hoops_ai.ml.EXPERIMENTAL.EmbeddingFlowModel

class hoops_ai.ml.EXPERIMENTAL.EmbeddingFlowModel(result_dir=None, log_file='Embedding_model_training_errors.log', generate_stream_cache_for_visu=False, face_weight_mode='power', face_alpha=1.3, face_tau=2.0, face_mix_uniform=0.05, face_eps=1e-06, edge_weight_mode='power', edge_alpha=1.3, edge_tau=2.0, edge_mix_uniform=0.05, edge_eps=1e-06, use_face_type_onehot=True, face_type_num_classes=16, face_type_max=15, uv_channels=7, lr=0.0003, weight_decay=0.0, emb_dim=1024, proj_dim=512, surf_in_ch=23, curve_in_ch=6, proj_hidden=1024, use_bn=True, temp_init=0.05, temp_min=0.01, temp_max=0.2, surf_c=(32, 64, 128), curve_c=(32, 64, 128), conv_kernel=3, conv_pad=1, aug_noise_std=0.005, aug_scale_min=0.85, aug_scale_max=1.2, p_hflip=0.5, p_vflip=0.5, p_rot90=0.5, p_cutout=0.3, cutout_frac_min=0.08, cutout_frac_max=0.2, channel_jitter_std=0.005, curve_noise_std_xyz=0.005, curve_noise_std_other=0.003, curve_p_reverse=0.5, curve_p_dropout=0.04, curve_augment_smooth=False, node_weight_drop_prob=0.5, edge_weight_drop_prob=0.5, weight_beta_a=10.0, weight_beta_b=10.0, loss_type='hard', hard_pos_weight=2.0, hard_neg_weight=3.0, sc_weight=0.0, proj=False, load_checkpoint_using_nn_module=False)

Bases: FlowModel

FlowModel adapter so FlowTrainer can train embedding contrastive model.

Parameters:

• result_dir (str)

• log_file (str)

• generate_stream_cache_for_visu (bool)

• face_weight_mode (str)

• face_alpha (float)

• face_tau (float)

• face_mix_uniform (float)

• face_eps (float)

• edge_weight_mode (str)

• edge_alpha (float)

• edge_tau (float)

• edge_mix_uniform (float)

• edge_eps (float)

• use_face_type_onehot (bool)

• face_type_num_classes (int)

• face_type_max (int)

• uv_channels (int)

• lr (float)

• weight_decay (float)

• emb_dim (int)

• proj_dim (int)

• surf_in_ch (int)

• curve_in_ch (int)

• proj_hidden (int)

• use_bn (bool)

• temp_init (float)

• temp_min (float)

• temp_max (float)

• surf_c (tuple)

• curve_c (tuple)

• conv_kernel (int)

• conv_pad (int)

• aug_noise_std (float)

• aug_scale_min (float)

• aug_scale_max (float)

• p_hflip (float)

• p_vflip (float)

• p_rot90 (float)

• p_cutout (float)

• cutout_frac_min (float)

• cutout_frac_max (float)

• channel_jitter_std (float)

• curve_noise_std_xyz (float)

• curve_noise_std_other (float)

• curve_p_reverse (float)

• curve_p_dropout (float)

• curve_augment_smooth (bool)

• node_weight_drop_prob (float)

• edge_weight_drop_prob (float)

• weight_beta_a (float)

• weight_beta_b (float)

• loss_type (str)

• hard_pos_weight (float)

• hard_neg_weight (float)

• sc_weight (float)

• proj (bool)

• load_checkpoint_using_nn_module (bool)

collate_function(batch)

Return a collated batch for this model.

Return type:

Any

convert_encoded_data_to_graph(storage, graph_handler, filename)

Converts encoded data from storage into a graph representation, which serves as input for the ML model.

Parameters:

• storage (DataStorage)

• graph_handler (MLStorage)

• filename (str)

Return type:

Dict[str, Any]

encode_cad_data(cad_file, cad_loader, storage)

Opens the CAD file and encodes its data into a format suitable for machine learning. Stores the encoded data using the provided storage handler.

Parameters:

• cad_file (str)

• cad_loader (CADLoader)

• storage (DataStorage)

Return type:

Tuple[int, int]

encode_label_data(label_storage, storage)

Uses the LabelStorage object to retrieve the labeling information for a given input Stores the label data for the specific machine learning Task

return the str key when the label data is found in the storage object and the size of the label data

Parameters:

• label_storage (LabelStorage)

• storage (DataStorage)

Return type:

Tuple[str, int]

load_model_input_from_files(graph_file, data_id, label_file=None)

Loads a single graph from a file to be used as input for the machine learning model.

Parameters:

• graph_file (str)

• data_id (int)

• label_file (str)

Return type:

Any

make_weights(a, mode='power', alpha=0.5, tau=2.0, mix_uniform=0.0, eps=1e-06)

metrics()

Publish/push the ml metrics after training the model

model_name()

Provides the name of the model.

Return type:

str

predict_and_postprocess(batch)

Post-processes and formats the raw model output into a structured prediction.

Return type:

Any

retrieve_model(check_point=None)

Retrieves the PyTorch Lightning model used in this flow.

Parameters:

check_point (str)

Return type:

pytorch_lightning.LightningModule