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Add missing copyright, cleanup and fix docs (#110)
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* Add missing copyright and cleanup files

* Fixing the labels in bug-report.md

* Add symbolic link of notebooks in docs

* Revert of adding symbolic link of notebooks in docs

* Add symbolic link of notebooks in docs
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zhiqwang authored May 20, 2021
1 parent 66b8330 commit 218c428
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Showing 9 changed files with 24 additions and 10 deletions.
2 changes: 1 addition & 1 deletion .github/ISSUE_TEMPLATE/bug-report.md
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Expand Up @@ -2,7 +2,7 @@
name: "\U0001F41B Bug Report"
about: Create a report to help us improve torchvision
title: ''
labels: bug
labels: bug / fix
assignees: ''

---
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1 change: 1 addition & 0 deletions docs/source/notebooks/export_relay_inference_tvm.ipynb
1 change: 1 addition & 0 deletions docs/source/notebooks/load_model_as_ultralytics.ipynb
1 change: 1 addition & 0 deletions docs/source/notebooks/visualize_jit_models.ipynb
8 changes: 7 additions & 1 deletion yolort/utils/graph_utils.py
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@@ -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

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8 changes: 5 additions & 3 deletions yolort/utils/image_utils.py
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Expand Up @@ -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):
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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
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11 changes: 6 additions & 5 deletions yolort/utils/yolo2coco.py
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@@ -1,3 +1,4 @@
# Copyright (c) 2020, Zhiqiang Wang. All Rights Reserved.
from pathlib import Path
import argparse

Expand All @@ -7,7 +8,7 @@
from .builtin_meta import COCO_CATEGORIES


class Yolo2Coco:
class YOLO2COCO:

def __init__(self, root, split):
self.info = {
Expand All @@ -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 = [{
Expand All @@ -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)

Expand Down Expand Up @@ -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()

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