"The input frozen model %s (%s) does not exist! Please check the path of the frozen model. "%(self.run_opt.init_frz_model,os.path.abspath(self.run_opt.init_frz_model))
)frome
# get the model type from the frozen model(self.run_opt.init_frz_model)
doc_neuron='Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.'
doc_resnet_dt='Whether to use a "Timestep" in the skip connection'
doc_seed='Random seed for parameter initialization'
doc_activation_function=f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
doc_precision=f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_trainable='If the parameters in the embedding net are trainable'
doc_sel_a='A list of integers. The length of the list should be the same as the number of atom types in the system. `sel_a[i]` gives the selected number of type-i neighbors. The full relative coordinates of the neighbors are used by the descriptor.'
doc_sel_r='A list of integers. The length of the list should be the same as the number of atom types in the system. `sel_r[i]` gives the selected number of type-i neighbors. Only relative distance of the neighbors are used by the descriptor. sel_a[i] + sel_r[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius.'
doc_rcut='The cut-off radius. The default value is 6.0'
doc_axis_rule='A list of integers. The length should be 6 times of the number of types. \n\n\
- axis_rule[i*6+0]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.\n\n\
- axis_rule[i*6+1]: type of the atom defining the first axis of type-i atom.\n\n\
- axis_rule[i*6+2]: index of the axis atom defining the first axis. Note that the neighbors with the same class and type are sorted according to their relative distance.\n\n\
- axis_rule[i*6+3]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.\n\n\
- axis_rule[i*6+4]: type of the atom defining the second axis of type-i atom.\n\n\
- axis_rule[i*6+5]: class of the atom defining the second axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.'
doc_sel='This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\
- `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\
- `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".'
doc_rcut='The cut-off radius.'
doc_rcut_smth='Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`'
doc_neuron='Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.'
doc_axis_neuron='Size of the submatrix of G (embedding matrix).'
doc_activation_function=f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
doc_resnet_dt='Whether to use a "Timestep" in the skip connection'
doc_type_one_side='Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets'
doc_precision=f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_trainable='If the parameters in the embedding net is trainable'
doc_seed='Random seed for parameter initialization'
doc_exclude_types='The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.'
doc_set_davg_zero='Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used'
doc_sel='This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\
- `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\
- `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".'
doc_rcut='The cut-off radius.'
doc_rcut_smth='Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`'
doc_neuron='Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.'
doc_activation_function=f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
doc_resnet_dt='Whether to use a "Timestep" in the skip connection'
doc_precision=f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_trainable='If the parameters in the embedding net are trainable'
doc_seed='Random seed for parameter initialization'
doc_set_davg_zero='Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used'
doc_sel='This parameter set the number of selected neighbors for each type of atom. It can be:\n\n\
- `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. `sel[i]` is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.\n\n\
- `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".'
doc_rcut='The cut-off radius.'
doc_rcut_smth='Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`'
doc_neuron='Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.'
doc_activation_function=f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
doc_resnet_dt='Whether to use a "Timestep" in the skip connection'
doc_type_one_side='Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets'
doc_precision=f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_trainable='If the parameters in the embedding net are trainable'
doc_seed='Random seed for parameter initialization'
doc_exclude_types='The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.'
doc_set_davg_zero='Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used'
doc_sel='This parameter set the number of selected neighbors. Note that this parameter is a little different from that in other descriptors. Instead of separating each type of atoms, only the summation matters. And this number is highly related with the efficiency, thus one should not make it too large. Usually 200 or less is enough, far away from the GPU limitation 4096. It can be:\n\n\
- `int`. The maximum number of neighbor atoms to be considered. We recommend it to be less than 200. \n\n\
- `List[int]`. The length of the list should be the same as the number of atom types in the system. `sel[i]` gives the selected number of type-i neighbors. Only the summation of `sel[i]` matters, and it is recommended to be less than 200.\
- `str`. Can be "auto:factor" or "auto". "factor" is a float number larger than 1. This option will automatically determine the `sel`. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the "factor". Finally the number is wraped up to 4 divisible. The option "auto" is equivalent to "auto:1.1".'
doc_rcut='The cut-off radius.'
doc_rcut_smth='Where to start smoothing. For example the 1/r term is smoothed from `rcut` to `rcut_smth`'
doc_neuron='Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.'
doc_axis_neuron='Size of the submatrix of G (embedding matrix).'
doc_activation_function=f'The activation function in the embedding net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
doc_resnet_dt='Whether to use a "Timestep" in the skip connection'
doc_type_one_side='Whether to consider the information from only one side or both sides.'
doc_precision=f'The precision of the embedding net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_trainable='If the parameters in the embedding net is trainable'
doc_seed='Random seed for parameter initialization'
doc_exclude_types='The excluded pairs of types which have no interaction with each other. For example, `[[0, 1]]` means no interaction between type 0 and type 1.'
doc_set_davg_zero='Set the normalization average to zero. This option should be set when `atom_ener` in the energy fitting is used'
doc_attn='The length of hidden vectors in attention layers'
doc_attn_layer='The number of attention layers'
doc_attn_dotr='Whether to do dot product with the normalized relative coordinates'
doc_attn_mask='Whether to do mask on the diagonal in the attention matrix'
doc_descrpt_type=f'The type of the descritpor. See explanation below. \n\n\
- `loc_frame`: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.\n\n\
- `se_e2_a`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor.\n\n\
- `se_e2_r`: Used by the smooth edition of Deep Potential. Only the distance between atoms is used to construct the descriptor.\n\n\
- `se_e3`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Three-body embedding will be used by this descriptor.\n\n\
- `se_a_tpe`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Type embedding will be used by this descriptor.\n\n\
- `se_atten`: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism will be used by this descriptor.\n\n\
- `hybrid`: Concatenate of a list of descriptors as a new descriptor.'
doc_numb_fparam='The dimension of the frame parameter. If set to >0, file `fparam.npy` should be included to provided the input fparams.'
doc_numb_aparam='The dimension of the atomic parameter. If set to >0, file `aparam.npy` should be included to provided the input aparams.'
doc_neuron='The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.'
doc_activation_function=f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
doc_precision=f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_resnet_dt='Whether to use a "Timestep" in the skip connection'
doc_trainable='Whether the parameters in the fitting net are trainable. This option can be\n\n\
- bool: True if all parameters of the fitting net are trainable, False otherwise.\n\n\
- list of bool: Specifies if each layer is trainable. Since the fitting net is composed by hidden layers followed by a output layer, the length of tihs list should be equal to len(`neuron`)+1.'
doc_rcond='The condition number used to determine the inital energy shift for each type of atoms.'
doc_seed='Random seed for parameter initialization of the fitting net'
doc_atom_ener='Specify the atomic energy in vacuum for each type'
doc_neuron='The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.'
doc_activation_function=f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
doc_resnet_dt='Whether to use a "Timestep" in the skip connection'
doc_precision=f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_scale='The output of the fitting net (polarizability matrix) will be scaled by ``scale``'
#doc_diag_shift = 'The diagonal part of the polarizability matrix will be shifted by ``diag_shift``. The shift operation is carried out after ``scale``.'
doc_fit_diag='Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.'
doc_sel_type='The atom types for which the atomic polarizability will be provided. If not set, all types will be selected.'
doc_seed='Random seed for parameter initialization of the fitting net'
# YWolfeee: user can decide whether to use shift diag
doc_shift_diag='Whether to shift the diagonal of polar, which is beneficial to training. Default is true.'
doc_neuron='The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.'
doc_activation_function=f'The activation function in the fitting net. Supported activation functions are {list_to_doc(ACTIVATION_FN_DICT.keys())} Note that "gelu" denotes the custom operator version, and "gelu_tf" denotes the TF standard version.'
doc_resnet_dt='Whether to use a "Timestep" in the skip connection'
doc_precision=f'The precision of the fitting net parameters, supported options are {list_to_doc(PRECISION_DICT.keys())} Default follows the interface precision.'
doc_sel_type='The atom types for which the atomic dipole will be provided. If not set, all types will be selected.'
doc_seed='Random seed for parameter initialization of the fitting net'
# YWolfeee: Delete global polar mode, merge it into polar mode and use loss setting to support.
deffitting_variant_type_args():
doc_descrpt_type='The type of the fitting. See explanation below. \n\n\
- `ener`: Fit an energy model (potential energy surface).\n\n\
- `dipole`: Fit an atomic dipole model. Global dipole labels or atomic dipole labels for all the selected atoms (see `sel_type`) should be provided by `dipole.npy` in each data system. The file either has number of frames lines and 3 times of number of selected atoms columns, or has number of frames lines and 3 columns. See `loss` parameter.\n\n\
- `polar`: Fit an atomic polarizability model. Global polarizazbility labels or atomic polarizability labels for all the selected atoms (see `sel_type`) should be provided by `polarizability.npy` in each data system. The file eith has number of frames lines and 9 times of number of selected atoms columns, or has number of frames lines and 9 columns. See `loss` parameter.\n\n'
doc_model_name="The name of the frozen dipole model file."
doc_model_charge_map=f"The charge of the WFCC. The list length should be the same as the {make_link('sel_type','model/fitting_net[dipole]/sel_type')}. "
doc_sys_charge_map=f"The charge of real atoms. The list length should be the same as the {make_link('type_map','model/type_map')}"
doc_ewald_h=f"The grid spacing of the FFT grid. Unit is A"
doc_ewald_beta=f"The splitting parameter of Ewald sum. Unit is A^{-1}"
doc_model_file=f"The input model file, which will be compressed by the DeePMD-kit."
doc_table_config=f"The arguments of model compression, including extrapolate(scale of model extrapolation), stride(uniform stride of tabulation's first and second table), and frequency(frequency of tabulation overflow check)."
doc_min_nbor_dist=f"The nearest distance between neighbor atoms saved in the frozen model."
doc_type_map='A list of strings. Give the name to each type of atoms. It is noted that the number of atom type of training system must be less than 128 in a GPU environment.'
doc_data_stat_nbatch='The model determines the normalization from the statistics of the data. This key specifies the number of `frames` in each `system` used for statistics.'
doc_data_stat_protect='Protect parameter for atomic energy regression.'
doc_type_embedding="The type embedding."
doc_descrpt='The descriptor of atomic environment.'
doc_fitting='The fitting of physical properties.'
doc_modifier='The modifier of model output.'
doc_use_srtab='The table for the short-range pairwise interaction added on top of DP. The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes. The first colume is the distance between atoms. The second to the last columes are energies for pairs of certain types. For example we have two atom types, 0 and 1. The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.'
doc_smin_alpha='The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor. This distance is calculated by softmin. This parameter is the decaying parameter in the softmin. It is only required when `use_srtab` is provided.'
doc_sw_rmin='The lower boundary of the interpolation between short-range tabulated interaction and DP. It is only required when `use_srtab` is provided.'
doc_sw_rmax='The upper boundary of the interpolation between short-range tabulated interaction and DP. It is only required when `use_srtab` is provided.'
returnf'The prefactor of {item} loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the {item} label should be provided by file {item}.npy in each data system. If both start_pref_{item} and limit_pref_{item} are set to 0, then the {item} will be ignored.'
deflimit_pref(item):
returnf'The prefactor of {item} loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.'
defloss_ener():
doc_start_pref_e=start_pref('energy')
doc_limit_pref_e=limit_pref('energy')
doc_start_pref_f=start_pref('force')
doc_limit_pref_f=limit_pref('force')
doc_start_pref_v=start_pref('virial')
doc_limit_pref_v=limit_pref('virial')
doc_start_pref_ae=start_pref('atom_ener')
doc_limit_pref_ae=limit_pref('atom_ener')
doc_start_pref_pf=start_pref('atom_pref')
doc_limit_pref_pf=limit_pref('atom_pref')
doc_relative_f='If provided, relative force error will be used in the loss. The difference of force will be normalized by the magnitude of the force in the label with a shift given by `relative_f`, i.e. DF_i / ( || F || + relative_f ) with DF denoting the difference between prediction and label and || F || denoting the L2 norm of the label.'
doc_enable_atom_ener_coeff="If true, the energy will be computed as \sum_i c_i E_i. c_i should be provided by file atom_ener_coeff.npy in each data system, otherwise it's 1."
# YWolfeee: Modified to support tensor type of loss args.
defloss_tensor():
#doc_global_weight = "The prefactor of the weight of global loss. It should be larger than or equal to 0. If only `pref` is provided or both are not provided, training will be global mode, i.e. the shape of 'polarizability.npy` or `dipole.npy` should be #frams x [9 or 3]."
#doc_local_weight = "The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If only `pref_atomic` is provided, training will be atomic mode, i.e. the shape of `polarizability.npy` or `dipole.npy` should be #frames x ([9 or 3] x #selected atoms). If both `pref` and `pref_atomic` are provided, training will be combined mode, and atomic label should be provided as well."
doc_global_weight="The prefactor of the weight of global loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to global label, i.e. 'polarizability.npy` or `dipole.npy`, whose shape should be #frames x [9 or 3]. If it's larger than 0.0, this npy should be included."
doc_local_weight="The prefactor of the weight of atomic loss. It should be larger than or equal to 0. If controls the weight of loss corresponding to atomic label, i.e. `atomic_polarizability.npy` or `atomic_dipole.npy`, whose shape should be #frames x ([9 or 3] x #selected atoms). If it's larger than 0.0, this npy should be included. Both `pref` and `pref_atomic` should be provided, and either can be set to 0.0."
doc_loss='The type of the loss. When the fitting type is `ener`, the loss type should be set to `ener` or left unset. When the fitting type is `dipole` or `polar`, the loss type should be set to `tensor`. \n\.'
'This key can be provided with a list that specifies the systems, or be provided with a string ' \
'by which the prefix of all systems are given and the list of the systems is automatically generated.'
doc_set_prefix=f'The prefix of the sets in the {link_sys}.'
doc_batch_size=f'This key can be \n\n\
- list: the length of which is the same as the {link_sys}. The batch size of each system is given by the elements of the list.\n\n\
- int: all {link_sys} use the same batch size.\n\n\
- string "auto": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.\n\n\
- string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.'
doc_auto_prob_style='Determine the probability of systems automatically. The method is assigned by this key and can be\n\n\
- "prob_uniform" : the probability all the systems are equal, namely 1.0/self.get_nsystems()\n\n\
- "prob_sys_size" : the probability of a system is proportional to the number of batches in the system\n\n\
- "prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;..." : the list of systems is devided into blocks. A block is specified by `stt_idx:end_idx:weight`, where `stt_idx` is the starting index of the system, `end_idx` is then ending (not including) index of the system, the probabilities of the systems in this block sums up to `weight`, and the relatively probabilities within this block is proportional to the number of batches in the system.'
'This key can be provided with a list that specifies the systems, or be provided with a string ' \
'by which the prefix of all systems are given and the list of the systems is automatically generated.'
doc_set_prefix=f'The prefix of the sets in the {link_sys}.'
doc_batch_size=f'This key can be \n\n\
- list: the length of which is the same as the {link_sys}. The batch size of each system is given by the elements of the list.\n\n\
- int: all {link_sys} use the same batch size.\n\n\
- string "auto": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.\n\n\
- string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.'
doc_auto_prob_style='Determine the probability of systems automatically. The method is assigned by this key and can be\n\n\
- "prob_uniform" : the probability all the systems are equal, namely 1.0/self.get_nsystems()\n\n\
- "prob_sys_size" : the probability of a system is proportional to the number of batches in the system\n\n\
- "prob_sys_size;stt_idx:end_idx:weight;stt_idx:end_idx:weight;..." : the list of systems is devided into blocks. A block is specified by `stt_idx:end_idx:weight`, where `stt_idx` is the starting index of the system, `end_idx` is then ending (not including) index of the system, the probabilities of the systems in this block sums up to `weight`, and the relatively probabilities within this block is proportional to the number of batches in the system.'
doc_sys_probs="A list of float if specified. " \
"Should be of the same length as `systems`, " \
"specifying the probability of each system."
doc_numb_btch="An integer that specifies the number of systems to be sampled for each validation period."
