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layers.py
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from __future__ import division
import paddle.fluid as fluid
import numpy as np
import os
# cudnn is not better when batch size is 1.
use_cudnn = False
if 'ce_mode' in os.environ:
use_cudnn = False
def cal_padding(img_size, stride, filter_size, dilation=1):
"""Calculate padding size."""
valid_filter_size = dilation * (filter_size - 1) + 1
if img_size % stride == 0:
out_size = max(filter_size - stride, 0)
else:
out_size = max(filter_size - (img_size % stride), 0)
return out_size // 2, out_size - out_size // 2
def instance_norm(input, name=None):
# TODO([email protected]): Check the accuracy when using fluid.layers.layer_norm.
# return fluid.layers.layer_norm(input, begin_norm_axis=2)
helper = fluid.layer_helper.LayerHelper("instance_norm", **locals())
dtype = helper.input_dtype()
epsilon = 1e-5
mean = fluid.layers.reduce_mean(input, dim=[2, 3], keep_dim=True)
var = fluid.layers.reduce_mean(
fluid.layers.square(input - mean), dim=[2, 3], keep_dim=True)
if name is not None:
scale_name = name + "_scale"
offset_name = name + "_offset"
scale_param = fluid.ParamAttr(
name=scale_name,
initializer=fluid.initializer.TruncatedNormal(1.0, 0.02),
trainable=True)
offset_param = fluid.ParamAttr(
name=offset_name,
initializer=fluid.initializer.Constant(0.0),
trainable=True)
scale = helper.create_parameter(
attr=scale_param, shape=input.shape[1:2], dtype=dtype)
offset = helper.create_parameter(
attr=offset_param, shape=input.shape[1:2], dtype=dtype)
tmp = fluid.layers.elementwise_mul(x=(input - mean), y=scale, axis=1)
tmp = tmp / fluid.layers.sqrt(var + epsilon)
tmp = fluid.layers.elementwise_add(tmp, offset, axis=1)
return tmp
def conv2d(input,
num_filters=64,
filter_size=7,
stride=1,
stddev=0.02,
padding="VALID",
name="conv2d",
norm=True,
relu=True,
relufactor=0.0):
"""Wrapper for conv2d op to support VALID and SAME padding mode."""
need_crop = False
if padding == "SAME":
top_padding, bottom_padding = cal_padding(input.shape[2], stride,
filter_size)
left_padding, right_padding = cal_padding(input.shape[2], stride,
filter_size)
height_padding = bottom_padding
width_padding = right_padding
if top_padding != bottom_padding or left_padding != right_padding:
height_padding = top_padding + stride
width_padding = left_padding + stride
need_crop = True
else:
height_padding = 0
width_padding = 0
padding = [height_padding, width_padding]
param_attr = fluid.ParamAttr(
name=name + "_w",
initializer=fluid.initializer.TruncatedNormal(scale=stddev))
bias_attr = fluid.ParamAttr(
name=name + "_b", initializer=fluid.initializer.Constant(0.0))
conv = fluid.layers.conv2d(
input,
num_filters,
filter_size,
name=name,
stride=stride,
padding=padding,
use_cudnn=use_cudnn,
param_attr=param_attr,
bias_attr=bias_attr)
if need_crop:
conv = fluid.layers.crop(
conv,
shape=(-1, conv.shape[1], conv.shape[2] - 1, conv.shape[3] - 1),
offsets=(0, 0, 1, 1))
if norm:
conv = instance_norm(input=conv, name=name + "_norm")
if relu:
conv = fluid.layers.leaky_relu(conv, alpha=relufactor)
return conv
def deconv2d(input,
out_shape,
num_filters=64,
filter_size=7,
stride=1,
stddev=0.02,
padding="VALID",
name="conv2d",
norm=True,
relu=True,
relufactor=0.0):
"""Wrapper for deconv2d op to support VALID and SAME padding mode."""
need_crop = False
if padding == "SAME":
top_padding, bottom_padding = cal_padding(out_shape[0], stride,
filter_size)
left_padding, right_padding = cal_padding(out_shape[1], stride,
filter_size)
height_padding = top_padding
width_padding = left_padding
if top_padding != bottom_padding or left_padding != right_padding:
need_crop = True
else:
height_padding = 0
width_padding = 0
padding = [height_padding, width_padding]
param_attr = fluid.ParamAttr(
name=name + "_w",
initializer=fluid.initializer.TruncatedNormal(scale=stddev))
bias_attr = fluid.ParamAttr(
name=name + "_b", initializer=fluid.initializer.Constant(0.0))
conv = fluid.layers.conv2d_transpose(
input,
num_filters,
name=name,
filter_size=filter_size,
stride=stride,
padding=padding,
use_cudnn=use_cudnn,
param_attr=param_attr,
bias_attr=bias_attr)
if need_crop:
conv = fluid.layers.crop(
conv,
shape=(-1, conv.shape[1], conv.shape[2] - 1, conv.shape[3] - 1),
offsets=(0, 0, 0, 0))
if norm:
conv = instance_norm(input=conv, name=name + "_norm")
if relu:
conv = fluid.layers.leaky_relu(conv, alpha=relufactor)
return conv