def make_labels(self):
self.labels["score"].text = str(self.score)
self.labels["lines"].text = str(self.lines)
self.labels["level"].text = str(self.current_level)
self.pause_label = util.make_label(
self.pause_text,
32, 320, 240, (255, 255, 255, 255), True, None)
self.init_blocks_label = util.make_label(
self.init_blocks_text,
12, 320, 200, (255, 255, 255, 255), True, None)
for name, label in self.labels.items():
pos = util.res(*label.original_pos)
label.x = pos["w"]
label.y = pos["h"]
label.font_size = pos["wr"] * label.original_size
self.labels[name] = label
def make_labels(self):
self.labels = []
for c in range(0, 9):
color = (255, 255, 255, 255)
if self.writer_index == c:
color = (100, 255, 100, 255)
self.labels.append(util.make_label(
self.chars[self.character_ids[c]].replace(" ", "."),
26, 20+c*26, 10, color, False, None))
color = (255, 255, 255, 255)
if self.writer_index == 9:
color = (100, 255, 100, 255)
self.labels.append(util.make_label(
"OK",
26, 260, 10, color, False, None))
no_highscores = False
if self.config_string not in self.highscores:
no_highscores = True
elif len(self.highscores[self.config_string]) == 0:
no_highscores = True
if no_highscores:
self.labels.append(util.make_label(
"No Highscores for this config yet!",
12,
320,
400,
(255, 255, 255, 255), True, None))
else:
number = 1
height = 400
fs = 12
self.labels.append(util.make_label(
"pos{}name{}score{}lines{}date".format(
" "*6,
" "*16,
" "*11,
" "*22
), fs, 16, height, (255, 255, 255, 255), False, None))
height -= fs
scores = self.highscores[self.config_string]
for player_score in sorted(scores,
key=lambda k: k["score"],
reverse=True):
self.labels.append(util.make_label(
"{} {} {} {} {}".format(
str(number).rjust(2, " "),
str(player_score["name"]).rjust(9, " "),
str(player_score["score"]).rjust(20, " "),
str(player_score["lines"]).rjust(15, " "),
str(self.dt(player_score["date"])).rjust(25, " ")
), fs, 24, height, (255, 255, 255, 255), False, None))
height -= fs
number += 1
if number > 30:
break
self.labels.append(util.make_label(
"Highscores", 40, 320, 440, (255, 255, 255, 255), True, None))
self.labels.append(util.make_label(
"Score: {}".format(self.score),
26, 320, 10, (255, 255, 255, 255), False, None))
def figure_def(pargs, kwargs, node):
"""Handle [% figure slug=slug img=img alt=alt caption=cap %] figure definition."""
slug = kwargs["slug"]
figure = util.get_config("figures")[slug]
label = util.make_label("figure", figure.number)
width = f'width="{figure.width}"' if figure.width else ""
return dedent(f"""\
<figure id="{figure.slug}">
<img src="./{figure.img}" alt="{figure.alt}" {width}/>
<figcaption markdown="1">{label}: {figure.caption}</figcaption>
</figure>
""")
def section_ref(pargs, kwargs, node):
"""Handle [% x slug %] section reference."""
if len(pargs) != 1:
util.fail(f"Badly-formatted 'x' shortcode {pargs} in {node.filepath}")
headings = util.get_config("headings")
slug = pargs[0]
heading = headings[slug]
label = util.make_label("part", heading.number)
anchor = f"#{slug}" if (len(heading.number) > 1) else ""
return f'<a href="@root/{heading.fileslug}/{anchor}">{label}</a>'
def _patch_heading(match):
"""Modify a single heading."""
headings = util.get_config("headings")
prefix = match.group(1)
text = match.group(2)
attributes = match.group(3) or ""
slug = match.group(4)
if slug is None:
return f"{prefix} {text} {attributes}".rstrip()
else:
label = util.make_label("part", headings[slug].number)
return f"{prefix} {label}: {text} {attributes}"
def main():
p = argparse.ArgumentParser(description='Display a kernel.')
p.add_argument('-out', help='output to *.png file instead of viewing')
p.add_argument('k', nargs='*', help='path to kernel(s)')
args = p.parse_args()
out = None
for fn in args.k:
print('Loading', fn)
step, kernel = util.load_kernel(fn)
print(' Step', step)
print(' Kernel shape is', kernel.shape)
print(' Min', np.min(kernel))
print(' Max', np.max(kernel))
print(' Mean', np.mean(kernel))
print(' Sum', np.sum(kernel))
print(' Sum of abs', np.sum(np.abs(kernel)))
print(' RMS', np.sqrt(np.mean(kernel * kernel)))
render = util.vis_hwoi(kernel, doubles=2)
render = util.hstack([render, util.make_label(fn)], 5)
if out is None:
out = render
else:
out = util.vstack([out, render], 5)
out = util.border(out, 5)
if args.out is not None:
util.save_image(args.out, out)
print('Written to', args.out)
else:
print('Press ESC to close window.')
def render_fn():
return out
util.viewer(None, render_fn)
def _replace(match):
caption = match.group(1)
slug = match.group(2)
table = util.get_config("tables")[slug]
label = util.make_label("table", table.number)
return f'<div class="table"><table id="{slug}"><caption>{label}: {caption}</caption>'
"""Handle [% t slug %] table reference."""
# Shortcode used improperly.
if len(pargs) != 1:
util.fail(f"Badly-formatted 't' shortcode {pargs} in {node.filepath}")
# Haven't collected information yet.
if (tables := util.get_config("tables")) is None:
return ""
# Create cross-reference.
slug = pargs[0]
if slug not in tables:
util.fail(
f"Unknown table cross-reference slug {slug} in {node.filepath}")
table = tables[slug]
label = util.make_label("table", table.number)
return f'<a class="tblref" href="@root/{table.fileslug}/#{slug}">{label}</a>'
@ivy.filters.register(ivy.filters.Filter.NODE_HTML)
def table_caption(text, node):
"""Get the caption in the right place."""
def _replace(match):
caption = match.group(1)
slug = match.group(2)
table = util.get_config("tables")[slug]
label = util.make_label("table", table.number)
return f'<div class="table"><table id="{slug}"><caption>{label}: {caption}</caption>'
return util.TABLE_DIV.sub(_replace, text)
def main(argv=()):
if argv[1:]:
raise ValueError("leftover arguments: %r" % argv[1:])
config = H(data_dir=FLAGS.data_dir,
base_output_dir=FLAGS.base_output_dir,
basename=FLAGS.basename,
resume=FLAGS.resume,
trace_fraction=FLAGS.trace_fraction)
if FLAGS.hpfile:
with open(FLAGS.hpfile) as hpfile:
hpstring = hpfile.read()
else:
hpstring = FLAGS.hp
config.hp = H(util.parse_hp(hpstring))
print str(config.hp)
config.label = util.make_label(config)
dirname = "%s_%s" % (datetime.datetime.now().isoformat(), config.label)
dirname = dirname[:255] # >:-(((((((((((((((((((((((((((((((((((((((((
config.output_dir = os.path.join(config.base_output_dir, dirname)
data = datasets.MscocoTF(config)
config.hp.caption.depth = data.caption_depth
# NOTE: all hyperparameters must be set at this point
prepare_run_directory(config)
model = models.Model(config.hp)
config.hp.masker.image = config.hp.image # -_-
config.masker = maskers.make(config.hp.masker.kind, hp=config.hp.masker)
config.global_step = tf.Variable(0, name="global_step", trainable=False)
trainer = Trainer(data, model, config)
tf.get_variable_scope().reuse_variables()
evaluator = Evaluator(data, model, config)
earlystopper = EarlyStopper(config, trainer.graph.lr_decay_op)
supervisor = tf.train.Supervisor(logdir=config.output_dir, summary_op=None)
with supervisor.managed_session() as session:
while True:
global_step = tf.train.global_step(session, config.global_step)
if supervisor.should_stop():
print "supervisor says should stop"
break
if earlystopper.should_stop(global_step):
print "earlystopper says should stop"
break
trainer(session, supervisor)
sys.stdout.write("\r%i " % global_step)
sys.stdout.flush()
if global_step % config.hp.validate.interval == 0:
values = evaluator(session, supervisor)
earlystopper.track(global_step, values.model.loss, session)
print "%5i loss:%10f loss asked:%10f loss given:%10f lr:%6g best:%10f age:%6i" % (
global_step,
values.model.loss,
values.model.loss_asked,
values.model.loss_given,
# NOTE: earlystopper.best_loss is best of median filtered losses
trainer.graph.lr.eval(session),
earlystopper.best_loss,
earlystopper.age)
if global_step >= config.hp.num_steps:
print "hp.num_steps reached"
break
def _modify_headings(node):
node.text = util.HEADING.sub(_patch_heading, node.text)
headings = util.get_config("headings")
if node.slug in headings:
node.meta["major"] = util.make_label("part", headings[node.slug].number)
def __init__(self, config):
super().__init__()
self.config = config
self.name = "game"
self.desired_scene = "game"
self.ready = False
self.entities = []
self.batch = pyglet.graphics.Batch()
self.block_sizes = []
self.blocks = {}
self.bags = {}
self.score = 0
self.current_level = config["first_level"]
self.paused = True
self.pause_text = "Generating polyominoes"
self.init_blocks_text = ""
self.pause_label = util.make_label(
self.pause_text,
32, 320, 240, (255, 255, 255, 255), True, None)
self.init_blocks_label = util.make_label(
"", 12, 320, 200, (255, 255, 255, 255), True, None)
self.shade = Shade((30, 30, 30, 150))
self.entities.append(self.shade)
self.lines = 0
# labels
height = 470
fs = 14
self.labels = {}
for label in ["score", "lines", "level"]:
label_config = [
fs, 540, height, (255, 255, 255, 255), False, self.batch
]
self.labels["text_{}".format(label)] = util.make_label(
label.title(), *label_config)
self.labels["text_{}".format(label)].original_pos = [540, height]
self.labels["text_{}".format(label)].original_size = fs
height -= fs
label_config = [
fs, 540, height, (255, 255, 255, 255), False, self.batch
]
self.labels[label] = util.make_label("", *label_config)
self.labels[label].original_pos = [540, height]
self.labels[label].original_size = fs
height -= fs
# block grid
self.block_grid = []
for i in range(0, config["height"]):
self.block_grid.append([])
for _ in range(0, config["width"]):
self.block_grid[i].append(None)
# graphical grid size
max_width = 540
max_height = 480
self.grid_size = int(min([
max_height / (config["height"] + 1),
max_width / (config["width"] + 2)
]))
# walls
wall = {
"x": 0,
"y": 480 - int(config["height"]*self.grid_size + self.grid_size),
"width": int(self.grid_size),
"height": int(config["height"]*self.grid_size)
}
self.entities.append(Wall(wall, self.batch))
width = config["width"]*int(self.grid_size) + 2*int(self.grid_size)
wall["x"] = width - int(self.grid_size)
self.entities.append(Wall(wall, self.batch))
wall["x"] = 0
wall["width"] = width
wall["height"] = int(self.grid_size)
self.entities.append(Wall(wall, self.batch))
# initial speed
spl = config["speed_per_level"]
speed = 10 / config["speed"] + spl * (self.current_level - 1)
pyglet.clock.schedule_interval(self.game_loop, speed)
# loop counter
self.loop_counter = 0
def make_labels(self):
self.labels = []
# title
self.labels.append(util.make_label(
"Polyominomania", 66, 320, 440,
(255, 255, 200, 255), True, None))
# config list
height = 350
for item in self.list_items:
color = (255, 255, 255, 255)
if self.selected_in_list == item:
color = (255, 255, 120, 255)
name = item.replace(".json", "")[:32]
if name != item.replace(".json", ""):
name += ".."
self.labels.append(util.make_label(
name, 10, 10, height, color, False, None))
height -= 10
# if a valid config is found, show details about the config
# else list the problem in red
if self.valid_config:
color = (255, 255, 255, 255)
head_color = (200, 200, 255, 255)
height = 350
fs = 12
# Basic information
self.labels.append(util.make_label(
"Information", 18, 270, height, head_color, False, None))
height -= fs
for part_of_desc in util.split(self.config["description"], 40):
self.labels.append(util.make_label(
part_of_desc, fs, 270, height, color, False, None))
height -= fs
color = (200, 255, 200, 255)
self.labels.append(util.make_label(
self.config_log, fs, 270, height, color, False, None))
height -= fs
color = (255, 255, 255, 255)
polyomino_string = "Polyominoes: {}".format(
" ".join(self.config["polyominoes"]))
for part_of_poly in util.split(polyomino_string, 40):
self.labels.append(util.make_label(
part_of_poly,
fs, 270, height, color, False, None))
height -= fs
self.labels.append(util.make_label(
"Lines needed per level: {}".format(
self.config["lines_per_level"]),
fs, 270, height, color, False, None))
height -= fs
self.labels.append(util.make_label(
"First level: {}".format(
self.config["first_level"]),
fs, 270, height, color, False, None))
height -= fs
self.labels.append(util.make_label(
"Next pieces: {}".format(
self.config["next_pieces"]),
fs, 270, height, color, False, None))
height -= fs
self.labels.append(util.make_label(
"Ghost piece: {}".format(
"visible" if self.config["ghost"] else "not visible"),
fs, 270, height, color, False, None))
height -= fs
self.labels.append(util.make_label(
"Grid size: {}x{}".format(
self.config["width"],
self.config["height"]),
fs, 270, height, color, False, None))
height -= 18
# Scoring information
self.labels.append(util.make_label(
"Scoring", 18, 270, height, head_color, False, None))
height -= fs
for field in ["polyomino", "softdrop", "harddrop", "level_up"]:
if self.config["scoring"][field] > 0:
self.labels.append(util.make_label(
"{} bonus: {}".format(
field.title().replace("_", " "),
self.config["scoring"][field]),
fs, 270, height, color, False, None))
height -= fs
lines_string = "lines:"
for i in range(1, len(self.config["scoring"]["lines"]) + 1):
lines_string += " {}".format(
self.config["scoring"]["lines"][str(i)])
for part_of_lines in util.split(lines_string, 40):
self.labels.append(util.make_label(
part_of_lines,
fs, 270, height, color, False, None))
height -= fs
lines_level_string = "This does not increase per level"
limit = len(self.config["scoring"]["lines_per_level"]) + 1
for i in range(1, limit):
if self.config["scoring"]["lines_per_level"][str(i)] > 0:
lines_level_string = "This does increase per level"
break
self.labels.append(util.make_label(
lines_level_string,
fs, 270, height, color, False, None))
height -= fs
else:
color = (255, 200, 200, 255)
height = 350
fs = 16
# Show the error information
for part_of_log in util.split(self.config_log, 36):
self.labels.append(util.make_label(
part_of_log, fs, 270, height, color, False, None))
height -= fs
# Select button instructions
self.labels.append(util.make_label(
"Press Enter or Space to select",
18, 270, 10, (255, 255, 255, 255), False, None))
"""Handle [% f slug %] figure reference."""
# Badly-formatted shortcode.
if len(pargs) != 1:
util.fail(f"Badly-formatted 'f' shortcode {pargs} in {node.filepath}")
# Haven't collected information yet.
if (figures := util.get_config("figures")) is None:
return ""
# Create cross-reference.
slug = pargs[0]
if slug not in figures:
util.fail(
f"Unknown figure cross-reference slug {slug} in {node.filepath}")
figure = figures[slug]
label = util.make_label("figure", figure.number)
return f'<a class="figref" href="@root/{figure.fileslug}/#{slug}">{label}</a>'
@ivy.events.register(ivy.events.Event.EXIT_BUILD)
def copy_files():
"""Copy all referenced images files."""
# Wrong part of the cycle.
if (figures := util.get_config("figures")) is None:
return
# Copy files.
for fig in figures.values():
src, dst = util.make_copy_paths(fig.node, fig.img)
shutil.copy(src, dst)
def train(self, load_weight=False, print_every=500):
MALE, FEMALE = 0, 1
FAKE, REAL = 0, 1
LAST_EPOCH = 0
# If pretrained weights exist, load them and get last epoch and iteration
if load_weight is True:
LAST_EPOCH = int(util.read_log())
self.load_pretrained(LAST_EPOCH)
for epoch in range(LAST_EPOCH, self.num_epoch):
self.set_optimizer(epoch)
for iters, (image, label) in enumerate(self.dloader):
# If a batch has only female or male images not mixed, just discard that batch.
# It seems that it makes results even worse.
if torch.sum(label == 0) == 0 or torch.sum(label == 1) == 0:
continue
male_num = torch.sum(label == 0)
female_num = torch.sum(label == 1)
image, label = image.type(self.dtype), label.type(self.ltype)
male_img, female_img = util.gender_divider(image, label)
image, label = Variable(image), Variable(label)
male_img, female_img = Variable(male_img), Variable(female_img)
"""
1. Train D_M (Discriminator for male)
Step 1. Hope D_M(male_img) == 1
Step 2. Hope D_M(fake_img) == 0
Step 3. Minimize classification loss
"""
# D_M(male_img) == 1
real_loss, fake_loss = 0, 0
if male_num is not 0:
real_score, _ = self.D_M(male_img)
real_label = util.make_label(real_score.size(),
label=REAL,
noisy=True).type(self.dtype)
real_label = Variable(real_label)
# Loss for real male image
real_loss = self.criterion_gan(real_score, real_label)
# Hope D_M(fake_img) == 0
if female_num is not 0:
fake_img = self.G_FM(female_img)
fake_score, _ = self.D_M(fake_img)
fake_label = util.make_label(fake_score.size(),
label=FAKE,
noisy=False).type(self.dtype)
fake_label = Variable(fake_label)
# Loss for fake male image
fake_loss = self.criterion_gan(fake_score, fake_label)
# Minimize classofication loss
_, gender_score = self.D_M(image)
cls_loss = self.criterion_cls(gender_score, label)
# Final D_M loss(Multitask learning)
D_loss = real_loss + fake_loss + cls_loss
# Update
self.all_optim_zero_grad()
D_loss.backward()
self.optim_D_M.step()
"""
2. Train D_F (Discriminator for female)
Step 1. Hope D_F(female_img) == 1
Step 2. Hope D_F(fake_img) == 0
Step 3. Minimize classification loss
"""
# Hope D_F(female_img) == 1
real_loss, fake_loss = 0, 0
if female_num is not 0:
real_score, _ = self.D_F(female_img)
real_label = util.make_label(real_score.size(),
label=REAL,
noisy=True).type(self.dtype)
real_label = Variable(real_label)
# Loss for real female image
real_loss = self.criterion_gan(real_score, real_label)
# Hope D_F(fake_img) == 0
if male_num is not 0:
fake_img = self.G_MF(male_img)
fake_score, _ = self.D_F(fake_img)
fake_label = util.make_label(fake_score.size(),
label=FAKE,
noisy=False).type(self.dtype)
fake_label = Variable(fake_label)
# Loss for fake female image
fake_loss = self.criterion_gan(fake_score, fake_label)
# Minimize classification loss
_, gender_score = self.D_F(image)
cls_loss = self.criterion_cls(gender_score, label)
# Final D_F loss
D_loss = real_loss + fake_loss + cls_loss
# Get classification accuracy
accuracy = util.get_cls_accuracy(gender_score.data, label.data)
# Update
self.all_optim_zero_grad()
D_loss.backward()
self.optim_D_F.step()
"""
3. Traing G_MF, G_FM with process of
<1> Male(Real image) -> <2> Female(Fake image) -> <3> Male(Cycle)
Step 1. Hope D_F(<2>) == 1
Step 2. Hope <2> to be classified as female
Step 3. Hope <1> == <3>
"""
if male_num is not 0:
fake_img = self.G_MF(male_img)
fake_score, gender_score = self.D_F(fake_img)
# Hope D_F((2)) == 1
real_label = util.make_label(fake_score.size(),
label=REAL,
noisy=False).type(self.dtype)
real_label = Variable(real_label)
gan_loss = self.criterion_gan(fake_score, real_label)
# Hope <2> to be classified as female
female_label = util.make_label(gender_score.size(0),
label=FEMALE,
noisy=False).type(
self.ltype)
female_label = Variable(female_label)
cls_loss = self.cls_lambda * self.criterion_cls(
gender_score, female_label)
# Hope <1> == <3>
cycle_img = self.G_FM(fake_img)
cyc_loss = self.cyc_lambda * self.criterion_cyc(
cycle_img, male_img)
# Final loss
G_loss = gan_loss + cls_loss + cyc_loss
# Update
self.all_optim_zero_grad()
G_loss.backward()
self.optim_G_MF.step()
self.optim_G_FM.step()
"""
4. Traing G_MF, G_FM with process of
<1> Female(Real image) -> <2> Male(Fake image) -> <3> Female(Cycle)
Step 1. Hope D_M(<2>) == 1
Step 2. Hope <2> to be classified as male
Step 3. Hope <1> == <3>
"""
if female_num is not 0:
fake_img = self.G_FM(female_img)
fake_score, gender_score = self.D_M(fake_img)
# Hope D_M(<2>) == 1
real_label = util.make_label(fake_score.size(),
label=REAL,
noisy=False).type(self.dtype)
real_label = Variable(real_label)
gan_loss = self.criterion_gan(fake_score, real_label)
# Hope <2> to be classified as male
male_label = util.make_label(gender_score.size(0),
label=MALE,
noisy=False).type(self.ltype)
male_label = Variable(male_label)
cls_loss = self.cls_lambda * self.criterion_cls(
gender_score, male_label)
# Hope <1> == <3>
cycle_img = self.G_MF(fake_img)
cyc_loss = self.cyc_lambda * self.criterion_cyc(
cycle_img, female_img)
# Final loss
G_loss = gan_loss + cls_loss + cyc_loss
# Update
self.all_optim_zero_grad()
G_loss.backward()
self.optim_G_MF.step()
self.optim_G_FM.step()
if iters % print_every == 0:
util.print_result(epoch, iters, accuracy, D_loss, G_loss,
gan_loss, cyc_loss, cls_loss)
# Save parameters
util.save_weight(self.D_M,
self.D_F,
self.G_MF,
self.G_FM,
num_epoch=epoch)
util.write_log(epoch)