Merge branch 'main' into 'main'

yolov5增加了mpi单机多卡和多机多卡启动方式，其readme文件进行了更新，对maskrcnn的debug输出日志进行了删除，并更新了该模型的readme文件

See merge request dcutoolkit/deeplearing/dlexamples_new!46

PyTorch/Compute-Vision/Objection/yolov5/utils/__init__.py

-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-utils/initialization
-"""
-
-
-def notebook_init(verbose=True):
-    # Check system software and hardware
-    print('Checking setup...')
-
-    import os
-    import shutil
-
-    from utils.general import check_requirements, emojis, is_colab
-    from utils.torch_utils import select_device  # imports
-
-    check_requirements(('psutil', 'IPython'))
-    import psutil
-    from IPython import display  # to display images and clear console output
-
-    if is_colab():
-        shutil.rmtree('/content/sample_data', ignore_errors=True)  # remove colab /sample_data directory
-
-    if verbose:
-        # System info
-        # gb = 1 / 1000 ** 3  # bytes to GB
-        gib = 1 / 1024 ** 3  # bytes to GiB
-        ram = psutil.virtual_memory().total
-        total, used, free = shutil.disk_usage("/")
-        display.clear_output()
-        s = f'({os.cpu_count()} CPUs, {ram * gib:.1f} GB RAM, {(total - free) * gib:.1f}/{total * gib:.1f} GB disk)'
-    else:
-        s = ''
-
-    select_device(newline=False)
-    print(emojis(f'Setup complete ✅ {s}'))
-    return display

PyTorch/Compute-Vision/Objection/yolov5/utils/activations.py

@@ -18,8 +18,8 @@ class SiLU(nn.Module):  # export-friendly version of nn.SiLU()
 class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
     @staticmethod
     def forward(x):
-        # return x * F.hardsigmoid(x)  # for TorchScript and CoreML
-        return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0  # for TorchScript, CoreML and ONNX
+        # return x * F.hardsigmoid(x)  # for torchscript and CoreML
+        return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX
 
 
 # Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------

PyTorch/Compute-Vision/Objection/yolov5/utils/augmentations.py

@@ -3,13 +3,14 @@
 Image augmentation functions
 """
 
+import logging
 import math
 import random
 
 import cv2
 import numpy as np
 
-from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box
+from utils.general import colorstr, segment2box, resample_segments, check_version
 from utils.metrics import bbox_ioa
 
 
@@ -19,7 +20,7 @@ class Albumentations:
         self.transform = None
         try:
             import albumentations as A
-            check_version(A.__version__, '1.0.3', hard=True)  # version requirement
+            check_version(A.__version__, '1.0.3')  # version requirement
 
             self.transform = A.Compose([
                 A.Blur(p=0.01),
@@ -31,11 +32,11 @@ class Albumentations:
                 A.ImageCompression(quality_lower=75, p=0.0)],
                 bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
 
-            LOGGER.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
+            logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
         except ImportError:  # package not installed, skip
             pass
         except Exception as e:
-            LOGGER.info(colorstr('albumentations: ') + f'{e}')
+            logging.info(colorstr('albumentations: ') + f'{e}')
 
     def __call__(self, im, labels, p=1.0):
         if self.transform and random.random() < p:
@@ -123,7 +124,7 @@ def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleF
 
 def random_perspective(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
                        border=(0, 0)):
-    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
+    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
     # targets = [cls, xyxy]
 
     height = im.shape[0] + border[0] * 2  # shape(h,w,c)
@@ -269,7 +270,7 @@ def mixup(im, labels, im2, labels2):
     return im, labels
 
 
-def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)
+def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)
     # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
     w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
     w2, h2 = box2[2] - box2[0], box2[3] - box2[1]

PyTorch/Compute-Vision/Objection/yolov5/utils/autobatch.py

-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Auto-batch utils
-"""
-
-from copy import deepcopy
-
-import numpy as np
-import torch
-from torch.cuda import amp
-
-from utils.general import LOGGER, colorstr
-from utils.torch_utils import profile
-
-
-def check_train_batch_size(model, imgsz=640):
-    # Check YOLOv5 training batch size
-    with amp.autocast():
-        return autobatch(deepcopy(model).train(), imgsz)  # compute optimal batch size
-
-
-def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
-    # Automatically estimate best batch size to use `fraction` of available CUDA memory
-    # Usage:
-    #     import torch
-    #     from utils.autobatch import autobatch
-    #     model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
-    #     print(autobatch(model))
-
-    prefix = colorstr('AutoBatch: ')
-    LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
-    device = next(model.parameters()).device  # get model device
-    if device.type == 'cpu':
-        LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
-        return batch_size
-
-    d = str(device).upper()  # 'CUDA:0'
-    properties = torch.cuda.get_device_properties(device)  # device properties
-    t = properties.total_memory / 1024 ** 3  # (GiB)
-    r = torch.cuda.memory_reserved(device) / 1024 ** 3  # (GiB)
-    a = torch.cuda.memory_allocated(device) / 1024 ** 3  # (GiB)
-    f = t - (r + a)  # free inside reserved
-    LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
-
-    batch_sizes = [1, 2, 4, 8, 16]
-    try:
-        img = [torch.zeros(b, 3, imgsz, imgsz) for b in batch_sizes]
-        y = profile(img, model, n=3, device=device)
-    except Exception as e:
-        LOGGER.warning(f'{prefix}{e}')
-
-    y = [x[2] for x in y if x]  # memory [2]
-    batch_sizes = batch_sizes[:len(y)]
-    p = np.polyfit(batch_sizes, y, deg=1)  # first degree polynomial fit
-    b = int((f * fraction - p[1]) / p[0])  # y intercept (optimal batch size)
-    LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%)')
-    return b

PyTorch/Compute-Vision/Objection/yolov5/utils/downloads.py

@@ -49,12 +49,9 @@ def attempt_download(file, repo='ultralytics/yolov5'):  # from utils.downloads i
         name = Path(urllib.parse.unquote(str(file))).name  # decode '%2F' to '/' etc.
         if str(file).startswith(('http:/', 'https:/')):  # download
             url = str(file).replace(':/', '://')  # Pathlib turns :// -> :/
-            file = name.split('?')[0]  # parse authentication https://url.com/file.txt?auth...
-            if Path(file).is_file():
-                print(f'Found {url} locally at {file}')  # file already exists
-            else:
-                safe_download(file=file, url=url, min_bytes=1E5)
-            return file
+            name = name.split('?')[0]  # parse authentication https://url.com/file.txt?auth...
+            safe_download(file=name, url=url, min_bytes=1E5)
+            return name
 
         # GitHub assets
         file.parent.mkdir(parents=True, exist_ok=True)  # make parent dir (if required)
@@ -63,12 +60,12 @@ def attempt_download(file, repo='ultralytics/yolov5'):  # from utils.downloads i
             assets = [x['name'] for x in response['assets']]  # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...]
             tag = response['tag_name']  # i.e. 'v1.0'
         except:  # fallback plan
-            assets = ['yolov5n.pt', 'yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt',
-                      'yolov5n6.pt', 'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt']
+            assets = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt',
+                      'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt']
             try:
                 tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
             except:
-                tag = 'v6.0'  # current release
+                tag = 'v5.0'  # current release
 
         if name in assets:
             safe_download(file,

PyTorch/Compute-Vision/Objection/yolov5/utils/flask_rest_api/restapi.py

@@ -5,8 +5,8 @@ import argparse
 import io
 
 import torch
-from flask import Flask, request
 from PIL import Image
+from flask import Flask, request
 
 app = Flask(__name__)
 

PyTorch/Compute-Vision/Objection/yolov5/utils/google_app_engine/additional_requirements.txt

 # add these requirements in your app on top of the existing ones
-pip==21.1
+pip==19.2
 Flask==1.0.2
 gunicorn==19.9.0