Cleanup: split GT boxes and class IDs tensors.

inspect_data.ipynb

@@ -438,11 +438,12 @@
    ],
    "source": [
     "image_id = np.random.choice(dataset.image_ids, 1)[0]\n",
-    "image, image_meta, bbox, mask = modellib.load_image_gt(\n",
+    "image, image_meta, class_ids, bbox, mask = modellib.load_image_gt(\n",
     "    dataset, config, image_id, use_mini_mask=False)\n",
     "\n",
     "log(\"image\", image)\n",
     "log(\"image_meta\", image_meta)\n",
+    "log(\"class_ids\", class_ids)\n",
     "log(\"bbox\", bbox)\n",
     "log(\"mask\", mask)\n",
     "\n",
@@ -466,7 +467,7 @@
     }
    ],
    "source": [
-    "visualize.display_instances(image, bbox[:,:4], mask, bbox[:,4], dataset.class_names)"
+    "visualize.display_instances(image, bbox, mask, class_ids, dataset.class_names)"
    ]
   },
   {
@@ -496,7 +497,7 @@
    ],
    "source": [
     "# Add augmentation and mask resizing.\n",
-    "image, image_meta, bbox, mask = modellib.load_image_gt(\n",
+    "image, image_meta, class_ids, bbox, mask = modellib.load_image_gt(\n",
     "    dataset, config, image_id, augment=True, use_mini_mask=True)\n",
     "log(\"mask\", mask)\n",
     "display_images([image]+[mask[:,:,i] for i in range(min(mask.shape[-1], 7))])"
@@ -520,7 +521,7 @@
    ],
    "source": [
     "mask = utils.expand_mask(bbox, mask, image.shape)\n",
-    "visualize.display_instances(image, bbox[:,:4], mask, bbox[:,4], dataset.class_names)"
+    "visualize.display_instances(image, bbox, mask, class_ids, dataset.class_names)"
    ]
   },
   {
@@ -634,7 +635,7 @@
     "\n",
     "# Load and draw random image\n",
     "image_id = np.random.choice(dataset.image_ids, 1)[0]\n",
-    "image, image_meta, _, _ = modellib.load_image_gt(dataset, config, image_id)\n",
+    "image, image_meta, _, _, _ = modellib.load_image_gt(dataset, config, image_id)\n",
     "fig, ax = plt.subplots(1, figsize=(10, 10))\n",
     "ax.imshow(image)\n",
     "levels = len(config.BACKBONE_SHAPES)\n",

inspect_model.ipynb

@@ -313,21 +313,17 @@
    ],
    "source": [
     "image_id = random.choice(dataset.image_ids)\n",
-    "image, image_meta, gt_bbox, gt_mask =\\\n",
+    "image, image_meta, gt_class_id, gt_bbox, gt_mask =\\\n",
     "    modellib.load_image_gt(dataset, config, image_id, use_mini_mask=False)\n",
     "info = dataset.image_info[image_id]\n",
     "print(\"image ID: {}.{} ({}) {}\".format(info[\"source\"], info[\"id\"], image_id, \n",
     "                                       dataset.image_reference(image_id)))\n",
-    "gt_class_id = gt_bbox[:, 4]\n",
-    "\n",
     "# Run object detection\n",
     "results = model.detect([image], verbose=1)\n",
     "\n",
     "# Display results\n",
     "ax = get_ax(1)\n",
     "r = results[0]\n",
-    "# visualize.display_instances(image, gt_bbox[:,:4], gt_mask, gt_bbox[:,4], \n",
-    "#                             dataset.class_names, ax=ax[0], title=\"Ground Truth\")\n",
     "visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], \n",
     "                            dataset.class_names, r['scores'], ax=ax,\n",
     "                            title=\"Predictions\")\n",
@@ -361,7 +357,7 @@
    ],
    "source": [
     "# Draw precision-recall curve\n",
-    "AP, precisions, recalls, overlaps = utils.compute_ap(gt_bbox[:,:4], gt_bbox[:,4], \n",
+    "AP, precisions, recalls, overlaps = utils.compute_ap(gt_bbox, gt_class_id, \n",
     "                                          r['rois'], r['class_ids'], r['scores'])\n",
     "visualize.plot_precision_recall(AP, precisions, recalls)"
    ]
@@ -384,7 +380,7 @@
    ],
    "source": [
     "# Grid of ground truth objects and their predictions\n",
-    "visualize.plot_overlaps(gt_bbox[:, 4], r['class_ids'], r['scores'],\n",
+    "visualize.plot_overlaps(gt_class_id, r['class_ids'], r['scores'],\n",
     "                        overlaps, dataset.class_names)"
    ]
   },
@@ -422,7 +418,7 @@
     "    APs = []\n",
     "    for image_id in image_ids:\n",
     "        # Load image\n",
-    "        image, image_meta, gt_bbox, gt_mask =\\\n",
+    "        image, image_meta, gt_class_id, gt_bbox, gt_mask =\\\n",
     "            modellib.load_image_gt(dataset, config,\n",
     "                                   image_id, use_mini_mask=False)\n",
     "        # Run object detection\n",
@@ -430,7 +426,7 @@
     "        # Compute AP\n",
     "        r = results[0]\n",
     "        AP, precisions, recalls, overlaps =\\\n",
-    "            utils.compute_ap(gt_bbox[:,:4], gt_bbox[:,4],\n",
+    "            utils.compute_ap(gt_bbox, gt_class_id,\n",
     "                              r['rois'], r['class_ids'], r['scores'])\n",
     "        APs.append(AP)\n",
     "    return APs\n",
@@ -493,7 +489,7 @@
     "# target_rpn_match is 1 for positive anchors, -1 for negative anchors\n",
     "# and 0 for neutral anchors.\n",
     "target_rpn_match, target_rpn_bbox = modellib.build_rpn_targets(\n",
-    "    image.shape, model.anchors, gt_bbox, model.config)\n",
+    "    image.shape, model.anchors, gt_class_id, gt_bbox, model.config)\n",
     "log(\"target_rpn_match\", target_rpn_match)\n",
     "log(\"target_rpn_bbox\", target_rpn_bbox)\n",
     "\n",
@@ -568,8 +564,9 @@
     "pillar = model.keras_model.get_layer(\"ROI\").output  # node to start searching from\n",
     "\n",
     "# TF 1.4 introduces a new version of NMS. Search for both names to support TF 1.3 and 1.4\n",
-    "nms_node = (model.ancestor(pillar, \"ROI/rpn_non_max_suppression:0\")\n",
-    "            or model.ancestor(pillar, \"ROI/rpn_non_max_suppression/NonMaxSuppressionV2:0\"))\n",
+    "nms_node = model.ancestor(pillar, \"ROI/rpn_non_max_suppression:0\")\n",
+    "if nms_node is None:\n",
+    "    nms_node = model.ancestor(pillar, \"ROI/rpn_non_max_suppression/NonMaxSuppressionV2:0\")\n",
     "\n",
     "rpn = model.run_graph([image], [\n",
     "    (\"rpn_class\", model.keras_model.get_layer(\"rpn_class\").output),\n",

train_shapes.ipynb

@@ -907,16 +907,17 @@
    "source": [
     "# Test on a random image\n",
     "image_id = random.choice(dataset_val.image_ids)\n",
-    "original_image, image_meta, gt_bbox, gt_mask =\\\n",
+    "original_image, image_meta, gt_class_id, gt_bbox, gt_mask =\\\n",
     "    modellib.load_image_gt(dataset_val, inference_config, \n",
     "                           image_id, use_mini_mask=False)\n",
     "\n",
     "log(\"original_image\", original_image)\n",
     "log(\"image_meta\", image_meta)\n",
+    "log(\"gt_class_id\", gt_bbox)\n",
     "log(\"gt_bbox\", gt_bbox)\n",
     "log(\"gt_mask\", gt_mask)\n",
     "\n",
-    "visualize.display_instances(original_image, gt_bbox[:,:4], gt_mask, gt_bbox[:,4], \n",
+    "visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id, \n",
     "                            dataset_train.class_names, figsize=(8, 8))"
    ]
   },
@@ -981,7 +982,7 @@
     "APs = []\n",
     "for image_id in image_ids:\n",
     "    # Load image and ground truth data\n",
-    "    image, image_meta, gt_bbox, gt_mask =\\\n",
+    "    image, image_meta, gt_class_id, gt_bbox, gt_mask =\\\n",
     "        modellib.load_image_gt(dataset_val, inference_config,\n",
     "                               image_id, use_mini_mask=False)\n",
     "    molded_images = np.expand_dims(modellib.mold_image(image, inference_config), 0)\n",
@@ -990,7 +991,7 @@
     "    r = results[0]\n",
     "    # Compute AP\n",
     "    AP, precisions, recalls, overlaps =\\\n",
-    "        utils.compute_ap(gt_bbox[:,:4], gt_bbox[:,4],\n",
+    "        utils.compute_ap(gt_bbox, gt_class_id,\n",
     "                         r[\"rois\"], r[\"class_ids\"], r[\"scores\"])\n",
     "    APs.append(AP)\n",
     "    \n",