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c50dde6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | import json
import logging
import math
import time
import torch
from training.misc import is_main_process
from open_clip import get_cast_dtype
from .distributed import is_master
from .zero_shot import multi_gpu_sync, 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()
def format_time(seconds):
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
seconds = int(seconds % 60)
return f"{hours}h {minutes}m {seconds}s"
@torch.no_grad()
def student_teacher_ensemble(student, teacher, alpha=0.5):
target_state_dict = {}
for k, v in student.items():
if k in teacher:
target_state_dict[k] = v * alpha + teacher[k] * (1.0 - alpha)
else:
continue
return target_state_dict
def train_one_epoch(model, teacher_model, vfm_model, method, data, epoch, optimizer, scaler, scheduler, writer, args):
autocast = get_autocast(args.precision)
model.train()
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()
epoch_start_time = 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 = method(batch, model, teacher_model, vfm_model, args)
total_loss = sum(losses.values())
losses["loss"] = total_loss
backward(total_loss, scaler)
if scaler is not None:
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()
batch_time_m.update(time.time() - end)
end = time.time()
batch_count = i_accum + 1
# compute the epoch remaining time
elapsed_time = time.time() - epoch_start_time
avg_iteration_time = elapsed_time / batch_count
remaining_iterations = num_batches_per_epoch - batch_count
estimated_remaining_time = avg_iteration_time * remaining_iterations
formatted_eta = format_time(estimated_remaining_time)
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)
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"ETA: {formatted_eta} "
f"Data (t): {data_time_m.avg:.3f} "
f"Batch (t): {batch_time_m.avg:.3f} "
f"LR: {optimizer.param_groups[0]['lr']:6f} "+ 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,
"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-1}. "
+ 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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