def render(self, is_lock):
bimpy.indent(10)
bimpy.text('- Plane')
bimpy.same_line()
bimpy_tools.help_marker(
'generate with random points\n' \
'* plane random: random on whole plane\n' \
'* balanced random: balanced positive and negative samples'
)
bimpy.push_item_width(140)
if bimpy.begin_combo('strategy##plane_random_generator',
self._select_strategy):
for item in self._strategy_list:
is_selected = bimpy.Bool(self._select_strategy == item)
if bimpy.selectable(item, is_selected) and not is_lock:
self._select_strategy = item
if is_selected.value:
bimpy.set_item_default_focus()
bimpy.end_combo()
bimpy.pop_item_width()
bimpy.unindent(10)
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 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, is_lock):
bimpy.indent(10)
bimpy.text('- SGD')
bimpy.same_line()
bimpy_tools.help_marker('torch.optim.SGD')
flags = bimpy.InputTextFlags.EnterReturnsTrue
if is_lock:
flags |= bimpy.InputTextFlags.ReadOnly
bimpy.push_item_width(120)
if bimpy.input_float('lr##sgd_optimizer', self._lr, flags=flags):
self._lr.value = max(0.0, self._lr.value)
if bimpy.input_float('momentum##sgd_optimizer',
self._momentum,
flags=flags):
self._momentum.value = max(0.0, self._momentum.value)
if bimpy.input_float('dampening##sgd_optimizer',
self._dampening,
flags=flags):
self._dampening.value = max(0.0, self._dampening.value)
if bimpy.input_float('weight_decay##sgd_optimizer',
self._weight_decay,
flags=flags):
self._weight_decay.value = max(0.0, self._weight_decay.value)
if bimpy.checkbox('nesterov##sgd_optimizer', self._nesterov):
self._hint_nesterov = False
if self._nesterov.value:
if self._momentum.value == 0 or self._dampening.value > 0:
self._nesterov.value = False
self._hint_nesterov = True
bimpy.same_line()
bimpy_tools.help_marker(
'Nesterov momentum requires a momentum and zero dampening',
self._hint_nesterov)
bimpy.pop_item_width()
bimpy.unindent(10)
def render(self, is_lock):
bimpy.set_next_tree_node_open(True, bimpy.Condition.FirstUseEver)
if not bimpy.tree_node('trainning##train_component'):
return
flags = bimpy.InputTextFlags.EnterReturnsTrue
if is_lock:
flags |= bimpy.InputTextFlags.ReadOnly
bimpy.push_item_width(120)
if bimpy.input_int('epoch##train_component',
self._epoch,
0,
0,
flags=flags):
self._epoch.value = max(1, self._epoch.value)
if bimpy.input_int('batch_size##train_component',
self._batch_size,
0,
0,
flags=flags):
self._batch_size.value = max(1, self._batch_size.value)
if bimpy.input_int('batch_per_epoch##train_component',
self._batch_per_epoch,
0,
0,
flags=flags):
self._batch_per_epoch.value = max(1, self._batch_per_epoch.value)
if bimpy.input_int('valid_size##train_component',
self._valid_size,
0,
0,
flags=flags):
self._valid_size.value = max(0, self._valid_size.value)
bimpy.pop_item_width()
bimpy.tree_pop()
def render(self, is_lock):
bimpy.indent(10)
bimpy.text('- Linear layer init')
bimpy.same_line()
bimpy_tools.help_marker(
'Initializer used in torch.nn.Linear, use Kaiming uniform')
bimpy.push_item_width(120)
flags = bimpy.InputTextFlags.EnterReturnsTrue
if is_lock:
flags |= bimpy.InputTextFlags.ReadOnly
if bimpy.input_float('a##sgd_optimizer', self._a, flags=flags):
self._a.value = max(0.0, self._a.value)
if bimpy.begin_combo('mode##linear_layer_init', self._select_mode):
for item in self._mode_list:
is_selected = bimpy.Bool(self._select_mode == item)
if bimpy.selectable(item, is_selected) and not is_lock:
self._select_mode = item
if is_selected.value:
bimpy.set_item_default_focus()
bimpy.end_combo()
if bimpy.begin_combo('nonlinearity##linear_layer_init',
self._select_nonlinearity):
for item in self._nonlinearity_list:
is_selected = bimpy.Bool(self._select_nonlinearity == item)
if bimpy.selectable(item, is_selected) and not is_lock:
self._select_nonlinearity = item
if is_selected.value:
bimpy.set_item_default_focus()
bimpy.end_combo()
bimpy.pop_item_width()
bimpy.unindent(10)
def render(self, is_lock):
bimpy.indent(10)
bimpy.text('- MSE loss')
bimpy.same_line()
bimpy_tools.help_marker('torch.nn.MSELoss')
bimpy.push_item_width(120)
if bimpy.begin_combo('reduction##mse_loss_fn', self._select_redution):
for item in self._reduction_list:
is_selected = bimpy.Bool(self._select_redution == item)
if bimpy.selectable(item, is_selected) and not is_lock:
self._select_redution = item
if is_selected.value:
bimpy.set_item_default_focus()
bimpy.end_combo()
bimpy.pop_item_width()
bimpy.unindent(10)
def render(self, is_lock):
bimpy.indent(10)
bimpy.text('- Adam')
bimpy.same_line()
bimpy_tools.help_marker('torch.optim.Adam')
flags = bimpy.InputTextFlags.EnterReturnsTrue
if is_lock:
flags |= bimpy.InputTextFlags.ReadOnly
bimpy.push_item_width(140)
if bimpy.input_float('lr##adam_optimizer', self._lr, flags=flags):
self._lr.value = max(0.0, self._lr.value)
if bimpy.input_float2('momentum##adam_optimizer',
self._betas_first,
self._betas_second,
flags=flags):
self._betas_first.value = max(0.0, self._betas_first.value)
self._betas_second.value = max(0.0, self._betas_second.value)
if bimpy.input_float('eps##adam_optimizer',
self._eps,
decimal_precision=8,
flags=flags):
self._dampening.value = max(0.0, self._eps.value)
if bimpy.input_float('weight_decay##adam_optimizer',
self._weight_decay,
flags=flags):
self._weight_decay.value = max(0.0, self._weight_decay.value)
bimpy.checkbox('amsgrad##adam_optimizer', self._amsgrad)
bimpy.pop_item_width()
bimpy.unindent(10)
def render(self, is_lock):
bimpy.indent(10)
bimpy.text('- Raw Point')
bimpy.same_line()
bimpy_tools.help_marker('generate with raw points')
bimpy.push_item_width(120)
if bimpy.begin_combo('strategy##raw_point_generator',
self._select_strategy):
for item in self._strategy_list:
is_selected = bimpy.Bool(self._select_strategy == item)
if bimpy.selectable(item, is_selected) and not is_lock:
self._select_strategy = item
if is_selected.value:
bimpy.set_item_default_focus()
bimpy.end_combo()
bimpy.pop_item_width()
bimpy.unindent(10)
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!",
flags=(bimpy.WindowFlags.AlwaysAutoResize
| bimpy.WindowFlags.NoTitleBar)):
bimpy.text("Some text")
if bimpy.button("Some button"):
print("!!!")
bimpy.combo("Combo2!", selectedItem, mylist)
bimpy.end()
def render(self, is_lock):
bimpy.set_next_tree_node_open(True, bimpy.Condition.FirstUseEver)
if not bimpy.tree_node('lines##lines_component'):
return
# number setting
flags = bimpy.InputTextFlags.EnterReturnsTrue
if is_lock:
flags |= bimpy.InputTextFlags.ReadOnly
if bimpy.input_int('number##lines_component', self._line_number, 1, 1, flags):
self._line_number.value = max(3, self._line_number.value)
if self._last_line_number_value > self._line_number.value:
self._line_data = self._line_data[:self._line_number.value] # cut back points
else:
self._line_data.extend([
[bimpy.Float(0), bimpy.Float(0), bimpy.Float(0)]
for _ in range(self._last_line_number_value, self._line_number.value)
])
self._last_line_number_value = self._line_number.value
# print('line number change to {}'.format(self._line_number.value))
# show line value setting
bimpy.set_next_tree_node_open(self._line_number.value < 10, bimpy.Condition.FirstUseEver)
self._highlight_line_index = None
if bimpy.tree_node('line value ({})'.format(self._line_number.value)):
for index in range(self._line_number.value):
bimpy.push_item_width(210)
bimpy.input_float3(
'{:<3d}'.format(index),
self._line_data[index][0],
self._line_data[index][1],
self._line_data[index][2],
flags=flags
)
bimpy.pop_item_width()
if bimpy.is_item_hovered():
self._highlight_line_index = index
bimpy.same_line()
if bimpy.button('rev##lines_component{}'.format(index)) and not is_lock:
for j in range(3):
self._line_data[index][j].value = -self._line_data[index][j].value
if bimpy.is_item_hovered():
self._highlight_line_index = index
bimpy.same_line()
if bimpy.button('draw##lines_component{}'.format(index)) and not is_lock:
self._waitting_draw_line_index = index
bimpy.set_window_focus('canvas window##canvas')
if bimpy.is_item_hovered():
self._highlight_line_index = index
bimpy.tree_pop()
# random setting
bimpy.new_line()
if bimpy.button('random##lines_component') and not is_lock:
initializer = self.initializer_component.build_initializer()
random_lines = initializer.random(self._line_number.value)
for index in range(self._line_number.value):
self._line_data[index][0].value = random_lines[index].a
self._line_data[index][1].value = random_lines[index].b
self._line_data[index][2].value = random_lines[index].c
self.initializer_component.render(is_lock)
bimpy.tree_pop()
def render(self, ctx, windows_info):
# calculate autoly
self.pos = bimpy.Vec2(
windows_info['file_brewswer_ui']['x'] +
windows_info['file_brewswer_ui']['w'] + conf.margin, conf.margin)
self.size = bimpy.Vec2(
ctx.width() - self.pos.x - conf.margin,
ctx.height() - 3 * conf.margin - conf.meta_info_height)
bimpy.set_next_window_pos(self.pos, bimpy.Condition.Always)
bimpy.set_next_window_size(self.size, bimpy.Condition.Always)
bimpy.begin(
LANG.image_shower_ui_title, bimpy.Bool(True),
bimpy.WindowFlags.NoCollapse | bimpy.WindowFlags.NoMove
| bimpy.WindowFlags.NoResize
| bimpy.WindowFlags.HorizontalScrollbar)
###########UI###########
# modal part
if self.im is not None:
bimpy.set_cursor_pos(bimpy.Vec2(0.0, conf.margin * 3))
bimpy.image(self.im)
# if image is loaded
if self.labels is not None:
for i, label in enumerate(self.labels):
color = self.COLORS[self.classes.index(label)]
# print((self.bbox[i][0], self.bbox[i][1] - 10))
# show on the left bottom of the picture
bimpy.set_cursor_pos(
bimpy.Vec2(self.bbox[i][0] + 10, self.bbox[i][3] + 10))
# set style
bimpy.push_id_int(i)
if conf.show_yolo_confience:
bimpy.button(
label + ' ' +
str(format(self.confidence[i] * 100, '.2f')) + '%')
else:
bimpy.button(label)
if bimpy.is_item_hovered(i):
s = "{} ({})\n{}"
label = label[0].upper() + label[1:]
s = s.format(
label,
str(format(self.confidence[i] * 100, '.2f')) + '%',
LANG.click_to_view_more)
bimpy.set_tooltip(s)
if bimpy.is_item_active():
self.select_label = label
bimpy.pop_id()
# bimpy.set_cursor_pos(bimpy.Vec2(conf.margin, self.size.y - conf.margin * 2))
bimpy.set_cursor_pos(bimpy.Vec2(conf.margin, conf.margin * 1.5))
if bimpy.button(LANG.smart_analyse) == True:
self.object_detection()
bimpy.same_line()
bimpy.checkbox(LANG.auto, self.auto)
### Resize ###
bimpy.same_line()
bimpy.push_item_width(150)
bimpy.drag_float(LANG.drag, self.scale, 1.0, 10, 1000)
bimpy.pop_item_width()
if abs(self.last_scale - self.scale.value) > 4.:
xx = self.size.x * self.scale.value / 100.
yy = (self.size.y - 45 - 40) * self.scale.value / 100.
im = self.i_s.resize(self.raw_im, xx, yy)
self.now_im = im
self.set_im(im)
# set to save computation
self.last_scale = self.scale.value
# if selected obj
if self.select_label != '':
# print(self.select_label)
windows_info['retrival_ui'][
'self'].select_label = self.select_label
bimpy.open_popup('{}: {}'.format(LANG.retrieve, self.select_label))
# reset
self.select_label = ''
windows_info['retrival_ui']['self'].retrival()
########################
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