MUTarget + CLEAN 240315

The primary objective of this branch is to integrate MUTarget with contrastive learning while ensuring minimal modifications to the original MUTarget codebase.

New Warm Starting Strategies 240315

In the train.train_loop, determine in advance whether there is a need to extend the batch; if so, do the extension on the batched data. Afterwards, in model.Encoder::forward(), process batched data through the corresponding conditional branches based on different scenarios to obtain the various classification_head, motif_logits, and projection_head.

Changes in train.train_loop, model.Encoder::forward, data.LocalizationDataset::get_pos_samples & get_neg_samples (sample_with_weight)

New Configuration Parameters Description 240304

Below are the descriptions for some important new configuration parameters, specifically regarding the use of SupCon and other related settings:

• apply: This parameter determines whether to use SupCon in the model training process. Setting it to False disables the use of SupCon.

• n_pos: Represents the number of positive samples to be used for each anchor in the contrastive learning setup.

• n_neg: Indicates the number of negative samples for each anchor.

• temperature: A scaling parameter used in the loss function of contrastive learning models.

• hard_neg: A boolean parameter that, when set to True, indicates the model should select harder negative samples for computing the loss. Hard negative samples are those that are more challenging for the model to correctly distinguish.

• weight: Set it as 1. This parameter was previously used but is no longer necessary.

• warm_start: Specifies the epoch at which warm starting ends.

Changes in data.py 240304

LocalizationDataset Class

• __getitem__ Method Enhancement: A new return value pos_neg is added. When using SupCon, this return value contains a list [pos_samples, neg_samples], where pos_samples and neg_samples are lists of samples used for contrastive learning. The code to get pos_neg is executed even when not in warm starting, although its results are not utilized.

• get_pos_samples Method: Identifies the positive samples for a given anchor index. A sample qualifies as positive if it matches at least one category with the anchor. For instance, an anchor [0100 0000] and a positive [1100 0000]. If the selected number of positive samples is less than n_pos, it is randomly multiplied to match n_pos.

• get_neg_samples Method: Finds the negative samples for a specified anchor index. The categories of negative samples must not overlap with the anchor. For example, an anchor [0100 0000] and a negative [0011 0000].

• hard_mining Method: If get_neg_samples opts for hard mining mode, this function is called to select negative sample template. For each category, it selects the hardest negative template based on a distance map file, ensures there's no overlap among hardest negative template with the anchor, and excludes any overlapping items in the negative template. If exclusion results in [0000 0000], it abandons hard mining for a standard negative sample selection.

• prepare_samples Method: Fixed a bug that caused failures when reading datasets containing dual data.

Changes in train.py 240304

• train_loop Function: Added conditions to check for SupCon usage and warm_starting status. If in warm starting and using SupCon, loss_function and loss_function_pro are not computed, and their corresponding networks are not engaged. Only loss_function_supcon is calculated.

Changes in model.py 240304

• Encoder Class: Depending on the use of SupCon and warm_starting status, it chooses between connecting (ParallelLinearDecoders and Linear) or only LayerNormNet. LayerNormNet is from CLEAN. When connecting LayerNormNet, pos_neg is processed and input as [bsz, 2(0:pos, 1:neg), n_pos(or n_neg), 5(variables)] -> [n_pos, 5, bsz] + [n_neg, 5, bsz]. For each positive and negative sample, embeddings are obtained and concatenated into [bcz, (1+npos+nneg), len(embedding)]. The projection head is then fetched, formatting the concatenation as [bcz, (1+npos+nneg), len(projection)].

Changes in loss.py 240304

• SupConHardLoss Function: Originates from CLEAN.