Merge pull request #3 from robin-maillot/move_nanovare_dependencies_t…

…o_core Move nanovare dependencies to core
robin-maillot · Nov 6, 2020 · 2b1bd73 · 2b1bd73
2 parents f6eca9a + c0b5591
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diff --git a/Dockerfile b/Dockerfile
@@ -1,5 +1,5 @@
 # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:20.08-py3
+FROM nvcr.io/nvidia/pytorch:20.10-py3
 
 # Install dependencies
 RUN pip install --upgrade pip

diff --git a/README.md b/README.md
@@ -21,14 +21,14 @@ This repository represents Ultralytics open-source research into future object d
 
 | Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
 |---------- |------ |------ |------ | -------- | ------| ------ |------  |  :------: |
-| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/tag/v3.0)    | 37.0     | 37.0     | 56.2     | **2.4ms** | **416** || 7.5M   | 13.2B
-| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/tag/v3.0)    | 44.3     | 44.3     | 63.2     | 3.4ms     | 294     || 21.8M  | 39.4B
-| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/tag/v3.0)    | 47.7     | 47.7     | 66.5     | 4.4ms     | 227     || 47.8M  | 88.1B
-| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/tag/v3.0)    | **49.2** | **49.2** | **67.7** | 6.9ms     | 145     || 89.0M  | 166.4B
+| [YOLOv5s](https://github.com/ultralytics/yolov5/releases)    | 37.0     | 37.0     | 56.2     | **2.4ms** | **416** || 7.5M   | 13.2B
+| [YOLOv5m](https://github.com/ultralytics/yolov5/releases)    | 44.3     | 44.3     | 63.2     | 3.4ms     | 294     || 21.8M  | 39.4B
+| [YOLOv5l](https://github.com/ultralytics/yolov5/releases)    | 47.7     | 47.7     | 66.5     | 4.4ms     | 227     || 47.8M  | 88.1B
+| [YOLOv5x](https://github.com/ultralytics/yolov5/releases)    | **49.2** | **49.2** | **67.7** | 6.9ms     | 145     || 89.0M  | 166.4B
 | | | | | | || |
-| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/tag/v3.0) + TTA|**50.8**| **50.8** | **68.9** | 25.5ms    | 39      || 89.0M  | 354.3B
+| [YOLOv5x](https://github.com/ultralytics/yolov5/releases) + TTA|**50.8**| **50.8** | **68.9** | 25.5ms    | 39      || 89.0M  | 354.3B
 | | | | | | || |
-| [YOLOv3-SPP](https://github.com/ultralytics/yolov5/releases/tag/v3.0) | 45.6     | 45.5     | 65.2     | 4.5ms     | 222     || 63.0M  | 118.0B
+| [YOLOv3-SPP](https://github.com/ultralytics/yolov5/releases) | 45.6     | 45.5     | 65.2     | 4.5ms     | 222     || 63.0M  | 118.0B
 
 ** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.  
 ** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. **Reproduce** by `python test.py --data coco.yaml --img 640 --conf 0.001`  
@@ -55,7 +55,8 @@ To install yolov5 as a module you can either:
 
 ## Tutorials
 
-* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)
+* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) (🚀  recommended)
+* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) (🚀  NEW)
 * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
 * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
 * [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251)
@@ -138,7 +139,7 @@ optional arguments:
 
 ## Inference
 
-Inference can be run on most common media formats. Model [checkpoints](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) are downloaded automatically if available. Results are saved to `./inference/output`.
+detect.py runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `inference/output`.
 ```bash
 $ python detect.py --source 0  # webcam
                             file.jpg  # image 
@@ -150,22 +151,43 @@ $ python detect.py --source 0  # webcam
                             http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8  # http stream
 ```
 
-To run inference on examples in the `./inference/images` folder:
-
+To run inference on example images in `inference/images`:
 ```bash
-$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
+$ python detect.py --source inference/images --weights yolov5s.pt --conf 0.25
 
-Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
-Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
+Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, output='inference/output', save_conf=False, save_txt=False, source='inference/images', update=False, view_img=False, weights='yolov5s.pt')
+Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16160MB)
 
-Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
+Downloading https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5s.pt to yolov5s.pt... 100%|██████████████| 14.5M/14.5M [00:00<00:00, 21.3MB/s]
 
-image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
-image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
-Results saved to /content/yolov5/inference/output
+Fusing layers... 
+Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients
+image 1/2 yolov5/inference/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.013s)
+image 2/2 yolov5/inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.013s)
+Results saved to yolov5/inference/output
+Done. (0.124s)
 ```
+<img src="https://user-images.githubusercontent.com/26833433/97107365-685a8d80-16c7-11eb-8c2e-83aac701d8b9.jpeg" width="500">  
+
+### PyTorch Hub
+
+To run **batched inference** with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36):
+```python
+import torch
+from PIL import Image
 
-<img src="https://user-images.githubusercontent.com/26833433/83082816-59e54880-a039-11ea-8abe-ab90cc1ec4b0.jpeg" width="500">  
+# Model
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).fuse().eval()  # yolov5s.pt
+model = model.autoshape()  # for autoshaping of PIL/cv2/np inputs and NMS
+
+# Images
+img1 = Image.open('zidane.jpg')
+img2 = Image.open('bus.jpg')
+imgs = [img1, img2]  # batched list of images
+
+# Inference
+prediction = model(imgs, size=640)  # includes NMS
+```
 
 
 ## Nanovare Training

diff --git a/data/hyp.finetune.yaml b/data/hyp.finetune.yaml
@@ -15,7 +15,7 @@ weight_decay: 0.00036
 warmup_epochs: 2.0
 warmup_momentum: 0.5
 warmup_bias_lr: 0.05
-giou: 0.0296
+box: 0.0296
 cls: 0.243
 cls_pw: 0.631
 obj: 0.301

diff --git a/data/hyp.scratch.yaml b/data/hyp.scratch.yaml
@@ -10,7 +10,7 @@ weight_decay: 0.0005  # optimizer weight decay 5e-4
 warmup_epochs: 3.0  # warmup epochs (fractions ok)
 warmup_momentum: 0.8  # warmup initial momentum
 warmup_bias_lr: 0.1  # warmup initial bias lr
-giou: 0.05  # box loss gain
+box: 0.05  # box loss gain
 cls: 0.5  # cls loss gain
 cls_pw: 1.0  # cls BCELoss positive_weight
 obj: 1.0  # obj loss gain (scale with pixels)

diff --git a/data/scripts/get_coco.sh b/data/scripts/get_coco.sh
@@ -8,14 +8,17 @@
 #     /yolov5
 
 # Download/unzip labels
-echo 'Downloading COCO 2017 labels ...'
 d='../' # unzip directory
-f='coco2017labels.zip' && curl -L https://github.com/ultralytics/yolov5/releases/download/v1.0/$f -o $f
-unzip -q $f -d $d && rm $f
+url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+f='coco2017labels.zip'                                                                 # 68 MB
+echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
 
 # Download/unzip images
-echo 'Downloading COCO 2017 images ...'
 d='../coco/images' # unzip directory
-f='train2017.zip' && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d $d && rm $f # 19G, 118k images
-f='val2017.zip' && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d $d && rm $f   # 1G, 5k images
-# f='test2017.zip' && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d $d && rm $f  # 7G,  41k images
+url=http://images.cocodataset.org/zips/
+f1='train2017.zip' # 19G, 118k images
+f2='val2017.zip'   # 1G, 5k images
+f3='test2017.zip'  # 7G, 41k images (optional)
+for f in $f1 $f2; do
+  echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
+done
diff --git a/data/scripts/get_voc.sh b/data/scripts/get_voc.sh
@@ -8,79 +8,23 @@
 #     /yolov5
 
 start=$(date +%s)
-
-# handle optional download dir
-if [ -z "$1" ]; then
-  # navigate to ~/tmp
-  echo "navigating to ../tmp/ ..."
-  mkdir -p ../tmp
-  cd ../tmp/
-else
-  # check if is valid directory
-  if [ ! -d $1 ]; then
-    echo $1 "is not a valid directory"
-    exit 0
-  fi
-  echo "navigating to" $1 "..."
-  cd $1
-fi
-
-echo "Downloading VOC2007 trainval ..."
-# Download data
-curl -LO http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
-echo "Downloading VOC2007 test data ..."
-curl -LO http://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
-echo "Done downloading."
-
-# Extract data
-echo "Extracting trainval ..."
-tar -xf VOCtrainval_06-Nov-2007.tar
-echo "Extracting test ..."
-tar -xf VOCtest_06-Nov-2007.tar
-echo "removing tars ..."
-rm VOCtrainval_06-Nov-2007.tar
-rm VOCtest_06-Nov-2007.tar
-
-end=$(date +%s)
-runtime=$((end - start))
-
-echo "Completed in" $runtime "seconds"
-
-start=$(date +%s)
-
-# handle optional download dir
-if [ -z "$1" ]; then
-  # navigate to ~/tmp
-  echo "navigating to ../tmp/ ..."
-  mkdir -p ../tmp
-  cd ../tmp/
-else
-  # check if is valid directory
-  if [ ! -d $1 ]; then
-    echo $1 "is not a valid directory"
-    exit 0
-  fi
-  echo "navigating to" $1 "..."
-  cd $1
-fi
-
-echo "Downloading VOC2012 trainval ..."
-# Download data
-curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
-echo "Done downloading."
-
-# Extract data
-echo "Extracting trainval ..."
-tar -xf VOCtrainval_11-May-2012.tar
-echo "removing tar ..."
-rm VOCtrainval_11-May-2012.tar
+mkdir -p ../tmp
+cd ../tmp/
+
+# Download/unzip images and labels
+d='.' # unzip directory
+url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+f1=VOCtrainval_06-Nov-2007.zip # 446MB, 5012 images
+f2=VOCtest_06-Nov-2007.zip     # 438MB, 4953 images
+f3=VOCtrainval_11-May-2012.zip # 1.95GB, 17126 images
+for f in $f1 $f2 $f3; do
+  echo 'Downloading' $url$f ' ...' && curl -L $url$f -o $f && unzip -q $f -d $d && rm $f # download, unzip, remove
+done
 
 end=$(date +%s)
 runtime=$((end - start))
-
 echo "Completed in" $runtime "seconds"
 
-cd ../tmp
 echo "Spliting dataset..."
 python3 - "$@" <<END
 import xml.etree.ElementTree as ET

diff --git a/detect.py b/detect.py
@@ -1,6 +1,5 @@
 import argparse
 import os
-import platform
 import shutil
 import time
 from pathlib import Path
@@ -18,17 +17,17 @@
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
-def detect(info_dict, save_img=False):
+def detect(save_img=False):
     out, source, weights, view_img, save_txt, imgsz = \
-        info_dict["output"], info_dict["source"], info_dict["weights"], info_dict["view_img"], info_dict["save_txt"], info_dict["img_size"]
+        opt.save_dir, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
     webcam = source.isnumeric() or source.startswith(('rtsp://', 'rtmp://', 'http://')) or source.endswith('.txt')
 
     # Initialize
     set_logging()
-    device = select_device(info_dict["device"])
-    if os.path.exists(out):
-        shutil.rmtree(out)  # delete output folder
-    os.makedirs(out)  # make new output folder
+    device = select_device(opt.device)
+    if os.path.exists(out):  # output dir
+        shutil.rmtree(out)  # delete dir
+    os.makedirs(out)  # make new dir
     half = device.type != 'cpu'  # half precision only supported on CUDA
 
     # Load model
@@ -37,6 +36,13 @@ def detect(info_dict, save_img=False):
     if half:
         model.half()  # to FP16
 
+    # Second-stage classifier
+    classify = False
+    if classify:
+        modelc = load_classifier(name='resnet101', n=2)  # initialize
+        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
+        modelc.to(device).eval()
+
     # Set Dataloader
     vid_path, vid_writer = None, None
     if webcam:
@@ -64,12 +70,16 @@ def detect(info_dict, save_img=False):
 
         # Inference
         t1 = time_synchronized()
-        pred = model(img, augment=info_dict["augment"])[0]
+        pred = model(img, augment=opt.augment)[0]
 
         # Apply NMS
-        pred = non_max_suppression(pred, info_dict["conf_thres"], info_dict["iou_thres"], classes=info_dict["classes"], agnostic=info_dict["agnostic_nms"])
+        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
         t2 = time_synchronized()
 
+        # Apply Classifier
+        if classify:
+            pred = apply_classifier(pred, modelc, img, im0s)
+
         # Process detections
         for i, det in enumerate(pred):  # detections per image
             if webcam:  # batch_size >= 1
@@ -94,8 +104,9 @@ def detect(info_dict, save_img=False):
                 for *xyxy, conf, cls in reversed(det):
                     if save_txt:  # Write to file
                         xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                        line = (cls, conf, *xywh) if opt.save_conf else (cls, *xywh)  # label format
                         with open(txt_path + '.txt', 'a') as f:
-                            f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
+                            f.write(('%g ' * len(line) + '\n') % line)
 
                     if save_img or view_img:  # Add bbox to image
                         label = '%s %.2f' % (names[int(cls)], conf)
@@ -137,13 +148,14 @@ def detect(info_dict, save_img=False):
     parser = argparse.ArgumentParser()
     parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
     parser.add_argument('--source', type=str, default='inference/images', help='source')  # file/folder, 0 for webcam
-    parser.add_argument('--output', type=str, default='inference/output', help='output folder')  # output folder
     parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
-    parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
-    parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
+    parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--view-img', action='store_true', help='display results')
     parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+    parser.add_argument('--save-dir', type=str, default='inference/output', help='directory to save results')
     parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
     parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
     parser.add_argument('--augment', action='store_true', help='augmented inference')
@@ -154,7 +166,7 @@ def detect(info_dict, save_img=False):
     with torch.no_grad():
         if opt.update:  # update all models (to fix SourceChangeWarning)
             for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
-                detect(vars(opt))
+                detect()
                 strip_optimizer(opt.weights)
         else:
-            detect(vars(opt))
+            detect()