def render(self, is_running):
bimpy.set_next_tree_node_open(True, bimpy.Condition.FirstUseEver)
if not bimpy.tree_node('progress##train_phase_component'):
return
self._is_trainning_start = False
self._is_trainning_stop = False
if self._is_trainning:
if bimpy.button('stop##train_phase_component'):
# print('stop')
self._is_trainning = False
self._is_trainning_stop = True
if not is_running:
# print('run over stop')
self._is_trainning = False
self._is_trainning_stop = True
else:
if bimpy.button('start##train_phase_component'):
# print('start {}'.format(is_running))
if not is_running:
self._is_trainning = True
self._is_trainning_start = True
if self._trainning_progress is not None:
bimpy.text('epoch: {} / {}'.format(
self._trainning_progress['epoch_now'],
self._trainning_progress['epoch_all'],
))
bimpy.tree_pop()
def main():
global CTX
ctx = bimpy.Context()
ctx.init(1200, 1200, "Image")
with ctx:
bimpy.themes.set_light_theme()
socket_thread = threading.Thread(target = thread_socket_func, args = (8883, ))
socket_thread.start()
message_thread = threading.Thread(target = message_thread_func)
message_thread.start()
previous_n_points = 0
clear_flag = False
while not ctx.should_close():
with ctx:
bimpy.set_next_window_pos(bimpy.Vec2(120, 120), bimpy.Condition.Once)
bimpy.set_next_window_size(bimpy.Vec2(400, 400), bimpy.Condition.Once)
bimpy.begin("Window #1")
bimpy.text("This is text!")
if bimpy.button("Send A Lot Of Messages"):
temp_messages = [make_message_text("log", "Message #" + str(i)) for i in range(560)]
send_messages(CTX.message_pipeline, temp_messages)
if bimpy.button("Send A Lot Of Random Points"):
temp_messages = [make_message_point("point", (random.randrange(400 + i), random.randrange(400 + i))) for i in range(20)]
send_messages(CTX.message_pipeline, temp_messages)
if bimpy.button("Clear Flag"):
clear_flag = not clear_flag
bimpy.text("Text from events:\n%s" % (CTX.data.text))
bimpy.end()
draw_window_drawing(ctx, 400, 400, "Sample Drawing", CTX.data.points)
log("Exited rendering thread")
log("Sending exit to message_thread")
send_message_text(CTX.message_pipeline, "exit", "")
log("Waiting for message_thread")
message_thread.join()
CTX.server_socket_running = False
if CTX.server_socket:
for c, info in CTX.server_socket.connections:
c.send(b'exit')
log("Waiting for socket_thread")
socket_thread.join()
log("Bye")
def drawPathFinder(columns_count=7, lines_count=5):
bimpy.columns(columns_count, None, borders_vertical)
i = 0
while i != columns_count:
i += 1
if borders_horizontal and bimpy.get_column_index == 0:
bimpy.separator()
#bimpy.text("{}".format("a"))
bimpy.button("Auto Path", bimpy.Vec2(100,100))
bimpy.next_column()
bimpy.columns(1)
if borders_horizontal:
bimpy.separator()
def scene_tree(self):
if bimpy.button("New Node"):
self.new_node()
bimpy.same_line()
if bimpy.button("Delete Node"):
if self.selected_node is not None:
def rem(l):
if self.selected_node in l:
l.remove(self.selected_node)
self.walk(self.root_nodes, rem)
def pnodes(l):
remove_n = None
for n in l:
node_flags = bimpy.OpenOnArrow | bimpy.OpenOnDoubleClick | bimpy.DefaultOpen
if n == self.selected_node:
node_flags |= bimpy.Selected
if len(n.children) == 0:
node_flags |= bimpy.Leaf | bimpy.NoTreePushOnOpen
node_open = bimpy.tree_node_ex(n.id, node_flags, n.name)
if bimpy.is_item_clicked():
if self.selected_node == n:
self.selected_node = None
else:
self.selected_node = n
if self.selected_node is not None and self.selected_node == n and self.ctrl_x:
self.copied_node = n
self.selected_node = None
remove_n = n
if node_open and len(n.children) != 0:
pnodes(n.children)
bimpy.tree_pop()
if remove_n is not None:
l.remove(remove_n)
if self.copied_node is not None and self.selected_node is not None and self.ctrl_v:
self.selected_node.children.append(self.copied_node)
self.copied_node = None
pnodes(self.root_nodes)
def main():
selected_compiler = bimpy.Int()
ctx = bimpy.Context()
ctx.init(WIDTH, HEIGHT, "Virtual enviroment manager")
environments = getAvailableEnviroments()
compilers_list = list(data.compilers.keys())
show_new_env_menu = False
while (not ctx.should_close()):
with ctx:
bimpy.set_next_window_pos(bimpy.Vec2(0, 0), bimpy.Condition.Once)
bimpy.set_next_window_size(bimpy.Vec2(WIDTH, HEIGHT),
bimpy.Condition.Once)
bimpy.begin("Enviroments",bimpy.Bool(True), \
bimpy.WindowFlags.NoCollapse and bimpy.WindowFlags.NoResize)
bimpy.text(sys.version)
bimpy.columns(2)
for enviroment in environments:
if bimpy.button(enviroment):
compiler = list(data.compilers.values())[
selected_compiler.
value] if selected_compiler.value != 0 else ""
subprocess.call(
['start', environments[enviroment], compiler],
shell=True)
bimpy.next_column()
if bimpy.button("O##" + enviroment):
subprocess.Popen(r'explorer /select,' +
os.path.dirname(environments[enviroment]))
#os.startfile(os.path.realpath(os.path.dirname(environments[enviroment])))
bimpy.next_column()
bimpy.columns(1)
if bimpy.combo("Compiler", selected_compiler, compilers_list):
pass
# if bimpy.button("Add new enviroment"):
# new_env_ctx = BimpyContext(WIDTH, HEIGHT, "New enviroment menu")
# while(not new_env_ctx.ctx.should_close()):
# with new_env_ctx.ctx:
# bimpy.begin("dsad")
# bimpy.text("d")
# bimpy.end()
# if bimpy.button("Create new enviroment"):
bimpy.end()
def retrival(self):
###########UI###########
size = bimpy.Vec2(500, 750)
bimpy.set_next_window_size(size, bimpy.Condition.Once)
if bimpy.begin_popup_modal('{}: {}'.format(LANG.retrieve, self.select_label)) \
and self.select_label != '' and self.first_init:
for idx, file in enumerate(
self.r.label2pic[self.select_label.lower()]):
if idx != 0:
bimpy.separator()
img = self.r.get_thumbnail(file)
bimpy.text(file)
bimpy.image(img)
bimpy.separator()
if bimpy.button(LANG.retrieve_close):
bimpy.clode_current_popup()
bimpy.end_popup()
########################
t = {
'x': bimpy.get_window_pos().x,
'y': bimpy.get_window_pos().y,
'w': bimpy.get_window_size().x,
'h': bimpy.get_window_size().y,
'self': self,
}
return t
def drawMainTab(columns_count=7, lines_count=5):
bimpy.columns(columns_count, None, borders_vertical)
i = 0
col_c = 0
while col_c > lines_count:
col_c += 1
while i != columns_count:
if borders_horizontal and bimpy.get_column_index == 0:
bimpy.separator()
#bimpy.text("{}".format("a"))
bimpy.button(field_names[i], bimpy.Vec2(100,100))
bimpy.next_column()
i += 1
columns_count = 0
bimpy.columns(1)
if borders_horizontal:
bimpy.separator()
def on_update(self):
bp.text("Hello, world!")
if bp.button("OK"):
print(self.string.value)
bp.input_text('string', self.string, 256)
bp.slider_float("float", self.f, 0, 1)
def ranged_slider(name, value, range_info):
f = bimpy.Float(value)
ret = None
rng = (range_info.max - range_info.min)
spd = .0025 * rng if rng > 0 else .1
if bimpy.drag_float("##v_" + name, f, spd, range_info.min, range_info.max):
ret = ps.ConfigurationInfo.ClampFloat(range_info, f.value)
bimpy.same_line()
if bimpy.button("init##b_" + name):
ret = range_info.initial
return ret
def visualize(self):
bimpy.begin(type(self).__name__ + " - " + str(id(self)), bimpy.Bool(True))
bimpy.input_text("Text", self.__state["text"], 256)
button_pressed = bimpy.button("Plot", bimpy.Vec2(40, 20))
if button_pressed or self.__state["toggle"]:
if button_pressed:
self.__state["toggle"] ^= True
if len(self.__state["text"].value) > 0:
bimpy.plot_lines("Graph", self.encode(self.__state["text"].value.encode()))
bimpy.end()
def render(self, is_lock):
bimpy.set_next_tree_node_open(True, bimpy.Condition.FirstUseEver)
if not bimpy.tree_node('convex points##convex_component'):
return
bimpy.same_line()
bimpy_tools.help_marker('Convex points should be presented in counter-clockwise order')
flags = bimpy.InputTextFlags.EnterReturnsTrue
if is_lock:
flags |= bimpy.InputTextFlags.ReadOnly
last_convex_number_value = self._convex_number.value
if bimpy.input_int('number##convex_component', self._convex_number, 1, 1, flags):
self._convex_number.value = max(3, self._convex_number.value)
if last_convex_number_value > self._convex_number.value:
self._convex_data = self._convex_data[:self._convex_number.value] # cut back points
else:
self._convex_data.extend([
[bimpy.Float(0), bimpy.Float(0)]
for _ in range(last_convex_number_value, self._convex_number.value)
])
# show convex value setting
bimpy.set_next_tree_node_open(self._convex_number.value < 10, bimpy.Condition.FirstUseEver)
if bimpy.tree_node('convex value ({})##convex_component'.format(self._convex_number.value)):
for index in range(self._convex_number.value):
bimpy.push_item_width(210)
bimpy.input_float2(
'{:<3d}'.format(index),
self._convex_data[index][0],
self._convex_data[index][1],
flags=flags
)
bimpy.pop_item_width()
bimpy.tree_pop()
# draw part
bimpy.new_line()
if bimpy.button('draw convex##convex_component') and not is_lock:
self._convex_data_backup = [[item[0].value, item[1].value]
for item in self._convex_data]
self._convex_draw_flag = True
self._convex_data = []
self._convex_number.value = 0
bimpy.tree_pop()
def view_batch(bp):
bimpy.begin("Batch")
bimpy.button("Open files...")
bimpy.button("Open configurations...")
bimpy.separator()
bimpy.text("Regions:")
bimpy.separator()
bimpy.button("Render Files")
bimpy.end()
def show(self, ctx):
# Add a layer to the MLP
bimpy.push_item_width(80)
bimpy.input_int("nr neurons", self.ui.nr_neurons)
bimpy.same_line()
bimpy.combo("transfer", self.ui.transfer, self.ui.transfer_names)
if (self.ui.nr_neurons.value > 0):
bimpy.same_line()
if (bimpy.button("add layer")):
transfer = self.ui.transfer_names[self.ui.transfer.value]
self.model.add_layer(transfer, self.ui.nr_neurons.value)
bimpy.pop_item_width()
self.model.show(ctx)
show_io_plot(self.ui.input, self.model)
def render(self):
if not self._size:
self._size = bimpy.Vec2(400, 600)
bimpy.set_next_window_size(self._size)
if bimpy.begin(self.name,
self.opened,
flags=bimpy.WindowFlags.NoCollapse
| bimpy.WindowFlags.MenuBar):
if bimpy.begin_menu_bar():
bimpy.menu_item('Reload', 'Cmd+R', self.click_states['reload'])
bimpy.end_menu_bar()
for folder_name, folder_files in self.tree.items():
if bimpy.collapsing_header(folder_name):
for item, button_name in folder_files:
if bimpy.button(button_name):
item_path = '{0}/{1}'.format(folder_name, item)
try:
new_savegame = SaveGameFrame(
PS2WrappedBinBackend, item_path, self)
self.child_frames.append(new_savegame)
except KeyboardInterrupt as e:
raise e
except Exception as e:
print(e)
bimpy.same_line()
bimpy.text(item)
bimpy.end()
for child_frame in self.child_frames:
child_frame.render()
import bimpy as bp
context = bp.Context()
context.init(600, 600, "Hello, Bimpy!")
label_content = bp.String()
spinner_content = bp.Float()
while not context.should_close():
with context:
bp.text("Hello, Bimpy!")
if bp.button("Ok"):
print("Hello!")
bp.input_text("string", label_content, 256)
bp.slider_float("float", spinner_content, 0.0, 1.0)
res = ((current_time) - (g_last_timestamp))
g_last_timestamp = current_time
return res
def fail(msg):
print(((bcolors.FAIL) + ("{:8d} FAIL ".format(milli_since_last())) +
(msg) + (bcolors.ENDC)))
sys.stdout.flush()
def plog(msg):
print(((bcolors.OKGREEN) + ("{:8d} LOG ".format(milli_since_last())) +
(msg) + (bcolors.ENDC)))
sys.stdout.flush()
args = docopt.docopt(__doc__, version="0.0.1")
if (args["--verbose"]):
print(args)
ctx = b.Context()
ctx.init(600, 600, "Hello")
str = b.String()
f = b.Float()
while (not (ctx.should_close())):
with ctx:
b.text("hello world")
if (b.button("OK")):
print(str.value)
b.input_text("string", str, 256)
b.slider_float("float", f, (0.0e+0), (1.e+0))
import bimpy
ctx = bimpy.Context()
ctx.init(600, 600, "Hello")
str = bimpy.String()
f = bimpy.Float();
while(not ctx.should_close()):
with ctx:
bimpy.text("Hello, world!")
if bimpy.button("OK"):
print(str.value)
bimpy.input_text('string', str, 256)
bimpy.slider_float("float", f, 0.0, 1.0)
bimpy.add_circle_filled(point * m + center, 5,
0xAF000000 + colors[i], 100)
axis_ = np.matmul(axis, S * 2.0)
axis_ = np.matmul(axis_, R) + P
bimpy.add_line(center + bimpy.Vec2(axis_[0, 0], axis_[0, 1]) * m,
center + bimpy.Vec2(axis_[1, 0], axis_[1, 1]) * m,
0xFFFF0000, 1)
bimpy.add_line(center + bimpy.Vec2(axis_[2, 0], axis_[2, 1]) * m,
center + bimpy.Vec2(axis_[3, 0], axis_[3, 1]) * m,
0xFFFF0000, 1)
bimpy.end()
bimpy.set_next_window_pos(bimpy.Vec2(20, 640), bimpy.Condition.Once)
bimpy.set_next_window_size(bimpy.Vec2(800, 140), bimpy.Condition.Once)
bimpy.begin("Controls")
bimpy.input_int("Data points count", DATA_POINTS)
bimpy.input_int("Clasters count", CLASTERS)
bimpy.slider_float("std", std, 0.0, 3.0)
if bimpy.button("Generate data"):
generate_fake_data()
bimpy.end()
ctx.render()
if opened.value:
if bimpy.begin("Hello!", opened=opened):
bimpy.columns(4, "mycolumns")
bimpy.separator()
bimpy.text("Some text")
bimpy.next_column()
bimpy.text("Some text")
bimpy.next_column()
bimpy.text("Some text")
bimpy.next_column()
bimpy.text("Some text")
bimpy.separator()
bimpy.columns(1)
if bimpy.button("Some button"):
a = 0
print("!!!")
bimpy.progress_bar(a)
bimpy.combo("Combo!", selectedItem, mylist)
bimpy.push_item_width(-10.0)
bimpy.plot_lines("Some plot", vals, graph_size=bimpy.Vec2(0, 300))
bimpy.pop_item_width()
a += 0.01
bimpy.end()
if bimpy.begin("Hello2!",
import sys
import os
if os.path.exists("../cmake-build-debug/"):
print('Running Debugging session!')
sys.path.insert(0, "../cmake-build-debug/")
import bimpy as bp
ctx = bp.Context()
ctx.init(600, 600, "Hello")
str = bp.String()
f = bp.Float()
while not ctx.should_close():
with ctx:
bp.begin_root("")
bp.text("Hello, world!")
if bp.button("OK"):
print(str.value)
bp.input_text('string', str, 256)
bp.slider_float("float", f, 0.0, 1.0)
bp.end()
def sample(cfg, logger):
model = Model(
startf=cfg.MODEL.START_CHANNEL_COUNT,
layer_count= cfg.MODEL.LAYER_COUNT,
maxf=cfg.MODEL.MAX_CHANNEL_COUNT,
latent_size=cfg.MODEL.LATENT_SPACE_SIZE,
truncation_psi=cfg.MODEL.TRUNCATIOM_PSI,
truncation_cutoff=cfg.MODEL.TRUNCATIOM_CUTOFF,
mapping_layers=cfg.MODEL.MAPPING_LAYERS,
channels=3)
model.eval()
logger.info("Trainable parameters generator:")
count_parameters(model.generator)
model_dict = {
'generator_s': model.generator,
'mapping_fl_s': model.mapping,
'dlatent_avg': model.dlatent_avg,
}
checkpointer = Checkpointer(cfg,
model_dict,
logger=logger,
save=True)
checkpointer.load()
ctx = bimpy.Context()
remove = bimpy.Bool(False)
layers = bimpy.Int(8)
ctx.init(1800, 1600, "Styles")
rnd = np.random.RandomState(5)
latents = rnd.randn(1, cfg.MODEL.LATENT_SPACE_SIZE)
sample = torch.tensor(latents).float().cuda()
def update_image(sample):
with torch.no_grad():
torch.manual_seed(0)
model.eval()
x_rec = model.generate(layers.value, remove.value, z=sample)
#model.generator.set(l.value, c.value)
resultsample = ((x_rec * 0.5 + 0.5) * 255).type(torch.long).clamp(0, 255)
resultsample = resultsample.cpu()[0, :, :, :]
return resultsample.type(torch.uint8).transpose(0, 2).transpose(0, 1)
with torch.no_grad():
save_image(model.generate(8, True, z=sample) * 0.5 + 0.5, 'sample.png')
im = bimpy.Image(update_image(sample))
while(not ctx.should_close()):
with ctx:
bimpy.set_window_font_scale(2.0)
if bimpy.checkbox('REMOVE BLOB', remove):
im = bimpy.Image(update_image(sample))
if bimpy.button('NEXT'):
latents = rnd.randn(1, cfg.MODEL.LATENT_SPACE_SIZE)
sample = torch.tensor(latents).float().cuda()
im = bimpy.Image(update_image(sample))
if bimpy.slider_int("Layers", layers, 0, 8):
im = bimpy.Image(update_image(sample))
bimpy.image(im, bimpy.Vec2(1024, 1024))
if (pinNumber.value > 13):
pinNumber = bimpy.Int(13)
if (minPWM.value > 255):
minPWM = bimpy.Int(255)
if (minPWM.value < 0):
minPWM = bimpy.Int(0)
if (maxPWM.value > 255):
maxPWM = bimpy.Int(255)
if (maxPWM.value < 1):
maxPWM = bimpy.Int(1)
if (minPWM.value > maxPWM.value):
minPWM = bimpy.Int(maxPWM.value)
bimpy.input_float('Light interval (seconds)', maxInterval, 0.100000000000000)
if (maxInterval.value < 0.100000000):
maxInterval = bimpy.Float(0.100000000000000)
if bimpy.button(textToggleButton,bimpy.Vec2(208,0)):
toggleLight = not toggleLight
if (toggleLight == True):
plot_values = array('f',[])
textToggleButton = 'Stop flashing LED'
threading.Thread(target=flashLED, args=()).start()
if (toggleLight == False):
textToggleButton = 'Start flashing LED'
if (checkPWM() == 1):
bimpy.input_int('PWM Min',minPWM,1)
bimpy.input_int('PWM Max',maxPWM,1)
bimpy.plot_lines("",plot_values,0,'(' + str(pinNumber.value) + ')',0,1,bimpy.Vec2(0,50))
if bimpy.button('Exit',bimpy.Vec2(208,0)):
currentBoard.exit()
exit()
bimpy.end()
def sample(cfg, logger):
torch.cuda.set_device(0)
model = Model(startf=cfg.MODEL.START_CHANNEL_COUNT,
layer_count=cfg.MODEL.LAYER_COUNT,
maxf=cfg.MODEL.MAX_CHANNEL_COUNT,
latent_size=cfg.MODEL.LATENT_SPACE_SIZE,
truncation_psi=cfg.MODEL.TRUNCATIOM_PSI,
truncation_cutoff=cfg.MODEL.TRUNCATIOM_CUTOFF,
mapping_layers=cfg.MODEL.MAPPING_LAYERS,
channels=cfg.MODEL.CHANNELS,
generator=cfg.MODEL.GENERATOR,
encoder=cfg.MODEL.ENCODER)
model.cuda(0)
model.eval()
model.requires_grad_(False)
decoder = model.decoder
encoder = model.encoder
mapping_tl = model.mapping_d
mapping_fl = model.mapping_f
dlatent_avg = model.dlatent_avg
logger.info("Trainable parameters generator:")
count_parameters(decoder)
logger.info("Trainable parameters discriminator:")
count_parameters(encoder)
arguments = dict()
arguments["iteration"] = 0
model_dict = {
'discriminator_s': encoder,
'generator_s': decoder,
'mapping_tl_s': mapping_tl,
'mapping_fl_s': mapping_fl,
'dlatent_avg': dlatent_avg
}
checkpointer = Checkpointer(cfg, model_dict, {}, logger=logger, save=False)
extra_checkpoint_data = checkpointer.load()
model.eval()
layer_count = cfg.MODEL.LAYER_COUNT
def encode(x):
Z, _ = model.encode(x, layer_count - 1, 1)
Z = Z.repeat(1, model.mapping_f.num_layers, 1)
return Z
def decode(x):
layer_idx = torch.arange(2 * layer_count)[np.newaxis, :, np.newaxis]
ones = torch.ones(layer_idx.shape, dtype=torch.float32)
coefs = torch.where(layer_idx < model.truncation_cutoff, ones, ones)
# x = torch.lerp(model.dlatent_avg.buff.data, x, coefs)
return model.decoder(x, layer_count - 1, 1, noise=True)
path = 'dataset_samples/faces/realign1024x1024'
paths = list(os.listdir(path))
paths.sort()
paths_backup = paths[:]
randomize = bimpy.Bool(True)
current_file = bimpy.String("")
ctx = bimpy.Context()
attribute_values = [bimpy.Float(0) for i in indices]
W = [
torch.tensor(np.load("principal_directions/direction_%d.npy" % i),
dtype=torch.float32) for i in indices
]
rnd = np.random.RandomState(5)
def loadNext():
img = np.asarray(Image.open(path + '/' + paths[0]))
current_file.value = paths[0]
paths.pop(0)
if len(paths) == 0:
paths.extend(paths_backup)
if img.shape[2] == 4:
img = img[:, :, :3]
im = img.transpose((2, 0, 1))
x = torch.tensor(np.asarray(im, dtype=np.float32),
device='cpu',
requires_grad=True).cuda() / 127.5 - 1.
if x.shape[0] == 4:
x = x[:3]
needed_resolution = model.decoder.layer_to_resolution[-1]
while x.shape[2] > needed_resolution:
x = F.avg_pool2d(x, 2, 2)
if x.shape[2] != needed_resolution:
x = F.adaptive_avg_pool2d(x,
(needed_resolution, needed_resolution))
img_src = ((x * 0.5 + 0.5) * 255).type(torch.long).clamp(
0, 255).cpu().type(torch.uint8).transpose(0,
2).transpose(0,
1).numpy()
latents_original = encode(x[None, ...].cuda())
latents = latents_original[0, 0].clone()
latents -= model.dlatent_avg.buff.data[0]
for v, w in zip(attribute_values, W):
v.value = (latents * w).sum()
for v, w in zip(attribute_values, W):
latents = latents - v.value * w
return latents, latents_original, img_src
def loadRandom():
latents = rnd.randn(1, cfg.MODEL.LATENT_SPACE_SIZE)
lat = torch.tensor(latents).float().cuda()
dlat = mapping_fl(lat)
layer_idx = torch.arange(2 * layer_count)[np.newaxis, :, np.newaxis]
ones = torch.ones(layer_idx.shape, dtype=torch.float32)
coefs = torch.where(layer_idx < model.truncation_cutoff, ones, ones)
dlat = torch.lerp(model.dlatent_avg.buff.data, dlat, coefs)
x = decode(dlat)[0]
img_src = ((x * 0.5 + 0.5) * 255).type(torch.long).clamp(
0, 255).cpu().type(torch.uint8).transpose(0,
2).transpose(0,
1).numpy()
latents_original = dlat
latents = latents_original[0, 0].clone()
latents -= model.dlatent_avg.buff.data[0]
for v, w in zip(attribute_values, W):
v.value = (latents * w).sum()
for v, w in zip(attribute_values, W):
latents = latents - v.value * w
return latents, latents_original, img_src
latents, latents_original, img_src = loadNext()
ctx.init(1800, 1600, "Styles")
def update_image(w, latents_original):
with torch.no_grad():
w = w + model.dlatent_avg.buff.data[0]
w = w[None, None, ...].repeat(1, model.mapping_f.num_layers, 1)
layer_idx = torch.arange(model.mapping_f.num_layers)[np.newaxis, :,
np.newaxis]
cur_layers = (7 + 1) * 2
mixing_cutoff = cur_layers
styles = torch.where(layer_idx < mixing_cutoff, w,
latents_original)
x_rec = decode(styles)
resultsample = ((x_rec * 0.5 + 0.5) * 255).type(torch.long).clamp(
0, 255)
resultsample = resultsample.cpu()[0, :, :, :]
return resultsample.type(torch.uint8).transpose(0,
2).transpose(0, 1)
im_size = 2**(cfg.MODEL.LAYER_COUNT + 1)
im = update_image(latents, latents_original)
print(im.shape)
im = bimpy.Image(im)
display_original = True
seed = 0
while not ctx.should_close():
with ctx:
new_latents = latents + sum(
[v.value * w for v, w in zip(attribute_values, W)])
if display_original:
im = bimpy.Image(img_src)
else:
im = bimpy.Image(update_image(new_latents, latents_original))
bimpy.begin("Principal directions")
bimpy.columns(2)
bimpy.set_column_width(0, im_size + 20)
bimpy.image(im)
bimpy.next_column()
for v, label in zip(attribute_values, labels):
bimpy.slider_float(label, v, -40.0, 40.0)
bimpy.checkbox("Randomize noise", randomize)
if randomize.value:
seed += 1
torch.manual_seed(seed)
if bimpy.button('Next'):
latents, latents_original, img_src = loadNext()
display_original = True
if bimpy.button('Display Reconstruction'):
display_original = False
if bimpy.button('Generate random'):
latents, latents_original, img_src = loadRandom()
display_original = False
if bimpy.input_text(
"Current file", current_file,
64) and os.path.exists(path + '/' + current_file.value):
paths.insert(0, current_file.value)
latents, latents_original, img_src = loadNext()
bimpy.end()
if True:
if bimpy.begin("",
flags=(bimpy.WindowFlags.NoSavedSettings
| bimpy.WindowFlags.NoMove
| bimpy.WindowFlags.NoResize)):
bimpy.text("NeX")
bimpy.push_style_var(bimpy.Style.Alpha, 255)
bimpy.set_window_size("", bimpy.Vec2(450, 250))
bimpy.text("")
#bimpy.set_style(bimpy.GuiStyle.alpha)
if MinCPS.value > MaxCPS.value:
MinCPS = bimpy.Float(MaxCPS.value)
if bimpy.collapsing_header("AutoClicker"):
bimpy.slider_float("CPS Min", MinCPS, 5.0, 25.0)
bimpy.slider_float("CPS Max", MaxCPS, 5.0, 25.0)
if bimpy.button("Toggle"):
Toggle = not Toggle
if bimpy.collapsing_header("Colors"):
bimpy.color_edit("Main Bar", BarColor)
bimpy.color_edit("Headers", HeaderColor)
bimpy.color_edit("Hovered Header", HeaderHoverColor)
bimpy.color_edit("Text", TextColor)
bimpy.color_edit("Slider Thumbs", SliderColor)
bimpy.color_edit("Activated Thumbs", SliderActiveColor)
bimpy.color_edit("CheckBox Marks", CheckMarkColor)
bimpy.push_style_color(bimpy.Colors.TitleBgActive, BarColor)
bimpy.push_style_color(bimpy.Colors.Header, HeaderColor)
bimpy.push_style_color(bimpy.Colors.HeaderHovered,
HeaderHoverColor)
bimpy.push_style_color(bimpy.Colors.Text, TextColor)
bimpy.push_style_color(bimpy.Colors.SliderGrab, SliderColor)
def sample(cfg, logger):
model = Model(startf=cfg.MODEL.START_CHANNEL_COUNT,
layer_count=cfg.MODEL.LAYER_COUNT,
maxf=cfg.MODEL.MAX_CHANNEL_COUNT,
latent_size=cfg.MODEL.LATENT_SPACE_SIZE,
truncation_psi=cfg.MODEL.TRUNCATIOM_PSI,
truncation_cutoff=cfg.MODEL.TRUNCATIOM_CUTOFF,
mapping_layers=cfg.MODEL.MAPPING_LAYERS,
channels=3)
del model.discriminator
model.eval()
#torch.cuda.manual_seed_all(110)
logger.info("Trainable parameters generator:")
count_parameters(model.generator)
if False:
model_dict = {
'generator': model.generator,
'mapping': model.mapping,
'dlatent_avg': model.dlatent_avg,
}
else:
model_dict = {
'generator_s': model.generator,
'mapping_s': model.mapping,
'dlatent_avg': model.dlatent_avg,
}
checkpointer = Checkpointer(cfg, model_dict, logger=logger, save=True)
file_name = 'karras2019stylegan-ffhq'
# file_name = 'results/model_final'
checkpointer.load(file_name=file_name + '.pth')
# checkpointer.save('final_stripped')
#sample_b = torch.randn(1, cfg.MODEL.LATENT_SPACE_SIZE).view(-1, cfg.MODEL.LATENT_SPACE_SIZE)
# for i in range(100):
# if i % 20 == 0:
# sample_a = sample_b
# sample_b = torch.randn(1, cfg.MODEL.LATENT_SPACE_SIZE).view(-1, cfg.MODEL.LATENT_SPACE_SIZE)
# x = (i % 20) / 20.0
# sample = sample_a * (1.0 - x) + sample_b * x
# save_sample(model, sample, i)
print(model.generator.get_statistics(8))
# print(model.discriminator.get_statistics(8))
ctx = bimpy.Context()
ctx.init(1800, 1600, "Styles")
rnd = np.random.RandomState(5)
latents = rnd.randn(1, cfg.MODEL.LATENT_SPACE_SIZE)
sample = torch.tensor(latents).float().cuda()
def update_image(sample):
with torch.no_grad():
model.eval()
x_rec = model.generate(model.generator.layer_count - 1,
1,
z=sample)
resultsample = ((x_rec * 0.5 + 0.5) * 255).type(torch.long).clamp(
0, 255)
resultsample = resultsample.cpu()[0, :, :, :]
return resultsample.type(torch.uint8).transpose(0,
2).transpose(0, 1)
im = update_image(sample)
print(im.shape)
im = bimpy.Image(im)
while (not ctx.should_close()):
with ctx:
im = bimpy.Image(update_image(sample))
bimpy.image(im)
# if bimpy.button('Ok'):
if bimpy.button('NEXT'):
latents = rnd.randn(1, cfg.MODEL.LATENT_SPACE_SIZE)
sample = torch.tensor(latents).float().cuda()
# im = bimpy.Image(update_image(sample))
#bimpy.set_window_font_scale(2.0)
exit()
rnd = np.random.RandomState(111011)
latents = rnd.randn(1, cfg.MODEL.LATENT_SPACE_SIZE)
sample = torch.tensor(latents).float().cuda(
) # torch.randn(16, cfg.MODEL.LATENT_SPACE_SIZE).view(-1, cfg.MODEL.LATENT_SPACE_SIZE)
save_sample(model, sample, 0)
im_count = 16
canvas = np.zeros([3, im_size * (im_count + 2), im_size * (im_count + 2)])
cut_layer_b = 0
cut_layer_e = 2
styles = model.mapping(sample)
styles = list(styles.split(1, 1))
for i in range(im_count):
torch.cuda.manual_seed_all(110)
style = [x[i] for x in styles]
style = torch.cat(style, dim=0)[None, ...]
rec = model.generator.decode(style, cfg.MODEL.LAYER_COUNT - 1, 0.7)
place(canvas, rec[0], 1, 2 + i)
place(canvas, rec[0], 2 + i, 1)
for i in range(im_count):
for j in range(im_count):
style_a = [x[i] for x in styles[:cut_layer_b]]
style_b = [x[j] for x in styles[cut_layer_b:cut_layer_e]]
style_c = [x[i] for x in styles[cut_layer_e:]]
style = style_a + style_b + style_c
torch.cuda.manual_seed_all(110)
style = torch.cat(style, dim=0)[None, ...]
rec = model.generator.decode(style, cfg.MODEL.LAYER_COUNT - 1, 0.7)
place(canvas, rec[0], 2 + i, 2 + j)
save_image(torch.Tensor(canvas), 'reconstruction.png')
bimpy.themes.set_light_theme()
taille_cible = bimpy.Int(150)
largeur_cible = bimpy.Int(150)
while not ctx.should_close():
ctx.new_frame()
bimpy.set_next_window_pos(bimpy.Vec2(0, 0), bimpy.Condition.Once)
bimpy.set_next_window_size(bimpy.Vec2(800, 400), bimpy.Condition.Once)
bimpy.begin("Controls")
bimpy.input_text("Nom du fichier", nom, 15)
if bimpy.button("Visualisation"):
while (i < 1000000):
num = i / 1000000
bimpy.progress_bar(num)
bimpy.end()
ctx.render()
i = i + 1
if bimpy.button("Debut du Scan"):
a = 2
if bimpy.button("Visualisation du resultat"):
a = 3
bimpy.text("Choix des formats de sortie")
bimpy.checkbox("VTK", vtk)
bimpy.checkbox("STL", stl)
def render(self, ctx, windows_info):
pos = bimpy.Vec2(conf.margin, conf.margin)
size_min = bimpy.Vec2(conf.min_file_browser_width,
ctx.height() - 2 * conf.margin)
size_max = bimpy.Vec2(conf.max_file_browser_width,
ctx.height() - 2 * conf.margin)
bimpy.set_next_window_pos(pos, bimpy.Condition.Once)
bimpy.set_next_window_size_constraints(size_min, size_max)
bimpy.begin(LANG.file_brewswer_ui_title, bimpy.Bool(True),
bimpy.WindowFlags.NoCollapse | bimpy.WindowFlags.NoMove)
###########UI###########
if bimpy.button(LANG.file_brewswer_ui_refresh) == True:
self.fb.refresh_file_list()
bimpy.same_line()
if bimpy.button(LANG.about) == True:
bimpy.open_popup(LANG.about)
# call render about ui
# print(dir(windows_info['about_ui']))
windows_info['about_ui']['self'].about()
for idx, f_name in enumerate(self.fb.file_list):
# print(self.selected.value)
if bimpy.selectable(
f_name.split('\\')[-1], self.selected.value == idx):
self.selected.value = idx
if self.selected.value != -1 and self.selected.value != self.preidx:
self.preidx = self.selected.value
windows_info['image_shower_ui']['self'].update_pic(f_name)
windows_info['meta_info_ui']['self'].update_meta_info(
f_name)
# progress bar
if not self.fb.q.empty():
self.process = self.fb.q.get()
f, d = self.process[-2], self.process[-1]
# update if new
if d != {}:
self.fb.pp.yolo_res[f] = d
self.process = (self.process[0] + 1, self.process[1])
if self.process[0] == self.process[1]:
with open('yolo_res', 'wb') as f:
pickle.dump(self.fb.pp.yolo_res, f)
# build retrieval index
windows_info['retrival_ui']['self'].init = False
sz = bimpy.get_window_size()
bimpy.set_cursor_pos(bimpy.Vec2(conf.margin, sz.y - conf.margin * 2))
bimpy.push_item_width(sz.x - conf.margin * 3 - 60)
process = self.process
bimpy.progress_bar(process[0] / float(process[1]),
bimpy.Vec2(0.0, 0.0),
"{}/{}".format(process[0], process[1]))
bimpy.same_line()
if bimpy.button(LANG.reindex) == True and process[0] == process[1]:
self.fb.refresh()
########################
t = {
'x': bimpy.get_window_pos().x,
'y': bimpy.get_window_pos().y,
'w': bimpy.get_window_size().x,
'h': bimpy.get_window_size().y,
'self': self,
}
bimpy.end()
return t
c = b.Context()
c.init(1200, 1200, "bimpy test")
img = Image.new(
"RGBA",
(512, 512),
)
px = img.load()
for x in range(512):
for y in range(512):
r = int(255.0 * float(x) / 512.0)
g = int(255.0 * float(y) / 512.0)
px[x, y] = (r, g, max(255 - r - g, 0), 255)
b_img = b.Image(img)
b_f1 = b.Float()
b_f2 = b.Float()
b_f3 = b.Float()
while not c.should_close():
with c:
b.text("hi")
if b.button("cat"):
print("mew")
b.input_float("float1", b_f1, 0.0, 1.0)
b.image(b_img)
b.slider_float3("float", b_f1, b_f2, b_f3, 0.0, 1.0)
if bimpy.begin_main_menu_bar():
if bimpy.menu_item('Save', ''):
save_data(args.save_path)
if bimpy.menu_item('Load', ''):
load_data(args.save_path)
bimpy.end_main_menu_bar(
) # According to bimpy docs, this is a special case where end is called inside the if.
if bimpy.begin("Video", opened=tab_video_view):
is_placing_rect = True
s = bimpy.text(args.base_path_video)
b_i = bimpy.Int(display_frame)
bimpy.slider_int("Frame", b_i, 0, video_len, "%d")
if bimpy.button(" < Prev (z) ") or bimpy.is_key_released(ord('Z')):
b_i.value -= 1
bimpy.same_line()
bimpy.checkbox("Autoplay (c to stop)", is_autoplay)
if bimpy.is_key_down(ord('C')):
is_autoplay.value = False
bimpy.same_line()
if bimpy.button(" Next > (x) ") or bimpy.is_key_released(
ord('X')) or is_autoplay.value:
b_i.value += 1
if display_frame != b_i.value:
simulate_to_frame(b_i.value)
bimpy.combo('Label used for annotation', current_label_idx, all_labels)
def __buttons_window(self):
bimpy.set_next_window_pos(bimpy.Vec2(5, 635), bimpy.Condition.Once)
bimpy.set_next_window_size(bimpy.Vec2(1005, 128), bimpy.Condition.Once)
bimpy.begin("Exercise Control",
flags=bimpy.WindowFlags(4) | bimpy.WindowFlags(1)
| bimpy.WindowFlags(2))
bimpy.push_font(self.fonts.fonts[16]["cond"])
bimpy.text_colored(bimpy.Vec4(1, 0, 0, 1), "Controls")
bimpy.separator()
bimpy.pop_font()
bimpy.push_font(self.fonts.fonts[16]["cond"])
bimpy.text("")
bimpy.same_line(0, 150)
if self.pause_button_state == 1:
bimpy.push_style_color(bimpy.Colors(0), bimpy.Vec4(0, 0, 0, 1))
bimpy.push_style_color(bimpy.Colors(21),
bimpy.Vec4(0.8, 0.8, 0, 1))
bimpy.push_style_color(bimpy.Colors(22), bimpy.Vec4(1, 1, 0, 1))
bimpy.push_style_color(bimpy.Colors(23),
bimpy.Vec4(0.5, 0.5, 0, 1))
pause_button_text = "Pause Exercise"
elif self.pause_button_state == 0:
bimpy.push_style_color(bimpy.Colors(0), bimpy.Vec4(0, 0, 0, 1))
bimpy.push_style_color(bimpy.Colors(21),
bimpy.Vec4(0.0, 0.8, 0.0, 1))
bimpy.push_style_color(bimpy.Colors(22),
bimpy.Vec4(0.0, 1, 0.0, 1))
bimpy.push_style_color(bimpy.Colors(23),
bimpy.Vec4(0.0, 0.5, 0.0, 1))
pause_button_text = "Resume Exercise"
elif self.pause_button_state == -1:
bimpy.push_style_color(bimpy.Colors(0), bimpy.Vec4(0, 0, 0, 1))
bimpy.push_style_color(bimpy.Colors(21),
bimpy.Vec4(0.4, 0.4, 0.4, 1))
bimpy.push_style_color(bimpy.Colors(22),
bimpy.Vec4(0.4, 0.4, 0.4, 1))
bimpy.push_style_color(bimpy.Colors(23),
bimpy.Vec4(0.4, 0.4, 0.4, 1))
pause_button_text = "------ -------"
if bimpy.button(pause_button_text, bimpy.Vec2(250, 75)):
if self.pause_button_state == 1:
self.pause_button_state = 0
self.data.is_exercising = False
elif self.pause_button_state == 0:
self.pause_button_state = 1
self.data.is_exercising = True
bimpy.pop_style_color()
bimpy.pop_style_color()
bimpy.pop_style_color()
bimpy.pop_style_color()
bimpy.same_line(450, 150)
if self.stop_button_enabled:
bimpy.push_style_color(bimpy.Colors(0), bimpy.Vec4(0, 0, 0, 1))
bimpy.push_style_color(bimpy.Colors(21), bimpy.Vec4(0.8, 0, 0, 1))
bimpy.push_style_color(bimpy.Colors(22), bimpy.Vec4(1, 0, 0, 1))
bimpy.push_style_color(bimpy.Colors(23), bimpy.Vec4(0.5, 0, 0, 1))
else:
bimpy.push_style_color(bimpy.Colors(0), bimpy.Vec4(0, 0, 0, 1))
bimpy.push_style_color(bimpy.Colors(21),
bimpy.Vec4(0.4, 0.4, 0.4, 1))
bimpy.push_style_color(bimpy.Colors(22),
bimpy.Vec4(0.4, 0.4, 0.4, 1))
bimpy.push_style_color(bimpy.Colors(23),
bimpy.Vec4(0.4, 0.4, 0.4, 1))
self.pause_button_state = -1
if bimpy.button("Stop Exercise", bimpy.Vec2(250, 75)):
self.data.is_exercising = False
self.stop_button_enabled = False
bimpy.pop_style_color()
bimpy.pop_style_color()
bimpy.pop_style_color()
bimpy.pop_style_color()
bimpy.pop_font()
bimpy.end()
def show_demo_window():
bp.begin_root(menu=True)
# Menu Bar
if bp.begin_menu_bar():
if bp.begin_menu("Menu"):
bp.end_menu()
if bp.begin_menu("Examples"):
bp.end_menu()
if bp.begin_menu("Tools"):
bp.end_menu()
bp.end_menu_bar()
global clicked
if bp.button("Button"):
clicked += 1
if clicked & 1:
bp.same_line()
bp.text("Thanks for clicking me!")
bp.checkbox("checkbox", check)
bp.radio_button("radio a", e, 0)
bp.same_line()
bp.radio_button("radio b", e, 1)
bp.same_line()
bp.radio_button("radio c", e, 2)
# Color buttons, demonstrate using PushID() to add unique identifier in the ID stack, and changing style.
for i in range(7):
if i > 0:
bp.same_line()
bp.push_id_int(i)
bp.push_style_color(bp.Colors.Button, bp.Vec4(i / 7.0, 0.6, 0.6, 1.0))
bp.push_style_color(bp.Colors.ButtonHovered,
bp.Vec4(i / 7.0, 0.7, 0.7, 1.0))
bp.push_style_color(bp.Colors.ButtonActive,
bp.Vec4(i / 7.0, 0.8, 0.8, 1.0))
bp.button("Click")
bp.pop_style_color(3)
bp.pop_id()
# Use AlignTextToFramePadding() to align text baseline to the baseline of framed elements (otherwise a Text+SameLine+Button sequence will have the text a little too high by default)
bp.align_text_to_frame_padding()
bp.text("Hold to repeat:")
bp.same_line()
# Arrow buttons with Repeater
spacing = bp.get_style().item_inner_spacing.x
bp.push_button_repeat(True)
global counter
if bp.arrow_button("##left", bp.Direction.Left):
counter -= 1
bp.same_line(0.0, spacing)
if bp.arrow_button("##right", bp.Direction.Right):
counter += 1
bp.pop_button_repeat()
bp.same_line()
bp.text("%d" % counter)
bp.text("Hover over me")
if bp.is_item_hovered():
bp.set_tooltip("I am a tooltip")
bp.same_line()
bp.text("- or me")
if bp.is_item_hovered():
bp.begin_tooltip()
bp.text("I am a fancy tooltip")
arr = [0.6, 0.1, 1.0, 0.5, 0.92, 0.1, 0.2]
bp.plot_lines("Curve", arr)
bp.end_tooltip()
bp.separator()
bp.label_text("label", "Value")
# Using the _simplified_ one-liner Combo() api here
# See "Combo" section for examples of how to use the more complete BeginCombo()/EndCombo() api.
items = [
"AAAA", "BBBB", "CCCC", "DDDD", "EEEE", "FFFF", "GGGG", "HHHH", "IIII",
"JJJJ", "KKKK", "LLLLLLL", "MMMM", "OOOOOOO"
]
bp.combo("combo", item_current, items)
bp.same_line()
help_marker(
"Refer to the \"Combo\" section below for an explanation of the full BeginCombo/EndCombo API, and demonstration of various flags.\n"
)
# To wire InputText() with std::string or any other custom string type,
# see the "Text Input > Resize Callback" section of this demo, and the misc/cpp/imgui_stdlib.h file.
bp.input_text("input text", str0, 128)
bp.same_line()
help_marker(
"USER:\nHold SHIFT or use mouse to select text.\n"
"CTRL+Left/Right to word jump.\n"
"CTRL+A or double-click to select all.\n"
"CTRL+X,CTRL+C,CTRL+V clipboard.\n"
"CTRL+Z,CTRL+Y undo/redo.\n"
"ESCAPE to revert.\n\nPROGRAMMER:\nYou can use the ImGuiInputTextFlags_CallbackResize facility if you need to wire InputText() to a dynamic string type. See misc/cpp/imgui_stdlib.h for an example (this is not demonstrated in imgui_demo.cpp)."
)
bp.end()
