提交 90f0425b 编写于 作者: W WuHaobo

add regnet architecture

上级 3c22467c
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
import math
import numpy as np
__all__ = [
"RegNetX_200MF", "RegNetX_4GF", "RegNetX_32GF", "RegNetY_200MF",
"RegNetY_4GF", "RegNetY_32GF"
]
class RegNet():
def __init__(self, w_a, w_0, w_m, d, group_w, bot_mul, q=8, se_on=False):
self.w_a = w_a
self.w_0 = w_0
self.w_m = w_m
self.d = d
self.q = q
self.group_w = group_w
self.bot_mul = bot_mul
# Stem type
self.stem_type = "simple_stem_in"
# Stem width
self.stem_w = 32
# Block type
self.block_type = "res_bottleneck_block"
# Stride of each stage
self.stride = 2
# Squeeze-and-Excitation (RegNetY)
self.se_on = se_on
self.se_r = 0.25
def quantize_float(self, f, q):
"""Converts a float to closest non-zero int divisible by q."""
return int(round(f / q) * q)
def adjust_ws_gs_comp(self, ws, bms, gs):
"""Adjusts the compatibility of widths and groups."""
ws_bot = [int(w * b) for w, b in zip(ws, bms)]
gs = [min(g, w_bot) for g, w_bot in zip(gs, ws_bot)]
ws_bot = [
self.quantize_float(w_bot, g) for w_bot, g in zip(ws_bot, gs)
]
ws = [int(w_bot / b) for w_bot, b in zip(ws_bot, bms)]
return ws, gs
def get_stages_from_blocks(self, ws, rs):
"""Gets ws/ds of network at each stage from per block values."""
ts = [
w != wp or r != rp
for w, wp, r, rp in zip(ws + [0], [0] + ws, rs + [0], [0] + rs)
]
s_ws = [w for w, t in zip(ws, ts[:-1]) if t]
s_ds = np.diff([d for d, t in zip(range(len(ts)), ts) if t]).tolist()
return s_ws, s_ds
def generate_regnet(self, w_a, w_0, w_m, d, q=8):
"""Generates per block ws from RegNet parameters."""
assert w_a >= 0 and w_0 > 0 and w_m > 1 and w_0 % q == 0
ws_cont = np.arange(d) * w_a + w_0
ks = np.round(np.log(ws_cont / w_0) / np.log(w_m))
ws = w_0 * np.power(w_m, ks)
ws = np.round(np.divide(ws, q)) * q
num_stages, max_stage = len(np.unique(ws)), ks.max() + 1
ws, ws_cont = ws.astype(int).tolist(), ws_cont.tolist()
return ws, num_stages, max_stage, ws_cont
def init_weights(self, op_type, filter_size=0, num_channels=0, name=None):
if op_type == 'conv':
fan_out = num_channels * filter_size * filter_size
param_attr = ParamAttr(
name=name + "_weights",
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=math.sqrt(2.0 / fan_out)))
bias_attr = False
elif op_type == 'bn':
param_attr = ParamAttr(
name=name + "_scale",
initializer=fluid.initializer.Constant(0.0))
bias_attr = ParamAttr(
name=name + "_offset",
initializer=fluid.initializer.Constant(0.0))
elif op_type == 'final_bn':
param_attr = ParamAttr(
name=name + "_scale",
initializer=fluid.initializer.Constant(1.0))
bias_attr = ParamAttr(
name=name + "_offset",
initializer=fluid.initializer.Constant(0.0))
return param_attr, bias_attr
def net(self, input, class_dim=1000):
# Generate RegNet ws per block
b_ws, num_s, max_s, ws_cont = self.generate_regnet(
self.w_a, self.w_0, self.w_m, self.d, self.q)
# Convert to per stage format
ws, ds = self.get_stages_from_blocks(b_ws, b_ws)
# Generate group widths and bot muls
gws = [self.group_w for _ in range(num_s)]
bms = [self.bot_mul for _ in range(num_s)]
# Adjust the compatibility of ws and gws
ws, gws = self.adjust_ws_gs_comp(ws, bms, gws)
# Use the same stride for each stage
ss = [self.stride for _ in range(num_s)]
# Use SE for RegNetY
se_r = self.se_r
# Construct the model
# Group params by stage
stage_params = list(zip(ds, ws, ss, bms, gws))
# Construct the stem
conv = self.conv_bn_layer(
input=input,
num_filters=self.stem_w,
filter_size=3,
stride=2,
padding=1,
act='relu',
name="stem_conv")
# Construct the stages
for block, (d, w_out, stride, bm, gw) in enumerate(stage_params):
for i in range(d):
# Stride apply to the first block of the stage
b_stride = stride if i == 0 else 1
conv_name = 's' + str(block + 1) + '_b' + str(i +
1) # chr(97 + i)
conv = self.bottleneck_block(
input=conv,
num_filters=w_out,
stride=b_stride,
bm=bm,
gw=gw,
se_r=self.se_r,
name=conv_name)
pool = fluid.layers.pool2d(
input=conv, pool_type='avg', global_pooling=True)
out = fluid.layers.fc(
input=pool,
size=class_dim,
param_attr=ParamAttr(
name="fc_0.w_0",
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=0.01)),
bias_attr=ParamAttr(
name="fc_0.b_0", initializer=fluid.initializer.Constant(0.0)))
return out
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
padding=0,
act=None,
name=None,
final_bn=False):
param_attr, bias_attr = self.init_weights(
op_type='conv',
filter_size=filter_size,
num_channels=num_filters,
name=name)
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
act=None,
param_attr=param_attr,
bias_attr=bias_attr,
name=name + '.conv2d.output.1')
bn_name = name + '_bn'
if final_bn:
param_attr, bias_attr = self.init_weights(
op_type='final_bn', name=bn_name)
else:
param_attr, bias_attr = self.init_weights(
op_type='bn', name=bn_name)
return fluid.layers.batch_norm(
input=conv,
act=act,
name=bn_name + '.output.1',
param_attr=param_attr,
bias_attr=bias_attr,
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance', )
# todo: to check the bn layer's eps and momentum, and relu_inplace
def shortcut(self, input, ch_out, stride, name):
ch_in = input.shape[1]
if ch_in != ch_out or stride != 1:
return self.conv_bn_layer(
input=input,
num_filters=ch_out,
filter_size=1,
stride=stride,
padding=0,
act=None,
name=name)
else:
return input
def bottleneck_block(self, input, num_filters, stride, bm, gw, se_r, name):
# Compute the bottleneck width
w_b = int(round(num_filters * bm))
# Compute the number of groups
num_gs = w_b // gw
conv0 = self.conv_bn_layer(
input=input,
num_filters=w_b,
filter_size=1,
stride=1,
padding=0,
act='relu',
name=name + "_branch2a")
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=w_b,
filter_size=3,
stride=stride,
padding=1,
groups=num_gs,
act='relu',
name=name + "_branch2b")
# Squeeze-and-Excitation (SE)
if self.se_on:
w_se = int(round(input.shape[1] * se_r))
conv1 = self.squeeze_excitation(
input=conv1,
num_channels=w_b,
reduction_channels=w_se,
name=name + "_branch2se")
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters,
filter_size=1,
stride=1,
padding=0,
act=None,
name=name + "_branch2c",
final_bn=True)
short = self.shortcut(
input, num_filters, stride, name=name + "_branch1")
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
def squeeze_excitation(self,
input,
num_channels,
reduction_channels,
name=None):
pool = fluid.layers.pool2d(
input=input, pool_size=0, pool_type='avg', global_pooling=True)
fan_out = num_channels
squeeze = fluid.layers.conv2d(
input=pool,
num_filters=reduction_channels,
filter_size=1,
act='relu',
param_attr=ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=math.sqrt(2.0 / fan_out)),
name=name + '_sqz_weights'),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
excitation = fluid.layers.conv2d(
input=squeeze,
num_filters=num_channels,
filter_size=1,
act='sigmoid',
param_attr=ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=math.sqrt(2.0 / fan_out)),
name=name + '_exc_weights'),
bias_attr=ParamAttr(name=name + '_exc_offset'))
scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
return scale
def RegNetX_200MF():
model = RegNet(
w_a=36.44, w_0=24, w_m=2.49, d=13, group_w=8, bot_mul=1.0, q=8)
return model
def RegNetX_4GF():
model = RegNet(
w_a=38.65, w_0=96, w_m=2.43, d=23, group_w=40, bot_mul=1.0, q=8)
return model
def RegNetX_32GF():
model = RegNet(
w_a=69.86, w_0=320, w_m=2.0, d=23, group_w=168, bot_mul=1.0, q=8)
return model
def RegNetY_200MF():
model = RegNet(
w_a=36.44,
w_0=24,
w_m=2.49,
d=13,
group_w=8,
bot_mul=1.0,
q=8,
se_on=True)
return model
def RegNetY_4GF():
model = RegNet(
w_a=31.41,
w_0=96,
w_m=2.24,
d=22,
group_w=64,
bot_mul=1.0,
q=8,
se_on=True)
return model
def RegNetY_32GF():
model = RegNet(
w_a=115.89,
w_0=232,
w_m=2.53,
d=20,
group_w=232,
bot_mul=1.0,
q=8,
se_on=True)
return model
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