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PaddleRS/paddlers/models/ppcls/arch/backbone/model_zoo/regnet.py

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# 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.
# Code was based on https://github.com/facebookresearch/pycls
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"RegNetX_200MF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_200MF_pretrained.pdparams",
"RegNetX_4GF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams",
"RegNetX_32GF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_32GF_pretrained.pdparams",
"RegNetY_200MF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_200MF_pretrained.pdparams",
"RegNetY_4GF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_4GF_pretrained.pdparams",
"RegNetY_32GF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_32GF_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
def quantize_float(f, q):
"""Converts a float to closest non-zero int divisible by q."""
return int(round(f / q) * q)
def adjust_ws_gs_comp(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 = [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(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(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
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
padding=0,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=ParamAttr(name=name + ".conv2d.output.1.w_0"),
bias_attr=ParamAttr(name=name + ".conv2d.output.1.b_0"))
bn_name = name + "_bn"
self._batch_norm = BatchNorm(
num_filters,
act=act,
param_attr=ParamAttr(name=bn_name + ".output.1.w_0"),
bias_attr=ParamAttr(bn_name + ".output.1.b_0"),
moving_mean_name=bn_name + "_mean",
moving_variance_name=bn_name + "_variance")
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
stride,
bm,
gw,
se_on,
se_r,
shortcut=True,
name=None):
super(BottleneckBlock, self).__init__()
# Compute the bottleneck width
w_b = int(round(num_filters * bm))
# Compute the number of groups
num_gs = w_b // gw
self.se_on = se_on
self.conv0 = ConvBNLayer(
num_channels=num_channels,
num_filters=w_b,
filter_size=1,
padding=0,
act="relu",
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
num_channels=w_b,
num_filters=w_b,
filter_size=3,
stride=stride,
padding=1,
groups=num_gs,
act="relu",
name=name + "_branch2b")
if se_on:
w_se = int(round(num_channels * se_r))
self.se_block = SELayer(
num_channels=w_b,
num_filters=w_b,
reduction_ratio=w_se,
name=name + "_branch2se")
self.conv2 = ConvBNLayer(
num_channels=w_b,
num_filters=num_filters,
filter_size=1,
act=None,
name=name + "_branch2c")
if not shortcut:
self.short = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
stride=stride,
name=name + "_branch1")
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
if self.se_on:
conv1 = self.se_block(conv1)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
self.pool2d_gap = AdaptiveAvgPool2D(1)
self._num_channels = num_channels
med_ch = int(num_channels / reduction_ratio)
stdv = 1.0 / math.sqrt(num_channels * 1.0)
self.squeeze = Linear(
num_channels,
med_ch,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name=name + "_sqz_weights"),
bias_attr=ParamAttr(name=name + "_sqz_offset"))
stdv = 1.0 / math.sqrt(med_ch * 1.0)
self.excitation = Linear(
med_ch,
num_filters,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name=name + "_exc_weights"),
bias_attr=ParamAttr(name=name + "_exc_offset"))
def forward(self, input):
pool = self.pool2d_gap(input)
pool = paddle.reshape(pool, shape=[-1, self._num_channels])
squeeze = self.squeeze(pool)
squeeze = F.relu(squeeze)
excitation = self.excitation(squeeze)
excitation = F.sigmoid(excitation)
excitation = paddle.reshape(
excitation, shape=[-1, self._num_channels, 1, 1])
out = input * excitation
return out
class RegNet(nn.Layer):
def __init__(self,
w_a,
w_0,
w_m,
d,
group_w,
bot_mul,
q=8,
se_on=False,
class_num=1000):
super(RegNet, self).__init__()
# Generate RegNet ws per block
b_ws, num_s, max_s, ws_cont = generate_regnet(w_a, w_0, w_m, d, q)
# Convert to per stage format
ws, ds = get_stages_from_blocks(b_ws, b_ws)
# Generate group widths and bot muls
gws = [group_w for _ in range(num_s)]
bms = [bot_mul for _ in range(num_s)]
# Adjust the compatibility of ws and gws
ws, gws = adjust_ws_gs_comp(ws, bms, gws)
# Use the same stride for each stage
ss = [2 for _ in range(num_s)]
# Use SE for RegNetY
se_r = 0.25
# Construct the model
# Group params by stage
stage_params = list(zip(ds, ws, ss, bms, gws))
# Construct the stem
stem_type = "simple_stem_in"
stem_w = 32
block_type = "res_bottleneck_block"
self.conv = ConvBNLayer(
num_channels=3,
num_filters=stem_w,
filter_size=3,
stride=2,
padding=1,
act="relu",
name="stem_conv")
self.block_list = []
for block, (d, w_out, stride, bm, gw) in enumerate(stage_params):
shortcut = False
for i in range(d):
num_channels = stem_w if block == i == 0 else in_channels
# 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)
bottleneck_block = self.add_sublayer(
conv_name,
BottleneckBlock(
num_channels=num_channels,
num_filters=w_out,
stride=b_stride,
bm=bm,
gw=gw,
se_on=se_on,
se_r=se_r,
shortcut=shortcut,
name=conv_name))
in_channels = w_out
self.block_list.append(bottleneck_block)
shortcut = True
self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = w_out
stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
self.out = Linear(
self.pool2d_avg_channels,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
bias_attr=ParamAttr(name="fc_0.b_0"))
def forward(self, inputs):
y = self.conv(inputs)
for block in self.block_list:
y = block(y)
y = self.pool2d_avg(y)
y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def RegNetX_200MF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet(
w_a=36.44,
w_0=24,
w_m=2.49,
d=13,
group_w=8,
bot_mul=1.0,
q=8,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_200MF"], use_ssld=use_ssld)
return model
def RegNetX_4GF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet(
w_a=38.65,
w_0=96,
w_m=2.43,
d=23,
group_w=40,
bot_mul=1.0,
q=8,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_4GF"], use_ssld=use_ssld)
return model
def RegNetX_32GF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet(
w_a=69.86,
w_0=320,
w_m=2.0,
d=23,
group_w=168,
bot_mul=1.0,
q=8,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
def RegNetY_200MF(pretrained=False, use_ssld=False, **kwargs):
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,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
def RegNetY_4GF(pretrained=False, use_ssld=False, **kwargs):
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,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
def RegNetY_32GF(pretrained=False, use_ssld=False, **kwargs):
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,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model