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)