Merge pull request #13 from hmorimitsu/plotly

Plotly

.gitignore

@@ -2,8 +2,6 @@ ptlflow_logs/
 ptlflow_scripts/
 outputs/
 
-docs/source/_*
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

docs/source/results/accuracy_plot.rst

@@ -5,25 +5,27 @@ EPE vs. Outliers
 Sintel Clean
 ============
 
-.. image:: sintel_clean_epe_outlier-drop_kitti_sintel.png
-  :width: 600
-  :alt: Plot of end-point-error vs. outliers of the available models on the Sintel Clean dataset.
+.. raw:: html
+
+  <iframe src="../_static/sintel_clean_epe_outlier-drop_kitti_sintel.html" height="600px" width="100%"></iframe>
 
 Sintel Final
 ============
 
-.. image:: sintel_final_epe_outlier-drop_kitti_sintel.png
-  :width: 600
-  :alt: Plot of end-point-error vs. outliers of the available models on the Sintel Final dataset.
+.. raw:: html
+
+  <iframe src="../_static/sintel_final_epe_outlier-drop_kitti_sintel.html" height="600px" width="100%"></iframe>
 
 KITTI 2012
 ==========
-.. image:: kitti_2012_epe_outlier-drop_kitti_sintel.png
-  :width: 600
-  :alt: Plot of end-point-error vs. outliers of the available models on the Kitti 2012 dataset.
+
+.. raw:: html
+
+  <iframe src="../_static/kitti_2012_epe_outlier-drop_kitti_sintel.html" height="600px" width="100%"></iframe>
 
 KITTI 2015
 ==========
-.. image:: kitti_2015_epe_outlier-drop_kitti_sintel.png
-  :width: 600
-  :alt: Plot of end-point-error vs. outliers of the available models on the Kitti 2015 dataset.
+
+.. raw:: html
+
+  <iframe src="../_static/kitti_2015_epe_outlier-drop_kitti_sintel.html" height="600px" width="100%"></iframe>

docs/source/results/speed_plot.rst

@@ -2,9 +2,9 @@
 Inference speed vs. Trainable parameters
 ========================================
 
-.. image:: speed_plot-all.png
-  :width: 600
-  :alt: Plot of inference speed vs. trainable parameters of the available models.
+.. raw:: html
+
+  <iframe src="../_static/speed_plot-all.html" height="600px" width="100%"></iframe>
 
 Environment
 -----------

requirements.txt

@@ -1,13 +1,12 @@
-matplotlib >= 3.0
 numpy >= 1.17.0
 opencv-python >= 4.0.0.21
 pandas >= 1.1.0
 pillow >= 5.0
+plotly >= 5.0.0
 pypng >= 0.0.16
 pytorch-lightning >= 1.1.0
 requests >= 2.0.0
 scipy >= 1.0.0
-seaborn >= 0.9
 tabulate >= 0.8.3
 torch >= 1.7.0
 torchmetrics >= 0.2

setup.cfg

@@ -32,16 +32,15 @@ classifiers =
 packages = find:
 include_package_data = True
 install_requires =
-    matplotlib >= 3.0
     numpy >= 1.17.0
     opencv-python >= 4.0.0.21
     pandas >= 1.1.0
     pillow >= 5.0
+    plotly >= 5.0.0
     pypng >= 0.0.16
     pytorch-lightning >= 1.1.0
     requests >= 2.0.0
     scipy >= 1.0.0
-    seaborn >= 0.9
     tabulate >= 0.8.3
     torch >= 1.7.0
     torchmetrics >= 0.2

speed_benchmark.py

@@ -20,10 +20,9 @@
 from pathlib import Path
 from typing import Union
 
-import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import seaborn as sns
+import plotly.express as px
 import torch
 from tqdm import tqdm
 
@@ -156,13 +155,19 @@ def save_plot(
     log10_col = TABLE_COLS[1]+'(Log10)'
     df_tmp = df.copy()
     df_tmp[log10_col] = np.log10(df[TABLE_COLS[1]])
-    plt.figure(figsize=(10, 10))
-    sns.scatterplot(data=df_tmp, x=log10_col, y=TABLE_COLS[2], hue=TABLE_COLS[0], style=TABLE_COLS[0], s=300)
-    plt.legend(loc=2, borderaxespad=0)
-    plt.title('Parameters x Forward time')
-    plt.tight_layout()
-    plt.savefig(output_dir / f'speed_plot-{model_name}.png')
-    plt.close()
+
+    fig = px.scatter(
+        df, x=TABLE_COLS[1], y=TABLE_COLS[2], color=TABLE_COLS[0], symbol=TABLE_COLS[0], log_x=True,
+        title='Parameters x Forward time')
+    fig.update_traces(
+        marker={
+            'size': 20,
+            'line': {'width': 2, 'color': 'DarkSlateGrey'}},
+        selector={'mode': 'markers'})
+    fig.update_layout(
+        title_font_size=30
+    )
+    fig.write_html(output_dir / f'speed_plot-{model_name}.html')
 
 
 if __name__ == '__main__':
@@ -173,5 +178,6 @@ def save_plot(
         df = benchmark(args)
     else:
         df = pd.read_csv(args.csv_path)
+        Path(args.output_path).mkdir(parents=True, exist_ok=True)
         save_plot(args.output_path, args.model, df)
     print(f'Results saved to {str(args.output_path)}.')

summary_metrics.py

@@ -26,9 +26,8 @@
 import argparse
 from pathlib import Path
 
-import matplotlib.pyplot as plt
 import pandas as pd
-import seaborn as sns
+import plotly.express as px
 
 
 def _init_parser() -> argparse.ArgumentParser:
@@ -110,16 +109,21 @@ def save_plots(
 
     for dataset_name, col_pair_dict in metric_pairs.items():
         col1, col2 = col_pair_dict.values()
-        plt.figure(figsize=(10, 10))
-        sns.scatterplot(data=df, x=col1, y=col2, hue=df.columns[0], style=df.columns[1], s=300)
-        plt.legend(loc=4, borderaxespad=0)
-        plt.title(f'{dataset_name} - {args.chosen_metrics[0]} x {args.chosen_metrics[1]}')
-        plt.tight_layout()
+        fig = px.scatter(
+            df, x=col1, y=col2, color=df.columns[0], symbol=df.columns[1],
+            title=f'{dataset_name} - {args.chosen_metrics[0]} x {args.chosen_metrics[1]}')
+        fig.update_traces(
+            marker={
+                'size': 20,
+                'line': {'width': 2, 'color': 'DarkSlateGrey'}},
+            selector={'mode': 'markers'})
+        fig.update_layout(
+            title_font_size=30
+        )
         file_name = f'{dataset_name}_{args.chosen_metrics[0]}_{args.chosen_metrics[1]}'
         if args.drop_checkpoints is not None and len(args.drop_checkpoints) > 0:
             file_name += f'-drop_{"_".join(args.drop_checkpoints)}'
-        plt.savefig(args.output_dir / (file_name+'.png'))
-        plt.close()
+        fig.write_html(args.output_dir / (file_name+'.html'))
 
 
 def summarize(

tests/test_summary_metrics.py

@@ -29,6 +29,6 @@ def test_summary(tmp_path: Path) -> None:
 
     assert len(list(tmp_path.glob('**/*.md'))) > 0
     assert len(list(tmp_path.glob('**/*.csv'))) > 0
-    assert len(list(tmp_path.glob('**/*.png'))) > 0
+    assert len(list(tmp_path.glob('**/*.html'))) > 0
 
     shutil.rmtree(tmp_path)