diff --git a/.github/ISSUE_TEMPLATE/-question.md b/.github/ISSUE_TEMPLATE/-question.md new file mode 100644 index 000000000000..2c22aea70a7b --- /dev/null +++ b/.github/ISSUE_TEMPLATE/-question.md @@ -0,0 +1,13 @@ +--- +name: "❓Question" +about: Ask a general question +title: '' +labels: question +assignees: '' + +--- + +## ❔Question + + +## Additional context diff --git a/Dockerfile b/Dockerfile index 357c6dbc4cb9..66eeb83c4ab5 100644 --- a/Dockerfile +++ b/Dockerfile @@ -25,10 +25,10 @@ COPY . /usr/src/app # t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t # Pull and Run -# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host $t bash +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host $t # Pull and Run with local directory access -# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t bash +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t # Kill all # sudo docker kill "$(sudo docker ps -q)" diff --git a/README.md b/README.md index c6b638003c62..b669724e7310 100755 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ This repository represents Ultralytics open-source research into future object d | [YOLOv5m](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 43.4 | 43.4 | 62.4 | 3.0ms | 333 || 21.8M | 39.4B | [YOLOv5l](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 46.6 | 46.7 | 65.4 | 3.9ms | 256 || 47.8M | 88.1B | [YOLOv5x](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | **48.4** | **48.4** | **66.9** | 6.1ms | 164 || 89.0M | 166.4B -| [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B +| [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B ** APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy. @@ -54,10 +54,11 @@ $ pip install -U -r requirements.txt Inference can be run on most common media formats. Model [checkpoints](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) are downloaded automatically if available. Results are saved to `./inference/output`. ```bash -$ python detect.py --source file.jpg # image +$ python detect.py --source 0 # webcam + file.jpg # image file.mp4 # video - ./dir # directory - 0 # webcam + path/ # directory + path/*.jpg # glob rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream ``` @@ -93,10 +94,11 @@ $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size ## Reproduce Our Environment -To access an up-to-date working environment (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled), consider a: +YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled): -- **Google Cloud** Deep Learning VM with $300 free credit offer: See our [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) -- **Google Colab Notebook** with 12 hours of free GPU time. Open In Colab +- **Google Colab Notebook** with free GPU: Open In Colab +- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5) +- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) - **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker) diff --git a/data/coco.yaml b/data/coco.yaml index 291149aeed60..26f2db9e9761 100644 --- a/data/coco.yaml +++ b/data/coco.yaml @@ -1,13 +1,13 @@ # COCO 2017 dataset http://cocodataset.org # Download command: bash yolov5/data/get_coco2017.sh -# Train command: python train.py --data ./data/coco.yaml -# Dataset should be placed next to yolov5 folder: +# Train command: python train.py --data coco.yaml +# Default dataset location is next to /yolov5: # /parent_folder # /coco # /yolov5 -# train and val datasets (image directory or *.txt file with image paths) +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] train: ../coco/train2017.txt # 118k images val: ../coco/val2017.txt # 5k images test: ../coco/test-dev2017.txt # 20k images for submission to https://competitions.codalab.org/competitions/20794 diff --git a/data/coco128.yaml b/data/coco128.yaml index 2b6184890cdb..a7ac848195c3 100644 --- a/data/coco128.yaml +++ b/data/coco128.yaml @@ -1,15 +1,15 @@ # COCO 2017 dataset http://cocodataset.org - first 128 training images -# Download command: python -c "from yolov5.utils.google_utils import gdrive_download; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip')" -# Train command: python train.py --data ./data/coco128.yaml -# Dataset should be placed next to yolov5 folder: +# Download command: python -c "from yolov5.utils.google_utils import *; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', 'coco128.zip')" +# Train command: python train.py --data coco128.yaml +# Default dataset location is next to /yolov5: # /parent_folder # /coco128 # /yolov5 -# train and val datasets (image directory or *.txt file with image paths) -train: ../coco128/images/train2017/ -val: ../coco128/images/train2017/ +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../coco128/images/train2017/ # 128 images +val: ../coco128/images/train2017/ # 128 images # number of classes nc: 80 diff --git a/data/get_coco2017.sh b/data/get_coco2017.sh index 03b2c7e89301..aa031dfb6a4e 100755 --- a/data/get_coco2017.sh +++ b/data/get_coco2017.sh @@ -1,12 +1,13 @@ #!/bin/bash # COCO 2017 dataset http://cocodataset.org # Download command: bash yolov5/data/get_coco2017.sh -# Train command: python train.py --data ./data/coco.yaml -# Dataset should be placed next to yolov5 folder: +# Train command: python train.py --data coco.yaml +# Default dataset location is next to /yolov5: # /parent_folder # /coco # /yolov5 + # Download labels from Google Drive, accepting presented query filename="coco2017labels.zip" fileid="1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L" diff --git a/data/get_voc.sh b/data/get_voc.sh index b7e66d003133..3eaad6b56efb 100644 --- a/data/get_voc.sh +++ b/data/get_voc.sh @@ -1,11 +1,12 @@ # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/ # Download command: bash ./data/get_voc.sh # Train command: python train.py --data voc.yaml -# Dataset should be placed next to yolov5 folder: +# Default dataset location is next to /yolov5: # /parent_folder # /VOC # /yolov5 + start=`date +%s` # handle optional download dir diff --git a/data/voc.yaml b/data/voc.yaml index efe22308ad47..12d05fd9d143 100644 --- a/data/voc.yaml +++ b/data/voc.yaml @@ -1,14 +1,15 @@ # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/ # Download command: bash ./data/get_voc.sh # Train command: python train.py --data voc.yaml -# Dataset should be placed next to yolov5 folder: +# Default dataset location is next to /yolov5: # /parent_folder # /VOC # /yolov5 -# train and val datasets (image directory or *.txt file with image paths) -train: ../VOC/images/train/ -val: ../VOC/images/val/ + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../VOC/images/train/ # 16551 images +val: ../VOC/images/val/ # 4952 images # number of classes nc: 20 diff --git a/detect.py b/detect.py index d02f0a922817..bfe46048aea9 100644 --- a/detect.py +++ b/detect.py @@ -2,7 +2,7 @@ import torch.backends.cudnn as cudnn -from utils import google_utils +from models.experimental import * from utils.datasets import * from utils.utils import * @@ -20,8 +20,7 @@ def detect(save_img=False): half = device.type != 'cpu' # half precision only supported on CUDA # Load model - google_utils.attempt_download(weights) - model = torch.load(weights, map_location=device)['model'].float().eval() # load FP32 model + model = attempt_load(weights, map_location=device) # load FP32 model imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size if half: model.half() # to FP16 @@ -129,7 +128,7 @@ def detect(save_img=False): if save_txt or save_img: print('Results saved to %s' % os.getcwd() + os.sep + out) - if platform == 'darwin': # MacOS + if platform == 'darwin' and not opt.update: # MacOS os.system('open ' + save_path) print('Done. (%.3fs)' % (time.time() - t0)) @@ -137,7 +136,7 @@ def detect(save_img=False): if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default='weights/yolov5s.pt', help='model.pt path') + parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam parser.add_argument('--output', type=str, default='inference/output', help='output folder') # output folder parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') @@ -146,7 +145,7 @@ def detect(save_img=False): parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--view-img', action='store_true', help='display results') parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') parser.add_argument('--update', action='store_true', help='update all models') diff --git a/hubconf.py b/hubconf.py index 7ca9d93bf0fc..29e93bdf2135 100644 --- a/hubconf.py +++ b/hubconf.py @@ -28,14 +28,18 @@ def create(name, pretrained, channels, classes): pytorch model """ config = os.path.join(os.path.dirname(__file__), 'models', '%s.yaml' % name) # model.yaml path - model = Model(config, channels, classes) - if pretrained: - ckpt = '%s.pt' % name # checkpoint filename - google_utils.attempt_download(ckpt) # download if not found locally - state_dict = torch.load(ckpt, map_location=torch.device('cpu'))['model'].float().state_dict() # to FP32 - state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter - model.load_state_dict(state_dict, strict=False) # load - return model + try: + model = Model(config, channels, classes) + if pretrained: + ckpt = '%s.pt' % name # checkpoint filename + google_utils.attempt_download(ckpt) # download if not found locally + state_dict = torch.load(ckpt, map_location=torch.device('cpu'))['model'].float().state_dict() # to FP32 + state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter + model.load_state_dict(state_dict, strict=False) # load + return model + except Exception as e: + help_url = 'https://github.com/ultralytics/yolov5/issues/36' + print('%s\nCache maybe be out of date. Delete cache and retry. See %s for help.' % (e, help_url)) def yolov5s(pretrained=False, channels=3, classes=80): diff --git a/models/experimental.py b/models/experimental.py index 146a61b67a44..a22f6bbf60e8 100644 --- a/models/experimental.py +++ b/models/experimental.py @@ -1,6 +1,7 @@ # This file contains experimental modules from models.common import * +from utils import google_utils class CrossConv(nn.Module): @@ -118,4 +119,23 @@ def forward(self, x, augment=False): y = [] for module in self: y.append(module(x, augment)[0]) - return torch.cat(y, 1), None # ensembled inference output, train output + # y = torch.stack(y).max(0)[0] # max ensemble + # y = torch.cat(y, 1) # nms ensemble + y = torch.stack(y).mean(0) # mean ensemble + return y, None # inference, train output + + +def attempt_load(weights, map_location=None): + # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a + model = Ensemble() + for w in weights if isinstance(weights, list) else [weights]: + google_utils.attempt_download(w) + model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model + + if len(model) == 1: + return model[-1] # return model + else: + print('Ensemble created with %s\n' % weights) + for k in ['names', 'stride']: + setattr(model, k, getattr(model[-1], k)) + return model # return ensemble diff --git a/models/export.py b/models/export.py index c11c0a391197..2097df51c0b0 100644 --- a/models/export.py +++ b/models/export.py @@ -31,7 +31,7 @@ # TorchScript export try: print('\nStarting TorchScript export with torch %s...' % torch.__version__) - f = opt.weights.replace('.pt', '.torchscript') # filename + f = opt.weights.replace('.pt', '.torchscript.pt') # filename ts = torch.jit.trace(model, img) ts.save(f) print('TorchScript export success, saved as %s' % f) @@ -61,7 +61,8 @@ import coremltools as ct print('\nStarting CoreML export with coremltools %s...' % ct.__version__) - model = ct.convert(ts, inputs=[ct.ImageType(name='images', shape=img.shape)]) # convert + # convert model from torchscript and apply pixel scaling as per detect.py + model = ct.convert(ts, inputs=[ct.ImageType(name='images', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) f = opt.weights.replace('.pt', '.mlmodel') # filename model.save(f) print('CoreML export success, saved as %s' % f) diff --git a/models/yolo.py b/models/yolo.py index 3fd87a336c68..da96a3102084 100644 --- a/models/yolo.py +++ b/models/yolo.py @@ -1,4 +1,5 @@ import argparse +from copy import deepcopy from models.experimental import * @@ -43,20 +44,21 @@ def _make_grid(nx=20, ny=20): class Model(nn.Module): - def __init__(self, model_cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes + def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes super(Model, self).__init__() - if type(model_cfg) is dict: - self.md = model_cfg # model dict + if isinstance(cfg, dict): + self.yaml = cfg # model dict else: # is *.yaml import yaml # for torch hub - with open(model_cfg) as f: - self.md = yaml.load(f, Loader=yaml.FullLoader) # model dict + self.yaml_file = Path(cfg).name + with open(cfg) as f: + self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict # Define model - if nc and nc != self.md['nc']: - print('Overriding %s nc=%g with nc=%g' % (model_cfg, self.md['nc'], nc)) - self.md['nc'] = nc # override yaml value - self.model, self.save = parse_model(self.md, ch=[ch]) # model, savelist, ch_out + if nc and nc != self.yaml['nc']: + print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc)) + self.yaml['nc'] = nc # override yaml value + self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist, ch_out # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) # Build strides, anchors @@ -72,8 +74,7 @@ def __init__(self, model_cfg='yolov5s.yaml', ch=3, nc=None): # model, input cha # Init weights, biases torch_utils.initialize_weights(self) - self._initialize_biases() # only run once - torch_utils.model_info(self) + self.info() print('') def forward(self, x, augment=False, profile=False): @@ -148,17 +149,21 @@ def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers m.conv = torch_utils.fuse_conv_and_bn(m.conv, m.bn) # update conv m.bn = None # remove batchnorm m.forward = m.fuseforward # update forward - torch_utils.model_info(self) + self.info() return self -def parse_model(md, ch): # model_dict, input_channels(3) + def info(self): # print model information + torch_utils.model_info(self) + + +def parse_model(d, ch): # model_dict, input_channels(3) print('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) - anchors, nc, gd, gw = md['anchors'], md['nc'], md['depth_multiple'], md['width_multiple'] + anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] na = (len(anchors[0]) // 2) # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out - for i, (f, n, m, args) in enumerate(md['backbone'] + md['head']): # from, number, module, args + for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): try: diff --git a/requirements.txt b/requirements.txt index 1100495b9c0d..bca726aa33f0 100755 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ Cython numpy==1.17 opencv-python -torch>=1.4 +torch>=1.5.1 matplotlib pillow tensorboard diff --git a/test.py b/test.py index 1cfae9591287..faad3477fd77 100644 --- a/test.py +++ b/test.py @@ -1,9 +1,8 @@ import argparse import json -from utils import google_utils +from models.experimental import * from utils.datasets import * -from utils.utils import * def test(data, @@ -18,34 +17,33 @@ def test(data, verbose=False, model=None, dataloader=None, + save_dir='', merge=False): # Initialize/load model and set device - if model is None: - training = False - merge = opt.merge # use Merge NMS + training = model is not None + if training: # called by train.py + device = next(model.parameters()).device # get model device + + else: # called directly device = torch_utils.select_device(opt.device, batch_size=batch_size) + merge = opt.merge # use Merge NMS # Remove previous - for f in glob.glob('test_batch*.jpg'): + for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')): os.remove(f) # Load model - google_utils.attempt_download(weights) - model = torch.load(weights, map_location=device)['model'].float().fuse().to(device) # load to FP32 + model = attempt_load(weights, map_location=device) # load FP32 model imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99 # if device.type != 'cpu' and torch.cuda.device_count() > 1: # model = nn.DataParallel(model) - else: # called by train.py - training = True - device = next(model.parameters()).device # get model device - # Half - half = device.type != 'cpu' and torch.cuda.device_count() == 1 # half precision only supported on single-GPU + half = device.type != 'cpu' # half precision only supported on CUDA if half: - model.half() # to FP16 + model.half() # Configure model.eval() @@ -56,11 +54,11 @@ def test(data, niou = iouv.numel() # Dataloader - if dataloader is None: # not training + if not training: img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images - dataloader = create_dataloader(path, imgsz, batch_size, int(max(model.stride)), opt, + dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, hyp=None, augment=False, cache=False, pad=0.5, rect=True)[0] seen = 0 @@ -71,7 +69,7 @@ def test(data, loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class = [], [], [], [] for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): - img = img.to(device) + 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) @@ -160,10 +158,10 @@ def test(data, # Plot images if batch_i < 1: - f = 'test_batch%g_gt.jpg' % batch_i # filename - plot_images(img, targets, paths, f, names) # ground truth - f = 'test_batch%g_pred.jpg' % batch_i - plot_images(img, output_to_target(output, width, height), paths, f, names) # predictions + f = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i) # filename + plot_images(img, targets, paths, str(f), names) # ground truth + f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i) + plot_images(img, output_to_target(output, width, height), paths, str(f), names) # predictions # Compute statistics stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy @@ -193,7 +191,7 @@ def test(data, if save_json and map50 and len(jdict): imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files] f = 'detections_val2017_%s_results.json' % \ - (weights.split(os.sep)[-1].replace('.pt', '') if weights else '') # filename + (weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '') # filename print('\nCOCO mAP with pycocotools... saving %s...' % f) with open(f, 'w') as file: json.dump(jdict, file) @@ -226,7 +224,7 @@ def test(data, if __name__ == '__main__': parser = argparse.ArgumentParser(prog='test.py') - parser.add_argument('--weights', type=str, default='weights/yolov5s.pt', help='model.pt path') + parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path') parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch') parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') diff --git a/train.py b/train.py index 29399e275fd8..bc0f53ac9205 100644 --- a/train.py +++ b/train.py @@ -22,15 +22,10 @@ print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex') mixed_precision = False # not installed -wdir = 'weights' + os.sep # weights dir -os.makedirs(wdir, exist_ok=True) -last = wdir + 'last.pt' -best = wdir + 'best.pt' -results_file = 'results.txt' - # Hyperparameters -hyp = {'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3) - 'momentum': 0.937, # SGD momentum +hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD + 'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3) + 'momentum': 0.937, # SGD momentum/Adam beta1 'weight_decay': 5e-4, # optimizer weight decay 'giou': 0.05, # giou loss gain 'cls': 0.58, # cls loss gain @@ -47,21 +42,24 @@ 'translate': 0.0, # image translation (+/- fraction) 'scale': 0.5, # image scale (+/- gain) 'shear': 0.0} # image shear (+/- deg) -print(hyp) -# Overwrite hyp with hyp*.txt (optional) -f = glob.glob('hyp*.txt') -if f: - print('Using %s' % f[0]) - for k, v in zip(hyp.keys(), np.loadtxt(f[0])): - hyp[k] = v -# Print focal loss if gamma > 0 -if hyp['fl_gamma']: - print('Using FocalLoss(gamma=%g)' % hyp['fl_gamma']) +def train(hyp, tb_writer, opt, device): + print(f'Hyperparameters {hyp}') + log_dir = tb_writer.log_dir if tb_writer else 'runs/evolution' # run directory + wdir = str(Path(log_dir) / 'weights') + os.sep # weights directory + + os.makedirs(wdir, exist_ok=True) + last = wdir + 'last.pt' + best = wdir + 'best.pt' + results_file = log_dir + os.sep + 'results.txt' + # Save run settings + with open(Path(log_dir) / 'hyp.yaml', 'w') as f: + yaml.dump(hyp, f, sort_keys=False) + with open(Path(log_dir) / 'opt.yaml', 'w') as f: + yaml.dump(vars(opt), f, sort_keys=False) -def train(hyp, tb_writer, opt, device): epochs = opt.epochs # 300 batch_size = opt.batch_size # batch size per process. total_batch_size = opt.total_batch_size @@ -77,7 +75,8 @@ def train(hyp, tb_writer, opt, device): data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict train_path = data_dict['train'] test_path = data_dict['val'] - nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes + nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names']) # number classes, names + assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check # Remove previous results if local_rank in [-1, 0]: @@ -85,7 +84,7 @@ def train(hyp, tb_writer, opt, device): os.remove(f) # Create model - model = Model(opt.cfg, nc=data_dict['nc']).to(device) + model = Model(opt.cfg, nc=nc).to(device) # Image sizes gs = int(max(model.stride)) # grid size (max stride) @@ -112,8 +111,11 @@ def train(hyp, tb_writer, opt, device): else: pg0.append(v) # all else - optimizer = optim.Adam(pg0, lr=hyp['lr0']) if opt.adam else \ - optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) + if hyp['optimizer'] == 'adam': # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR + optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum + else: + optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) + optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay optimizer.add_param_group({'params': pg2}) # add pg2 (biases) print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) @@ -164,7 +166,6 @@ def train(hyp, tb_writer, opt, device): # Scheduler https://arxiv.org/pdf/1812.01187.pdf lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.9 + 0.1 # cosine scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) - scheduler.last_epoch = start_epoch - 1 # do not move # https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822 # plot_lr_scheduler(optimizer, scheduler, epochs) @@ -188,6 +189,7 @@ def train(hyp, tb_writer, opt, device): dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, local_rank=opt.local_rank) mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class + nb = len(dataloader) # number of batches assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Correct your labels or your model.' % (mlc, nc, opt.cfg) # Testloader @@ -202,11 +204,10 @@ def train(hyp, tb_writer, opt, device): model.hyp = hyp # attach hyperparameters to model model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou) model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights - model.names = data_dict['names'] + model.names = names # Class frequency - # TODO: - if 0: #tb_writer: + if tb_writer: labels = np.concatenate(dataset.labels, 0) c = torch.tensor(labels[:, 0]) # classes # cf = torch.bincount(c.long(), minlength=nc) + 1. @@ -221,10 +222,10 @@ def train(hyp, tb_writer, opt, device): # Start training t0 = time.time() - nb = len(dataloader) # number of batches - n_burn = max(3 * nb, 1e3) # burn-in iterations, max(3 epochs, 1k iterations) + nw = max(3 * nb, 1e3) # number of warmup iterations, max(3 epochs, 1k iterations) maps = np.zeros(nc) # mAP per class results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification' + scheduler.last_epoch = start_epoch - 1 # do not move if opt.local_rank in [0, -1]: print('Image sizes %g train, %g test' % (imgsz, imgsz_test)) print('Using %g dataloader workers' % dataloader.num_workers) @@ -265,11 +266,11 @@ def train(hyp, tb_writer, opt, device): optimizer.zero_grad() for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- ni = i + nb * epoch # number integrated batches (since train start) - imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0 + imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0 - # Burn-in - if ni <= n_burn: - xi = [0, n_burn] # x interp + # Warmup + if ni <= nw: + xi = [0, nw] # x interp # model.gr = np.interp(ni, xi, [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou) accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round()) for j, x in enumerate(optimizer.param_groups): @@ -314,19 +315,20 @@ def train(hyp, tb_writer, opt, device): # Print if opt.local_rank in [-1, 0]: - # TODO: all_reduce mloss if in DDP mode. mloss = (mloss * i + loss_items) / (i + 1) # update mean losses mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0) # (GB) s = ('%10s' * 2 + '%10.4g' * 6) % ( '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) pbar.set_description(s) + # Plot if ni < 3: - f = 'train_batch%g.jpg' % ni # filename + f = str(Path(log_dir) / ('train_batch%g.jpg' % ni)) # filename result = plot_images(images=imgs, targets=targets, paths=paths, fname=f) if tb_writer and result is not None: tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) # tb_writer.add_graph(model, imgs) # add model to tensorboard + # end batch ------------------------------------------------------------------------------------------------ # Scheduler @@ -336,7 +338,7 @@ def train(hyp, tb_writer, opt, device): if opt.local_rank in [-1, 0]: # mAP if ema is not None: - ema.update_attr(model) + ema.update_attr(model, include=['md', 'nc', 'hyp', 'gr', 'names', 'stride']) final_epoch = epoch + 1 == epochs if not opt.notest or final_epoch: # Calculate mAP results, maps, times = test.test(opt.data, @@ -345,7 +347,8 @@ def train(hyp, tb_writer, opt, device): save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'), model=ema.ema.module if hasattr(ema.ema, 'module') else ema.ema, single_cls=opt.single_cls, - dataloader=testloader) + dataloader=testloader, + save_dir=log_dir) # Explicitly keep the shape. # Write with open(results_file, 'a') as f: @@ -398,6 +401,7 @@ def train(hyp, tb_writer, opt, device): if not opt.evolve: plot_results() # save as results.png print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) + dist.destroy_process_group() if device.type != 'cpu' and torch.cuda.device_count() > 1 else None torch.cuda.empty_cache() return results @@ -405,13 +409,15 @@ def train(hyp, tb_writer, opt, device): if __name__ == '__main__': parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path') + parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') + parser.add_argument('--hyp', type=str, default='', help='hyp.yaml path (optional)') parser.add_argument('--epochs', type=int, default=300) parser.add_argument('--batch-size', type=int, default=16, help="batch size for all gpus.") - parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='*.cfg path') - parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path') parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='train,test sizes') parser.add_argument('--rect', action='store_true', help='rectangular training') - parser.add_argument('--resume', action='store_true', help='resume training from last.pt') + parser.add_argument('--resume', nargs='?', const='get_last', default=False, + help='resume from given path/to/last.pt, or most recent run if blank.') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--notest', action='store_true', help='only test final epoch') parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') @@ -421,17 +427,24 @@ def train(hyp, tb_writer, opt, device): parser.add_argument('--weights', type=str, default='', help='initial weights path') parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--adam', action='store_true', help='use adam optimizer') - parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') # Parameter For DDP. parser.add_argument('--local_rank', type=int, default=-1, help="Extra parameter for DDP implementation. Don't use it manually.") opt = parser.parse_args() + + last = get_latest_run() if opt.resume == 'get_last' else opt.resume # resume from most recent run + if last and not opt.weights: + print(f'Resuming training from {last}') opt.weights = last if opt.resume and not opt.weights else opt.weights with torch_distributed_zero_first(opt.local_rank): check_git_status() opt.cfg = check_file(opt.cfg) # check file opt.data = check_file(opt.data) # check file + if opt.hyp: # update hyps + opt.hyp = check_file(opt.hyp) # check file + with open(opt.hyp) as f: + hyp.update(yaml.load(f, Loader=yaml.FullLoader)) # update hyps opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size) opt.total_batch_size = opt.batch_size @@ -452,7 +465,7 @@ def train(hyp, tb_writer, opt, device): if not opt.evolve: if opt.local_rank in [-1, 0]: print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/') - tb_writer = SummaryWriter(comment=opt.name) + tb_writer = SummaryWriter(log_dir=increment_dir('runs/exp', opt.name)) else: tb_writer = None train(hyp, tb_writer, opt, device) diff --git a/tutorial.ipynb b/tutorial.ipynb index 6c776431ac24..d3418ccf85f2 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -6,6 +6,7 @@ "name": "YOLOv5 Tutorial", "provenance": [], "collapsed_sections": [], + "toc_visible": true, "include_colab_link": true }, "kernelspec": { @@ -34,7 +35,8 @@ "source": [ "\n", "\n", - "This notebook was developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com." + "This notebook was written by Ultralytics LLC, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n", + "For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com." ] }, { @@ -44,7 +46,7 @@ "colab_type": "text" }, "source": [ - "#Initial Setup\n", + "# Setup\n", "\n", "Clone repo, install dependencies, `%cd` into `./yolov5` folder and check GPU." ] @@ -54,11 +56,15 @@ "metadata": { "id": "wbvMlHd_QwMG", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "outputId": "669566b2-391f-4596-f290-110e2e177946" }, "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone repo\n", - "!pip install -r yolov5/requirements.txt # install dependencies\n", + "!pip install -qr yolov5/requirements.txt # install dependencies (ignore errors)\n", "%cd yolov5\n", "\n", "import torch\n", @@ -68,8 +74,16 @@ "clear_output()\n", "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))" ], - "execution_count": 0, - "outputs": [] + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Setup complete. Using torch 1.5.1+cu101 _CudaDeviceProperties(name='Tesla T4', major=7, minor=5, total_memory=15079MB, multi_processor_count=40)\n" + ], + "name": "stdout" + } + ] }, { "cell_type": "markdown", @@ -78,9 +92,9 @@ "colab_type": "text" }, "source": [ - "#1. Inference\n", + "# 1. Inference\n", "\n", - "Run inference with a pretrained checkpoint on contents of `/inference/images` folder. Models are downloaded automatically from our Google Drive [folder](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) if available." + "Run inference with a pretrained checkpoint on contents of `/inference/images` folder. Models are auto-downloaded from [Google Drive](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)." ] }, { @@ -88,17 +102,17 @@ "metadata": { "id": "zR9ZbuQCH7FX", "colab_type": "code", - "outputId": "528fcc04-2393-437a-84d2-092becbaefbe", "colab": { "base_uri": "https://localhost:8080/", "height": 488 - } + }, + "outputId": "528fcc04-2393-437a-84d2-092becbaefbe" }, "source": [ - "!python detect.py --weights yolov5s.pt --img 416 --conf 0.4 --source ./inference/images/\n", + "!python detect.py --weights yolov5s.pt --img 416 --conf 0.4 --source inference/images/\n", "Image(filename='inference/output/zidane.jpg', width=600)" ], - "execution_count": 14, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -150,14 +164,14 @@ }, "source": [ "# Example syntax (do not run cell)\n", - "!python detect.py --source ./file.jpg # image \n", - " ./file.mp4 # video\n", - " ./dir # directory\n", + "!python detect.py --source file.jpg # image \n", + " file.mp4 # video\n", + " dir/ # directory\n", " 0 # webcam\n", " 'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa' # rtsp\n", " 'http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8' # http" ], - "execution_count": 0, + "execution_count": null, "outputs": [] }, { @@ -167,8 +181,8 @@ "colab_type": "text" }, "source": [ - "#2. Test\n", - "Test a model on COCO val or test-dev dataset to determine trained accuracy. Models are downloaded automatically from our Google Drive [folder](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) if available. To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be 1-2% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation." + "# 2. Test\n", + "Test a model on COCO val or test-dev dataset to determine trained accuracy. Models are auto-downloaded from [Google Drive](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be 1-2% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation." ] }, { @@ -178,7 +192,7 @@ "colab_type": "text" }, "source": [ - "###2.1 val2017\n", + "### 2.1 val2017\n", "Download COCO val 2017 dataset, 1GB, 5000 images, and test model accuracy." ] }, @@ -191,80 +205,19 @@ "base_uri": "https://localhost:8080/", "height": 33 }, - "outputId": "e49d6899-0feb-4bc7-e384-513c7d384060" + "outputId": "df037a5d-efae-4687-9ff7-22a48fd7f801" }, "source": [ "# Download COCO val2017\n", "gdrive_download('1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43','coco2017val.zip') # val2017 dataset\n", "!mv ./coco ../ # move folder alongside /yolov5" ], - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Downloading https://drive.google.com/uc?export=download&id=1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43 as coco2017val.zip... unzipping... Done (37.1s)\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "WBWS_QSc__se", - "colab_type": "code", - "outputId": "02b54431-b993-4acd-c8a3-9c3231662718", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 602 - } - }, - "source": [ - "# Run YOLOv5s on COCO val2017\n", - "!python test.py --weights yolov5s.pt --data ./data/coco.yaml --img 640" - ], - "execution_count": 8, + "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ - "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='././data/coco.yaml', device='', img_size=640, iou_thres=0.65, save_json=True, single_cls=False, task='val', verbose=False, weights='yolov5s.pt')\n", - "Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)\n", - "\n", - "Model Summary: 165 layers, 7.07417e+06 parameters, 7.07417e+06 gradients\n", - "Caching labels ../coco/val2017.txt (4952 found, 48 missing, 0 empty, 0 duplicate, for 5000 images): 100% 5000/5000 [00:00<00:00, 7822.09it/s]\n", - "Saving labels to ../coco/labels/val2017.npy for faster future loading\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:33<00:00, 1.67it/s]\n", - " all 5e+03 3.63e+04 0.334 0.633 0.534 0.336\n", - "Speed: 6.0/2.9/8.9 ms inference/NMS/total per 640x640 image at batch-size 32\n", - "\n", - "COCO mAP with pycocotools... saving detections_val2017_yolov5s_results.json...\n", - "loading annotations into memory...\n", - "Done (t=0.41s)\n", - "creating index...\n", - "index created!\n", - "Loading and preparing results...\n", - "DONE (t=5.70s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=89.04s).\n", - "Accumulating evaluation results...\n", - "DONE (t=14.04s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.544\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.378\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.188\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.397\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.459\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.296\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.496\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.557\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.358\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.618\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.700\n" + "Downloading https://drive.google.com/uc?export=download&id=1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43 as coco2017val.zip... unzipping... Done (11.2s)\n" ], "name": "stdout" } @@ -275,56 +228,56 @@ "metadata": { "id": "X58w8JLpMnjH", "colab_type": "code", - "outputId": "1cc32887-1fe3-4079-f66e-b6b756f96527", "colab": { "base_uri": "https://localhost:8080/", - "height": 586 - } + "height": 606 + }, + "outputId": "8c62a086-e312-46d1-b475-d90542eae545" }, "source": [ "# Run YOLOv5x on COCO val2017\n", - "!python test.py --weights yolov5x.pt --data ./data/coco.yaml --img 640" + "!python test.py --weights yolov5x.pt --data coco.yaml --img 672" ], - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ - "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='././data/coco.yaml', device='', img_size=640, iou_thres=0.65, save_json=True, single_cls=False, task='val', verbose=False, weights='yolov5x.pt')\n", + "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', img_size=672, iou_thres=0.65, merge=False, save_json=True, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n", "Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)\n", "\n", - "Model Summary: 381 layers, 9.59219e+07 parameters, 9.59219e+07 gradients\n", - "Caching labels ../coco/labels/val2017.npy (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 100% 5000/5000 [00:00<00:00, 16516.13it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [05:05<00:00, 1.95s/it]\n", - " all 5e+03 3.63e+04 0.393 0.742 0.652 0.455\n", - "Speed: 51.1/2.2/53.3 ms inference/NMS/total per 640x640 image at batch-size 32\n", + "Fusing layers... Model Summary: 284 layers, 8.89222e+07 parameters, 8.89222e+07 gradients\n", + "Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 100% 5000/5000 [00:00<00:00, 22899.17it/s]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [02:38<00:00, 1.01s/it]\n", + " all 5e+03 3.63e+04 0.426 0.746 0.66 0.469\n", + "Speed: 22.3/1.7/24.0 ms inference/NMS/total per 672x672 image at batch-size 32\n", "\n", - "COCO mAP with pycocotools... saving detections_val2017_yolov5x_results.json...\n", + "COCO mAP with pycocotools... saving detections_val2017__results.json...\n", "loading annotations into memory...\n", - "Done (t=0.39s)\n", + "Done (t=0.41s)\n", "creating index...\n", "index created!\n", "Loading and preparing results...\n", - "DONE (t=3.59s)\n", + "DONE (t=4.39s)\n", "creating index...\n", "index created!\n", "Running per image evaluation...\n", "Evaluate annotation type *bbox*\n", - "DONE (t=79.47s).\n", + "DONE (t=76.56s).\n", "Accumulating evaluation results...\n", - "DONE (t=12.20s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.470\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.659\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.515\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.310\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.516\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.610\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.362\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.597\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.656\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.504\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.704\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.787\n" + "DONE (t=11.02s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.484\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.668\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.528\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.311\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.534\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.628\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.371\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.609\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.662\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.501\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.714\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.807\n" ], "name": "stdout" } @@ -337,7 +290,7 @@ "colab_type": "text" }, "source": [ - "###2.2 test-dev2017\n", + "### 2.2 test-dev2017\n", "Download COCO test2017 dataset, 7GB, 40,000 images, to test model accuracy on test-dev set, 20,000 images. Results are saved to a `*.json` file which can be submitted to the evaluation server at https://competitions.codalab.org/competitions/20794." ] }, @@ -354,7 +307,7 @@ "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 7GB, 41k images\n", "!mv ./test2017 ./coco/images && mv ./coco ../ # move images into /coco and move /coco alongside /yolov5" ], - "execution_count": 0, + "execution_count": null, "outputs": [] }, { @@ -368,7 +321,7 @@ "# Run YOLOv5s on COCO test-dev2017 with argument --task test\n", "!python test.py --weights yolov5s.pt --data ./data/coco.yaml --task test" ], - "execution_count": 0, + "execution_count": null, "outputs": [] }, { @@ -380,7 +333,7 @@ "source": [ "# 3. Train\n", "\n", - "Download the 128-image tutorial training dataset `./data/coco128.yaml`, start tensorboard and train a `yolov5s.yaml` model for **5 epochs**. Note that actual training is typically much longer, around **300-1000 epochs**, depending on your dataset." + "Download https://www.kaggle.com/ultralytics/coco128, a small 128-image tutorial dataset, start tensorboard and train YOLOv5s from a pretrained checkpoint for 3 epochs (actual training is much longer, around **300-1000 epochs**, depending on your dataset)." ] }, { @@ -388,28 +341,40 @@ "metadata": { "id": "Knxi2ncxWffW", "colab_type": "code", - "outputId": "cbb574a9-861d-4f47-ca0c-32166bd3361e", "colab": { "base_uri": "https://localhost:8080/", "height": 33 - } + }, + "outputId": "35815e93-7d6e-4fee-c050-a4a565d51648" }, "source": [ - "# Download tutorial dataset coco128.yaml\n", - "gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip') # tutorial dataset\n", + "# Download coco128\n", + "gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip') # coco128 dataset\n", "!mv ./coco128 ../ # move folder alongside /yolov5" ], - "execution_count": 2, + "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ - "Downloading https://drive.google.com/uc?export=download&id=1n_oKgR81BJtqk75b00eAjdv03qVCQn2f as coco128.zip... unzipping... Done (5.8s)\n" + "Downloading https://drive.google.com/uc?export=download&id=1n_oKgR81BJtqk75b00eAjdv03qVCQn2f as coco128.zip... unzipping... Done (5.3s)\n" ], "name": "stdout" } ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "_pOkGLv1dMqh", + "colab_type": "text" + }, + "source": [ + "Train a YOLOv5s model on coco128 by specifying model config file `--cfg models/yolo5s.yaml`, and dataset config file `--data data/coco128.yaml`. Start training from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights ''`. Pretrained weights are auto-downloaded from [Google Drive](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J).\n", + "\n", + "**All training results are saved to `runs/exp0`** for the first experiment, then `runs/exp1`, `runs/exp2` etc. for subsequent experiments.\n" + ] + }, { "cell_type": "code", "metadata": { @@ -418,115 +383,98 @@ "colab": {} }, "source": [ - "# Start tensorboard\n", + "# Start tensorboard (optional)\n", "%load_ext tensorboard\n", "%tensorboard --logdir runs" ], - "execution_count": 0, + "execution_count": null, "outputs": [] }, - { - "cell_type": "markdown", - "metadata": { - "id": "_pOkGLv1dMqh", - "colab_type": "text" - }, - "source": [ - "Train a YOLOv5s model on the coco128 dataset by specifying model configuration file `--cfg ./models/yolo5s.yaml`, and a dataset configuration file `--data ./data/coco128.yaml`. Start training from pretrained `--weights yolov5s.pt`, or from scratch (randomly initialized weights) using `--weights ''`. Pretrained checkpoints are downloaded automatically from our Google Drive [folder](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) if available.\n" - ] - }, { "cell_type": "code", "metadata": { "id": "1NcFxRcFdJ_O", "colab_type": "code", - "outputId": "836944be-b677-4614-889b-da120cda0304", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 - } + }, + "outputId": "121b5b2e-bc8e-4648-ee1c-8d2795176db6" }, "source": [ - "# Train YOLOv5s on coco128 for 5 epochs\n", - "!python train.py --img 640 --batch 16 --epochs 5 --data ./data/coco128.yaml --cfg ./models/yolov5s.yaml --weights yolov5s.pt --name tutorial --nosave --cache" + "# Train YOLOv5s on coco128 for 3 epochs\n", + "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --cfg yolov5s.yaml --weights yolov5s.pt --nosave --cache" ], - "execution_count": 5, + "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ - "Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex\n", - "{'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.58, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.014, 'hsv_s': 0.68, 'hsv_v': 0.36, 'degrees': 0.0, 'translate': 0.0, 'scale': 0.5, 'shear': 0.0}\n", - "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='././models/yolov5s.yaml', data='././data/coco128.yaml', device='', epochs=5, evolve=False, img_size=[640], multi_scale=False, name='tutorial', nosave=True, notest=False, rect=False, resume=False, single_cls=False, weights='yolov5s.pt')\n", + "Namespace(batch_size=16, bucket='', cache_images=True, cfg='./models/yolov5s.yaml', data='./data/coco128.yaml', device='', epochs=3, evolve=False, hyp='', img_size=[640], multi_scale=False, name='', noautoanchor=False, nosave=True, notest=False, rect=False, resume=False, single_cls=False, weights='yolov5s.pt')\n", "Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)\n", "\n", - "2020-06-14 19:45:19.668043: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n", + "2020-07-11 20:37:09.422496: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n", "Start Tensorboard with \"tensorboard --logdir=runs\", view at http://localhost:6006/\n", + "Hyperparameters {'optimizer': 'SGD', 'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.58, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.014, 'hsv_s': 0.68, 'hsv_v': 0.36, 'degrees': 0.0, 'translate': 0.0, 'scale': 0.5, 'shear': 0.0}\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 3520 models.common.Focus [3, 32, 3] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1] \n", + " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", + " 4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3] \n", + " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", + " 6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3] \n", + " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", + " 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n", + " 9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n", + " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 models.common.Concat [1] \n", + " 13 -1 1 378624 models.common.BottleneckCSP [512, 256, 1, False] \n", + " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 16 [-1, 4] 1 0 models.common.Concat [1] \n", + " 17 -1 1 95104 models.common.BottleneckCSP [256, 128, 1, False] \n", + " 18 -1 1 32895 torch.nn.modules.conv.Conv2d [128, 255, 1, 1] \n", + " 19 -2 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 20 [-1, 14] 1 0 models.common.Concat [1] \n", + " 21 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False] \n", + " 22 -1 1 65535 torch.nn.modules.conv.Conv2d [256, 255, 1, 1] \n", + " 23 -2 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 24 [-1, 10] 1 0 models.common.Concat [1] \n", + " 25 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n", + " 26 -1 1 130815 torch.nn.modules.conv.Conv2d [512, 255, 1, 1] \n", + " 27 [-1, 22, 18] 1 0 models.yolo.Detect [80, [[116, 90, 156, 198, 373, 326], [30, 61, 62, 45, 59, 119], [10, 13, 16, 30, 33, 23]]]\n", + "Model Summary: 191 layers, 7.46816e+06 parameters, 7.46816e+06 gradients\n", "\n", - " from n params module arguments \n", - " 0 -1 1 3520 models.common.Focus [3, 32, 3] \n", - " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", - " 2 -1 1 20672 models.common.Bottleneck [64, 64] \n", - " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", - " 4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3] \n", - " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", - " 6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3] \n", - " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", - " 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n", - " 9 -1 1 1905152 models.common.BottleneckCSP [512, 512, 2] \n", - " 10 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n", - " 11 -1 1 130815 torch.nn.modules.conv.Conv2d [512, 255, 1, 1] \n", - " 12 -2 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", - " 13 [-1, 6] 1 0 models.common.Concat [1] \n", - " 14 -1 1 197120 models.common.Conv [768, 256, 1, 1] \n", - " 15 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False] \n", - " 16 -1 1 65535 torch.nn.modules.conv.Conv2d [256, 255, 1, 1] \n", - " 17 -2 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", - " 18 [-1, 4] 1 0 models.common.Concat [1] \n", - " 19 -1 1 49408 models.common.Conv [384, 128, 1, 1] \n", - " 20 -1 1 78720 models.common.BottleneckCSP [128, 128, 1, False] \n", - " 21 -1 1 32895 torch.nn.modules.conv.Conv2d [128, 255, 1, 1] \n", - " 22 [-1, 16, 11] 1 0 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]]]\n", - "Model Summary: 165 layers, 7.07417e+06 parameters, 7.07417e+06 gradients\n", + "Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n", + "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 20484.22it/s]\n", + "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 156.07it/s]\n", + "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 22082.55it/s]\n", + "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 152.91it/s]\n", "\n", - "Optimizer groups: 54 .bias, 60 conv.weight, 51 other\n", - "Reading image shapes: 100% 128/128 [00:00<00:00, 8158.02it/s]\n", - "Caching labels ../coco128/labels/train2017 (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 7608.18it/s]\n", - "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 149.70it/s]\n", - "Caching labels ../coco128/labels/train2017 (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 8165.09it/s]\n", - "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 145.34it/s]\n", - "Label width-height: n mean min max matching recall\n", - " 929 105.8 1.23 640 0.4736 0.9903\n", + "Analyzing anchors... Best Possible Recall (BPR) = 0.9935\n", "Image sizes 640 train, 640 test\n", "Using 2 dataloader workers\n", - "Starting training for 5 epochs...\n", + "Starting training for 3 epochs...\n", "\n", " Epoch gpu_mem GIoU obj cls total targets img_size\n", - " 0/4 7.3G 0.04362 0.07954 0.02032 0.1435 208 640: 100% 8/8 [00:09<00:00, 1.24s/it]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:07<00:00, 1.03it/s]\n", - " all 128 929 0.421 0.704 0.66 0.42\n", + " 0/2 6.84G 0.04376 0.06831 0.02 0.1321 225 640: 100% 8/8 [00:09<00:00, 1.22s/it]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:09<00:00, 1.24s/it]\n", + " all 128 929 0.34 0.762 0.69 0.446\n", "\n", " Epoch gpu_mem GIoU obj cls total targets img_size\n", - " 1/4 13.9G 0.04609 0.07819 0.01886 0.1431 167 640: 100% 8/8 [00:03<00:00, 2.34it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 3.34it/s]\n", - " all 128 929 0.418 0.713 0.659 0.421\n", + " 1/2 6.06G 0.04333 0.08225 0.02207 0.1476 182 640: 100% 8/8 [00:03<00:00, 2.17it/s]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 3.28it/s]\n", + " all 128 929 0.342 0.755 0.687 0.447\n", "\n", " Epoch gpu_mem GIoU obj cls total targets img_size\n", - " 2/4 13.9G 0.04508 0.07012 0.01977 0.135 198 640: 100% 8/8 [00:03<00:00, 2.33it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 3.50it/s]\n", - " all 128 929 0.409 0.71 0.664 0.421\n", - "\n", - " Epoch gpu_mem GIoU obj cls total targets img_size\n", - " 3/4 13.9G 0.04577 0.08308 0.02038 0.1492 222 640: 100% 8/8 [00:03<00:00, 2.34it/s]\n", - " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 3.50it/s]\n", - " all 128 929 0.429 0.71 0.67 0.426\n", - "\n", - " Epoch gpu_mem GIoU obj cls total targets img_size\n", - " 4/4 13.9G 0.04608 0.07352 0.01855 0.1382 149 640: 100% 8/8 [00:03<00:00, 2.32it/s]\n", + " 2/2 6.06G 0.0444 0.07251 0.01855 0.1355 216 640: 100% 8/8 [00:03<00:00, 2.15it/s]\n", " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 3.46it/s]\n", - " all 128 929 0.423 0.716 0.663 0.422\n", - "Optimizer stripped from weights/last_tutorial.pt\n", - "5 epochs completed in 0.012 hours.\n", + " all 128 929 0.354 0.759 0.689 0.45\n", + "Optimizer stripped from runs/exp0/weights/last.pt, 15.2MB\n", + "3 epochs completed in 0.009 hours.\n", "\n" ], "name": "stdout" @@ -540,9 +488,9 @@ "colab_type": "text" }, "source": [ - "#4. Visualize\n", + "# 4. Visualize\n", "\n", - "After training starts, view `train*.jpg` images to see training images, labels and augmentation effects. Note a mosaic dataloader is used for training (shown below), a new dataloading concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)." + "View `runs/exp0/train*.jpg` images to see training images, labels and augmentation effects. A **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)." ] }, { @@ -550,16 +498,16 @@ "metadata": { "id": "W40tI99_7BcH", "colab_type": "code", - "outputId": "1c838e44-79fe-433f-a334-59a037ee322e", "colab": { "base_uri": "https://localhost:8080/", "height": 917 - } + }, + "outputId": "1c838e44-79fe-433f-a334-59a037ee322e" }, "source": [ - "Image(filename='./train_batch1.jpg', width=900) # view augmented training mosaics" + "Image(filename='runs/exp0/train_batch1.jpg', width=900) # view augmented training mosaics" ], - "execution_count": 0, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -586,7 +534,7 @@ "colab_type": "text" }, "source": [ - "View `test_batch0_gt.jpg` to see test batch 0 ground truth labels." + "View `test_batch0_gt.jpg` to see test batch 0 *ground truth* labels." ] }, { @@ -594,16 +542,16 @@ "metadata": { "id": "PF9MLHDb7tB6", "colab_type": "code", - "outputId": "b7a874f7-dad3-4611-e777-56c724c7ee81", "colab": { "base_uri": "https://localhost:8080/", "height": 647 - } + }, + "outputId": "b7a874f7-dad3-4611-e777-56c724c7ee81" }, "source": [ - "Image(filename='./test_batch0_gt.jpg', width=900) # view test image labels" + "Image(filename='runs/exp0/test_batch0_gt.jpg', width=900) # view test image labels" ], - "execution_count": 0, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -630,7 +578,7 @@ "colab_type": "text" }, "source": [ - "View `test_batch0_pred.jpg` to see test batch 0 predictions." + "View `test_batch0_pred.jpg` to see test batch 0 *predictions*." ] }, { @@ -638,16 +586,16 @@ "metadata": { "id": "ycP4UTEZ82_I", "colab_type": "code", - "outputId": "c7c1238d-e0fa-4fc5-f393-bf5bce55d245", "colab": { "base_uri": "https://localhost:8080/", "height": 647 - } + }, + "outputId": "c7c1238d-e0fa-4fc5-f393-bf5bce55d245" }, "source": [ - "Image(filename='./test_batch0_pred.jpg', width=900) # view test image predictions" + "Image(filename='runs/exp0/test_batch0_pred.jpg', width=900) # view test image predictions" ], - "execution_count": 0, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -674,30 +622,29 @@ "colab_type": "text" }, "source": [ - "Training losses and performance metrics are saved to Tensorboard and also to a `results.txt` logfile. `results.txt` is plotted as `results.png` after training completes. Partially completed `results.txt` files can be plotted with `from utils.utils import plot_results; plot_results()`. Here we show YOLOv5s trained on coco128 to 100 epochs, starting from scratch (orange), and starting from pretrained `yolov5s.pt` weights (blue)." + "Training losses and performance metrics are saved to Tensorboard and also to a `runs/exp0/results.txt` logfile. `results.txt` is plotted as `results.png` after training completes. Partially completed `results.txt` files can be plotted with `from utils.utils import plot_results; plot_results()`. Here we show YOLOv5s trained on coco128 to 300 epochs, starting from scratch (blue), and from pretrained `yolov5s.pt` (orange)." ] }, { "cell_type": "code", "metadata": { - "id": "C60XAsyv6OPe", + "id": "MDznIqPF7nk3", "colab_type": "code", - "outputId": "70c2254e-9caf-46b0-afbe-f7fe1967b19b", "colab": { "base_uri": "https://localhost:8080/", "height": 517 - } + }, + "outputId": "c1146425-643e-49ab-de25-73216f0dde23" }, "source": [ - "from utils.utils import plot_results; plot_results() # plot results.txt as results.png\n", - "Image(filename='./results.png', width=1000) # view results.png" + "from utils.utils import plot_results; plot_results() # plot results.txt files as results.png" ], - "execution_count": 0, + "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { - "image/png": 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cF154AX/+858BaMNXx44dQ0JCwiB+EiIiIiIiInIFf3dPgIRHoepA6dUmtHb2ICzIH6njR0AaEWJ5Rw/U0aXGnZ5ek8/XNt9m+0Ei8koVFRX62ykpKWa3jYqKglQqhUKhwI0bN9DQ0IBRo0YJZszu7m5kZ2dDrVZjzpw5ePbZZ22eG3mX0qtN0GjcO4cudS/yiuXYnWX+tU5ERERERMKVk5ODDz/8EJ9//jkuXryIpKQk5OTkIDExESqVCvn5+Th58iQAQCKRYNeuXYMa75VXXtFXdBaJRFizZg0uX76My5cvm91v+vTpGDdu3KDGJiIiIiIiosFhAIv0zitasL1YjqNVygGLlvGRoZh1zwiMkQQjQOznNcEsVbv56hiK5g4XzYSIyLWqqqr0t+Pi4ixuHxcXB4VCod/XngCWs8bcvHkzKioqEBQUhLfeegsikcjmuZlTW1tr9nldW0USjtbOHndPAQBQXKnE55fqMTeRFdmIiIiIiDyRv78/Dhw4gCeffBKffPIJ6uuh/HhrAAAgAElEQVTrsWnTpgHbRUdHY9++fZgyZcqgxtOFuQBAo9Hgd7/7nVX7vfPOO8jKyhrU2ERERERERDQ4DGARAKDoQh1y88vRpTZeDUqubINc2dbnMZEISEuIxJr0eI+tEtVkIYBVywAWEXmplpYfWqONHDnS4vYjRowwuq+7xzx//jxef/11AMCGDRsQHx9v19zMkUqlDj8mOVdYkHAucX+19ww2LZqC5amx7p4KERERERHZISwsDB9//DE++ugj/O1vf0NZWRmUSiXCwsJwzz33YPHixVi5ciWGDx/u7qkSERERERGRGwlndYrc5ryixWz4yhSNBpBVKnG8qgEbFyV65MJis6UKWKrbLpoJEZFrtbX9EKoNCgqyuH1wcLD+dmtrqyDG7OnpQXZ2Nrq7u5GUlIR169bZNS/yPqnjR0Akgs1tCP39gKd+EoPYkUNxvbEd731VAzOdiq2i0QCvHLyIg+e+x6s/T/TY0DoRERERka9btGgRFi1aZPf+WVlZFqtUHTt2zO7jExERERERkXsxgEXYXiy3OXxlSK3ReOzCoqUKWNca21BwWoHWzh6vabtIROQttm7dirNnz0IsFuPtt9+Gv79zLmt0bRBNqaurw3333eeUsck+0ogQpCVEQlaptHqfkUMDsTsrpc91zOLp0cjeU2bxesEap2uakbnzFPKWJSNj6phBH4+IiIiIiIiIiIiIiIiEgwEsH6dQdeBolfWLk+acrmnG4j+XYPGMaEwYFQoACBD7CTq4pGq/Y/b5b1W3se6Dr/s8Fh8ZigVTo5A5UyrIn4mIyBqhoaFobm4GAHR2diI0NNTs9rdv/1ARMCwszO1jXr58Gb///e8BALm5uZg5c6Zdc7JGdHS0045NzrMmPR4n5Y1WhcwD/EQDwlcAkCSV4C9ZKViy80t0q20sp2VEl7oXufnlKFgV7FGBdSIiIiIiIiIiIiIiIjKPASwfV3q1yeb2POaoNUDB6doBj4tEQFpCJNakxwtqwdGeihZyZRvksmrskFUjbZLwfiYiImtIJBJ9GKqxsdFiGKqpqanPvu4cs7e3F9nZ2bhz5w5iY2OxadMmu+ZD3i1JKkHesmSLbZYDxX7IW5Zs8lyeJJVgx7J77WrXbEyXuhd5xXLszkoZ9LGIiIiIiIiIiIiIiIhIGBjA8nGtnT0uGUejAWSVShyvasDGRYlYnhrrknEtUbXZ31JIA+3PdFLeyHZCRORxEhIScO3aNQDAtWvXEBsba3Z73ba6fd05ZkVFBUpLSwEAU6ZMwR//+Eej+7e0tOhv37x5E5s3b9bfX7duHYYMGWLzz0CeJWPqGBSsCkZesRyyKmWf0LlIBDycEIlcK4LUuuNs/uQSymqaBz0vWZUStc0diA5nJU0iIiIiIiIiIiIiIiJvwACWjwsLcu1LQK3R4JWDF7Hr+FU8fu9Yt7fxa+6wP4ClY007IYWqA6VXm9Da2SPoloxE5DumTZuGoqIiAEBZWRnS0tJMbnvjxg0oFAoAQGRkJEaNGuXWMTUGKZpPP/0Un376qcWxW1pa8Oqrr+rvP//88wxg+YgkqQS7s1KgUHXgq2sqtHZ2IywoAKnjI2wKQCVJJShY/QDeLa3Bqx9dGFQFUY0G2H+6FmvnTrT/IERERERERERERERERCQYfu6eALlX6vgREIlcP66i+TbyZNV4aOtRZO8pw3lFi+WdnMCeFoTG6NoJ9Xde0YLsPWV46I2jWPfB1/j9J5ew7oOv8dAb7v25iYgyMjL0tw8dOmR2288++0x/e+HChR41JpGONCIES2ZE4+lZcVgyI9ru6lPLU2Owa/mMQc/nzaPVKLpQN+jjEBERERERERERERERkfsxgOXjpBEhSEuIdNv4ujZ+mTtPuWURUuWgABbwQzshnaILdcjceQqySuWAKhm6lozu+rmJiGbPno2oqCgAwLFjx3D27Fmj26nVauTl5envL1261O1jJicnQ6PRWPxj2MIwJiamz3MSifmWc0TmzJsShYcnDe76Sd2rQW5+OcPYRD6qsLAQmZmZiI2NRVBQECIjI/HAAw/gjTfewK1btwQ5pkajQWlpKTZv3oxHHnkEsbGxCA4ORlBQEH70ox8hIyMD27dv79MCmIiIiIiIiIiIiMhXMIBFWJMej0Cxe18KXepevPD+OZcvQqraHBfA0miA0qsqANrKV7n55ehS95rdR9e+kIuvRORqYrEYGzZs0N9fsWIFlErlgO1efvlllJeXAwBmzZqF+fPnGz3enj17IBKJIBKJMGfOHJeMSeROjrh+MlVBk4i8V1tbGxYtWoRFixbhgw8+QE1NDe7cuYOGhgacOnUK69evx9SpU1FaWiqoMa9cuYJx48bh/vvvx6uvvorPPvsMNTU16OzsxJ07d1BXV4fDhw/j17/+NSZMmIADBw44bP5EREREREREREREnsDf3RMg90uSSpC3LNmqwJAzdfdqsOx/S/Hsg3HInCmFNMK+1kDWutOjRuudHoces7WzGwCwvVhu9d+lbvF1d1aKQ+dCRGRJTk4OPvzwQ3z++ee4ePEikpKSkJOTg8TERKhUKuTn5+PkyZMAAIlEgl27dnnkmETO4Kjrp+JKJd468Q38/fwQFuSP1PEjnH4NRETuoVarkZmZiaKiIgDA6NGjB5wDS0pKoFAosHDhQpSUlGDy5MmCGFOlUqG2thYAMGTIEKSlpWHWrFkYN24chgwZgurqarz33nu4fPkympqa8Mtf/hL5+fn45S9/Oaj5ExEREREREREREXkKBrAIAJAxdQwKVgUjr1iO4sqB1UhcpaNLjTxZNXbIqpE2KRJr0uORJHVOm6iWjm6HHzMsKAAKVQeOVtn2d6hrXxgdzgVXInIdf39/HDhwAE8++SQ++eQT1NfXY9OmTQO2i46Oxr59+zBlyhSPHJPIWXTXT5s/uYSymma7j/Ofn1X2uR8fGYoFU6NcEkgnItd5++239UGoxMREyGQyjB49Wv/8c889hxdffBHbtm1Dc3MzVq5ciRMnTghmTKlUinXr1mH58uUIDw8f8PxLL72EX//613jzzTfR29uL1atXY968eWz7S0RERERERERERD6BLQhJL0kqwe6sFHyxPg256fGYODoUIjfNRQNAVqnE4j9/iXdLrztljCYHth8EAJEISB0fgdKrTdBobNvXsH0hEZErhYWF4eOPP8bBgwexePFiSKVSDBkyBCNHjsRPfvITbNmyBRcuXMADDzzg0WMSOUuSVIKC1Q9g82NTIXLQhZNc2YY8WTUe2noU2XvK2KqYyAuo1Wps3LhRf3/v3r19glA6W7ZsQXJyMgDgiy++wJEjRwQx5rRp01BdXY0XXnjBaPgK0Iasd+zYgenTpwPQVs06ePCg3fMnIiIiIiIiIiIi8iSsgEUDSCNCsHbuRKydOxEKVQe+uqZCa2c3Kr67iX+c/c6lc1FrNHjl4EXsOn4Vj9871qGVIFTtjg1gPZwQiejwELR23rBrf137QnI+haoDpVeb0NrZw3ZPRHctWrQIixYtsnv/rKwsZGVluXRMS2JjY6GxNRFLZKflqTFQ3upEnqzaYcfUBdJPyhuRtywZGVPHOOzYRORaJ06cQF1dHQBg9uzZ+pBSf2KxGLm5ucjOzgYA5OfnY968eW4fc+jQoVaNKRKJkJmZibNnzwIAvv76a7vmTkRERERERERERORpGMAis6QRIX2CKQ+MH4F1B762ucLTYCmabyNPVo08WfWg2/LowjdfftPksPkFiv2Qmx4PAAgLsu9/q7CgAIfNh4w7r2jB9mI5jlYp+7yGRSIgLcG5LS+JiMj7Zc6UYsfRaodfJ3Wpe5GbX46CVcE8TxF5qEOHDulvL1y40Oy2CxYsMLqfJ4wJAMOGDdPfvn379qCORUREREREREREROQpGMAimyyZKUVokD9eyD+HbrV7qorIlW2Qy6qxQ1aNtEl9QzPmKhuZCt8MlthPhLxlyfo5DA+2PUila19IzlN0oQ65+eXoUvcOeE6jYYURIiIaPGlECNISIiGrVDr82F3qXuQVy7E7K8XhxyYi56uoqNDfTkkx//9xVFQUpFIpFAoFbty4gYaGBowaNcojxuw/bkxMjF3HICIiIiIiIiIiIvI0DGCRzTKmjsEHq4KRVyyHzMFhJlvo2vIcq1TioYkj8V1LJ6qVbeg/nfjIUMRHhuLzyzecEhq7Z+RQ1DR14C8nr+F6Uzvy//WtzcfQtS/UMRUkY+s8+5xXtJgMXxlihREiIhqsNenx+OJKA7p7HX/NUVypxOeX6jE3Mcrhx3YWXrsQaVVVVelvx8XFWdw+Li4OCoVCv689YSh3jNnc3Ix9+/bp7z/yyCM2H4OIiIiIiIiIiIjIEzGARXZJkkqwOysFClUHvrqmQmtnN7rVGoiggb/YDxXf3cRH5d9D7YTFx/56ARy70mjyebmyDXJlm9PGv6Jsw+uHKu3e37B9obkqXSGBYnR0qfs8xtZ51tleLLcYvtJhhREiIhqMJKkEy34yDn87VeOU46969yzefPJeq6s1uisAxba/RH21tLTob48cOdLi9iNGjDC6r9DH/O1vf4vm5mYAwKOPPopp06bZfIza2lqzz9fV1dk1NyIiIiIiIiIiIiJnYgCLBkUaEWJyEe//vT8WecVyFDuhDY87+PuJoO7VDKiwNRgB4h/aF5prkQdgQPgKYOs8ayhUHThaZdtrUFalRG1zR5+qZERERNaKHTHUacdW92rwwvvn8MFq89Ua3RmAYttfooHa2n74QkhQUJDF7YODg/W3W1tbPWLMnTt34p133gEASCQSbN++3eZjAIBUKrVrPyIiIiIiIiIiIiJ38nP3BMh76apkfbE+Dbnp8RgaKHb3lOwiEgEpMeEA4NDwFQA8cM9ITPnRcKtb5Jmia513XmHfN9W9WenVJpvbZGo0QOlVlXMmREREXi8syLnfceju1WDZ/5biv45UQaHqGPB80YU6ZO48BVnlwIqaugBU5s5TKLrg+Coytrb95bULkXf49NNP8cILLwAA/Pz88M477yA2Nta9kyIiIiIiIiIiIiJyIQawyOmkESFYO3ci3s9JRYBY5O7pWGVu4mi89otE/CEzCV+sT0NYcAB6nNBO8fiVBjz0xlFk7ymzO3yl06XuxcsHvja6EOvLWjt77Nyv28EzISIiX5E6fgRETr7k6ehSI09WjYe2aq8jdEEmWwJQL7x/zuEBKHva/hL5gtDQUP3tzs5Oi9vfvn1bfzssLEzQY/7zn//EkiVL0NPTA5FIhLfeeguPPfaYbZM1oFAozP7517/+ZfexiYiIiIiIiIiIiJyFASxymSSpBDuW3YtAsfBfdrJKJcYMD8KSGdHQaGBzCztbaDRAU3uXQ451ub51wEKsr7O3CklYUICDZ0JERL5CGhGCtIRIl4ylQd+KVrYEoLp7NQ69ZhhM218ibyeR/NDys7Gx0eL2TU1NRvcV2pgymQyPPvooOjs7IRKJ8D//8z945plnbJ+sgejoaLN/xoxh61IiIiIiIiIiIiISHuEnYcirZEwdg4JV9yN9kmsWJe2l7tXo2+LY08LOnfovxNpDoepAwWkF/nLyGgpOKzy6qpY9VUhEIiB1fIRzJkRERD5hTXq8S0PnuopWRyttC0A1tXdhyc4vHdKOkG1/iUxLSEjQ37527ZrF7Q23MdxXSGPKZDL84he/0FfOevPNN7Fy5Uo7ZkpERERERERERETk+RjAIpdLkkqwOysFX6xPQ256PMZFBLt7Skbp2uLY28LO3brUvfoQmSFz4arzihZk7ynDQ28cxboPvsbvP7mEdR98rW+T6IlVteypQvJwQiSiw0OcNCMiIvIFSVIJ8pYluzSE1d2rgT2Z8W61xug1g63Y9pfItGnTpulvl5WVmd32xo0bUCgUAIDIyEiMGjVKcGPqwlcdHdrPEjt27MDq1avtmicRERERERERERGRN2AAi9xGGhGCtXMn4sT6h7H5sakQ+9lYpsgFZFVK9PRa18ZHiHQhMsByuGrnsW+QufMUZJXKAdUrNJrBV9VyJ1uqkASK/ZCbHu/kGRERkS8wrPxpazVGVzO8ZrAX2/4SmZaRkaG/fejQIbPbfvbZZ/rbCxcuFNyY/cNX27dvx/PPP2/3PImIiIiIiIiIiIi8gX2rJEQOtjw1BtPGDkdesRzFNrbOcSZtEEkEkQge1YbQkKxKifdKa7Dx40voUg8Mk+nCVTIr/t51VbUKVgUjSSpxxnSdQleF5Ln3z0Hda/ofMlDsh7xlyR71sxERkbDpKn8qVB346poKrZ3d6FZrIIIG/mI/hAUFIHV8BDZ8dNGqc7EzyaqUqG3usLsKpK7try3XTGz7S75i9uzZiIqKQn19PY4dO4azZ89i+vTpA7ZTq9XIy8vT31+6dKmgxjx27Fif8NV///d/Izc31+45EhEREREREREREXkLVsAiwejfmnDi6FB3TwkAECAW2dzCTkg0GmBD4UWj4St7OKJChjtkTB2D/yd1nMnn7xk1FAWr7kfG1DEunBUREfkKaUQIlsyIxtOz4vCrh8Yj56F78PSsOCyZEY3o8BCbqjU6i0YD7JBV92lPbAu2/SUyTSwWY8OGDfr7K1asgFI5MHT58ssvo7y8HAAwa9YszJ8/3+jx9uzZA5FIBJFIhDlz5rhkzOPHj+ORRx7pE75as2aN0W2JiIiIiIiIiIiIfA0rYJHg6FoTrp07EQpVBwrO1OLguVp8q7rtlvmEBQVgTXo8TsobHRZicjVzVZ/sMdgKGe4yxF9s8rnU8SNY+YqIiNxGV63xhffPodvB521b7CtTYF+ZAvGRoVgwNQqZM6WQRlh/vrflmoltf8nX5OTk4MMPP8Tnn3+OixcvIikpCTk5OUhMTIRKpUJ+fj5OnjwJAJBIJNi1a5dgxiwvL+8Tvpo/fz5iYmJw8OBBs+OPHDkSDz744KB/DiIiIiIiIiIiIiKhYwCLBM0wjPVuaQ1eK7zo8DCRObq2ONHhIchblozc/HKPDWE5kkYD7D9di7VzJ7p7KjZpaLtj8rnvW9wT8CMiItLJmDoGH6wORvaeMjS1d7l1LnJlG+SyauyQVSNtUiTWpMdbFVTWBclWv3fWbCvCALGIbX/J5/j7++PAgQN48skn8cknn6C+vh6bNm0asF10dDT27duHKVOmCGbM8vJytLe36+8fPnwYhw8ftjj+7NmzcezYMbvnT0REREREREREROQp2IKQPMby1Bj8Y/UDSJ8UCZHI/LZB/o55aRu2xcmYOgYFq+63anxf8ObRahRdqHP3NGzS2GZ6Mfv7lk4XzoSIiMi4JKkEf8lKQYBYGBcbGgCySiUyd56y+ryfMXUMRoUOMbvN9qXJbPtLPiksLAwff/wxDh48iMWLF0MqlWLIkCEYOXIkfvKTn2DLli24cOECHnjgAY8ek4iIiIiIiIiIiMjXsAIWeZQkqQS7s1KgUHXgq2sqtHZ2o1utgQga+Iv9EBYUoK9YNdiKWcba4hiOf+hCPf7zs8uO+LE8krpXg9z8chSsCvaY6hWNrayARUREwpcklWDHsnsFVXmzS91r9Xn/Vmc3lGbOuQDg78fvgZBvW7RoERYtWmT3/llZWcjKynLZmPaMR0RERERERERERORLGMAijySNCIE0IsTsNstTYzBt7HDkFcshq1KabYPTX6DYz2xbHGlECH710HiUXm2CrFJpy9S9Spe6F3nFcuzOSnH3VKzS1G56Mbj1Tg9udXZjWFCAC2dERERknLbyZjDyiuUoFsi1hrXn/Sv1rRaPVd3QhnmOmhgREREREREREREREZGbMYBFXs1YxaywoAAMD/bH3/+lGBDMEom0bQdz0+Otquq0Jj0eJ+WNVlWnEIsAtX3FuARNVqVEbXOHvlWjUPX2atBkpgUhoK2CNSyKASwiIhIGw+uYjR9fwj8v33D3lKw673/5TZPF41Qr24w+rlB1oPRqE1o7exAW5I/U8SMshu6JiIiIiIiIiIiIiIjcjQEs8gnGKmbNTYwaEMzStS+0VpJUgrxlyRZbBOkqau0/XWtXxaxAsR9+HD0cp2uabd7X2TQaoPSqCktmOGdx1FELsTdvd6PHQjvK71tuY1LUMHunSkRE5BTSiBC89otEFFfesKmipzNoNMD+07VYO3din8cVqg4UnFbg0IV6yE2Eqwxd+v4WCk4r9Of34cEB+HuZAkeNhOPTEiKxxspwPBERERERERERERERkTswgEU+zZpWhpYYtgiyVFFrzPBgqytm6ejCW2OGByNz5ymb9nWVsutN0Gg0Dq1WcV7Rgu3FcoctxDa2mW4/qPN9S6c9UyUiInI6aUQI0hIiBdH6eIdMjsiwQCxPjdWfr22dV2V9K9Z98LXF7TQaQFapxEl5I/KWJSNj6hh7p01EREREREREREREROQ0DGAROYCpVof9K2pZWzELAEQAHp7Utx2itfu62r6yWuwrq+3zWHxkKBZMjULmTKnNYayiC3Umf057F2IbLbQfBIDKur7VONj2iIiIhMSW1sfOpNEArxy8iP86cgUtt7thocCkQ3Spe5GbX46CVcGshEVERERERERERERERILDABaRA1lTUctcxSwAmBgZigXTxiBzZvSAdoiG+xYLoAKGOXJlG+SyauyQVSNtkumKVf1bDA4PDrAqZGbrQqw1FbDe/epbvPvVt/r7bHtERERCYkuQ25BIBESEBKKp3XIY2Raqjm6HHs+SLnUv8orl2J2V4tJxiYiIiIiIiIiIiIiILBF0AKuwsBB79+5FWVkZ6uvrMWzYMEyYMAGPP/44Vq5ciWHDhnnUmEePHkV6ejo0dxM3MTExuH79uoNmTp7E2opZ5vbddfwbvH6o0kUztp8G2opVX1xpwLKfjEPsiKH6oNXfyxQDWgzawpaFWGsCWP2x7REREQmNNUHuWRNG4keSIPiL/fTXF01tXYJtZWwLWZUStc0dFq+XiIiIiIiIiIiIiIiIXEmQAay2tjY89dRTKCws7PN4Q0MDGhoacOrUKezYsQP79+9HamqqR4zZ0dGBZ599Vh++IgKsq5hlysJpY/D/F1XaHV5yte5eDf52qsbhx7V2IbbJihaEprDtERERCYk9Qe7o8BDBtjK2hUYD7JBV4/m0CWwTTEQ26V95l+3GiYiIiIiIiIiIyJEEF8BSq9XIzMxEUVERAGD06NHIyclBYmIiVCoV8vPzUVJSAoVCgYULF6KkpASTJ08W/Ji/+93vcPXqVQwdOhTt7e2Dmi8RoA1vpSVEQibwVoTOptEApVdVWDLD/OKJPRWwDLHtERERCY2tQW5L1bM8xb4yBfaVKRAfGYoFU6OQOVMKaUQIwxVEZNR5RQu2F8uNVt7t/z5CREREREREREREZC/BBbDefvttfRAqMTERMpkMo0eP1j//3HPP4cUXX8S2bdvQ3NyMlStX4sSJE4Ie88svv8Sf/vQnAMDmzZvxm9/8ZlDzJdJZkx6Pk/JGj65k4Qitnd0WtxlsAAtg2yMiIvJ8xqpndas1OHFFiZPVTe6enk3kyjbIZdXIk1VjxNBAqNq7YJitEImAtIRIrEmPZwVLIh9VdKHObOU/3fvIDlk10ibx/YKIiIiIiIiIiIjs5+fuCRhSq9XYuHGj/v7evXv7BKF0tmzZguTkZADAF198gSNHjgh2zM7OTmRnZ6O3txdPPPEEHnvsMbvnStRfklSCvGXJCBQL6n9llwsLCrC4TcMgWhDq6KptEREReTppRAiWzIjG07Pi8KuHxuP1xT+GSOTuWdmvqV/4CtCet2WVSmTuPIWiC3VumRcRuc95RYvVbVc14PsFERERERERERERDY6gUhsnTpxAXZ32l52zZ8/G9OnTjW4nFouRm5urv5+fny/YMV977TVUVVVBIpHoq2AROZK2ndD9SJ8UaXThdGJkKJ5+IBb/sXAS4iNDXT9BJxOJgNTxERa3a3JABSzAumpbREREnkbX2tgbdal7kZtfjvOKFndPhYhcaHux3OZKwV3qXrzw/jm+XxAREREReYPmGuDce0Dp/2j/21zj7hkRERGRlxNUC8JDhw7pby9cuNDstgsWLDC6n5DGPH36NLZt2wYA2Lp1K6KionD9+nX7JkpkhrF2QmFBAUgdH9GnXd59cSOQufOU1QsRw4MDcPO2sANHDydEWmwJqNFoHNKCEDBebUuh6kDp1Sa0dvYgLMgfqeNHQBrBNoVERORZvLm1cZe6F3nFcuzOSnH3VIjIBRSqDhytUtq1b3evBtl7yvCXrBS2IyQiIiIi8kTfnQGObQHkR4A+9bJFQPw8YM5LwNgZ7podEREReTFBBbAqKir0t1NSzC+OREVFQSqVQqFQ4MaNG2hoaMCoUaMEM2Z3dzeys7OhVqsxZ84cPPvsszbPjchW0ogQs8EfXctCS604xCIRfr8oEVPHSmwKbLlaoNgPuenxFrdr71Kjs9sxP4Oq/Q4Uqg5II0JwXtGC7cVyHK1SQmPwOU4kAtISIrEmPZ6LNkRE5DGsvU7wVLIqJWqbOxAdHsLwNJGXK73a1Of63FZN7V1Y/OcSbFw0BctTYx02LyIiIiIin9dcA1w/Cdy5BQwZBsQ+CITH2L6NKZcKgQPPAOouI09qAPlh4OpR4IndQOKjg/5xiIiIiAwJKoBVVVWlvx0XF2dx+7i4OCgUCv2+9gSwnDXm5s2bUVFRgaCgILz11lsQGesNNwi1tbVmn9e1VSTqT9uyMBh5xXLIjASHHk6IRK5BcChvWTJeeP8cunsHsYLhJBt+MRlJUkmfRdTuuwvGAWI//YKq2oFz/8/PKvH6oUpM+dEwVNa1osfIsTUaQFapxEl5I/KWJSNj6hiHjU9ERORMuuuEzZ9cQllNs7un41AaDfCbfefh7ydC6bUmhqeJvFhrZ8+gj6HWAK8cvIiD577Hqz9P5HsDEREREZG9mmuAc+8ClwuBhir0rUoFYOQk4J7Z2kOxx70AACAASURBVIevHTe9zZRHgeTlpsNY350xE74yoO7Sbje8iJWwiIiIyKEEFcBqaWnR3x45cqTF7UeMGGF0X3ePef78ebz++usAgA0bNiA+3nKFHltJpVKHH5N8h7UtCwHtQuwHq4ORvacMTe0WPri42CsHL+KPn8uhau/q/3Gsj+jwYIeOq9EAF767ZXG7LnUvcvPLUbAqmAs2RETkMZKkEhSsfgDvltZgw0cXYG+OeVxEMKaNleDIpXp0q4UR5C67rjL6OMPTRN4lLMhxv+o4XdOMx98swWP3/ghTx0pYNY+IiIiIyFr6VoCHzW/XWKn9Y2mb45XA8TdMtxE8tsVy+EpH3QUc3wo8uc+67YmIiIisIKgAVltbm/52UFCQxe2Dg38IVbS2tgpizJ6eHmRnZ6O7uxtJSUlYt26dXfMicgVLLQt1kqQS/CUrBUt2fimYBVQda0Jhtc23XTAT47rUvcgrlmN3lvkWp0REREKzPDUG08YOxysHK1BhRfBYR9fK+Km7bbvOK1qMVt4UIoanibxD6vgREIngsPecXgD/OPc9/nHue/1j8ZGhWDA1CpkzpQxjEREREZH3MmwHqO7WPiYOsNwa0GwrwMG420bwGxkwIwsYcY92XrebAfkR2w515TDQ8i0gGefgORIREZGvElQAyxts3boVZ8+ehVgsxttvvw1/f+f8FevaIJpSV1eH++67zyljk29KkkqwY9m9yM0vR9fdNn9kHVmVErXNHQOqixEREQldklSCj1/4KY5crMcf/ynH5TrTQSxjrYx1x+hfefN6Yzve/9e3ggt2AwxPE3kDaUQI0hIiIatUOm0MubINclk1dsiqkTaJLUyJiIiIyMvoq1cdwYB2gHoi49WorG0FOBi93UDZ/w7yIBptuCz5SYdMiYiIiEhQAazQ0FA0NzcDADo7OxEaGmp2+9u3f6hqExYW5vYxL1++jN///vcAgNzcXMycOdOuOVkjOjraaccmMiVj6hgUrAo2WsVCJAJmjgvHOUULeuztVeSlNBqg9KoKS2YwgEVERJ5p3pQozJsS1SdE1a3WQAQN/MV+JlsZG+pfeXPx9GjBVsZieJrI861Jj8dJeaPTvzyigbaF6fGqBmxclIjld6v/ERERERF5LKurV92tRnX1KPDEbmBMkjbQ9GWec8NXjtRpfcVvIiIiIksEFcCSSCT6MFRjY6PFMFRTU1Offd05Zm9vL7Kzs3Hnzh3ExsZi06ZNds2HSOiMVbEwXHQtulDHKllGVNbzgxwREXk+a9sXW6P/NUVl/S28U3IdagEEuTUaYP/pWqydO9HdUyEiOyVJJchbluyyzyZqjQavHLyIg+e+x6s/T2Q1LCIiIiLyTPZUr1J3AftX3L3j/s/0Ngka5u4ZEBERkRcRVAArISEB165dAwBcu3YNsbGxZrfXbavb151jVlRUoLS0FAAwZcoU/PGPfzS6f0tLi/72zZs3sXnzZv39devWYciQITb/DETuYGoB1lyVLF/2Tsl1zIwJR8bUMe6eChERkaAYXlPMjAkXTJD7zaPVSBwTxnM3kQcz/GxS7MR2hIZO1zRj8Z+/ZDUsIiIiIvJMx7bYWb3KExcCREDsg+6eBBEREXkRQQWwpk2bhqKiIgBAWVkZ0tLSTG5748YNKBQKAEBkZCRGjRrl1jE1BimTTz/9FJ9++qnFsVtaWvDqq6/q7z///PMMYJFXMFUla3iwP/7+L4VPBrPUvRrk5pejYFUwvw1PRERkgqUgd5C/Hzp7XBPOUvdq8ML75/DBap67iTyZ4WeTgjO12P3FVbR3qZ06JqthEREREZFHaq4B5EfcPQvXmTgfkIxz9yyIiIjIiwgqgJWRkYE33ngDAHDo0CGsX7/e5LafffaZ/vbChQs9akwiX2GsStbcxCij7Qs1GvR5TNV+B//5WaWbZu4cXepe5BXLsTsrxejzClUHSq82obWzB2FB/kgdP8JhbZ6IiIg8haV2x++W1uC1wosuaVXY3atB9p4y/CUrhQEKIg8njQjB2rkTkT4pEkt2folutfPfQ07XNCNz5ynkLUtmNT0iIiIiEr7rJ+GZlazsIA4EZpteDyQiIiKyh6ACWLNnz0ZUVBTq6+tx7NgxnD17FtOnTx+wnVqtRl5env7+0qVL3T5mcnJynypYply/fh1xcXEAgJiYGFy/ft3uuRN5KlPtCw0fU6g68PqhSq+rlCWrUqK2uQPR4T/8rOcVLdheLMfRfpU+RCIgLSESa9LjuehLREQ+x9T1wvLUGEwbO9xl7Y6b2ruwZOeX2LHsXgYoiLxAklSCHcvudVm70y51L6vpEREREZEwNddoQ1d3bgFDhgHN1909I9cQiYEndgNjZ7h7JkRERORl/Nw9AUNisRgbNmzQ31+xYgWUSuWA7V5++WWUl5cDAGbNmoX58+cbPd6ePXsgEokgEokwZ84cl4xJRI4hjQhBWkKku6fhcBoNUHpVpb9fdKEOmTtPQVY5cAFZowFklUpk7jyFogt1Lp4pERGRcOmqZJ1Yl4Y/ZCbh2Z/GQewnctp43WptK+HzihanjUFErqNtd3o/UmLCXTJed68Gy/63FP91pAoKVYdLxiQiIiIiMum7M8B7vwS2JwEf/RtQ9LL2vye2untmrvHQi0Dio+6eBREREXkhQVXAAoCcnBx8+OGH+Pzzz3Hx4kUkJSUhJycHiYmJUKlUyM/Px8mTJwEAEokEu3bt8sgxiciyNenxOClvdMk301NiwnG+9qZLxtp/+ls0td2BsvUO9nx53WILJX5rnoiIyDjDKlkzY8KdWtHGUithIvIsSVIJClY/4LK2ph1dauTJqrFDVo20SaxyS0RERERO0L+iVeyDQHhM320uFQIHngHUXe6Zo9uJgHuXu3sSRERE5KUEF8Dy9/fHgQMH8OSTT+KTTz5BfX09Nm3aNGC76Oho7Nu3D1OmTPHIMYnIsiSpBHnLkp3eHiRQ7IdXfp6Iupu3XdKK5F/XmvGva8027aP71vyzD8Yhc6bUaEsmIiIiX6ataBOMvGI5iisHVrR1hOJKJf6/wgt45sHxPBcTeQldW9PNn1xCWY1t1+j20EBb5fZ4VQM2LkrE8tRYp49JRERERF6mf9AqSAKc/SsgPwLtFaeBkZOAKY8CycuBjkYfD18BmDgfkIxz9yyIiIjISwkugAUAYWFh+Pjjj/HRRx/hb3/7G8rKyqBUKhEWFoZ77rkHixcvxsqVKzF8+HCPHpOILNMtpjprQSRQ7Ie8ZclIkkq034K/u3ArqxrYEtDd+K15IiIi83StCRWqDhScqUXBaQXqbnY6dIw9X9Zgz5c1iI8MxYKpUQxGE3kBV1fDAgC1RoNXDl7EruNX8fi9Y/leQkRERESWfXcGOLbFeNDKlMZK4HglcPwNIGSkb4evxIHA7PXungURERF5MZFGI7SIATlCbW0tpFIpAEChUCA6OtrNMyIaPGsWRPwAzE4Yhe9abkN+o83kx1CRCHg4IRK5JkJMClUH/nS0GvvKFI6ZvBOIRSJ+a94Ivv+Rq/E1RyRsRy7W44//lONy3S2nHF8E+HQwmu+B5GrOfs2dV7S47QsZDHYSkTk855Kr8TVHJDBe1zpQBDx9CGi+BnTeAtTd2g/YfgFA0N32iZ++CMgPO2Y4cSDwxG4g8VGLm/L9j1yNrzki8lXe+P4nyApYRETG6NqDGFsQMRaoUqg68NU1FVo7u9Gt1kAEDfzFfggLCkDq+AhEh5te1JBGhCBhdJizf6RB4bfmiYiILJs3JQrzpkTprwsq62/hnZLrDqtww3ZiRN7FsJKe4WeJupYOFFcq8a3qttPGlivbIJdVI09WjclRYfjN3ImYNyXKaeMRERERkYf47oyXha+gbQUYc7/2jylzXgKuHh3kzy3SjjV7PTB2xiCOQ0RE5CH6tyqOfVD7eP/HwmOcO6Yjj+9BGMAiIo9ibEHEVKBKGhEyqEBSWJBnvEUqmm9b1ZpQoepA6dUmtHb2ICzIH6njRzCwRUREPsPwumBmTDheeP8cuh3YZkwXjD547nv86qHxuHm7m+dcIg9m7LPEa49aV5XXES7Xt+JXe89gzPAhmJ8YhTGSYASI/fieQkREROSLjm3xrvCVta0Ax87QVq2yGD7zA5L+DzAm2XglLck4R82ciIhImJprgHPvApcLgYYqWNWqeOQk4J7ZwLBoQBxgX3DKZHtkERA/Txum9rEAtGekC4iI+hlsuMoaqeNHQCSCy1uP2MtUBY7zihZsL5bjqJGqYWkJvtsyiYiIfFfG1DH4YHUwsveUoandsb/EPl3TjNN7z/R5jOdcIu+hq8q7+ZNLKKtpdvp4dTfvYM+pmgGPs10hERERkY9orrm7qOkldK0ArV2MTXwUGF4EHN8KXDmMAYu7rG5FRETewp4qUvoAlB0texsrtX/6sCE4ZbY9skY7p6tHrW4B7C0YwCIiMkEaEYK0hEjIKpXunopNDFsTThgVihPyRqiNpMg0Gm1g66S8EXnLkpExdYwbZktEROQeSVIJ/pKVgiU7v0S32rlpa55zibxLklSCgtUP4N3SGmz46AKcXAzLKMN2hQxjERERETmQrYufhturu7WPiQP63jbW/sfStuEx2mMfeglWVbEQvEGEpcbOAJ7cp/37qCkBOm+xuhUREXkPk1WkoK1SNeVRIHn5wOuRst3AofVAb48DJ3M3OFX9T2DhG0DKM8avjToarWuPrO7Sbje8yGfC0gxgERGZsSY9HifljehS97p7KjZTNN+Govm2xe261L3IzS9HwapgVuUgIiKfkiSVYMeye/Hce+eMhpUdjedcIu+iq4bljGp6tmAYi4iIiAj2VY0wZOvip7ntHSFkFNDR4PjjusK4VOCBXKDzpmPDUuExtv2bEhERCZ3ZKlLQVqg6Xgkcf+OHylQAUPTvgKLUefPSqIFP1wKH/wPoMbLWHDDU+vbI6i5tJcsn9zl2jgLFABYRkRlJUgnyliUjN7/cbAgrUOyHhKhQVHx3y4Wzc5wudS9eLDiPXz00HqnjR3CxhoiIfEbG1DH4n+Ui/Kpf20Bn4TmXyLu4spqeNRjGIiIiIp9jb9UIw8BW0zfA2T0/VKLqr//iZ+Rk4NSfHFxxoh9PDF+JxMDCPwAp2e6eCRERkbAYq5jZVg98tcvK64m7lanknwN+IqBX7dTp6hkLXwFAd7ttx7lyGGj51icqVzKARURkQcbUMShYFYy8YjlkVUoYFsgQiYCHEyKRmx4PAMjcecojq2UB2sWadR98DZEISEuIxJr0eFbnICIinzBvShQenuS6tsO6cy4ABiSIvICump6lL224Wv8w1qx7RmCMJBgBYj90352no2633+lB/a1ORA0LwtAh/vrnw4L8GTYlIiIi57G3aoTdlat0i5+HBzNrLzSI9oJERESeyNrKmw6vmNkLCOdXTzbQaP++kp9090ScjgEsIiIrJEkl2J2VAoWqA19dU6G1sxthQQFIHR+B6PAfFhOsqZYldBoNIKtU4niVEotnRGPCqFAAXEAhIiLv5q62w7qAxA5ZNdImRWJpihQ3b3ejtbOH510iD2L4pY1iF4U5bSFXtkGubHPb+IMNgBm+HypUHSi92oTWzh6T2xAREZEP+O4McCDbdNWqPu4Gp74p1t51ZuUqbycOBDL3OL69IBERkRD1D1oFSYCzf7Wu8mbZbuDQel536HR6ZhcpWzGARURkA2lEiNlf6At94cUWag1QcLp2wOOskEVERN7I2rbDzqKBNgDdvwqX7rzrzGCWYZiBAQYi+xl+aaPgTC0OnqvFtyoTpdp9jKMCYCGBYnR0mS+zby7sJdT3OL4PExGRxzLWTkccYL4SxGCP01wDnHsXKH3TyvCVAS6ADo44EHhiNzDpEXfPhIiIyLnsqVylr7y5FRAHAepOp07R4wQNc/cMXIIBLCIiB+u/8FJ0oQ7yG20OKSwpBLoKWSfljchbloyMqWPcPSUiIiKHEGKQWnfeNdYecXJUGH4zdyLmTYmy69jnFS3YXizHUSMtlhm2JrKfNCIEa+dOxNq5E/FuaQ1eK7wIda+3fBpwL0vhK8C6sJepkJapVoqObtmou/19y218+U0TqpUDPy+yRS2R8BQWFmLv3r0oKytDfX09hg0bhgkTJuDxxx/HypUrMWyY4xcU3DEmkVWsWpQU/dD2z1RbOluOM2URcPEjtv9zKj8gfJw25Nbn34MtBsm1eM4l8gDWtuBz1piGgW1Ttw3nZUvY21KLY2swfNWPSPv37ANEGo2GvwX0QrW1tZBKpQAAhUKB6OhoN8+IyLcZti6s+O4mPir/3uwiTICfCPOmjEbFdzcF/a15sQh4elYcEqLCBPMtbb7/kavxNUfknQyD1FduuK9tlzXGDB+CzBlSmxbpiy7UWaz2FSj2sxi25nsguZonvubOK1oEFewkz2NNC0dXh8cs3WYlL8fzxPc/b9HW1oannnoKhYWFJreRSqXYv38/UlNTPXbM/via8xDOXPw0dWxbFyV1VZMSH+17zO/LgYoCQGM5YE0uIA4AnvjLD/9ONSU+22KQ73/uw3MuX3PkAcyFp/u34NOx9Xqlf1jqZi1w7TjQUDVwTGsEDAW6281vM3IScM9s7eHL3ub1iaNNzACe3DfgYW98/2MAy0t544uVyJvoFmFkRipOPJwQiVyDihOe9K35wbb7cETrDb7/kavxNUfk/TypnZg1FVPOK1qwZOeX6FZbvrYIFPuhYNX9Jith8T2QXM2TX3OeFOwkchRrwmODCZL5UtDLk9//PJlarcbPf/5zFBUVAQBGjx6NnJwcJCYmQqVSIT8/HyUlJQCA8PBwlJSUYPLkyR43pjF8zQmcycVPK6pO9dd/UTRIApz9q/GF1eExwC0FoLG1bbsfIJECLd8OPCa539BR2kVRVrcCwPc/d+E5l685ssFgAtiDaR9cths4tN66lr4jJwKRU4CGy8aDU7rA07DoHypXDTZoRcIkDgSyi4xeZ3jj+x8DWF7KG1+sRN7IsDJWWFAAUsdHIDp84C+Ovelb8/1/AR8W5I/hwQH4e5nCIS2Q+P5HrsbXHJFv8eRgtO6c+7t/VKCp3foS2umTIrE7K8Xoc3wPJFfzltecLoxVcFqBupssS080WLYGvTyx2pe3vP95ml27dmHVqlUAgMTERMhkMowePbrPNi+++CK2bdsGAPjpT3+KEydOeNyYxvA1J2BWLX76AT/+JfCj5B8WV++0Aq11QNgYYEgYFzlJyy8AeOYww1cG+P7nHjzn8jUneI6qOmnqOLrHm6/3PV8D1gWUjAWarN3X0nFu1gJXioCWGtt/XvJtIjGQuUdbYdMIb3z/YwDLS3nji5WIPKsChyOJRSJsXJSI5amxFrfl+x+5Gl9zRL7nvKIFmz+5hLKaZndPxSVEIuCL9WlGQ+J8DyRX88bX3JGL9fjjP+W4XHfL3VMhokEwDIFZU8nL1uCWN77/CZ1arYZUKkVdXR0A4MyZM5g+fbrR7WbOnIny8nIAwOHDhzFv3jyPGdOUQb3mjFVW6B/+AYwvDgrxtlDmzsVPsooImDgfGJcKHP1P860iDdtDkh7Pua7Hc66Dz7necM4T0twHE3qy9jjWtMgj8kSzXwLS/t3k0954zvV39wSIiMh60ogQrJ07EWvnTvSoChyDpdZo8MrBizh47nu8+vNEq6thEREROUOSVIKC1Q/4zLlYowFKr6qwZIb3t3cicod5U6Iwb0pUnxaF8httrENB5GHkyjbIlba1F7Wn6jO5zokTJ/SLsrNnzza6KAsAYrEYubm5yM7OBgDk5+fbvTDrjjEdymRbPCJyGr8AYObTQMQ9QNDdSiqScdrn4h4Cjm8FrhzGgFaVE+cDs9ez8hUJAs+5duA5VxgaK7V/BovhK3KVwKFAl6tebyLg3uUuGks4GMAiIvJQy1NjMG3scK9pTWiN0zXNyNx5CnnLkpExdYy7p0NERD7O8Fws69dG19u0dna7ewpEXs/wyxaGrcq71RrUtXSg5JsmXLlhW7iDiIRNowFklUqclDfyc64AHTp0SH974cKFZrddsGCB0f08YUyHuVQIHHjGfLUdInIgK0JUY2cAT+7TVsipKQE6bw0MaREJAM+5NuI5l4jMEYmBhX8AJqQbP/8bXhd8Xw5U7Ac0asfPY+J8n7zeYACLiMiDJUkl2J2V4lPflu9S9yI3vxwFq4L5DWEiInI7w3PxV9dU2HX8G5urX3iCsKAAd0+ByKdII0KMtiUzvO5nGIvIe/BzrjBVVFTob6ekpJjdNioqClKpFAqFAjdu3EBDQwNGjRrlEWM6xHdnuBBM5CrhMcCPl2orSli7qBkeo/1DJFA859qA51wiMmfoKG34WhfONnb+739d8JOcuxUzixw3D3GgNiTugxjAIiLyAua+LS+CBjda7+Cdkute0yKpS92LvGI5dmeZ/2BERETkKrqwRHxkKDJ3nkKXutfdU3IYkQhIHR/h7mkQEQZe9zOMReQ9+DlXeKqqqvS34+LiLG4fFxcHhUKh39eehVl3jOkQx7ZwIZgIAOAHJP0fYEwyoO4GbtUCV48DDZUY0CJsZAJwTxoQHmdd9QldNYuUbKf+BETuwHOuDXjOJSJT/AL6hq+sZVgxs/w9bZU9Y9cu1rYvFAcCT+z22TbHDGAREXkZU9+WnxkTjtz8cq9ZEJZVKVHb3IHo8IE/KxERkbskSSXIW5bsVefchxMieb4lEiCGsYi8Dz/nCktLS4v+9siRIy1uP2LECKP7CnXM2tpas8/X1dVZd6DmGkB+xKaxibyPhXaA1rQA1FefOIy+C55WtBok8nA85/KcS0T9iAOBtP8Avj1lXWUqR4SewmOAtH/X/jF17WI2pMVrFoABLCIin5ExdQwKVgUjr1gOWZUSGg8vhqXRAKVXVVgyg7+YJiIiYbF0zg3y90Nnj2eEswLFfshNj3f3NIjIAlNhLG9vT07kbfg5V1ja2n4ItAYFBVncPjg4WH+7tbVV8GNKpVKbtjfp+kkM+HY8kbcZOgpob8SA1/qoSUDiIsvtAK1pAWhYfcJSWIvIy/CcayWec4m8hB8Q/zOgRWFFiOnX7gk9mbp2sSak5eMYwCIi8iFJUgl2Z6X0aVPY3NGN09dVOHW1yeNCWa2d3e6eAhERkVHGzrlhQQFIHR+B6PAQvFtag9cKLwq6PXCAWIS8ZclIkkrcPRWvU1hYiL1796KsrAz19fUYNmwYJkyYgMcffxwrV67EsGHDBD1mdXU1du3ahUOHDkGhUECtVmPs2LH42c9+hpycHCQnJzt8/mQ9S+3J/cV+Dr/ddqcH9bfuIGpYEIYO8UddSwdKvmliAIzIDvycSx7nzi13z4DIgAgYlwrUngZ6HfR+Kg7UBqNCRrpmkdGasBYR+Saec4k8nJGwlDUhJqGGnnjNYhQDWEREPshYm0JjizM3Wu/gnZLrgl0cDgsKcPcUiIiIzDLVGnh5agymjR0u2MqUI4cGYndWCsNXDtbW1oannnoKhYWFfR5vaGhAQ0MDTp06hR07dmD//v1ITU0V5JhvvfUWfv3rX+P27dt9Hr9y5QquXLmCXbt2YcOGDdiwYYND5k+DY+o9yFUGGwAzF+SaGBmKWRNGAtAw7EVehZ9zhSM0NBTNzc0AgM7OToSGhprd3vDcGBYWJvgxFQqF2efr6upw3333WT7QEMcHx4nsEnEPsOKgdhHyUiFw4BlA3TW4Y/Zv58NFRiKn4DmX51wiz3Q3UDV9BVB33kSFKpivmGlriImhJ8FjAIuIiACYXpyZGROO3PxydKmF1SpJJAJSx0e4expERER2M1Ula3iwP/7+L4XbglkBfiKGr5xArVYjMzMTRUVFAIDRo0cjJycHiYmJUKlUyM/PR0lJCRQKBRYuXIiSkhJMnjxZUGO+++67WLlyJQDAz88PS5cuRXp6Ovz9/VFSUoK//vWvuHPnDl577TUMGTIEL7300qDmT57PUQEwU9UETW1jS5BLSIYGitHepXb3NMiN+DlXWCQSiX5htrGx0eLCbFNTU599hT5mdHS07RM0JvZBACKwJRI5jkj7hqix4XeR4kDgif/9YVEz8VFgeBFwfCtwpci+OTijnQ8RGcVzrpV4ziVyL78AYObT2tB3/+pTkx4RXoUqcgsGsIiIyKyMqWNQsCpYcBU6Hk6IHLDoQkRE5ImMBRTmJkYZDRxoNEDBmVoUXajDlRttDp9LoNiPbQed5O2339YHoRITEyGTyTB69Gj988899xxefPFFbNu2Dc3NzVi5ciVOnDghmDEbGhrw3HPPAdCGrz788EM8+uij+udXrFiBp59+Gunp6ejo6MArr7yCxx57DAkJCYP6GYgA64Jc1mxjqSJX/1aKzmrZ2P+2YahMoerQv88LPTBGjsfPucKSkJCAa9euAQCuXbuG2NhYs9vrttXt6yljDlp4DBA/D5Afds/4JBx+d5ebenvsPIBB6AmwPjzVv0qVztgZ2taBzTVA+XsmqlIYVK7ovMnFUiI34TnXSjznEmmNmgSMnwMMj9aGotTd2myi7vatWuDqcdPVqMbP0d42tc0ANgSzWaHK5zGARUREFhmr0OHOb5IHiv2Qmx7vwhGJiIhcz1SYYO3ciVg7d2KfRXpHhLFSYsPxyiOJDF85gVqtxsaNG/X39+7d2ycIpbNlyxYUFxejvLwcX3zxBY4cOYJ58+YJYsw//OEPuHXrFgBtcMswfKWTmpqKTZs24be//S16enqwceNGvP/++3bNn8gZ3N2S0RrSiJA+7/PWtnB0V3jMU6uNCRE/5wrPtGnT9EHmsrIypKWlmdz2xo0b+vZCkZGRGDVqlMeM6RBzXgKuHh18uzfyUMaCU4dhdjGz/8KpsdCTteEpS4uh4THaihSsSkEkWDzn2oDnXBKUu+fimFnA1/uBGxdg9vwfOBToarf+8NZcL5hjzXnfcBtdkKuzFWirA8J+pL2OwEWt7wAAHvlJREFU4PUC2YABLCIispqpBQtXtvtgZQ7nKCwsxN69e1FWVob6+noMGzYMEyZMwOOPP46VK1di2DDH95cf7JjfffcdvvjiC5w+fRpnzpzB999/j8bGRty6dQtDhw7FuHHjcN9992HZsmVIT093+PyJiNyt/yK9vRVTxCIRfr8oEU+lxjprqj7vxIkTqKurAwDMnj0b06dPN7qdWCxGbm4usrOzAQD5+fl2B7AcPea+ffv0t3/zm9+YHDcnJwcbNmxAe3s7CgsLcfv2bQQHB9v1MxD5Ok8IjJliS3jMniCZtwW9+DlXmDIyMvDGG28AAA4dOoT169eb3Pazzz7T3164cKFHjekQY2doKxAdeIYLwu4ydBTQ3ohBtaUyuihqpEKUYZUJc8Gp/ouZ9iycOjo8xaoURILEc64NeM4lQ7YGmgzZVQnKYN/ERcC9y384F8/Kte78b2qbwVwvmGLNeZ/XBuRgDGAREdGg2dPuw9wvzEcODURTe1ff77SJtO0YctPj+UtpB2pra8NTTz2FwsLCPo83NDSgoaEBp06dwo4dO7B//36kpqYKaszXX38db775ptHnbt68iYqKClRUVGD37t1IT09Hfn6+e7+dRETkROYqppg65/Lc6jqHDh3S37b0y9oFCxYY3c+dY166dAk1NTUAgMmTJyMuLs7kscLCwvDTn/4URUVFaG9vx/Hjx5GRkWHr9InIw7kqPGZv0Eso1b54Lha22bNnIyoqCvX19Th27BjOnj1rNNCsVquRl5env7906VKPGtNhEh8FhhdZV/2IHKP/4qelBU3Dag6BoZYXRQez+OmMxUwukBJ5LZ5zbcRzrnsNJvRkzXEixmvP8VE/7nu+HkygyZpwkyOCUQw8ETGARURErmFN9aywoACkjo9AdHiIycfJcdRqNTIzM/WllkePHo2cnBwkJiZCpVIhPz8fJSUlUCgUWLhwIUpKSjB58mRBjRkYGIgZM2ZgxowZiI+Px6hRoxAQEICGhgaUlpaioKAAt2/fRnFxMebMmYPTp0+zCgcReT1bz7nkfBUVFfrbKSkpZreNioqCVCqFQqHAjRs30NDQYFeA2JFj2nIs3Ta6c31FRQUDWETkNEKoEmYsBGZNJS+ei4VPLBZjw4YN+Ld/+zcAwIoVKyCTyRAZGdlnu5dffhnl5eUAgFmzZmH+/PlGj7dnzx48/fTTALSLvseOHXP6mC43dobp6kemwj/WLBS6+7bQ5m5q8dMRC5pcFCUiN+A51w7mzrnedM4T0twHE3qy5jjuru7IawAih2AAi4iI3MrUL8yF8It0b/f222/rF0cTExMhk8kwevRo/fPPPfccXnzxRWzbtg3Nzc1YuXIlTpw4IZgx165diy1btmDo0KFGn1+9ejU2btyIn/3sZ/jmm29w6dIl/OlPf8K6desG9TMQEXkqnlvdp6qqSn/bXPUow20UCoV+X3sCWI4c055jGduXiMgb8fzq3XJycvDhhx/i888/x8WLF5GUlDTgS0QnT54EAEgkEuzatcsjx3Q4LuAREdH/be/OY6yszj+APzCDwMgoyiLojGKNQQdwK0gFdRSqKFppsSrQVugydWk0rVpLtcUSYxq7aKqpLUarVCuljVEEgdYgKFCpqKUqotUAssgqoGyjMNzfH4T7Y5mZu8yd7c7nk9zkXO/znnOuOXm/QR7fmyGZmyWZ2zg0PQE10IAFAC1QVVVVjB8/Pvn+iSeeOKARap977703Zs2aFYsWLYq5c+fGP//5z7j44oubxJpf+MIXUq7Zo0eP+NWvfhVXXnllRERMnTpVAxYADW7Lli3JcefOnVPWd+rUqdprG2vNhtz/qlWrav18zZo1Gc0HAHVRWFgYTz/9dIwaNSqmTZsWa9eujbvvvvuQupKSkpg8eXL06tWrWa4JAI1N5gKQD1o39gYAgIb38ssvJ/8Cs7y8vNrft4/Y+yjmm2++Ofl+0qRJzWrNiDjgD8Zr166t01wAkI1t27Ylx+3atUtZv//P5W7durXR12zI/ZeWltb6OvvsszOaDwDqqri4OKZOnRrPPvtsDB8+PEpLS6Nt27bRuXPn6N+/f9x7773x9ttvx4ABA5r1mgDQ2GQuAM2dJ2ABQAs0Y8aM5Hjo0KG11l566aXVXtcc1oyI+OCDD5Ljbt261WkuAAAAWqZhw4bFsGHDsr5+zJgxMWbMmAZdEwCaI5kLQHOlAQsAWqC33norOe7Xr1+ttd26dYvS0tJYuXJlrFu3LjZs2BBdunRpFmuuX78+xo4dm3z/9a9/PeM5AKCuOnToEJs3b46IiMrKyujQoUOt9Tt37kyOi4uLG33N/a+trKxMuXZd9r9y5cpaP1+zZo2nYAEAAAAATY4GLABogd57773k+MQTT0xZf+KJJyb/QvS9997LqhmqPtdcvnx5LFq0KCIi9uzZE5s2bYrXXnst/vrXv8Ynn3wSEXufqnXjjTdmvG8AqKuOHTsmm6E2btyYshnq448/PuDaxl5z//cbN25MuXZd9l9SUpJRPQAAAABAU6ABCwBaoC1btiTHnTt3TlnfqVOnaq9tKmvOnDkzbrjhhmo/O/744+OGG26I22+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"text/plain": [ "" ] @@ -708,83 +655,103 @@ "width": 1000 } }, - "execution_count": 9 + "execution_count": 29 } ] }, { "cell_type": "markdown", "metadata": { - "id": "IEijrePND_2I", + "id": "Zelyeqbyt3GD", "colab_type": "text" }, "source": [ - "## 5. Appendix" + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):\n", + "\n", + "- **Google Colab Notebook** with free GPU: \"Open\n", + "- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5)\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) \n", + "- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)" ] }, { - "cell_type": "code", + "cell_type": "markdown", "metadata": { - "id": "gI6NoBev8Ib1", - "colab_type": "code", - "colab": {} + "id": "IEijrePND_2I", + "colab_type": "text" }, "source": [ - "# Re-clone\n", - "%cd ..\n", - "!rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5\n", - "%cd yolov5" - ], - "execution_count": 0, - "outputs": [] + "# Appendix\n", + "\n", + "Optional extras below. Unit tests validate repo functionality and should be run on any PRs submitted.\n" + ] }, { "cell_type": "code", "metadata": { - "id": "5OGhWlyAYQlS", + "id": "gI6NoBev8Ib1", "colab_type": "code", "colab": {} }, "source": [ - "# Apex install\n", - "git clone https://github.com/NVIDIA/apex && cd apex && pip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" . --user && cd .. && rm -rf apex" + "# Re-clone repo\n", + "%cd ..\n", + "!rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5\n", + "%cd yolov5" ], - "execution_count": 0, + "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { - "id": "JaTFNJHvFBy4", + "id": "Z2AvpeKfrbsT", "colab_type": "code", "colab": {} }, "source": [ - "# Test GCP checkpoint on COCO val2017\n", + "# Test GCP ckpt\n", "%%shell\n", - "x=best*.pt\n", - "gsutil cp gs://*/*/weights/$x .\n", - "python test.py --weights $x --data ./data/coco.yaml --img 736" + "for x in best*\n", + "do\n", + " gsutil cp gs://*/*/*/$x.pt .\n", + " python test.py --weights $x.pt --data coco.yaml --img 672\n", + "done" ], - "execution_count": 0, + "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { - "id": "wuVlYvfBVTuD", + "id": "FGH0ZjkGjejy", "colab_type": "code", "colab": {} }, "source": [ - "# Test multiple models on COCO val2017\n", + "# YOLOv5 unit tests\n", "%%shell\n", - "for x in yolov5s yolov5m yolov5l yolov5x\n", - "do \n", - " python test.py --weights $x.pt --data ./data/coco.yaml --img 640 --conf 0.001\n", + "cd .. && rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5 && cd yolov5\n", + "export PYTHONPATH=\"$PWD\" # to run *.py. files in subdirectories\n", + "pip install -qr requirements.txt onnx\n", + "python3 -c \"from utils.google_utils import *; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', 'coco128.zip')\" && mv ./coco128 ../\n", + "for x in yolov5s #yolov5m yolov5l yolov5x # models\n", + "do\n", + " python train.py --weights $x.pt --cfg $x.yaml --epochs 4 --img 320 --device 0 # train\n", + " for di in 0 cpu # inference devices\n", + " do\n", + " python detect.py --weights $x.pt --device $di # detect official\n", + " python detect.py --weights runs/exp0/weights/last.pt --device $di # detect custom\n", + " python test.py --weights $x.pt --device $di # test official\n", + " python test.py --weights runs/exp0/weights/last.pt --device $di # test custom\n", + " done\n", + " python models/yolo.py --cfg $x.yaml # inspect\n", + " python models/export.py --weights $x.pt --img 640 --batch 1 # export\n", "done" ], - "execution_count": 0, + "execution_count": null, "outputs": [] } ] diff --git a/utils/datasets.py b/utils/datasets.py index b01201f46dd3..a3a5531f8f54 100755 --- a/utils/datasets.py +++ b/utils/datasets.py @@ -26,6 +26,11 @@ break +def get_hash(files): + # Returns a single hash value of a list of files + return sum(os.path.getsize(f) for f in files if os.path.isfile(f)) + + def exif_size(img): # Returns exif-corrected PIL size s = img.size # (width, height) @@ -50,7 +55,7 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa rect=rect, # rectangular training cache_images=cache, single_cls=opt.single_cls, - stride=stride, + stride=int(stride), pad=pad) batch_size = min(batch_size, len(dataset)) @@ -67,35 +72,39 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa class LoadImages: # for inference def __init__(self, path, img_size=640): - path = str(Path(path)) # os-agnostic - files = [] - if os.path.isdir(path): - files = sorted(glob.glob(os.path.join(path, '*.*'))) - elif os.path.isfile(path): - files = [path] + p = str(Path(path)) # os-agnostic + p = os.path.abspath(p) # absolute path + if '*' in p: + files = sorted(glob.glob(p)) # glob + elif os.path.isdir(p): + files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir + elif os.path.isfile(p): + files = [p] # files + else: + raise Exception('ERROR: %s does not exist' % p) images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats] videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats] - nI, nV = len(images), len(videos) + ni, nv = len(images), len(videos) self.img_size = img_size self.files = images + videos - self.nF = nI + nV # number of files - self.video_flag = [False] * nI + [True] * nV + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv self.mode = 'images' if any(videos): self.new_video(videos[0]) # new video else: self.cap = None - assert self.nF > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \ - (path, img_formats, vid_formats) + assert self.nf > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \ + (p, img_formats, vid_formats) def __iter__(self): self.count = 0 return self def __next__(self): - if self.count == self.nF: + if self.count == self.nf: raise StopIteration path = self.files[self.count] @@ -106,7 +115,7 @@ def __next__(self): if not ret_val: self.count += 1 self.cap.release() - if self.count == self.nF: # last video + if self.count == self.nf: # last video raise StopIteration else: path = self.files[self.count] @@ -114,14 +123,14 @@ def __next__(self): ret_val, img0 = self.cap.read() self.frame += 1 - print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nF, self.frame, self.nframes, path), end='') + print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nf, self.frame, self.nframes, path), end='') else: # Read image self.count += 1 img0 = cv2.imread(path) # BGR assert img0 is not None, 'Image Not Found ' + path - print('image %g/%g %s: ' % (self.count, self.nF, path), end='') + print('image %g/%g %s: ' % (self.count, self.nf, path), end='') # Padded resize img = letterbox(img0, new_shape=self.img_size)[0] @@ -139,7 +148,7 @@ def new_video(self, path): self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) def __len__(self): - return self.nF # number of files + return self.nf # number of files class LoadWebcam: # for inference @@ -284,19 +293,21 @@ class LoadImagesAndLabels(Dataset): # for training/testing 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): try: - path = str(Path(path)) # os-agnostic - parent = str(Path(path).parent) + os.sep - if os.path.isfile(path): # file - with open(path, 'r') as f: - f = f.read().splitlines() - f = [x.replace('./', parent) if x.startswith('./') else x for x in f] # local to global path - elif os.path.isdir(path): # folder - f = glob.iglob(path + os.sep + '*.*') - else: - raise Exception('%s does not exist' % path) + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = str(Path(p)) # os-agnostic + parent = str(Path(p).parent) + os.sep + if os.path.isfile(p): # file + with open(p, 'r') as t: + t = t.read().splitlines() + f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path + elif os.path.isdir(p): # folder + f += glob.iglob(p + os.sep + '*.*') + else: + raise Exception('%s does not exist' % p) self.img_files = [x.replace('/', os.sep) for x in f if os.path.splitext(x)[-1].lower() in img_formats] - except: - raise Exception('Error loading data from %s. See %s' % (path, help_url)) + except Exception as e: + raise Exception('Error loading data from %s: %s\nSee %s' % (path, e, help_url)) n = len(self.img_files) assert n > 0, 'No images found in %s. See %s' % (path, help_url) @@ -315,20 +326,22 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r self.stride = stride # Define labels - self.label_files = [x.replace('images', 'labels').replace(os.path.splitext(x)[-1], '.txt') - for x in self.img_files] - - # Read image shapes (wh) - sp = path.replace('.txt', '') + '.shapes' # shapefile path - try: - with open(sp, 'r') as f: # read existing shapefile - s = [x.split() for x in f.read().splitlines()] - assert len(s) == n, 'Shapefile out of sync' - except: - s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')] - np.savetxt(sp, s, fmt='%g') # overwrites existing (if any) + self.label_files = [x.replace('images', 'labels').replace(os.path.splitext(x)[-1], '.txt') for x in + self.img_files] + + # Check cache + cache_path = str(Path(self.label_files[0]).parent) + '.cache' # cached labels + if os.path.isfile(cache_path): + cache = torch.load(cache_path) # load + if cache['hash'] != get_hash(self.label_files + self.img_files): # dataset changed + cache = self.cache_labels(cache_path) # re-cache + else: + cache = self.cache_labels(cache_path) # cache - self.shapes = np.array(s, dtype=np.float64) + # Get labels + labels, shapes = zip(*[cache[x] for x in self.img_files]) + self.shapes = np.array(shapes, dtype=np.float64) + self.labels = list(labels) # Rectangular Training https://github.com/ultralytics/yolov3/issues/232 if self.rect: @@ -338,6 +351,7 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r 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] @@ -354,33 +368,11 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride # Cache labels - self.imgs = [None] * n - self.labels = [np.zeros((0, 5), dtype=np.float32)] * n create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False nm, nf, ne, ns, nd = 0, 0, 0, 0, 0 # number missing, found, empty, datasubset, duplicate - np_labels_path = str(Path(self.label_files[0]).parent) + '.npy' # saved labels in *.npy file - if os.path.isfile(np_labels_path): - s = np_labels_path # print string - x = np.load(np_labels_path, allow_pickle=True) - if len(x) == n: - self.labels = x - labels_loaded = True - else: - s = path.replace('images', 'labels') - pbar = tqdm(self.label_files) for i, file in enumerate(pbar): - if labels_loaded: - l = self.labels[i] - # np.savetxt(file, l, '%g') # save *.txt from *.npy file - else: - try: - with open(file, 'r') as f: - l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32) - except: - nm += 1 # print('missing labels for image %s' % self.img_files[i]) # file missing - continue - + l = self.labels[i] # label if l.shape[0]: assert l.shape[1] == 5, '> 5 label columns: %s' % file assert (l >= 0).all(), 'negative labels: %s' % file @@ -426,15 +418,13 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r ne += 1 # print('empty labels for image %s' % self.img_files[i]) # file empty # os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i])) # remove - pbar.desc = 'Caching labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % ( - s, nf, nm, ne, nd, n) - assert nf > 0 or n == 20288, 'No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url) - if not labels_loaded and n > 1000: - print('Saving labels to %s for faster future loading' % np_labels_path) - np.save(np_labels_path, self.labels) # save for next time + pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % ( + cache_path, nf, nm, ne, nd, n) + assert nf > 0, 'No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url) # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) - if cache_images: # if training + self.imgs = [None] * n + if cache_images: gb = 0 # Gigabytes of cached images pbar = tqdm(range(len(self.img_files)), desc='Caching images') self.img_hw0, self.img_hw = [None] * n, [None] * n @@ -443,15 +433,31 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r gb += self.imgs[i].nbytes pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9) - # Detect corrupted images https://medium.com/joelthchao/programmatically-detect-corrupted-image-8c1b2006c3d3 - detect_corrupted_images = False - if detect_corrupted_images: - from skimage import io # conda install -c conda-forge scikit-image - for file in tqdm(self.img_files, desc='Detecting corrupted images'): - try: - _ = io.imread(file) - except: - print('Corrupted image detected: %s' % file) + def cache_labels(self, path='labels.cache'): + # Cache dataset labels, check images and read shapes + x = {} # dict + pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files)) + for (img, label) in pbar: + try: + l = [] + image = Image.open(img) + image.verify() # PIL verify + # _ = io.imread(img) # skimage verify (from skimage import io) + shape = exif_size(image) # image size + assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels' + if os.path.isfile(label): + with open(label, 'r') as f: + l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32) # labels + if len(l) == 0: + l = np.zeros((0, 5), dtype=np.float32) + x[img] = [l, shape] + except Exception as e: + x[img] = None + print('WARNING: %s: %s' % (img, e)) + + x['hash'] = get_hash(self.label_files + self.img_files) + torch.save(x, path) # save for next time + return x def __len__(self): return len(self.img_files) @@ -472,6 +478,13 @@ def __getitem__(self, index): img, labels = load_mosaic(self, index) shapes = None + # MixUp https://arxiv.org/pdf/1710.09412.pdf + # if random.random() < 0.5: + # img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1)) + # r = np.random.beta(0.3, 0.3) # mixup ratio, alpha=beta=0.3 + # img = (img * r + img2 * (1 - r)).astype(np.uint8) + # labels = np.concatenate((labels, labels2), 0) + else: # Load image img, (h0, w0), (h, w) = load_image(self, index) @@ -683,8 +696,8 @@ def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding elif scaleFill: # stretch dw, dh = 0.0, 0.0 - new_unpad = new_shape - ratio = new_shape[0] / shape[1], new_shape[1] / shape[0] # width, height ratios + 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 diff --git a/utils/google_utils.py b/utils/google_utils.py index 0de6aa33daff..0a3dec1d4bab 100644 --- a/utils/google_utils.py +++ b/utils/google_utils.py @@ -9,10 +9,10 @@ def attempt_download(weights): # Attempt to download pretrained weights if not found locally - weights = weights.strip() + weights = weights.strip().replace("'", '') msg = weights + ' missing, try downloading from https://drive.google.com/drive/folders/1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J' - r = 1 + r = 1 # return if len(weights) > 0 and not os.path.isfile(weights): d = {'yolov3-spp.pt': '1mM67oNw4fZoIOL1c8M3hHmj66d8e-ni_', # yolov3-spp.yaml 'yolov5s.pt': '1R5T6rIyy3lLwgFXNms8whc-387H0tMQO', # yolov5s.yaml @@ -27,7 +27,7 @@ def attempt_download(weights): if not (r == 0 and os.path.exists(weights) and os.path.getsize(weights) > 1E6): # weights exist and > 1MB os.remove(weights) if os.path.exists(weights) else None # remove partial downloads - s = "curl -L -o %s 'https://storage.googleapis.com/ultralytics/yolov5/ckpt/%s'" % (weights, file) + s = "curl -L -o %s 'storage.googleapis.com/ultralytics/yolov5/ckpt/%s'" % (weights, file) r = os.system(s) # execute, capture return values # Error check @@ -36,8 +36,7 @@ def attempt_download(weights): raise Exception(msg) -def gdrive_download(id='1HaXkef9z6y5l4vUnCYgdmEAj61c6bfWO', name='coco.zip'): - # https://gist.github.com/tanaikech/f0f2d122e05bf5f971611258c22c110f +def gdrive_download(id='1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', name='coco128.zip'): # Downloads a file from Google Drive, accepting presented query # from utils.google_utils import *; gdrive_download() t = time.time() @@ -47,12 +46,12 @@ def gdrive_download(id='1HaXkef9z6y5l4vUnCYgdmEAj61c6bfWO', name='coco.zip'): os.remove('cookie') if os.path.exists('cookie') else None # Attempt file download - os.system("curl -c ./cookie -s -L \"https://drive.google.com/uc?export=download&id=%s\" > /dev/null" % id) + os.system("curl -c ./cookie -s -L \"drive.google.com/uc?export=download&id=%s\" > /dev/null" % id) if os.path.exists('cookie'): # large file - s = "curl -Lb ./cookie \"https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=%s\" -o %s" % ( + s = "curl -Lb ./cookie \"drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=%s\" -o %s" % ( id, name) else: # small file - s = "curl -s -L -o %s 'https://drive.google.com/uc?export=download&id=%s'" % (name, id) + s = "curl -s -L -o %s 'drive.google.com/uc?export=download&id=%s'" % (name, id) r = os.system(s) # execute, capture return values os.remove('cookie') if os.path.exists('cookie') else None @@ -71,6 +70,7 @@ def gdrive_download(id='1HaXkef9z6y5l4vUnCYgdmEAj61c6bfWO', name='coco.zip'): print('Done (%.1fs)' % (time.time() - t)) return r + # def upload_blob(bucket_name, source_file_name, destination_blob_name): # # Uploads a file to a bucket # # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python diff --git a/utils/torch_utils.py b/utils/torch_utils.py index 786b01896d50..71cb73d8f1c6 100644 --- a/utils/torch_utils.py +++ b/utils/torch_utils.py @@ -174,33 +174,32 @@ def scale_img(img, ratio=1.0, same_shape=False): # img(16,3,256,416), r=ratio return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean +def copy_attr(a, b, include=(), exclude=()): + # Copy attributes from b to a, options to only include [...] and to exclude [...] + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith('_') or k in exclude: + continue + else: + setattr(a, k, v) + + class ModelEMA: """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models Keep a moving average of everything in the model state_dict (parameters and buffers). This is intended to allow functionality like https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage A smoothed version of the weights is necessary for some training schemes to perform well. - E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use - RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA - smoothing of weights to match results. Pay attention to the decay constant you are using - relative to your update count per epoch. - To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but - disable validation of the EMA weights. Validation will have to be done manually in a separate - process, or after the training stops converging. This class is sensitive where it is initialized in the sequence of model init, GPU assignment and distributed training wrappers. - I've tested with the sequence in my own train.py for torch.DataParallel, apex.DDP, and single-GPU. """ - def __init__(self, model, decay=0.9999, device=''): + def __init__(self, model, decay=0.9999, updates=0): # Create EMA - self.ema = deepcopy(model.module if is_parallel(model) else model) # FP32 EMA - self.ema.eval() - self.updates = 0 # number of EMA updates + self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA + # if next(model.parameters()).device.type != 'cpu': + # self.ema.half() # FP16 EMA + self.updates = updates # number of EMA updates self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs) - self.device = device # perform ema on different device from model if set - if device: - self.ema.to(device) for p in self.ema.parameters(): p.requires_grad_(False) @@ -217,15 +216,6 @@ def update(self, model): v *= d v += (1. - d) * msd[k].detach() - def update_attr(self, model): - # Assign attributes (which may change during training) - for k, v in model.__dict__.items(): - # TODO: This is uglyy. Custom attributes should have some specific naming strategy. - if not (k.startswith('_') or k in ["process_group", "reducer"] or - isinstance(v, (torch.distributed.ProcessGroupNCCL, torch.distributed.Reducer))): - try: - pickle.dumps(v) - except Exception: - continue - else: - setattr(self.ema, k, v) + def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): + # Update EMA attributes + copy_attr(self.ema, model, include, exclude) diff --git a/utils/utils.py b/utils/utils.py index 8fa044dba29d..fdca9b2828cb 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -51,9 +51,15 @@ def init_seeds(seed=0): torch_utils.init_seeds(seed=seed) +def get_latest_run(search_dir='./runs'): + # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) + last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) + return max(last_list, key=os.path.getctime) + + def check_git_status(): # Suggest 'git pull' if repo is out of date - if platform in ['linux', 'darwin']: + if platform in ['linux', 'darwin'] and not os.path.isfile('/.dockerenv'): s = subprocess.check_output('if [ -d .git ]; then git fetch && git status -uno; fi', shell=True).decode('utf-8') if 'Your branch is behind' in s: print(s[s.find('Your branch is behind'):s.find('\n\n')] + '\n') @@ -187,7 +193,7 @@ def xywh2xyxy(x): 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 = max(img1_shape) / max(img0_shape) # gain = old / new + 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] @@ -514,6 +520,7 @@ def build_targets(p, targets, model): off = torch.tensor([[1, 0], [0, 1], [-1, 0], [0, -1]], device=targets.device).float() # overlap offsets at = torch.arange(na).view(na, 1).repeat(1, nt) # anchor tensor, same as .repeat_interleave(nt) + g = 0.5 # offset style = 'rect4' for i in range(det.nl): anchors = det.anchors[i] @@ -528,7 +535,6 @@ def build_targets(p, targets, model): a, t = at[j], t.repeat(na, 1, 1)[j] # filter # overlaps - g = 0.5 # offset gxy = t[:, 2:4] # grid xy z = torch.zeros_like(gxy) if style == 'rect2': @@ -647,14 +653,12 @@ def strip_optimizer(f='weights/best.pt'): # from utils.utils import *; strip_op x['optimizer'] = None x['model'].half() # to FP16 torch.save(x, f) - print('Optimizer stripped from %s' % f) + print('Optimizer stripped from %s, %.1fMB' % (f, os.path.getsize(f) / 1E6)) def create_pretrained(f='weights/best.pt', s='weights/pretrained.pt'): # from utils.utils import *; create_pretrained() # create pretrained checkpoint 's' from 'f' (create_pretrained(x, x) for x in glob.glob('./*.pt')) - device = torch.device('cpu') - x = torch.load(s, map_location=device) - + x = torch.load(f, map_location=torch.device('cpu')) x['optimizer'] = None x['training_results'] = None x['epoch'] = -1 @@ -662,7 +666,7 @@ def create_pretrained(f='weights/best.pt', s='weights/pretrained.pt'): # from u for p in x['model'].parameters(): p.requires_grad = True torch.save(x, s) - print('%s saved as pretrained checkpoint %s' % (f, s)) + print('%s saved as pretrained checkpoint %s, %.1fMB' % (f, s, os.path.getsize(s) / 1E6)) def coco_class_count(path='../coco/labels/train2014/'): @@ -891,10 +895,7 @@ def fitness(x): def output_to_target(output, width, height): - """ - Convert a YOLO model output to target format - [batch_id, class_id, x, y, w, h, conf] - """ + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] if isinstance(output, torch.Tensor): output = output.cpu().numpy() @@ -915,6 +916,16 @@ def output_to_target(output, width, height): return np.array(targets) +def increment_dir(dir, comment=''): + # Increments a directory runs/exp1 --> runs/exp2_comment + n = 0 # number + d = sorted(glob.glob(dir + '*')) # directories + if len(d): + d = d[-1].replace(dir, '') + n = int(d[:d.find('_')] if '_' in d else d) + 1 # increment + return dir + str(n) + ('_' + comment if comment else '') + + # Plotting functions --------------------------------------------------------------------------------------------------- def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy @@ -1045,7 +1056,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max return mosaic -def plot_lr_scheduler(optimizer, scheduler, epochs=300): +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): # Plot LR simulating training for full epochs optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals y = [] @@ -1059,7 +1070,7 @@ def plot_lr_scheduler(optimizer, scheduler, epochs=300): plt.xlim(0, epochs) plt.ylim(0) plt.tight_layout() - plt.savefig('LR.png', dpi=200) + plt.savefig(Path(save_dir) / 'LR.png', dpi=200) def plot_test_txt(): # from utils.utils import *; plot_test() @@ -1124,7 +1135,7 @@ def plot_study_txt(f='study.txt', x=None): # from utils.utils import *; plot_st plt.savefig(f.replace('.txt', '.png'), dpi=200) -def plot_labels(labels): +def plot_labels(labels, save_dir=''): # plot dataset labels c, b = labels[:, 0], labels[:, 1:].transpose() # classees, boxes @@ -1145,7 +1156,7 @@ def hist2d(x, y, n=100): ax[2].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet') ax[2].set_xlabel('width') ax[2].set_ylabel('height') - plt.savefig('labels.png', dpi=200) + plt.savefig(Path(save_dir) / 'labels.png', dpi=200) plt.close() @@ -1191,7 +1202,8 @@ def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_re fig.savefig(f.replace('.txt', '.png'), dpi=200) -def plot_results(start=0, stop=0, bucket='', id=(), labels=()): # from utils.utils import *; plot_results() +def plot_results(start=0, stop=0, bucket='', id=(), labels=(), + save_dir=''): # from utils.utils import *; plot_results() # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training fig, ax = plt.subplots(2, 5, figsize=(12, 6)) ax = ax.ravel() @@ -1201,7 +1213,7 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=()): # from utils.ut os.system('rm -rf storage.googleapis.com') files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id] else: - files = glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt') + files = glob.glob(str(Path(save_dir) / 'results*.txt')) + glob.glob('../../Downloads/results*.txt') for fi, f in enumerate(files): try: results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T @@ -1222,4 +1234,4 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=()): # from utils.ut fig.tight_layout() ax[1].legend() - fig.savefig('results.png', dpi=200) + fig.savefig(Path(save_dir) / 'results.png', dpi=200)