Delete clipseg/training.py

clipseg/training.py

@@ -1,266 +0,0 @@
-import torch
-import inspect
-import json
-import yaml
-import math
-import os
-import sys
-
-from general_utils import log
-
-import numpy as np
-from functools import partial
-from os.path import expanduser, join, isfile, basename
-
-from torch.cuda.amp import autocast, GradScaler
-from torch.optim.lr_scheduler import LambdaLR
-from contextlib import nullcontext
-from torch.utils.data import DataLoader
-
-from general_utils import TrainingLogger, get_attribute, filter_args, log, training_config_from_cli_args
-
-
-def cosine_warmup_lr(i, warmup=10, max_iter=90):
-    """ Cosine LR with Warmup """
-    if i < warmup:
-        return (i+1)/(warmup+1)
-    else:
-        return 0.5 + 0.5*math.cos(math.pi*(((i-warmup)/(max_iter- warmup))))
-
-
-def validate(model, dataset, config):
-    data_loader = torch.utils.data.DataLoader(dataset, batch_size=4, shuffle=False)
-
-    metric_class, use_metric = config.val_metric_class, config.use_val_metric
-    loss_fn = get_attribute(config.loss)
-
-    model.eval()
-    model.cuda()
-
-    if metric_class is not None:
-        metric = get_attribute(metric_class)()
-
-    with torch.no_grad():
-
-        i, losses = 0, []
-        for data_x, data_y in data_loader:
-
-            data_x = [x.cuda() if isinstance(x, torch.Tensor) else x for x in data_x]
-            data_y = [x.cuda() if isinstance(x, torch.Tensor) else x for x in data_y]
-
-            prompts = model.sample_prompts(data_x[1], prompt_list=('a photo of a {}',))
-            pred, visual_q, _, _  = model(data_x[0], prompts, return_features=True)
-
-            if metric_class is not None:
-                metric.add([pred], data_y)
-
-            # pred = model(data_x[0], prompts)
-            # loss = loss_fn(pred[0], data_y[0])
-            loss = loss_fn(pred, data_y[0])
-            losses += [float(loss)]
-
-            i += 1
-
-            if config.val_max_iterations is not None and i > config.val_max_iterations:
-                break
-
-    if use_metric is None:
-        return np.mean(losses), {}, False
-    else:
-        metric_scores = {m: s for m, s in zip(metric.names(), metric.value())} if metric is not None else {}
-        return np.mean(losses), metric_scores, True
-
-
-def main():
-
-    config = training_config_from_cli_args()
-
-    val_interval, best_val_loss, best_val_score = config.val_interval, float('inf'), float('-inf')
-
-    model_cls = get_attribute(config.model)
-    _, model_args, _ = filter_args(config, inspect.signature(model_cls).parameters)
-    model = model_cls(**model_args).cuda()
-
-    dataset_cls = get_attribute(config.dataset)
-    _, dataset_args, _ = filter_args(config, inspect.signature(dataset_cls).parameters)
-
-    dataset = dataset_cls(**dataset_args)
-
-    log.info(f'Train dataset {dataset.__class__.__name__} (length: {len(dataset)})')
-
-    if val_interval is not None:
-        dataset_val_args = {k[4:]: v for k,v in config.items() if k.startswith('val_') and k != 'val_interval'}
-        _, dataset_val_args, _ = filter_args(dataset_val_args, inspect.signature(dataset_cls).parameters)
-        print('val args', {**dataset_args, **{'split': 'val', 'aug': 0}, **dataset_val_args})
-
-        dataset_val = dataset_cls(**{**dataset_args, **{'split': 'val', 'aug': 0}, **dataset_val_args})
-
-    # optimizer
-    opt_cls = get_attribute(config.optimizer)
-    if config.optimize == 'torch.optim.SGD':
-        opt_args = {'momentum': config.momentum if 'momentum' in config else 0}
-    else:
-        opt_args = {}
-    opt = opt_cls(model.parameters(), lr=config.lr, **opt_args)
-
-    if config.lr_scheduler == 'cosine':
-        assert config.T_max is not None and config.eta_min is not None
-        lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt, config.T_max, config.eta_min)
-    elif config.lr_scheduler == 'warmup_cosine':        
-        lr_scheduler = LambdaLR(opt, partial(cosine_warmup_lr, max_iter=(config.max_iterations), warmup=config.warmup))
-    else:
-        lr_scheduler = None
-
-    batch_size, max_iterations = config.batch_size, config.max_iterations
-
-    loss_fn = get_attribute(config.loss)
-
-    if config.amp:
-        log.info('Using AMP')
-        autocast_fn = autocast
-        scaler = GradScaler()
-    else:
-        autocast_fn, scaler = nullcontext, None
-
-
-    save_only_trainable = True
-    data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=4)
-
-    # disable config when hyperparam. opt. to avoid writing logs.
-    tracker_config = config if not config.hyperparameter_optimization else None
-
-    with TrainingLogger(log_dir=config.name, model=model, config=tracker_config) as logger:
-
-        i = 0
-        while True:
-            for data_x, data_y in data_loader:
-
-                # between caption and output feature.
-                # 1. Sample random captions
-                # 2. Check alignment with CLIP
-
-                # randomly mix text and visual support conditionals
-                if config.mix:
-
-                    assert config.mask.startswith('text_and')
-
-                    with autocast_fn():
-                        # data_x[1] = text label
-                        prompts = model.sample_prompts(data_x[1])
-
-                        # model.clip_model()
-
-                        text_cond = model.compute_conditional(prompts)
-                        if model.__class__.__name__ == 'CLIPDensePredTMasked':
-                            # when mask=='separate'
-                            visual_s_cond, _, _ = model.visual_forward_masked(data_x[2].cuda(), data_x[3].cuda())
-                        else:
-                            # data_x[2] = visual prompt
-                            visual_s_cond, _, _ = model.visual_forward(data_x[2].cuda())
-
-                    max_txt = config.mix_text_max if config.mix_text_max is not None else 1
-                    batch_size = text_cond.shape[0]
-
-                    # sample weights for each element in batch
-                    text_weights = torch.distributions.Uniform(config.mix_text_min, max_txt).sample((batch_size,))[:, None]
-                    text_weights = text_weights.cuda()
-
-                    if dataset.__class__.__name__ == 'PhraseCut':
-                        # give full weight to text where support_image is invalid
-                        visual_is_valid = data_x[4] if model.__class__.__name__ == 'CLIPDensePredTMasked' else data_x[3]
-                        text_weights = torch.max(text_weights[:,0], 1 - visual_is_valid.float().cuda()).unsqueeze(1)
-
-                    cond = text_cond * text_weights + visual_s_cond * (1 - text_weights)
-
-                else:
-                    # no mix
-                    
-                    if model.__class__.__name__ == 'CLIPDensePredTMasked':
-                        # compute conditional vector using CLIP masking
-                        with autocast_fn():
-                            assert config.mask == 'separate'
-                            cond, _, _ = model.visual_forward_masked(data_x[1].cuda(), data_x[2].cuda())
-                    else:
-                        cond = data_x[1]
-                        if isinstance(cond, torch.Tensor):
-                            cond = cond.cuda()
-
-                with autocast_fn():
-                    visual_q = None
-
-                    pred, visual_q, _, _  = model(data_x[0].cuda(), cond, return_features=True)
-
-                    loss = loss_fn(pred, data_y[0].cuda())
-
-                    if torch.isnan(loss) or torch.isinf(loss):
-                        # skip if loss is nan
-                        log.warning('Training stopped due to inf/nan loss.')
-                        sys.exit(-1)
-
-                    extra_loss = 0
-                    loss += extra_loss
-
-                opt.zero_grad()
-
-                if scaler is None:
-                    loss.backward()
-                    opt.step()
-                else:
-                    scaler.scale(loss).backward()
-                    scaler.step(opt)
-                    scaler.update()
-
-                if lr_scheduler is not None:
-                    lr_scheduler.step()
-                    if i % 2000 == 0:
-                        current_lr = [g['lr'] for g in opt.param_groups][0]
-                        log.info(f'current lr: {current_lr:.5f} ({len(opt.param_groups)} parameter groups)')
-
-                logger.iter(i=i, loss=loss)                    
-                i += 1
-
-                if i >= max_iterations:
-
-                    if not isfile(join(logger.base_path, 'weights.pth')):
-                        # only write if no weights were already written
-                        logger.save_weights(only_trainable=save_only_trainable)
-                    
-                    sys.exit(0)
-
-                    
-                if config.checkpoint_iterations is not None and i in config.checkpoint_iterations:
-                    logger.save_weights(only_trainable=save_only_trainable, weight_file=f'weights_{i}.pth')
-
-                
-                if val_interval is not None and i % val_interval == val_interval - 1:
-
-                    val_loss, val_scores, maximize = validate(model, dataset_val, config)
-                    
-                    if len(val_scores) > 0:
-
-                        score_str = f', scores: ' + ', '.join(f'{k}: {v}' for k, v in val_scores.items())
-                        
-                        if maximize and val_scores[config.use_val_metric] > best_val_score:
-                            logger.save_weights(only_trainable=save_only_trainable)
-                            best_val_score = val_scores[config.use_val_metric]
-
-                        elif not maximize and val_scores[config.use_val_metric] < best_val_score:
-                            logger.save_weights(only_trainable=save_only_trainable)
-                            best_val_score = val_scores[config.use_val_metric]
-
-                    else:
-                        score_str = ''
-                        # if no score is used, fall back to loss
-                        if val_loss < best_val_loss:
-                            logger.save_weights(only_trainable=save_only_trainable)
-                            best_val_loss = val_loss
-                    
-                    log.info(f'Validation loss: {val_loss}' + score_str)
-                    logger.iter(i=i, val_loss=val_loss, extra_loss=float(extra_loss), **val_scores)
-                    model.train()
-
-            print('epoch complete')
-
-
-if __name__ == '__main__':
-    main()