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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torchvision | |
| from . import resnet, resnext | |
| try: | |
| from lib.nn import SynchronizedBatchNorm2d | |
| except ImportError: | |
| from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d | |
| class SegmentationModuleBase(nn.Module): | |
| def __init__(self): | |
| super(SegmentationModuleBase, self).__init__() | |
| def pixel_acc(pred, label, ignore_index=-1): | |
| _, preds = torch.max(pred, dim=1) | |
| valid = (label != ignore_index).long() | |
| acc_sum = torch.sum(valid * (preds == label).long()) | |
| pixel_sum = torch.sum(valid) | |
| acc = acc_sum.float() / (pixel_sum.float() + 1e-10) | |
| return acc | |
| def part_pixel_acc(pred_part, gt_seg_part, gt_seg_object, object_label, valid): | |
| mask_object = (gt_seg_object == object_label) | |
| _, pred = torch.max(pred_part, dim=1) | |
| acc_sum = mask_object * (pred == gt_seg_part) | |
| acc_sum = torch.sum(acc_sum.view(acc_sum.size(0), -1), dim=1) | |
| acc_sum = torch.sum(acc_sum * valid) | |
| pixel_sum = torch.sum(mask_object.view(mask_object.size(0), -1), dim=1) | |
| pixel_sum = torch.sum(pixel_sum * valid) | |
| return acc_sum, pixel_sum | |
| def part_loss(pred_part, gt_seg_part, gt_seg_object, object_label, valid): | |
| mask_object = (gt_seg_object == object_label) | |
| loss = F.nll_loss(pred_part, gt_seg_part * mask_object.long(), reduction='none') | |
| loss = loss * mask_object.float() | |
| loss = torch.sum(loss.view(loss.size(0), -1), dim=1) | |
| nr_pixel = torch.sum(mask_object.view(mask_object.shape[0], -1), dim=1) | |
| sum_pixel = (nr_pixel * valid).sum() | |
| loss = (loss * valid.float()).sum() / torch.clamp(sum_pixel, 1).float() | |
| return loss | |
| class SegmentationModule(SegmentationModuleBase): | |
| def __init__(self, net_enc, net_dec, labeldata, loss_scale=None): | |
| super(SegmentationModule, self).__init__() | |
| self.encoder = net_enc | |
| self.decoder = net_dec | |
| self.crit_dict = nn.ModuleDict() | |
| if loss_scale is None: | |
| self.loss_scale = {"object": 1, "part": 0.5, "scene": 0.25, "material": 1} | |
| else: | |
| self.loss_scale = loss_scale | |
| # criterion | |
| self.crit_dict["object"] = nn.NLLLoss(ignore_index=0) # ignore background 0 | |
| self.crit_dict["material"] = nn.NLLLoss(ignore_index=0) # ignore background 0 | |
| self.crit_dict["scene"] = nn.NLLLoss(ignore_index=-1) # ignore unlabelled -1 | |
| # Label data - read from json | |
| self.labeldata = labeldata | |
| object_to_num = {k: v for v, k in enumerate(labeldata['object'])} | |
| part_to_num = {k: v for v, k in enumerate(labeldata['part'])} | |
| self.object_part = {object_to_num[k]: | |
| [part_to_num[p] for p in v] | |
| for k, v in labeldata['object_part'].items()} | |
| self.object_with_part = sorted(self.object_part.keys()) | |
| self.decoder.object_part = self.object_part | |
| self.decoder.object_with_part = self.object_with_part | |
| def forward(self, feed_dict, *, seg_size=None): | |
| if seg_size is None: # training | |
| if feed_dict['source_idx'] == 0: | |
| output_switch = {"object": True, "part": True, "scene": True, "material": False} | |
| elif feed_dict['source_idx'] == 1: | |
| output_switch = {"object": False, "part": False, "scene": False, "material": True} | |
| else: | |
| raise ValueError | |
| pred = self.decoder( | |
| self.encoder(feed_dict['img'], return_feature_maps=True), | |
| output_switch=output_switch | |
| ) | |
| # loss | |
| loss_dict = {} | |
| if pred['object'] is not None: # object | |
| loss_dict['object'] = self.crit_dict['object'](pred['object'], feed_dict['seg_object']) | |
| if pred['part'] is not None: # part | |
| part_loss = 0 | |
| for idx_part, object_label in enumerate(self.object_with_part): | |
| part_loss += self.part_loss( | |
| pred['part'][idx_part], feed_dict['seg_part'], | |
| feed_dict['seg_object'], object_label, feed_dict['valid_part'][:, idx_part]) | |
| loss_dict['part'] = part_loss | |
| if pred['scene'] is not None: # scene | |
| loss_dict['scene'] = self.crit_dict['scene'](pred['scene'], feed_dict['scene_label']) | |
| if pred['material'] is not None: # material | |
| loss_dict['material'] = self.crit_dict['material'](pred['material'], feed_dict['seg_material']) | |
| loss_dict['total'] = sum([loss_dict[k] * self.loss_scale[k] for k in loss_dict.keys()]) | |
| # metric | |
| metric_dict= {} | |
| if pred['object'] is not None: | |
| metric_dict['object'] = self.pixel_acc( | |
| pred['object'], feed_dict['seg_object'], ignore_index=0) | |
| if pred['material'] is not None: | |
| metric_dict['material'] = self.pixel_acc( | |
| pred['material'], feed_dict['seg_material'], ignore_index=0) | |
| if pred['part'] is not None: | |
| acc_sum, pixel_sum = 0, 0 | |
| for idx_part, object_label in enumerate(self.object_with_part): | |
| acc, pixel = self.part_pixel_acc( | |
| pred['part'][idx_part], feed_dict['seg_part'], feed_dict['seg_object'], | |
| object_label, feed_dict['valid_part'][:, idx_part]) | |
| acc_sum += acc | |
| pixel_sum += pixel | |
| metric_dict['part'] = acc_sum.float() / (pixel_sum.float() + 1e-10) | |
| if pred['scene'] is not None: | |
| metric_dict['scene'] = self.pixel_acc( | |
| pred['scene'], feed_dict['scene_label'], ignore_index=-1) | |
| return {'metric': metric_dict, 'loss': loss_dict} | |
| else: # inference | |
| output_switch = {"object": True, "part": True, "scene": True, "material": True} | |
| pred = self.decoder(self.encoder(feed_dict['img'], return_feature_maps=True), | |
| output_switch=output_switch, seg_size=seg_size) | |
| return pred | |
| def conv3x3(in_planes, out_planes, stride=1, has_bias=False): | |
| "3x3 convolution with padding" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=1, bias=has_bias) | |
| def conv3x3_bn_relu(in_planes, out_planes, stride=1): | |
| return nn.Sequential( | |
| conv3x3(in_planes, out_planes, stride), | |
| SynchronizedBatchNorm2d(out_planes), | |
| nn.ReLU(inplace=True), | |
| ) | |
| class ModelBuilder: | |
| def __init__(self): | |
| pass | |
| # custom weights initialization | |
| def weights_init(m): | |
| classname = m.__class__.__name__ | |
| if classname.find('Conv') != -1: | |
| nn.init.kaiming_normal_(m.weight.data, nonlinearity='relu') | |
| elif classname.find('BatchNorm') != -1: | |
| m.weight.data.fill_(1.) | |
| m.bias.data.fill_(1e-4) | |
| #elif classname.find('Linear') != -1: | |
| # m.weight.data.normal_(0.0, 0.0001) | |
| def build_encoder(self, arch='resnet50_dilated8', fc_dim=512, weights=''): | |
| pretrained = True if len(weights) == 0 else False | |
| if arch == 'resnet34': | |
| raise NotImplementedError | |
| orig_resnet = resnet.__dict__['resnet34'](pretrained=pretrained) | |
| net_encoder = Resnet(orig_resnet) | |
| elif arch == 'resnet34_dilated8': | |
| raise NotImplementedError | |
| orig_resnet = resnet.__dict__['resnet34'](pretrained=pretrained) | |
| net_encoder = ResnetDilated(orig_resnet, | |
| dilate_scale=8) | |
| elif arch == 'resnet34_dilated16': | |
| raise NotImplementedError | |
| orig_resnet = resnet.__dict__['resnet34'](pretrained=pretrained) | |
| net_encoder = ResnetDilated(orig_resnet, | |
| dilate_scale=16) | |
| elif arch == 'resnet50': | |
| orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained) | |
| net_encoder = Resnet(orig_resnet) | |
| elif arch == 'resnet101': | |
| orig_resnet = resnet.__dict__['resnet101'](pretrained=pretrained) | |
| net_encoder = Resnet(orig_resnet) | |
| elif arch == 'resnext101': | |
| orig_resnext = resnext.__dict__['resnext101'](pretrained=pretrained) | |
| net_encoder = Resnet(orig_resnext) # we can still use class Resnet | |
| else: | |
| raise Exception('Architecture undefined!') | |
| # net_encoder.apply(self.weights_init) | |
| if len(weights) > 0: | |
| # print('Loading weights for net_encoder') | |
| net_encoder.load_state_dict( | |
| torch.load(weights, map_location=lambda storage, loc: storage), strict=False) | |
| return net_encoder | |
| def build_decoder(self, nr_classes, | |
| arch='ppm_bilinear_deepsup', fc_dim=512, | |
| weights='', use_softmax=False): | |
| if arch == 'upernet_lite': | |
| net_decoder = UPerNet( | |
| nr_classes=nr_classes, | |
| fc_dim=fc_dim, | |
| use_softmax=use_softmax, | |
| fpn_dim=256) | |
| elif arch == 'upernet': | |
| net_decoder = UPerNet( | |
| nr_classes=nr_classes, | |
| fc_dim=fc_dim, | |
| use_softmax=use_softmax, | |
| fpn_dim=512) | |
| else: | |
| raise Exception('Architecture undefined!') | |
| net_decoder.apply(self.weights_init) | |
| if len(weights) > 0: | |
| # print('Loading weights for net_decoder') | |
| net_decoder.load_state_dict( | |
| torch.load(weights, map_location=lambda storage, loc: storage), strict=False) | |
| return net_decoder | |
| class Resnet(nn.Module): | |
| def __init__(self, orig_resnet): | |
| super(Resnet, self).__init__() | |
| # take pretrained resnet, except AvgPool and FC | |
| self.conv1 = orig_resnet.conv1 | |
| self.bn1 = orig_resnet.bn1 | |
| self.relu1 = orig_resnet.relu1 | |
| self.conv2 = orig_resnet.conv2 | |
| self.bn2 = orig_resnet.bn2 | |
| self.relu2 = orig_resnet.relu2 | |
| self.conv3 = orig_resnet.conv3 | |
| self.bn3 = orig_resnet.bn3 | |
| self.relu3 = orig_resnet.relu3 | |
| self.maxpool = orig_resnet.maxpool | |
| self.layer1 = orig_resnet.layer1 | |
| self.layer2 = orig_resnet.layer2 | |
| self.layer3 = orig_resnet.layer3 | |
| self.layer4 = orig_resnet.layer4 | |
| def forward(self, x, return_feature_maps=False): | |
| conv_out = [] | |
| x = self.relu1(self.bn1(self.conv1(x))) | |
| x = self.relu2(self.bn2(self.conv2(x))) | |
| x = self.relu3(self.bn3(self.conv3(x))) | |
| x = self.maxpool(x) | |
| x = self.layer1(x); conv_out.append(x); | |
| x = self.layer2(x); conv_out.append(x); | |
| x = self.layer3(x); conv_out.append(x); | |
| x = self.layer4(x); conv_out.append(x); | |
| if return_feature_maps: | |
| return conv_out | |
| return [x] | |
| # upernet | |
| class UPerNet(nn.Module): | |
| def __init__(self, nr_classes, fc_dim=4096, | |
| use_softmax=False, pool_scales=(1, 2, 3, 6), | |
| fpn_inplanes=(256,512,1024,2048), fpn_dim=256): | |
| # Lazy import so that compilation isn't needed if not being used. | |
| from .prroi_pool import PrRoIPool2D | |
| super(UPerNet, self).__init__() | |
| self.use_softmax = use_softmax | |
| # PPM Module | |
| self.ppm_pooling = [] | |
| self.ppm_conv = [] | |
| for scale in pool_scales: | |
| # we use the feature map size instead of input image size, so down_scale = 1.0 | |
| self.ppm_pooling.append(PrRoIPool2D(scale, scale, 1.)) | |
| self.ppm_conv.append(nn.Sequential( | |
| nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False), | |
| SynchronizedBatchNorm2d(512), | |
| nn.ReLU(inplace=True) | |
| )) | |
| self.ppm_pooling = nn.ModuleList(self.ppm_pooling) | |
| self.ppm_conv = nn.ModuleList(self.ppm_conv) | |
| self.ppm_last_conv = conv3x3_bn_relu(fc_dim + len(pool_scales)*512, fpn_dim, 1) | |
| # FPN Module | |
| self.fpn_in = [] | |
| for fpn_inplane in fpn_inplanes[:-1]: # skip the top layer | |
| self.fpn_in.append(nn.Sequential( | |
| nn.Conv2d(fpn_inplane, fpn_dim, kernel_size=1, bias=False), | |
| SynchronizedBatchNorm2d(fpn_dim), | |
| nn.ReLU(inplace=True) | |
| )) | |
| self.fpn_in = nn.ModuleList(self.fpn_in) | |
| self.fpn_out = [] | |
| for i in range(len(fpn_inplanes) - 1): # skip the top layer | |
| self.fpn_out.append(nn.Sequential( | |
| conv3x3_bn_relu(fpn_dim, fpn_dim, 1), | |
| )) | |
| self.fpn_out = nn.ModuleList(self.fpn_out) | |
| self.conv_fusion = conv3x3_bn_relu(len(fpn_inplanes) * fpn_dim, fpn_dim, 1) | |
| # background included. if ignore in loss, output channel 0 will not be trained. | |
| self.nr_scene_class, self.nr_object_class, self.nr_part_class, self.nr_material_class = \ | |
| nr_classes['scene'], nr_classes['object'], nr_classes['part'], nr_classes['material'] | |
| # input: PPM out, input_dim: fpn_dim | |
| self.scene_head = nn.Sequential( | |
| conv3x3_bn_relu(fpn_dim, fpn_dim, 1), | |
| nn.AdaptiveAvgPool2d(1), | |
| nn.Conv2d(fpn_dim, self.nr_scene_class, kernel_size=1, bias=True) | |
| ) | |
| # input: Fusion out, input_dim: fpn_dim | |
| self.object_head = nn.Sequential( | |
| conv3x3_bn_relu(fpn_dim, fpn_dim, 1), | |
| nn.Conv2d(fpn_dim, self.nr_object_class, kernel_size=1, bias=True) | |
| ) | |
| # input: Fusion out, input_dim: fpn_dim | |
| self.part_head = nn.Sequential( | |
| conv3x3_bn_relu(fpn_dim, fpn_dim, 1), | |
| nn.Conv2d(fpn_dim, self.nr_part_class, kernel_size=1, bias=True) | |
| ) | |
| # input: FPN_2 (P2), input_dim: fpn_dim | |
| self.material_head = nn.Sequential( | |
| conv3x3_bn_relu(fpn_dim, fpn_dim, 1), | |
| nn.Conv2d(fpn_dim, self.nr_material_class, kernel_size=1, bias=True) | |
| ) | |
| def forward(self, conv_out, output_switch=None, seg_size=None): | |
| output_dict = {k: None for k in output_switch.keys()} | |
| conv5 = conv_out[-1] | |
| input_size = conv5.size() | |
| ppm_out = [conv5] | |
| roi = [] # fake rois, just used for pooling | |
| for i in range(input_size[0]): # batch size | |
| roi.append(torch.Tensor([i, 0, 0, input_size[3], input_size[2]]).view(1, -1)) # b, x0, y0, x1, y1 | |
| roi = torch.cat(roi, dim=0).type_as(conv5) | |
| ppm_out = [conv5] | |
| for pool_scale, pool_conv in zip(self.ppm_pooling, self.ppm_conv): | |
| ppm_out.append(pool_conv(F.interpolate( | |
| pool_scale(conv5, roi.detach()), | |
| (input_size[2], input_size[3]), | |
| mode='bilinear', align_corners=False))) | |
| ppm_out = torch.cat(ppm_out, 1) | |
| f = self.ppm_last_conv(ppm_out) | |
| if output_switch['scene']: # scene | |
| output_dict['scene'] = self.scene_head(f) | |
| if output_switch['object'] or output_switch['part'] or output_switch['material']: | |
| fpn_feature_list = [f] | |
| for i in reversed(range(len(conv_out) - 1)): | |
| conv_x = conv_out[i] | |
| conv_x = self.fpn_in[i](conv_x) # lateral branch | |
| f = F.interpolate( | |
| f, size=conv_x.size()[2:], mode='bilinear', align_corners=False) # top-down branch | |
| f = conv_x + f | |
| fpn_feature_list.append(self.fpn_out[i](f)) | |
| fpn_feature_list.reverse() # [P2 - P5] | |
| # material | |
| if output_switch['material']: | |
| output_dict['material'] = self.material_head(fpn_feature_list[0]) | |
| if output_switch['object'] or output_switch['part']: | |
| output_size = fpn_feature_list[0].size()[2:] | |
| fusion_list = [fpn_feature_list[0]] | |
| for i in range(1, len(fpn_feature_list)): | |
| fusion_list.append(F.interpolate( | |
| fpn_feature_list[i], | |
| output_size, | |
| mode='bilinear', align_corners=False)) | |
| fusion_out = torch.cat(fusion_list, 1) | |
| x = self.conv_fusion(fusion_out) | |
| if output_switch['object']: # object | |
| output_dict['object'] = self.object_head(x) | |
| if output_switch['part']: | |
| output_dict['part'] = self.part_head(x) | |
| if self.use_softmax: # is True during inference | |
| # inference scene | |
| x = output_dict['scene'] | |
| x = x.squeeze(3).squeeze(2) | |
| x = F.softmax(x, dim=1) | |
| output_dict['scene'] = x | |
| # inference object, material | |
| for k in ['object', 'material']: | |
| x = output_dict[k] | |
| x = F.interpolate(x, size=seg_size, mode='bilinear', align_corners=False) | |
| x = F.softmax(x, dim=1) | |
| output_dict[k] = x | |
| # inference part | |
| x = output_dict['part'] | |
| x = F.interpolate(x, size=seg_size, mode='bilinear', align_corners=False) | |
| part_pred_list, head = [], 0 | |
| for idx_part, object_label in enumerate(self.object_with_part): | |
| n_part = len(self.object_part[object_label]) | |
| _x = F.interpolate(x[:, head: head + n_part], size=seg_size, mode='bilinear', align_corners=False) | |
| _x = F.softmax(_x, dim=1) | |
| part_pred_list.append(_x) | |
| head += n_part | |
| output_dict['part'] = part_pred_list | |
| else: # Training | |
| # object, scene, material | |
| for k in ['object', 'scene', 'material']: | |
| if output_dict[k] is None: | |
| continue | |
| x = output_dict[k] | |
| x = F.log_softmax(x, dim=1) | |
| if k == "scene": # for scene | |
| x = x.squeeze(3).squeeze(2) | |
| output_dict[k] = x | |
| if output_dict['part'] is not None: | |
| part_pred_list, head = [], 0 | |
| for idx_part, object_label in enumerate(self.object_with_part): | |
| n_part = len(self.object_part[object_label]) | |
| x = output_dict['part'][:, head: head + n_part] | |
| x = F.log_softmax(x, dim=1) | |
| part_pred_list.append(x) | |
| head += n_part | |
| output_dict['part'] = part_pred_list | |
| return output_dict | |