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figures.py
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import os
import pickle
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import torch
from pytorch_pretrained_biggan.model import Generator as orig_biggan
from model.began import Generator128
from model.biggan import BigGanSkip
from model.dcgan import Generator as dcgan_generator
from model.vae import VAE
from utils import (load_trained_net, parse_baseline_results_folder,
parse_results_folder)
# Store the original forward before monkey patch in BigGanSkip
orig_biggan_forward = orig_biggan.forward
def cs_plot(split):
def name_fn(row):
if row['model'].startswith('lasso-dct'):
return 'Lasso-DCT'
elif row['n_cuts'] == 0:
return 'No Surgery'
else:
return 'With Surgery'
def ratio_fn(row):
if row['type'] == 'DCGAN':
image_size = 64
elif row['type'] in ['BEGAN', 'VAE']:
image_size = 128
return row['n_measure'] / (3 * image_size * image_size)
df_baseline = pd.read_pickle(
'./final_runs/processed_results/df_baseline_results.pkl')
df_results = pd.read_pickle(
'./final_runs/processed_results/df_results.pkl')
if split == 'test_celeba':
split_name = [
'test_celeba64_cropped100',
'test_celeba128_cropped100',
]
elif split == 'ood-coco':
split_name = ['ood-coco100']
df1 = pd.concat([
df_baseline.loc[(df_baseline['model'] == 'lasso-dct-64')
& (df_baseline['split'].isin(split_name))],
df_results.loc[(df_results['fm'] == 'GaussianCompressiveSensing')
& (df_results['model'] == 'dcgan_cs') &
(df_results['n_cuts'].isin([0, 1])) &
(df_results['split'].isin(split_name))]
])
df1.loc[:, 'type'] = 'DCGAN'
df2 = pd.concat([
df_baseline.loc[(df_baseline['model'] == 'lasso-dct-128')
& (df_baseline['split'].isin(split_name))],
df_results.loc[(df_results['fm'] == 'GaussianCompressiveSensing')
& (df_results['model'] == 'began_cs') &
(df_results['n_cuts'].isin([0, 2])) &
(df_results['split'].isin(split_name))]
])
df2.loc[:, 'type'] = 'BEGAN'
df3 = pd.concat([
df_baseline.loc[(df_baseline['model'] == 'lasso-dct-128')
& (df_baseline['split'].isin(split_name))],
df_results.loc[(df_results['fm'] == 'GaussianCompressiveSensing')
& (df_results['model'].isin(['vanilla_vae_cs'])) &
(df_results['n_cuts'].isin([0, 1])) &
(df_results['split'].isin(split_name))]
])
df3.loc[:, 'type'] = 'VAE'
df = pd.concat([df1, df2, df3])
df['undersampling_ratio'] = df.apply(lambda row: ratio_fn(row), axis=1)
df['model_name'] = df.apply(lambda row: name_fn(row), axis=1)
sns.set(rc={'figure.figsize': (16, 9)})
sns.set_style('ticks')
sns.set_context('poster', font_scale=1.6)
filled_markers = ['D', 'o', 'v', 'd', '^', '*', 'X']
legend_order = ['With Surgery', 'No Surgery', 'Lasso-DCT']
if split == 'test_celeba':
ylim = (10, 40)
yt = range(10, 41, 5)
elif split == 'ood-coco':
ylim = (10, 35)
yt = range(10, 36, 5)
plt.figure()
g = sns.FacetGrid(df,
col="type",
hue='model_name',
hue_order=legend_order,
hue_kws=dict(marker=filled_markers),
height=12,
sharey=False,
aspect=1.3,
legend_out=True,
xlim=(0, 0.5),
ylim=ylim)
g = (g.map(sns.lineplot,
"undersampling_ratio",
"psnr",
ci='sd',
markersize=20))
(g.set_xlabels(r"Undersampling Ratio ($m/n$)").set_ylabels(
"PSNR (dB)").set_titles("{col_name}",
size=36).fig.subplots_adjust(bottom=0.25))
g.set(xticks=[0, 0.1, 0.2, 0.3, 0.4, 0.5], yticks=yt)
handles = g._legend_data.values()
labels = g._legend_data.keys()
g.fig.legend(handles=handles, labels=labels, loc='lower center', ncol=3)
os.makedirs('./figures/cs_plot', exist_ok=True)
plt.savefig((f'./figures/cs_plot/'
f'cs_plot_split={split}.pdf'),
dpi=300,
bbox_inches='tight')
def cs_baselines_plot(split):
def _name_fn(row):
if row['model'].startswith('mgan'):
return 'MGAN'
elif row['model'].startswith('iagan'):
return 'IAGAN'
elif row['model'].startswith('deep_decoder'):
return 'DD'
else:
return 'With Surgery'
def _ratio_fn(row):
if row['type'] == 'DCGAN':
image_size = 64
elif row['type'] in ['BEGAN', 'VAE']:
image_size = 128
return row['n_measure'] / (3 * image_size * image_size)
df_results = pd.read_pickle(
'./final_runs/processed_results/df_results.pkl')
if split == 'test_celeba':
split_name = ['test_celeba64_cropped100', 'test_celeba128_cropped100']
elif split == 'ood-coco':
split_name = ['ood-coco100']
df1 = df_results.loc[((df_results['model'] == 'dcgan_cs') &
(df_results['n_cuts'] == 1)) |
(df_results['model'] == 'mgan_dcgan_cs') |
(df_results['model'] == 'iagan_dcgan_cs') |
(df_results['model'] == 'deep_decoder_64_cs')].copy()
df1['type'] = 'DCGAN'
df2 = df_results.loc[
((df_results['model'] == 'began_cs') & (df_results['n_cuts'] == 2)) |
(df_results['model'] == 'mgan_began_cs') |
(df_results['model'] == 'iagan_began_cs') |
(df_results['model'] == 'deep_decoder_128_cs')].copy()
df2['type'] = 'BEGAN'
df3 = df_results.loc[
((df_results['model'] == 'vanilla_vae_cs') &
(df_results['n_cuts'] == 1)) |
(df_results['model'] == 'mgan_vanilla_vae_cs') |
(df_results['model'] == 'iagan_vanilla_vae_cs') |
(df_results['model'] == 'deep_decoder_128_cs')].copy()
df3['type'] = 'VAE'
df = pd.concat([df1, df2, df3])
df = df[df['split'].isin(split_name)]
df['model_name'] = df.apply(lambda row: _name_fn(row), axis=1)
df['undersampling_ratio'] = df.apply(lambda row: _ratio_fn(row), axis=1)
sns.set(rc={'figure.figsize': (16, 9)})
sns.set_style('ticks')
sns.set_context('poster', font_scale=1.6)
filled_markers = ['D', 'o', 'v', 'd', '^', '*', 'X']
col_order = ['DCGAN', 'BEGAN', 'VAE']
hue_order = [
'With Surgery',
'IAGAN',
'DD',
'MGAN',
]
if split == 'test_celeba':
ylim = (15, 45)
yt = range(15, 46, 5)
elif split == 'ood-coco':
ylim = (15, 40)
yt = range(15, 41, 5)
plt.figure()
g = sns.FacetGrid(
data=df,
col='type',
col_order=col_order,
hue='model_name',
hue_order=hue_order,
hue_kws=dict(marker=filled_markers),
height=12,
sharey=False,
aspect=1.3,
legend_out=True,
xlim=(0, 0.5),
ylim=ylim,
)
g = (g.map(sns.lineplot,
'undersampling_ratio',
'psnr',
ci='sd',
markersize=20))
(g.set_xlabels("Undersampling Ratio (m/n)").set_ylabels(
"PSNR (dB)").set_titles("{col_name}",
size=36).fig.subplots_adjust(bottom=0.25))
g.set(xticks=[0, 0.1, 0.2, 0.3, 0.4, 0.5], yticks=yt)
handles = g._legend_data.values()
labels = g._legend_data.keys()
g.fig.legend(handles=handles, labels=labels, loc='lower center', ncol=4)
os.makedirs('./figures/cs_baselines', exist_ok=True)
plt.savefig((f'./figures/cs_baselines/'
f'cs_baselines_plot_split={split}.pdf'),
dpi=300,
bbox_inches='tight')
def opt_error_plot():
def split_fn(row):
if row['split'] == 'train_celeba128_cropped100':
return 'Train'
elif row['split'] == 'test_celeba128_cropped100':
return 'Test'
elif row['split'] == 'ood-coco100':
return 'OOD'
elif row['split'] == 'began_generated100':
return 'Fake'
else:
return ''
def type_fn(row):
if row['n_cuts'] == 0:
return 'No Surgery'
else:
return 'With Surgery'
with open('./final_runs/processed_results/df_results.pkl', 'rb') as f:
df = pickle.load(f)
df = df.loc[((df['model'] == 'began_opt_error_fake_imgs') &
(df['split'].isin([
'train_celeba128_cropped100', 'test_celeba128_cropped100',
'began_generated100', 'ood-coco100'
])))]
df['split_name'] = df.apply(lambda row: split_fn(row), axis=1)
df['type'] = df.apply(lambda row: type_fn(row), axis=1)
sns.set(rc={'figure.figsize': (16, 9)})
sns.set_context('poster', font_scale=1.8)
sns.set_style('ticks')
# st.set_style("ticks", {"ytick.major.size": 5})
legend_order = ['No Surgery', 'With Surgery']
split_order = ['Train', 'Test', 'OOD', 'Fake']
plt.figure()
ax = sns.boxplot(data=df,
x="split_name",
y="psnr",
order=split_order,
hue='type',
hue_order=legend_order,
palette=sns.color_palette("hls", 2),
dodge=True)
ax.set_xlabel("Image distribution")
ax.set_ylabel("PSNR (dB)")
ax.set_ylim(10, 60)
ax.set_yticks(range(10, 61, 10))
ax.set_title("Optimization Error")
# Remove legend title
handles, labels = ax.get_legend_handles_labels()
ax.legend(frameon=False,
handles=handles[0:],
labels=labels[0:],
loc='upper left',
fontsize=24)
os.makedirs('./figures/opt_error', exist_ok=True)
plt.savefig(f'./figures/opt_error/opt_error.pdf',
dpi=300,
bbox_inches='tight')
def best_cuts_plot(model):
def split_fn(row):
if 'celeba' in row['split']:
return 'CelebA'
elif 'ood' in row['split']:
return 'COCO'
else:
return ''
df_results = pd.read_pickle(
'./final_runs/processed_results/df_results.pkl')
df = df_results.loc[df_results['model'] == model]
df.loc[:, 'split_name'] = df.apply(lambda row: split_fn(row), axis=1)
sns.set(rc={'figure.figsize': (16, 9)})
sns.set_style('ticks')
sns.set_context('poster', font_scale=1.8)
filled_markers = ['d', 'o']
legend_order = ['CelebA', 'COCO']
if model == 'dcgan_cs_n_cuts':
title = 'DCGAN'
xlim = (0, 4)
xticks = list(range(0, 5))
ylim = (5, 35)
elif model == 'began_cs_n_cuts':
title = 'BEGAN'
xlim = (0, 13)
xticks = list(range(0, 14))
ylim = (5, 35)
elif model == 'vanilla_vae_cs_n_cuts':
title = 'VAE'
xlim = (0, 5)
xticks = list(range(0, 6))
ylim = (5, 35)
else:
raise NotImplementedError()
plt.figure()
ax = sns.lineplot(data=df,
x='n_cuts',
y='psnr',
hue='split_name',
hue_order=legend_order,
ci='sd',
style='split_name',
style_order=legend_order,
markers=filled_markers,
markersize=20,
dashes=False)
ax.set_title(title, fontsize=50)
ax.set_xlabel('No. of cuts')
ax.set_ylabel('PSNR (dB)')
ax.set_xlim(xlim)
ax.set_xticks(xticks)
ax.set_ylim(ylim)
# # Remove legend title
handles, labels = ax.get_legend_handles_labels()
ax.legend(frameon=False,
handles=handles[1:],
labels=labels[1:],
loc='upper right')
os.makedirs('./figures/best_cuts', exist_ok=True)
plt.savefig((f'./figures/best_cuts/'
f'best_cuts_model={model}.pdf'),
dpi=300,
bbox_inches='tight')
def noop_plot():
with open('./final_runs/processed_results/df_results.pkl', 'rb') as f:
df_results = pickle.load(f)
def _split_fn(row):
if 'celeba' in row['split']:
return 'Test'
elif row['split'] == 'ood-coco100':
return 'OOD'
else:
return ''
# Filters
df = df_results.loc[(df_results['model'].isin(
['began_noop', 'vanilla_vae_noop', 'dcgan_noop']))
& (df_results['split'].isin([
'test_celeba64_cropped100',
'test_celeba128_cropped100', 'ood-coco100'
]))]
df = df.loc[((df['model'] == 'began_noop') & (df['n_cuts'].isin([0, 2]))) |
((df['model'] == 'vanilla_vae_noop') &
(df['n_cuts'].isin([0, 1]))) |
((df['model'] == 'dcgan_noop') & (df['n_cuts'].isin([0, 1])))]
df = df.loc[df['fm'] == 'NoOp']
df.loc[:, 'model_name'] = pd.Series(df.apply(lambda row: _name_fn(row),
axis=1),
index=df.index).values
df.loc[:, 'type'] = pd.Series(df.apply(lambda row: _type_fn(row), axis=1),
index=df.index).values
df.loc[:, 'split_name'] = pd.Series(df.apply(lambda row: _split_fn(row),
axis=1),
index=df.index).values
sns.set(rc={'figure.figsize': (16, 9)})
sns.set_style('ticks')
sns.set_context('poster', font_scale=1.4)
legend_order = ['No Surgery', 'With Surgery']
model_order = ['DCGAN', 'BEGAN', 'VAE']
split_order = ['Test', 'OOD']
g = sns.catplot(data=df,
x="model_name",
order=model_order,
y="psnr",
col="split_name",
col_order=split_order,
hue='type',
hue_order=legend_order,
palette=sns.color_palette("hls", 2),
kind="box",
height=8,
aspect=1.4,
legend=False)
(g.set_xlabels("").set_xticklabels(model_order, fontsize=24).set_ylabels(
"PSNR (dB)").set_titles("{col_name}").fig.subplots_adjust(top=0.85,
bottom=0.2,
wspace=0.1,
hspace=0.0))
g.set(ylim=(0, 50), yticks=range(0, 51, 10))
handles = g._legend_data.values()
labels = g._legend_data.keys()
g.fig.legend(handles=handles,
labels=labels,
loc='lower center',
ncol=2,
fontsize=28)
os.makedirs('./figures/noop_plot', exist_ok=True)
plt.savefig(f'./figures/noop_plot/noop_box.pdf',
dpi=300,
bbox_inches='tight')
def _name_fn(row):
"""
Need to filter out before using this function
"""
if row['model'].startswith('dcgan'):
return 'DCGAN'
elif row['model'].startswith('began'):
return 'BEGAN'
elif row['model'].startswith('vanilla_vae'):
return 'VAE'
elif row['model'].startswith('beta_vae'):
return 'β-VAE'
elif row['model'].startswith('biggan'):
return 'BigGAN'
else:
return ''
def _type_fn(row):
"""
Need to filter out before using this function
"""
if row['n_cuts'] == 0:
return 'No Surgery'
else:
return 'With Surgery'
def _fm_fn(row):
if row['fm'] == 'InpaintingScatter':
return f'Inp({int(row["fraction_kept"] * 100) :d}%)'
elif row['fm'] == 'SuperResolution':
return f'SR({int(1/row["scale_factor"]):d}x)'
else:
return ''
def inverse_plot(split):
with open('./final_runs/processed_results/df_results.pkl', 'rb') as f:
df_results = pickle.load(f)
if split == 'test_celeba':
df = df_results.loc[
((df_results['model'] == 'biggan_inv') &
(df_results['split'] == 'celebahq20')) |
((df_results['model'].
isin(['began_inv', 'beta_vae_inv', 'vanilla_vae_inv'])) &
(df_results['split'] == 'test_celeba128_cropped20')) |
((df_results['model'].isin(['dcgan_inv'])) &
(df_results['split'] == 'test_celeba64_cropped20'))]
elif split == 'ood-coco':
df = df_results.loc[(df_results['model'].isin([
'began_inv', 'biggan_inv', 'vanilla_vae_inv', 'beta_vae_inv',
'dcgan_inv'
])) & (df_results['split'] == 'ood-coco20')]
else:
raise KeyError()
df = df.loc[((df['model'] == 'began_inv') & (df['n_cuts'].isin([0, 3]))) |
((df['model'] == 'biggan_inv') & (df['n_cuts'].isin([0, 7]))) |
((df['model'] == 'vanilla_vae_inv') &
(df['n_cuts'].isin([0, 2]))) |
((df['model'] == 'beta_vae_inv') & (df['n_cuts'].isin([0, 2])))
| ((df['model'] == 'dcgan_inv') & (df['n_cuts'].isin([0, 1])))]
df.loc[:, 'model_name'] = pd.Series(df.apply(lambda row: _name_fn(row),
axis=1),
index=df.index).values
df.loc[:, 'type'] = pd.Series(df.apply(lambda row: _type_fn(row), axis=1),
index=df.index).values
df.loc[:, 'fm_name'] = pd.Series(df.apply(lambda row: _fm_fn(row), axis=1),
index=df.index).values
sns.set(rc={'figure.figsize': (16, 9)})
sns.set_style('ticks')
sns.set_context('poster', font_scale=1.4)
legend_order = ['No Surgery', 'With Surgery']
fm_order = ['Inp(10%)', 'SR(4x)']
if split == 'test_celeba':
model_order = ['DCGAN', 'BEGAN', 'VAE', 'β-VAE', 'BigGAN']
elif split in ['ood-coco']:
model_order = ['DCGAN', 'BEGAN', 'VAE', 'β-VAE', 'BigGAN']
if split == 'test_celeba':
title = 'Test'
elif split == 'ood-coco':
title = 'OOD'
g = sns.catplot(
data=df,
x="model_name",
y="psnr",
col="fm_name",
hue='type',
order=model_order,
col_order=fm_order,
hue_order=legend_order,
palette=sns.color_palette("hls", 2),
kind="box",
dodge=True,
height=8,
sharey=True,
legend=False,
)
if split == 'test_celeba':
model_order = ['DCGAN', 'BEGAN', 'VAE', 'β-VAE', 'BigGAN*']
elif split in ['ood-coco20']:
model_order = ['DCGAN', 'BEGAN', 'VAE', 'β-VAE', 'BigGAN']
(g.set_xlabels("").set_xticklabels(model_order, fontsize=24).set_ylabels(
"PSNR (dB)").set_titles("{col_name}").fig.subplots_adjust(top=0.85,
bottom=0.25,
wspace=0,
hspace=.1))
g.fig.suptitle(title)
g.set(ylim=(0, 35), yticks=range(0, 36, 5))
handles = g._legend_data.values()
labels = g._legend_data.keys()
g.fig.legend(handles=handles, labels=labels, loc='lower center', ncol=2)
os.makedirs('./figures/inverse_plot', exist_ok=True)
plt.savefig(f'./figures/inverse_plot/inverse_catplot_split={split}.pdf',
dpi=300,
bbox_inches='tight')
def cs_other_init_plot(split, n_measure):
def init_name_fn(row):
if row['z_init_mode'] == 'zero':
return 'Zero'
elif row['z_init_mode'] == 'lasso_inverse':
return 'LassoInv'
elif row['z_init_mode'] == 'normal':
return f'N(0,{int(row["limit"])})'
elif row['z_init_mode'] == 'clamped_normal':
return 'CN(0,1)'
else:
return 'Lasso-DCT'
with open('./final_runs/processed_results/df_baseline_results.pkl',
'rb') as f:
df_baseline = pickle.load(f)
with open('./final_runs/processed_results/df_results.pkl', 'rb') as f:
df_results = pickle.load(f)
df = pd.concat([
df_baseline.loc[(df_baseline['model'] == 'lasso-dct-128')
& (df_baseline['n_measure'] == n_measure)],
df_results.loc[(df_results['fm'] == 'GaussianCompressiveSensing')
& (df_results['model'] == 'began_cs_other_init') &
(df_results['n_cuts'].isin([2])) &
(df_results['n_measure'] == n_measure)]
])
df = df.loc[df['split'] == split]
df['init_name'] = df.apply(lambda row: init_name_fn(row), axis=1)
sns.set(rc={'figure.figsize': (20, 10)})
sns.set_style('ticks')
sns.set_context('poster', font_scale=2)
mode_order = [
'Lasso-DCT', 'Zero', 'CN(0,1)', 'LassoInv', 'N(0,5)', 'N(0,10)'
]
plt.figure()
ax = sns.boxplot(
x='init_name',
y='psnr',
data=df,
order=mode_order,
)
if 'celeba' in split:
title = f'Test'
elif split == 'ood-coco100':
title = f'OOD'
ax.set_ylim(10, 40)
ax.set_xlabel('Initialization Strategy')
ax.set_xticklabels(mode_order, fontsize=36)
ax.set_ylabel('PSNR (dB)')
ax.set_title(title)
os.makedirs('./figures/cs_other_init', exist_ok=True)
plt.savefig((f'./figures/cs_other_init/'
f'cs_other_init_n_measure={n_measure}_split={split}.pdf'),
dpi=300,
bbox_inches='tight')
def noop_single_image(img_name, split, model, size, n_cuts):
if model == 'dcgan_noop':
# title_fontsize = 40
# label_fontsize = 32
# model_name = "DCGAN"
z_lr = 1
n_steps = 100
elif model == 'began_noop':
# title_fontsize = 40
# label_fontsize = 32
# model_name = "BEGAN"
z_lr = 1
n_steps = 100
elif model in ['vanilla_vae_noop']:
# title_fontsize = 40
# label_fontsize = 32
# model_name = 'VAE' if model == 'vanilla_vae' else 'β-VAE'
z_lr = 1
n_steps = 40
else:
raise NotImplementedError(model)
fig, ax = plt.subplots(1, 3, figsize=(10, 20))
fig.subplots_adjust(0, 0, 1, 1, 0, 0)
# Original
orig = torch.load(
f'./final_runs/images/{split}/{img_name}/{size}/original.pt').cpu(
).numpy().transpose([1, 2, 0])
ax[0].imshow(np.clip(orig, 0, 1))
# ax[0].set_xlabel('Original', fontsize=label_fontsize)
ax[0].set_xticks([])
ax[0].set_yticks([])
ax[0].set_frame_on(False)
# Naive model
n_0_path_a = f'./final_runs/results/{model}/n_cuts=0/{split}/{img_name}/NoOp/optimizer=lbfgs.n_steps={n_steps}.z_lr={z_lr}.restarts=3.n_cuts=0.z_init_mode=clamped_normal.limit=1'
# n_0_path_b = f'./final_runs/results/{model}/n_cuts=0/{split}/{img_name}/NoOp/optimizer=lbfgs.n_steps={n_steps}.z_lr={z_lr}.z_init_mode=clamped_normal.recover_batch_size=1.limit=1.n_cuts=0'
try:
n0_img = torch.load(
f'{n_0_path_a}/recovered.pt').cpu().numpy().transpose([1, 2, 0])
# n0_psnr = pickle.load(open(f'{n_0_path_a}/psnr_clamped.pkl', 'rb'))
except FileNotFoundError:
n0_img = torch.load(
f'{n_0_path_b}/recovered.pt').cpu().numpy().transpose([1, 2, 0])
# n0_psnr = pickle.load(open(f'{n_0_path_b}/psnr_clamped.pkl', 'rb'))
# ax[1].set_title(model_name, fontsize=title_fontsize)
# ax[1].set_xlabel(f'No Surgery\n{n0_psnr:0.2f}dB', fontsize=label_fontsize)
ax[1].imshow(np.clip(n0_img, 0, 1))
ax[1].set_xticks([])
ax[1].set_yticks([])
ax[1].set_frame_on(False)
# Best model
n_chosen_path_a = f'./final_runs/results/{model}/n_cuts={n_cuts}/{split}/{img_name}/NoOp/optimizer=lbfgs.n_steps={n_steps}.z_lr={z_lr}.restarts=3.n_cuts={n_cuts}.z_init_mode=clamped_normal.limit=1'
# n_chosen_path_b = f'./final_runs/results/{model}/n_cuts={n_cuts}/{split}/{img_name}/NoOp/optimizer=lbfgs.n_steps={n_steps}.z_lr={z_lr}.z_init_mode=clamped_normal.recover_batch_size=1.limit=1.n_cuts={n_cuts}'
try:
n_chosen_img = torch.load(
f'{n_chosen_path_a}/recovered.pt').cpu().numpy().transpose(
[1, 2, 0])
# n_chosen_psnr = pickle.load(
# open(f'{n_chosen_path_a}/psnr_clamped.pkl', 'rb'))
except FileNotFoundError:
n_chosen_img = torch.load(
f'{n_chosen_path_b}/recovered.pt').cpu().numpy().transpose(
[1, 2, 0])
# n_chosen_psnr = pickle.load(
# open(f'{n_chosen_path_b}/psnr_clamped.pkl', 'rb'))
# ax[2].set_xlabel(f'With Surgery\n{n_chosen_psnr:0.2f}dB', fontsize=label_fontsize)
ax[2].imshow(np.clip(n_chosen_img, 0, 1))
ax[2].set_xticks([])
ax[2].set_yticks([])
ax[2].set_frame_on(False)
return fig
def noop_images(top_n):
with open('./final_runs/processed_results/df_results.pkl', 'rb') as f:
df = pickle.load(f)
params = {
'dcgan_noop': {
'n_cuts': 1,
'size': 64,
},
'began_noop': {
'n_cuts': 2,
'size': 128,
},
'vanilla_vae_noop': {
'n_cuts': 1,
'size': 128
},
}
for split in [
'test_celeba64_cropped100', 'test_celeba128_cropped100',
'ood-coco100'
]:
for model, kwargs in params.items():
os.makedirs(f'./figures/noop_images/{model}', exist_ok=True)
imgs = get_top_noop(df, split, model, kwargs['n_cuts'], top_n)
if len(imgs) == 0:
continue
for img in imgs:
noop_single_image(img, split, model, kwargs['size'],
kwargs['n_cuts'])
plt.savefig(
f'./figures/noop_images/{model}/{model}_{split}_{img}.png',
dpi=300,
bbox_inches='tight')
plt.close()
def inverse_single_image(img_name, split, model, fm, size, n_cuts):
if model == 'biggan_inv':
z_lr = 1.5
n_steps = 25
elif model in ['vanilla_vae_inv', 'beta_vae_inv']:
z_lr = 1
n_steps = 25
elif model == 'began_inv':
z_lr = 1
n_steps = 25
elif model == 'dcgan_inv':
z_lr = 0.1
n_steps = 25
else:
raise NotImplementedError(model)
# Original
fig = plt.figure(constrained_layout=False)
gs = fig.add_gridspec(1, 1, left=0, right=0.14, wspace=0, hspace=0)
orig = torch.load(
f'./final_runs/images/{split}/{img_name}/{size}/original.pt').cpu(
).numpy().transpose([1, 2, 0])
ax = fig.add_subplot(gs[0, 0])
ax.imshow(np.clip(orig, 0, 1))
ax.set_xticks([])
ax.set_yticks([])
ax.set_frame_on(False)
# Inpainting Degraded, No Surgery, With Surgery
# Degraded
fm = 'InpaintingScatter.fraction_kept=0.1'
gs = fig.add_gridspec(1, 3, left=0.15, right=0.57, wspace=0, hspace=0)
ax = fig.add_subplot(gs[0, 0])
deg = torch.load(f'./final_runs/images/{split}/{img_name}/{size}/{fm}.pt'
).cpu().numpy().transpose([1, 2, 0])
ax.imshow(np.clip(deg, 0, 1))
ax.set_xticks([])
ax.set_yticks([])
ax.set_frame_on(False)
# Naive model
ax = fig.add_subplot(gs[0, 1])
n_0_path = f'./final_runs/results/{model}/n_cuts=0/{split}/{img_name}/{fm}/optimizer=lbfgs.n_steps={n_steps}.z_lr={z_lr}.recover_batch_size=1.n_cuts=0.z_init_mode=clamped_normal.limit=1'
n0_img = torch.load(f'{n_0_path}/recovered.pt').cpu().numpy().transpose(
[1, 2, 0])
ax.imshow(np.clip(n0_img, 0, 1))
ax.set_xticks([])
ax.set_yticks([])
ax.set_frame_on(False)
# Best model
ax = fig.add_subplot(gs[0, 2])
n_chosen_path = f'./final_runs/results/{model}/n_cuts={n_cuts}/{split}/{img_name}/{fm}/optimizer=lbfgs.n_steps={n_steps}.z_lr={z_lr}.recover_batch_size=1.n_cuts={n_cuts}.z_init_mode=clamped_normal.limit=1'
n_chosen_img = torch.load(
f'{n_chosen_path}/recovered.pt').cpu().numpy().transpose([1, 2, 0])
ax.imshow(np.clip(n_chosen_img, 0, 1))
ax.set_xticks([])
ax.set_yticks([])
ax.set_frame_on(False)
# SuperResolution Degraded, No Surgery, With Surgery
# Degraded
fm = 'SuperResolution.scale_factor=0.25.mode=bilinear'
gs = fig.add_gridspec(1, 3, left=0.58, right=1, wspace=0, hspace=0)
ax = fig.add_subplot(gs[0, 0])
deg = torch.load(f'./final_runs/images/{split}/{img_name}/{size}/{fm}.pt'
).cpu().numpy().transpose([1, 2, 0])
ax.imshow(np.clip(deg, 0, 1))
ax.set_xticks([])
ax.set_yticks([])
ax.set_frame_on(False)
# Naive model
ax = fig.add_subplot(gs[0, 1])
n_0_path = f'./final_runs/results/{model}/n_cuts=0/{split}/{img_name}/{fm}/optimizer=lbfgs.n_steps={n_steps}.z_lr={z_lr}.recover_batch_size=1.n_cuts=0.z_init_mode=clamped_normal.limit=1'
n0_img = torch.load(f'{n_0_path}/recovered.pt').cpu().numpy().transpose(
[1, 2, 0])
ax.imshow(np.clip(n0_img, 0, 1))
ax.set_xticks([])
ax.set_yticks([])
ax.set_frame_on(False)
# Best model
ax = fig.add_subplot(gs[0, 2])
n_chosen_path = f'./final_runs/results/{model}/n_cuts={n_cuts}/{split}/{img_name}/{fm}/optimizer=lbfgs.n_steps={n_steps}.z_lr={z_lr}.recover_batch_size=1.n_cuts={n_cuts}.z_init_mode=clamped_normal.limit=1'
n_chosen_img = torch.load(
f'{n_chosen_path}/recovered.pt').cpu().numpy().transpose([1, 2, 0])
ax.imshow(np.clip(n_chosen_img, 0, 1))
ax.set_xticks([])
ax.set_yticks([])
ax.set_frame_on(False)
return fig
def get_top_inverse(df, split, model, n_cuts, n):
df1 = df[(df['split'] == split) & (df['model'] == model) &
(df['n_cuts'] == n_cuts) & ((df['fraction_kept'] == 0.1) |
(df['scale_factor'] == 0.25))]
return df1.groupby(['image_name']).mean().sort_values(
by='psnr', ascending=False).head(n).index.values.tolist()
def get_top_noop(df, split, model, n_cuts, n):
df1 = df[(df['split'] == split) & (df['model'] == model) &
(df['n_cuts'] == n_cuts) & (df['fm'] == 'NoOp')]
return df1.groupby(['image_name']).mean().sort_values(
by='psnr', ascending=False).head(n).index.values.tolist()
def get_top_cs(df, split, model, n_cuts, n_measure, n):
df1 = df[(df['split'] == split) & (df['model'] == model) &
(df['n_cuts'] == n_cuts) & (df['n_measure'] == n_measure)]
return df1.groupby(['image_name']).mean().sort_values(
by='psnr', ascending=False).head(n).index.values.tolist()
def inverse_images(top_n):
with open('./final_runs/processed_results/df_results.pkl', 'rb') as f:
df = pickle.load(f)
params1 = {
'began_inv': {
'n_cuts': 3,
'size': 128,
},
'dcgan_inv': {
'n_cuts': 1,
'size': 64
},
'vanilla_vae_inv': {
'n_cuts': 2,
'size': 128
},
'beta_vae_inv': {
'n_cuts': 2,
'size': 128
},
}
params2 = {
'biggan_inv': {
'n_cuts': 7,
'size': 512
},
}
# everything but biggan...
for split in [
'test_celeba64_cropped20', 'test_celeba128_cropped20', 'ood-coco20'
]:
for fm in [
'InpaintingScatter.fraction_kept=0.1',
'SuperResolution.scale_factor=0.25.mode=bilinear'
]:
for model, kwargs in params1.items():
os.makedirs(f'./figures/inverse_output_images/{model}',
exist_ok=True)
imgs = get_top_inverse(df, split, model, kwargs['n_cuts'],
top_n)
for img in imgs:
inverse_single_image(img, split, model, fm, kwargs['size'],
kwargs['n_cuts'])
plt.savefig(
f'./figures/inverse_output_images/{model}/{model}_{split}_{img}.png',
dpi=300,
bbox_inches='tight')
plt.close()
# biggan
for split in ['celebahq20', 'ood-coco20']:
for fm in [
'InpaintingScatter.fraction_kept=0.1',
'SuperResolution.scale_factor=0.25.mode=bilinear'
]:
for model, kwargs in params2.items():
os.makedirs(f'./figures/inverse_output_images/{model}',
exist_ok=True)
imgs = get_top_inverse(df, split, model, kwargs['n_cuts'],
top_n)
for img in imgs:
inverse_single_image(img, split, model, fm, kwargs['size'],
kwargs['n_cuts'])
plt.savefig(
f'./figures/inverse_output_images/{model}/{model}_{split}_{img}.png',
dpi=300,
bbox_inches='tight')
plt.close()
def cs_single_image(img_name, split, model, n_measure, n_cuts):
fontsize = 18
if n_measure == 8000:
size = 128
elif n_measure == 2000:
size = 64
else:
raise ValueError()
if model in ['vanilla_vae_cs', 'beta_vae_cs']:
z_lr = 1
n_steps = 25
elif model == 'began_cs':
z_lr = 1
n_steps = 25
elif model == 'dcgan_cs':
z_lr = 0.1
n_steps = 25
else:
raise NotImplementedError(model)
fig, ax = plt.subplots(1, 4)
# plt.axis('off')
fig.subplots_adjust(0, 0, 1, 1, 0, 0)
# Original
orig = torch.load(