Mapping is correct now and the baseline results looking a lot better

anushkaray · anushkaray · commit 6bfa952789b0 · 2022-03-21T18:09:56.000Z
diff --git a/mmseg/core/evaluation/metrics.py b/mmseg/core/evaluation/metrics.py
@@ -54,7 +54,7 @@ def intersect_and_union(pred_label,
     """
 
     if isinstance(pred_label, str):
-        pred_label = torch.from_numpy(np.load(pred_label))
+        pred_label = torch.from_numpy(np.load(pred_label)) 
     else:
         pred_label = torch.from_numpy((pred_label))
 
@@ -63,26 +63,20 @@ def intersect_and_union(pred_label,
             mmcv.imread(label, flag='unchanged', backend='pillow'))
     else:
         label = torch.from_numpy(label)
-    # print("label map ", label_map)
-    if label_map is not None:
-        for old_id, new_id in label_map.items():
-            label[label == old_id] = new_id
+    ## I commented this out because this was being done twice (once in loading and once here)
+    # if label_map is not None:
+    #     for old_id, new_id in label_map.items():
+    #         label[label == old_id] = new_id
     if reduce_zero_label:
         label[label == 0] = 255
         label = label - 1
         label[label == 254] = 255
-    # label = tf.rgb_to_grayscale(label, 1)
-    # print("label shape ", label.shape)
-    # print("pred label: ", torch.max(pred_label))
-    label = label[:,:,0]
-    # print("label shape after ", label.shape)
     mask = (label != ignore_index)
-    # print("pred label: ", pred_label)
-    # print("pred label shape: ", pred_label.shape)
-    # print("mask: ", mask)
-    # print("mask shape: ", mask.shape)
     pred_label = pred_label[mask]
     label = label[mask]
+    
+    print("Predicted Label (metrics.py) ", pred_label)
+    print("Ground Truth Label (metrics.py) ", label)
 
     intersect = pred_label[pred_label == label]
     area_intersect = torch.histc(
diff --git a/mmseg/datasets/aerial.py b/mmseg/datasets/aerial.py
@@ -18,8 +18,10 @@ class AerialDataset(CustomDataset):
     The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is fixed to
     '.png'.
     """
-    AERIAL_CLASSES = ('sidewalk', 'earth', 'grass', 'sand', 'water', 'rock', 'swimming pool', 'plant', 
-               'building', 'wall', 'windowpane', 'door', 'fence', 'pole', 'person', 'animal', 'car', 'bicycle', 'tree', 'television receiver', 'microwave', 'coffee table', 'trade name', 'sconce')
+    AERIAL_CLASSES = ('sconce', 'sidewalk', 'earth', 'grass', 'sand', 'water', 'rock', 'swimming pool', 'plant', 
+               'building', 'wall', 'windowpane', 'door', 'fence', 'pole', 'person', 'animal', 'car', 'bicycle', 'tree', 'television receiver', 'microwave', 'coffee table', 'trade name')
+    
+    # AERIAL_CLASSES = ('wall', 'building', 'tree', 'windowpane',  'grass', 'sidewalk', 'person','earth', 'door', 'plant','car', 'water','fence', 'rock', 'sand', 'coffee table', 'television receiver', 'pole','swimming pool','trade name', 'microwave', 'animal', 'bicycle','sconce')
     CLASSES = (
         'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
         'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
@@ -46,7 +48,12 @@ class AerialDataset(CustomDataset):
         'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
         'clock', 'flag')
 
-    AERIAL_PALETTE = [[128, 64, 128], [130, 76, 0], [0, 102, 0], [112, 103, 87], [28, 42, 168], [48, 41, 30], [0, 50, 89], [107, 142, 35], [70, 70, 70], [102, 102, 156], [254, 228, 12], [254, 148, 12], [190, 153, 153], [153, 153, 153], [255, 22, 96], [102, 51, 0], [9, 143, 150], [119, 11, 32], [51, 51, 0], [190, 250, 190], [112, 150, 146], [2, 135, 115], [255, 0, 0], [0, 0, 0]]
+    AERIAL_PALETTE = [[0, 0, 0], [128, 64, 128], [130, 76, 0], [0, 102, 0], [112, 103, 87], [28, 42, 168], [48, 41, 30], [0, 50, 89], [107, 142, 35], [70, 70, 70], [102, 102, 156], [254, 228, 12], [254, 148, 12], [190, 153, 153], [153, 153, 153], [255, 22, 96], [102, 51, 0], [9, 143, 150], [119, 11, 32], [51, 51, 0], [190, 250, 190], [112, 150, 146], [2, 135, 115], [255, 0, 0]]
+
+
+    # AERIAL_PALETTE = [[102, 102, 156], [70, 70, 70], [51, 51, 0], [254, 228, 12], [0, 102, 0], [128, 64, 128], [255, 22, 96], [130, 76, 0], [254, 148, 12], [107, 142, 35], [9, 143, 150], [28, 42, 168], [190, 153, 153], [48, 41, 30], [112, 103, 87],[2, 135, 115], [190, 250, 190],[153, 153, 153], [0, 50, 89], [255, 0, 0],[112, 150, 146],[102, 51, 0],  [190, 250, 190], [0, 0, 0]]
+    
+    
     PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
                [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
                [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
@@ -96,6 +103,8 @@ def __init__(self, **kwargs):
             **kwargs
             )
         self.CLASSES, self.PALETTE = self.get_classes_and_palette(self.AERIAL_CLASSES, self.AERIAL_PALETTE)
+        self.gt_seg_map_loader_cfg = None
+        self.reduce_zero_label = False
         # print("self.classes len ", len(self.CLASSES))
         # print("self.palette len ", len(self.PALETTE))
 
diff --git a/mmseg/datasets/custom.py b/mmseg/datasets/custom.py
@@ -13,6 +13,7 @@
 from mmseg.utils import get_root_logger
 from .builder import DATASETS
 from .pipelines import Compose, LoadAnnotations
+import torch
 
 
 @DATASETS.register_module()
@@ -245,6 +246,7 @@ def get_gt_seg_map_by_idx(self, index):
         results = dict(ann_info=ann_info)
         self.pre_pipeline(results)
         self.gt_seg_map_loader(results)
+        # print("GT CONFIG IN CUSTOM ", self.gt_seg_map_loader_cfg)
         return results['gt_semantic_seg']
 
     def get_gt_seg_maps(self, efficient_test=None):
@@ -280,17 +282,19 @@ def pre_eval(self, preds, indices):
             indices = [indices]
         if not isinstance(preds, list):
             preds = [preds]
-
+        # print("indices in customs: ", indices)
         pre_eval_results = []
-        # print("preds shape: ", preds[0].shape)
         for pred, index in zip(preds, indices):
-            seg_map = self.get_gt_seg_map_by_idx(index)
-            # print("seg map shape: ", seg_map.shape)
-            # print("pred shape: ", pred.shape)
+            # print("index in customs: ", index)
+            seg_map = self.get_gt_seg_map_by_idx(index) 
+            print("pred in custom: ", np.amax(pred))
+            print("seg map in custom: ", np.amax(seg_map))
             pre_eval_results.append(
                 intersect_and_union(pred, seg_map, len(self.CLASSES),
                                     self.ignore_index, self.label_map,
                                     self.reduce_zero_label))
+            
+     
 
         return pre_eval_results
 
@@ -347,6 +351,7 @@ def get_classes_and_palette(self, classes=None, palette=None):
     def get_palette_for_custom_classes(self, class_names, palette=None):
 
         if self.label_map is not None:
+            # print("LABEL MAP IS NOT NONE IN CUSTOM")
             # return subset of palette
             palette = []
             for old_id, new_id in sorted(
@@ -357,6 +362,7 @@ def get_palette_for_custom_classes(self, class_names, palette=None):
 
         elif palette is None:
             if self.PALETTE is None:
+                # print("PALETTE IS NOT OURS IT IS RANDOM IN CUSTOM")
                 # Get random state before set seed, and restore
                 # random state later.
                 # It will prevent loss of randomness, as the palette
@@ -368,6 +374,7 @@ def get_palette_for_custom_classes(self, class_names, palette=None):
                 palette = np.random.randint(0, 255, size=(len(class_names), 3))
                 np.random.set_state(state)
             else:
+                # print("PALETTE IS OURS IN CUSTOM")
                 palette = self.PALETTE
 
         return palette
diff --git a/mmseg/datasets/pipelines/loading.py b/mmseg/datasets/pipelines/loading.py
@@ -5,6 +5,7 @@
 import numpy as np
 
 from ..builder import PIPELINES
+from PIL import Image
 
 
 @PIPELINES.register_module()
@@ -120,7 +121,6 @@ def __call__(self, results):
         Returns:
             dict: The dict contains loaded semantic segmentation annotations.
         """
-        print("AYOOOOOOOOOOOO")
 
         if self.file_client is None:
             self.file_client = mmcv.FileClient(**self.file_client_args)
@@ -130,20 +130,51 @@ def __call__(self, results):
                                 results['ann_info']['seg_map'])
         else:
             filename = results['ann_info']['seg_map']
-        img_bytes = self.file_client.get(filename)
-        gt_semantic_seg = mmcv.imfrombytes(
-            img_bytes, flag='unchanged',
-            backend=self.imdecode_backend).squeeze().astype(np.uint8)
+        print("Filename (Loding.py) ", filename)
+        
+        gt_semantic_seg = np.asarray(Image.open(filename))
+        print("GT Semantic Seg Before Changing Labels (Loading.py): ", gt_semantic_seg)
+        # img_bytes = self.file_client.get(filename)
+        
+        # gt_semantic_seg = mmcv.imfrombytes(
+        #     img_bytes, flag='unchanged', channel_order='rgb',
+        #     backend=self.imdecode_backend).squeeze().astype(np.uint8)
+
+        # print("LABEL MAP IN LOAD ANN ", results['label_map'])
         # modify if custom classes
         if results.get('label_map', None) is not None:
+            # print("LINE 142 OF LOADANN")
+            # updated = set()
+            # for x in range(len(gt_semantic_seg)):
+            #     for y in range(len(gt_semantic_seg[x])):
+            #         for old_id, new_id in results['label_map'].items():
+            #             if (x,y) not in updated and gt_semantic_seg[x][y] == new_id:
+            #                 gt_semantic_seg[x][y] = old_id
+            #                 updated.add((x,y))
+
+            # for old_id, new_id in results['label_map'].items():
+            #     if new_id not in updated:
+            #         if new_id in gt_semantic_seg:
+            #             gt_semantic_seg[gt_semantic_seg == new_id] = old_id
+            #             updated.add(old_id)
+            ### This is the original code
+            # for old_id, new_id in results['label_map'].items():
+            #     gt_semantic_seg[gt_semantic_seg == new_id] = old_id
+            updated = set()
             for old_id, new_id in results['label_map'].items():
-                gt_semantic_seg[gt_semantic_seg == old_id] = new_id
+                indices = np.argwhere(gt_semantic_seg == new_id)
+                for index in indices:
+                    if tuple(index) not in updated:
+                        gt_semantic_seg[index[0]][index[1]] = old_id
+                        updated.add(tuple(index))
+                         
         # reduce zero_label
         if self.reduce_zero_label:
             # avoid using underflow conversion
             gt_semantic_seg[gt_semantic_seg == 0] = 255
             gt_semantic_seg = gt_semantic_seg - 1
             gt_semantic_seg[gt_semantic_seg == 254] = 255
+        print("GT Semantic Seg After Changing Labels (Loading.py) ", gt_semantic_seg)
         results['gt_semantic_seg'] = gt_semantic_seg
         results['seg_fields'].append('gt_semantic_seg')
         return results
diff --git a/notebooks/resize.ipynb b/notebooks/resize.ipynb
