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| import pdb |
| import cv2 |
| import os |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import numpy as np |
|
|
| from lib.models.backbones.backbone_selector import BackboneSelector |
| from lib.models.tools.module_helper import ModuleHelper |
| from lib.utils.helpers.offset_helper import DTOffsetConfig |
| from lib.models.backbones.hrnet.hrnet_backbone import BasicBlock |
|
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|
|
| class SegFix_HRNet(nn.Module): |
| def __init__(self, configer): |
| super(SegFix_HRNet, self).__init__() |
| self.configer = configer |
| self.backbone = BackboneSelector(configer).get_backbone() |
| backbone_name = self.configer.get('network', 'backbone') |
| width = int(backbone_name[-2:]) |
| if 'hrnet2x' in backbone_name: |
| in_channels = width * 31 |
| else: |
| in_channels = width * 15 |
|
|
| num_masks = 2 |
| num_directions = DTOffsetConfig.num_classes |
|
|
| mid_channels = 256 |
|
|
| self.dir_head = nn.Sequential( |
| nn.Conv2d(in_channels, |
| mid_channels, |
| kernel_size=1, |
| stride=1, |
| padding=0, |
| bias=False), |
| ModuleHelper.BNReLU(mid_channels, |
| bn_type=self.configer.get( |
| 'network', 'bn_type')), |
| nn.Conv2d(mid_channels, |
| num_directions, |
| kernel_size=1, |
| stride=1, |
| padding=0, |
| bias=False)) |
| self.mask_head = nn.Sequential( |
| nn.Conv2d(in_channels, |
| mid_channels, |
| kernel_size=1, |
| stride=1, |
| padding=0, |
| bias=False), |
| ModuleHelper.BNReLU(mid_channels, |
| bn_type=self.configer.get( |
| 'network', 'bn_type')), |
| nn.Conv2d(mid_channels, |
| num_masks, |
| kernel_size=1, |
| stride=1, |
| padding=0, |
| bias=False)) |
|
|
| def forward(self, x_): |
| x = self.backbone(x_) |
| _, _, h, w = x[0].size() |
|
|
| feat1 = x[0] |
| for i in range(1, len(x)): |
| x[i] = F.interpolate(x[i], |
| size=(h, w), |
| mode='bilinear', |
| align_corners=True) |
|
|
| feats = torch.cat(x, 1) |
| mask_map = self.mask_head(feats) |
| dir_map = self.dir_head(feats) |
| return mask_map, dir_map |
|
|
