Spaces:
Running
Running
File size: 7,621 Bytes
a80d6bb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 |
import torch
import torch.nn as nn
import torch.nn.init as init
from .nets.backbone import HourglassBackbone, SuperpointBackbone
from .nets.junction_decoder import SuperpointDecoder
from .nets.heatmap_decoder import PixelShuffleDecoder
from .nets.descriptor_decoder import SuperpointDescriptor
def get_model(model_cfg=None, loss_weights=None, mode="train"):
""" Get model based on the model configuration. """
# Check dataset config is given
if model_cfg is None:
raise ValueError("[Error] The model config is required!")
# List the supported options here
print("\n\n\t--------Initializing model----------")
supported_arch = ["simple"]
if not model_cfg["model_architecture"] in supported_arch:
raise ValueError(
"[Error] The model architecture is not in supported arch!")
if model_cfg["model_architecture"] == "simple":
model = SOLD2Net(model_cfg)
else:
raise ValueError(
"[Error] The model architecture is not in supported arch!")
# Optionally register loss weights to the model
if mode == "train":
if loss_weights is not None:
for param_name, param in loss_weights.items():
if isinstance(param, nn.Parameter):
print("\t [Debug] Adding %s with value %f to model"
% (param_name, param.item()))
model.register_parameter(param_name, param)
else:
raise ValueError(
"[Error] the loss weights can not be None in dynamic weighting mode during training.")
# Display some summary info.
print("\tModel architecture: %s" % model_cfg["model_architecture"])
print("\tBackbone: %s" % model_cfg["backbone"])
print("\tJunction decoder: %s" % model_cfg["junction_decoder"])
print("\tHeatmap decoder: %s" % model_cfg["heatmap_decoder"])
print("\t-------------------------------------")
return model
class SOLD2Net(nn.Module):
""" Full network for SOLD². """
def __init__(self, model_cfg):
super(SOLD2Net, self).__init__()
self.name = model_cfg["model_name"]
self.cfg = model_cfg
# List supported network options
self.supported_backbone = ["lcnn", "superpoint"]
self.backbone_net, self.feat_channel = self.get_backbone()
# List supported junction decoder options
self.supported_junction_decoder = ["superpoint_decoder"]
self.junction_decoder = self.get_junction_decoder()
# List supported heatmap decoder options
self.supported_heatmap_decoder = ["pixel_shuffle",
"pixel_shuffle_single"]
self.heatmap_decoder = self.get_heatmap_decoder()
# List supported descriptor decoder options
if "descriptor_decoder" in self.cfg:
self.supported_descriptor_decoder = ["superpoint_descriptor"]
self.descriptor_decoder = self.get_descriptor_decoder()
# Initialize the model weights
self.apply(weight_init)
def forward(self, input_images):
# The backbone
features = self.backbone_net(input_images)
# junction decoder
junctions = self.junction_decoder(features)
# heatmap decoder
heatmaps = self.heatmap_decoder(features)
outputs = {"junctions": junctions, "heatmap": heatmaps}
# Descriptor decoder
if "descriptor_decoder" in self.cfg:
outputs["descriptors"] = self.descriptor_decoder(features)
return outputs
def get_backbone(self):
""" Retrieve the backbone encoder network. """
if not self.cfg["backbone"] in self.supported_backbone:
raise ValueError(
"[Error] The backbone selection is not supported.")
# lcnn backbone (stacked hourglass)
if self.cfg["backbone"] == "lcnn":
backbone_cfg = self.cfg["backbone_cfg"]
backbone = HourglassBackbone(**backbone_cfg)
feat_channel = 256
elif self.cfg["backbone"] == "superpoint":
backbone_cfg = self.cfg["backbone_cfg"]
backbone = SuperpointBackbone()
feat_channel = 128
else:
raise ValueError(
"[Error] The backbone selection is not supported.")
return backbone, feat_channel
def get_junction_decoder(self):
""" Get the junction decoder. """
if (not self.cfg["junction_decoder"]
in self.supported_junction_decoder):
raise ValueError(
"[Error] The junction decoder selection is not supported.")
# superpoint decoder
if self.cfg["junction_decoder"] == "superpoint_decoder":
decoder = SuperpointDecoder(self.feat_channel,
self.cfg["backbone"])
else:
raise ValueError(
"[Error] The junction decoder selection is not supported.")
return decoder
def get_heatmap_decoder(self):
""" Get the heatmap decoder. """
if not self.cfg["heatmap_decoder"] in self.supported_heatmap_decoder:
raise ValueError(
"[Error] The heatmap decoder selection is not supported.")
# Pixel_shuffle decoder
if self.cfg["heatmap_decoder"] == "pixel_shuffle":
if self.cfg["backbone"] == "lcnn":
decoder = PixelShuffleDecoder(self.feat_channel,
num_upsample=2)
elif self.cfg["backbone"] == "superpoint":
decoder = PixelShuffleDecoder(self.feat_channel,
num_upsample=3)
else:
raise ValueError("[Error] Unknown backbone option.")
# Pixel_shuffle decoder with single channel output
elif self.cfg["heatmap_decoder"] == "pixel_shuffle_single":
if self.cfg["backbone"] == "lcnn":
decoder = PixelShuffleDecoder(
self.feat_channel, num_upsample=2, output_channel=1)
elif self.cfg["backbone"] == "superpoint":
decoder = PixelShuffleDecoder(
self.feat_channel, num_upsample=3, output_channel=1)
else:
raise ValueError("[Error] Unknown backbone option.")
else:
raise ValueError(
"[Error] The heatmap decoder selection is not supported.")
return decoder
def get_descriptor_decoder(self):
""" Get the descriptor decoder. """
if (not self.cfg["descriptor_decoder"]
in self.supported_descriptor_decoder):
raise ValueError(
"[Error] The descriptor decoder selection is not supported.")
# SuperPoint descriptor
if self.cfg["descriptor_decoder"] == "superpoint_descriptor":
decoder = SuperpointDescriptor(self.feat_channel)
else:
raise ValueError(
"[Error] The descriptor decoder selection is not supported.")
return decoder
def weight_init(m):
""" Weight initialization function. """
# Conv2D
if isinstance(m, nn.Conv2d):
init.xavier_normal_(m.weight.data)
if m.bias is not None:
init.normal_(m.bias.data)
# Batchnorm
elif isinstance(m, nn.BatchNorm2d):
init.normal_(m.weight.data, mean=1, std=0.02)
init.constant_(m.bias.data, 0)
# Linear
elif isinstance(m, nn.Linear):
init.xavier_normal_(m.weight.data)
init.normal_(m.bias.data)
else:
pass
|