Automatically download trained model file

demo.ipynb

@@ -45,10 +45,11 @@
     "# Directory to save logs and trained model\n",
     "MODEL_DIR = os.path.join(ROOT_DIR, \"logs\")\n",
     "\n",
-    "# Path to trained weights file\n",
-    "# Download this file and place in the root of your \n",
-    "# project (See README file for details)\n",
+    "# Local path to trained weights file\n",
     "COCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n",
+    "# Download COCO trained weights from Releases if needed\n",
+    "if not os.path.exists(COCO_MODEL_PATH):\n",
+    "    utils.download_trained_weights(COCO_MODEL_PATH)\n",
     "\n",
     "# Directory of images to run detection on\n",
     "IMAGE_DIR = os.path.join(ROOT_DIR, \"images\")"
@@ -144,6 +145,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
+    "collapsed": true,
     "scrolled": false
    },
    "outputs": [],
@@ -282,7 +284,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

inspect_model.ipynb

@@ -49,10 +49,11 @@
     "# Directory to save logs and trained model\n",
     "MODEL_DIR = os.path.join(ROOT_DIR, \"logs\")\n",
     "\n",
-    "# Path to trained weights file\n",
-    "# Download this file and place in the root of your \n",
-    "# project (See README file for details)\n",
+    "# Local path to trained weights file\n",
     "COCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n",
+    "# Download COCO trained weights from Releases if needed\n",
+    "if not os.path.exists(COCO_MODEL_PATH):\n",
+    "    utils.download_trained_weights(COCO_MODEL_PATH)\n",
     "\n",
     "# Path to Shapes trained weights\n",
     "SHAPES_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_shapes.h5\")"
@@ -1377,7 +1378,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

inspect_weights.ipynb

@@ -44,8 +44,11 @@
     "# Directory to save logs and trained model\n",
     "MODEL_DIR = os.path.join(ROOT_DIR, \"logs\")\n",
     "\n",
-    "# Path to COCO trained weights\n",
+    "# Local path to trained weights file\n",
     "COCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n",
+    "# Download COCO trained weights from Releases if needed\n",
+    "if not os.path.exists(COCO_MODEL_PATH):\n",
+    "    utils.download_trained_weights(COCO_MODEL_PATH)\n",
     "\n",
     "# Path to Shapes trained weights\n",
     "SHAPES_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_shapes.h5\")"
@@ -266,7 +269,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

train_shapes.ipynb

@@ -51,8 +51,11 @@
     "# Directory to save logs and trained model\n",
     "MODEL_DIR = os.path.join(ROOT_DIR, \"logs\")\n",
     "\n",
-    "# Path to COCO trained weights\n",
-    "COCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")"
+    "# Local path to trained weights file\n",
+    "COCO_MODEL_PATH = os.path.join(ROOT_DIR, \"mask_rcnn_coco.h5\")\n",
+    "# Download COCO trained weights from Releases if needed\n",
+    "if not os.path.exists(COCO_MODEL_PATH):\n",
+    "    utils.download_trained_weights(COCO_MODEL_PATH)"
    ]
   },
   {
@@ -1024,7 +1027,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

utils.py

@@ -16,6 +16,11 @@ import tensorflow as tf
 import scipy.misc
 import skimage.color
 import skimage.io
+import urllib.request
+import shutil
+
+# URL from which to download the latest COCO trained weights
+COCO_MODEL_URL = "https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5"
 
 
 ############################################################
@@ -688,3 +693,16 @@ def batch_slice(inputs, graph_fn, batch_size, names=None):
         result = result[0]
 
     return result
+
+
+def download_trained_weights(coco_model_path, verbose=1):
+    """Download COCO trained weights from Releases.
+
+    coco_model_path: local path of COCO trained weights
+    """
+    if verbose > 0:
+        print("Downloading pretrained model to " + coco_model_path + " ...")
+    with urllib.request.urlopen(COCO_MODEL_URL) as resp, open(coco_model_path, 'wb') as out:
+        shutil.copyfileobj(resp, out)
+    if verbose > 0:
+        print("... done downloading pretrained model!")