Add scripts to generate plots from paper (microsoft#186)

* Add scripts to generate plots from paper

* Style fix

experiments/plot_bar_chart.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+df1 = pd.read_csv("original-benchmark-results.csv")
+df2 = pd.read_csv("warped-benchmark-results.csv")
+
+mean1 = df1.groupby("sampler").mean()
+mean2 = df2.groupby("sampler").mean()
+
+cached1 = (
+    df1[(df1["cached"]) & (df1["sampler"] != "resnet18")].groupby("sampler").mean()
+)
+cached2 = (
+    df2[(df2["cached"]) & (df2["sampler"] != "resnet18")].groupby("sampler").mean()
+)
+not_cached1 = (
+    df1[(~df1["cached"]) & (df1["sampler"] != "resnet18")].groupby("sampler").mean()
+)
+not_cached2 = (
+    df2[(~df2["cached"]) & (df2["sampler"] != "resnet18")].groupby("sampler").mean()
+)
+
+print("cached, original\n", cached1)
+print("cached, warped\n", cached2)
+print("not cached, original\n", not_cached1)
+print("not cached, warped\n", not_cached2)
+
+cmap = sns.color_palette()
+
+labels = ["GridGeoSampler", "RandomBatchGeoSampler", "RandomGeoSampler"]
+
+fig, ax = plt.subplots()
+x = np.arange(3)
+width = 0.2
+
+rects1 = ax.bar(
+    x - width * 3 / 2,
+    not_cached1["rate"],
+    width,
+    label="Raw Data, Not Cached",
+    color=cmap[0],
+)
+rects2 = ax.bar(
+    x - width * 1 / 2,
+    not_cached2["rate"],
+    width,
+    label="Preprocessed, Not Cached",
+    color=cmap[1],
+)
+rects2 = ax.bar(
+    x + width * 1 / 2, cached1["rate"], width, label="Raw Data, Cached", color=cmap[2]
+)
+rects3 = ax.bar(
+    x + width * 3 / 2,
+    cached2["rate"],
+    width,
+    label="Preprocessed, Cached",
+    color=cmap[3],
+)
+
+ax.set_ylabel("sampling rate (patches/sec)", fontsize=12)
+ax.set_xticks(x)
+ax.set_xticklabels(labels, fontsize=12)
+ax.tick_params(axis="x", labelrotation=10)
+ax.legend(fontsize="large")
+
+plt.gca().spines.right.set_visible(False)
+plt.gca().spines.top.set_visible(False)
+plt.tight_layout()
+plt.show()

experiments/plot_dataloader_benchmark.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+df = pd.read_csv("warped-benchmark-results.csv")
+
+random_cached = df[(df["sampler"] == "RandomGeoSampler") & (df["cached"])]
+random_batch_cached = df[(df["sampler"] == "RandomBatchGeoSampler") & (df["cached"])]
+grid_cached = df[(df["sampler"] == "GridGeoSampler") & (df["cached"])]
+other = [
+    ("RandomGeoSampler", random_cached),
+    ("RandomBatchGeoSampler", random_batch_cached),
+    ("GridGeoSampler", grid_cached),
+]
+
+cmap = sns.color_palette()
+
+ax = plt.gca()
+
+for i, (label, df) in enumerate(other):
+    df = df.groupby("batch_size")
+    ax.plot(df.mean().index, df.mean()["rate"], color=cmap[i], label=label)
+    ax.fill_between(
+        df.mean().index, df.min()["rate"], df.max()["rate"], color=cmap[i], alpha=0.2
+    )
+
+
+ax.set_xscale("log")
+ax.set_xticks([16, 32, 64, 128, 256])
+ax.set_xticklabels([16, 32, 64, 128, 256], fontsize=12)
+ax.set_xlabel("batch size", fontsize=12)
+ax.set_ylabel("sampling rate (patches/sec)", fontsize=12)
+ax.legend(loc="center right", fontsize="large")
+
+plt.gca().spines.right.set_visible(False)
+plt.gca().spines.top.set_visible(False)
+plt.tight_layout()
+plt.show()

experiments/plot_percentage_benchmark.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+df1 = pd.read_csv("original-benchmark-results.csv")
+df2 = pd.read_csv("warped-benchmark-results.csv")
+
+random_cached1 = df1[(df1["sampler"] == "RandomGeoSampler") & (df1["cached"])]
+random_cached2 = df2[(df2["sampler"] == "RandomGeoSampler") & (df2["cached"])]
+random_cachedp = random_cached1
+random_cachedp["rate"] /= random_cached2["rate"]
+
+random_batch_cached1 = df1[
+    (df1["sampler"] == "RandomBatchGeoSampler") & (df1["cached"])
+]
+random_batch_cached2 = df2[
+    (df2["sampler"] == "RandomBatchGeoSampler") & (df2["cached"])
+]
+random_batch_cachedp = random_batch_cached1
+random_batch_cachedp["rate"] /= random_batch_cached2["rate"]
+
+grid_cached1 = df1[(df1["sampler"] == "GridGeoSampler") & (df1["cached"])]
+grid_cached2 = df2[(df2["sampler"] == "GridGeoSampler") & (df2["cached"])]
+grid_cachedp = grid_cached1
+grid_cachedp["rate"] /= grid_cached2["rate"]
+
+other = [
+    ("RandomGeoSampler (cached)", random_cachedp),
+    ("RandomBatchGeoSampler (cached)", random_batch_cachedp),
+    ("GridGeoSampler (cached)", grid_cachedp),
+]
+
+cmap = sns.color_palette()
+
+ax = plt.gca()
+
+for i, (label, df) in enumerate(other):
+    df = df.groupby("batch_size")
+    ax.plot([16, 32, 64, 128, 256], df.mean()["rate"], color=cmap[i], label=label)
+    ax.fill_between(
+        df.mean().index, df.min()["rate"], df.max()["rate"], color=cmap[i], alpha=0.2
+    )
+
+
+ax.set_xscale("log")
+ax.set_xticks([16, 32, 64, 128, 256])
+ax.set_xticklabels([16, 32, 64, 128, 256])
+ax.set_xlabel("batch size")
+ax.set_ylabel("% sampling rate (patches/sec)")
+ax.legend()
+plt.show()