Merge pull request #33 from LikeLy-Journey/revert-32-pointRend

Revert "Add PointRend"

segmentron/config/config.py

@@ -80,15 +80,11 @@ def remove_irrelevant_cfg(self):
         from ..models.model_zoo import MODEL_REGISTRY
         model_list = MODEL_REGISTRY.get_list()
         model_list_lower = [x.lower() for x in model_list]
-
+        # print('model_list:', model_list)
         assert model_name.lower() in model_list_lower, "Expected model name in {}, but received {}"\
             .format(model_list, model_name)
         pop_keys = []
         for key in self.MODEL.keys():
-            if key.lower() in model_list_lower:
-                if model_name.lower() == 'pointrend' and \
-                    key.lower() == self.MODEL.POINTREND.BASEMODEL.lower():
-                    continue
             if key.lower() in model_list_lower and key.lower() != model_name.lower():
                 pop_keys.append(key)
         for key in pop_keys:

segmentron/config/settings.py

@@ -174,9 +174,6 @@
 cfg.MODEL.CGNET.STAGE2_BLOCK_NUM = 3
 cfg.MODEL.CGNET.STAGE3_BLOCK_NUM = 21
 
-########################## PointRend config ##################################
-cfg.MODEL.POINTREND.BASEMODEL = 'DeepLabV3_Plus'
-
 ########################## hrnet config ######################################
 cfg.MODEL.HRNET.PRETRAINED_LAYERS = ['*']
 cfg.MODEL.HRNET.STEM_INPLANES = 64

segmentron/data/dataloader/pascal_aug.py

@@ -73,8 +73,6 @@ def __getitem__(self, index):
             img, target = self._sync_transform(img, target)
         elif self.mode == 'val':
             img, target = self._val_sync_transform(img, target)
-        elif self.mode == 'testval':
-            img, target = self._val_sync_transform(img, target)
         else:
             raise RuntimeError('unknown mode for dataloader: {}'.format(self.mode))
         # general resize, normalize and toTensor

segmentron/data/dataloader/pascal_voc.py

@@ -1,7 +1,6 @@
 """Pascal VOC Semantic Segmentation Dataset."""
 import os
 import torch
-import logging
 import numpy as np
 
 from PIL import Image
@@ -85,7 +84,6 @@ def __getitem__(self, index):
             img, mask = self._val_sync_transform(img, mask)
         else:
             assert self.mode == 'testval'
-            logging.warn("Use mode of testval, you should set batch size=1")
             img, mask = self._img_transform(img), self._mask_transform(mask)
         # general resize, normalize and toTensor
         if self.transform is not None:

segmentron/models/__init__.py

@@ -25,4 +25,3 @@
 from .espnetv2 import ESPNetV2
 from .enet import ENet
 from .edanet import EDANet
-from .pointrend import PointRend

segmentron/models/backbones/build.py

@@ -3,7 +3,6 @@
 import logging
 import torch.utils.model_zoo as model_zoo
 
-from ...utils.download import download
 from ...utils.registry import Registry
 from ...config import cfg
 
@@ -43,14 +42,7 @@ def load_backbone_pretrained(model, backbone):
             return
         else:
             logging.info('load backbone pretrained model from url..')
-            try:
-                msg = model.load_state_dict(model_zoo.load_url(model_urls[backbone]), strict=False)
-            except Exception as e:
-                logging.warning(e)
-                logging.info('Use torch download failed, try custom method!')
-
-                msg = model.load_state_dict(torch.load(download(model_urls[backbone], 
-                        path=os.path.join(torch.hub._get_torch_home(), 'checkpoints'))), strict=False)
+            msg = model.load_state_dict(model_zoo.load_url(model_urls[backbone]), strict=False)
             logging.info(msg)
 
 

segmentron/solver/loss.py

@@ -6,7 +6,6 @@
 
 from torch.autograd import Variable
 from .lovasz_losses import lovasz_softmax
-from ..models.pointrend import point_sample
 from ..data.dataloader import datasets
 from ..config import cfg
 
@@ -361,32 +360,6 @@ def forward(self, *inputs):
         return dict(loss=self._aux_forward(*inputs))
 
 
-class PointRendLoss(nn.CrossEntropyLoss):
-    def __init__(self, aux=True, aux_weight=0.2, ignore_index=-1, **kwargs):
-        super(PointRendLoss, self).__init__(ignore_index=ignore_index)
-        self.aux = aux
-        self.aux_weight = aux_weight
-        self.ignore_index = ignore_index
-
-    def forward(self, *inputs, **kwargs):
-        result, gt = tuple(inputs)
-
-        pred = F.interpolate(result["coarse"], gt.shape[-2:], mode="bilinear", align_corners=True)
-        seg_loss = F.cross_entropy(pred, gt, ignore_index=self.ignore_index)
-
-        gt_points = point_sample(
-            gt.float().unsqueeze(1),
-            result["points"],
-            mode="nearest",
-            align_corners=False
-        ).squeeze_(1).long()
-        points_loss = F.cross_entropy(result["rend"], gt_points, ignore_index=self.ignore_index)
-
-        loss = seg_loss + points_loss
-
-        return dict(loss=loss)
-
-
 def get_segmentation_loss(model, use_ohem=False, **kwargs):
     if use_ohem:
         return MixSoftmaxCrossEntropyOHEMLoss(**kwargs)
@@ -400,13 +373,11 @@ def get_segmentation_loss(model, use_ohem=False, **kwargs):
         logging.info('Use dice loss!')
         return DiceLoss(**kwargs)
 
+
     model = model.lower()
     if model == 'icnet':
         return ICNetLoss(**kwargs)
     elif model == 'encnet':
         return EncNetLoss(**kwargs)
-    elif model == 'pointrend':
-        logging.info('Use pointrend loss!')
-        return PointRendLoss(**kwargs)
     else:
         return MixSoftmaxCrossEntropyLoss(**kwargs)

segmentron/utils/score.py

@@ -30,7 +30,10 @@ def update(self, preds, labels):
         """
 
         def reduce_tensor(tensor):
-            rt = tensor.clone()
+            if isinstance(tensor, torch.Tensor):
+                rt = tensor.clone()
+            else:
+                rt = copy.deepcopy(tensor)
             dist.all_reduce(rt, op=dist.ReduceOp.SUM)
             return rt
 

tools/eval.py

@@ -47,10 +47,9 @@ def __init__(self, args):
         # create network
         self.model = get_segmentation_model().to(self.device)
 
-        if hasattr(self.model, 'encoder') and hasattr(self.model.encoder, 'named_modules') and \
-            cfg.MODEL.BN_EPS_FOR_ENCODER:
-            logging.info('set bn custom eps for bn in encoder: {}'.format(cfg.MODEL.BN_EPS_FOR_ENCODER))
-            self.set_batch_norm_attr(self.model.encoder.named_modules(), 'eps', cfg.MODEL.BN_EPS_FOR_ENCODER)
+        if hasattr(self.model, 'encoder') and cfg.MODEL.BN_EPS_FOR_ENCODER:
+                logging.info('set bn custom eps for bn in encoder: {}'.format(cfg.MODEL.BN_EPS_FOR_ENCODER))
+                self.set_batch_norm_attr(self.model.encoder.named_modules(), 'eps', cfg.MODEL.BN_EPS_FOR_ENCODER)
 
         if args.distributed:
             self.model = nn.parallel.DistributedDataParallel(self.model,