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import json
import logging
import math
import time
import torch
from open_clip import get_cast_dtype
from .distributed import is_master
from .zero_shot import zero_shot_eval
from .precision import get_autocast
import os
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def postprocess_clip_output(model_out):
return {
"image_features": model_out[0],
"text_features": model_out[1],
"logit_scale": model_out[2]
}
def unwrap_model(model):
if hasattr(model, 'module'):
return model.module
else:
return model
def backward(total_loss, scaler):
if scaler is not None:
scaler.scale(total_loss).backward()
else:
total_loss.backward()
@torch.no_grad()
def student_teacher_ensemble(student, teacher, alpha=0.5):
target_state_dict = {}
for k, v in student.items():
target_state_dict[k] = v * alpha + teacher[k] * (1.0 - alpha)
return target_state_dict
def train_one_epoch(model, method, data, loss, epoch, optimizer, scaler, scheduler, dist_P_VLM, dist_model, args):
device = torch.device(args.device)
autocast = get_autocast(args.precision)
cast_dtype = get_cast_dtype(args.precision)
model.train()
if dist_model is not None:
dist_model.eval()
if dist_P_VLM is not None:
dist_P_VLM.eval()
data['train'].set_epoch(epoch) # set epoch in process safe manner via sampler or shared_epoch
dataloader = data['train'].dataloader
num_batches_per_epoch = dataloader.num_batches // args.accum_freq
sample_digits = math.ceil(math.log(dataloader.num_samples + 1, 10))
losses_m = {}
batch_time_m = AverageMeter()
data_time_m = AverageMeter()
end = time.time()
for i, batch in enumerate(dataloader):
i_accum = i // args.accum_freq
step = num_batches_per_epoch * epoch + i_accum
if not args.skip_scheduler:
scheduler(step)
data_time_m.update(time.time() - end)
optimizer.zero_grad()
assert args.accum_freq == 1, "accum freq disabled"
with autocast():
losses, batch_size, logit_scale = method(batch, model, dist_P_VLM, dist_model, loss, device, cast_dtype,
args.distributed, args)
total_loss = sum(losses.values())
losses["loss"] = total_loss
backward(total_loss, scaler)
if scaler is not None:
if args.horovod:
optimizer.synchronize()
scaler.unscale_(optimizer)
if args.grad_clip_norm is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)
with optimizer.skip_synchronize():
scaler.step(optimizer)
else:
if args.grad_clip_norm is not None:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)
scaler.step(optimizer)
scaler.update()
else:
if args.grad_clip_norm is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)
optimizer.step()
# Note: we clamp to 4.6052 = ln(100), as in the original paper.
with torch.no_grad():
unwrap_model(model).logit_scale.clamp_(0, math.log(100))
batch_time_m.update(time.time() - end)
end = time.time()
batch_count = i_accum + 1
if is_master(args) and (i_accum % args.log_every_n_steps == 0 or batch_count == num_batches_per_epoch):
# batch_size = len(images)
num_samples = batch_count * batch_size * args.accum_freq * args.world_size
samples_per_epoch = dataloader.num_samples
percent_complete = 100.0 * batch_count / num_batches_per_epoch
# NOTE loss is coarsely sampled, just master node and per log update
for key, val in losses.items():
if key not in losses_m:
losses_m[key] = AverageMeter()
losses_m[key].update(val.item(), batch_size)
logit_scale_scalar = logit_scale.item()
loss_log = " ".join(
[
f"{loss_name.capitalize()}: {loss_m.val:#.5g} ({loss_m.avg:#.5g})"
for loss_name, loss_m in losses_m.items()
]
)
samples_per_second = args.accum_freq * args.batch_size * args.world_size / batch_time_m.val
samples_per_second_per_gpu = args.accum_freq * args.batch_size / batch_time_m.val
logging.info(
f"Train Epoch: {epoch} [{num_samples:>{sample_digits}}/{samples_per_epoch} ({percent_complete:.0f}%)] "
f"Data (t): {data_time_m.avg:.3f} "
f"Batch (t): {batch_time_m.avg:.3f}, {samples_per_second:#g}/s, {samples_per_second_per_gpu:#g}/s/gpu "
f"LR: {optimizer.param_groups[0]['lr']:5f} "
f"Logit Scale: {logit_scale_scalar:.3f} " + loss_log
)
# Save train loss / etc. Using non avg meter values as loggers have their own smoothing
log_data = {
"data_time": data_time_m.val,
"batch_time": batch_time_m.val,
"samples_per_second": samples_per_second,
"samples_per_second_per_gpu": samples_per_second_per_gpu,
"scale": logit_scale_scalar,
"lr": optimizer.param_groups[0]["lr"]
}
log_data.update({name:val.val for name,val in losses_m.items()})
# resetting batch / data time meters per log window
batch_time_m.reset()
data_time_m.reset()
def evaluate(model, data, epoch, args):
metrics = {}
model.eval()
zero_shot_metrics = zero_shot_eval(model, data, epoch, args)
if not is_master(args):
return {}
metrics.update(zero_shot_metrics)
if not metrics:
return metrics
keys = ''.join([f"{k}, " for k in metrics.keys() if 'all' in k])[:-2]
values = ''.join([f'{round(v, 4):.4f}, ' for k, v in metrics.items() if 'all' in k])[:-2]
logging.info(
f"Eval Epoch: {epoch}. "
+ f"{keys}: {values}."
)
# TODO save the results as plots
logging.info(metrics)
if args.save_logs:
with open(os.path.join(args.checkpoint_path, "results.json"), "a+") as f:
f.write(json.dumps(metrics))
f.write("\n")
return metrics
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