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Fixed speed of COCO dataset parsing #1888

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merged 6 commits into from
Mar 6, 2024
Merged

Fixed speed of COCO dataset parsing #1888

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00a9101
Cherry pick COCO parsing from feature/ALG-000_memory-efficient-coco-d…
BloodAxe Mar 6, 2024
6b0e115
Merge branch 'master' into feature/SG-000-fix-coco-parsing
BloodAxe Mar 6, 2024
e000753
Provide explicit shape for extracted ann boxes to address an edge cas…
BloodAxe Mar 6, 2024
b9d6a22
Ensure empty annotations has bboxes of shape [0,4] for broadcasting t…
BloodAxe Mar 6, 2024
6194a43
Added explicit dtypes
BloodAxe Mar 6, 2024
e1fbc5a
Merge branch 'master' into feature/SG-000-fix-coco-parsing
BloodAxe Mar 6, 2024
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31 changes: 19 additions & 12 deletions src/super_gradients/training/datasets/detection_datasets/coco_format_detection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
import dataclasses
import json
import os
from collections import defaultdict

import numpy as np
from typing import List, Optional, Tuple
Expand Down Expand Up @@ -208,16 +209,16 @@ def parse_coco_into_detection_annotations(
category_names = np.array([category["name"] for category in coco["categories"]], dtype=str)

# Extract box annotations
ann_box_xyxy = xywh_to_xyxy_inplace(np.array([annotation["bbox"] for annotation in coco["annotations"]], dtype=np.float32), image_shape=None)
ann_box_xyxy = xywh_to_xyxy_inplace(np.array([annotation["bbox"] for annotation in coco["annotations"]], dtype=np.float32).reshape(-1, 4), image_shape=None)

ann_category_id = np.array([annotation["category_id"] for annotation in coco["annotations"]], dtype=int)
ann_iscrowd = np.array([annotation["iscrowd"] for annotation in coco["annotations"]], dtype=bool)
ann_image_ids = np.array([annotation["image_id"] for annotation in coco["annotations"]], dtype=int)
ann_category_id = np.array([annotation["category_id"] for annotation in coco["annotations"]], dtype=int).reshape(-1)
ann_iscrowd = np.array([annotation["iscrowd"] for annotation in coco["annotations"]], dtype=bool).reshape(-1)
ann_image_ids = np.array([annotation["image_id"] for annotation in coco["annotations"]], dtype=int).reshape(-1)

# Extract image stuff
img_ids = np.array([img["id"] for img in coco["images"]], dtype=int)
img_paths = np.array([img["file_name"] if "file_name" in img else "{:012}".format(img["id"]) + ".jpg" for img in coco["images"]], dtype=str)
img_width_height = np.array([(img["width"], img["height"]) for img in coco["images"]], dtype=int)
img_ids = [img["id"] for img in coco["images"]]
img_paths = [img["file_name"] if "file_name" in img else "{:012}".format(img["id"]) + ".jpg" for img in coco["images"]]
img_width_height = [(img["width"], img["height"]) for img in coco["images"]]

# Now, we can drop the annotations that belongs to the excluded classes
if int(class_ids_to_ignore is not None) + int(exclude_classes is not None) + int(include_classes is not None) > 1:
Expand Down Expand Up @@ -273,9 +274,15 @@ def parse_coco_into_detection_annotations(

annotations = []

for img_id, image_path, (image_width, image_height) in zip(img_ids, img_paths, img_width_height):
mask = ann_image_ids == img_id
img_id2ann_box_xyxy = defaultdict(list)
img_id2ann_iscrowd = defaultdict(list)
img_id2ann_category_id = defaultdict(list)
for ann_image_id, _ann_box_xyxy, _ann_iscrowd, _ann_category_id in zip(ann_image_ids, ann_box_xyxy, ann_iscrowd, ann_category_id):
img_id2ann_box_xyxy[ann_image_id].append(_ann_box_xyxy)
img_id2ann_iscrowd[ann_image_id].append(_ann_iscrowd)
img_id2ann_category_id[ann_image_id].append(_ann_category_id)

for img_id, image_path, (image_width, image_height) in zip(img_ids, img_paths, img_width_height):
if image_path_prefix is not None:
image_path = os.path.join(image_path_prefix, image_path)

Expand All @@ -284,9 +291,9 @@ def parse_coco_into_detection_annotations(
image_path=image_path,
image_width=image_width,
image_height=image_height,
ann_boxes_xyxy=ann_box_xyxy[mask],
ann_is_crowd=ann_iscrowd[mask],
ann_labels=ann_category_id[mask],
ann_boxes_xyxy=np.asarray(img_id2ann_box_xyxy[img_id], dtype=np.float32).reshape(-1, 4),
ann_is_crowd=np.asarray(img_id2ann_iscrowd[img_id], dtype=bool).reshape(-1),
ann_labels=np.asarray(img_id2ann_category_id[img_id], dtype=int).reshape(-1),
)
annotations.append(ann)

Expand Down
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