ClothingGAN / visualize.py
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Init code
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# Copyright 2020 Erik Härkönen. All rights reserved.
# This file is licensed to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. You may obtain a copy
# of the License at http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS
# OF ANY KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.
# Patch for broken CTRL+C handler
# https://github.com/ContinuumIO/anaconda-issues/issues/905
import os
os.environ['FOR_DISABLE_CONSOLE_CTRL_HANDLER'] = '1'
import torch, json, numpy as np
from types import SimpleNamespace
import matplotlib.pyplot as plt
from pathlib import Path
from os import makedirs
from PIL import Image
from netdissect import proggan, nethook, easydict, zdataset
from netdissect.modelconfig import create_instrumented_model
from estimators import get_estimator
from models import get_instrumented_model
from scipy.cluster.vq import kmeans
import re
import sys
import datetime
import argparse
from tqdm import trange
from config import Config
from decomposition import get_random_dirs, get_or_compute, get_max_batch_size, SEED_VISUALIZATION
from utils import pad_frames
def x_closest(p):
distances = np.sqrt(np.sum((X - p)**2, axis=-1))
idx = np.argmin(distances)
return distances[idx], X[idx]
def make_gif(imgs, duration_secs, outname):
head, *tail = [Image.fromarray((x * 255).astype(np.uint8)) for x in imgs]
ms_per_frame = 1000 * duration_secs / instances
head.save(outname, format='GIF', append_images=tail, save_all=True, duration=ms_per_frame, loop=0)
def make_mp4(imgs, duration_secs, outname):
import shutil
import subprocess as sp
FFMPEG_BIN = shutil.which("ffmpeg")
assert FFMPEG_BIN is not None, 'ffmpeg not found, install with "conda install -c conda-forge ffmpeg"'
assert len(imgs[0].shape) == 3, 'Invalid shape of frame data'
resolution = imgs[0].shape[0:2]
fps = int(len(imgs) / duration_secs)
command = [ FFMPEG_BIN,
'-y', # overwrite output file
'-f', 'rawvideo',
'-vcodec','rawvideo',
'-s', f'{resolution[0]}x{resolution[1]}', # size of one frame
'-pix_fmt', 'rgb24',
'-r', f'{fps}',
'-i', '-', # imput from pipe
'-an', # no audio
'-c:v', 'libx264',
'-preset', 'slow',
'-crf', '17',
str(Path(outname).with_suffix('.mp4')) ]
frame_data = np.concatenate([(x * 255).astype(np.uint8).reshape(-1) for x in imgs])
with sp.Popen(command, stdin=sp.PIPE, stdout=sp.PIPE, stderr=sp.PIPE) as p:
ret = p.communicate(frame_data.tobytes())
if p.returncode != 0:
print(ret[1].decode("utf-8"))
raise sp.CalledProcessError(p.returncode, command)
def make_grid(latent, lat_mean, lat_comp, lat_stdev, act_mean, act_comp, act_stdev, scale=1, n_rows=10, n_cols=5, make_plots=True, edit_type='latent'):
from notebooks.notebook_utils import create_strip_centered
inst.remove_edits()
x_range = np.linspace(-scale, scale, n_cols, dtype=np.float32) # scale in sigmas
rows = []
for r in range(n_rows):
curr_row = []
out_batch = create_strip_centered(inst, edit_type, layer_key, [latent],
act_comp[r], lat_comp[r], act_stdev[r], lat_stdev[r], act_mean, lat_mean, scale, 0, -1, n_cols)[0]
for i, img in enumerate(out_batch):
curr_row.append(('c{}_{:.2f}'.format(r, x_range[i]), img))
rows.append(curr_row[:n_cols])
inst.remove_edits()
if make_plots:
# If more rows than columns, make several blocks side by side
n_blocks = 2 if n_rows > n_cols else 1
for r, data in enumerate(rows):
# Add white borders
imgs = pad_frames([img for _, img in data])
coord = ((r * n_blocks) % n_rows) + ((r * n_blocks) // n_rows)
plt.subplot(n_rows//n_blocks, n_blocks, 1 + coord)
plt.imshow(np.hstack(imgs))
# Custom x-axis labels
W = imgs[0].shape[1] # image width
P = imgs[1].shape[1] # padding width
locs = [(0.5*W + i*(W+P)) for i in range(n_cols)]
plt.xticks(locs, ["{:.2f}".format(v) for v in x_range])
plt.yticks([])
plt.ylabel(f'C{r}')
plt.tight_layout()
plt.subplots_adjust(top=0.96) # make room for suptitle
return [img for row in rows for img in row]
######################
### Visualize results
######################
if __name__ == '__main__':
global max_batch, sample_shape, feature_shape, inst, args, layer_key, model
args = Config().from_args()
t_start = datetime.datetime.now()
timestamp = lambda : datetime.datetime.now().strftime("%d.%m %H:%M")
print(f'[{timestamp()}] {args.model}, {args.layer}, {args.estimator}')
# Ensure reproducibility
torch.manual_seed(0) # also sets cuda seeds
np.random.seed(0)
# Speed up backend
torch.backends.cudnn.benchmark = True
torch.autograd.set_grad_enabled(False)
has_gpu = torch.cuda.is_available()
device = torch.device('cuda' if has_gpu else 'cpu')
layer_key = args.layer
layer_name = layer_key #layer_key.lower().split('.')[-1]
basedir = Path(__file__).parent.resolve()
outdir = basedir / 'out'
# Load model
inst = get_instrumented_model(args.model, args.output_class, layer_key, device, use_w=args.use_w)
model = inst.model
feature_shape = inst.feature_shape[layer_key]
latent_shape = model.get_latent_shape()
print('Feature shape:', feature_shape)
# Layout of activations
if len(feature_shape) != 4: # non-spatial
axis_mask = np.ones(len(feature_shape), dtype=np.int32)
else:
axis_mask = np.array([0, 1, 1, 1]) # only batch fixed => whole activation volume used
# Shape of sample passed to PCA
sample_shape = feature_shape*axis_mask
sample_shape[sample_shape == 0] = 1
# Load or compute components
dump_name = get_or_compute(args, inst)
data = np.load(dump_name, allow_pickle=False) # does not contain object arrays
X_comp = data['act_comp']
X_global_mean = data['act_mean']
X_stdev = data['act_stdev']
X_var_ratio = data['var_ratio']
X_stdev_random = data['random_stdevs']
Z_global_mean = data['lat_mean']
Z_comp = data['lat_comp']
Z_stdev = data['lat_stdev']
n_comp = X_comp.shape[0]
data.close()
# Transfer components to device
tensors = SimpleNamespace(
X_comp = torch.from_numpy(X_comp).to(device).float(), #-1, 1, C, H, W
X_global_mean = torch.from_numpy(X_global_mean).to(device).float(), # 1, C, H, W
X_stdev = torch.from_numpy(X_stdev).to(device).float(),
Z_comp = torch.from_numpy(Z_comp).to(device).float(),
Z_stdev = torch.from_numpy(Z_stdev).to(device).float(),
Z_global_mean = torch.from_numpy(Z_global_mean).to(device).float(),
)
transformer = get_estimator(args.estimator, n_comp, args.sparsity)
tr_param_str = transformer.get_param_str()
# Compute max batch size given VRAM usage
max_batch = args.batch_size or (get_max_batch_size(inst, device) if has_gpu else 1)
print('Batch size:', max_batch)
def show():
if args.batch_mode:
plt.close('all')
else:
plt.show()
print(f'[{timestamp()}] Creating visualizations')
# Ensure visualization gets new samples
torch.manual_seed(SEED_VISUALIZATION)
np.random.seed(SEED_VISUALIZATION)
# Make output directories
est_id = f'spca_{args.sparsity}' if args.estimator == 'spca' else args.estimator
outdir_comp = outdir/model.name/layer_key.lower()/est_id/'comp'
outdir_inst = outdir/model.name/layer_key.lower()/est_id/'inst'
outdir_summ = outdir/model.name/layer_key.lower()/est_id/'summ'
makedirs(outdir_comp, exist_ok=True)
makedirs(outdir_inst, exist_ok=True)
makedirs(outdir_summ, exist_ok=True)
# Measure component sparsity (!= activation sparsity)
sparsity = np.mean(X_comp == 0) # percentage of zero values in components
print(f'Sparsity: {sparsity:.2f}')
def get_edit_name(mode):
if mode == 'activation':
is_stylegan = 'StyleGAN' in args.model
is_w = layer_key in ['style', 'g_mapping']
return 'W' if (is_stylegan and is_w) else 'ACT'
elif mode == 'latent':
return model.latent_space_name()
elif mode == 'both':
return 'BOTH'
else:
raise RuntimeError(f'Unknown edit mode {mode}')
# Only visualize applicable edit modes
if args.use_w and layer_key in ['style', 'g_mapping']:
edit_modes = ['latent'] # activation edit is the same
else:
edit_modes = ['activation', 'latent']
# Summary grid, real components
for edit_mode in edit_modes:
plt.figure(figsize = (14,12))
plt.suptitle(f"{args.estimator.upper()}: {model.name} - {layer_name}, {get_edit_name(edit_mode)} edit", size=16)
make_grid(tensors.Z_global_mean, tensors.Z_global_mean, tensors.Z_comp, tensors.Z_stdev, tensors.X_global_mean,
tensors.X_comp, tensors.X_stdev, scale=args.sigma, edit_type=edit_mode, n_rows=14)
plt.savefig(outdir_summ / f'components_{get_edit_name(edit_mode)}.jpg', dpi=300)
show()
if args.make_video:
components = 15
instances = 150
# One reasonable, one over the top
for sigma in [args.sigma, 3*args.sigma]:
for c in range(components):
for edit_mode in edit_modes:
frames = make_grid(tensors.Z_global_mean, tensors.Z_global_mean, tensors.Z_comp[c:c+1, :, :], tensors.Z_stdev[c:c+1], tensors.X_global_mean,
tensors.X_comp[c:c+1, :, :], tensors.X_stdev[c:c+1], n_rows=1, n_cols=instances, scale=sigma, make_plots=False, edit_type=edit_mode)
plt.close('all')
frames = [x for _, x in frames]
frames = frames + frames[::-1]
make_mp4(frames, 5, outdir_comp / f'{get_edit_name(edit_mode)}_sigma{sigma}_comp{c}.mp4')
# Summary grid, random directions
# Using the stdevs of the principal components for same norm
random_dirs_act = torch.from_numpy(get_random_dirs(n_comp, np.prod(sample_shape)).reshape(-1, *sample_shape)).to(device)
random_dirs_z = torch.from_numpy(get_random_dirs(n_comp, np.prod(inst.input_shape)).reshape(-1, *latent_shape)).to(device)
for edit_mode in edit_modes:
plt.figure(figsize = (14,12))
plt.suptitle(f"{model.name} - {layer_name}, random directions w/ PC stdevs, {get_edit_name(edit_mode)} edit", size=16)
make_grid(tensors.Z_global_mean, tensors.Z_global_mean, random_dirs_z, tensors.Z_stdev,
tensors.X_global_mean, random_dirs_act, tensors.X_stdev, scale=args.sigma, edit_type=edit_mode, n_rows=14)
plt.savefig(outdir_summ / f'random_dirs_{get_edit_name(edit_mode)}.jpg', dpi=300)
show()
# Random instances w/ components added
n_random_imgs = 10
latents = model.sample_latent(n_samples=n_random_imgs)
for img_idx in trange(n_random_imgs, desc='Random images', ascii=True):
#print(f'Creating visualizations for random image {img_idx+1}/{n_random_imgs}')
z = latents[img_idx][None, ...]
# Summary grid, real components
for edit_mode in edit_modes:
plt.figure(figsize = (14,12))
plt.suptitle(f"{args.estimator.upper()}: {model.name} - {layer_name}, {get_edit_name(edit_mode)} edit", size=16)
make_grid(z, tensors.Z_global_mean, tensors.Z_comp, tensors.Z_stdev,
tensors.X_global_mean, tensors.X_comp, tensors.X_stdev, scale=args.sigma, edit_type=edit_mode, n_rows=14)
plt.savefig(outdir_summ / f'samp{img_idx}_real_{get_edit_name(edit_mode)}.jpg', dpi=300)
show()
if args.make_video:
components = 5
instances = 150
# One reasonable, one over the top
for sigma in [args.sigma, 3*args.sigma]: #[2, 5]:
for edit_mode in edit_modes:
imgs = make_grid(z, tensors.Z_global_mean, tensors.Z_comp, tensors.Z_stdev, tensors.X_global_mean, tensors.X_comp, tensors.X_stdev,
n_rows=components, n_cols=instances, scale=sigma, make_plots=False, edit_type=edit_mode)
plt.close('all')
for c in range(components):
frames = [x for _, x in imgs[c*instances:(c+1)*instances]]
frames = frames + frames[::-1]
make_mp4(frames, 5, outdir_inst / f'{get_edit_name(edit_mode)}_sigma{sigma}_img{img_idx}_comp{c}.mp4')
print('Done in', datetime.datetime.now() - t_start)