remove check_requirements

classify/predict.py

@@ -46,7 +46,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 from models.common import DetectMultiBackend
 from utils.augmentations import classify_transforms
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
-from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
+from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow,  colorstr, cv2,
                            increment_path, print_args, strip_optimizer)
 from utils.plots import Annotator
 from utils.torch_utils import select_device, smart_inference_mode
@@ -217,7 +217,6 @@ def parse_opt():
 
 
 def main(opt):
-    check_requirements(exclude=('tensorboard', 'thop'))
     run(**vars(opt))
 
 

classify/train.py

@@ -41,7 +41,7 @@ from models.experimental import attempt_load
 from models.yolo import ClassificationModel, DetectionModel
 from utils.dataloaders import create_classification_dataloader
 from utils.general import (DATASETS_DIR, LOGGER, TQDM_BAR_FORMAT, WorkingDirectory, check_git_info, check_git_status,
-                           check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save)
+                           colorstr, download, increment_path, init_seeds, print_args, yaml_save)
 from utils.loggers import GenericLogger
 from utils.plots import imshow_cls
 from utils.torch_utils import (ModelEMA, de_parallel, model_info, reshape_classifier_output, select_device, smart_DDP,
@@ -300,7 +300,6 @@ def main(opt):
     if RANK in {-1, 0}:
         print_args(vars(opt))
         check_git_status()
-        check_requirements()
 
     # DDP mode
     device = select_device(opt.device, batch_size=opt.batch_size)

classify/val.py

@@ -36,7 +36,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.common import DetectMultiBackend
 from utils.dataloaders import create_classification_dataloader
-from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr,
+from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, colorstr,
                            increment_path, print_args)
 from utils.torch_utils import select_device, smart_inference_mode
 
@@ -161,7 +161,6 @@ def parse_opt():
 
 
 def main(opt):
-    check_requirements(exclude=('tensorboard', 'thop'))
     run(**vars(opt))
 
 

data/Objects365.yaml

@@ -386,9 +386,8 @@ names:
 download: |
   from tqdm import tqdm
 
-  from utils.general import Path, check_requirements, download, np, xyxy2xywhn
+  from utils.general import Path, download, np, xyxy2xywhn
 
-  check_requirements(('pycocotools>=2.0',))
   from pycocotools.coco import COCO
 
   # Make Directories

detect.py

@@ -44,7 +44,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.common import DetectMultiBackend
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
-from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
+from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, colorstr, cv2,
                            increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
 from utils.plots import Annotator, colors, save_one_box
 from utils.torch_utils import select_device, smart_inference_mode
@@ -252,7 +252,6 @@ def parse_opt():
 
 
 def main(opt):
-    check_requirements(exclude=('tensorboard', 'thop'))
     run(**vars(opt))
 
 

export.py

@@ -77,7 +77,7 @@ if platform.system() != 'Windows':
 from models.experimental import attempt_load
 from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel, Segment
 from utils.dataloaders import LoadImages
-from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
+from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_version,
                            check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save)
 from utils.torch_utils import select_device, smart_inference_mode
 
@@ -116,7 +116,6 @@ def try_export(inner_func):
 @try_export
 def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')):
     # YOLOv5 ONNX export
-    check_requirements('onnx>=1.12.0')
     import onnx
 
     LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
@@ -157,7 +156,6 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
     if simplify:
         try:
             cuda = torch.cuda.is_available()
-            check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
             import onnxsim
 
             LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
@@ -172,7 +170,6 @@ def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX
 @try_export
 def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
     # YOLOv5 OpenVINO export
-    check_requirements('openvino-dev')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
     import openvino.inference_engine as ie
 
     LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
@@ -198,8 +195,6 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
     try:
         import tensorrt as trt
     except Exception:
-        if platform.system() == 'Linux':
-            check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
         import tensorrt as trt
 
     if trt.__version__[0] == '7':  # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
@@ -255,7 +250,6 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
 @try_export
 def export_rknn(model, batch_size, int8, data, prefix=colorstr('RKNN:')):
     # YOLOv5 RKNN export
-    check_requirements('rknn-toolkit2')
     from rknn.api import RKNN
     # Create RKNN object
     rknn = RKNN(verbose=False)

hubconf.py

@@ -34,12 +34,11 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
     from models.experimental import attempt_load
     from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
     from utils.downloads import attempt_download
-    from utils.general import LOGGER, check_requirements, intersect_dicts, logging
+    from utils.general import LOGGER, intersect_dicts, logging
     from utils.torch_utils import select_device
 
     if not verbose:
         LOGGER.setLevel(logging.WARNING)
-    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

@@ -27,7 +27,7 @@ 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,
+from utils.general import (LOGGER, ROOT, Profile, check_suffix, check_version, colorstr,
                            increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
                            xyxy2xywh, yaml_load)
 from utils.plots import Annotator, colors, save_one_box
@@ -415,11 +415,9 @@ class DetectMultiBackend(nn.Module):
                 stride, names = int(d['stride']), d['names']
         elif dnn:  # ONNX OpenCV DNN
             LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
-            check_requirements('opencv-python>=4.5.4')
             net = cv2.dnn.readNetFromONNX(w)
         elif onnx:  # ONNX Runtime
             LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
-            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
             import onnxruntime
             providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
             session = onnxruntime.InferenceSession(w, providers=providers)
@@ -429,7 +427,6 @@ class DetectMultiBackend(nn.Module):
                 stride, names = int(meta['stride']), eval(meta['names'])
         elif xml:  # OpenVINO
             LOGGER.info(f'Loading {w} for OpenVINO inference...')
-            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
             from openvino.runtime import Core, Layout, get_batch
             ie = Core()
             if not Path(w).is_file():  # if not *.xml
@@ -531,7 +528,6 @@ class DetectMultiBackend(nn.Module):
             raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
         elif paddle:  # PaddlePaddle
             LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
-            check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
             import paddle.inference as pdi
             if not Path(w).is_file():  # if not *.pdmodel
                 w = next(Path(w).rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
@@ -544,7 +540,6 @@ class DetectMultiBackend(nn.Module):
             output_names = predictor.get_output_names()
         elif triton:  # NVIDIA Triton Inference Server
             LOGGER.info(f'Using {w} as Triton Inference Server...')
-            check_requirements('tritonclient[all]')
             from utils.triton import TritonRemoteModel
             model = TritonRemoteModel(url=w)
             nhwc = model.runtime.startswith('tensorflow')

segment/predict.py

@@ -44,7 +44,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.common import DetectMultiBackend
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
-from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
+from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, colorstr, cv2,
                            increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
                            strip_optimizer)
 from utils.plots import Annotator, colors, save_one_box
@@ -275,7 +275,6 @@ def parse_opt():
 
 
 def main(opt):
-    check_requirements(exclude=('tensorboard', 'thop'))
     run(**vars(opt))
 
 

segment/train.py

@@ -48,7 +48,7 @@ from utils.autobatch import check_train_batch_size
 from utils.callbacks import Callbacks
 from utils.downloads import attempt_download, is_url
 from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info,
-                           check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr,
+                           check_git_status, check_img_size, check_suffix, check_yaml, colorstr,
                            get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
                            labels_to_image_weights, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save)
 from utils.loggers import GenericLogger
@@ -509,7 +509,6 @@ def main(opt, callbacks=Callbacks()):
     if RANK in {-1, 0}:
         print_args(vars(opt))
         check_git_status()
-        check_requirements()
 
     # Resume
     if opt.resume and not opt.evolve:  # resume from specified or most recent last.pt

segment/val.py

@@ -44,7 +44,7 @@ from models.common import DetectMultiBackend
 from models.yolo import SegmentationModel
 from utils.callbacks import Callbacks
 from utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size,
-                           check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path,
+                           check_yaml, coco80_to_coco91_class, colorstr, increment_path,
                            non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
 from utils.metrics import ConfusionMatrix, box_iou
 from utils.plots import output_to_target, plot_val_study
@@ -160,7 +160,6 @@ def run(
         callbacks=Callbacks(),
 ):
     if save_json:
-        check_requirements('pycocotools>=2.0.6')
         process = process_mask_native  # more accurate
     else:
         process = process_mask  # faster
@@ -434,7 +433,6 @@ def parse_opt():
 
 
 def main(opt):
-    check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
 
     if opt.task in ('train', 'val', 'test'):  # run normally
         if opt.conf_thres > 0.001:  # https://github.com/ultralytics/yolov5/issues/1466

train.py

@@ -49,7 +49,7 @@ from utils.callbacks import Callbacks
 from utils.dataloaders import create_dataloader
 from utils.downloads import attempt_download, is_url
 from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info,
-                           check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr,
+                           check_img_size, check_suffix, check_yaml, colorstr,
                            get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
                            labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer,
                            yaml_save)
@@ -480,8 +480,6 @@ def main(opt, callbacks=Callbacks()):
     # Checks
     if RANK in {-1, 0}:
         print_args(vars(opt))
-        check_git_status()
-        check_requirements()
 
     # Resume (from specified or most recent last.pt)
     if opt.resume and not opt.evolve:

utils/__init__.py

@@ -54,7 +54,7 @@ def notebook_init(verbose=True):
     import os
     import shutil
 
-    from utils.general import check_font, check_requirements, is_colab
+    from utils.general import check_font, is_colab
     from utils.torch_utils import select_device  # imports
 
     check_font()

utils/dataloaders.py

@@ -30,7 +30,7 @@ from tqdm import tqdm
 
 from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
                                  letterbox, mixup, random_perspective)
-from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements,
+from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, 
                            check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy,
                            xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
 from utils.torch_utils import torch_distributed_zero_first
@@ -191,7 +191,6 @@ class LoadScreenshots:
     # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
     def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
         # source = [screen_number left top width height] (pixels)
-        check_requirements('mss')
         import mss
 
         source, *params = source.split()
@@ -356,7 +355,6 @@ class LoadStreams:
             st = f'{i + 1}/{n}: {s}... '
             if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'):  # if source is YouTube video
                 # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
-                check_requirements(('pafy', 'youtube_dl==2020.12.2'))
                 import pafy
                 s = pafy.new(s).getbest(preftype='mp4').url  # YouTube URL
             s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam

utils/general.py

@@ -355,7 +355,6 @@ def check_git_status(repo='ultralytics/yolov5', branch='master'):
 @WorkingDirectory(ROOT)
 def check_git_info(path='.'):
     # YOLOv5 git info check, return {remote, branch, commit}
-    check_requirements('gitpython')
     import git
     try:
         repo = git.Repo(path)
@@ -387,29 +386,6 @@ def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=Fals
     return result
 
 
-@TryExcept()
-def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=''):
-    # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages or single package str)
-    prefix = colorstr('red', 'bold', 'requirements:')
-    check_python()  # check python version
-    if isinstance(requirements, Path):  # requirements.txt file
-        file = requirements.resolve()
-        assert file.exists(), f'{prefix} {file} not found, check failed.'
-        with file.open() as f:
-            requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]
-    elif isinstance(requirements, str):
-        requirements = [requirements]
-
-    s = ''
-    n = 0
-    for r in requirements:
-        try:
-            pkg.require(r)
-        except (pkg.VersionConflict, pkg.DistributionNotFound):  # exception if requirements not met
-            s += f'"{r}" '
-            n += 1
-
-
 def check_img_size(imgsz, s=32, floor=0):
     # Verify image size is a multiple of stride s in each dimension
     if isinstance(imgsz, int):  # integer i.e. img_size=640

utils/plots.py

@@ -20,7 +20,7 @@ import torch
 from PIL import Image, ImageDraw, ImageFont
 
 from utils import TryExcept, threaded
-from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
+from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, clip_boxes, increment_path,
                            is_ascii, xywh2xyxy, xyxy2xywh)
 from utils.metrics import fitness
 from utils.segment.general import scale_image
@@ -62,8 +62,6 @@ def check_pil_font(font=FONT, size=10):
         try:
             check_font(font)
             return ImageFont.truetype(str(font), size)
-        except TypeError:
-            check_requirements('Pillow>=8.4.0')  # known issue https://github.com/ultralytics/yolov5/issues/5374
         except URLError:  # not online
             return ImageFont.load_default()
 

val.py

@@ -39,7 +39,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 from models.common import DetectMultiBackend
 from utils.callbacks import Callbacks
 from utils.dataloaders import create_dataloader
-from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements,
+from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size,
                            check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression,
                            print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
 from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
@@ -311,7 +311,6 @@ def run(
             json.dump(jdict, f)
 
         try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
-            check_requirements('pycocotools>=2.0.6')
             from pycocotools.coco import COCO
             from pycocotools.cocoeval import COCOeval
 
@@ -371,7 +370,6 @@ def parse_opt():
 
 
 def main(opt):
-    check_requirements(exclude=('tensorboard', 'thop'))
 
     if opt.task in ('train', 'val', 'test'):  # run normally
         if opt.conf_thres > 0.001:  # https://github.com/ultralytics/yolov5/issues/1466