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supernet_engine_prompt.py
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supernet_engine_prompt.py
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import math
import sys
from typing import Iterable, Optional
from timm.utils.model import unwrap_model
import torch
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
from lib import utils
import random
import time
def sample_configs(choices, is_visual_prompt_tuning=False,is_adapter=False,is_LoRA=False,is_prefix=False):
config = {}
depth = choices['depth']
if is_visual_prompt_tuning == False and is_adapter == False and is_LoRA == False and is_prefix==False:
visual_prompt_depth = random.choice(choices['visual_prompt_depth'])
lora_depth = random.choice(choices['lora_depth'])
adapter_depth = random.choice(choices['adapter_depth'])
prefix_depth = random.choice(choices['prefix_depth'])
config['visual_prompt_dim'] = [random.choice(choices['visual_prompt_dim']) for _ in range(visual_prompt_depth)] + [0] * (depth - visual_prompt_depth)
config['lora_dim'] = [random.choice(choices['lora_dim']) for _ in range(lora_depth)] + [0] * (depth - lora_depth)
config['adapter_dim'] = [random.choice(choices['adapter_dim']) for _ in range(adapter_depth)] + [0] * (depth - adapter_depth)
config['prefix_dim'] = [random.choice(choices['prefix_dim']) for _ in range(prefix_depth)] + [0] * (depth - prefix_depth)
else:
if is_visual_prompt_tuning:
config['visual_prompt_dim'] = [choices['super_prompt_tuning_dim']] * (depth)
else:
config['visual_prompt_dim'] = [0] * (depth)
if is_adapter:
config['adapter_dim'] = [choices['super_adapter_dim']] * (depth)
else:
config['adapter_dim'] = [0] * (depth)
if is_LoRA:
config['lora_dim'] = [choices['super_LoRA_dim']] * (depth)
else:
config['lora_dim'] = [0] * (depth)
if is_prefix:
config['prefix_dim'] = [choices['super_prefix_dim']] * (depth)
else:
config['prefix_dim'] = [0] * (depth)
return config
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None,
amp: bool = True, teacher_model: torch.nn.Module = None,
teach_loss: torch.nn.Module = None, choices=None, mode='super', retrain_config=None,is_visual_prompt_tuning=False,is_adapter=False,is_LoRA=False,is_prefix=False):
model.train()
criterion.train()
# set random seed
random.seed(epoch)
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
if mode == 'retrain':
config = retrain_config
model_module = unwrap_model(model)
print(config)
model_module.set_sample_config(config=config)
print(model_module.get_sampled_params_numel(config))
for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
samples = samples.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
# sample random config
if mode == 'super':
# sample
config = sample_configs(choices=choices,is_visual_prompt_tuning=is_visual_prompt_tuning,is_adapter=is_adapter,is_LoRA=is_LoRA,is_prefix=is_prefix)
# print("current iter config: {}".format(config))
model_module = unwrap_model(model)
model_module.set_sample_config(config=config)
elif mode == 'retrain':
config = retrain_config
model_module = unwrap_model(model)
model_module.set_sample_config(config=config)
if mixup_fn is not None:
samples, targets = mixup_fn(samples, targets)
if amp:
with torch.cuda.amp.autocast():
if teacher_model:
with torch.no_grad():
teach_output = teacher_model(samples)
_, teacher_label = teach_output.topk(1, 1, True, True)
outputs = model(samples)
loss = 1/2 * criterion(outputs, targets) + 1/2 * teach_loss(outputs, teacher_label.squeeze())
else:
outputs = model(samples)
loss = criterion(outputs, targets)
else:
outputs = model(samples)
if teacher_model:
with torch.no_grad():
teach_output = teacher_model(samples)
_, teacher_label = teach_output.topk(1, 1, True, True)
loss = 1 / 2 * criterion(outputs, targets) + 1 / 2 * teach_loss(outputs, teacher_label.squeeze())
else:
loss = criterion(outputs, targets)
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
optimizer.zero_grad()
# this attribute is added by timm on one optimizer (adahessian)
if amp:
is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
loss_scaler(loss, optimizer, clip_grad=max_norm,
parameters=model.parameters(), create_graph=is_second_order)
else:
loss.backward()
optimizer.step()
torch.cuda.synchronize()
if model_ema is not None:
model_ema.update(model)
metric_logger.update(loss=loss_value)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(data_loader, model, device, amp=True, choices=None, mode='super', retrain_config=None,is_visual_prompt_tuning=False,is_adapter=False,is_LoRA=False,is_prefix=False):
criterion = torch.nn.CrossEntropyLoss()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
# switch to evaluation mode
model.eval()
if mode == 'super':
config = sample_configs(choices=choices,is_visual_prompt_tuning=is_visual_prompt_tuning,is_adapter=is_adapter,is_LoRA=is_LoRA,is_prefix=False)
model_module = unwrap_model(model)
model_module.set_sample_config(config=config)
else:
config = retrain_config
model_module = unwrap_model(model)
model_module.set_sample_config(config=config)
print("sampled model config: {}".format(config))
parameters = model_module.get_sampled_params_numel(config)
print("sampled model parameters: {}".format(parameters))
for images, target in metric_logger.log_every(data_loader, 10, header):
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
# compute output
if amp:
with torch.cuda.amp.autocast():
output = model(images)
loss = criterion(output, target)
else:
output = model(images)
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
batch_size = images.shape[0]
metric_logger.update(loss=loss.item())
metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
.format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}