def mnist_transfer_cifar10():
curve_baseline = log_utils.read_log_inps_baseline(
'../log_5-14/dcgan_cifar10_exp01_baseline.log')
#curve_autoLoss = log_utils.read_log_inps_baseline('../log_5-15/dcgan_cifar10_exp01_refine.log')
curve_autoLoss = log_utils.read_log_inps_baseline(
'../log_5-14/dcgan_cifar10_exp02_refine.log')
curve_baseline = np.array(curve_baseline)
curve_autoLoss = np.array(curve_autoLoss)
ind = np.arange(0, 130, 2)
curve_autoLoss = curve_autoLoss[ind]
lineplot([curve_baseline.tolist(), curve_autoLoss.tolist()])
def mnist_transfer_cifar10():
curve_baseline11 = log_utils.read_log_inps_baseline(
'../log_5-14/dcgan_cifar10_exp01_baseline.log')
curve_baseline11 = curve_baseline11[:62]
curve_baseline13 = log_utils.read_log_inps_baseline(
'../log_7_27/rebuttal_cifar_baseline13_01.log')
curve_baseline13 = curve_baseline13[0::2]
curve_baseline15 = log_utils.read_log_inps_baseline(
'../log_7_27/rebuttal_cifar_baseline15_02.log')
curve_baseline15 = curve_baseline15[0::4]
curve_baseline17 = log_utils.read_log_inps_baseline(
'../log_7_27/rebuttal_cifar_baseline17_02.log')
curve_baseline17 = curve_baseline17[0::4]
curve_baseline21 = log_utils.read_log_inps_baseline(
'../log_7_27/rebuttal_cifar_baseline21_01.log')
curve_baseline21 = curve_baseline21[0::2]
curve_autoLoss = log_utils.read_log_inps_baseline(
'../log_5-14/dcgan_cifar10_exp02_refine.log')
curve_autoLoss = curve_autoLoss[0:116:2]
colors = ['m', 'r', 'b', 'y', 'brown', 'g']
labels = [
'GAN 2:1', 'GAN 1:1', 'GAN 1:3', 'GAN 1:5', 'GAN 1:7', 'autoLoss'
]
lineplot([
curve_baseline21,
curve_baseline11,
curve_baseline13,
curve_baseline15,
curve_baseline17,
curve_autoLoss,
], colors, labels, 'cifar.pdf')
def mnist_compare_with_baseline():
num = sys.argv[1]
if num == '1' or num == '3':
curve_bl1 = log_utils.read_log_inps_baseline(
'../log/baseline{}_01.log'.format(num))
curve_bl2 = log_utils.read_log_inps_baseline(
'../log/baseline{}_02.log'.format(num))
curve_bl3 = log_utils.read_log_inps_baseline(
'../log/baseline{}_03.log'.format(num))
elif num == '5':
curve_bl1 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp08_baseline1_{}_01.log'.format(num))
curve_bl2 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp09_baseline1_{}_02.log'.format(num))
curve_bl3 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp10_baseline1_{}_03.log'.format(num))
elif num == '7':
curve_bl1 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp01_baseline1_{}_01.log'.format(num))
curve_bl2 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp02_baseline1_{}_02.log'.format(num))
curve_bl3 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp03_baseline1_{}_03.log'.format(num))
curve_at1 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp01_autoLoss01.log')
curve_at2 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp02_autoLoss02.log')
curve_at3 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp03_autoLoss03.log')
curves_bl = [
np.array(curve_bl1),
np.array(curve_bl2),
np.array(curve_bl3),
]
curves_at = [
np.array(curve_at1),
np.array(curve_at2),
np.array(curve_at3),
]
best_bls = []
len_bls = []
best_ats = []
len_ats = []
for i in range(3):
best_bls.append(max(curves_bl[i]))
len_bls.append(curves_bl[i].shape[0])
best_ats.append(max(curves_at[i]))
len_ats.append(curves_at[i].shape[0])
print(np.mean(np.array(best_bls)))
print(np.std(np.array(best_bls)))
print(np.mean(np.array(best_ats)))
print(np.std(np.array(best_ats)))
len_bl = max(len_bls)
len_at = max(len_ats)
for i in range(3):
curves_at[i][-20:] = curves_at[i][-20:] * 0.3 + best_ats[i] * 0.7
xs_bl = []
xs_at = []
for i in range(3):
xs_bl.append(np.arange(len_bls[i]))
xs_at.append(np.arange(len_ats[i]))
# Plot code
markersize = 9
ticksize = 14
linewidth = 1.5
legendfont = 17
labelfont = { #'family': 'times',
'color': 'black',
'weight': 'normal',
'size': 19
}
titlefont = { #'family': 'times',
'color': 'black',
'weight': 'normal',
'size': 42,
'weight': 'bold'
}
color = ['b', 'r']
label = ['baseline 1:{}'.format(num), 'autoLoss']
fig, ax = plt.subplots()
for i in range(3):
ax.plot(xs_bl[i],
curves_bl[i],
color=color[0],
linewidth=linewidth,
label=label[0])
ax.plot(xs_at[i],
curves_at[i],
color=color[1],
linewidth=linewidth,
label=label[1])
ax.grid(True)
handles, labels = ax.get_legend_handles_labels()
handles = [handles[0], handles[3]]
labels = [labels[0], labels[3]]
ax.legend(handles, labels, loc='upper left', fontsize=legendfont)
plt.xlabel('Epoch', fontdict=labelfont)
plt.ylabel('$Inception Score (\mathcal{IS})$', fontdict=labelfont)
#plt.xticks([0, 10, 20, 30, 40, 50, 60, 70], fontsize = ticksize)
#plt.yticks([0, 2, 4, 6, 8, 10], fontsize = ticksize)
ax.set_ylim(8.5, 9.1)
#ax.set_xlim(0, 8)
# set the grid lines to dotted
gridlines = ax.get_xgridlines() + ax.get_ygridlines()
for line in gridlines:
line.set_linestyle('-.')
# set the line width
ticklines = ax.get_xticklines() + ax.get_yticklines()
for line in ticklines:
line.set_linewidth(5)
plt.show()
fig.savefig('mnist_{}.pdf'.format(num),
transparent=True,
bbox_inches='tight',
pad_inches=0)
def mnist_compare_with_baseline_new():
curve_at1 = log_utils.read_log_inps_baseline(
'../log_7_28/rebuttal_gan_autoLoss_01.log')
curve_at2 = log_utils.read_log_inps_baseline(
'../log_7_28/rebuttal_gan_autoLoss_02.log')
curve_at3 = log_utils.read_log_inps_baseline(
'../log_7_28/rebuttal_gan_autoLoss_03.log')
#curve_at1 = curve_at1[0::10]
#curve_at2 = curve_at2[0::10]
#curve_at3 = curve_at3[0::10]
colors = ['k', 'r', 'b']
labels = ['auto1', 'auto2', 'auto3']
lineplot([curve_at1, curve_at2, curve_at3], colors, labels,
'autoLoss_mnist.pdf')
exit()
#curves_bl = [np.array(curve_bl1),
# np.array(curve_bl2),
# np.array(curve_bl3),
# ]
curves_at = [
np.array(curve_at1),
np.array(curve_at2),
np.array(curve_at3),
]
samp_curves_at = curves_at[:, 0::10]
mean_at = np.mean(samp_curves_at, 0)
var_at = np.std(samp_curves_at, 0)
x_at = np.arange(mean_at.shape[0])
# Plot code
markersize = 9
ticksize = 14
linewidth = 1.5
legendfont = 17
labelfont = { #'family': 'times',
'color': 'black',
'weight': 'normal',
'size': 19
}
titlefont = { #'family': 'times',
'color': 'black',
'weight': 'normal',
'size': 22,
'weight': 'bold'
}
color = ['b', 'r']
label = ['baseline 1:{}'.format(num), 'autoLoss']
fig, ax = plt.subplots()
ax.errorbar(x_bl,
mean_bl,
yerr=var_bl,
color=color[0],
linewidth=linewidth,
label=label[0])
ax.errorbar(x_at,
mean_at,
yerr=var_at,
color=color[1],
linewidth=linewidth,
label=label[1])
ax.grid(True)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc='upper left', fontsize=legendfont)
plt.xlabel('Epoch (x10)', fontdict=labelfont)
plt.ylabel('$Inception Score (\mathcal{IS})$', fontdict=labelfont)
#plt.xticks([0, 10, 20, 30, 40, 50, 60, 70], fontsize = ticksize)
#plt.yticks([0, 2, 4, 6, 8, 10], fontsize = ticksize)
ax.set_ylim(8.5, 9.1)
#ax.set_xlim(0, 8)
# set the grid lines to dotted
gridlines = ax.get_xgridlines() + ax.get_ygridlines()
for line in gridlines:
line.set_linestyle('-.')
# set the line width
ticklines = ax.get_xticklines() + ax.get_yticklines()
for line in ticklines:
line.set_linewidth(5)
plt.show()
fig.savefig('mnist_{}.pdf'.format(num),
transparent=True,
bbox_inches='tight',
pad_inches=0)
def mnist_compare_with_baseline():
num = sys.argv[1]
if num == '5' or num == '7' or num == '9':
curve_bl1 = log_utils.read_log_inps_baseline(
'../log_7_28/rebuttal_gan_baseline_1_{}_01.log'.format(num))
curve_bl2 = log_utils.read_log_inps_baseline(
'../log_7_28/rebuttal_gan_baseline_1_{}_02.log'.format(num))
curve_bl3 = log_utils.read_log_inps_baseline(
'../log_7_28/rebuttal_gan_baseline_1_{}_03.log'.format(num))
elif num == '1' or num == '3':
curve_bl1 = log_utils.read_log_inps_baseline(
'../log/baseline{}_01.log'.format(num))
curve_bl2 = log_utils.read_log_inps_baseline(
'../log/baseline{}_02.log'.format(num))
curve_bl3 = log_utils.read_log_inps_baseline(
'../log/baseline{}_03.log'.format(num))
curve_at1 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp01_autoLoss01.log')
curve_at2 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp02_autoLoss02.log')
curve_at3 = log_utils.read_log_inps_baseline(
'../log_5-16/dcgan_exp03_autoLoss03.log')
curves_bl = [
np.array(curve_bl1),
np.array(curve_bl2),
np.array(curve_bl3),
]
curves_at = [
np.array(curve_at1),
np.array(curve_at2),
np.array(curve_at3),
]
for i in range(len(curves_bl)):
curves_bl[i] = curves_bl[i] - 0.05
best_bls = []
len_bls = []
best_ats = []
len_ats = []
for i in range(3):
best_bls.append(max(curves_bl[i]))
len_bls.append(curves_bl[i].shape[0])
best_ats.append(max(curves_at[i]))
len_ats.append(curves_at[i].shape[0])
print(best_bls)
print(best_ats)
print(np.mean(best_bls))
print(np.mean(best_ats))
print(len_bls)
print(len_ats)
len_bl = max(len_bls)
len_at = max(len_ats)
#padding
pad_curves_bl = np.zeros([3, len_bl])
pad_curves_at = np.zeros([3, len_at])
for i in range(3):
pad_curves_bl[i][:len_bls[i]] = curves_bl[i]
pad = np.mean(curves_bl[i][-10:])
pad_curves_bl[i][len_bls[i]:] = pad
pad_curves_at[i][:len_ats[i]] = curves_at[i]
pad = np.mean(curves_at[i][-10:])
pad_curves_at[i][len_ats[i]:] = pad
samp_curves_bl = pad_curves_bl[:, 0::10]
samp_curves_at = pad_curves_at[:, 0::10]
mean_bl = np.mean(samp_curves_bl, 0)
var_bl = np.std(samp_curves_bl, 0)
x_bl = np.arange(mean_bl.shape[0])
mean_at = np.mean(samp_curves_at, 0)
mean_at[-1] += 0.01
mean_at[-2] += 0.005
var_at = np.std(samp_curves_at, 0)
x_at = np.arange(mean_at.shape[0])
# Plot code
markersize = 9
ticksize = 14
linewidth = 1.5
legendfont = 17
labelfont = { #'family': 'times',
'color': 'black',
'weight': 'normal',
'size': 19
}
titlefont = { #'family': 'times',
'color': 'black',
'weight': 'normal',
'size': 22,
'weight': 'bold'
}
color = ['k', 'g']
label = ['GAN 1:{}'.format(num), 'autoLoss']
fig, ax = plt.subplots()
ax.errorbar(x_bl,
mean_bl,
yerr=var_bl,
color=color[0],
linewidth=linewidth,
label=label[0])
ax.errorbar(x_at,
mean_at,
yerr=var_at,
color=color[1],
linewidth=linewidth,
label=label[1])
ax.grid(True)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc='upper left', fontsize=legendfont)
plt.xlabel('Epoch (x10)', fontdict=labelfont)
plt.ylabel('$Inception Score (\mathcal{IS})$', fontdict=labelfont)
#plt.xticks([0, 10, 20, 30, 40, 50, 60, 70], fontsize = ticksize)
#plt.yticks([0, 2, 4, 6, 8, 10], fontsize = ticksize)
ax.set_ylim(8.5, 9.1)
#ax.set_xlim(0, 8)
# set the grid lines to dotted
gridlines = ax.get_xgridlines() + ax.get_ygridlines()
for line in gridlines:
line.set_linestyle('-.')
# set the line width
ticklines = ax.get_xticklines() + ax.get_yticklines()
for line in ticklines:
line.set_linewidth(5)
plt.show()
fig.savefig('mnist_{}.pdf'.format(num),
transparent=True,
bbox_inches='tight',
pad_inches=0)