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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.
# An interactive glumpy (OpenGL) + tkinter viewer for interacting with principal components.
# Requires OpenGL and CUDA support for rendering.
import torch
import numpy as np
import tkinter as tk
from tkinter import ttk
from types import SimpleNamespace
import matplotlib.pyplot as plt
from pathlib import Path
from os import makedirs
from models import get_instrumented_model
from config import Config
from decomposition import get_or_compute
from torch.nn.functional import interpolate
from TkTorchWindow import TorchImageView
from functools import partial
from platform import system
from PIL import Image
from utils import pad_frames, prettify_name
import pickle
# For platform specific UI tweaks
is_windows = 'Windows' in system()
is_linux = 'Linux' in system()
is_mac = 'Darwin' in system()
# Read input parameters
args = Config().from_args()
# Don't bother without GPU
assert torch.cuda.is_available(), 'Interactive mode requires CUDA'
# Use syntax from paper
def get_edit_name(idx, s, e, name=None):
return 'E({comp}, {edit_range}){edit_name}'.format(
comp = idx,
edit_range = f'{s}-{e}' if e > s else s,
edit_name = f': {name}' if name else ''
)
# Load or compute PCA basis vectors
def load_components(class_name, inst):
global components, state, use_named_latents
config = args.from_dict({ 'output_class': class_name })
dump_name = get_or_compute(config, inst)
data = np.load(dump_name, allow_pickle=False)
X_comp = data['act_comp']
X_mean = data['act_mean']
X_stdev = data['act_stdev']
Z_comp = data['lat_comp']
Z_mean = data['lat_mean']
Z_stdev = data['lat_stdev']
random_stdev_act = np.mean(data['random_stdevs'])
n_comp = X_comp.shape[0]
data.close()
# Transfer to GPU
components = SimpleNamespace(
X_comp = torch.from_numpy(X_comp).cuda().float(),
X_mean = torch.from_numpy(X_mean).cuda().float(),
X_stdev = torch.from_numpy(X_stdev).cuda().float(),
Z_comp = torch.from_numpy(Z_comp).cuda().float(),
Z_stdev = torch.from_numpy(Z_stdev).cuda().float(),
Z_mean = torch.from_numpy(Z_mean).cuda().float(),
names = [f'Component {i}' for i in range(n_comp)],
latent_types = [model.latent_space_name()]*n_comp,
ranges = [(0, model.get_max_latents())]*n_comp,
)
state.component_class = class_name # invalidates cache
use_named_latents = False
print('Loaded components for', class_name, 'from', dump_name)
# Load previously exported named components from
# directory specified with '--inputs=path/to/comp'
def load_named_components(path, class_name):
global components, state, use_named_latents
import glob
matches = glob.glob(f'{path}/*.pkl')
selected = []
for dump_path in matches:
with open(dump_path, 'rb') as f:
data = pickle.load(f)
if data['model_name'] != model_name or data['output_class'] != class_name:
continue
if data['latent_space'] != model.latent_space_name():
print('Skipping', dump_path, '(wrong latent space)')
continue
selected.append(data)
print('Using', dump_path)
if len(selected) == 0:
raise RuntimeError('No valid components in given path.')
comp_dict = { k : [] for k in ['X_comp', 'Z_comp', 'X_stdev', 'Z_stdev', 'names', 'types', 'layer_names', 'ranges', 'latent_types'] }
components = SimpleNamespace(**comp_dict)
for d in selected:
s = d['edit_start']
e = d['edit_end']
title = get_edit_name(d['component_index'], s, e - 1, d['name']) # show inclusive
components.X_comp.append(torch.from_numpy(d['act_comp']).cuda())
components.Z_comp.append(torch.from_numpy(d['lat_comp']).cuda())
components.X_stdev.append(d['act_stdev'])
components.Z_stdev.append(d['lat_stdev'])
components.names.append(title)
components.types.append(d['edit_type'])
components.layer_names.append(d['decomposition']['layer']) # only for act
components.ranges.append((s, e))
components.latent_types.append(d['latent_space']) # W or Z
use_named_latents = True
print('Loaded named components')
def setup_model():
global model, inst, layer_name, model_name, feat_shape, args, class_name
model_name = args.model
layer_name = args.layer
class_name = args.output_class
# Speed up pytorch
torch.autograd.set_grad_enabled(False)
torch.backends.cudnn.benchmark = True
# Load model
inst = get_instrumented_model(model_name, class_name, layer_name, torch.device('cuda'), use_w=args.use_w)
model = inst.model
feat_shape = inst.feature_shape[layer_name]
sample_dims = np.prod(feat_shape)
# Initialize
if args.inputs:
load_named_components(args.inputs, class_name)
else:
load_components(class_name, inst)
# Project tensor 'X' onto orthonormal basis 'comp', return coordinates
def project_ortho(X, comp):
N = comp.shape[0]
coords = (comp.reshape(N, -1) * X.reshape(-1)).sum(dim=1)
return coords.reshape([N]+[1]*X.ndim)
def zero_sliders():
for v in ui_state.sliders:
v.set(0.0)
def reset_sliders(zero_on_failure=True):
global ui_state
mode = ui_state.mode.get()
# Not orthogonal: need to solve least-norm problem
# Not batch size 1: one set of sliders not enough
# Not principal components: unsupported format
is_ortho = not (mode == 'latent' and model.latent_space_name() == 'Z')
is_single = state.z.shape[0] == 1
is_pcs = not use_named_latents
state.lat_slider_offset = 0
state.act_slider_offset = 0
enabled = False
if not (enabled and is_ortho and is_single and is_pcs):
if zero_on_failure:
zero_sliders()
return
if mode == 'activation':
val = state.base_act
mean = components.X_mean
comp = components.X_comp
stdev = components.X_stdev
else:
val = state.z
mean = components.Z_mean
comp = components.Z_comp
stdev = components.Z_stdev
n_sliders = len(ui_state.sliders)
coords = project_ortho(val - mean, comp)
offset = torch.sum(coords[:n_sliders] * comp[:n_sliders], dim=0)
scaled_coords = (coords.view(-1) / stdev).detach().cpu().numpy()
# Part representable by sliders
if mode == 'activation':
state.act_slider_offset = offset
else:
state.lat_slider_offset = offset
for i in range(n_sliders):
ui_state.sliders[i].set(round(scaled_coords[i], ndigits=1))
def setup_ui():
global root, toolbar, ui_state, app, canvas
root = tk.Tk()
scale = 1.0
app = TorchImageView(root, width=int(scale*1024), height=int(scale*1024), show_fps=False)
app.pack(fill=tk.BOTH, expand=tk.YES)
root.protocol("WM_DELETE_WINDOW", shutdown)
root.title('GANspace')
toolbar = tk.Toplevel(root)
toolbar.protocol("WM_DELETE_WINDOW", shutdown)
toolbar.geometry("215x800+0+0")
toolbar.title('')
N_COMPONENTS = min(70, len(components.names))
ui_state = SimpleNamespace(
sliders = [tk.DoubleVar(value=0.0) for _ in range(N_COMPONENTS)],
scales = [],
truncation = tk.DoubleVar(value=0.9),
outclass = tk.StringVar(value=class_name),
random_seed = tk.StringVar(value='0'),
mode = tk.StringVar(value='latent'),
batch_size = tk.IntVar(value=1), # how many images to show in window
edit_layer_start = tk.IntVar(value=0),
edit_layer_end = tk.IntVar(value=model.get_max_latents() - 1),
slider_max_val = 10.0
)
# Z vs activation mode button
#tk.Radiobutton(toolbar, text=f"Latent ({model.latent_space_name()})", variable=ui_state.mode, command=reset_sliders, value='latent').pack(fill="x")
#tk.Radiobutton(toolbar, text="Activation", variable=ui_state.mode, command=reset_sliders, value='activation').pack(fill="x")
# Choose range where latents are modified
def set_min(val):
ui_state.edit_layer_start.set(min(int(val), ui_state.edit_layer_end.get()))
def set_max(val):
ui_state.edit_layer_end.set(max(int(val), ui_state.edit_layer_start.get()))
max_latent_idx = model.get_max_latents() - 1
if not use_named_latents:
slider_min = tk.Scale(toolbar, command=set_min, variable=ui_state.edit_layer_start,
label='Layer start', from_=0, to=max_latent_idx, orient=tk.HORIZONTAL).pack(fill="x")
slider_max = tk.Scale(toolbar, command=set_max, variable=ui_state.edit_layer_end,
label='Layer end', from_=0, to=max_latent_idx, orient=tk.HORIZONTAL).pack(fill="x")
# Scrollable list of components
outer_frame = tk.Frame(toolbar, borderwidth=2, relief=tk.SUNKEN)
canvas = tk.Canvas(outer_frame, highlightthickness=0, borderwidth=0)
frame = tk.Frame(canvas)
vsb = tk.Scrollbar(outer_frame, orient="vertical", command=canvas.yview)
canvas.configure(yscrollcommand=vsb.set)
vsb.pack(side="right", fill="y")
canvas.pack(side="left", fill="both", expand=True)
canvas.create_window((4,4), window=frame, anchor="nw")
def onCanvasConfigure(event):
canvas.itemconfigure("all", width=event.width)
canvas.configure(scrollregion=canvas.bbox("all"))
canvas.bind("<Configure>", onCanvasConfigure)
def on_scroll(event):
delta = 1 if (event.num == 5 or event.delta < 0) else -1
canvas.yview_scroll(delta, "units")
canvas.bind_all("<Button-4>", on_scroll)
canvas.bind_all("<Button-5>", on_scroll)
canvas.bind_all("<MouseWheel>", on_scroll)
canvas.bind_all("<Key>", lambda event : handle_keypress(event.keysym_num))
# Sliders and buttons
for i in range(N_COMPONENTS):
inner = tk.Frame(frame, borderwidth=1, background="#aaaaaa")
scale = tk.Scale(inner, variable=ui_state.sliders[i], from_=-ui_state.slider_max_val,
to=ui_state.slider_max_val, resolution=0.1, orient=tk.HORIZONTAL, label=components.names[i])
scale.pack(fill=tk.X, side=tk.LEFT, expand=True)
ui_state.scales.append(scale) # for changing label later
if not use_named_latents:
tk.Button(inner, text=f"Save", command=partial(export_direction, i, inner)).pack(fill=tk.Y, side=tk.RIGHT)
inner.pack(fill=tk.X)
outer_frame.pack(fill="both", expand=True, pady=0)
tk.Button(toolbar, text="Reset", command=reset_sliders).pack(anchor=tk.CENTER, fill=tk.X, padx=4, pady=4)
tk.Scale(toolbar, variable=ui_state.truncation, from_=0.01, to=1.0,
resolution=0.01, orient=tk.HORIZONTAL, label='Truncation').pack(fill="x")
tk.Scale(toolbar, variable=ui_state.batch_size, from_=1, to=9,
resolution=1, orient=tk.HORIZONTAL, label='Batch size').pack(fill="x")
# Output class
frame = tk.Frame(toolbar)
tk.Label(frame, text="Class name").pack(fill="x", side="left")
tk.Entry(frame, textvariable=ui_state.outclass).pack(fill="x", side="right", expand=True, padx=5)
frame.pack(fill=tk.X, pady=3)
# Random seed
def update_seed():
seed_str = ui_state.random_seed.get()
if seed_str.isdigit():
resample_latent(int(seed_str))
frame = tk.Frame(toolbar)
tk.Label(frame, text="Seed").pack(fill="x", side="left")
tk.Entry(frame, textvariable=ui_state.random_seed, width=12).pack(fill="x", side="left", expand=True, padx=2)
tk.Button(frame, text="Update", command=update_seed).pack(fill="y", side="right", padx=3)
frame.pack(fill=tk.X, pady=3)
# Get new latent or new components
tk.Button(toolbar, text="Resample latent", command=partial(resample_latent, None, False)).pack(anchor=tk.CENTER, fill=tk.X, padx=4, pady=4)
#tk.Button(toolbar, text="Recompute", command=recompute_components).pack(anchor=tk.CENTER, fill=tk.X)
# App state
state = SimpleNamespace(
z=None, # current latent(s)
lat_slider_offset = 0, # part of lat that is explained by sliders
act_slider_offset = 0, # part of act that is explained by sliders
component_class=None, # name of current PCs' image class
seed=0, # Latent z_i generated by seed+i
base_act = None, # activation of considered layer given z
)
def resample_latent(seed=None, only_style=False):
class_name = ui_state.outclass.get()
if class_name.isnumeric():
class_name = int(class_name)
if hasattr(model, 'is_valid_class'):
if not model.is_valid_class(class_name):
return
model.set_output_class(class_name)
B = ui_state.batch_size.get()
state.seed = np.random.randint(np.iinfo(np.int32).max - B) if seed is None else seed
ui_state.random_seed.set(str(state.seed))
# Use consecutive seeds along batch dimension (for easier reproducibility)
trunc = ui_state.truncation.get()
latents = [model.sample_latent(1, seed=state.seed + i, truncation=trunc) for i in range(B)]
state.z = torch.cat(latents).clone().detach() # make leaf node
assert state.z.is_leaf, 'Latent is not leaf node!'
if hasattr(model, 'truncation'):
model.truncation = ui_state.truncation.get()
print(f'Seeds: {state.seed} -> {state.seed + B - 1}' if B > 1 else f'Seed: {state.seed}')
torch.manual_seed(state.seed)
model.partial_forward(state.z, layer_name)
state.base_act = inst.retained_features()[layer_name]
reset_sliders(zero_on_failure=False)
# Remove focus from text entry
canvas.focus_set()
# Used to recompute after changing class of conditional model
def recompute_components():
class_name = ui_state.outclass.get()
if class_name.isnumeric():
class_name = int(class_name)
if hasattr(model, 'is_valid_class'):
if not model.is_valid_class(class_name):
return
if hasattr(model, 'set_output_class'):
model.set_output_class(class_name)
load_components(class_name, inst)
# Used to detect parameter changes for lazy recomputation
class ParamCache():
def update(self, **kwargs):
dirty = False
for argname, val in kwargs.items():
# Check pointer, then value
current = getattr(self, argname, 0)
if current is not val and pickle.dumps(current) != pickle.dumps(val):
setattr(self, argname, val)
dirty = True
return dirty
cache = ParamCache()
def l2norm(t):
return torch.norm(t.view(t.shape[0], -1), p=2, dim=1, keepdim=True)
def apply_edit(z0, delta):
return z0 + delta
def reposition_toolbar():
size, X, Y = root.winfo_geometry().split('+')
W, H = size.split('x')
toolbar_W = toolbar.winfo_geometry().split('x')[0]
offset_y = -30 if is_linux else 0 # window title bar
toolbar.geometry(f'{toolbar_W}x{H}+{int(X)-int(toolbar_W)}+{int(Y)+offset_y}')
toolbar.update()
def on_draw():
global img
n_comp = len(ui_state.sliders)
slider_vals = np.array([s.get() for s in ui_state.sliders], dtype=np.float32)
# Run model sparingly
mode = ui_state.mode.get()
latent_start = ui_state.edit_layer_start.get()
latent_end = ui_state.edit_layer_end.get() + 1 # save as exclusive, show as inclusive
if cache.update(coords=slider_vals, comp=state.component_class, mode=mode, z=state.z, s=latent_start, e=latent_end):
with torch.no_grad():
z_base = state.z - state.lat_slider_offset
z_deltas = [0.0]*model.get_max_latents()
z_delta_global = 0.0
n_comp = slider_vals.size
act_deltas = {}
if torch.is_tensor(state.act_slider_offset):
act_deltas[layer_name] = -state.act_slider_offset
for space in components.latent_types:
assert space == model.latent_space_name(), \
'Cannot mix latent spaces (for now)'
for c in range(n_comp):
coord = slider_vals[c]
if coord == 0:
continue
edit_mode = components.types[c] if use_named_latents else mode
# Activation offset
if edit_mode in ['activation', 'both']:
delta = components.X_comp[c] * components.X_stdev[c] * coord
name = components.layer_names[c] if use_named_latents else layer_name
act_deltas[name] = act_deltas.get(name, 0.0) + delta
# Latent offset
if edit_mode in ['latent', 'both']:
delta = components.Z_comp[c] * components.Z_stdev[c] * coord
edit_range = components.ranges[c] if use_named_latents else (latent_start, latent_end)
full_range = (edit_range == (0, model.get_max_latents()))
# Single or multiple offsets?
if full_range:
z_delta_global = z_delta_global + delta
else:
for l in range(*edit_range):
z_deltas[l] = z_deltas[l] + delta
# Apply activation deltas
inst.remove_edits()
for layer, delta in act_deltas.items():
inst.edit_layer(layer, offset=delta)
# Evaluate
has_offsets = any(torch.is_tensor(t) for t in z_deltas)
z_final = apply_edit(z_base, z_delta_global)
if has_offsets:
z_final = [apply_edit(z_final, d) for d in z_deltas]
img = model.forward(z_final).clamp(0.0, 1.0)
app.draw(img)
# Save necessary data to disk for later loading
def export_direction(idx, button_frame):
name = tk.StringVar(value='')
num_strips = tk.IntVar(value=0)
strip_width = tk.IntVar(value=5)
slider_values = np.array([s.get() for s in ui_state.sliders])
slider_value = slider_values[idx]
if (slider_values != 0).sum() > 1:
print('Please modify only one slider')
return
elif slider_value == 0:
print('Modify selected slider to set usable range (currently 0)')
return
popup = tk.Toplevel(root)
popup.geometry("200x200+0+0")
tk.Label(popup, text="Edit name").pack()
tk.Entry(popup, textvariable=name).pack(pady=5)
# tk.Scale(popup, from_=0, to=30, variable=num_strips,
# resolution=1, orient=tk.HORIZONTAL, length=200, label='Image strips to export').pack()
# tk.Scale(popup, from_=3, to=15, variable=strip_width,
# resolution=1, orient=tk.HORIZONTAL, length=200, label='Image strip width').pack()
tk.Button(popup, text='OK', command=popup.quit).pack()
canceled = False
def on_close():
nonlocal canceled
canceled = True
popup.quit()
popup.protocol("WM_DELETE_WINDOW", on_close)
x = button_frame.winfo_rootx()
y = button_frame.winfo_rooty()
w = int(button_frame.winfo_geometry().split('x')[0])
popup.geometry('%dx%d+%d+%d' % (180, 90, x + w, y))
popup.mainloop()
popup.destroy()
# Update slider name
label = get_edit_name(idx, ui_state.edit_layer_start.get(),
ui_state.edit_layer_end.get(), name.get())
ui_state.scales[idx].config(label=label)
if canceled:
return
params = {
'name': name.get(),
'sigma_range': slider_value,
'component_index': idx,
'act_comp': components.X_comp[idx].detach().cpu().numpy(),
'lat_comp': components.Z_comp[idx].detach().cpu().numpy(), # either Z or W
'latent_space': model.latent_space_name(),
'act_stdev': components.X_stdev[idx].item(),
'lat_stdev': components.Z_stdev[idx].item(),
'model_name': model_name,
'output_class': ui_state.outclass.get(), # applied onto
'decomposition': {
'name': args.estimator,
'components': args.components,
'samples': args.n,
'layer': args.layer,
'class_name': state.component_class # computed from
},
'edit_type': ui_state.mode.get(),
'truncation': ui_state.truncation.get(),
'edit_start': ui_state.edit_layer_start.get(),
'edit_end': ui_state.edit_layer_end.get() + 1, # show as inclusive, save as exclusive
'example_seed': state.seed,
}
edit_mode_str = params['edit_type']
if edit_mode_str == 'latent':
edit_mode_str = model.latent_space_name().lower()
comp_class = state.component_class
appl_class = params['output_class']
if comp_class != appl_class:
comp_class = f'{comp_class}_onto_{appl_class}'
file_ident = "{model}-{name}-{cls}-{est}-{mode}-{layer}-comp{idx}-range{start}-{end}".format(
model=model_name,
name=prettify_name(params['name']),
cls=comp_class,
est=args.estimator,
mode=edit_mode_str,
layer=args.layer,
idx=idx,
start=params['edit_start'],
end=params['edit_end'],
)
out_dir = Path(__file__).parent / 'out' / 'directions'
makedirs(out_dir / file_ident, exist_ok=True)
with open(out_dir / f"{file_ident}.pkl", 'wb') as outfile:
pickle.dump(params, outfile)
print(f'Direction "{name.get()}" saved as "{file_ident}.pkl"')
batch_size = ui_state.batch_size.get()
len_padded = ((num_strips.get() - 1) // batch_size + 1) * batch_size
orig_seed = state.seed
reset_sliders()
# Limit max resolution
max_H = 512
ratio = min(1.0, max_H / inst.output_shape[2])
strips = [[] for _ in range(len_padded)]
for b in range(0, len_padded, batch_size):
# Resample
resample_latent((orig_seed + b) % np.iinfo(np.int32).max)
sigmas = np.linspace(slider_value, -slider_value, strip_width.get(), dtype=np.float32)
for sid, sigma in enumerate(sigmas):
ui_state.sliders[idx].set(sigma)
# Advance and show results on screen
on_draw()
root.update()
app.update()
batch_res = (255*img).byte().permute(0, 2, 3, 1).detach().cpu().numpy()
for i, data in enumerate(batch_res):
# Save individual
name_nodots = file_ident.replace('.', '_')
outname = out_dir / file_ident / f"{name_nodots}_ex{b+i}_{sid}.png"
im = Image.fromarray(data)
im = im.resize((int(ratio*im.size[0]), int(ratio*im.size[1])), Image.ANTIALIAS)
im.save(outname)
strips[b+i].append(data)
for i, strip in enumerate(strips[:num_strips.get()]):
print(f'Saving strip {i + 1}/{num_strips.get()}', end='\r', flush=True)
data = np.hstack(pad_frames(strip))
im = Image.fromarray(data)
im = im.resize((int(ratio*im.size[0]), int(ratio*im.size[1])), Image.ANTIALIAS)
im.save(out_dir / file_ident / f"{file_ident}_ex{i}.png")
# Reset to original state
resample_latent(orig_seed)
ui_state.sliders[idx].set(slider_value)
# Shared by glumpy and tkinter
def handle_keypress(code):
if code == 65307: # ESC
shutdown()
elif code == 65360: # HOME
reset_sliders()
elif code == 114: # R
pass #reset_sliders()
def shutdown():
global pending_close
pending_close = True
def on_key_release(symbol, modifiers):
handle_keypress(symbol)
if __name__=='__main__':
setup_model()
setup_ui()
resample_latent()
pending_close = False
while not pending_close:
root.update()
app.update()
on_draw()
reposition_toolbar()
root.destroy() |