init model

README.md

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+---
+license: apache-2.0
+tags:
+- RyzenAI
+- object-detection
+- vision
+- YOLO
+- Pytorch
+datasets:
+- COCO
+metrics:
+- mAP
+---
+# YOLOv5s model trained on COCO
+
+YOLOv5s is the small version of YOLOv5 model trained on COCO object detection (118k annotated images) at resolution 640x640. It was released in [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5).
+
+We develop a modified version that could be supported by [AMD Ryzen AI](https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html).
+
+
+## Model description
+
+YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
+
+
+## Intended uses & limitations
+
+You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=amd/yolov5) to look for all available YOLOv5 models.
+
+
+## How to use
+
+### Installation
+
+   Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI.
+   Run the following script to install pre-requisites for this model.
+   ```bash
+   pip install -r requirements.txt 
+   ```
+
+
+### Data Preparation (optional: for accuracy evaluation)
+
+The dataset MSCOCO2017 contains 118287 images for training and 5000 images for validation.
+
+Download COCO dataset and create directories in your code like this:
+  ```plain
+  └── datasets
+       └── coco
+             ├── annotations
+             |   ├── instances_val2017.json
+             |   └── ...
+             ├── labels
+             |   ├── val2017
+             |   |   ├── 000000000139.txt
+             |       ├── 000000000285.txt
+             |       └── ...
+             ├── images
+             |   ├── val2017
+             |   |   ├── 000000000139.jpg
+             |       ├── 000000000285.jpg
+             └── val2017.txt
+  ```
+1. put the val2017 image folder under images directory or use a softlink
+2. the labels folder and val2017.txt above are generate by **general_json2yolo.py**
+3. modify the coco.yaml like this:
+```markdown
+path: /path/to/your/datasets/coco  # dataset root dir
+train: train2017.txt  # train images (relative to 'path') 118287 images
+val: val2017.txt  # val images (relative to 'path') 5000 images
+```
+
+
+### Test & Evaluation
+
+ - Code snippet from [`onnx_inference.py`](onnx_inference.py) on how to use
+```python
+    args = make_parser().parse_args()
+    onnx_path = args.model
+    onnx_model = onnxruntime.InferenceSession(onnx_path)
+    grid = np.load("./grid.npy", allow_pickle=True)
+    anchor_grid = np.load("./anchor_grid.npy", allow_pickle=True)
+    path = args.image_path 
+    new_path = args.output_path
+    conf_thres, iou_thres, classes, agnostic_nms, max_det = 0.25, 0.45, None, False, 1000
+
+    img0 = cv2.imread(path)
+    img = pre_process(img0)
+    onnx_input = {onnx_model.get_inputs()[0].name: img}
+    onnx_output = onnx_model.run(None, onnx_input)
+    onnx_output = post_process(onnx_output)
+    pred = non_max_suppression(
+        onnx_output[0], conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det
+    )
+    colors = Colors()
+    det = pred[0]
+    im0 = img0.copy()
+    annotator = Annotator(im0, line_width=2, example=str(names))
+    if len(det):
+        # Rescale boxes from img_size to im0 size
+        det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
+
+        # Write results
+        for *xyxy, conf, cls in reversed(det):
+            c = int(cls)  # integer class
+            label = f"{names[c]} {conf:.2f}"
+            annotator.box_label(xyxy, label, color=colors(c, True))
+    # Stream results
+    im0 = annotator.result()
+    cv2.imwrite(new_path, im0)
+```
+
+ - Run inference for a single image
+  ```python
+  python onnx_inference.py -m ./yolov5s_qat.onnx -i /Path/To/Your/Image --ipu --provider_config /Path/To/Your/Provider_config
+  ```
+*Note: __vaip_config.json__ is located at the setup package of Ryzen AI (refer to [Installation](#installation))*
+ - Test accuracy of the quantized model
+  ```python
+  python onnx_eval.py -m ./yolov5s_qat.onnx --ipu --provider_config /Path/To/Your/Provider_config
+  ```
+
+### Performance
+
+|Metric |Accuracy on IPU|
+| :----:  | :----: |
+|AP\@0.50:0.95|0.356|
+
+
+```bibtex
+@software{glenn_jocher_2021_5563715,
+  author       = {Glenn Jocher et. al.},
+  title        = {{ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, 
+                   Roboflow integration, TensorFlow export, OpenCV
+                   DNN support}},
+  month        = oct,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = {v6.0},
+  doi          = {10.5281/zenodo.5563715},
+  url          = {https://doi.org/10.5281/zenodo.5563715}
+}
+```

anchor_grid.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ccef53e0f9fb34a86d85a3a93d79250598b85025e156294afc34d673bf3965ad
+size 202917

coco.yaml

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+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO 2017 dataset http://cocodataset.org
+# Example usage: python train.py --data coco.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco  ← downloads here
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ./datasets/coco  # dataset root dir
+train: train2017.txt
+val: val2017.txt
+#train: train2017.txt  # train images (relative to 'path') 118287 images
+#val: val2017.txt  # train images (relative to 'path') 5000 images
+#test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+nc: 80  # number of classes
+names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+        'hair drier', 'toothbrush']  # class names

general_json2yolo.py

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+import json
+from collections import defaultdict
+import sys
+import pathlib
+import numpy as np
+from tqdm import tqdm
+CURRENT_DIR = pathlib.Path(__file__).parent
+sys.path.append(str(CURRENT_DIR))
+from utils import *
+
+
+def convert_coco_json(json_dir='../coco/annotations/', use_segments=False, cls91to80=False):
+    save_dir = make_dirs()  # output directory
+    coco80 = coco91_to_coco80_class()
+
+    # Import json
+    for json_file in sorted(Path(json_dir).resolve().glob('*.json')):
+        if not str(json_file).endswith("instances_val2017.json"):
+            continue
+        fn = Path(save_dir) / 'labels' / json_file.stem.replace('instances_', '')  # folder name
+        fn.mkdir()
+        with open(json_file) as f:
+            data = json.load(f)
+
+        # Create image dict
+        images = {'%g' % x['id']: x for x in data['images']}
+        # Create image-annotations dict
+        imgToAnns = defaultdict(list)
+        for ann in data['annotations']:
+            imgToAnns[ann['image_id']].append(ann)
+        
+        txt_file = open(Path(save_dir / 'val2017').with_suffix('.txt'), 'a')
+        # Write labels file
+        for img_id, anns in tqdm(imgToAnns.items(), desc=f'Annotations {json_file}'):
+            img = images['%g' % img_id]
+            h, w, f = img['height'], img['width'], img['file_name']
+            bboxes = []
+            segments = []
+
+            txt_file.write('./images/' + '/'.join(img['coco_url'].split('/')[-2:]) + '\n')
+            for ann in anns:
+                if ann['iscrowd']:
+                    continue
+                # The COCO box format is [top left x, top left y, width, height]
+                box = np.array(ann['bbox'], dtype=np.float64)
+                box[:2] += box[2:] / 2  # xy top-left corner to center
+                box[[0, 2]] /= w  # normalize x
+                box[[1, 3]] /= h  # normalize y
+                if box[2] <= 0 or box[3] <= 0:  # if w <= 0 and h <= 0
+                    continue
+
+                cls = coco80[ann['category_id'] - 1] if cls91to80 else ann['category_id'] - 1  # class
+                box = [cls] + box.tolist()
+                if box not in bboxes:
+                    bboxes.append(box)
+                # Segments
+                if use_segments:
+                    if len(ann['segmentation']) > 1:
+                        s = merge_multi_segment(ann['segmentation'])
+                        s = (np.concatenate(s, axis=0) / np.array([w, h])).reshape(-1).tolist()
+                    else:
+                        s = [j for i in ann['segmentation'] for j in i]  # all segments concatenated
+                        s = (np.array(s).reshape(-1, 2) / np.array([w, h])).reshape(-1).tolist()
+                    s = [cls] + s
+                    if s not in segments:
+                        segments.append(s)
+
+            # Write
+            with open((fn / f).with_suffix('.txt'), 'a') as file:
+                for i in range(len(bboxes)):
+                    line = *(segments[i] if use_segments else bboxes[i]),  # cls, box or segments
+                    file.write(('%g ' * len(line)).rstrip() % line + '\n')
+        txt_file.close()
+
+def min_index(arr1, arr2):
+    """Find a pair of indexes with the shortest distance. 
+    Args:
+        arr1: (N, 2).
+        arr2: (M, 2).
+    Return:
+        a pair of indexes(tuple).
+    """
+    dis = ((arr1[:, None, :] - arr2[None, :, :]) ** 2).sum(-1)
+    return np.unravel_index(np.argmin(dis, axis=None), dis.shape)
+
+
+def merge_multi_segment(segments):
+    """Merge multi segments to one list.
+    Find the coordinates with min distance between each segment,
+    then connect these coordinates with one thin line to merge all 
+    segments into one.
+
+    Args:
+        segments(List(List)): original segmentations in coco's json file.
+            like [segmentation1, segmentation2,...], 
+            each segmentation is a list of coordinates.
+    """
+    s = []
+    segments = [np.array(i).reshape(-1, 2) for i in segments]
+    idx_list = [[] for _ in range(len(segments))]
+
+    # record the indexes with min distance between each segment
+    for i in range(1, len(segments)):
+        idx1, idx2 = min_index(segments[i - 1], segments[i])
+        idx_list[i - 1].append(idx1)
+        idx_list[i].append(idx2)
+
+    # use two round to connect all the segments
+    for k in range(2):
+        # forward connection
+        if k == 0:
+            for i, idx in enumerate(idx_list):
+                # middle segments have two indexes
+                # reverse the index of middle segments
+                if len(idx) == 2 and idx[0] > idx[1]:
+                    idx = idx[::-1]
+                    segments[i] = segments[i][::-1, :]
+
+                segments[i] = np.roll(segments[i], -idx[0], axis=0)
+                segments[i] = np.concatenate([segments[i], segments[i][:1]])
+                # deal with the first segment and the last one
+                if i in [0, len(idx_list) - 1]:
+                    s.append(segments[i])
+                else:
+                    idx = [0, idx[1] - idx[0]]
+                    s.append(segments[i][idx[0]:idx[1] + 1])
+
+        else:
+            for i in range(len(idx_list) - 1, -1, -1):
+                if i not in [0, len(idx_list) - 1]:
+                    idx = idx_list[i]
+                    nidx = abs(idx[1] - idx[0])
+                    s.append(segments[i][nidx:])
+    return s
+
+
+if __name__ == '__main__':
+
+    convert_coco_json('./datasets/coco/annotations',  # directory with *.json
+                        use_segments=True,
+                        cls91to80=True)

grid.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1eb0131b1a854173fa17c0ddb74004257d389c29051e40e67c67dab230b6dba
+size 202917

onnx_eval.py

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+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+import onnxruntime
+import numpy as np
+import torch
+from tqdm import tqdm
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+import sys
+import pathlib
+CURRENT_DIR = pathlib.Path(__file__).parent
+sys.path.append(str(CURRENT_DIR))
+from utils import create_dataloader, coco80_to_coco91_class, check_dataset, box_iou, non_max_suppression, post_process, scale_coords, xyxy2xywh, xywh2xyxy, \
+                  increment_path, colorstr, ap_per_class
+
+
+def save_one_txt(predn, save_conf, shape, file):
+    # Save one txt result
+    gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
+    for *xyxy, conf, cls in predn.tolist():
+        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
+        with open(file, 'a') as f:
+            f.write(('%g ' * len(line)).rstrip() % line + '\n')
+
+
+def save_one_json(predn, jdict, path, class_map):
+    # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
+    image_id = int(path.stem) if path.stem.isnumeric() else path.stem
+    box = xyxy2xywh(predn[:, :4])  # xywh
+    box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
+    for p, b in zip(predn.tolist(), box.tolist()):
+        jdict.append({'image_id': image_id,
+                      'category_id': class_map[int(p[5])],
+                      'bbox': [round(x, 3) for x in b],
+                      'score': round(p[4], 5)})
+
+
+def process_batch(detections, labels, iouv):
+    """
+    Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.
+    Arguments:
+        detections (Array[N, 6]), x1, y1, x2, y2, conf, class
+        labels (Array[M, 5]), class, x1, y1, x2, y2
+    Returns:
+        correct (Array[N, 10]), for 10 IoU levels
+    """
+    correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)
+    iou = box_iou(labels[:, 1:], detections[:, :4])
+    x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5]))  # IoU above threshold and classes match
+    if x[0].shape[0]:
+        matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detection, iou]
+        if x[0].shape[0] > 1:
+            matches = matches[matches[:, 2].argsort()[::-1]]
+            matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+            # matches = matches[matches[:, 2].argsort()[::-1]]
+            matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+        matches = torch.Tensor(matches).to(iouv.device)
+        correct[matches[:, 1].long()] = matches[:, 2:3] >= iouv
+    return correct
+
+
+def run(data,
+        weights=None,  # model.pt path(s)
+        batch_size=32,  # batch size
+        imgsz=640,  # inference size (pixels)
+        conf_thres=0.001,  # confidence threshold
+        iou_thres=0.6,  # NMS IoU threshold
+        task='val',  # val, test
+        single_cls=False,  # treat as single-class dataset
+        save_txt=False,  # save results to *.txt
+        save_hybrid=False,  # save label+prediction hybrid results to *.txt
+        save_conf=False,  # save confidences in --save-txt labels
+        save_json=False,  # save a COCO-JSON results file
+        project=ROOT / 'runs/val',  # save to project/name
+        name='exp',  # save to project/name
+        exist_ok=False,  # existing project/name ok, do not increment
+        half=True,  # use FP16 half-precision inference
+        plots=False,
+        onnx_weights="./yolov5s_qat.onnx",
+        ipu=False,
+        provider_config='',
+        ):
+    # Initialize/load model and set device
+    device = torch.device('cpu')
+
+    # Directories
+    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+    # Load model
+    if isinstance(onnx_weights, list):
+        onnx_weights = onnx_weights[0]
+    if ipu:
+        providers = ["VitisAIExecutionProvider"]
+        provider_options = [{"config_file": provider_config}]
+        onnx_model = onnxruntime.InferenceSession(onnx_weights, providers=providers, provider_options=provider_options)
+    else:
+        onnx_model = onnxruntime.InferenceSession(onnx_weights)
+
+    # Data
+    data = check_dataset(data)  # check
+    gs = 32  # grid size (max stride)
+
+    is_coco = isinstance(data.get('val'), str) and data['val'].endswith('val2017.txt')  # COCO dataset
+    nc = 1 if single_cls else int(data['nc'])  # number of classes
+    iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
+    niou = iouv.numel()
+
+    # Dataloader
+    pad = 0.0 if task == 'speed' else 0.5
+    task = 'val'  # path to val/test images
+    dataloader = create_dataloader(data[task], imgsz, batch_size, gs, single_cls, pad=pad, rect=False,
+                                    prefix=colorstr(f'{task}: '), workers=8)[0]
+
+    seen = 0
+    names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+        'hair drier', 'toothbrush'] 
+    class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
+    s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
+    dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
+    loss = torch.zeros(3, device=device)
+    jdict, stats, ap, ap_class = [], [], [], []                                      
+
+    for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s, total=len(dataloader))):
+        img = img.to(device, non_blocking=True)
+        img = img.half() if half else img.float()  # uint8 to fp16/32
+        img /= 255.0  # 0 - 255 to 0.0 - 1.0
+        targets = targets.to(device)
+        nb, _, height, width = img.shape  # batch size, channels, height, width
+
+        outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: img.cpu().numpy()})
+        outputs = [torch.tensor(item).to(device) for item in outputs]
+        outputs = post_process(outputs)
+        out, train_out = outputs[0], outputs[1]
+
+        # Run NMS
+        targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
+        lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
+        out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
+
+        # Statistics per image
+        for si, pred in enumerate(out):
+            labels = targets[targets[:, 0] == si, 1:]
+            nl = len(labels)
+            tcls = labels[:, 0].tolist() if nl else []  # target class
+            path, shape = Path(paths[si]), shapes[si][0]
+            seen += 1
+
+            if len(pred) == 0:
+                if nl:
+                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
+                continue
+
+            # Predictions
+            if single_cls:
+                pred[:, 5] = 0
+            predn = pred.clone()
+            scale_coords(img[si].shape[1:], predn[:, :4], shape, shapes[si][1])  # native-space pred
+
+            # Evaluate
+            if nl:
+                tbox = xywh2xyxy(labels[:, 1:5])  # target boxes
+                scale_coords(img[si].shape[1:], tbox, shape, shapes[si][1])  # native-space labels
+                labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels
+                correct = process_batch(predn, labelsn, iouv)
+            else:
+                correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)
+            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))  # (correct, conf, pcls, tcls)
+
+            # Save/log
+            if save_txt:
+                save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
+            if save_json:
+                save_one_json(predn, jdict, path, class_map)  # append to COCO-JSON dictionary
+
+    # Compute statistics
+    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
+    if len(stats) and stats[0].any():
+        p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
+        ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95
+        mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
+        nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
+    else:
+        nt = torch.zeros(1)
+
+    # Print results
+    pf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print format
+    print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
+
+    # Save JSON
+    if save_json and len(jdict):
+        w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
+        anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json')  # annotations json
+        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
+        print(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
+        with open(pred_json, 'w') as f:
+            json.dump(jdict, f)
+
+        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
+            anno = COCO(anno_json)  # init annotations api
+            pred = anno.loadRes(pred_json)  # init predictions api
+            eval = COCOeval(anno, pred, 'bbox')
+            if is_coco:
+                eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]  # image IDs to evaluate
+            eval.evaluate()
+            eval.accumulate()
+            eval.summarize()
+            map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
+        except Exception as e:
+            print(f'pycocotools unable to run: {e}')
+
+    s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+    print(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    maps = np.zeros(nc) + map
+    for i, c in enumerate(ap_class):
+        maps[c] = ap[i]
+    return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, 0
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data', type=str, default='./coco.yaml', help='path to your dataset.yaml')
+    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
+    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
+    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
+    parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.65, help='NMS IoU threshold')
+    parser.add_argument('--task', default='val', help='val, test')
+    parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
+    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
+    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+    parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
+    parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name')
+    parser.add_argument('--name', default='exp', help='save to project/name')
+    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
+    parser.add_argument('-m', '--onnx_weights', default='./yolov5s_qat.onnx', nargs='+', type=str, help='path to your onnx_weights')
+    parser.add_argument('--ipu', action='store_true', help='flag for ryzen ai')
+    parser.add_argument('--provider_config', default='', type=str, help='provider config for ryzen ai')
+    opt = parser.parse_args()
+    opt.save_json |= opt.data.endswith('coco.yaml')
+    opt.save_txt |= opt.save_hybrid
+    return opt
+
+
+def main(opt):
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

onnx_inference.py

@@ -0,0 +1,137 @@
+import onnxruntime
+import numpy as np
+import cv2
+import torch
+import sys
+import pathlib
+CURRENT_DIR = pathlib.Path(__file__).parent
+sys.path.append(str(CURRENT_DIR))
+import argparse
+from utils import (
+    letterbox,
+    non_max_suppression,
+    scale_coords,
+    Annotator,
+    Colors,
+)
+
+
+def pre_process(img):
+    img = letterbox(img, [640, 640], stride=32, auto=False)[0]
+    # Convert
+    img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
+    img = np.ascontiguousarray(img)
+    img = img.astype("float32")
+    img = img / 255.0
+    img = img[np.newaxis, :]
+    return img
+
+
+def post_process(x):
+    x = list(x)
+    z = []  # inference output
+    stride = [8, 16, 32]
+    for i in range(3):
+        bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
+        x[i] = (
+            torch.tensor(x[i])
+            .view(bs, 3, 85, ny, nx)
+            .permute(0, 1, 3, 4, 2)
+            .contiguous()
+        )
+        y = x[i].sigmoid()
+        xy = (y[..., 0:2] * 2.0 - 0.5 + grid[i]) * stride[i]
+        wh = (y[..., 2:4] * 2) ** 2 * anchor_grid[i]
+        y = torch.cat((xy, wh, y[..., 4:]), -1)
+        z.append(y.view(bs, -1, 85))
+
+    return (torch.cat(z, 1), x)
+
+
+def make_parser():
+    parser = argparse.ArgumentParser("onnxruntime inference sample")
+    parser.add_argument(
+        "-m",
+        "--model",
+        type=str,
+        default="./yolov5s_qat.onnx",
+        help="input your onnx model.",
+    )
+    parser.add_argument(
+        "-i",
+        "--image_path",
+        type=str,
+        default='./demo.jpg',
+        help="path to your input image.",
+    )
+    parser.add_argument(
+        "-o",
+        "--output_path",
+        type=str,
+        default='./demo_infer.jpg',
+        help="path to your output directory.",
+    )
+    parser.add_argument(
+        '--ipu', 
+        action='store_true', 
+        help='flag for ryzen ai'
+    )
+    parser.add_argument(
+        '--provider_config', 
+        default='', 
+        type=str, 
+        help='provider config for ryzen ai'
+    )
+    return parser
+
+
+names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+    'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+    'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+    'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+    'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+    'hair drier', 'toothbrush'] 
+
+
+if __name__ == '__main__':
+    args = make_parser().parse_args()
+    onnx_path = args.model
+    if args.ipu:
+        providers = ["VitisAIExecutionProvider"]
+        provider_options = [{"config_file": args.provider_config}]
+        onnx_model = onnxruntime.InferenceSession(onnx_path, providers=providers, provider_options=provider_options)
+    else:
+        onnx_model = onnxruntime.InferenceSession(onnx_path)
+    grid = np.load("./grid.npy", allow_pickle=True)
+    anchor_grid = np.load("./anchor_grid.npy", allow_pickle=True)
+    path = args.image_path 
+    new_path = args.output_path
+    conf_thres, iou_thres, classes, agnostic_nms, max_det = 0.25, 0.45, None, False, 1000
+
+    img0 = cv2.imread(path)
+    img = pre_process(img0)
+    onnx_input = {onnx_model.get_inputs()[0].name: img}
+    onnx_output = onnx_model.run(None, onnx_input)
+    onnx_output = post_process(onnx_output)
+    pred = non_max_suppression(
+        onnx_output[0], conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det
+    )
+    colors = Colors()
+    det = pred[0]
+    im0 = img0.copy()
+    annotator = Annotator(im0, line_width=2, example=str(names))
+    if len(det):
+        # Rescale boxes from img_size to im0 size
+        det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
+
+        # Write results
+        for *xyxy, conf, cls in reversed(det):
+            c = int(cls)  # integer class
+            label = f"{names[c]} {conf:.2f}"
+            annotator.box_label(xyxy, label, color=colors(c, True))
+    # Stream results
+    im0 = annotator.result()
+    cv2.imwrite(new_path, im0)

requirements.txt

@@ -0,0 +1,37 @@
+# pip install -r requirements.txt
+
+# Base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.2
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+tqdm>=4.41.0
+torch==1.8.0 
+torchvision==0.9.0
+
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# wandb
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=4.1  # CoreML export
+onnx>=1.9.0  # ONNX export
+# onnxruntime
+# onnx-simplifier>=0.3.6  # ONNX simplifier
+# scikit-learn==0.19.2  # CoreML quantization
+# tensorflow>=2.4.1  # TFLite export
+# tensorflowjs>=3.9.0  # TF.js export
+
+# Extras --------------------------------------
+# albumentations>=1.0.3
+# Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
+pycocotools>=2.0  # COCO mAP
+# roboflow
+thop  # FLOPs computation

utils.py

@@ -0,0 +1,990 @@
+import numpy as np
+import cv2
+from pathlib import Path
+import torch
+import time
+import torchvision
+import re
+import glob
+from torch.utils.data import Dataset
+import yaml
+import os
+from multiprocessing.pool import ThreadPool, Pool
+from tqdm import tqdm
+from itertools import repeat
+import logging
+from PIL import Image, ExifTags
+import hashlib
+import sys
+import pathlib
+CURRENT_DIR = pathlib.Path(__file__).parent
+sys.path.append(str(CURRENT_DIR))
+# Parameters
+IMG_FORMATS = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo']
+NUM_THREADS = min(8, os.cpu_count())
+img_formats = IMG_FORMATS  # acceptable image suffixes
+vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv']  # acceptable video suffixes
+
+# Get orientation exif tag
+for orientation in ExifTags.TAGS.keys():
+    if ExifTags.TAGS[orientation] == 'Orientation':
+        break
+
+
+def make_dirs(dir='./datasets/coco'):
+    # Create folders
+    dir = Path(dir)
+    for p in [dir / 'labels']:
+        p.mkdir(parents=True, exist_ok=True)  # make dir
+    return dir
+
+
+def coco91_to_coco80_class():  # converts 80-index (val2014) to 91-index (paper)
+    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
+    x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, None, 24, 25, None,
+         None, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
+         51, 52, 53, 54, 55, 56, 57, 58, 59, None, 60, None, None, 61, None, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+         None, 73, 74, 75, 76, 77, 78, 79, None]
+    return x
+
+
+def is_ascii(s=""):
+    # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
+    s = str(s)  # convert list, tuple, None, etc. to str
+    return len(s.encode().decode("ascii", "ignore")) == len(s)
+
+
+def is_chinese(s="人工智能"):
+    # Is string composed of any Chinese characters?
+    return re.search("[\u4e00-\u9fff]", s)
+
+
+def letterbox(
+    im,
+    new_shape=(640, 640),
+    color=(114, 114, 114),
+    auto=True,
+    scaleFill=False,
+    scaleup=True,
+    stride=32,
+):
+    # Resize and pad image while meeting stride-multiple constraints
+    shape = im.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+    if not scaleup:  # only scale down, do not scale up (for better val mAP)
+        r = min(r, 1.0)
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+    if auto:  # minimum rectangle
+        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
+    elif scaleFill:  # stretch
+        dw, dh = 0.0, 0.0
+        new_unpad = (new_shape[1], new_shape[0])
+        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    im = cv2.copyMakeBorder(
+        im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color
+    )  # add border
+    return im, ratio, (dw, dh)
+
+
+def xyxy2xywh(x):
+    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
+    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
+    y[:, 2] = x[:, 2] - x[:, 0]  # width
+    y[:, 3] = x[:, 3] - x[:, 1]  # height
+    return y
+
+
+def xywh2xyxy(x):
+    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+
+def non_max_suppression(
+    prediction,
+    conf_thres=0.25,
+    iou_thres=0.45,
+    classes=None,
+    agnostic=False,
+    multi_label=False,
+    labels=(),
+    max_det=300,
+):
+    """Runs Non-Maximum Suppression (NMS) on inference results
+
+    Returns:
+         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
+    """
+
+    nc = prediction.shape[2] - 5  # number of classes
+    xc = prediction[..., 4] > conf_thres  # candidates
+
+    # Checks
+    assert (
+        0 <= conf_thres <= 1
+    ), f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
+    assert (
+        0 <= iou_thres <= 1
+    ), f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"
+
+    # Settings
+    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
+    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
+    time_limit = 10.0  # seconds to quit after
+    redundant = True  # require redundant detections
+    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
+    merge = False  # use merge-NMS
+
+    t = time.time()
+    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
+    for xi, x in enumerate(prediction):  # image index, image inference
+        # Apply constraints
+        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
+        x = x[xc[xi]]  # confidence
+
+        # Cat apriori labels if autolabelling
+        if labels and len(labels[xi]):
+            l = labels[xi]
+            v = torch.zeros((len(l), nc + 5), device=x.device)
+            v[:, :4] = l[:, 1:5]  # box
+            v[:, 4] = 1.0  # conf
+            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
+            x = torch.cat((x, v), 0)
+
+        # If none remain process next image
+        if not x.shape[0]:
+            continue
+
+        # Compute conf
+        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
+
+        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+        box = xywh2xyxy(x[:, :4])
+
+        # Detections matrix nx6 (xyxy, conf, cls)
+        if multi_label:
+            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
+            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
+        else:  # best class only
+            conf, j = x[:, 5:].max(1, keepdim=True)
+            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
+
+        # Filter by class
+        if classes is not None:
+            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+
+        # Apply finite constraint
+        # if not torch.isfinite(x).all():
+        #     x = x[torch.isfinite(x).all(1)]
+
+        # Check shape
+        n = x.shape[0]  # number of boxes
+        if not n:  # no boxes
+            continue
+        elif n > max_nms:  # excess boxes
+            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
+
+        # Batched NMS
+        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
+        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
+        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
+        if i.shape[0] > max_det:  # limit detections
+            i = i[:max_det]
+        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
+            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
+            weights = iou * scores[None]  # box weights
+            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(
+                1, keepdim=True
+            )  # merged boxes
+            if redundant:
+                i = i[iou.sum(1) > 1]  # require redundancy
+
+        output[xi] = x[i]
+        if (time.time() - t) > time_limit:
+            print(f"WARNING: NMS time limit {time_limit}s exceeded")
+            break  # time limit exceeded
+
+    return output
+
+
+def clip_coords(boxes, shape):
+    # Clip bounding xyxy bounding boxes to image shape (height, width)
+    if isinstance(boxes, torch.Tensor):  # faster individually
+        boxes[:, 0].clamp_(0, shape[1])  # x1
+        boxes[:, 1].clamp_(0, shape[0])  # y1
+        boxes[:, 2].clamp_(0, shape[1])  # x2
+        boxes[:, 3].clamp_(0, shape[0])  # y2
+    else:  # np.array (faster grouped)
+        boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1])  # x1, x2
+        boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0])  # y1, y2
+
+
+def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
+    # Rescale coords (xyxy) from img1_shape to img0_shape
+    if ratio_pad is None:  # calculate from img0_shape
+        gain = min(
+            img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]
+        )  # gain  = old / new
+        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (
+            img1_shape[0] - img0_shape[0] * gain
+        ) / 2  # wh padding
+    else:
+        gain = ratio_pad[0][0]
+        pad = ratio_pad[1]
+
+    coords[:, [0, 2]] -= pad[0]  # x padding
+    coords[:, [1, 3]] -= pad[1]  # y padding
+    coords[:, :4] /= gain
+    clip_coords(coords, img0_shape)
+    return coords
+
+
+class Annotator:
+    # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
+    def __init__(
+        self,
+        im,
+        line_width=None,
+        font_size=None,
+        font="Arial.ttf",
+        pil=False,
+        example="abc",
+    ):
+        assert (
+            im.data.contiguous
+        ), "Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images."
+        self.pil = pil or not is_ascii(example) or is_chinese(example)
+        self.im = im
+        self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2)  # line width
+
+    def box_label(
+        self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255)
+    ):
+        # Add one xyxy box to image with label
+        p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
+        cv2.rectangle(
+            self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA
+        )
+        if label:
+            tf = max(self.lw - 1, 1)  # font thickness
+            w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[
+                0
+            ]  # text width, height
+            outside = p1[1] - h - 3 >= 0  # label fits outside box
+            p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
+            cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
+            cv2.putText(
+                self.im,
+                label,
+                (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
+                0,
+                self.lw / 3,
+                txt_color,
+                thickness=tf,
+                lineType=cv2.LINE_AA,
+            )
+
+    def rectangle(self, xy, fill=None, outline=None, width=1):
+        # Add rectangle to image (PIL-only)
+        self.draw.rectangle(xy, fill, outline, width)
+
+    def result(self):
+        # Return annotated image as array
+        return np.asarray(self.im)
+
+
+class Colors:
+    # Ultralytics color palette https://ultralytics.com/
+    def __init__(self):
+        # hex = matplotlib.colors.TABLEAU_COLORS.values()
+        hex = (
+            "FF3838",
+            "FF9D97",
+            "FF701F",
+            "FFB21D",
+            "CFD231",
+            "48F90A",
+            "92CC17",
+            "3DDB86",
+            "1A9334",
+            "00D4BB",
+            "2C99A8",
+            "00C2FF",
+            "344593",
+            "6473FF",
+            "0018EC",
+            "8438FF",
+            "520085",
+            "CB38FF",
+            "FF95C8",
+            "FF37C7",
+        )
+        self.palette = [self.hex2rgb("#" + c) for c in hex]
+        self.n = len(self.palette)
+
+    def __call__(self, i, bgr=False):
+        c = self.palette[int(i) % self.n]
+        return (c[2], c[1], c[0]) if bgr else c
+
+    @staticmethod
+    def hex2rgb(h):  # rgb order (PIL)
+        return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4))
+
+
+def create_dataloader(path, imgsz, batch_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0,
+                      rect=False, rank=-1, workers=8, image_weights=False, quad=False, prefix=''):
+
+    dataset = LoadImagesAndLabels(path, imgsz, batch_size,
+                                  augment=augment,  # augment images
+                                  hyp=hyp,  # augmentation hyperparameters
+                                  rect=rect,  # rectangular training
+                                  cache_images=cache,
+                                  single_cls=single_cls,
+                                  stride=int(stride),
+                                  pad=pad,
+                                  image_weights=image_weights,
+                                  prefix=prefix)
+
+    batch_size = min(batch_size, len(dataset))
+    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, workers])  # number of workers
+    sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
+    loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader
+    # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader()
+    dataloader = loader(dataset,
+                        batch_size=batch_size,
+                        num_workers=nw,
+                        sampler=sampler,
+                        pin_memory=True,
+                        collate_fn=LoadImagesAndLabels.collate_fn)
+    return dataloader, dataset
+
+
+class LoadImagesAndLabels(Dataset):
+    # YOLOv5 train_loader/val_loader, loads images and labels for training and validation
+    cache_version = 0.5  # dataset labels *.cache version
+
+    def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
+                 cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''):
+        self.img_size = img_size
+        self.augment = augment
+        self.hyp = hyp
+        self.image_weights = image_weights
+        self.rect = False if image_weights else rect
+        self.mosaic = False  # load 4 images at a time into a mosaic (only during training)
+        self.mosaic_border = [-img_size // 2, -img_size // 2]
+        self.stride = stride
+        self.path = path
+        self.albumentations = None
+
+        f = []  # image files
+        for p in path if isinstance(path, list) else [path]:
+            p = Path(p)  # os-agnostic
+            if p.is_dir():  # dir
+                f += glob.glob(str(p / '**' / '*.*'), recursive=True)
+                # f = list(p.rglob('**/*.*'))  # pathlib
+            elif p.is_file():  # file
+                with open(p, 'r') as t:
+                    t = t.read().strip().splitlines()
+                    parent = str(p.parent) + os.sep
+                    f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path
+                    # f += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
+            else:
+                raise Exception(f'{prefix}{p} does not exist')
+        self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS])
+        # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats])  # pathlib
+        assert self.img_files, f'{prefix}No images found'
+
+        # Check cache
+        self.label_files = img2label_paths(self.img_files)  # labels
+        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')
+        try:
+            cache, exists = np.load(cache_path, allow_pickle=True).item(), True  # load dict
+            assert cache['version'] == self.cache_version  # same version
+            assert cache['hash'] == get_hash(self.label_files + self.img_files)  # same hash
+        except:
+            cache, exists = self.cache_labels(cache_path, prefix), False  # cache
+
+        # Display cache
+        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupted, total
+        if exists:
+            d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
+            tqdm(None, desc=prefix + d, total=n, initial=n)  # display cache results
+            if cache['msgs']:
+                logging.info('\n'.join(cache['msgs']))  # display warnings
+
+        # Read cache
+        [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items
+        labels, shapes, self.segments = zip(*cache.values())
+        self.labels = list(labels)
+        self.shapes = np.array(shapes, dtype=np.float64)
+        self.img_files = list(cache.keys())  # update
+        self.label_files = img2label_paths(cache.keys())  # update
+        if single_cls:
+            for x in self.labels:
+                x[:, 0] = 0
+
+        n = len(shapes)  # number of images
+        bi = np.floor(np.arange(n) / batch_size).astype(int)  # batch index
+        nb = bi[-1] + 1  # number of batches
+        self.batch = bi  # batch index of image
+        self.n = n
+        self.indices = range(n)
+
+        # Rectangular Training
+        if self.rect:
+            # Sort by aspect ratio
+            s = self.shapes  # wh
+            ar = s[:, 1] / s[:, 0]  # aspect ratio
+            irect = ar.argsort()
+            self.img_files = [self.img_files[i] for i in irect]
+            self.label_files = [self.label_files[i] for i in irect]
+            self.labels = [self.labels[i] for i in irect]
+            self.shapes = s[irect]  # wh
+            ar = ar[irect]
+
+            # Set training image shapes
+            shapes = [[1, 1]] * nb
+            for i in range(nb):
+                ari = ar[bi == i]
+                mini, maxi = ari.min(), ari.max()
+                if maxi < 1:
+                    shapes[i] = [maxi, 1]
+                elif mini > 1:
+                    shapes[i] = [1, 1 / mini]
+
+            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride
+
+        # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
+        self.imgs, self.img_npy = [None] * n, [None] * n
+        if cache_images:
+            if cache_images == 'disk':
+                self.im_cache_dir = Path(Path(self.img_files[0]).parent.as_posix() + '_npy')
+                self.img_npy = [self.im_cache_dir / Path(f).with_suffix('.npy').name for f in self.img_files]
+                self.im_cache_dir.mkdir(parents=True, exist_ok=True)
+            gb = 0  # Gigabytes of cached images
+            self.img_hw0, self.img_hw = [None] * n, [None] * n
+            results = ThreadPool(NUM_THREADS).imap(lambda x: load_image(*x), zip(repeat(self), range(n)))
+            pbar = tqdm(enumerate(results), total=n)
+            for i, x in pbar:
+                if cache_images == 'disk':
+                    if not self.img_npy[i].exists():
+                        np.save(self.img_npy[i].as_posix(), x[0])
+                    gb += self.img_npy[i].stat().st_size
+                else:
+                    self.imgs[i], self.img_hw0[i], self.img_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
+                    gb += self.imgs[i].nbytes
+                pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB {cache_images})'
+            pbar.close()
+
+    def cache_labels(self, path=Path('./labels.cache'), prefix=''):
+        # Cache dataset labels, check images and read shapes
+        x = {}  # dict
+        nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
+        desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels..."
+        with Pool(NUM_THREADS) as pool:
+            pbar = tqdm(pool.imap(verify_image_label, zip(self.img_files, self.label_files, repeat(prefix))), desc=desc, total=len(self.img_files))
+            for im_file, l, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
+                nm += nm_f
+                nf += nf_f
+                ne += ne_f
+                nc += nc_f
+                if im_file:
+                    x[im_file] = [l, shape, segments]
+                if msg:
+                    msgs.append(msg)
+                pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupted"
+
+        pbar.close()
+        if msgs:
+            logging.info('\n'.join(msgs))
+        x['hash'] = get_hash(self.label_files + self.img_files)
+        x['results'] = nf, nm, ne, nc, len(self.img_files)
+        x['msgs'] = msgs  # warnings
+        x['version'] = self.cache_version  # cache version
+        try:
+            np.save(path, x)  # save cache for next time
+            path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix
+            logging.info(f'{prefix}New cache created: {path}')
+        except Exception as e:
+            logging.info(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}')  # path not writeable
+        return x
+
+    def __len__(self):
+        return len(self.img_files)
+
+    # def __iter__(self):
+    #     self.count = -1
+    #     print('ran dataset iter')
+    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
+    #     return self
+
+    def __getitem__(self, index):
+        index = self.indices[index]  # linear, shuffled, or image_weights
+
+        hyp = self.hyp
+        mosaic = self.mosaic
+
+        # Load image
+        img, (h0, w0), (h, w) = load_image(self, index)
+
+        # Letterbox
+        shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
+        img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
+        shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling
+
+        labels = self.labels[index].copy()
+        if labels.size:  # normalized xywh to pixel xyxy format
+            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
+
+        nl = len(labels)  # number of labels
+        if nl:
+            labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3)
+
+        labels_out = torch.zeros((nl, 6))
+        if nl:
+            labels_out[:, 1:] = torch.from_numpy(labels)
+
+        # Convert
+        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
+        img = np.ascontiguousarray(img)
+
+        return torch.from_numpy(img), labels_out, self.img_files[index], shapes
+
+    @staticmethod
+    def collate_fn(batch):
+        img, label, path, shapes = zip(*batch)  # transposed
+        for i, l in enumerate(label):
+            l[:, 0] = i  # add target image index for build_targets()
+        return torch.stack(img, 0), torch.cat(label, 0), path, shapes
+
+
+def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
+    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
+    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
+    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
+    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
+    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
+    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
+         35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
+         64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
+    return x
+
+
+def check_dataset(data, autodownload=True):
+    # Download and/or unzip dataset if not found locally
+    # Usage: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128_with_yaml.zip
+
+    # Download (optional)
+    extract_dir = ''
+
+    # Read yaml (optional)
+    if isinstance(data, (str, Path)):
+        with open(data, errors='ignore') as f:
+            data = yaml.safe_load(f)  # dictionary
+
+    # Parse yaml
+    path = extract_dir or Path(data.get('path') or '')  # optional 'path' default to '.'
+    for k in 'train', 'val', 'test':
+        if data.get(k):  # prepend path
+            data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]
+
+    assert 'nc' in data, "Dataset 'nc' key missing."
+    if 'names' not in data:
+        data['names'] = [f'class{i}' for i in range(data['nc'])]  # assign class names if missing
+    train, val, test, s = [data.get(x) for x in ('train', 'val', 'test', 'download')]
+    if val:
+        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
+        if not all(x.exists() for x in val):
+            print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
+
+    return data  # dictionary
+
+
+def box_iou(box1, box2):
+    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+    """
+    Return intersection-over-union (Jaccard index) of boxes.
+    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+    Arguments:
+        box1 (Tensor[N, 4])
+        box2 (Tensor[M, 4])
+    Returns:
+        iou (Tensor[N, M]): the NxM matrix containing the pairwise
+            IoU values for every element in boxes1 and boxes2
+    """
+
+    def box_area(box):
+        # box = 4xn
+        return (box[2] - box[0]) * (box[3] - box[1])
+
+    area1 = box_area(box1.T)
+    area2 = box_area(box2.T)
+
+    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
+    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
+    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)
+
+
+def increment_path(path, exist_ok=False, sep='', mkdir=False):
+    # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
+    path = Path(path)  # os-agnostic
+    if path.exists() and not exist_ok:
+        suffix = path.suffix
+        path = path.with_suffix('')
+        dirs = glob.glob(f"{path}{sep}*")  # similar paths
+        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
+        i = [int(m.groups()[0]) for m in matches if m]  # indices
+        n = max(i) + 1 if i else 2  # increment number
+        path = Path(f"{path}{sep}{n}{suffix}")  # update path
+    dir = path if path.suffix == '' else path.parent  # directory
+    if not dir.exists() and mkdir:
+        dir.mkdir(parents=True, exist_ok=True)  # make directory
+    return path
+
+
+def colorstr(*input):
+    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
+    *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string
+    colors = {'black': '\033[30m',  # basic colors
+              'red': '\033[31m',
+              'green': '\033[32m',
+              'yellow': '\033[33m',
+              'blue': '\033[34m',
+              'magenta': '\033[35m',
+              'cyan': '\033[36m',
+              'white': '\033[37m',
+              'bright_black': '\033[90m',  # bright colors
+              'bright_red': '\033[91m',
+              'bright_green': '\033[92m',
+              'bright_yellow': '\033[93m',
+              'bright_blue': '\033[94m',
+              'bright_magenta': '\033[95m',
+              'bright_cyan': '\033[96m',
+              'bright_white': '\033[97m',
+              'end': '\033[0m',  # misc
+              'bold': '\033[1m',
+              'underline': '\033[4m'}
+    return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
+
+
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
+    # Arguments
+        tp:  True positives (nparray, nx1 or nx10).
+        conf:  Objectness value from 0-1 (nparray).
+        pred_cls:  Predicted object classes (nparray).
+        target_cls:  True object classes (nparray).
+        plot:  Plot precision-recall curve at mAP@0.5
+        save_dir:  Plot save directory
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+
+    # Sort by objectness
+    i = np.argsort(-conf)
+    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+
+    # Find unique classes
+    unique_classes = np.unique(target_cls)
+    nc = unique_classes.shape[0]  # number of classes, number of detections
+
+    # Create Precision-Recall curve and compute AP for each class
+    px, py = np.linspace(0, 1, 1000), []  # for plotting
+    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
+    for ci, c in enumerate(unique_classes):
+        i = pred_cls == c
+        n_l = (target_cls == c).sum()  # number of labels
+        n_p = i.sum()  # number of predictions
+
+        if n_p == 0 or n_l == 0:
+            continue
+        else:
+            # Accumulate FPs and TPs
+            fpc = (1 - tp[i]).cumsum(0)
+            tpc = tp[i].cumsum(0)
+
+            # Recall
+            recall = tpc / (n_l + 1e-16)  # recall curve
+            r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
+
+            # Precision
+            precision = tpc / (tpc + fpc)  # precision curve
+            p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
+
+            # AP from recall-precision curve
+            for j in range(tp.shape[1]):
+                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+                if plot and j == 0:
+                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
+
+    # Compute F1 (harmonic mean of precision and recall)
+    f1 = 2 * p * r / (p + r + 1e-16)
+
+    i = f1.mean(0).argmax()  # max F1 index
+    return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32')
+
+
+def compute_ap(recall, precision):
+    """ Compute the average precision, given the recall and precision curves
+    # Arguments
+        recall:    The recall curve (list)
+        precision: The precision curve (list)
+    # Returns
+        Average precision, precision curve, recall curve
+    """
+
+    # Append sentinel values to beginning and end
+    mrec = np.concatenate(([0.0], recall, [1.0]))
+    mpre = np.concatenate(([1.0], precision, [0.0]))
+
+    # Compute the precision envelope
+    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
+
+    # Integrate area under curve
+    method = 'interp'  # methods: 'continuous', 'interp'
+    if method == 'interp':
+        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
+        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
+    else:  # 'continuous'
+        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
+
+    return ap, mpre, mrec
+
+
+def output_to_target(output):
+    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
+    targets = []
+    for i, o in enumerate(output):
+        for *box, conf, cls in o.cpu().numpy():
+            targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])
+    return np.array(targets)
+
+
+def check_yaml(file, suffix=('.yaml', '.yml')):
+    # Search/download YAML file (if necessary) and return path, checking suffix
+    return check_file(file, suffix)
+
+
+def check_file(file, suffix=''):
+    # Search/download file (if necessary) and return path
+    check_suffix(file, suffix)  # optional
+    file = str(file)  # convert to str()
+    return file
+
+
+def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
+    # Check file(s) for acceptable suffixes
+    if file and suffix:
+        if isinstance(suffix, str):
+            suffix = [suffix]
+        for f in file if isinstance(file, (list, tuple)) else [file]:
+            assert Path(f).suffix.lower() in suffix, f"{msg}{f} acceptable suffix is {suffix}"
+
+
+def img2label_paths(img_paths):
+    # Define label paths as a function of image paths
+    sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep  # /images/, /labels/ substrings
+    return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]
+
+
+def exif_size(img):
+    # Returns exif-corrected PIL size
+    s = img.size  # (width, height)
+    try:
+        rotation = dict(img._getexif().items())[orientation]
+        if rotation == 6:  # rotation 270
+            s = (s[1], s[0])
+        elif rotation == 8:  # rotation 90
+            s = (s[1], s[0])
+    except:
+        pass
+
+    return s
+
+
+def verify_image_label(args):
+    # Verify one image-label pair
+    im_file, lb_file, prefix = args
+    nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', []  # number (missing, found, empty, corrupt), message, segments
+    try:
+        # verify images
+        im = Image.open(im_file)
+        im.verify()  # PIL verify
+        shape = exif_size(im)  # image size
+        assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
+        assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'
+        if im.format.lower() in ('jpg', 'jpeg'):
+            with open(im_file, 'rb') as f:
+                f.seek(-2, 2)
+                if f.read() != b'\xff\xd9':  # corrupt JPEG
+                    Image.open(im_file).save(im_file, format='JPEG', subsampling=0, quality=100)  # re-save image
+                    msg = f'{prefix}WARNING: corrupt JPEG restored and saved {im_file}'
+
+        # verify labels
+        if os.path.isfile(lb_file):
+            nf = 1  # label found
+            with open(lb_file, 'r') as f:
+                l = [x.split() for x in f.read().strip().splitlines() if len(x)]
+                if any([len(x) > 8 for x in l]):  # is segment
+                    classes = np.array([x[0] for x in l], dtype=np.float32)
+                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l]  # (cls, xy1...)
+                    l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
+                l = np.array(l, dtype=np.float32)
+            if len(l):
+                assert l.shape[1] == 5, 'labels require 5 columns each'
+                assert (l >= 0).all(), 'negative labels'
+                assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
+                assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels'
+            else:
+                ne = 1  # label empty
+                l = np.zeros((0, 5), dtype=np.float32)
+        else:
+            nm = 1  # label missing
+            l = np.zeros((0, 5), dtype=np.float32)
+        return im_file, l, shape, segments, nm, nf, ne, nc, msg
+    except Exception as e:
+        nc = 1
+        msg = f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}'
+        return [None, None, None, None, nm, nf, ne, nc, msg]
+
+
+def segments2boxes(segments):
+    # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
+    boxes = []
+    for s in segments:
+        x, y = s.T  # segment xy
+        boxes.append([x.min(), y.min(), x.max(), y.max()])  # cls, xyxy
+    return xyxy2xywh(np.array(boxes))  # cls, xywh
+
+
+def get_hash(paths):
+    # Returns a single hash value of a list of paths (files or dirs)
+    size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes
+    h = hashlib.md5(str(size).encode())  # hash sizes
+    h.update(''.join(paths).encode())  # hash paths
+    return h.hexdigest()  # return hash
+
+
+class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
+    """ Dataloader that reuses workers
+
+    Uses same syntax as vanilla DataLoader
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
+        self.iterator = super().__iter__()
+
+    def __len__(self):
+        return len(self.batch_sampler.sampler)
+
+    def __iter__(self):
+        for i in range(len(self)):
+            yield next(self.iterator)
+
+
+class _RepeatSampler(object):
+    """ Sampler that repeats forever
+
+    Args:
+        sampler (Sampler)
+    """
+
+    def __init__(self, sampler):
+        self.sampler = sampler
+
+    def __iter__(self):
+        while True:
+            yield from iter(self.sampler)
+
+
+def load_image(self, i):
+    # loads 1 image from dataset index 'i', returns im, original hw, resized hw
+    im = self.imgs[i]
+    if im is None:  # not cached in ram
+        npy = self.img_npy[i]
+        if npy and npy.exists():  # load npy
+            im = np.load(npy)
+        else:  # read image
+            path = self.img_files[i]
+            im = cv2.imread(path)  # BGR
+            assert im is not None, 'Image Not Found ' + path
+        h0, w0 = im.shape[:2]  # orig hw
+        r = self.img_size / max(h0, w0)  # ratio
+        if r != 1:  # if sizes are not equal
+            im = cv2.resize(im, (int(w0 * r), int(h0 * r)),
+                            interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR)
+        return im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
+    else:
+        return self.imgs[i], self.img_hw0[i], self.img_hw[i]  # im, hw_original, hw_resized
+
+
+def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
+    # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw  # top left x
+    y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh  # top left y
+    y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw  # bottom right x
+    y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh  # bottom right y
+    return y
+
+
+def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
+    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
+    if clip:
+        clip_coords(x, (h - eps, w - eps))  # warning: inplace clip
+    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+    y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w  # x center
+    y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h  # y center
+    y[:, 2] = (x[:, 2] - x[:, 0]) / w  # width
+    y[:, 3] = (x[:, 3] - x[:, 1]) / h  # height
+    return y
+
+
+def post_process(x):
+    grid = np.load("./grid.npy", allow_pickle=True)
+    anchor_grid = np.load("./anchor_grid.npy", allow_pickle=True)
+    x = list(x)
+    z = []  # inference output
+    stride = [8, 16, 32]
+    for i in range(3):
+        bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
+        x[i] = (
+            torch.tensor(x[i])
+            .view(bs, 3, 85, ny, nx)
+            .permute(0, 1, 3, 4, 2)
+            .contiguous()
+        )
+        y = x[i].sigmoid()
+        xy = (y[..., 0:2] * 2.0 - 0.5 + grid[i]) * stride[i]
+        wh = (y[..., 2:4] * 2) ** 2 * anchor_grid[i]
+        y = torch.cat((xy, wh, y[..., 4:]), -1)
+        z.append(y.view(bs, -1, 85))
+
+    return (torch.cat(z, 1), x)

yolov5s_qat.onnx

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