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"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import logging
import os
import wandb
import torch
import torch.distributed as dist
from common.dist_utils import get_rank, get_world_size, is_main_process, is_dist_avail_and_initialized
from common.logger import MetricLogger, SmoothedValue
from common.registry import registry
from datasets.data_utils import prepare_sample
from typing import Optional, Dict, List, Union, Tuple
import torch.nn.functional as F
import ipdb
from sklearn.metrics import cohen_kappa_score, accuracy_score, confusion_matrix
import numpy as np
class BaseTask:
def __init__(self, **kwargs):
super().__init__()
self.inst_id_key = "instance_id"
self.wandb_initialized = False
def init_wandb(self, cfg):
if is_main_process():
self.wandb_initialized = True
@classmethod
def setup_task(cls, **kwargs):
return cls()
def build_model(self, cfg):
model_config = cfg.model_cfg
model_cls = registry.get_model_class(model_config.arch)
return model_cls.from_config(model_config)
def build_datasets(self, cfg):
"""
Build a dictionary of datasets, keyed by split 'train', 'valid', 'test'.
Download dataset and annotations automatically if not exist.
Args:
cfg (common.config.Config): _description_
Returns:
dict: Dictionary of torch.utils.data.Dataset objects by split.
"""
datasets = dict()
datasets_config = cfg.datasets_cfg
# print('datasets_config',datasets_config)
assert len(datasets_config) > 0, "At least one dataset has to be specified."
for name in datasets_config:
dataset_config = datasets_config[name]
builder = registry.get_builder_class(name)(dataset_config)
dataset = builder.build_datasets()
dataset['train'].name = name #?
if 'sample_ratio' in dataset_config:
dataset['train'].sample_ratio = dataset_config.sample_ratio
print(f"Loaded dataset: {name} with {len(dataset['train'])} samples.")
datasets[name] = dataset
return datasets
def train_step(self, model, samples):
outputs = model(samples)
loss, modal, task = outputs['loss'], outputs['modal'], outputs['task_type']
return loss, modal, task
def valid_step(self, model, samples):
"""
Validation step function to compute predictions and prepare for QWK calculation.
"""
model.eval()
with torch.no_grad():
outputs = model(samples)
loss = outputs['loss']
logits = outputs.get('logits', None)
if logits is None:
# Handle case where model outputs probabilities directly
probs = outputs.get('probs', None)
if probs is not None:
preds = torch.argmax(probs, dim=1)
else:
preds = torch.argmax(logits, dim=1)
labels = samples['labels']
score_labels = samples.get('score_labels', None)
# print("logits", logits.shape)
return [{
'loss': loss.item(),
'pred': pred.item(),
'logits': logits,
'label': label.item(),
'score_label': score_label.item() if score_label is not None else None,
'modal': outputs['modal'],
'task_type': outputs['task_type']
} for pred, label, score_label in zip(preds, labels, score_labels)]
# print('NOT YET')
# raise NotImplementedError
def before_evaluation(self, model, dataset, **kwargs):
model.before_evaluation(dataset=dataset, task_type=type(self))
def after_evaluation(self, val_result, **kwargs):
loss = val_result['loss']
qwk = val_result['qwk']
val_log = {
'agg_metrics': qwk
}
return val_log
def inference_step(self):
raise NotImplementedError
def evaluation(self, model, data_loader, cuda_enabled=True):
metric_logger = MetricLogger(delimiter=" ")
header = "Evaluation"
# TODO make it configurable
print_freq = 10
results = []
for samples in metric_logger.log_every(data_loader, print_freq, header):
samples = prepare_sample(samples, cuda_enabled=cuda_enabled)
eval_output = self.valid_step(model=model, samples=samples)
results.extend(eval_output)
if is_dist_avail_and_initialized():
dist.barrier()
return results
def train_epoch(
self,
epoch,
model,
data_loader,
optimizer,
lr_scheduler,
scaler=None,
cuda_enabled=False,
log_freq=50,
accum_grad_iters=1,
):
return_dict, metric_logger = self._train_inner_loop(
epoch=epoch,
iters_per_epoch=lr_scheduler.iters_per_epoch,
model=model,
data_loader=data_loader,
optimizer=optimizer,
scaler=scaler,
lr_scheduler=lr_scheduler,
log_freq=log_freq,
cuda_enabled=cuda_enabled,
accum_grad_iters=accum_grad_iters,
)
# Log metrics to wandb
if is_main_process() and self.wandb_initialized:
wandb.log({
"epoch": epoch,
"train/loss": float(return_dict["loss"]),
"train/lr": float(return_dict["lr"]),
"train/modal": return_dict["modal"],
"train/task": return_dict["task"],
})
return return_dict, metric_logger
def train_iters(
self,
epoch,
start_iters,
iters_per_inner_epoch,
model,
data_loader,
optimizer,
lr_scheduler,
scaler=None,
cuda_enabled=False,
log_freq=50,
accum_grad_iters=1,
):
return self._train_inner_loop(
epoch=epoch,
start_iters=start_iters,
iters_per_epoch=iters_per_inner_epoch,
model=model,
data_loader=data_loader,
optimizer=optimizer,
scaler=scaler,
lr_scheduler=lr_scheduler,
log_freq=log_freq,
cuda_enabled=cuda_enabled,
accum_grad_iters=accum_grad_iters,
)
def _train_inner_loop(
self,
epoch,
iters_per_epoch,
model,
data_loader,
optimizer,
lr_scheduler,
scaler=None,
start_iters=None,
log_freq=50,
cuda_enabled=False,
accum_grad_iters=1,
):
"""
An inner training loop compatible with both epoch-based and iter-based training.
When using epoch-based, training stops after one epoch; when using iter-based,
training stops after #iters_per_epoch iterations.
"""
use_amp = scaler is not None
if not hasattr(data_loader, "__next__"):
# convert to iterator if not already
data_loader = iter(data_loader)
metric_logger = MetricLogger(delimiter=" ")
metric_logger.add_meter("lr", SmoothedValue(window_size=1, fmt="{global_avg:.8e}"))
metric_logger.add_meter("loss", SmoothedValue(window_size=1, fmt="{global_avg:.3f}"))
# if iter-based runner, schedule lr based on inner epoch.
logging.info(
"Start training epoch {}, {} iters per inner epoch.".format(
epoch, iters_per_epoch
)
)
header = "Epoch: [{}]".format(epoch)
if start_iters is None:
# epoch-based runner
inner_epoch = epoch
else:
# In iter-based runner, we schedule the learning rate based on iterations.
inner_epoch = start_iters // iters_per_epoch
header = header + "; inner epoch [{}]".format(inner_epoch)
for i in metric_logger.log_every(range(iters_per_epoch), log_freq, header):
# if using iter-based runner, we stop after iters_per_epoch iterations.
if i >= iters_per_epoch:
break
samples = next(data_loader)
samples = prepare_sample(samples, cuda_enabled=cuda_enabled)
samples.update(
{
"epoch": inner_epoch,
"num_iters_per_epoch": iters_per_epoch,
"iters": i,
}
)
lr_scheduler.step(cur_epoch=inner_epoch, cur_step=i)
torch.autograd.set_detect_anomaly(True)
with torch.cuda.amp.autocast(enabled=use_amp):
loss, modal, task = self.train_step(model=model, samples=samples)
with torch.autograd.detect_anomaly():
if use_amp:
scaler.scale(loss).backward()
else:
loss.backward()
# scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
# update gradients every accum_grad_iters iterations
if (i + 1) % accum_grad_iters == 0:
if use_amp:
scaler.unscale_(optimizer)
scaler.step(optimizer)
scaler.update()
else:
optimizer.step()
optimizer.zero_grad()
metric_logger.update(loss=loss.item())
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
metric_logger.update(modal=modal)
metric_logger.update(task=task)
if is_main_process() and self.wandb_initialized:
wandb.log({
"train/step": i + epoch * iters_per_epoch,
"train/loss_step": loss.item(),
"train/lr_step": optimizer.param_groups[0]["lr"],
"train/modal_step": modal,
"train/task_step": task,
})
# after train_epoch()
# gather the stats from all processes
metric_logger.synchronize_between_processes()
logging.info("Averaged stats: " + str(metric_logger.global_avg()))
return_dict = {}
for k, meter in metric_logger.meters.items():
if not isinstance(meter, str):
return_dict.update({k: "{:.3f}".format(meter.global_avg)})
# return_dict = {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
return_dict.update({'modal': modal, 'task': task})
return return_dict, metric_logger
@staticmethod
def save_result(result, result_dir, filename, remove_duplicate=""):
import json
result_file = os.path.join(
result_dir, "%s_rank%d.json" % (filename, get_rank())
)
final_result_file = os.path.join(result_dir, "%s.json" % filename)
json.dump(result, open(result_file, "w"))
if is_dist_avail_and_initialized():
dist.barrier()
if is_main_process():
logging.warning("rank %d starts merging results." % get_rank())
# combine results from all processes
result = []
for rank in range(get_world_size()):
result_file = os.path.join(
result_dir, "%s_rank%d.json" % (filename, rank)
)
res = json.load(open(result_file, "r"))
result += res
if remove_duplicate:
result_new = []
id_list = []
for res in result:
if res[remove_duplicate] not in id_list:
id_list.append(res[remove_duplicate])
result_new.append(res)
result = result_new
json.dump(result, open(final_result_file, "w"))
print("result file saved to %s" % final_result_file)
if wandb.run is not None:
wandb.log({"final_results": result})
return final_result_file
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