Merge pull request #44 from augmentedstartups/dev

Merge dev

README.md

@@ -61,6 +61,8 @@ pip install cython-bbox asone onnxruntime-gpu==1.12.1
 pip install super-gradients==3.1.1
 # for CPU
 pip install torch torchvision
+# for GPU
+pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113
 ```
 </details>
 
@@ -70,7 +72,8 @@ pip install torch torchvision
 ```shell
 python -m venv .env
 .env\Scripts\activate
-pip install numpy Cython
+pip install numpy Cython 
+pip install lap
 pip install -e git+https://github.com/samson-wang/cython_bbox.git#egg=cython-bbox
 
 pip install asone onnxruntime-gpu==1.12.1
@@ -96,10 +99,6 @@ pip install cython-bbox asone
 pip install super-gradients==3.1.1
 # for CPU
 pip install torch torchvision
-
-# for GPU
-pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113
-
 ```
 </details>
 
@@ -213,11 +212,11 @@ detector = ASOne(detector=asone.YOLOX_S_PYTORCH, use_cuda=True)
 
 # For macOs
 # YOLO5
-detector = ASOne(detector=asone.YOLOV5X_MLMODEL, use_cuda=True)
+detector = ASOne(detector=asone.YOLOV5X_MLMODEL)
 # YOLO7
-detector = ASOne(detector=asone.YOLO7_MLMODEL, use_cuda=True)
+detector = ASOne(detector=asone.YOLOV7_MLMODEL)
 # YOLO8
-detector = ASOne(detector=asone.YOLOV8L_MLMODEL, use_cuda=True)
+detector = ASOne(detector=asone.YOLOV8L_MLMODEL)
 ```
 
 </details>
@@ -425,7 +424,6 @@ To setup ASOne using Docker follow instructions given in [docker setup](asone/li
 - [x] YOLO-NAS
 - [ ] SAM Integration
 
-
 |Offered By: |Maintained By:|
 |-------------|-------------|
 |[![AugmentedStarups](https://user-images.githubusercontent.com/107035454/195115263-d3271ef3-973b-40a4-83c8-0ade8727dd40.png)](https://augmentedstartups.com)|[![AxcelerateAI](https://user-images.githubusercontent.com/107035454/195114870-691c8a52-fcf0-462e-9e02-a720fc83b93f.png)](https://axcelerate.ai/)|

asone/demo_detector.py

@@ -5,6 +5,9 @@
 import argparse
 import time
 import os
+import sys
+import torch
+
 
 def main(args):
     filter_classes = args.filter_classes
@@ -15,8 +18,17 @@ def main(args):
     if filter_classes:
         filter_classes = filter_classes.split(',')
 
+    if args.use_cuda and torch.cuda.is_available():
+        args.use_cuda = True
+    else:
+        args.use_cuda = False
 
-    detector = ASOne(asone.YOLOV7_PYTORCH, weights=args.weights, use_cuda=args.use_cuda)
+    if sys.platform.startswith('darwin'):
+        detector = asone.YOLOV7_MLMODEL 
+    else:
+        detector = asone.YOLOV7_PYTORCH
+
+    detector = ASOne(detector, weights=args.weights, use_cuda=args.use_cuda)
 
     cap = cv2.VideoCapture(video_path)
     width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)

asone/demo_tracker.py

@@ -18,7 +18,7 @@ def main(args):
 
 
     detect = ASOne(tracker=asone.BYTETRACK, detector=asone.YOLOV7_PYTORCH, 
-                   use_cuda=True)
+                   use_cuda=args.use_cuda)
 
     track = detect.track_video(video_path, output_dir=args.output_path, 
                                save_result=args.save, display=args.display,

asone/pose_estimators/yolov7_pose/models/common.py

@@ -12,10 +12,10 @@
 from PIL import Image
 from torch.cuda import amp
 
-from utils.datasets import letterbox
-from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
-from utils.plots import color_list, plot_one_box
-from utils.torch_utils import time_synchronized
+from asone.pose_estimators.yolov7_pose.utils.datasets import letterbox
+from asone.pose_estimators.yolov7_pose.utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
+from asone.pose_estimators.yolov7_pose.utils.plots import color_list, plot_one_box
+from asone.pose_estimators.yolov7_pose.utils.torch_utils import time_synchronized
 
 
 ##### basic ####

asone/pose_estimators/yolov7_pose/models/experimental.py

@@ -3,9 +3,9 @@
 import torch
 import torch.nn as nn
 
-from models.common import Conv, DWConv
-from utils.google_utils import attempt_download
-
+from .common import Conv, DWConv
+from asone.pose_estimators.yolov7_pose.utils.google_utils import attempt_download
+from asone.pose_estimators.yolov7_pose.utils.yolov7_pose_utils import yolov7_in_syspath
 
 class CrossConv(nn.Module):
     # Cross Convolution Downsample
@@ -239,7 +239,8 @@ def attempt_load(weights, map_location=None):
     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
         attempt_download(w)
-        ckpt = torch.load(w, map_location=map_location)  # load
+        with yolov7_in_syspath():
+            ckpt = torch.load(w, map_location=map_location)  # load
         model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model
 
     # Compatibility updates

asone/pose_estimators/yolov7_pose/utils/datasets.py

@@ -28,7 +28,7 @@
 
 from .general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \
     resample_segments, clean_str
-from utils.torch_utils import torch_distributed_zero_first
+from .torch_utils import torch_distributed_zero_first
 
 # Parameters
 help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'

asone/pose_estimators/yolov7_pose/utils/yolov7_pose_utils.py

@@ -0,0 +1,20 @@
+import contextlib
+from pathlib import Path
+import sys
+
+@contextlib.contextmanager
+def yolov7_in_syspath():
+    """
+    Temporarily add yolov5 folder to `sys.path`.
+    
+    torch.hub handles it in the same way: https://github.com/pytorch/pytorch/blob/75024e228ca441290b6a1c2e564300ad507d7af6/torch/hub.py#L387
+    
+    Proper fix for: #22, #134, #353, #1155, #1389, #1680, #2531, #3071   
+    No need for such workarounds: #869, #1052, #2949
+    """
+    yolov7_folder_dir = str(Path(__file__).parents[1].absolute())
+    try:
+        sys.path.insert(0, yolov7_folder_dir)
+        yield
+    finally:
+        sys.path.remove(yolov7_folder_dir)

asone/pose_estimators/yolov7_pose/yolov7.py

@@ -5,19 +5,13 @@
 import matplotlib.pyplot as plt
 from torchvision import transforms
 from .utils.datasets import letterbox
-from utils.torch_utils import select_device
-from models.experimental import attempt_load
+from .utils.torch_utils import select_device
+from .models.experimental import attempt_load
 from .utils.general import non_max_suppression_kpt,strip_optimizer,xyxy2xywh
 from .utils.plots import output_to_keypoint, plot_skeleton_kpts,colors,plot_one_box_kpt
+import os
+import sys
 
-def scale_bboxes(bboxes, org_img_shape, resized_img_shape):
-    # Rescaling Bounding Boxes 
-    # print(bboxes[:, :2])
-    # exit()
-    bboxes[:, :2] /= np.array([resized_img_shape[1], resized_img_shape[0]])
-    bboxes[:, :2] *= np.array([org_img_shape[1], org_img_shape[0]])  
-
-    return bboxes
 
 class Yolov7PoseEstimator:
     def __init__(self, weights="yolov7-w6-pose.pt", use_cuda=True): 

main.py

@@ -1,16 +1,29 @@
+import sys
 import argparse
 import asone
 from asone import ASOne
+import torch
+
 
 def main(args):
     filter_classes = args.filter_classes
 
     if filter_classes:
         filter_classes = ['person']
-
+    # Check if cuda available
+    if args.use_cuda and torch.cuda.is_available():
+        args.use_cuda = True
+    else:
+        args.use_cuda = False
+
+    if sys.platform.startswith('darwin'):
+        detector = asone.YOLOV7_MLMODEL 
+    else:
+        detector = asone.YOLOV7_PYTORCH
+
     detect = ASOne(
         tracker=asone.BYTETRACK,
-        detector=asone.YOLOV7_PYTORCH,
+        detector=detector,
         weights=args.weights,
         use_cuda=args.use_cuda
         )

requirements.txt

@@ -12,14 +12,10 @@ pandas
 tabulate
 typing-extensions==3.10.0.2
 wheel
-torch==1.12.1
-torchvision
 numpy==1.23.3
-Cython
-ultralytics
 asone-ocr
 motpy
-
+ultralytics==8.0.109
 torchreid==0.2.5
 tensorboard
 protobuf==3.20

setup.py

@@ -9,7 +9,7 @@
 URL = 'https://github.com/axcelerateai/asone'
 DOWNLOAD_URL = URL
 
-VERSION = '0.3.0'
+VERSION = '0.3.2'
 
 with open('README.md') as f:
     long_description = f.read()