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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)
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)
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:
if use_metric is None:
return np.mean(losses), {}, False
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}
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))
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()
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())
# 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)
# 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())
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.')
extra_loss = 0
loss += extra_loss
if scaler is None:
if lr_scheduler is not None:
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
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:
best_val_score = val_scores[config.use_val_metric]
elif not maximize and val_scores[config.use_val_metric] < best_val_score:
best_val_score = val_scores[config.use_val_metric]
score_str = ''
# if no score is used, fall back to loss
if val_loss < best_val_loss:
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)
print('epoch complete')
if __name__ == '__main__':