added m2f metris analysis and moved data analysis nb in a dir

data/test_set_annotated_only/.task_statistics.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36d9eaa9eb0c01c32f03fea8fb2046a9ce51b39d04c8ca202a3a13c9fa7835a0
+size 5620

scripts/m2f_metrics_analysis/m2f_main.ipynb

@@ -0,0 +1,240 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mask2former import Mask2Former\n",
+    "import torch as tr\n",
+    "import os\n",
+    "from datetime import datetime\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "from vre.utils import (FFmpegVideo, collage_fn, semantic_mapper, FakeVideo,\n",
+    "                       colorize_semantic_segmentation, image_resize, image_write)\n",
+    "from pathlib import Path\n",
+    "import pandas as pd\n",
+    "from torchmetrics.functional.classification import multiclass_stat_scores\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapi_mapping = {\n",
+    "    \"land\": [\"Terrain\", \"Sand\", \"Snow\"],\n",
+    "    \"forest\": [\"Vegetation\"],\n",
+    "    \"residential\": [\"Building\", \"Utility Pole\", \"Pole\", \"Fence\", \"Wall\", \"Manhole\", \"Street Light\", \"Curb\",\n",
+    "                    \"Guard Rail\", \"Caravan\", \"Junction Box\", \"Traffic Sign (Front)\", \"Billboard\", \"Banner\",\n",
+    "                    \"Mailbox\", \"Traffic Sign (Back)\", \"Bench\", \"Fire Hydrant\", \"Trash Can\", \"CCTV Camera\",\n",
+    "                    \"Traffic Light\", \"Barrier\", \"Rail Track\", \"Phone Booth\", \"Curb Cut\", \"Traffic Sign Frame\",\n",
+    "                    \"Bike Rack\"],\n",
+    "    \"road\": [\"Road\", \"Lane Marking - General\", \"Sidewalk\", \"Bridge\", \"Other Vehicle\", \"Motorcyclist\", \"Pothole\",\n",
+    "                \"Catch Basin\", \"Car Mount\", \"Tunnel\", \"Parking\", \"Service Lane\", \"Lane Marking - Crosswalk\",\n",
+    "                \"Pedestrian Area\", \"On Rails\", \"Bike Lane\", \"Crosswalk - Plain\"],\n",
+    "    \"little-objects\": [\"Car\", \"Person\", \"Truck\", \"Boat\", \"Wheeled Slow\", \"Trailer\", \"Ground Animal\", \"Bicycle\",\n",
+    "                        \"Motorcycle\", \"Bird\", \"Bus\", \"Ego Vehicle\", \"Bicyclist\", \"Other Rider\"],\n",
+    "    \"water\": [\"Water\"],\n",
+    "    \"sky\": [\"Sky\"],\n",
+    "    \"hill\": [\"Mountain\"]\n",
+    "}\n",
+    "\n",
+    "coco_mapping = {\n",
+    "    \"land\": [\"grass-merged\", \"dirt-merged\", \"sand\", \"gravel\", \"flower\", \"playingfield\", \"snow\", \"platform\"],\n",
+    "    \"forest\": [\"tree-merged\"],\n",
+    "    \"residential\": [\"building-other-merged\", \"house\", \"roof\", \"fence-merged\", \"wall-other-merged\", \"wall-brick\",\n",
+    "                    \"rock-merged\", \"tent\", \"bridge\", \"bench\", \"window-other\", \"fire hydrant\", \"traffic light\",\n",
+    "                    \"umbrella\", \"wall-stone\", \"clock\", \"chair\", \"sports ball\", \"floor-other-merged\",\n",
+    "                    \"floor-wood\", \"stop sign\", \"door-stuff\", \"banner\", \"light\", \"net\", \"surfboard\", \"frisbee\",\n",
+    "                    \"rug-merged\", \"potted plant\", \"parking meter\", \"tennis racket\", \"sink\", \"hair drier\",\n",
+    "                    \"food-other-merged\", \"curtain\", \"mirror-stuff\", \"baseball glove\", \"baseball bat\", \"zebra\",\n",
+    "                    \"spoon\", \"towel\", \"donut\", \"apple\", \"handbag\", \"couch\", \"orange\", \"wall-wood\",\n",
+    "                    \"window-blind\", \"pizza\", \"cabinet-merged\", \"skateboard\", \"remote\", \"bottle\", \"bed\",\n",
+    "                    \"table-merged\", \"backpack\", \"bear\", \"wall-tile\", \"cup\", \"scissors\", \"ceiling-merged\",\n",
+    "                    \"oven\", \"cell phone\", \"microwave\", \"toaster\", \"carrot\", \"fork\", \"giraffe\", \"paper-merged\",\n",
+    "                    \"cat\", \"book\", \"sandwich\", \"wine glass\", \"pillow\", \"blanket\", \"tie\", \"bowl\", \"snowboard\",\n",
+    "                    \"vase\", \"toothbrush\", \"toilet\", \"dining table\", \"laptop\", \"tv\", \"cardboard\", \"keyboard\",\n",
+    "                    \"hot dog\", \"cake\", \"knife\", \"suitcase\", \"refrigerator\", \"fruit\", \"shelf\", \"counter\", \"skis\",\n",
+    "                    \"banana\", \"teddy bear\", \"broccoli\", \"mouse\"],\n",
+    "    \"road\": [\"road\", \"railroad\", \"pavement-merged\", \"stairs\"],\n",
+    "    \"little-objects\": [\"truck\", \"car\", \"boat\", \"horse\", \"person\", \"train\", \"elephant\", \"bus\", \"bird\", \"sheep\",\n",
+    "                        \"cow\", \"motorcycle\", \"dog\", \"bicycle\", \"airplane\", \"kite\"],\n",
+    "    \"water\": [\"river\", \"water-other\", \"sea\"],\n",
+    "    \"sky\": [\"sky-other-merged\"],\n",
+    "    \"hill\": [\"mountain-merged\"]\n",
+    "}\n",
+    "\n",
+    "color_map = [[0, 255, 0], [0, 127, 0], [255, 255, 0], [255, 255, 255],\n",
+    "             [255, 0, 0], [0, 0, 255], [0, 255, 255], [127, 127, 63]]\n",
+    "\n",
+    "def eval(y: np.ndarray, gt: np.ndarray) -> float:\n",
+    "    tp, fp, _, fn = multiclass_stat_scores(tr.from_numpy(y), tr.from_numpy(gt), num_classes=8, average=None)[:, 0:4].T\n",
+    "    iou = (tp / (tp + fp + fn)).nan_to_num(0, 0, 0)\n",
+    "    weights = tr.FloatTensor([0.28172092, 0.30589653, 0.13341699, 0.05937348,\n",
+    "                              0.00474491, 0.05987466, 0.08660721, 0.06836531])\n",
+    "    iou_avg = (iou * weights).sum().item()\n",
+    "    return iou_avg\n",
+    "\n",
+    "def collage_fn2(images: list[np.ndarray], size: tuple[int, int], **kwargs):\n",
+    "    images_rsz = [image_resize(image, *size) for image in images]\n",
+    "    return collage_fn(images_rsz, **kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "video = FFmpegVideo((\"/export/home/proiecte/aux/mihai_cristian.pirvu/datasets/dronescapes/raw_data/videos\"\n",
+    "                     \"/norway_210821_DJI_0015_full/DJI_0015.MP4\"))\n",
+    "gt_dir = (\"/export/home/proiecte/aux/mihai_cristian.pirvu/datasets/dronescapes/data/\"\n",
+    "          \"test_set_annotated_only/semantic_segprop8/norway_210821_DJI_0015_full_\")\n",
+    "print(video)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_id = \"49189528_0\" # \"49189528_1\" (r50/mapillary), \"47429163_0\" (swin/coco), \"49189528_0\" (swin/mapillary)\n",
+    "os.environ[\"VRE_DEVICE\"] = device = \"cuda\" #\"cpu\"\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"7\"\n",
+    "\n",
+    "m2f_1 = Mask2Former(model_id, semantic_argmax_only=False, name=\"m2f\", dependencies=[])\n",
+    "m2f_2 = Mask2Former(\"47429163_0\", semantic_argmax_only=False, name=\"m2f\", dependencies=[])\n",
+    "m2f_3 = Mask2Former(\"49189528_1\", semantic_argmax_only=False, name=\"m2f\", dependencies=[])\n",
+    "\n",
+    "m2f_1.device = \"cuda\" if tr.cuda.is_available() else \"cpu\"\n",
+    "m2f_2.device = \"cuda\" if tr.cuda.is_available() else \"cpu\"\n",
+    "m2f_3.device = \"cuda\" if tr.cuda.is_available() else \"cpu\"\n",
+    "\n",
+    "metrics = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frame_ix = 900\n",
+    "def load_gt(ix: int) -> np.ndarray:\n",
+    "    gt_path = f\"{gt_dir}{ix}.npz\"\n",
+    "    assert Path(gt_path).exists(), gt_path\n",
+    "    gt_data = np.load(gt_path)[\"arr_0\"]\n",
+    "    return gt_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def m2f_do_one(m2f: Mask2Former, frame: np.ndarray, gt_data_shape, mapping: dict) -> tuple[np.ndarray, np.ndarray]:\n",
+    "    m2f_1.vre_free() if m2f_1.setup_called and id(m2f) != id(m2f_1) else None\n",
+    "    m2f_2.vre_free() if m2f_2.setup_called and id(m2f) != id(m2f_1) else None\n",
+    "    m2f_3.vre_free() if m2f_3.setup_called and id(m2f) != id(m2f_1) else None\n",
+    "    m2f.vre_setup() if not m2f.setup_called else None\n",
+    "\n",
+    "    now = datetime.now()\n",
+    "    m2f.data = None\n",
+    "    m2f.compute(FakeVideo(frame[None], fps=1), [0])\n",
+    "    print(f\"Pred took: {datetime.now() - now}\"); now = datetime.now()\n",
+    "    m2f_mapped = semantic_mapper(m2f.data.output.argmax(-1)[0], mapping, m2f.classes)\n",
+    "    m2f_mapped = image_resize(m2f_mapped, *gt_data_shape, interpolation=\"nearest\")\n",
+    "    print(f\"semantic_mapper took: {datetime.now() - now}\"); now = datetime.now()\n",
+    "    m2f_colorized = colorize_semantic_segmentation(m2f_mapped[None], list(mapping), color_map, rgb=rgb_rsz[None])[0]\n",
+    "    print(f\"colorize took: {datetime.now() - now}\"); now = datetime.now()\n",
+    "    return m2f_mapped, m2f_colorized\n",
+    "\n",
+    "def eval_and_store(frame, frame_ix, res_all: list[tuple[np.ndarray], np.ndarray], gt_color: np.ndarray,\n",
+    "                   columns: list[str]):\n",
+    "    collage_data = []\n",
+    "    for item in res_all:\n",
+    "        collage_data.extend([frame, item[1], gt_color])\n",
+    "    clg = collage_fn2(collage_data, size=gt_color.shape[0:2], rows_cols=(-1, 3))\n",
+    "    image_write(clg, f\"collage_{frame_ix}.png\")\n",
+    "    display(Image.fromarray(clg))\n",
+    "    evals = [eval(item[0], gt_data) for item in res_all]\n",
+    "\n",
+    "    try:\n",
+    "        metrics = pd.read_csv(\"metrics.csv\", index_col=0)\n",
+    "    except Exception as e:\n",
+    "        metrics = pd.DataFrame(None, columns=columns)\n",
+    "\n",
+    "    metrics.loc[frame_ix] = evals\n",
+    "    display(metrics.sort_index())\n",
+    "    metrics.to_csv(\"metrics.csv\")\n",
+    "\n",
+    "for frame_ix in [60, 120, 300, 600, 900, 1200, 1500]:\n",
+    "    frame, gt_data = video[frame_ix], load_gt(frame_ix)\n",
+    "    rgb_rsz = image_resize(frame, *gt_data.shape)\n",
+    "    gt_color = colorize_semantic_segmentation(gt_data[None], classes=list(mapi_mapping), color_map=color_map,\n",
+    "                                            rgb=rgb_rsz[None])[0]\n",
+    "    mapped1, colorized1 = m2f_do_one(m2f_1, frame, gt_data.shape, mapi_mapping)\n",
+    "    mapped2, colorized2 = m2f_do_one(m2f_2, frame, gt_data.shape, coco_mapping)\n",
+    "    mapped3, colorized3 = m2f_do_one(m2f_3, frame, gt_data.shape, mapi_mapping)\n",
+    "\n",
+    "    mapped1_rsz, colorized1_rsz = m2f_do_one(m2f_1, rgb_rsz, gt_data.shape, mapi_mapping)\n",
+    "    mapped2_rsz, colorized2_rsz = m2f_do_one(m2f_2, rgb_rsz, gt_data.shape, coco_mapping)\n",
+    "    mapped3_rsz, colorized3_rsz = m2f_do_one(m2f_3, rgb_rsz, gt_data.shape, mapi_mapping)\n",
+    "\n",
+    "    all_res = [\n",
+    "        (mapped1, colorized1), (mapped2, colorized2), (mapped3, colorized3),\n",
+    "        (mapped1_rsz, colorized1_rsz), (mapped2_rsz, colorized2_rsz), (mapped3_rsz, colorized3_rsz),\n",
+    "    ]\n",
+    "    columns = [\"swin_mapillary\", \"swin_coco\", \"r50_mapillary\",\n",
+    "               \"swin_mapillary_rsz\", \"swin_coco_rsz\", \"r50_mapillary_rsz\"]\n",
+    "\n",
+    "    eval_and_store(frame, frame_ix, all_res, gt_color, columns)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ngc",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

scripts/m2f_metrics_analysis/metrics.csv

@@ -0,0 +1,8 @@
+,swin_mapillary,swin_coco,r50_mapillary,swin_mapillary_rsz,swin_coco_rsz,r50_mapillary_rsz
+900,0.1421419978141784,0.2064806669950485,0.3687707483768463,0.2772172391414642,0.206145167350769,0.4239617884159088
+60,0.2167781591415405,0.2062840312719345,0.4351741373538971,0.3048166632652282,0.1999642997980117,0.4761459827423095
+120,0.2345813512802124,0.2026010006666183,0.4289666712284088,0.3349316716194153,0.199358657002449,0.45726078748703
+300,0.1704004108905792,0.2161678075790405,0.4017727971076965,0.2839266061782837,0.2174815833568573,0.4474448561668396
+600,0.1249909698963165,0.2117721140384674,0.3536447584629059,0.2163735926151275,0.2082659006118774,0.4042723476886749
+1200,0.2162990868091583,0.2044363170862198,0.3678789138793945,0.2966309785842895,0.2034457772970199,0.3900764584541321
+1500,0.2148686647415161,0.22754918038845062,0.35765504837036133,0.27770155668258667,0.22701863944530487,0.36417320370674133

scripts/{vre_data_analysis.py → vre_data_analysis/vre_data_analysis.py}

@@ -10,6 +10,7 @@ import io
 import base64
 import bs4
 from PIL import Image
+import seaborn as sns
 
 def extract_pil_from_b64_image(base64_buf: str) -> Image:
     return Image.open(io.BytesIO(base64.b64decode(base64_buf)))
@@ -43,6 +44,7 @@ def save_html(html_imgs: list[str], description: str, out_path: str):
     open(out_path, "w").write(str(html))
     print(f"Written html at '{out_path}'")
 
+
 def histogram_from_classification_task(reader: MultiTaskDataset, classif: SemanticRepresentation,
                                        n: int | None = None, mode: str = "sequential", **figkwargs) -> plt.Figure:
     fig = plt.Figure(**figkwargs)
@@ -59,10 +61,17 @@ def histogram_from_classification_task(reader: MultiTaskDataset, classif: Semant
     df = pd.DataFrame({"Labels": classif.classes, "Values": counts})
     df["Values"] = df["Values"] / df["Values"].sum()
     df = df.sort_values("Values", ascending=True)
-    df = df[df["Values"] > 0.01]
-    df.plot(x="Labels", y="Values", kind="barh", legend=False, color="skyblue", ax=fig.gca(), title=classif.name)
+    df = df[df["Values"] > 0.005]
+
+    ax = fig.gca()
+    sns.barplot(data=df, y="Labels", x="Values", palette="viridis", legend=True, ax=ax, width=1)
+
+    # Adjust y-axis tick positions and spacing
+    ax.set_title(classif.name, fontsize=14, fontweight='bold')
+    ax.set_ylabel("Labels", fontsize=12)
+
+    fig.set_size_inches(8, 2 if len(df) <= 2 else len(df) * 0.5)
     fig.gca().set_xlim(0, 1)
-    # fig.gca().set_ylabel("Values")
     fig.tight_layout()
     plt.close()
     return fig