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 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.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)