Train from --data path/to/dataset.zip feature

data/Argoverse_HD.yaml → data/Argoverse.yaml

@@ -1,6 +1,6 @@
 # YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
 # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
-# Example usage: python train.py --data Argoverse_HD.yaml
+# Example usage: python train.py --data Argoverse.yaml
 # parent
 # ├── yolov5
 # └── datasets

hubconf.py

@@ -27,7 +27,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
 
     from models.yolo import Model, attempt_load
     from utils.general import check_requirements, set_logging
-    from utils.google_utils import attempt_download
+    from utils.downloads import attempt_download
     from utils.torch_utils import select_device
 
     file = Path(__file__).absolute()

models/experimental.py

@@ -5,7 +5,7 @@
 import torch.nn as nn
 
 from models.common import Conv, DWConv
-from utils.google_utils import attempt_download
+from utils.downloads import attempt_download
 
 
 class CrossConv(nn.Module):

train.py

@@ -35,7 +35,7 @@
 from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
     strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
     check_requirements, print_mutation, set_logging, one_cycle, colorstr
-from utils.google_utils import attempt_download
+from utils.downloads import attempt_download
 from utils.loss import ComputeLoss
 from utils.plots import plot_labels, plot_evolution
 from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, de_parallel
@@ -78,9 +78,9 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     plots = not evolve  # create plots
     cuda = device.type != 'cpu'
     init_seeds(1 + RANK)
-    with open(data, encoding='ascii', errors='ignore') as f:
-        data_dict = yaml.safe_load(f)
-
+    with torch_distributed_zero_first(RANK):
+        data_dict = check_dataset(data)  # check
+    train_path, val_path = data_dict['train'], data_dict['val']
     nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
     names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
     assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}'  # check
@@ -106,9 +106,6 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
         LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}')  # report
     else:
         model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
-    with torch_distributed_zero_first(RANK):
-        check_dataset(data_dict)  # check
-    train_path, val_path = data_dict['train'], data_dict['val']
 
     # Freeze
     freeze = []  # parameter names to freeze (full or partial)

utils/datasets.py

@@ -884,11 +884,11 @@ def verify_image_label(args):
         return [None, None, None, None, nm, nf, ne, nc, msg]
 
 
-def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False):
+def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False, profile=False, hub=False):
     """ Return dataset statistics dictionary with images and instances counts per split per class
-    Usage1: from utils.datasets import *; dataset_stats('coco128.yaml', verbose=True)
-    Usage2: from utils.datasets import *; dataset_stats('../datasets/coco128.zip', verbose=True)
-
+    To run in parent directory: export PYTHONPATH="$PWD/yolov5"
+    Usage1: from utils.datasets import *; dataset_stats('coco128.yaml', autodownload=True)
+    Usage2: from utils.datasets import *; dataset_stats('../datasets/coco128_with_yaml.zip')
     Arguments
         path:           Path to data.yaml or data.zip (with data.yaml inside data.zip)
         autodownload:   Attempt to download dataset if not found locally
@@ -897,46 +897,80 @@ def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False):
 
     def round_labels(labels):
         # Update labels to integer class and 6 decimal place floats
-        return [[int(c), *[round(x, 6) for x in points]] for c, *points in labels]
+        return [[int(c), *[round(x, 4) for x in points]] for c, *points in labels]
 
     def unzip(path):
         # Unzip data.zip TODO: CONSTRAINT: path/to/abc.zip MUST unzip to 'path/to/abc/'
         if str(path).endswith('.zip'):  # path is data.zip
+            assert Path(path).is_file(), f'Error unzipping {path}, file not found'
             assert os.system(f'unzip -q {path} -d {path.parent}') == 0, f'Error unzipping {path}'
-            data_dir = path.with_suffix('')  # dataset directory
-            return True, data_dir, list(data_dir.rglob('*.yaml'))[0]  # zipped, data_dir, yaml_path
+            dir = path.with_suffix('')  # dataset directory
+            return True, str(dir), next(dir.rglob('*.yaml'))  # zipped, data_dir, yaml_path
         else:  # path is data.yaml
             return False, None, path
 
+    def hub_ops(f, max_dim=1920):
+        # HUB ops for 1 image 'f'
+        im = Image.open(f)
+        r = max_dim / max(im.height, im.width)  # ratio
+        if r < 1.0:  # image too large
+            im = im.resize((int(im.width * r), int(im.height * r)))
+        im.save(im_dir / Path(f).name, quality=75)  # save
+
     zipped, data_dir, yaml_path = unzip(Path(path))
     with open(check_file(yaml_path), encoding='ascii', errors='ignore') as f:
         data = yaml.safe_load(f)  # data dict
         if zipped:
             data['path'] = data_dir  # TODO: should this be dir.resolve()?
     check_dataset(data, autodownload)  # download dataset if missing
-    nc = data['nc']  # number of classes
-    stats = {'nc': nc, 'names': data['names']}  # statistics dictionary
+    hub_dir = Path(data['path'] + ('-hub' if hub else ''))
+    stats = {'nc': data['nc'], 'names': data['names']}  # statistics dictionary
     for split in 'train', 'val', 'test':
         if data.get(split) is None:
             stats[split] = None  # i.e. no test set
             continue
         x = []
-        dataset = LoadImagesAndLabels(data[split], augment=False, rect=True)  # load dataset
-        if split == 'train':
-            cache_path = Path(dataset.label_files[0]).parent.with_suffix('.cache')  # *.cache path
+        dataset = LoadImagesAndLabels(data[split])  # load dataset
         for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics'):
-            x.append(np.bincount(label[:, 0].astype(int), minlength=nc))
+            x.append(np.bincount(label[:, 0].astype(int), minlength=data['nc']))
         x = np.array(x)  # shape(128x80)
         stats[split] = {'instance_stats': {'total': int(x.sum()), 'per_class': x.sum(0).tolist()},
                         'image_stats': {'total': dataset.n, 'unlabelled': int(np.all(x == 0, 1).sum()),
                                         'per_class': (x > 0).sum(0).tolist()},
                         'labels': [{str(Path(k).name): round_labels(v.tolist())} for k, v in
                                    zip(dataset.img_files, dataset.labels)]}
 
+        if hub:
+            im_dir = hub_dir / 'images'
+            im_dir.mkdir(parents=True, exist_ok=True)
+            for _ in tqdm(ThreadPool(NUM_THREADS).imap(hub_ops, dataset.img_files), total=dataset.n, desc='HUB Ops'):
+                pass
+
+    # Profile
+    stats_path = hub_dir / 'stats.json'
+    if profile:
+        for _ in range(1):
+            file = stats_path.with_suffix('.npy')
+            t1 = time.time()
+            np.save(file, stats)
+            t2 = time.time()
+            x = np.load(file, allow_pickle=True)
+            print(f'stats.npy times: {time.time() - t2:.3f}s read, {t2 - t1:.3f}s write')
+
+            file = stats_path.with_suffix('.json')
+            t1 = time.time()
+            with open(file, 'w') as f:
+                json.dump(stats, f)  # save stats *.json
+            t2 = time.time()
+            with open(file, 'r') as f:
+                x = json.load(f)  # load hyps dict
+            print(f'stats.json times: {time.time() - t2:.3f}s read, {t2 - t1:.3f}s write')
+
     # Save, print and return
-    with open(cache_path.with_suffix('.json'), 'w') as f:
-        json.dump(stats, f)  # save stats *.json
+    if hub:
+        print(f'Saving {stats_path.resolve()}...')
+        with open(stats_path, 'w') as f:
+            json.dump(stats, f)  # save stats.json
     if verbose:
         print(json.dumps(stats, indent=2, sort_keys=False))
-        # print(yaml.dump([stats], sort_keys=False, default_flow_style=False))
     return stats

utils/google_utils.py → utils/downloads.py

@@ -1,4 +1,4 @@
-# Google utils: https://cloud.google.com/storage/docs/reference/libraries
+# Download utils
 
 import os
 import platform
@@ -115,6 +115,10 @@ def get_token(cookie="./cookie"):
                 return line.split()[-1]
     return ""
 
+
+# Google utils: https://cloud.google.com/storage/docs/reference/libraries ----------------------------------------------
+#
+#
 # 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

utils/general.py

@@ -24,7 +24,7 @@
 import torchvision
 import yaml
 
-from utils.google_utils import gsutil_getsize
+from utils.downloads import gsutil_getsize
 from utils.metrics import box_iou, fitness
 from utils.torch_utils import init_torch_seeds
 
@@ -224,16 +224,30 @@ def check_file(file):
 
 
 def check_dataset(data, autodownload=True):
-    # Download dataset if not found locally
-    path = Path(data.get('path', ''))  # optional 'path' field
-    if path:
-        for k in 'train', 'val', 'test':
-            if data.get(k):  # prepend path
-                data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]
+    # Download and/or unzip dataset if not found locally
+    # Usage: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128_with_yaml.zip
+
+    # Download (optional)
+    extract_dir = ''
+    if isinstance(data, (str, Path)) and str(data).endswith('.zip'):  # i.e. gs://bucket/dir/coco128.zip
+        download(data, dir='../datasets', unzip=True, delete=False, curl=False, threads=1)
+        data = next((Path('../datasets') / Path(data).stem).rglob('*.yaml'))
+        extract_dir, autodownload = data.parent, False
+
+    # Read yaml (optional)
+    if isinstance(data, (str, Path)):
+        with open(data, encoding='ascii', errors='ignore') as f:
+            data = yaml.safe_load(f)  # dictionary
+
+    # Parse yaml
+    path = extract_dir or Path(data.get('path') or '')  # optional 'path' default to '.'
+    for k in 'train', 'val', 'test':
+        if data.get(k):  # prepend path
+            data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]]
 
     assert 'nc' in data, "Dataset 'nc' key missing."
     if 'names' not in data:
-        data['names'] = [str(i) for i in range(data['nc'])]  # assign class names if missing
+        data['names'] = [f'class{i}' for i in range(data['nc'])]  # assign class names if missing
     train, val, test, s = [data.get(x) for x in ('train', 'val', 'test', 'download')]
     if val:
         val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
@@ -256,13 +270,17 @@ def check_dataset(data, autodownload=True):
             else:
                 raise Exception('Dataset not found.')
 
+    return data  # dictionary
+
 
 def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1):
-    # Multi-threaded file download and unzip function
+    # Multi-threaded file download and unzip function, used in data.yaml for autodownload
     def download_one(url, dir):
         # Download 1 file
         f = dir / Path(url).name  # filename
-        if not f.exists():
+        if Path(url).is_file():  # exists in current path
+            Path(url).rename(f)  # move to dir
+        elif not f.exists():
             print(f'Downloading {url} to {f}...')
             if curl:
                 os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -")  # curl download, retry and resume on fail
@@ -286,7 +304,7 @@ def download_one(url, dir):
         pool.close()
         pool.join()
     else:
-        for u in tuple(url) if isinstance(url, str) else url:
+        for u in [url] if isinstance(url, (str, Path)) else url:
             download_one(u, dir)