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plot.py
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Fri, Jun 7, 11:05

plot.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Plot functions to create figures.
"""
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import torch.nn as nn
from paths import FIGURES_DIR, DEBUG_DIR_MNIST_012
def plot_loss_and_acc(run_time, run_name, history):
"""Generate a plot with training loss and validation accuracy for a specific model."""
num_epochs = list(range(len(history)))
loss = [t[0] for t in history]
acc = [t[1] for t in history]
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
r1 = ax1.plot(num_epochs, loss, color='red', label='training loss')
ax1.set_xlabel('epoch')
ax1.set_ylabel('loss')
r2 = ax2.plot(num_epochs, acc, color='blue', label='validation accuracy')
ax2.set_ylabel('accuracy')
lns = r2 + r1
labs = [l.get_label() for l in lns]
leg = plt.legend(lns, labs, loc='center right', shadow=True)
leg.draw_frame(False)
plt.savefig(FIGURES_DIR + run_time + '_' + run_name + '_loss_and_acc_results.png')
plt.gcf().clear()
def plot_loss(run_time, run_name, history):
"""Generate a plot with training and validation loss for the given history."""
num_epochs = list(range(len(history)))
train_loss = [t[0] for t in history]
valid_loss = [t[1] for t in history]
fig, ax1 = plt.subplots()
train_curve = ax1.plot(num_epochs, train_loss, color='red', label='training')
valid_curve = ax1.plot(num_epochs, valid_loss, color='blue', label='validation')
ax1.set_xlabel('epoch')
ax1.set_ylabel('loss')
leg = plt.legend(loc='upper right', shadow=True)
leg.draw_frame(False)
plt.savefig(FIGURES_DIR + run_time + '_' + run_name + '_losses.png')
plt.gcf().clear()
def plot_error(run_time, run_name, history):
"""Generates a plot with training validation errors for the given history."""
num_epochs = list(range(len(history)))
train_errors = [t[0] for t in history]
valid_errors = [t[1] for t in history]
fig, ax1 = plt.subplots()
train_curve = ax1.plot(num_epochs, train_errors, color='red', label='training')
valid_curve = ax1.plot(num_epochs, valid_errors, color='blue', label='validation')
ax1.set_xlabel('epoch')
ax1.set_ylabel('error %')
leg = plt.legend(loc='upper right', shadow=True)
leg.draw_frame(False)
plt.savefig(FIGURES_DIR + run_time + '_' + run_name + '_errors.png')
plt.gcf().clear()
def colorbar(mappable):
"""Create a colorbar that matches properly the size of the image."""
ax = mappable.axes
fig = ax.figure
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
return fig.colorbar(mappable, cax=cax)
def show_filter(tensor, filter_name, num_filters, num_nodes, without_batch=False):
"""Show the filters of the intermediary layers given by the tensor."""
dim = int(np.sqrt(num_nodes))
if not without_batch:
# select the first batch
tensor = tensor.data[0]
else:
tensor = tensor.data
# keep only 3 filters in order to keep good quality in visualization
# if num_filters > 3:
# num_filters=3
# create the figure containing all the filters
fig = plt.figure(figsize=(15,5))
for idx in range(num_filters):
ax = fig.add_subplot(1, num_filters, idx+1) # this line adds sub-axes
im = ax.imshow(tensor[:,idx].contiguous().view(dim,dim), cmap='jet')
#ax.set_axis_off()
#cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
#colorbar(im)
# add suptitle and display the plot
plt.suptitle(filter_name)
plt.tight_layout(h_pad=1)
plt.show()
def show_filters_single_window(list, h, w, num_nodes, with_relu=False):
"""Show the filters of the intermediary layers given by the tensor."""
dim = int(np.sqrt(num_nodes))
relu = nn.ReLU(inplace=True)
# create the figure containing all the filters
fig = plt.figure(figsize=(15,10))
for i in range(len(list)):
for j in range(3):
ax = fig.add_subplot(h, w, i*3 + j+1) # this line adds sub-axes
if with_relu:
im = ax.imshow(relu(list[i]).detach()[0, :, j].contiguous().view(dim,dim), cmap='jet')
else:
im = ax.imshow(list[i].detach()[0, :, j].contiguous().view(dim,dim), cmap='jet')
ax.set_axis_off()
colorbar(im)
# add suptitle and display the plot
#plt.suptitle(filter_name)
plt.tight_layout(h_pad=1)
plt.show()
def show_cheb_poly_single_window(cheb_poly, num_filters, num_nodes):
"""Show chebyshev polynomial of the intermediary layers given by the tensor."""
dim = int(np.sqrt(num_nodes))
#cheb_poly shape : B x F x N x D
# create the figure containing all the filters
fig = plt.figure(figsize=(15,10))
for i in range(num_filters):
for j in range(10):
ax = fig.add_subplot(num_filters, 10, i*10 + j+1) # this line adds sub-axes
im = ax.imshow(cheb_poly.detach()[1, i, :, j].contiguous().view(dim,dim), cmap='jet')
#ax.set_axis_off()
#colorbar(im)
# add suptitle and display the plot
#plt.suptitle(filter_name)
plt.tight_layout(h_pad=1)
plt.show()
def plot_input(input, dim, index, num):
"""Plot the input images."""
fig = plt.figure(figsize=(15,10))
for i in range(num):
for j in range(num):
ax = fig.add_subplot(num, num, i*num + j+1)
im = ax.imshow(input.data[index + (i*num)+j].contiguous().view(dim,dim), cmap='jet')
plt.show()

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