python train.py --data data/proteinnet/casp12.pt --name model01 -lr -0.01 -e 30 -b 12 -m conv-enc -dm 256 -l drmsd 

usage: train.py [-h] [--data DATA] [--name NAME] [-lr LEARNING_RATE]
                [-e EPOCHS] [-b BATCH_SIZE] [-es EARLY_STOPPING]
                [-nws N_WARMUP_STEPS] [-cg CLIP]
                [-l {mse,drmsd,lndrmsd,combined}] [--train_only]
                [--lr_scheduling {noam,plateau}] [--patience PATIENCE]
                [--early_stopping_threshold EARLY_STOPPING_THRESHOLD]
                [-esm {train-mse,test-mse,valid-10-mse,valid-20-mse,valid-30-mse,valid-40-mse,valid-50-mse,valid-70-mse,valid-90-mse,train-drmsd,test-drmsd,valid-10-drmsd,valid-20-drmsd,valid-30-drmsd,valid-40-drmsd,valid-50-drmsd,valid-70-drmsd,valid-90-drmsd,train-lndrmsd,test-lndrmsd,valid-10-lndrmsd,valid-20-lndrmsd,valid-30-lndrmsd,valid-40-lndrmsd,valid-50-lndrmsd,valid-70-lndrmsd,valid-90-lndrmsd,train-combined,test-combined,valid-10-combined,valid-20-combined,valid-30-combined,valid-40-combined,valid-50-combined,valid-70-combined,valid-90-combined}]
                [--without_angle_means] [--eval_train EVAL_TRAIN]
                [-opt {adam,sgd}] [-fctf FRACTION_COMPLETE_TF]
                [-fsstf FRACTION_SUBSEQ_TF]
                [--skip_missing_res_train SKIP_MISSING_RES_TRAIN]
                [--repeat_train REPEAT_TRAIN] [-s SEED]
                [--combined_drmsd_weight COMBINED_DRMSD_WEIGHT]
                [--batching_order {descending,ascending,binned-random}]
                [--backbone_loss] [--sequential_drmsd_loss] [--bins BINS]
                [--train_eval_downsample TRAIN_EVAL_DOWNSAMPLE]
                [--automatically_determine_batch_size AUTOMATICALLY_DETERMINE_BATCH_SIZE]
                [-m MODEL] [-dm D_MODEL] [-dih D_INNER_HID] [-nh N_HEAD]
                [-nl N_LAYERS] [-do DROPOUT] [--postnorm]
                [--weight_decay WEIGHT_DECAY] [--conv1_size CONV1_SIZE]
                [--conv2_size CONV2_SIZE] [--conv3_size CONV3_SIZE]
                [--conv1_reduc CONV1_REDUC] [--conv2_reduc CONV2_REDUC]
                [--conv3_reduc CONV3_REDUC] [--use_embedding USE_EMBEDDING]
                [--conv_out_matches_dm CONV_OUT_MATCHES_DM]
                [--log_structure_step LOG_STRUCTURE_STEP]
                [--log_val_struct_step LOG_VAL_STRUCT_STEP]
                [--log_wandb_step LOG_WANDB_STEP] [--save_pngs SAVE_PNGS]
                [--no_cuda] [-c CLUSTER] [--restart] [--restart_opt]
                [--checkpoint_time_interval CHECKPOINT_TIME_INTERVAL]
                [--load_chkpt LOAD_CHKPT]

optional arguments:
  -h, --help            show this help message and exit

Required Args:
  --data DATA           Path to training data.
  --name NAME           The model name.

Training Args:
  -lr LEARNING_RATE, --learning_rate LEARNING_RATE
  -e EPOCHS, --epochs EPOCHS
  -b BATCH_SIZE, --batch_size BATCH_SIZE
  -es EARLY_STOPPING, --early_stopping EARLY_STOPPING
                        Stops if training hasn't improved in X epochs
  -nws N_WARMUP_STEPS, --n_warmup_steps N_WARMUP_STEPS
                        Number of warmup training steps when using lr-
                        scheduling as proposed in the originalTransformer
                        paper.
  -cg CLIP, --clip CLIP
                        Gradient clipping value.
  -l {mse,drmsd,lndrmsd,combined}, --loss {mse,drmsd,lndrmsd,combined}
                        Loss used to train the model. Can be root mean squared
                        error (RMSE), distance-based root mean squared
                        distance (DRMSD), length-normalized DRMSD (ln-DRMSD)
                        or a combination of RMSE and ln-DRMSD.
  --train_only          Train, validation, and testing sets are the same. Only
                        report train accuracy.
  --lr_scheduling {noam,plateau}
                        noam: Use learning rate scheduling as described in
                        Transformer paper, plateau: Decrease learning rate
                        after Validation loss plateaus.
  --patience PATIENCE   Number of epochs to wait before reducing LR for
                        plateau scheduler.
  --early_stopping_threshold EARLY_STOPPING_THRESHOLD
                        Threshold for considering improvements during
                        training/lr scheduling.
  -esm {train-mse,test-mse,valid-10-mse,valid-20-mse,valid-30-mse,valid-40-mse,valid-50-mse,valid-70-mse,valid-90-mse,train-drmsd,test-drmsd,valid-10-drmsd,valid-20-drmsd,valid-30-drmsd,valid-40-drmsd,valid-50-drmsd,valid-70-drmsd,valid-90-drmsd,train-lndrmsd,test-lndrmsd,valid-10-lndrmsd,valid-20-lndrmsd,valid-30-lndrmsd,valid-40-lndrmsd,valid-50-lndrmsd,valid-70-lndrmsd,valid-90-lndrmsd,train-combined,test-combined,valid-10-combined,valid-20-combined,valid-30-combined,valid-40-combined,valid-50-combined,valid-70-combined,valid-90-combined}, --early_stopping_metric {train-mse,test-mse,valid-10-mse,valid-20-mse,valid-30-mse,valid-40-mse,valid-50-mse,valid-70-mse,valid-90-mse,train-drmsd,test-drmsd,valid-10-drmsd,valid-20-drmsd,valid-30-drmsd,valid-40-drmsd,valid-50-drmsd,valid-70-drmsd,valid-90-drmsd,train-lndrmsd,test-lndrmsd,valid-10-lndrmsd,valid-20-lndrmsd,valid-30-lndrmsd,valid-40-lndrmsd,valid-50-lndrmsd,valid-70-lndrmsd,valid-90-lndrmsd,train-combined,test-combined,valid-10-combined,valid-20-combined,valid-30-combined,valid-40-combined,valid-50-combined,valid-70-combined,valid-90-combined}
                        Metric observed for early stopping and LR scheduling.
  --without_angle_means
                        Do not initialize the model with pre-computed angle
                        means.
  --eval_train EVAL_TRAIN
                        Perform an evaluation of the entire training set after
                        a training epoch.
  -opt {adam,sgd}, --optimizer {adam,sgd}
                        Training optimizer.
  -fctf FRACTION_COMPLETE_TF, --fraction_complete_tf FRACTION_COMPLETE_TF
                        Fraction of the time to use teacher forcing for every
                        timestep of the batch. Model trains fastest when this
                        is 1.
  -fsstf FRACTION_SUBSEQ_TF, --fraction_subseq_tf FRACTION_SUBSEQ_TF
                        Fraction of the time to use teacher forcing on a per-
                        timestep basis.
  --skip_missing_res_train SKIP_MISSING_RES_TRAIN
                        When training, skip over batches that have missing
                        residues. This can make trainingfaster if using
                        teacher forcing.
  --repeat_train REPEAT_TRAIN
                        Duplicate the training set X times. Useful for
                        training on small datasets.
  -s SEED, --seed SEED  The random number generator seed for numpy and torch.
  --combined_drmsd_weight COMBINED_DRMSD_WEIGHT
                        When combining losses, use weight w for loss = w *
                        drmsd + (1-w) * mse.
  --batching_order {descending,ascending,binned-random}
                        Method for constructuing minibatches of proteins
                        w.r.t. sequence length. Batches can be provided in
                        descending/ascending order, or 'binned-random' which
                        keeps the sequencesin a batch similar, while
                        randomizing the bins/batches.
  --backbone_loss       While training, only evaluate loss on the backbone.
  --sequential_drmsd_loss
                        Compute DRMSD loss without batch-level
                        parallelization.
  --bins BINS           Number of bins for protein dataset batching.
  --train_eval_downsample TRAIN_EVAL_DOWNSAMPLE
                        Fraction of training set to evaluate on each epoch.
  --automatically_determine_batch_size AUTOMATICALLY_DETERMINE_BATCH_SIZE, -adbs AUTOMATICALLY_DETERMINE_BATCH_SIZE
                        Experimentally determinethe maximum allowable
                        batchsize for training.

Model Args:
  -m MODEL, --model MODEL
                        Model architecture type. Encoder only or
                        encoder/decoder model.
  -dm D_MODEL, --d_model D_MODEL
                        Dimension of each sequence item in the model. Each
                        layer uses the same dimension for simplicity.
  -dih D_INNER_HID, --d_inner_hid D_INNER_HID
                        Dimmension of the inner layer of the feed-forward
                        layer at the end of every Transformer block.
  -nh N_HEAD, --n_head N_HEAD
                        Number of attention heads.
  -nl N_LAYERS, --n_layers N_LAYERS
                        Number of layers in the model. If using
                        encoder/decoder model, the encoder and decoder both
                        have this number of layers.
  -do DROPOUT, --dropout DROPOUT
                        Dropout applied between layers.
  --postnorm            Use post-layer normalization, as depicted in the
                        original figure for the Transformer model. May not
                        train as well as pre-layer normalization.
  --weight_decay WEIGHT_DECAY
                        Applies weight decay to model weights.
  --conv1_size CONV1_SIZE
                        Size of conv1 layer kernel for 'conv-enc' model.
  --conv2_size CONV2_SIZE
                        Size of conv2 layer kernel for 'conv-enc' model.
  --conv3_size CONV3_SIZE
                        Size of conv2 layer kernel for 'conv-enc' model.
  --conv1_reduc CONV1_REDUC
                        Factor by which conv1 layer reduces the number of
                        channels for 'conv-enc' model.
  --conv2_reduc CONV2_REDUC
                        Factor by which conv2 layer reduces the number of
                        channels for 'conv-enc' model.
  --conv3_reduc CONV3_REDUC
                        Factor by which conv2 layer reduces the number of
                        channels for 'conv-enc' model.
  --use_embedding USE_EMBEDDING
                        Whether or not to use embedding layer in the
                        transformer model.
  --conv_out_matches_dm CONV_OUT_MATCHES_DM
                        If True, the final convolution layer at the start of
                        the model will match the dimensionality of the
                        requested d_model. Used for ConvEnc models.

Saving Args:
  --log_structure_step LOG_STRUCTURE_STEP
                        Frequency of logging structure data during training.
  --log_val_struct_step LOG_VAL_STRUCT_STEP, -lvs LOG_VAL_STRUCT_STEP
                        During training, make predictions on 1 structure from
                        every validation set.
  --log_wandb_step LOG_WANDB_STEP
                        Frequency of logging to wandb during training.
  --save_pngs SAVE_PNGS, -png SAVE_PNGS
                        Save images when making structures.
  --no_cuda
  -c CLUSTER, --cluster CLUSTER
                        Set of parameters to facilitate training on a remote
                        cluster. Limited I/O, etc.
  --restart             Does not resume training.
  --restart_opt         Resumes training but does not load the optimizer
                        state.
  --checkpoint_time_interval CHECKPOINT_TIME_INTERVAL
                        The amount of time (in hours) after which a model
                        checkpoint is made, regardless of its performance.
  --load_chkpt LOAD_CHKPT
                        Path from which to load a model checkpoint.