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resnet.py
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resnet.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import math
from numbers import Integral
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.nn.initializer import Constant, Uniform
from paddle.regularizer import L2Decay
from paddle3d.apis import manager
from paddle3d.models import layers
from paddle3d.models.layers import reset_parameters
from paddle3d.utils import checkpoint
__all__ = ['ResNet']
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
dilation=1,
groups=1,
is_vd_mode=False,
act=None,
data_format='NCHW'):
super(ConvBNLayer, self).__init__()
if dilation != 1 and kernel_size != 3:
raise RuntimeError("When the dilation isn't 1," \
"the kernel_size should be 3.")
self.is_vd_mode = is_vd_mode
self.act = act
self._pool2d_avg = nn.AvgPool2D(
kernel_size=2,
stride=2,
padding=0,
ceil_mode=True,
data_format=data_format)
self._conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2 \
if dilation == 1 else dilation,
dilation=dilation,
groups=groups,
bias_attr=False,
data_format=data_format)
self._batch_norm = nn.BatchNorm2D(out_channels, data_format=data_format)
if self.act:
self._act = nn.ReLU()
def forward(self, inputs):
if self.is_vd_mode:
inputs = self._pool2d_avg(inputs)
y = self._conv(inputs)
y = self._batch_norm(y)
if self.act:
y = self._act(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
first_conv=False,
dilation=1,
is_vd_mode=False,
data_format='NCHW'):
super(BottleneckBlock, self).__init__()
self.data_format = data_format
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
act='relu',
data_format=data_format)
if first_conv and dilation != 1:
dilation //= 2
self.dilation = dilation
self.conv1 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
act='relu',
dilation=dilation,
data_format=data_format)
self.conv2 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels * 4,
kernel_size=1,
act=None,
data_format=data_format)
if if_first or stride == 1:
is_vd_mode = False
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels * 4,
kernel_size=1,
stride=stride,
is_vd_mode=is_vd_mode,
data_format=data_format)
self.shortcut = shortcut
# NOTE: Use the wrap layer for quantization training
self.relu = nn.ReLU()
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(short, conv2)
y = self.relu(y)
return y
class BasicBlock(nn.Layer):
def __init__(self,
in_channels,
out_channels,
stride,
dilation=1,
shortcut=True,
if_first=False,
is_vd_mode=False,
data_format='NCHW'):
super(BasicBlock, self).__init__()
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
dilation=dilation,
act='relu',
data_format=data_format)
self.conv1 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
dilation=dilation,
act=None,
data_format=data_format)
if if_first or stride == 1:
is_vd_mode = False
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=stride,
is_vd_mode=is_vd_mode,
data_format=data_format)
self.shortcut = shortcut
self.dilation = dilation
self.data_format = data_format
self.relu = nn.ReLU()
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(short, conv1)
y = self.relu(y)
return y
@manager.BACKBONES.add_component
class ResNet(nn.Layer):
def __init__(self,
layers=50,
output_stride=8,
multi_grid=(1, 1, 1),
return_idx=[3],
pretrained=None,
variant='b',
data_format='NCHW'):
"""
Residual Network, see https://arxiv.org/abs/1512.03385
Args:
variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently
layers (int, optional): The layers of ResNet_vd. The supported layers are (18, 34, 50, 101, 152, 200). Default: 50.
output_stride (int, optional): The stride of output features compared to input images. It is 8 or 16. Default: 8.
multi_grid (tuple|list, optional): The grid of stage4. Defult: (1, 1, 1).
pretrained (str, optional): The path of pretrained model.
"""
super(ResNet, self).__init__()
self.variant = variant
self.data_format = data_format
self.conv1_logit = None # for gscnn shape stream
self.layers = layers
self.norm_mean = paddle.to_tensor([0.485, 0.456, 0.406])
self.norm_std = paddle.to_tensor([0.229, 0.224, 0.225])
supported_layers = [18, 34, 50, 101, 152, 200]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(
supported_layers, layers)
if layers == 18:
depth = [2, 2, 2, 2]
elif layers == 34 or layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
elif layers == 200:
depth = [3, 12, 48, 3]
num_channels = [64, 256, 512, 1024
] if layers >= 50 else [64, 64, 128, 256]
num_filters = [64, 128, 256, 512]
# for channels of four returned stages
self.feat_channels = [c * 4 for c in num_filters
] if layers >= 50 else num_filters
dilation_dict = None
if output_stride == 8:
dilation_dict = {2: 2, 3: 4}
elif output_stride == 16:
dilation_dict = {3: 2}
self.return_idx = return_idx
if variant in ['c', 'd']:
conv_defs = [
[3, 32, 3, 2],
[32, 32, 3, 1],
[32, 64, 3, 1],
]
else:
conv_defs = [[3, 64, 7, 2]]
self.conv1 = nn.Sequential()
for (i, conv_def) in enumerate(conv_defs):
c_in, c_out, k, s = conv_def
self.conv1.add_sublayer(
str(i),
ConvBNLayer(
in_channels=c_in,
out_channels=c_out,
kernel_size=k,
stride=s,
act='relu',
data_format=data_format))
self.pool2d_max = nn.MaxPool2D(
kernel_size=3, stride=2, padding=1, data_format=data_format)
self.stage_list = []
if layers >= 50:
for block in range(len(depth)):
shortcut = False
block_list = []
for i in range(depth[block]):
if layers in [101, 152] and block == 2:
if i == 0:
conv_name = "res" + str(block + 2) + "a"
else:
conv_name = "res" + str(block + 2) + "b" + str(i)
else:
conv_name = "res" + str(block + 2) + chr(97 + i)
###############################################################################
# Add dilation rate for some segmentation tasks, if dilation_dict is not None.
dilation_rate = dilation_dict[
block] if dilation_dict and block in dilation_dict else 1
# Actually block here is 'stage', and i is 'block' in 'stage'
# At the stage 4, expand the the dilation_rate if given multi_grid
if block == 3:
dilation_rate = dilation_rate * multi_grid[i]
###############################################################################
bottleneck_block = self.add_sublayer(
'layer_%d_%d' % (block, i),
BottleneckBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block] * 4,
out_channels=num_filters[block],
stride=2 if i == 0 and block != 0
and dilation_rate == 1 else 1,
shortcut=shortcut,
if_first=block == i == 0,
first_conv=i == 0,
is_vd_mode=variant in ['c', 'd'],
dilation=dilation_rate,
data_format=data_format))
block_list.append(bottleneck_block)
shortcut = True
self.stage_list.append(block_list)
else:
for block in range(len(depth)):
shortcut = False
block_list = []
for i in range(depth[block]):
dilation_rate = dilation_dict[block] \
if dilation_dict and block in dilation_dict else 1
if block == 3:
dilation_rate = dilation_rate * multi_grid[i]
basic_block = self.add_sublayer(
'layer_%d_%d' % (block, i),
BasicBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block],
out_channels=num_filters[block],
stride=2 if i == 0 and block != 0 \
and dilation_rate == 1 else 1,
dilation=dilation_rate,
shortcut=shortcut,
if_first=block == i == 0,
is_vd_mode=variant in ['c', 'd'],
data_format=data_format))
block_list.append(basic_block)
shortcut = True
self.stage_list.append(block_list)
self.pretrained = pretrained
self.init_weight()
def forward(self, inputs):
image = self.preprocess(inputs)
y = self.conv1(image)
y = self.pool2d_max(y)
# A feature list saves the output feature map of each stage.
feat_list = []
for idx, stage in enumerate(self.stage_list):
for block in stage:
y = block(y)
if idx in self.return_idx:
feat_list.append(y)
return feat_list
def preprocess(self, images):
"""
Preprocess images
Args:
images [paddle.Tensor(N, 3, H, W)]: Input images
Return
x [paddle.Tensor(N, 3, H, W)]: Preprocessed images
"""
x = images
# Create a mask for padded pixels
mask = paddle.isnan(x)
# Match ResNet pretrained preprocessing
x = self.normalize(x, mean=self.norm_mean, std=self.norm_std)
# Make padded pixels = 0
a = paddle.zeros_like(x)
x = paddle.where(mask, a, x)
return x
def normalize(self, image, mean, std):
shape = paddle.shape(image)
if mean.shape:
mean = mean[..., :, None]
if std.shape:
std = std[..., :, None]
out = (image.reshape([shape[0], shape[1], shape[2] * shape[3]]) -
mean) / std
return out.reshape(shape)
def init_weight(self):
for sublayer in self.sublayers():
if isinstance(sublayer, nn.Conv2D):
reset_parameters(sublayer)