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March 18, 2018 19:03
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""" | |
Copyright (c) 2017, Gavin Weiguang Ding | |
All rights reserved. | |
Redistribution and use in source and binary forms, with or without | |
modification, are permitted provided that the following conditions are met: | |
1. Redistributions of source code must retain the above copyright notice, this | |
list of conditions and the following disclaimer. | |
2. Redistributions in binary form must reproduce the above copyright notice, | |
this list of conditions and the following disclaimer in the documentation | |
and/or other materials provided with the distribution. | |
3. Neither the name of the copyright holder nor the names of its contributors | |
may be used to endorse or promote products derived from this software | |
without specific prior written permission. | |
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | |
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE | |
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE | |
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR | |
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF | |
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS | |
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN | |
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) | |
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE | |
POSSIBILITY OF SUCH DAMAGE. | |
""" | |
import os | |
import numpy as np | |
import matplotlib.pyplot as plt | |
plt.rcdefaults() | |
from matplotlib.lines import Line2D | |
from matplotlib.patches import Rectangle | |
from matplotlib.patches import Circle | |
NumDots = 4 | |
NumConvMax = 8 | |
NumFcMax = 20 | |
White = 1. | |
Light = 0.7 | |
Medium = 0.5 | |
Dark = 0.3 | |
Darker = 0.15 | |
Black = 0. | |
def add_layer(patches, colors, size=(24, 24), num=5, | |
top_left=[0, 0], | |
loc_diff=[3, -3], | |
): | |
# add a rectangle | |
top_left = np.array(top_left) | |
loc_diff = np.array(loc_diff) | |
loc_start = top_left - np.array([0, size[0]]) | |
for ind in range(num): | |
patches.append(Rectangle(loc_start + ind * loc_diff, size[1], size[0])) | |
if ind % 2: | |
colors.append(Medium) | |
else: | |
colors.append(Light) | |
def add_layer_with_omission(patches, colors, size=(24, 24), | |
num=5, num_max=8, | |
num_dots=4, | |
top_left=[0, 0], | |
loc_diff=[3, -3], | |
): | |
# add a rectangle | |
top_left = np.array(top_left) | |
loc_diff = np.array(loc_diff) | |
loc_start = top_left - np.array([0, size[0]]) | |
this_num = min(num, num_max) | |
start_omit = (this_num - num_dots) // 2 | |
end_omit = this_num - start_omit | |
start_omit -= 1 | |
for ind in range(this_num): | |
if (num > num_max) and (start_omit < ind < end_omit): | |
omit = True | |
else: | |
omit = False | |
if omit: | |
patches.append( | |
Circle(loc_start + ind * loc_diff + np.array(size) / 2, 0.5)) | |
else: | |
patches.append(Rectangle(loc_start + ind * loc_diff, | |
size[1], size[0])) | |
if omit: | |
colors.append(Black) | |
elif ind % 2: | |
colors.append(Medium) | |
else: | |
colors.append(Light) | |
def add_mapping(patches, colors, start_ratio, end_ratio, patch_size, ind_bgn, | |
top_left_list, loc_diff_list, num_show_list, size_list): | |
start_loc = top_left_list[ind_bgn] \ | |
+ (num_show_list[ind_bgn] - 1) * np.array(loc_diff_list[ind_bgn]) \ | |
+ np.array([start_ratio[0] * (size_list[ind_bgn][1] - patch_size[1]), | |
- start_ratio[1] * (size_list[ind_bgn][0] - patch_size[0])] | |
) | |
end_loc = top_left_list[ind_bgn + 1] \ | |
+ (num_show_list[ind_bgn + 1] - 1) * np.array( | |
loc_diff_list[ind_bgn + 1]) \ | |
+ np.array([end_ratio[0] * size_list[ind_bgn + 1][1], | |
- end_ratio[1] * size_list[ind_bgn + 1][0]]) | |
patches.append(Rectangle(start_loc, patch_size[1], -patch_size[0])) | |
colors.append(Dark) | |
patches.append(Line2D([start_loc[0], end_loc[0]], | |
[start_loc[1], end_loc[1]])) | |
colors.append(Darker) | |
patches.append(Line2D([start_loc[0] + patch_size[1], end_loc[0]], | |
[start_loc[1], end_loc[1]])) | |
colors.append(Darker) | |
patches.append(Line2D([start_loc[0], end_loc[0]], | |
[start_loc[1] - patch_size[0], end_loc[1]])) | |
colors.append(Darker) | |
patches.append(Line2D([start_loc[0] + patch_size[1], end_loc[0]], | |
[start_loc[1] - patch_size[0], end_loc[1]])) | |
colors.append(Darker) | |
def label(xy, text, xy_off=[0, 4]): | |
plt.text(xy[0] + xy_off[0], xy[1] + xy_off[1], text, | |
family='sans-serif', size=8) | |
if __name__ == '__main__': | |
fc_unit_size = 2 | |
layer_width = 40 | |
flag_omit = True | |
patches = [] | |
colors = [] | |
fig, ax = plt.subplots() | |
############################ | |
# conv layers | |
size_list = [(32, 32), (18, 18), (10, 10), (6, 6), (4, 4)] | |
num_list = [3, 32, 32, 48, 48] | |
x_diff_list = [0, layer_width, layer_width, layer_width, layer_width] | |
text_list = ['Inputs'] + ['Feature\nmaps'] * (len(size_list) - 1) | |
loc_diff_list = [[3, -3]] * len(size_list) | |
num_show_list = list(map(min, num_list, [NumConvMax] * len(num_list))) | |
top_left_list = np.c_[np.cumsum(x_diff_list), np.zeros(len(x_diff_list))] | |
for ind in range(len(size_list)): | |
if flag_omit: | |
add_layer_with_omission(patches, colors, size=size_list[ind], | |
num=num_list[ind], | |
num_max=NumConvMax, | |
num_dots=NumDots, | |
top_left=top_left_list[ind], | |
loc_diff=loc_diff_list[ind]) | |
else: | |
add_layer(patches, colors, size=size_list[ind], | |
num=num_show_list[ind], | |
top_left=top_left_list[ind], loc_diff=loc_diff_list[ind]) | |
label(top_left_list[ind], text_list[ind] + '\n{}@{}x{}'.format( | |
num_list[ind], size_list[ind][0], size_list[ind][1])) | |
############################ | |
# in between layers | |
start_ratio_list = [[0.4, 0.5], [0.4, 0.8], [0.4, 0.5], [0.4, 0.8]] | |
end_ratio_list = [[0.4, 0.5], [0.4, 0.8], [0.4, 0.5], [0.4, 0.8]] | |
patch_size_list = [(5, 5), (2, 2), (5, 5), (2, 2)] | |
ind_bgn_list = range(len(patch_size_list)) | |
text_list = ['Convolution', 'Max-pooling', 'Convolution', 'Max-pooling'] | |
for ind in range(len(patch_size_list)): | |
add_mapping( | |
patches, colors, start_ratio_list[ind], end_ratio_list[ind], | |
patch_size_list[ind], ind, | |
top_left_list, loc_diff_list, num_show_list, size_list) | |
label(top_left_list[ind], text_list[ind] + '\n{}x{} kernel'.format( | |
patch_size_list[ind][0], patch_size_list[ind][1]), xy_off=[26, -65] | |
) | |
############################ | |
# fully connected layers | |
size_list = [(fc_unit_size, fc_unit_size)] * 3 | |
num_list = [768, 500, 2] | |
num_show_list = list(map(min, num_list, [NumFcMax] * len(num_list))) | |
x_diff_list = [sum(x_diff_list) + layer_width, layer_width, layer_width] | |
top_left_list = np.c_[np.cumsum(x_diff_list), np.zeros(len(x_diff_list))] | |
loc_diff_list = [[fc_unit_size, -fc_unit_size]] * len(top_left_list) | |
text_list = ['Hidden\nunits'] * (len(size_list) - 1) + ['Outputs'] | |
for ind in range(len(size_list)): | |
if flag_omit: | |
add_layer_with_omission(patches, colors, size=size_list[ind], | |
num=num_list[ind], | |
num_max=NumFcMax, | |
num_dots=NumDots, | |
top_left=top_left_list[ind], | |
loc_diff=loc_diff_list[ind]) | |
else: | |
add_layer(patches, colors, size=size_list[ind], | |
num=num_show_list[ind], | |
top_left=top_left_list[ind], | |
loc_diff=loc_diff_list[ind]) | |
label(top_left_list[ind], text_list[ind] + '\n{}'.format( | |
num_list[ind])) | |
text_list = ['Flatten\n', 'Fully\nconnected', 'Fully\nconnected'] | |
for ind in range(len(size_list)): | |
label(top_left_list[ind], text_list[ind], xy_off=[-10, -65]) | |
############################ | |
for patch, color in zip(patches, colors): | |
patch.set_color(color * np.ones(3)) | |
if isinstance(patch, Line2D): | |
ax.add_line(patch) | |
else: | |
patch.set_edgecolor(Black * np.ones(3)) | |
ax.add_patch(patch) | |
plt.tight_layout() | |
plt.axis('equal') | |
plt.axis('off') | |
plt.show() | |
fig.set_size_inches(8, 2.5) | |
fig_dir = './' | |
fig_ext = '.png' | |
fig.savefig(os.path.join(fig_dir, 'convnet_fig' + fig_ext), | |
bbox_inches='tight', pad_inches=0) |
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