367 lines
12 KiB
Python
367 lines
12 KiB
Python
import math
|
|
import paddle
|
|
from paddle import nn
|
|
|
|
BatchNorm2d = nn.BatchNorm2D
|
|
|
|
__all__ = [
|
|
"ResNet",
|
|
"resnet18",
|
|
"resnet34",
|
|
"resnet50",
|
|
"resnet101",
|
|
"deformable_resnet18",
|
|
"deformable_resnet50",
|
|
"resnet152",
|
|
]
|
|
|
|
model_urls = {
|
|
"resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth",
|
|
"resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth",
|
|
"resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth",
|
|
"resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth",
|
|
"resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
|
|
}
|
|
|
|
|
|
def constant_init(module, constant, bias=0):
|
|
module.weight = paddle.create_parameter(
|
|
shape=module.weight.shape,
|
|
dtype="float32",
|
|
default_initializer=paddle.nn.initializer.Constant(constant),
|
|
)
|
|
if hasattr(module, "bias"):
|
|
module.bias = paddle.create_parameter(
|
|
shape=module.bias.shape,
|
|
dtype="float32",
|
|
default_initializer=paddle.nn.initializer.Constant(bias),
|
|
)
|
|
|
|
|
|
def conv3x3(in_planes, out_planes, stride=1):
|
|
"""3x3 convolution with padding"""
|
|
return nn.Conv2D(
|
|
in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias_attr=False
|
|
)
|
|
|
|
|
|
class BasicBlock(nn.Layer):
|
|
expansion = 1
|
|
|
|
def __init__(self, inplanes, planes, stride=1, downsample=None, dcn=None):
|
|
super(BasicBlock, self).__init__()
|
|
self.with_dcn = dcn is not None
|
|
self.conv1 = conv3x3(inplanes, planes, stride)
|
|
self.bn1 = BatchNorm2d(planes, momentum=0.1)
|
|
self.relu = nn.ReLU()
|
|
self.with_modulated_dcn = False
|
|
if not self.with_dcn:
|
|
self.conv2 = nn.Conv2D(
|
|
planes, planes, kernel_size=3, padding=1, bias_attr=False
|
|
)
|
|
else:
|
|
from paddle.version.ops import DeformConv2D
|
|
|
|
deformable_groups = dcn.get("deformable_groups", 1)
|
|
offset_channels = 18
|
|
self.conv2_offset = nn.Conv2D(
|
|
planes, deformable_groups * offset_channels, kernel_size=3, padding=1
|
|
)
|
|
self.conv2 = DeformConv2D(
|
|
planes, planes, kernel_size=3, padding=1, bias_attr=False
|
|
)
|
|
self.bn2 = BatchNorm2d(planes, momentum=0.1)
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def forward(self, x):
|
|
residual = x
|
|
|
|
out = self.conv1(x)
|
|
out = self.bn1(out)
|
|
out = self.relu(out)
|
|
|
|
# out = self.conv2(out)
|
|
if not self.with_dcn:
|
|
out = self.conv2(out)
|
|
else:
|
|
offset = self.conv2_offset(out)
|
|
out = self.conv2(out, offset)
|
|
out = self.bn2(out)
|
|
|
|
if self.downsample is not None:
|
|
residual = self.downsample(x)
|
|
|
|
out += residual
|
|
out = self.relu(out)
|
|
|
|
return out
|
|
|
|
|
|
class Bottleneck(nn.Layer):
|
|
expansion = 4
|
|
|
|
def __init__(self, inplanes, planes, stride=1, downsample=None, dcn=None):
|
|
super(Bottleneck, self).__init__()
|
|
self.with_dcn = dcn is not None
|
|
self.conv1 = nn.Conv2D(inplanes, planes, kernel_size=1, bias_attr=False)
|
|
self.bn1 = BatchNorm2d(planes, momentum=0.1)
|
|
self.with_modulated_dcn = False
|
|
if not self.with_dcn:
|
|
self.conv2 = nn.Conv2D(
|
|
planes, planes, kernel_size=3, stride=stride, padding=1, bias_attr=False
|
|
)
|
|
else:
|
|
deformable_groups = dcn.get("deformable_groups", 1)
|
|
from paddle.vision.ops import DeformConv2D
|
|
|
|
offset_channels = 18
|
|
self.conv2_offset = nn.Conv2D(
|
|
planes,
|
|
deformable_groups * offset_channels,
|
|
stride=stride,
|
|
kernel_size=3,
|
|
padding=1,
|
|
)
|
|
self.conv2 = DeformConv2D(
|
|
planes, planes, kernel_size=3, padding=1, stride=stride, bias_attr=False
|
|
)
|
|
self.bn2 = BatchNorm2d(planes, momentum=0.1)
|
|
self.conv3 = nn.Conv2D(planes, planes * 4, kernel_size=1, bias_attr=False)
|
|
self.bn3 = BatchNorm2d(planes * 4, momentum=0.1)
|
|
self.relu = nn.ReLU()
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
self.dcn = dcn
|
|
self.with_dcn = dcn is not None
|
|
|
|
def forward(self, x):
|
|
residual = x
|
|
|
|
out = self.conv1(x)
|
|
out = self.bn1(out)
|
|
out = self.relu(out)
|
|
|
|
# out = self.conv2(out)
|
|
if not self.with_dcn:
|
|
out = self.conv2(out)
|
|
else:
|
|
offset = self.conv2_offset(out)
|
|
out = self.conv2(out, offset)
|
|
out = self.bn2(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv3(out)
|
|
out = self.bn3(out)
|
|
|
|
if self.downsample is not None:
|
|
residual = self.downsample(x)
|
|
|
|
out += residual
|
|
out = self.relu(out)
|
|
|
|
return out
|
|
|
|
|
|
class ResNet(nn.Layer):
|
|
def __init__(self, block, layers, in_channels=3, dcn=None):
|
|
self.dcn = dcn
|
|
self.inplanes = 64
|
|
super(ResNet, self).__init__()
|
|
self.out_channels = []
|
|
self.conv1 = nn.Conv2D(
|
|
in_channels, 64, kernel_size=7, stride=2, padding=3, bias_attr=False
|
|
)
|
|
self.bn1 = BatchNorm2d(64, momentum=0.1)
|
|
self.relu = nn.ReLU()
|
|
self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
|
|
self.layer1 = self._make_layer(block, 64, layers[0])
|
|
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dcn=dcn)
|
|
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dcn=dcn)
|
|
self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dcn=dcn)
|
|
|
|
if self.dcn is not None:
|
|
for m in self.modules():
|
|
if isinstance(m, Bottleneck) or isinstance(m, BasicBlock):
|
|
if hasattr(m, "conv2_offset"):
|
|
constant_init(m.conv2_offset, 0)
|
|
|
|
def _make_layer(self, block, planes, blocks, stride=1, dcn=None):
|
|
downsample = None
|
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
|
downsample = nn.Sequential(
|
|
nn.Conv2D(
|
|
self.inplanes,
|
|
planes * block.expansion,
|
|
kernel_size=1,
|
|
stride=stride,
|
|
bias_attr=False,
|
|
),
|
|
BatchNorm2d(planes * block.expansion, momentum=0.1),
|
|
)
|
|
|
|
layers = []
|
|
layers.append(block(self.inplanes, planes, stride, downsample, dcn=dcn))
|
|
self.inplanes = planes * block.expansion
|
|
for i in range(1, blocks):
|
|
layers.append(block(self.inplanes, planes, dcn=dcn))
|
|
self.out_channels.append(planes * block.expansion)
|
|
return nn.Sequential(*layers)
|
|
|
|
def forward(self, x):
|
|
x = self.conv1(x)
|
|
x = self.bn1(x)
|
|
x = self.relu(x)
|
|
x = self.maxpool(x)
|
|
|
|
x2 = self.layer1(x)
|
|
x3 = self.layer2(x2)
|
|
x4 = self.layer3(x3)
|
|
x5 = self.layer4(x4)
|
|
|
|
return x2, x3, x4, x5
|
|
|
|
|
|
def load_torch_params(paddle_model, torch_patams):
|
|
paddle_params = paddle_model.state_dict()
|
|
|
|
fc_names = ["classifier"]
|
|
for key, torch_value in torch_patams.items():
|
|
if "num_batches_tracked" in key:
|
|
continue
|
|
key = (
|
|
key.replace("running_var", "_variance")
|
|
.replace("running_mean", "_mean")
|
|
.replace("module.", "")
|
|
)
|
|
torch_value = torch_value.detach().cpu().numpy()
|
|
if key in paddle_params:
|
|
flag = [i in key for i in fc_names]
|
|
if any(flag) and "weight" in key: # ignore bias
|
|
new_shape = [1, 0] + list(range(2, torch_value.ndim))
|
|
print(
|
|
f"name: {key}, ori shape: {torch_value.shape}, new shape: {torch_value.transpose(new_shape).shape}"
|
|
)
|
|
torch_value = torch_value.transpose(new_shape)
|
|
paddle_params[key] = torch_value
|
|
else:
|
|
print(f"{key} not in paddle")
|
|
paddle_model.set_state_dict(paddle_params)
|
|
|
|
|
|
def load_models(model, model_name):
|
|
import torch.utils.model_zoo as model_zoo
|
|
|
|
torch_patams = model_zoo.load_url(model_urls[model_name])
|
|
load_torch_params(model, torch_patams)
|
|
|
|
|
|
def resnet18(pretrained=True, **kwargs):
|
|
"""Constructs a ResNet-18 model.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
"""
|
|
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
|
|
if pretrained:
|
|
assert (
|
|
kwargs.get("in_channels", 3) == 3
|
|
), "in_channels must be 3 whem pretrained is True"
|
|
print("load from imagenet")
|
|
load_models(model, "resnet18")
|
|
return model
|
|
|
|
|
|
def deformable_resnet18(pretrained=True, **kwargs):
|
|
"""Constructs a ResNet-18 model.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
"""
|
|
model = ResNet(BasicBlock, [2, 2, 2, 2], dcn=dict(deformable_groups=1), **kwargs)
|
|
if pretrained:
|
|
assert (
|
|
kwargs.get("in_channels", 3) == 3
|
|
), "in_channels must be 3 whem pretrained is True"
|
|
print("load from imagenet")
|
|
model.load_state_dict(model_zoo.load_url(model_urls["resnet18"]), strict=False)
|
|
return model
|
|
|
|
|
|
def resnet34(pretrained=True, **kwargs):
|
|
"""Constructs a ResNet-34 model.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
"""
|
|
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
|
|
if pretrained:
|
|
assert (
|
|
kwargs.get("in_channels", 3) == 3
|
|
), "in_channels must be 3 whem pretrained is True"
|
|
model.load_state_dict(model_zoo.load_url(model_urls["resnet34"]), strict=False)
|
|
return model
|
|
|
|
|
|
def resnet50(pretrained=True, **kwargs):
|
|
"""Constructs a ResNet-50 model.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
"""
|
|
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
|
|
if pretrained:
|
|
assert (
|
|
kwargs.get("in_channels", 3) == 3
|
|
), "in_channels must be 3 whem pretrained is True"
|
|
load_models(model, "resnet50")
|
|
return model
|
|
|
|
|
|
def deformable_resnet50(pretrained=True, **kwargs):
|
|
"""Constructs a ResNet-50 model with deformable conv.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
"""
|
|
model = ResNet(Bottleneck, [3, 4, 6, 3], dcn=dict(deformable_groups=1), **kwargs)
|
|
if pretrained:
|
|
assert (
|
|
kwargs.get("in_channels", 3) == 3
|
|
), "in_channels must be 3 whem pretrained is True"
|
|
model.load_state_dict(model_zoo.load_url(model_urls["resnet50"]), strict=False)
|
|
return model
|
|
|
|
|
|
def resnet101(pretrained=True, **kwargs):
|
|
"""Constructs a ResNet-101 model.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
"""
|
|
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
|
|
if pretrained:
|
|
assert (
|
|
kwargs.get("in_channels", 3) == 3
|
|
), "in_channels must be 3 whem pretrained is True"
|
|
model.load_state_dict(model_zoo.load_url(model_urls["resnet101"]), strict=False)
|
|
return model
|
|
|
|
|
|
def resnet152(pretrained=True, **kwargs):
|
|
"""Constructs a ResNet-152 model.
|
|
Args:
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
|
"""
|
|
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
|
|
if pretrained:
|
|
assert (
|
|
kwargs.get("in_channels", 3) == 3
|
|
), "in_channels must be 3 whem pretrained is True"
|
|
model.load_state_dict(model_zoo.load_url(model_urls["resnet152"]), strict=False)
|
|
return model
|
|
|
|
|
|
if __name__ == "__main__":
|
|
x = paddle.zeros([2, 3, 640, 640])
|
|
net = resnet50(pretrained=True)
|
|
y = net(x)
|
|
for u in y:
|
|
print(u.shape)
|
|
|
|
print(net.out_channels)
|