def extra_repr(self):
txt = strings.get_string_from_dict({
'input_dims':self.input_dims,
'in_dropout':self.in_dropout,
'out_dropout':self.out_dropout,
'dummy':self.dummy,
}, ', ', '=')
return txt
def extra_repr(self):
txt = strings.get_string_from_dict({
'activation':self.activation,
'residual_dropout':self.residual_dropout,
'input_dims':self.input_dims,
'fourier_dims':self.fourier_dims,
'kernel_size':self.kernel_size,
'mod_dropout':self.mod_dropout,
}, ', ', '=')
return txt
def extra_repr(self):
txt = strings.get_string_from_dict({
'input_dims':self.input_dims,
'output_dims':self.output_dims,
'max_curve_length':self.max_curve_length,
'in_dropout':self.in_dropout,
'out_dropout':self.out_dropout,
'bias':self.bias,
'bidirectional':self.bidirectional,
}, ', ', '=')
return txt
def extra_repr(self):
txt = strings.get_string_from_dict({
'te_features':self.te_features,
'min_te_period':self.min_te_period,
'max_te_period':self.max_te_period,
'te_periods':[f'{p:.3f}' for p in tensor_to_numpy(self.get_te_periods())],
'te_phases':[f'{p:.3f}' for p in tensor_to_numpy(self.get_te_phases())],
'te_scales':[f'{p:.5f}' for p in tensor_to_numpy(self.get_te_scales())],
'time_noise_window':self.time_noise_window,
'init_k_exp':self.init_k_exp,
'mod_dropout':self.mod_dropout,
}, ', ', '=')
return txt
def extra_repr(self):
txt = strings.get_string_from_dict(
{
'input_dims': self.input_dims,
'output_dims': self.output_dims,
'activation': self.activation,
'in_dropout': self.in_dropout,
'out_dropout': self.out_dropout,
'bias': self.bias,
'split_out': self.split_out,
'bias_value': self.bias_value,
}, ', ', '=')
return txt
def extra_repr(self):
txt = strings.get_string_from_dict({
'input_dims':self.input_dims,
'output_dims':self.output_dims,
'max_curve_length':self.max_curve_length,
'num_heads':self.num_heads,
'activation':self.activation,
'in_dropout':self.in_dropout,
'out_dropout':self.out_dropout,
'attn_dropout':self.attn_dropout,
'mlp_dropout':self.mlp_dropout,
'bias':self.bias,
}, ', ', '=')
return txt
def __repr__(self):
txt = f'CustomDataset('
txt += strings.get_string_from_dict({
'lcset_len':f'{len(self.lcset):,}',
'class_names':self.class_names,
'band_names':self.band_names,
'max_day':f'{self.max_day:.2f}',
'max_len': f'{self.max_len:,}',
'in_attrs':self.in_attrs,
'rec_attr':self.rec_attr,
'append_in_ddays':self.append_in_ddays,
'balanced_w_cdict':self.balanced_w_cdict,
'populations_cdict':self.populations_cdict,
}, ', ', '=')
txt += ')'
return txt
def extra_repr(self):
txt = strings.get_string_from_dict(
{
'input_dims': self.input_dims,
'input_space': self.input_space,
'output_dims': self.output_dims,
'output_space': self.get_output_space(),
'spatial_field': self.get_spatial_field(),
'cnn_kwargs': self.cnn_kwargs,
'pool_kwargs': self.pool_kwargs,
'padding_mode': self.padding_mode,
'activation': self.activation,
'in_dropout': self.in_dropout,
'out_dropout': self.out_dropout,
'bias': self.bias,
}, ', ', '=')
return txt
def get_model_name(model_name_dict):
return strings.get_string_from_dict(model_name_dict)