Merge pull request #2 from ultralytics/master

merge

README.md

@@ -168,22 +168,22 @@ python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 12
 <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-integrations-loop.png" width="100%" />
 
 <div align="center">
+  <a href="https://roboflow.com/?ref=ultralytics">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
+  <a href="https://cutt.ly/yolov5-readme-clearml">
+    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
   <a href="https://bit.ly/yolov5-readme-comet">
     <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-comet.png" width="10%" /></a>
   <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
   <a href="https://bit.ly/yolov5-deci-platform">
     <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
-  <a href="https://cutt.ly/yolov5-readme-clearml">
-    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
-  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
-  <a href="https://roboflow.com/?ref=ultralytics">
-    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
 </div>
 
-|Comet ⭐ NEW|Deci ⭐ NEW|ClearML ⭐ NEW|Roboflow|
+|Roboflow|ClearML ⭐ NEW|Comet ⭐ NEW|Deci ⭐ NEW|
 |:-:|:-:|:-:|:-:|
-|Visualize model metrics and predictions and upload models and datasets in realtime with [Comet](https://bit.ly/yolov5-readme-comet)|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics)|
+|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics)|Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!)|Visualize model metrics and predictions and upload models and datasets in realtime with [Comet](https://bit.ly/yolov5-readme-comet)|Automatically compile and quantize YOLOv5 for better inference performance in one click at [Deci](https://bit.ly/yolov5-deci-platform)|
 
 
 ## <div align="center">Why YOLOv5</div>

classify/predict.py

@@ -91,7 +91,7 @@ def run(
     # Dataloader
     bs = 1  # batch_size
     if webcam:
-        view_img = check_imshow()
+        view_img = check_imshow(warn=True)
         dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
         bs = len(dataset)
     elif screenshot:

detect.py

@@ -99,7 +99,7 @@ def run(
     # Dataloader
     bs = 1  # batch_size
     if webcam:
-        view_img = check_imshow()
+        view_img = check_imshow(warn=True)
         dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
         bs = len(dataset)
     elif screenshot:

hubconf.py

@@ -39,7 +39,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
 
     if not verbose:
         LOGGER.setLevel(logging.WARNING)
-    check_requirements(exclude=('ipython', 'opencv-python', 'tensorboard', 'thop'))
+    check_requirements(exclude=('opencv-python', 'tensorboard', 'thop'))
     name = Path(name)
     path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name  # checkpoint path
     try:

models/common.py

@@ -18,13 +18,15 @@
 import requests
 import torch
 import torch.nn as nn
+from IPython.display import display
 from PIL import Image
 from torch.cuda import amp
 
+from utils import TryExcept
 from utils.dataloaders import exif_transpose, letterbox
 from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
-                           increment_path, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy, xyxy2xywh,
-                           yaml_load)
+                           increment_path, is_notebook, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
+                           xyxy2xywh, yaml_load)
 from utils.plots import Annotator, colors, save_one_box
 from utils.torch_utils import copy_attr, smart_inference_mode
 
@@ -756,7 +758,7 @@ def _run(self, pprint=False, show=False, save=False, crop=False, render=False, l
 
             im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
             if show:
-                im.show(self.files[i])  # show
+                display(im) if is_notebook() else im.show(self.files[i])
             if save:
                 f = self.files[i]
                 im.save(save_dir / f)  # save
@@ -772,6 +774,7 @@ def _run(self, pprint=False, show=False, save=False, crop=False, render=False, l
                 LOGGER.info(f'Saved results to {save_dir}\n')
             return crops
 
+    @TryExcept('Showing images is not supported in this environment')
     def show(self, labels=True):
         self._run(show=True, labels=labels)  # show results
 

segment/predict.py

@@ -102,7 +102,7 @@ def run(
     # Dataloader
     bs = 1  # batch_size
     if webcam:
-        view_img = check_imshow()
+        view_img = check_imshow(warn=True)
         dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
         bs = len(dataset)
     elif screenshot:

segment/train.py

@@ -91,17 +91,6 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     data_dict = None
     if RANK in {-1, 0}:
         logger = GenericLogger(opt=opt, console_logger=LOGGER)
-        # loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
-        # if loggers.clearml:
-        #     data_dict = loggers.clearml.data_dict  # None if no ClearML dataset or filled in by ClearML
-        # if loggers.wandb:
-        #     data_dict = loggers.wandb.data_dict
-        #     if resume:
-        #         weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size
-        #
-        # # Register actions
-        # for k in methods(loggers):
-        #     callbacks.register_action(k, callback=getattr(loggers, k))
 
     # Config
     plots = not evolve and not opt.noplots  # create plots
@@ -400,7 +389,6 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                     'ema': deepcopy(ema.ema).half(),
                     'updates': ema.updates,
                     'optimizer': optimizer.state_dict(),
-                    # 'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
                     'opt': vars(opt),
                     'date': datetime.now().isoformat()}
 

train.py

@@ -53,7 +53,6 @@
                            one_cycle, print_args, print_mutation, strip_optimizer, yaml_save)
 from utils.loggers import Loggers
 from utils.loggers.comet.comet_utils import check_comet_resume
-from utils.loggers.wandb.wandb_utils import check_wandb_resume
 from utils.loss import ComputeLoss
 from utils.metrics import fitness
 from utils.plots import plot_evolve
@@ -375,7 +374,6 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                     'ema': deepcopy(ema.ema).half(),
                     'updates': ema.updates,
                     'optimizer': optimizer.state_dict(),
-                    'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
                     'opt': vars(opt),
                     'date': datetime.now().isoformat()}
 
@@ -483,7 +481,7 @@ def main(opt, callbacks=Callbacks()):
         check_requirements()
 
     # Resume (from specified or most recent last.pt)
-    if opt.resume and not check_wandb_resume(opt) and not check_comet_resume(opt) and not opt.evolve:
+    if opt.resume and not check_comet_resume(opt) and not opt.evolve:
         last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
         opt_yaml = last.parent.parent / 'opt.yaml'  # train options yaml
         opt_data = opt.data  # original dataset

utils/__init__.py

@@ -23,7 +23,7 @@ def __enter__(self):
 
     def __exit__(self, exc_type, value, traceback):
         if value:
-            print(emojis(f'{self.msg}{value}'))
+            print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
         return True
 
 

utils/autoanchor.py

@@ -26,7 +26,7 @@ def check_anchor_order(m):
         m.anchors[:] = m.anchors.flip(0)
 
 
-@TryExcept(f'{PREFIX}ERROR: ')
+@TryExcept(f'{PREFIX}ERROR')
 def check_anchors(dataset, model, thr=4.0, imgsz=640):
     # Check anchor fit to data, recompute if necessary
     m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()

utils/dataloaders.py

@@ -17,7 +17,6 @@
 from pathlib import Path
 from threading import Thread
 from urllib.parse import urlparse
-from zipfile import ZipFile
 
 import numpy as np
 import torch
@@ -31,7 +30,8 @@
 from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
                                  cutout, letterbox, mixup, random_perspective)
 from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, check_dataset, check_requirements, check_yaml, clean_str,
-                           cv2, is_colab, is_kaggle, segments2boxes, xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
+                           cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, xywh2xyxy, xywhn2xyxy,
+                           xyxy2xywhn)
 from utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
@@ -1005,13 +1005,18 @@ def verify_image_label(args):
 
 
 class HUBDatasetStats():
-    """ Return dataset statistics dictionary with images and instances counts per split per class
-    To run in parent directory: export PYTHONPATH="$PWD/yolov5"
-    Usage1: from utils.dataloaders import *; HUBDatasetStats('coco128.yaml', autodownload=True)
-    Usage2: from utils.dataloaders import *; HUBDatasetStats('path/to/coco128_with_yaml.zip')
+    """ Class for generating HUB dataset JSON and `-hub` dataset directory
+
     Arguments
         path:           Path to data.yaml or data.zip (with data.yaml inside data.zip)
         autodownload:   Attempt to download dataset if not found locally
+
+    Usage
+        from utils.dataloaders import HUBDatasetStats
+        stats = HUBDatasetStats('coco128.yaml', autodownload=True)  # usage 1
+        stats = HUBDatasetStats('path/to/coco128.zip')  # usage 2
+        stats.get_json(save=False)
+        stats.process_images()
     """
 
     def __init__(self, path='coco128.yaml', autodownload=False):
@@ -1048,7 +1053,7 @@ def _unzip(self, path):
         if not str(path).endswith('.zip'):  # path is data.yaml
             return False, None, path
         assert Path(path).is_file(), f'Error unzipping {path}, file not found'
-        ZipFile(path).extractall(path=path.parent)  # unzip
+        unzip_file(path, path=path.parent)
         dir = path.with_suffix('')  # dataset directory == zip name
         assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/'
         return True, str(dir), self._find_yaml(dir)  # zipped, data_dir, yaml_path

utils/downloads.py

@@ -5,12 +5,9 @@
 
 import logging
 import os
-import platform
 import subprocess
-import time
 import urllib
 from pathlib import Path
-from zipfile import ZipFile
 
 import requests
 import torch
@@ -109,81 +106,3 @@ def github_assets(repository, version='latest'):
                 error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag} or {url3}')
 
     return str(file)
-
-
-def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'):
-    # Downloads a file from Google Drive. from yolov5.utils.downloads import *; gdrive_download()
-    t = time.time()
-    file = Path(file)
-    cookie = Path('cookie')  # gdrive cookie
-    print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='')
-    if file.exists():
-        file.unlink()  # remove existing file
-    if cookie.exists():
-        cookie.unlink()  # remove existing cookie
-
-    # Attempt file download
-    out = "NUL" if platform.system() == "Windows" else "/dev/null"
-    os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}')
-    if os.path.exists('cookie'):  # large file
-        s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}'
-    else:  # small file
-        s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"'
-    r = os.system(s)  # execute, capture return
-    if cookie.exists():
-        cookie.unlink()  # remove existing cookie
-
-    # Error check
-    if r != 0:
-        if file.exists():
-            file.unlink()  # remove partial
-        print('Download error ')  # raise Exception('Download error')
-        return r
-
-    # Unzip if archive
-    if file.suffix == '.zip':
-        print('unzipping... ', end='')
-        ZipFile(file).extractall(path=file.parent)  # unzip
-        file.unlink()  # remove zip
-
-    print(f'Done ({time.time() - t:.1f}s)')
-    return r
-
-
-def get_token(cookie="./cookie"):
-    with open(cookie) as f:
-        for line in f:
-            if "download" in line:
-                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
-#
-#     storage_client = storage.Client()
-#     bucket = storage_client.get_bucket(bucket_name)
-#     blob = bucket.blob(destination_blob_name)
-#
-#     blob.upload_from_filename(source_file_name)
-#
-#     print('File {} uploaded to {}.'.format(
-#         source_file_name,
-#         destination_blob_name))
-#
-#
-# def download_blob(bucket_name, source_blob_name, destination_file_name):
-#     # Uploads a blob from a bucket
-#     storage_client = storage.Client()
-#     bucket = storage_client.get_bucket(bucket_name)
-#     blob = bucket.blob(source_blob_name)
-#
-#     blob.download_to_filename(destination_file_name)
-#
-#     print('Blob {} downloaded to {}.'.format(
-#         source_blob_name,
-#         destination_file_name))