From 07b4f5ea993ee19816125dcfcbefe7cbd9ff46be Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Thu, 16 Jul 2020 13:22:13 +0000
Subject: [PATCH] Refactored plot_loss() function
---
pytorch/graphics.py | 88 +++++++++++++++++++++++----------------------
1 file changed, 46 insertions(+), 42 deletions(-)
diff --git a/pytorch/graphics.py b/pytorch/graphics.py
index d142209..166f3bf 100644
--- a/pytorch/graphics.py
+++ b/pytorch/graphics.py
@@ -13,6 +13,7 @@ import numpy as np
import torch
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
+import matplotlib.lines as mlines
from matplotlib.colors import ListedColormap, BoundaryNorm
from matplotlib import cm as colormap
@@ -36,6 +37,11 @@ def contrast_stretching(image, alpha=2):
return norm
+def running_mean(x, w):
+ cumsum = np.cumsum(np.insert(x, 0, 0))
+ return (cumsum[w:] - cumsum[:-w]) / w
+
+
# plot_sample() plots a false color composite of the scene/tile together
# with the model prediction and the corresponding ground truth
def plot_sample(x, y, use_bands, labels, y_pred=None, figsize=(10, 10),
@@ -164,8 +170,8 @@ def plot_confusion_matrix(cm, labels, normalize=True,
return fig, ax
-def plot_loss(loss_file, figsize=(10, 10),
- colors=['lightgreen', 'skyblue', 'darkgreen', 'steelblue'],
+def plot_loss(loss_file, figsize=(10, 10), step=5,
+ colors=['lightgreen', 'green', 'skyblue', 'steelblue'],
outpath=os.path.join(os.getcwd(), '_graphics/')):
# load the model loss
@@ -176,60 +182,58 @@ def plot_loss(loss_file, figsize=(10, 10),
loss = {k: v[np.nonzero(v)].reshape(v.shape[0], -1) for k, v in
state.items() if k != 'epoch'}
+ # compute running mean with a window equal to the number of batches in
+ # an epoch
+ rm = {k: running_mean(v.flatten('F'), v.shape[0]) for k, v in loss.items()}
+
# number of epochs trained
- epochs = np.arange(0, state['epoch'] + 1)
+ epochs = np.arange(0, loss['tl'].shape[1])
# instanciate figure
fig, ax1 = plt.subplots(1, 1, figsize=figsize)
- # plot training and validation mean loss per epoch
- [ax1.plot(epochs, v.mean(axis=0),
- label=k.capitalize().replace('_', ' '), color=c, lw=2)
- for (k, v), c in zip(loss.items(), colors) if v.any() and 'loss' in k]
-
- # plot training loss per batch
- ax2 = ax1.twiny()
- [ax2.plot(v.flatten('F'), color=c, alpha=0.5)
- for (k, v), c in zip(loss.items(), colors) if 'loss' in k and
- 'validation' not in k]
-
- # plot training and validation mean accuracy per epoch
- ax3 = ax1.twinx()
- [ax3.plot(epochs, v.mean(axis=0),
- label=k.capitalize().replace('_', ' '), color=c, lw=2)
- for (k, v), c in zip(loss.items(), colors) if v.any() and 'accuracy'
- in k]
-
- # plot training accuracy per batch
- ax4 = ax3.twiny()
- [ax4.plot(v.flatten('F'), color=c, alpha=0.5)
- for (k, v), c in zip(loss.items(), colors) if 'accuracy' in k and
- 'validation' not in k]
+ # create axes for each parameter to plot
+ ax2 = ax1.twinx()
+ ax3 = ax1.twiny()
+ ax4 = ax2.twiny()
+
+ # list of axes
+ axes = [ax1, ax2, ax3, ax4]
+
+ # plot running mean loss and accuracy of the training dataset
+ [ax.plot(v, color=c) for (k, v), ax, c in zip(rm.items(), axes, colors)
+ if v.any()]
# axes properties and labels
- for ax in [ax2, ax4]:
+ nbatches = loss['tl'].shape[0]
+ for ax in [ax3, ax4]:
ax.set(xticks=[], xticklabels=[])
- ax1.set(xlabel='Epoch',
+ ax1.set(xticks=np.arange(0, nbatches * epochs[-1] + 1, nbatches * step),
+ xticklabels=epochs[::step],
+ xlabel='Epoch',
ylabel='Loss',
ylim=(0, 1))
- ax3.set(ylabel='Accuracy',
- ylim=(0, 1))
+ ax2.set(ylabel='Accuracy',
+ ylim=(0.5, 1))
# compute early stopping point
- if loss['validation_accuracy'].any():
- esepoch = np.argmax(loss['validation_accuracy'].mean(axis=0))
- esacc = np.max(loss['validation_accuracy'].mean(axis=0))
+ if loss['va'].any():
+ esepoch = np.argmax(loss['va'].mean(axis=0)) * nbatches
+ esacc = np.max(loss['va'].mean(axis=0))
ax1.vlines(esepoch, ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1],
ls='--', color='grey')
- ax1.text(esepoch - 1, ax1.get_ylim()[0] + 0.01,
- 'epoch = {}'.format(esepoch), ha='right', color='grey')
- ax1.text(esepoch + 1, ax1.get_ylim()[0] + 0.01,
- 'acc = {:.2f}%'.format(esacc * 100), ha='left',
- color='grey')
-
- # add legends
- ax1.legend(frameon=False, loc='lower left')
- ax3.legend(frameon=False, loc='upper left')
+ ax1.text(esepoch - nbatches, ax1.get_ylim()[0] + 0.01,
+ 'epoch = {}, accuracy = {:.1f}%'
+ .format(int(esepoch / nbatches), esacc * 100),
+ ha='right', color='grey')
+
+ # create a patch (proxy artist) for every color
+ ulabels = ['Training loss', 'Training accuracy',
+ 'Validation loss', 'Validation accuracy']
+ patches = [mlines.Line2D([], [], color=c, label=l) for c, l in
+ zip(colors, ulabels)]
+ # plot patches as legend
+ ax1.legend(handles=patches, loc='lower left', frameon=False)
# save figure
os.makedirs(outpath, exist_ok=True)
--
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