Dataset autodownload feature addition (#685)

* initial commit

* move download scripts into data/scripts

* new check_dataset() function in general.py

* move check_dataset() out of with context

* Update general.py

* DDP update

* Update general.py

data/coco.yaml

@@ -1,5 +1,4 @@
 # COCO 2017 dataset http://cocodataset.org
-# Download command: bash yolov5/data/get_coco2017.sh
 # Train command: python train.py --data coco.yaml
 # Default dataset location is next to /yolov5:
 #   /parent_folder
@@ -7,6 +6,9 @@
 #     /yolov5
 
 
+# download command/URL (optional)
+download: bash data/scripts/get_coco.sh
+
 # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
 train: ../coco/train2017.txt  # 118287 images
 val: ../coco/val2017.txt  # 5000 images

data/coco128.yaml

@@ -1,5 +1,4 @@
 # COCO 2017 dataset http://cocodataset.org - first 128 training images
-# Download command:  python -c "from yolov5.utils.google_utils import *; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', 'coco128.zip')"
 # Train command: python train.py --data coco128.yaml
 # Default dataset location is next to /yolov5:
 #   /parent_folder
@@ -7,6 +6,9 @@
 #     /yolov5
 
 
+# download command/URL (optional)
+download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
+
 # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
 train: ../coco128/images/train2017/  # 128 images
 val: ../coco128/images/train2017/  # 128 images

data/get_coco2017.sh

@@ -1,30 +0,0 @@
-#!/bin/bash
-# COCO 2017 dataset http://cocodataset.org
-# Download command: bash yolov5/data/get_coco2017.sh
-# Train command: python train.py --data coco.yaml
-# Default dataset location is next to /yolov5:
-#   /parent_folder
-#     /coco
-#     /yolov5
-
-
-# Download labels from Google Drive, accepting presented query
-filename="coco2017labels.zip"
-fileid="1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L"
-curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" > /dev/null
-curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}" -o ${filename}
-rm ./cookie
-
-# Unzip labels
-unzip -q ${filename}  # for coco.zip
-# tar -xzf ${filename}  # for coco.tar.gz
-rm ${filename}
-
-# Download and unzip images
-cd coco/images
-f="train2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f  # 19G, 118k images
-f="val2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f  # 1G, 5k images
-# f="test2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f  # 7G,  41k images
-
-# cd out
-cd ../..

data/scripts/get_coco.sh

@@ -0,0 +1,21 @@
+#!/bin/bash
+# COCO 2017 dataset http://cocodataset.org
+# Download command: bash data/scripts/get_coco.sh
+# Train command: python train.py --data coco.yaml
+# Default dataset location is next to /yolov5:
+#   /parent_folder
+#     /coco
+#     /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 $f
+
+# Download/unzip images
+echo 'Downloading COCO 2017 images ...'
+d='../coco/images' # unzip directory
+f='train2017.zip' && curl http://images.cocodataset.org/zips/$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

data/get_voc.sh → data/scripts/get_voc.sh

@@ -1,33 +1,32 @@
+#!/bin/bash
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
-# Download command: bash ./data/get_voc.sh
+# Download command: bash data/scripts/get_voc.sh
 # Train command: python train.py --data voc.yaml
 # Default dataset location is next to /yolov5:
 #   /parent_folder
 #     /VOC
 #     /yolov5
 
-
-start=`date +%s`
+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
+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 the data.
+# Download data
 curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
 echo "Downloading VOC2007 test data ..."
 curl -LO http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
@@ -42,44 +41,42 @@ echo "removing tars ..."
 rm VOCtrainval_06-Nov-2007.tar
 rm VOCtest_06-Nov-2007.tar
 
-end=`date +%s`
-runtime=$((end-start))
+end=$(date +%s)
+runtime=$((end - start))
 
 echo "Completed in" $runtime "seconds"
 
-start=`date +%s`
+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
+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 the data.
+# 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
 
-end=`date +%s`
-runtime=$((end-start))
+end=$(date +%s)
+runtime=$((end - start))
 
 echo "Completed in" $runtime "seconds"
 
@@ -144,8 +141,8 @@ for year, image_set in sets:
 
 END
 
-cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt
-cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt
+cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt >train.txt
+cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt
 
 python3 - "$@" <<END
 
@@ -211,5 +208,5 @@ for line in lines:
 
 END
 
-rm -rf ../tmp  # remove temporary directory
+rm -rf ../tmp # remove temporary directory
 echo "VOC download done."

data/voc.yaml

@@ -1,5 +1,4 @@
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
-# Download command: bash ./data/get_voc.sh
 # Train command: python train.py --data voc.yaml
 # Default dataset location is next to /yolov5:
 #   /parent_folder
@@ -7,6 +6,9 @@
 #     /yolov5
 
 
+# download command/URL (optional)
+download: bash data/scripts/get_voc.sh
+
 # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
 train: ../VOC/images/train/  # 16551 images
 val: ../VOC/images/val/  # 4952 images

test.py

@@ -13,7 +13,7 @@ from tqdm import tqdm
 from models.experimental import attempt_load
 from utils.datasets import create_dataloader
 from utils.general import (
-    coco80_to_coco91_class, check_file, check_img_size, compute_loss, non_max_suppression,
+    coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression,
     scale_coords, xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class)
 from utils.torch_utils import select_device, time_synchronized
 
@@ -68,6 +68,7 @@ def test(data,
     model.eval()
     with open(data) as f:
         data = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+    check_dataset(data)  # check
     nc = 1 if single_cls else int(data['nc'])  # number of classes
     iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
     niou = iouv.numel()

train.py

@@ -21,9 +21,9 @@ import test  # import test.py to get mAP after each epoch
 from models.yolo import Model
 from utils.datasets import create_dataloader
 from utils.general import (
-    check_img_size, torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors,
-    labels_to_image_weights, compute_loss, plot_images, fitness, strip_optimizer, plot_results,
-    get_latest_run, check_git_status, check_file, increment_dir, print_mutation, plot_evolution)
+    torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors, labels_to_image_weights,
+    compute_loss, plot_images, fitness, strip_optimizer, plot_results, get_latest_run, check_dataset, check_file,
+    check_git_status, check_img_size, increment_dir, print_mutation, plot_evolution)
 from utils.google_utils import attempt_download
 from utils.torch_utils import init_seeds, ModelEMA, select_device, intersect_dicts
 
@@ -51,6 +51,8 @@ def train(hyp, opt, device, tb_writer=None):
     init_seeds(2 + rank)
     with open(opt.data) as f:
         data_dict = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+    with torch_distributed_zero_first(rank):
+        check_dataset(data_dict)  # check
     train_path = data_dict['train']
     test_path = data_dict['val']
     nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names'])  # number classes, names

utils/general.py

@@ -128,6 +128,25 @@ def check_file(file):
         return files[0]  # return first file if multiple found
 
 
+def check_dataset(dict):
+    # Download dataset if not found
+    train, val = os.path.abspath(dict['train']), os.path.abspath(dict['val'])  # data paths
+    if not (os.path.exists(train) and os.path.exists(val)):
+        print('\nWARNING: Dataset not found, nonexistant paths: %s' % [train, val])
+        if 'download' in dict:
+            s = dict['download']
+            print('Attempting autodownload from: %s' % s)
+            if s.startswith('http') and s.endswith('.zip'):  # URL
+                f = Path(s).name  # filename
+                torch.hub.download_url_to_file(s, f)
+                r = os.system('unzip -q %s -d ../ && rm %s' % (f, f))
+            else:  # bash script
+                r = os.system(s)
+            print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure'))  # analyze return value
+        else:
+            Exception('Dataset autodownload unavailable.')
+
+
 def make_divisible(x, divisor):
     # Returns x evenly divisble by divisor
     return math.ceil(x / divisor) * divisor