minicoco dataset

minicoco.py

@@ -21,124 +21,124 @@ _URLS = {
 # fmt: off
 CLASS_INFOS = [
     # name  id  train
-    ('person', 1, 0), 
-    ('bicycle', 2, 1), 
-    ('car', 3, 2), 
-    ('motorcycle', 4, 3), 
-    ('airplane', 5, 4), 
-    ('bus', 6, 5), 
-    ('train', 7, 6), 
-    ('truck', 8, 7), 
-    ('boat', 9, 8), 
-    ('traffic light', 10, 9), 
-    ('fire hydrant', 11, 10), 
-    ('stop sign', 13, 11), 
-    ('parking meter', 14, 12), 
-    ('bench', 15, 13), 
-    ('bird', 16, 14), 
-    ('cat', 17, 15), 
-    ('dog', 18, 16), 
-    ('horse', 19, 17), 
-    ('sheep', 20, 18), 
-    ('cow', 21, 19), 
-    ('elephant', 22, 20), 
-    ('bear', 23, 21), 
-    ('zebra', 24, 22), 
-    ('giraffe', 25, 23), 
-    ('backpack', 27, 24), 
-    ('umbrella', 28, 25), 
-    ('handbag', 31, 26), 
-    ('tie', 32, 27), 
-    ('suitcase', 33, 28), 
-    ('frisbee', 34, 29), 
-    ('skis', 35, 30), 
-    ('snowboard', 36, 31), 
-    ('sports ball', 37, 32), 
-    ('kite', 38, 33), 
-    ('baseball bat', 39, 34), 
-    ('baseball glove', 40, 35), 
-    ('skateboard', 41, 36), 
-    ('surfboard', 42, 37), 
-    ('tennis racket', 43, 38), 
-    ('bottle', 44, 39), 
-    ('wine glass', 46, 40), 
-    ('cup', 47, 41), 
-    ('fork', 48, 42), 
-    ('knife', 49, 43), 
-    ('spoon', 50, 44), 
-    ('bowl', 51, 45), 
-    ('banana', 52, 46), 
-    ('apple', 53, 47), 
-    ('sandwich', 54, 48), 
-    ('orange', 55, 49), 
-    ('broccoli', 56, 50), 
-    ('carrot', 57, 51), 
-    ('hot dog', 58, 52), 
-    ('pizza', 59, 53), 
+    ('person', 1, 0),
+    ('bicycle', 2, 1),
+    ('car', 3, 2),
+    ('motorcycle', 4, 3),
+    ('airplane', 5, 4),
+    ('bus', 6, 5),
+    ('train', 7, 6),
+    ('truck', 8, 7),
+    ('boat', 9, 8),
+    ('traffic light', 10, 9),
+    ('fire hydrant', 11, 10),
+    ('stop sign', 13, 11),
+    ('parking meter', 14, 12),
+    ('bench', 15, 13),
+    ('bird', 16, 14),
+    ('cat', 17, 15),
+    ('dog', 18, 16),
+    ('horse', 19, 17),
+    ('sheep', 20, 18),
+    ('cow', 21, 19),
+    ('elephant', 22, 20),
+    ('bear', 23, 21),
+    ('zebra', 24, 22),
+    ('giraffe', 25, 23),
+    ('backpack', 27, 24),
+    ('umbrella', 28, 25),
+    ('handbag', 31, 26),
+    ('tie', 32, 27),
+    ('suitcase', 33, 28),
+    ('frisbee', 34, 29),
+    ('skis', 35, 30),
+    ('snowboard', 36, 31),
+    ('sports ball', 37, 32),
+    ('kite', 38, 33),
+    ('baseball bat', 39, 34),
+    ('baseball glove', 40, 35),
+    ('skateboard', 41, 36),
+    ('surfboard', 42, 37),
+    ('tennis racket', 43, 38),
+    ('bottle', 44, 39),
+    ('wine glass', 46, 40),
+    ('cup', 47, 41),
+    ('fork', 48, 42),
+    ('knife', 49, 43),
+    ('spoon', 50, 44),
+    ('bowl', 51, 45),
+    ('banana', 52, 46),
+    ('apple', 53, 47),
+    ('sandwich', 54, 48),
+    ('orange', 55, 49),
+    ('broccoli', 56, 50),
+    ('carrot', 57, 51),
+    ('hot dog', 58, 52),
+    ('pizza', 59, 53),
     ('donut', 60, 54),
-    ('cake', 61, 55), 
-    ('chair', 62, 56), 
-    ('couch', 63, 57), 
-    ('potted plant', 64, 58), 
-    ('bed', 65, 59), 
-    ('dining table', 67, 60), 
-    ('toilet', 70, 61), 
-    ('tv', 72, 62), 
-    ('laptop', 73, 63), 
-    ('mouse', 74, 64), 
-    ('remote', 75, 65), 
-    ('keyboard', 76, 66), 
-    ('cell phone', 77, 67), 
-    ('microwave', 78, 68), 
-    ('oven', 79, 69), 
-    ('toaster', 80, 70), 
-    ('sink', 81, 71), 
-    ('refrigerator', 82, 72), 
-    ('book', 84, 73), 
-    ('clock', 85, 74), 
-    ('vase', 86, 75), 
-    ('scissors', 87, 76), 
-    ('teddy bear', 88, 77), 
-    ('hair drier', 89, 78), 
+    ('cake', 61, 55),
+    ('chair', 62, 56),
+    ('couch', 63, 57),
+    ('potted plant', 64, 58),
+    ('bed', 65, 59),
+    ('dining table', 67, 60),
+    ('toilet', 70, 61),
+    ('tv', 72, 62),
+    ('laptop', 73, 63),
+    ('mouse', 74, 64),
+    ('remote', 75, 65),
+    ('keyboard', 76, 66),
+    ('cell phone', 77, 67),
+    ('microwave', 78, 68),
+    ('oven', 79, 69),
+    ('toaster', 80, 70),
+    ('sink', 81, 71),
+    ('refrigerator', 82, 72),
+    ('book', 84, 73),
+    ('clock', 85, 74),
+    ('vase', 86, 75),
+    ('scissors', 87, 76),
+    ('teddy bear', 88, 77),
+    ('hair drier', 89, 78),
     ('toothbrush', 90, 79)
 ]
 
 KEYPOINTS_INFOS=[
     # name  id  train
-    # ('nose', 1, 0), 
-    # ('left_eye', 2, 1), 
-    # ('right_eye', 3, 2), 
-    # ('left_ear', 4, 3), 
-    # ('right_ear', 5, 4), 
-    # ('left_shoulder', 6, 5), 
-    # ('right_shoulder', 7, 6), 
-    # ('left_elbow', 8, 7), 
-    # ('right_elbow', 9, 8), 
-    # ('left_wrist', 10, 9), 
-    # ('right_wrist', 11, 10), 
-    # ('left_hip', 12, 11), 
-    # ('right_hip', 13, 12), 
-    # ('left_knee', 14, 13), 
-    # ('right_knee', 15, 14), 
-    # ('left_ankle', 16, 15), 
+    # ('nose', 1, 0),
+    # ('left_eye', 2, 1),
+    # ('right_eye', 3, 2),
+    # ('left_ear', 4, 3),
+    # ('right_ear', 5, 4),
+    # ('left_shoulder', 6, 5),
+    # ('right_shoulder', 7, 6),
+    # ('left_elbow', 8, 7),
+    # ('right_elbow', 9, 8),
+    # ('left_wrist', 10, 9),
+    # ('right_wrist', 11, 10),
+    # ('left_hip', 12, 11),
+    # ('right_hip', 13, 12),
+    # ('left_knee', 14, 13),
+    # ('right_knee', 15, 14),
+    # ('left_ankle', 16, 15),
     # ('right_ankle', 17, 16)
     ('none', 1, 0),
-    ('nose', 2, 1), 
-    ('left_eye', 3, 2), 
-    ('right_eye', 4, 3), 
-    ('left_ear', 5, 4), 
-    ('right_ear', 6, 5), 
-    ('left_shoulder', 7, 6), 
-    ('right_shoulder', 8, 7), 
-    ('left_elbow', 9, 8), 
-    ('right_elbow', 10, 9), 
-    ('left_wrist', 11, 10), 
-    ('right_wrist', 12, 11), 
-    ('left_hip', 13, 12), 
-    ('right_hip', 14, 13), 
-    ('left_knee', 15, 14), 
-    ('right_knee', 16, 15), 
-    ('left_ankle', 17, 16), 
+    ('nose', 2, 1),
+    ('left_eye', 3, 2),
+    ('right_eye', 4, 3),
+    ('left_ear', 5, 4),
+    ('right_ear', 6, 5),
+    ('left_shoulder', 7, 6),
+    ('right_shoulder', 8, 7),
+    ('left_elbow', 9, 8),
+    ('right_elbow', 10, 9),
+    ('left_wrist', 11, 10),
+    ('right_wrist', 12, 11),
+    ('left_hip', 13, 12),
+    ('right_hip', 14, 13),
+    ('left_knee', 15, 14),
+    ('right_knee', 16, 15),
+    ('left_ankle', 17, 16),
     ('right_ankle', 18, 17)
 ]
 
@@ -195,7 +195,7 @@ captions_features = datasets.Features(
     }
 )
 
-keypoint_features = datasets.Features( 
+keypoint_features = datasets.Features(
     # 这里可能有点问题，因为模型的keypoint的标注的类别没别没有增加进来，
     # 有点复杂，后面再finetune，现在基本信息已经正确
     {
@@ -244,7 +244,7 @@ def get_captions_annotation(captions_path):
             "width":image["width"],
             "image_id":image['id']
         })
-    
+
     return anno_infos, images_infos
 
 
@@ -256,18 +256,18 @@ def get_instances_annotation(instances_path):
     images_infos = list()
 
     for instance_info in anno_instances['annotations']:
-        # instance_info = {'segmentation': [[510.66, 423.01, 511.72, 420.03, 510.45, 416.0, 510.34, 413.02, 
-        # 510.77, 410.26, 510.77, 407.5, 510.34, 405.16, 511.51, 402.83, 511.41, 400.49, 510.24, 398.16, 
-        # 509.39, 397.31, 504.61, 399.22, 502.17, 399.64, 500.89, 401.66, 500.47, 402.08, 499.09, 401.87, 
-        # 495.79, 401.98, 490.59, 401.77, 488.79, 401.77, 485.39, 398.58, 483.9, 397.31, 481.56, 396.35, 
-        # 478.48, 395.93, 476.68, 396.03, 475.4, 396.77, 473.92, 398.79, 473.28, 399.96, 473.49, 401.87, 
-        # 474.56, 403.47, 473.07, 405.59, 473.39, 407.71, 476.68, 409.41, 479.23, 409.73, 481.56, 410.69, 
-        # 480.4, 411.85, 481.35, 414.93, 479.86, 418.65, 477.32, 420.03, 476.04, 422.58, 479.02, 422.58, 
-        # 480.29, 423.01, 483.79, 419.93, 486.66, 416.21, 490.06, 415.57, 492.18, 416.85, 491.65, 420.24, 
-        # 492.82, 422.9, 493.56, 424.39, 496.43, 424.6, 498.02, 423.01, 498.13, 421.31, 497.07, 420.03, 
-        # 497.07, 415.15, 496.33, 414.51, 501.1, 411.96, 502.06, 411.32, 503.02, 415.04, 503.33, 418.12, 
-        # 501.1, 420.24, 498.98, 421.63, 500.47, 424.39, 505.03, 423.32, 506.2, 421.31, 507.69, 419.5, 
-        # 506.31, 423.32, 510.03, 423.01, 510.45, 423.01]], 'area': 702.1057499999998, 'iscrowd': 0, 
+        # instance_info = {'segmentation': [[510.66, 423.01, 511.72, 420.03, 510.45, 416.0, 510.34, 413.02,
+        # 510.77, 410.26, 510.77, 407.5, 510.34, 405.16, 511.51, 402.83, 511.41, 400.49, 510.24, 398.16,
+        # 509.39, 397.31, 504.61, 399.22, 502.17, 399.64, 500.89, 401.66, 500.47, 402.08, 499.09, 401.87,
+        # 495.79, 401.98, 490.59, 401.77, 488.79, 401.77, 485.39, 398.58, 483.9, 397.31, 481.56, 396.35,
+        # 478.48, 395.93, 476.68, 396.03, 475.4, 396.77, 473.92, 398.79, 473.28, 399.96, 473.49, 401.87,
+        # 474.56, 403.47, 473.07, 405.59, 473.39, 407.71, 476.68, 409.41, 479.23, 409.73, 481.56, 410.69,
+        # 480.4, 411.85, 481.35, 414.93, 479.86, 418.65, 477.32, 420.03, 476.04, 422.58, 479.02, 422.58,
+        # 480.29, 423.01, 483.79, 419.93, 486.66, 416.21, 490.06, 415.57, 492.18, 416.85, 491.65, 420.24,
+        # 492.82, 422.9, 493.56, 424.39, 496.43, 424.6, 498.02, 423.01, 498.13, 421.31, 497.07, 420.03,
+        # 497.07, 415.15, 496.33, 414.51, 501.1, 411.96, 502.06, 411.32, 503.02, 415.04, 503.33, 418.12,
+        # 501.1, 420.24, 498.98, 421.63, 500.47, 424.39, 505.03, 423.32, 506.2, 421.31, 507.69, 419.5,
+        # 506.31, 423.32, 510.03, 423.01, 510.45, 423.01]], 'area': 702.1057499999998, 'iscrowd': 0,
         # 'image_id': 289343, 'bbox': [473.07, 395.93, 38.65, 28.67], 'category_id': 18, 'id': 1768}
         bbox = instance_info['bbox']
         image_id = instance_info['image_id']
@@ -291,12 +291,12 @@ def get_instances_annotation(instances_path):
                     "classes":CATEGORY_ID2CLASS_NAMES[instance_info['category_id']]
                 }
             ]
-                
+
 
     for image in anno_instances['images']:
-        # image={'license': 4, 'file_name': '000000397133.jpg', 
-        # 'coco_url': 'http://images.cocodataset.org/val2017/000000397133.jpg', 
-        # 'height': 427, 'width': 640, 'date_captured': '2013-11-14 17:02:52', 
+        # image={'license': 4, 'file_name': '000000397133.jpg',
+        # 'coco_url': 'http://images.cocodataset.org/val2017/000000397133.jpg',
+        # 'height': 427, 'width': 640, 'date_captured': '2013-11-14 17:02:52',
         # 'flickr_url': 'http://farm7.staticflickr.com/6116/6255196340_da26cf2c9e_z.jpg', 'id': 397133}
         images_infos.append({
             "image_name":image['file_name'],
@@ -304,7 +304,7 @@ def get_instances_annotation(instances_path):
             "width":image["width"],
             "image_id":image['id']
         })
-    
+
     return anno_infos, images_infos
 
 
@@ -338,7 +338,7 @@ def get_keypoints_annotation(keypoints_path):
                     "num_keypoints":keypoint_info['num_keypoints'],
                 }
             ]
-                
+
 
     for image in anno_keypoints['images']:
         # image={'license': 4, 'file_name': '000000397133.jpg', 'coco_url': 'http://images.cocodataset.org/val2017/000000397133.jpg', 'height': 427, 'width': 640, 'date_captured': '2013-11-14 17:02:52', 'flickr_url': 'http://farm7.staticflickr.com/6116/6255196340_da26cf2c9e_z.jpg', 'id': 397133}
@@ -348,7 +348,7 @@ def get_keypoints_annotation(keypoints_path):
             "width":image["width"],
             "image_id":image['id']
         })
-    
+
     return anno_infos, images_infos
 
 
@@ -370,7 +370,7 @@ class MINICOCOConfig(datasets.BuilderConfig):
         self.task_name = task_name
 
 
-class PASCALDataset(datasets.GeneratorBasedBuilder):
+class MiniCOCODataset(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
         MINICOCOConfig(
@@ -472,7 +472,7 @@ class PASCALDataset(datasets.GeneratorBasedBuilder):
                     "width": image["width"],
                     "objects":[
                         {
-                            "bboxes": object_info["bbox"], 
+                            "bboxes": object_info["bbox"],
                             "classes": object_info["classes"]
                         }
                         for object_info in anno_infos[image['image_id']]
@@ -495,7 +495,7 @@ class PASCALDataset(datasets.GeneratorBasedBuilder):
                     "width": image["width"],
                     "objects":[
                         {
-                            "bboxes": object_info["bbox"], 
+                            "bboxes": object_info["bbox"],
                             "classes": object_info["classes"],
                             'segmentation':object_info['segmentation'],
                             'iscrowd':object_info['iscrowd']
@@ -503,7 +503,7 @@ class PASCALDataset(datasets.GeneratorBasedBuilder):
                         for object_info in anno_infos[image['image_id']]
                     ]
                 }
-                yield id_, example  
+                yield id_, example
 
         elif task_name=="keypoint":
             keypoints_path = os.path.join(filepath, "annotations", "person_keypoints_" + split + "2017.json")
@@ -519,8 +519,8 @@ class PASCALDataset(datasets.GeneratorBasedBuilder):
                     "height": image["height"],
                     "width": image["width"],
                     "objects":[
-                        {                    
-                            "bboxes": object_info["bbox"], 
+                        {
+                            "bboxes": object_info["bbox"],
                             "classes": object_info["classes"],
                             'keypoints':object_info['keypoints'],
                             "num_keypoints":object_info["num_keypoints"]
@@ -528,4 +528,4 @@ class PASCALDataset(datasets.GeneratorBasedBuilder):
                         for object_info in anno_infos[image['image_id']]
                     ]
                 }
-                yield id_, example
+                yield id_, example