Fix logs

.gitignore

@@ -1,5 +1,5 @@
 # Byte-compiled / optimized / DLL files
-__pycache__/
+*/__pycache__/
 *.py[cod]
 *$py.class
 

app.py

@@ -0,0 +1,99 @@
+from typing import Optional
+import dash
+import dash_core_components as dcc
+import dash_html_components as html
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import json
+import numpy as np
+
+import glob
+import os
+
+
+
+def collect_first_best(pattern: str, neg: Optional[str] = None):
+    files = []
+    for file in sorted(glob.glob(pattern), reverse=True):
+        if neg and neg in file:
+            continue
+        files.append(file)
+
+    figures = []
+    for file in files:
+        title = os.path.split(file)[1].split('.')[0]
+        df1 = pd.read_csv(file)
+        figures.append(px.box(df1, x="map_name", y="counts",
+                              color="algo_type",
+                              notched=True, title=title))
+    return figures
+
+
+def collect_line_plots(pattern: str, neg: Optional[str] = None):
+    files = []
+    for file in sorted(glob.glob(pattern), reverse=True):
+        if neg and neg in file:
+            continue
+        files.append(file)
+
+    lineplots = []
+    for file in files:
+        with open(file) as f:
+            data = json.loads(f.readline().rstrip('\n'))
+            title = os.path.split(file)[1].split('.')[0]
+            fig = go.Figure()
+            fig.update_layout(dict(title=title))
+            for map_name, counts in data.items():
+                uniform_mean = np.array(counts['uniform']['mean'])
+                uniform_std = np.array(counts['uniform']['std'])
+
+                y_u1 = (uniform_mean - uniform_std).tolist()
+                y_u2 = (uniform_mean + uniform_std).tolist()
+
+                roi_mean = np.array(counts['roi']['mean'])
+                roi_std = np.array(counts['roi']['std'])
+                y_r1 = (roi_mean - 0.5 * roi_std).tolist()
+                y_r2 = (roi_mean + roi_std).tolist()
+
+                x1 = list(range(len(uniform_mean)))
+                x2 = list(range(len(roi_mean)))
+
+                fig.add_trace(go.Scatter(x=x1 + x1[::-1], y=y_u1 + y_u2[::-1], fill='toself', name='RRT*-uniform_' + map_name))
+                fig.add_trace(go.Scatter(x=x2 + x2[::-1], y=y_r1 + y_r2[::-1], fill='toself', name='RRT*-roi_' + map_name))
+
+                #fig.add_trace(go.Scatter(x=x1, y=uniform_mean, name='RRT*-uniform_' + map_name))
+                #fig.add_trace(go.Scatter(x=x2, y=roi_mean, name='RRT*-roi_' + map_name))
+            fig.update_traces(mode='lines')
+            lineplots.append(fig)
+    return lineplots
+
+
+figures = collect_first_best('logs/collected_stats_gan*.csv')
+figures.extend(collect_first_best('logs/collected_stats_pix2pix*.csv'))
+figures.extend(collect_first_best('logs/gan*.csv', neg='moving_ai'))
+figures.extend(collect_first_best('logs/pix2pix*.csv', neg='moving_ai'))
+figures.extend(collect_first_best('logs/gan_moving_ai*.csv'))
+figures.extend(collect_first_best('logs/pix2pix_moving_ai*.csv'))
+
+lineplots = collect_line_plots('logs/collected_stats_gan*.plot')
+lineplots.extend(collect_line_plots('logs/collected_stats_pix2pix*.plot'))
+lineplots.extend(collect_line_plots('logs/gan*.plot', neg='moving_ai'))
+lineplots.extend(collect_line_plots('logs/pix2pix*.plot', neg='moving_ai'))
+lineplots.extend(collect_line_plots('logs/gan_moving_ai*.plot'))
+lineplots.extend(collect_line_plots('logs/pix2pix_moving_ai*.plot'))
+
+with open('box_plots.html', 'w') as f:
+    for fig in figures:
+        f.write(fig.to_html(full_html=False, include_plotlyjs='cdn'))
+with open('line_plots.html', 'w') as f:
+    for line in lineplots:
+        f.write(line.to_html(full_html=False, include_plotlyjs='cdn'))
+
+
+app = dash.Dash(__name__)
+app.layout = html.Div([
+    html.Div([dcc.Graph(figure=fig) for fig in figures]),
+    html.Div([dcc.Graph(figure=lineplot) for lineplot in lineplots])
+])
+app.run_server(debug=True)"

download.py

@@ -1,11 +1,13 @@
+import os
 import argparse
 from pathgan.data.utils import download_and_extract
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(prog = "top", description="Training GAN (from original paper")
     parser.add_argument("--url", type=str, help="Url of file.")
-    parser.add_argument("--root", type=str, default=".", help="Root path.")
+    parser.add_argument("--root", type=str, default="data", help="Root path.")
     parser.add_argument("--filename", type=str, default=None, help="Filname.")
     args = parser.parse_args()
+    os.makedirs(args.root, exist_ok=True)
     download_and_extract(args.root, args.url, args.filename)

notebooks/analyzes_of_connectivity.ipynb

@@ -2,14 +2,25 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "sys.path.append(os.path.dirname(os.getcwd()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "import cv2\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from PathGAN.data.utils import rgb2binary\n",
+    "from pathgan.utils import rgb2binary\n",
     "import matplotlib.pyplot as plt\n",
     "from tqdm.notebook import tqdm\n",
     "from PIL import Image, ImageDraw\n",
@@ -19,7 +30,7 @@
     "import sys\n",
     "from  pathlib import Path\n",
     "from collections import deque\n",
-    "from PathGAN.RRT_last import RRT"
+    "from pathgan.models.rrt import RRT"
    ]
   },
   {
@@ -28,8 +39,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dataset_path = './PathGAN/data/dataset/'\n",
-    "data_path = './PathGAN/data/' "
+    "dataset_path = 'data/generated_dataset/dataset/'\n",
+    "data_path = 'data/generated_dataset/'"
    ]
   },
   {
@@ -1140,7 +1151,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1154,9 +1165,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

pathgan/data/mpr_dataset.py

@@ -29,6 +29,8 @@ class MPRDataset(Dataset):
         Dataframe with map/task/roi pairs.
     transform: Callable
         Transforms for map/task/roi pairs.
+    to_binary: bool (default=False)
+        If to load data (image) as binary tensor.
     """
     def __init__(
         self,
@@ -37,14 +39,16 @@ def __init__(
         roi_dir: str,
         csv_file: pd.DataFrame,
         transform: Optional[Callable] = None,
-        test: bool = False,
+        return_meta: bool = False,
+        to_binary: bool = False,
     ):
         self.map_dir = map_dir
         self.point_dir = point_dir
         self.roi_dir = roi_dir
         self.csv_file = csv_file
         self.transform = transform
-        self.test = test
+        self.return_meta = return_meta
+        self.to_binary = to_binary
 
     def __len__(self) -> int:
         return len(self.csv_file)
@@ -54,19 +58,23 @@ def __getitem__(self, index: int) -> Dict[str, Any]:
         map_name = row["map"].split(".")[0]
         map_path = f"{self.map_dir}/{row['map']}"
         point_path = f"{self.point_dir}/{map_name}/{row['task']}"
-        if not self.test:
-            roi_path = f"{self.roi_dir}/{map_name}/{row['roi']}"
+        roi_path = f"{self.roi_dir}/{map_name}/{row['roi']}"
+        meta = {"map_path": map_path, "point_path": point_path, "roi_path": roi_path}
 
-        map_img = np.array(Image.open(map_path).convert('RGB'))
-        point_img = np.array(Image.open(point_path).convert('RGB'))
-        if not self.test:
-            roi_img = np.array(Image.open(roi_path).convert('RGB'))
+        if self.to_binary:
+            map_img = np.array(Image.open(map_path).convert("L"))
+            point_img = np.array(Image.open(point_path).convert("L"))
+            roi_img = np.array(Image.open(roi_path).convert("L"))
+        else:
+            map_img = np.array(Image.open(map_path).convert("RGB"))
+            point_img = np.array(Image.open(point_path).convert("RGB"))
+            roi_img = np.array(Image.open(roi_path).convert("RGB"))
 
         if self.transform is not None:
             map_img = self.transform(map_img)
             point_img = self.transform(point_img)
-            if not self.test:
-                roi_img = self.transform(roi_img)
-        if not self.test:
-            return map_img, point_img, roi_img
-        return map_img, point_img
+            roi_img = self.transform(roi_img)
+
+        if self.return_meta:
+            return map_img, point_img, roi_img, meta
+        return map_img, point_img, roi_img

pathgan/metrics/__init__.py

@@ -0,0 +1 @@
+from .metrics import *

pathgan/metrics/metrics.py

@@ -1,10 +1,33 @@
+import numpy as np
 import torch
 import torch.nn as nn
 from torch.nn import functional as F
 from torchvision.models import inception_v3
 from .functional import kl_divergence, covariance, frechet_distance 
 
 
+def intersection_over_union(roi_pred, roi_true):
+    roi_pred = np.sum(roi_pred, axis=-1)
+    roi_pred = (roi_pred < 0.5 * np.max(roi_pred)).astype(int)
+    roi_true = np.sum(roi_true, axis=-1)
+    roi_true = (roi_true < 0.5 * np.max(roi_true)).astype(int)
+    inter = (roi_pred * roi_true).sum()
+    union = roi_pred.sum() + roi_true.sum()
+    iou_value = inter / (union - inter + 1e-6)
+    return iou_value
+
+
+def jaccard_coefficient(roi_pred, roi_true):
+    roi_pred = np.sum(roi_pred, axis=-1)
+    roi_pred = (roi_pred < 0.5 * np.max(roi_pred)).astype(int)
+    roi_true = np.sum(roi_true, axis=-1)
+    roi_true = (roi_true < 0.5 * np.max(roi_true)).astype(int)
+    inter = (roi_pred * roi_true).sum()
+    union = roi_pred.sum() + roi_true.sum()
+    dice_value = 2 * inter / (union + 1e-6)
+    return dice_value
+
+
 class KLDivergence(nn.Module):
     """Kullback–Leibler divergence."""
     def __init__(self,):

pathgan/models/rrt/rrt_base.py

@@ -32,15 +32,15 @@ class PathDescription(object):
     """
     def __init__(
         self,
-        path: List,
+        path: Optional[List] = None,
         cost: float = float('inf'),
         time_sec: float = 0.,
         time_it: int = 0,
         samples_taken: int = 0,
         nodes_taken: int = 0,
     ):
         """Initialize."""
-        self.path = path
+        self.path = path if path is not None else []
         self.cost = cost
         self.time_sec = time_sec
         self.time_it = time_it

scripts/data/download_datasets.sh

@@ -0,0 +1,13 @@
+#!/usr/bin/env python
+
+echo "Downloading generated dataset..."
+
+python download.py \
+    --url https://github.com/akanametov/pathgan/releases/download/2.0/dataset.zip \
+    --root data/generated_dataset \
+
+echo "Downloading movingai dataset..."
+
+python download.py \
+    --url https://github.com/akanametov/pathgan/releases/download/2.0/movingai_dataset.zip \
+    --root data/movingai_dataset \

scripts/data/download_results.sh

@@ -0,0 +1,25 @@
+#!/usr/bin/env python
+
+echo "Downloading SAGAN results on generated dataset..."
+
+python download.py \
+    --url https://github.com/akanametov/pathgan/releases/download/2.0/results.zip \
+    --root data/generated_dataset \
+
+echo "Downloading SAGAN results on movingai dataset..."
+
+python download.py \
+    --url https://github.com/akanametov/pathgan/releases/download/2.0/movingai_results.zip \
+    --root data/movingai_dataset/movingai \
+
+echo "Downloading Pix2pix results on generated dataset..."
+
+python download.py \
+    --url https://github.com/akanametov/pathgan/releases/download/2.0/pixresults.zip \
+    --root data/generated_dataset \
+
+echo "Downloading Pix2pix results on movingai dataset..."
+
+python download.py \
+    --url https://github.com/akanametov/pathgan/releases/download/2.0/movingai_pixresults.zip \
+    --root data/movingai_dataset/movingai \

scripts/logs/get_logs_generated_pix2pix.sh

@@ -0,0 +1,12 @@
+#!/usr/bin/env python
+
+echo "Get Pix2pix logs on generated dataset..."
+
+python get_logs.py A \
+    --map_params "{'data_folder': './data/generated_dataset', 'maps_folder': 'maps', 'results_folder': 'pixresults', 'results_file': 'result.csv'}" \
+    --rrt_params "{'path_resolution': 1, 'step_len': 2, 'max_iter': 10000}" \
+    --mu 0.1 \
+    --gamma 10 \
+    --n 50 \
+    --output_dir logs/pix2pix \
+    --output_fname pix2pix_generated_logs.txt \

scripts/logs/get_logs_generated_sagan.sh

@@ -0,0 +1,12 @@
+#!/usr/bin/env python
+
+echo "Get SAGAN logs on generated dataset..."
+
+python get_logs.py A \
+    --map_params "{'data_folder': './data/generated_dataset', 'maps_folder': 'maps', 'results_folder': 'results', 'results_file': 'results.csv'}" \
+    --rrt_params "{'path_resolution': 1, 'step_len': 2, 'max_iter': 10000}" \
+    --mu 0.1 \
+    --gamma 10 \
+    --n 50 \
+    --output_dir logs/ \
+    --output_fname sagan_generated_logs.txt \

scripts/logs/get_logs_movingai_pix2pix.sh

@@ -0,0 +1,12 @@
+#!/usr/bin/env python
+
+echo "Get Pix2pix logs on movingai dataset..."
+
+python get_logs.py A \
+    --map_params "{'data_folder': './data/movingai_dataset', 'maps_folder': 'maps', 'results_folder': 'movingai/pixresults', 'results_file': 'pixresult.csv'}" \
+    --rrt_params "{'path_resolution': 1, 'step_len': 2, 'max_iter': 10000}" \
+    --mu 0.1 \
+    --gamma 10 \
+    --n 50 \
+    --output_dir logs/pix2pix \
+    --output_fname pix2pix_movingai_logs.txt \

scripts/logs/get_logs_movingai_sagan.sh

@@ -0,0 +1,12 @@
+#!/usr/bin/env python
+
+echo "Get SAGAN logs on movingai dataset..."
+
+python get_logs.py A \
+    --map_params "{'data_folder': './data/movingai_dataset', 'maps_folder': 'maps', 'results_folder': 'movingai/results', 'results_file': 'result.csv'}" \
+    --rrt_params "{'path_resolution': 1, 'step_len': 2, 'max_iter': 10000}" \
+    --mu 0.1 \
+    --gamma 10 \
+    --n 50 \
+    --output_dir logs/sagan \
+    --output_fname sagan_movingai_logs.txt \