Add missing copyright, cleanup and fix docs

.github/ISSUE_TEMPLATE/bug-report.md

@@ -2,7 +2,7 @@
 name: "\U0001F41B Bug Report"
 about: Create a report to help us improve torchvision
 title: ''
-labels: bug
+labels: bug / fix
 assignees: ''
 
 ---

docs/source/notebooks/export_onnx_inference_onnxruntime.ipynb

@@ -0,0 +1 @@
+../../../notebooks/export-onnx-inference-onnxruntime.ipynb

docs/source/notebooks/export_relay_inference_tvm.ipynb

@@ -0,0 +1 @@
+../../../notebooks/export-relay-inference-tvm.ipynb

docs/source/notebooks/inference_pytorch_export_libtorch.ipynb

@@ -0,0 +1 @@
+../../../notebooks/inference-pytorch-export-libtorch.ipynb

docs/source/notebooks/load_model_as_ultralytics.ipynb

@@ -0,0 +1 @@
+../../../notebooks/load-model-as-ultralytics.ipynb

docs/source/notebooks/visualize_jit_models.ipynb

@@ -0,0 +1 @@
+../../../notebooks/visualize-jit-models.ipynb

yolort/utils/graph_utils.py

@@ -1,6 +1,12 @@
+# Copyright (c) 2020, Thomas Viehmann. All Rights Reserved.
 """
-Plotting a graph with bad gradient nodes marked in red.
+Visualizing JIT Modules
+
 Mostly copy-paste from https://github.com/t-vi/pytorch-tvmisc/tree/master/hacks
+with license under the CC-BY-SA 4.0.
+
+Please link to Thomas's blog post or the original github source (linked from the
+blog post) with the attribution notice.
 """
 from graphviz import Digraph
 

yolort/utils/image_utils.py

@@ -11,13 +11,13 @@
 
 import torch
 from torch import Tensor
-from torchvision import transforms
+
 from torchvision.ops.boxes import box_convert
 
 from typing import Optional
 
 import logging
-logger = logging.getLogger(__name__)
+
 
 
 def plot_one_box(box, img, color=None, label=None, line_thickness=None):
@@ -47,7 +47,7 @@ def cv2_imshow(
     convert_bgr_to_rgb: bool = True,
 ) -> None:
     """
-    A replacement for cv2.imshow() for use in Jupyter notebooks.
+    A replacement of cv2.imshow() for using in Jupyter notebooks.
 
     Args:
         image (np.ndarray):. shape (N, M) or (N, M, 1) is an NxM grayscale image. shape (N, M, 3)
@@ -194,6 +194,8 @@ def load_names(category_path):
 
 @torch.no_grad()
 def overlay_boxes(detections, path, time_consume, args):
+    logger = logging.getLogger(__name__)
+
     img = cv2.imread(path) if args.save_img else None
 
     for i, pred in enumerate(detections):  # detections per image

yolort/utils/yolo2coco.py

@@ -1,3 +1,4 @@
+# Copyright (c) 2020, Zhiqiang Wang. All Rights Reserved.
 from pathlib import Path
 import argparse
 
@@ -7,7 +8,7 @@
 from .builtin_meta import COCO_CATEGORIES
 
 
-class Yolo2Coco:
+class YOLO2COCO:
 
     def __init__(self, root, split):
         self.info = {
@@ -24,8 +25,8 @@ def __init__(self, root, split):
         self.type = 'instances'
         self.split = split
         self.root_path = Path(root)
-        self.label_path = self.root_path.joinpath('labels')
-        self.annotation_root = self.root_path.joinpath('annotations')
+        self.label_path = self.root_path / 'labels'
+        self.annotation_root = self.root_path / 'annotations'
         Path(self.annotation_root).mkdir(parents=True, exist_ok=True)
 
         self.categories = [{
@@ -48,7 +49,7 @@ def generate(self, coco_type='instances', annotation_format='bbox'):
             'annotations': annotations,
             'categories': self.categories,
         }
-        output_path = self.annotation_root.joinpath(f'{coco_type}_{self.split}.json')
+        output_path = self.annotation_root / f'{coco_type}_{self.split}.json'
         with open(output_path, 'w') as json_file:
             json.dump(json_data, json_file, sort_keys=True)
 
@@ -124,5 +125,5 @@ def _get_annotation(vertex_info, height, width):
 
     args = parser.parse_args()
 
-    converter = Yolo2Coco(args.data_path, args.split)
+    converter = YOLO2COCO(args.data_path, args.split)
     converter.generate()