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 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 render(self):
bimpy.set_next_tree_node_open(True, bimpy.Condition.FirstUseEver)
if not bimpy.tree_node('result##train_result_component'):
return
for item in ['loss', 'acc']:
for name in self._measure_value:
bimpy.text('{:<6} {:<6}: {:.8f}'.format(
name, item,
self._measure_value[name][item],
))
bimpy.new_line()
bimpy.tree_pop()
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.set_next_tree_node_open(True, bimpy.Condition.FirstUseEver)
if not bimpy.tree_node('optimizer##optimizer_component'):
return
if bimpy.begin_combo('select##optimizer_component',
self._select_optimizer):
for item in self._optimizer_list:
is_selected = bimpy.Bool(self._select_optimizer == item)
if bimpy.selectable(item, is_selected) and not is_lock:
self._select_optimizer = item
if is_selected.value:
bimpy.set_item_default_focus()
bimpy.end_combo()
self._optimizer_map[self._select_optimizer].render(is_lock)
bimpy.tree_pop()
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()