer=UniformInitializer(
low=-k, high=k)),
bias_attr=False)
x = fluid.layers.concat(input=[conv1, conv2], axis=1)
gama, beta = self._bn_param_config(name, affine, "fr_bn")
x = fluid.layers.batch_norm(
x,
param_attr=gama,
bias_attr=beta,
moving_mean_name=name + "/fr_mean",
moving_variance_name=name + "/fr_variance")
return x
def model(x,
y,
c_in,
num_classes,
layers,
steps=4,
multiplier=4,
stem_multiplier=3,
name="model"):
c_curr = stem_multiplier * c_in
k = (1. / x.shape[1] / 3 / 3)**0.5
x = fluid.layers.conv2d(
x,
c_curr,
3,
padding=1,
param_attr=fluid.ParamAttr(name=name + "_conv_0",
initializer=UniformInitializer(low=-k,
high=k)),
bias_attr=False)
x = fluid.layers.batch_norm(
x,
param_attr=fluid.ParamAttr(name=name + "_bn0_scale",
initializer=ConstantInitializer(value=1)),
bias_attr=fluid.ParamAttr(name=name + "_bn0_offset",
initializer=ConstantInitializer(value=0)),
moving_mean_name=name + "_bn0_mean",
moving_variance_name=name + "_bn0_variance")
s0 = s1 = x
reduction_prev = False
c_curr = c_in
for i in range(layers):
if i in [layers // 3, 2 * layers // 3]:
c_curr *= 2
reduction = True
else:
reduction = False
s0, s1 = s1, cell(s0, s1, steps, multiplier, c_curr, reduction,
reduction_prev, name + "_l" + str(i))
reduction_prev = reduction
out = fluid.layers.pool2d(s1, pool_type='avg', global_pooling=True)
out = fluid.layers.squeeze(out, axes=[2, 3])
k = (1. / out.shape[1])**0.5
logits = fluid.layers.fc(
out,
num_classes,
param_attr=fluid.ParamAttr(name=name + "_fc_weights",
initializer=UniformInitializer(low=-k,
high=k)),
bias_attr=fluid.ParamAttr(name=name + "_fc_bias",
initializer=UniformInitializer(low=-k,
high=k)))
train_loss = fluid.layers.reduce_mean(
fluid.layers.softmax_with_cross_entropy(logits, y))
return logits, train_loss
def __init__(self, hidden_size, bias=False, init_scale=0.1):
super(AttentionLayer, self).__init__()
self.input_proj = Linear(
hidden_size,
hidden_size,
param_attr=ParamAttr(initializer=UniformInitializer(
low=-init_scale, high=init_scale)),
bias_attr=bias)
self.output_proj = Linear(
hidden_size + hidden_size,
hidden_size,
param_attr=ParamAttr(initializer=UniformInitializer(
low=-init_scale, high=init_scale)),
bias_attr=bias)
def _classifier(self, x, num_classes, name):
out = fluid.layers.pool2d(x, pool_type='avg', global_pooling=True)
out = fluid.layers.squeeze(out, axes=[2, 3])
k = (1. / out.shape[1])**0.5
out = fluid.layers.fc(
out,
num_classes,
param_attr=fluid.ParamAttr(name=name + "/fc_weights",
initializer=UniformInitializer(low=-k,
high=k)),
bias_attr=fluid.ParamAttr(name=name + "/fc_bias",
initializer=UniformInitializer(low=-k,
high=k)))
return out
def _relu_conv_bn(self,
x,
c_out,
kernel_size,
stride,
padding,
affine=True,
name=''):
x = fluid.layers.relu(x)
k = (1. / x.shape[1] / kernel_size / kernel_size)**0.5
x = fluid.layers.conv2d(
x,
c_out,
kernel_size,
stride=stride,
padding=padding,
param_attr=fluid.ParamAttr(
name=name + "/rcb_conv",
initializer=UniformInitializer(
low=-k, high=k)),
bias_attr=False)
gama, beta = self._bn_param_config(name, affine, "rcb_bn")
x = fluid.layers.batch_norm(
x,
param_attr=gama,
bias_attr=beta,
moving_mean_name=name + "/rcb_mean",
moving_variance_name=name + "/rcb_variance")
return x
def _dil_conv(self,
x,
c_out,
kernel_size,
stride,
padding,
dilation,
affine=True,
name=''):
c_in = x.shape[1]
x = fluid.layers.relu(x)
k = (1. / x.shape[1] / kernel_size / kernel_size)**0.5
x = fluid.layers.conv2d(
x,
c_in,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=c_in,
use_cudnn=False,
param_attr=fluid.ParamAttr(
name=name + "/dil_conv1",
initializer=UniformInitializer(
low=-k, high=k)),
bias_attr=False)
k = (1. / x.shape[1] / 1 / 1)**0.5
x = fluid.layers.conv2d(
x,
c_out,
1,
padding=0,
param_attr=fluid.ParamAttr(
name=name + "/dil_conv2",
initializer=UniformInitializer(
low=-k, high=k)),
bias_attr=False)
gama, beta = self._bn_param_config(name, affine, "dil_conv_bn")
x = fluid.layers.batch_norm(
x,
param_attr=gama,
bias_attr=beta,
moving_mean_name=name + "/dil_bn_mean",
moving_variance_name=name + "/dil_bn_variance")
return x
def __init__(self,
vocab_size,
embed_dim,
hidden_size,
num_layers,
dropout_prob=0.,
init_scale=0.1):
super(Decoder, self).__init__()
self.embedder = Embedding(
size=[vocab_size, embed_dim],
param_attr=ParamAttr(initializer=UniformInitializer(
low=-init_scale, high=init_scale)))
self.lstm_attention = RNN(DecoderCell(
num_layers, embed_dim, hidden_size, dropout_prob, init_scale),
is_reverse=False,
time_major=False)
self.output_layer = Linear(
hidden_size,
vocab_size,
param_attr=ParamAttr(initializer=UniformInitializer(
low=-init_scale, high=init_scale)),
bias_attr=False)
def __init__(self,
vocab_size,
embed_dim,
hidden_size,
num_layers,
dropout_prob=0.,
init_scale=0.1):
super(Encoder, self).__init__()
self.embedder = Embedding(
size=[vocab_size, embed_dim],
param_attr=ParamAttr(initializer=UniformInitializer(
low=-init_scale, high=init_scale)))
self.stack_lstm = RNN(EncoderCell(num_layers, embed_dim, hidden_size,
dropout_prob, init_scale),
is_reverse=False,
time_major=False)
def __init__(self,
num_layers,
input_size,
hidden_size,
dropout_prob=0.,
init_scale=0.1):
super(EncoderCell, self).__init__()
self.dropout_prob = dropout_prob
# use add_sublayer to add multi-layers
self.lstm_cells = []
for i in range(num_layers):
self.lstm_cells.append(
self.add_sublayer(
"lstm_%d" % i,
BasicLSTMCell(
input_size=input_size if i == 0 else hidden_size,
hidden_size=hidden_size,
param_attr=ParamAttr(initializer=UniformInitializer(
low=-init_scale, high=init_scale)))))
def conv_bn(x, c_out, kernel_size, padding, stride, name):
k = (1. / x.shape[1] / kernel_size / kernel_size)**0.5
conv1 = fluid.layers.conv2d(
x,
c_out,
kernel_size,
stride=stride,
padding=padding,
param_attr=fluid.ParamAttr(
name=name + "_conv", initializer=UniformInitializer(
low=-k, high=k)),
bias_attr=False)
bn1 = fluid.layers.batch_norm(
conv1,
param_attr=fluid.ParamAttr(
name=name + "_bn_scale", initializer=ConstantInitializer(value=1)),
bias_attr=fluid.ParamAttr(
name=name + "_bn_offset", initializer=ConstantInitializer(value=0)),
moving_mean_name=name + "_bn_mean",
moving_variance_name=name + "_bn_variance")
return bn1
def sep_conv(x, c_out, kernel_size, stride, padding, affine=True, name=''):
c_in = x.shape[1]
x = fluid.layers.relu(x)
k = (1. / x.shape[1] / kernel_size / kernel_size)**0.5
x = fluid.layers.conv2d(
x,
c_in,
kernel_size,
stride=stride,
padding=padding,
groups=c_in,
use_cudnn=False,
param_attr=fluid.ParamAttr(name=name + "_sep_conv_1_1",
initializer=UniformInitializer(low=-k,
high=k)),
bias_attr=False)
k = (1. / x.shape[1] / 1 / 1)**0.5
x = fluid.layers.conv2d(
x,
c_in,
1,
padding=0,
param_attr=fluid.ParamAttr(name=name + "_sep_conv_1_2",
initializer=UniformInitializer(low=-k,
high=k)),
bias_attr=False)
gama, beta = bn_param_config(name, affine, "sep_conv_bn1")
x = fluid.layers.batch_norm(x,
param_attr=gama,
bias_attr=beta,
moving_mean_name=name + "_sep_bn1_mean",
moving_variance_name=name +
"_sep_bn1_variance")
x = fluid.layers.relu(x)
k = (1. / x.shape[1] / kernel_size / kernel_size)**0.5
x = fluid.layers.conv2d(
x,
c_in,
kernel_size,
stride=1,
padding=padding,
groups=c_in,
use_cudnn=False,
param_attr=fluid.ParamAttr(name=name + "_sep_conv2_1",
initializer=UniformInitializer(low=-k,
high=k)),
bias_attr=False)
k = (1. / x.shape[1] / 1 / 1)**0.5
x = fluid.layers.conv2d(
x,
c_in,
1,
padding=0,
param_attr=fluid.ParamAttr(name=name + "_sep_conv2_2",
initializer=UniformInitializer(low=-k,
high=k)),
bias_attr=False)
gama, beta = bn_param_config(name, affine, "sep_conv_bn2")
x = fluid.layers.batch_norm(x,
param_attr=gama,
bias_attr=beta,
moving_mean_name=name + "_sep_bn2_mean",
moving_variance_name=name +
"_sep_bn2_variance")
return x
