Added classes for multi-label classification

coco_dataset_multi_label_script.py

@@ -1,6 +1,7 @@
 import json
 import os
 import datasets
+import torch
 
 
 class COCOBuilderConfig(datasets.BuilderConfig):
@@ -81,6 +82,8 @@ class COCODataset(datasets.GeneratorBasedBuilder):
             "file_name": datasets.Value("string"),
             "coco_url": datasets.Value("string"),
             "image_path": datasets.Value("string"),
+            "category_ids": datasets.Sequence(datasets.Value("int64")),
+            "category_one_hot": datasets.Sequence(datasets.Value("int64")),
         }
 
         features = datasets.Features(feature_dict)
@@ -120,7 +123,6 @@ class COCODataset(datasets.GeneratorBasedBuilder):
             "annotations_trainval": os.path.join(data_dir, "annotations_trainval2017.zip"),
             "image_info_test": os.path.join(data_dir, "image_info_test2017.zip"),
         }
-        archive_path = dl_manager.download_and_extract(_DL_URLS)
 
         splits = []
         for split in self.config.splits:
@@ -129,8 +131,9 @@ class COCODataset(datasets.GeneratorBasedBuilder):
                     name=datasets.Split.TRAIN,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "json_path": os.path.join(archive_path["annotations_trainval"], "annotations", "captions_train2017.json"),
-                        "image_dir": os.path.join(archive_path["train"], "train2017"),
+                        "captions_json_path": os.path.join(data_dir, "annotations", "captions_train2017.json"),
+                        "instances_json_path": os.path.join(data_dir, "annotations", "instances_train2017.json"),
+                        "image_dir": os.path.join(data_dir, "train2017"),
                         "split": "train",
                     }
                 )
@@ -139,8 +142,9 @@ class COCODataset(datasets.GeneratorBasedBuilder):
                     name=datasets.Split.VALIDATION,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "json_path": os.path.join(archive_path["annotations_trainval"], "annotations", "captions_val2017.json"),
-                        "image_dir": os.path.join(archive_path["val"], "val2017"),
+                        "captions_json_path": os.path.join(data_dir, "annotations", "captions_val2017.json"),
+                        "instances_json_path": os.path.join(data_dir, "annotations", "instances_val2017.json"),
+                        "image_dir": os.path.join(data_dir, "val2017"),
                         "split": "valid",
                     },
                 )
@@ -149,8 +153,9 @@ class COCODataset(datasets.GeneratorBasedBuilder):
                     name=datasets.Split.TEST,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "json_path": os.path.join(archive_path["image_info_test"], "annotations", "image_info_test2017.json"),
-                        "image_dir": os.path.join(archive_path["test"], "test2017"),
+                        "captions_json_path": os.path.join(data_dir, "annotations", "image_info_test2017.json"),
+                        "instances_json_path": os.path.join(data_dir, "annotations", "image_info_test2017.json"), # "instances_test2017.json
+                        "image_dir": os.path.join(data_dir, "test2017"),
                         "split": "test",
                     },
                 )
@@ -160,42 +165,150 @@ class COCODataset(datasets.GeneratorBasedBuilder):
             splits.append(dataset)
 
         return splits
+    
+    # instances.json
+#     {
+    #     "info": {
+    #         "year": "2020",
+    #         "version": "1",
+    #         "description": "Exported from roboflow.ai",
+    #         "contributor": "Roboflow",
+    #         "url": "https://app.roboflow.ai/datasets/hard-hat-sample/1",
+    #         "date_created": "2000-01-01T00:00:00+00:00"
+    #     },
+    #     "licenses": [
+    #         {
+    #             "id": 1,
+    #             "url": "https://creativecommons.org/publicdomain/zero/1.0/",
+    #             "name": "Public Domain"
+    #         }
+    #     ],
+    #     "categories": [
+    #         {
+    #             "id": 0,
+    #             "name": "Workers",
+    #             "supercategory": "none"
+    #         },
+    #         {
+    #             "id": 1,
+    #             "name": "head",
+    #             "supercategory": "Workers"
+    #         },
+    #         {
+    #             "id": 2,
+    #             "name": "helmet",
+    #             "supercategory": "Workers"
+    #         },
+    #         {
+    #             "id": 3,
+    #             "name": "person",
+    #             "supercategory": "Workers"
+    #         }
+    #     ],
+    #     "images": [
+    #         {
+    #             "id": 0,
+    #             "license": 1,
+    #             "file_name": "0001.jpg",
+    #             "height": 275,
+    #             "width": 490,
+    #             "date_captured": "2020-07-20T19:39:26+00:00"
+    #         }
+    #     ],
+    #     "annotations": [
+    #         {
+    #             "id": 0,
+    #             "image_id": 0,
+    #             "category_id": 2,
+    #             "bbox": [
+    #                 45,
+    #                 2,
+    #                 85,
+    #                 85
+    #             ],
+    #             "area": 7225,
+    #             "segmentation": [],
+    #             "iscrowd": 0
+    #         },
+    #         {
+    #             "id": 1,
+    #             "image_id": 0,
+    #             "category_id": 2,
+    #             "bbox": [
+    #                 324,
+    #                 29,
+    #                 72,
+    #                 81
+    #             ],
+    #             "area": 5832,
+    #             "segmentation": [],
+    #             "iscrowd": 0
+    #         }
+    #     ]
+    # }
 
     def _generate_examples(
         # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-        self, json_path, image_dir, split
+        self, captions_json_path, instances_json_path, image_dir, split
     ):
-        """ Yields examples as (key, example) tuples. """
+        """ Yields examples as (key, example, categories) tuples. """
         # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is here for legacy reason (tfds) and is not important in itself.
 
-        _features = ["image_id", "caption_id", "caption", "height", "width", "file_name", "coco_url", "image_path", "id"]
+        _features = ["image_id", "caption_id", "caption", "height", "width", "file_name", "coco_url", "image_path", "id", "category_ids", "category_one_hot"]
         features = list(_features)
 
         if split in "valid":
             split = "val"
 
-        with open(json_path, 'r', encoding='UTF-8') as fp:
-            data = json.load(fp)
+        with open(captions_json_path, 'r', encoding='UTF-8') as fp:
+            captions_data = json.load(fp)
+
+        with open(instances_json_path, 'r', encoding='UTF-8') as fp:
+            instances_data = json.load(fp)
 
         # list of dict
-        images = data["images"]
+        images = captions_data["images"]
+        instances_annotations = instances_data["annotations"]
         entries = images
 
+        self.classes = list(map(lambda x: {'id': x['id'], 'name': x['name']}, instances_data['categories']))
+        self.num_classes = len(self.classes)
+
         # build a dict of image_id -> image info dict
         d = {image["id"]: image for image in images}
 
+        # build a dict of image_id -> list of category_ids
+        cat_ids_dict = {}
+
+        for annotation in instances_annotations:
+            image_id = annotation["image_id"]
+            category_id = annotation["category_id"]
+            if image_id not in cat_ids_dict:
+                cat_ids_dict[image_id] = set([])
+            cat_ids_dict[image_id].add(category_id)
+
         # list of dict
         if split in ["train", "val"]:
-            annotations = data["annotations"]
+            annotations = captions_data["annotations"]       
 
             # build a dict of image_id ->
             for annotation in annotations:
                 _id = annotation["id"]
-                image_info = d[annotation["image_id"]]
+                image_id = annotation["image_id"]
+                image_info = d[image_id]
                 annotation.update(image_info)
                 annotation["id"] = _id
 
+                # Add the category_ids to the annotation
+                annotation["category_ids"] = cat_ids_dict[annotation["image_id"]] if annotation["image_id"] in cat_ids_dict else []
+                
+                annotation['category_one_hot'] = torch.zeros(len(self.classes))
+                for category_id in annotation["category_ids"]:
+                    # Get index of category_id in self.classes
+                    index = next((index for (index, d) in enumerate(self.classes) if d["id"] == category_id), None)
+                    annotation['category_one_hot'][index] = 1
+
             entries = annotations
 
         for id_, entry in enumerate(entries):
@@ -213,3 +326,14 @@ class COCODataset(datasets.GeneratorBasedBuilder):
             entry = {k: entry[k] for k in _features if k in entry}
 
             yield str((entry["image_id"], entry["caption_id"])), entry
+
+from datasets import load_dataset
+if __name__ == "__main__":
+    dataset = load_dataset(
+            "coco_dataset_multi_label_script/coco_dataset_multi_label_script.py",
+            "2017",
+            keep_in_memory=False,
+            splits=["valid"],
+            data_dir="/workspace/pixt/clip-training/data/mscoco",
+        )
+    print(dataset["validation"][0])