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train.py
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from abc import abstractmethod
import importlib
import os
import time
import random
import math
from functools import wraps
import sys
import copy
import numpy as np
np.set_printoptions(threshold=sys.maxsize)
import torch
import torch.nn as nn
from torch import multiprocessing
from torchvision import datasets, transforms
from torch.nn.modules.utils import _pair
from utils.model_profiling import model_profiling
from utils.transforms import Lighting
from utils.transforms import ImageFolderLMDB
from ultron_io import UltronIO
from utils.config import FLAGS
from utils.meters import *
from utils.model_profiling import compare_models
from models.quantizable_ops import EMA
from models.quantizable_ops import QuantizableConv2d, QuantizableLinear
import wandb
import datetime
import torch.cuda.amp as amp
#torch.autograd.set_detect_anomaly(True)
def get_exp_cycle_annealing(cycle_size_iter: int, temp_step: float, n: float):
"""
This function return the exp annealing function for the gumbel softmax.
:param cycle_size_iter: integer that defies the cycle size
:param temp_step: the step size coefficient
:param n: a float scaling of the iteration index
:return: a function which get an index and return a floating temperature value
"""
def temp_func(i):
if i < 0:
return 1.0
i = i % cycle_size_iter
return np.maximum(0.5, 1 * np.exp(-temp_step * np.round(i / n)))
return temp_func
def timing(f):
@wraps(f)
def wrap(*args, **kw):
if True: # is_master():
ts = time.time()
result = f(*args, **kw)
te = time.time()
print('func:{!r} took: {:2.4f} sec'.format(f.__name__, te-ts))
else:
result = f(*args, **kw)
return result
return wrap
def get_model():
"""get model"""
model_lib = importlib.import_module(FLAGS.model)
model = model_lib.Model(FLAGS.num_classes)
model_wrapper = torch.nn.DataParallel(model).cuda()
return model, model_wrapper
def data_transforms():
"""get transform of dataset"""
if FLAGS.data_transforms in [
'imagenet1k_basic', 'imagenet1k_inception', 'imagenet1k_mobile']:
if FLAGS.data_transforms == 'imagenet1k_inception':
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
crop_scale = 0.08
jitter_param = 0.4
lighting_param = 0.1
elif FLAGS.data_transforms == 'imagenet1k_basic':
if getattr(FLAGS, 'normalize', False):
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
else:
mean = [0.0, 0.0, 0.0]
std = [1.0, 1.0, 1.0]
#crop_scale = 0.08
#jitter_param = 0.4
#lighting_param = 0.1
elif FLAGS.data_transforms == 'imagenet1k_mobile':
if getattr(FLAGS, 'normalize', False):
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
else:
mean = [0.0, 0.0, 0.0]
std = [1.0, 1.0, 1.0]
#crop_scale = 0.25
#jitter_param = 0.4
#lighting_param = 0.1
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(224),# scale=(crop_scale, 1.0)),
#transforms.ColorJitter(
# brightness=jitter_param, contrast=jitter_param,
# saturation=jitter_param),
#Lighting(lighting_param),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
val_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
test_transforms = val_transforms
elif FLAGS.data_transforms == 'cifar':
if getattr(FLAGS, 'normalize', False):
mean = [0.4914, 0.4822, 0.4465]
std = [0.2023, 0.1994, 0.2010]
else:
mean = [0.0, 0.0, 0.0]
std = [1.0, 1.0, 1.0]
### me !! ###
train_transforms = transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
val_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
test_transforms = val_transforms
elif FLAGS.data_transforms == 'cinic':
if getattr(FLAGS, 'normalize', False):
mean = [0.4789, 0.4723, 0.4305]
std = [0.2421, 0.2383, 0.2587]
else:
mean = [0.0, 0.0, 0.0]
std = [1.0, 1.0, 1.0]
train_transforms = transforms.Compose([
transforms.RandomCrop(224, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
val_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
test_transforms = val_transforms
else:
try:
transforms_lib = importlib.import_module(FLAGS.data_transforms)
return transforms_lib.data_transforms()
except ImportError:
raise NotImplementedError(
'Data transform {} is not yet implemented.'.format(
FLAGS.data_transforms))
return train_transforms, val_transforms, test_transforms
def dataset(train_transforms, val_transforms, test_transforms):
"""get dataset for classification"""
if FLAGS.dataset == 'imagenet1k':
if not FLAGS.test_only:
train_set = datasets.ImageFolder(
os.path.join(FLAGS.dataset_dir, 'train'),
transform=train_transforms)
else:
train_set = None
val_set = datasets.ImageFolder(
os.path.join(FLAGS.dataset_dir, 'val'),
transform=val_transforms)
test_set = None
elif FLAGS.dataset == 'imagenet1k_lmdb':
if not FLAGS.test_only:
train_set = ImageFolderLMDB(
os.path.join(FLAGS.dataset_dir, 'train'),
transform=train_transforms)
else:
train_set = None
val_set = ImageFolderLMDB(
os.path.join(FLAGS.dataset_dir, 'val'),
transform=val_transforms)
test_set = None
elif FLAGS.dataset == 'imagenet1k_val50k':
if not FLAGS.test_only:
train_set = datasets.ImageFolder(
os.path.join(FLAGS.dataset_dir, 'train'),
transform=train_transforms)
seed = getattr(FLAGS, 'random_seed', 0)
random.seed(seed)
val_size = 50000
random.shuffle(train_set.samples)
train_set.samples = train_set.samples[val_size:]
else:
train_set = None
val_set = datasets.ImageFolder(
os.path.join(FLAGS.dataset_dir, 'val'),
transform=val_transforms)
test_set = None
elif FLAGS.dataset == 'CINIC10':
if not FLAGS.test_only:
train_set = datasets.ImageFolder(
os.path.join(FLAGS.dataset_dir, 'train'),
transform=train_transforms)
else:
train_set = None
val_set = datasets.ImageFolder(
os.path.join(FLAGS.dataset_dir, 'valid'),
transform=val_transforms)
test_set = datasets.ImageFolder(
os.path.join(FLAGS.dataset_dir, 'test'),
transform=val_transforms)
elif FLAGS.dataset == 'CIFAR10':
if not FLAGS.test_only:
train_set = datasets.CIFAR10(
FLAGS.dataset_dir,
transform = train_transforms,
download=True)
else:
train_set = None
val_set = datasets.CIFAR10(
FLAGS.dataset_dir,
train=False,
transform = val_transforms,
download=True)
test_set = None
elif FLAGS.dataset == 'CIFAR100':
if not FLAGS.test_only:
train_set = datasets.CIFAR100(
FLAGS.dataset_dir,
transform = train_transforms,
download=True)
else:
train_set = None
val_set = datasets.CIFAR100(
FLAGS.dataset_dir,
train=False,
transform = val_transforms,
download=True)
test_set = None
else:
try:
dataset_lib = importlib.import_module(FLAGS.dataset)
return dataset_lib.dataset(
train_transforms, val_transforms, test_transforms)
except ImportError:
raise NotImplementedError(
'Dataset {} is not yet implemented.'.format(FLAGS.dataset))
return train_set, val_set, test_set
def data_loader(train_set, val_set, test_set):
"""get data loader"""
train_loader = None
val_loader = None
test_loader = None
if getattr(FLAGS, 'batch_size', False):
if getattr(FLAGS, 'batch_size_per_gpu', False):
assert FLAGS.batch_size == (FLAGS.batch_size_per_gpu * FLAGS.num_gpus_per_job)
else:
assert FLAGS.batch_size % FLAGS.num_gpus_per_job == 0
FLAGS.batch_size_per_gpu = (FLAGS.batch_size // FLAGS.num_gpus_per_job)
elif getattr(FLAGS, 'batch_size_per_gpu', False):
FLAGS.batch_size = FLAGS.batch_size_per_gpu * FLAGS.num_gpus_per_job
else:
raise ValueError('batch size (per gpu) is not defined')
batch_size = int(FLAGS.batch_size)# / get_world_size())
if FLAGS.data_loader in ['imagenet1k_basic','cifar', 'cinic']:
train_sampler = None
val_sampler = None
if not FLAGS.test_only:
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=batch_size,
shuffle=(train_sampler is None),
sampler=train_sampler,
pin_memory=True,
num_workers=FLAGS.data_loader_workers,
drop_last=getattr(FLAGS, 'drop_last', False))
val_loader = torch.utils.data.DataLoader(
val_set,
batch_size=batch_size,
shuffle=False,
sampler=val_sampler,
pin_memory=True,
num_workers=FLAGS.data_loader_workers,
drop_last=getattr(FLAGS, 'drop_last', False))
test_loader = val_loader
else:
try:
data_loader_lib = importlib.import_module(FLAGS.data_loader)
return data_loader_lib.data_loader(train_set, val_set, test_set)
except ImportError:
raise NotImplementedError(
'Data loader {} is not yet implemented.'.format(
FLAGS.data_loader))
if train_loader is not None:
FLAGS.data_size_train = len(train_loader.dataset)
if val_loader is not None:
FLAGS.data_size_val = len(val_loader.dataset)
if test_loader is not None:
FLAGS.data_size_test = len(test_loader.dataset)
return train_loader, val_loader, test_loader
def lr_func(x, fun='cos'):
if fun == 'cos':
return math.cos( x * math.pi ) / 2 + 0.5
if fun == 'exp':
return math.exp( - x * 8 )
if fun == 'gaussian':
return ( math.exp( - x**2 * 8 ) + 0.02 ) / 1.02
if fun == 'butterworth':
return ( 1 / ( ( x * 3 ) ** 10 + 1 ) ** 0.5 + 0.02 ) / 1.02
if fun == 'mixed':
return ( math.cos( x * math.pi ) / 2 + 0.5 ) / ( ( x * 1.5 ) ** 20 + 1 ) ** 0.5
def get_lr_scheduler(optimizer, nBatch=None):
"""get learning rate"""
#warmup_epochs = getattr(FLAGS, 'lr_warmup_epochs', 0)
if FLAGS.lr_scheduler == 'multistep':
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=FLAGS.multistep_lr_milestones,
gamma=FLAGS.multistep_lr_gamma)
elif FLAGS.lr_scheduler == 'exp_decaying':
lr_dict = {}
for i in range(FLAGS.num_epochs):
if i == 0:
lr_dict[i] = 1
elif i % getattr(FLAGS, 'exp_decaying_period', 1) == 0:
lr_dict[i] = lr_dict[i-1] * FLAGS.exp_decaying_lr_gamma
else:
lr_dict[i] = lr_dict[i-1]
lr_lambda = lambda epoch: lr_dict[epoch] # noqa: E731
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lr_lambda)
elif FLAGS.lr_scheduler == 'exp_decaying_iter':
FLAGS.num_iters = FLAGS.num_epochs * nBatch
FLAGS.warmup_iters = FLAGS.warmup_epochs * nBatch
lr_dict = {}
for i in range(FLAGS.warmup_iters):
bs_ratio = 256 / FLAGS.batch_size
lr_dict[i] = (1 - bs_ratio) / FLAGS.warmup_iters * i + bs_ratio
for i in range(FLAGS.warmup_iters, FLAGS.num_iters):
#lr_dict[i] = math.exp(-(i - FLAGS.warmup_iters) / (FLAGS.num_iters - FLAGS.warmup_iters) * 8)
lr_dict[i] = lr_func((i - FLAGS.warmup_iters) / (FLAGS.num_iters - FLAGS.warmup_iters), 'exp')
lr_lambda = lambda itr: lr_dict[itr] # noqa: E731
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lr_lambda)
elif FLAGS.lr_scheduler == 'gaussian_iter':
FLAGS.num_iters = FLAGS.num_epochs * nBatch
FLAGS.warmup_iters = FLAGS.warmup_epochs * nBatch
lr_dict = {}
for i in range(FLAGS.warmup_iters):
bs_ratio = 256 / FLAGS.batch_size
lr_dict[i] = (1 - bs_ratio) / FLAGS.warmup_iters * i + bs_ratio
for i in range(FLAGS.warmup_iters, FLAGS.num_iters):
#lr_dict[i] = math.exp(-(i - FLAGS.warmup_iters)**2 / (FLAGS.num_iters - FLAGS.warmup_iters)**2 * 8)
lr_dict[i] = lr_func((i - FLAGS.warmup_iters) / (FLAGS.num_iters - FLAGS.warmup_iters), 'gaussian')
lr_lambda = lambda itr: lr_dict[itr] # noqa: E731
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lr_lambda)
elif FLAGS.lr_scheduler == 'butterworth_iter':
FLAGS.num_iters = FLAGS.num_epochs * nBatch
FLAGS.warmup_iters = FLAGS.warmup_epochs * nBatch
lr_dict = {}
for i in range(FLAGS.warmup_iters):
bs_ratio = 256 / FLAGS.batch_size
lr_dict[i] = (1 - bs_ratio) / FLAGS.warmup_iters * i + bs_ratio
for i in range(FLAGS.warmup_iters, FLAGS.num_iters):
lr_dict[i] = lr_func((i - FLAGS.warmup_iters) / (FLAGS.num_iters - FLAGS.warmup_iters), 'butterworth')
lr_lambda = lambda itr: lr_dict[itr] # noqa: E731
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lr_lambda)
elif FLAGS.lr_scheduler == 'mixed_iter':
FLAGS.num_iters = FLAGS.num_epochs * nBatch
FLAGS.warmup_iters = FLAGS.warmup_epochs * nBatch
lr_dict = {}
for i in range(FLAGS.warmup_iters):
bs_ratio = 256 / FLAGS.batch_size
lr_dict[i] = (1 - bs_ratio) / FLAGS.warmup_iters * i + bs_ratio
for i in range(FLAGS.warmup_iters, FLAGS.num_iters):
lr_dict[i] = lr_func((i - FLAGS.warmup_iters) / (FLAGS.num_iters - FLAGS.warmup_iters), 'mixed')
lr_lambda = lambda itr: lr_dict[itr] # noqa: E731
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lr_lambda)
elif FLAGS.lr_scheduler == 'linear_decaying':
num_epochs = FLAGS.num_epochs - FLAGS.warmup_epochs
lr_dict = {}
for i in range(FLAGS.num_epochs):
lr_dict[i] = 1. - (i - FLAGS.warmup_epochs) / FLAGS.num_epochs
lr_lambda = lambda epoch: lr_dict[epoch] # noqa: E731
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lr_lambda)
elif FLAGS.lr_scheduler == 'cos_annealing':
num_epochs = FLAGS.num_epochs - FLAGS.warmup_epochs
lr_dict = {}
for i in range(FLAGS.num_epochs):
lr_dict[i] = (1.0 + math.cos( (i - FLAGS.warmup_epochs) * math.pi / num_epochs)) / 2
lr_lambda = lambda epoch: lr_dict[epoch] # noqa: E731
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lr_lambda)
elif FLAGS.lr_scheduler == 'cos_annealing_iter':
FLAGS.num_iters = FLAGS.num_epochs * nBatch
FLAGS.warmup_iters = FLAGS.warmup_epochs * nBatch
lr_dict = {}
for i in range(FLAGS.warmup_iters):
bs_ratio = 256 / FLAGS.batch_size
lr_dict[i] = (1 - bs_ratio) / FLAGS.warmup_iters * i + bs_ratio
if getattr(FLAGS, 'warm_restart', False):
T = 10
T_iter = T * nBatch
start_iter = FLAGS.warmup_iters
while True:
if start_iter >= FLAGS.num_iters:
break
T_iter = min(T_iter, FLAGS.num_iters - start_iter)
for i in range(start_iter, start_iter + T_iter):
if i >= FLAGS.num_iters:
break
lr_dict[i] = (1.0 + math.cos((i - start_iter) * math.pi / T_iter)) / 2
start_iter += T_iter
T_iter *= 2
else:
for i in range(FLAGS.warmup_iters, FLAGS.num_iters):
lr_dict[i] = (1.0 + math.cos((i - FLAGS.warmup_iters) * math.pi / (FLAGS.num_iters - FLAGS.warmup_iters))) / 2
lr_lambda = lambda itr: lr_dict[itr] # noqa: E731
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lr_lambda)
else:
try:
lr_scheduler_lib = importlib.import_module(FLAGS.lr_scheduler)
return lr_scheduler_lib.get_lr_scheduler(optimizer)
except ImportError:
raise NotImplementedError(
'Learning rate scheduler {} is not yet implemented.'.format(
FLAGS.lr_scheduler))
return lr_scheduler
def get_optimizer(model):
"""get optimizer"""
if FLAGS.optimizer == 'sgd':
# all depthwise convolution (N, 1, x, x) has no weight decay
# weight decay only on normal conv and fc
model_params = []
for name, params in model.named_parameters():
ps = list(params.size())
if len(ps) == 4 and ps[1] != 1:
weight_decay = FLAGS.weight_decay
lr = FLAGS.lr
elif len(ps) == 2:
weight_decay = FLAGS.weight_decay
lr = FLAGS.lr
elif "lamda" in name:
weight_decay = 0
lr = getattr(FLAGS, "lr_lamda", FLAGS.lr)
else:
weight_decay = 0
lr = FLAGS.lr
item = {'params': params, 'weight_decay': weight_decay,
'lr': lr, 'momentum': FLAGS.momentum,
'nesterov': FLAGS.nesterov}
model_params.append(item)
optimizer = torch.optim.SGD(model_params)
elif FLAGS.optimizer == 'rmsprop':
optimizer = torch.optim.RMSprop(model.parameters(), lr=FLAGS.lr, alpha=FLAGS.optim_decay, eps=FLAGS.optim_eps, weight_decay=FLAGS.weight_decay, momentum=FLAGS.momentum)
else:
try:
optimizer_lib = importlib.import_module(FLAGS.optimizer)
return optimizer_lib.get_optimizer(model)
except ImportError:
raise NotImplementedError(
'Optimizer {} is not yet implemented.'.format(FLAGS.optimizer))
return optimizer
def set_random_seed(seed=None):
"""set random seed"""
if seed is None:
seed = getattr(FLAGS, 'random_seed', 0)
print('seed for random sampling: {}'.format(seed))
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def get_meters(phase):
"""util function for meters"""
def get_single_meter(phase, suffix=''):
meters = {}
meters['loss'] = ScalarMeter('{}_loss/{}'.format(phase, suffix))
for k in FLAGS.topk:
meters['top{}_error'.format(k)] = ScalarMeter(
'{}_top{}_error/{}'.format(phase, k, suffix))
return meters
assert phase in ['train', 'val', 'test', 'cal'], 'Invalid phase.'
meters = get_single_meter(phase)
if phase == 'val':
meters['best_val'] = ScalarMeter('best_val')
return meters
def profiling(model, use_cuda):
"""profiling on either gpu or cpu"""
print('Start model profiling, use_cuda:{}.'.format(use_cuda))
flops, params, bitops, bitops_max, bytesize, energy, latency = model_profiling(
model, FLAGS.image_size, FLAGS.image_size,
verbose=getattr(FLAGS, 'model_profiling_verbose', False))
return bitops, bytesize
def get_experiment_setting():
experiment_setting = 'ema_decay_{ema_decay}/fp_pretrained_{fp_pretrained}/bit_list_{bit_list}'.format(ema_decay=getattr(FLAGS, 'ema_decay', None), fp_pretrained=getattr(FLAGS, 'fp_pretrained_file', None) is not None, bit_list='_'.join([str(i) for i in getattr(FLAGS, 'bits_list', None)]))
if getattr(FLAGS, 'act_bits_list', False):
experiment_setting = os.path.join(experiment_setting, 'act_bits_list_{}'.format('_'.join([str(i) for i in FLAGS.act_bits_list])))
if getattr(FLAGS, 'double_side', False):
experiment_setting = os.path.join(experiment_setting, 'double_side_True')
if not getattr(FLAGS, 'rescale', False):
experiment_setting = os.path.join(experiment_setting, 'rescale_False')
if not getattr(FLAGS, 'calib_pact', False):
experiment_setting = os.path.join(experiment_setting, 'calib_pact_False')
experiment_setting = os.path.join(experiment_setting, 'kappa_{kappa}'.format(kappa=getattr(FLAGS, 'kappa', 1.0)))
if getattr(FLAGS, 'target_bitops', False):
experiment_setting = os.path.join(experiment_setting, 'target_bitops_{}'.format(getattr(FLAGS, 'target_bitops', False)))
if getattr(FLAGS, 'target_size', False):
experiment_setting = os.path.join(experiment_setting, 'target_size_{}'.format(getattr(FLAGS, 'target_size', False)))
if getattr(FLAGS, 'init_bit', False):
experiment_setting = os.path.join(experiment_setting, 'init_bit_{}'.format(getattr(FLAGS, 'init_bit', False)))
if getattr(FLAGS, 'unbiased', False):
experiment_setting = os.path.join(experiment_setting, f'unbiased_True')
print('Experiment settings: {}'.format(experiment_setting))
return experiment_setting
def forward_loss(model, criterion, inputs, targets, meter):
"""forward model and return loss"""
if getattr(FLAGS, 'normalize', False):
inputs = inputs #(128 * inputs).round_().clamp_(-128, 127)
else:
inputs = (255 * inputs).round_()
outputs = model(inputs)
loss = torch.mean(criterion(outputs, targets))
# topk
_, pred = outputs.topk(max(FLAGS.topk))
pred = pred.t()
correct = pred.eq(targets.view(1, -1).expand_as(pred))
correct_k = []
for k in FLAGS.topk:
correct_k.append(correct[:k].float().sum(0))
res = torch.cat(correct_k, dim=0)
res = res.cpu().detach().numpy()
bs = (res.size - 1) // len(FLAGS.topk)
for i, k in enumerate(FLAGS.topk):
error_list = list(1. - res[i*bs:(i+1)*bs])
if meter is not None:
meter['top{}_error'.format(k)].cache_list(error_list)
if meter is not None:
meter['loss'].cache(loss.tolist())
return loss
def bit_discretizing(model):
print('hard offset', FLAGS.hard_offset)
for m in model.modules():
if hasattr(m, 'bit_discretizing'):
print('bit discretized for ', m)
m.bit_discretizing()
def get_comp_cost_loss(model):
loss = 0.0
for name, m in model.named_modules():
try:
loss += getattr(m, 'comp_cost_loss', 0.0)
except:
print(f'loss.shape: {loss.shape}')
print(f"getattr(m, 'comp_cost_loss', 0.0).shape: {getattr(m, 'comp_cost_loss', 0.0).shape}")
exit()
target_bitops = getattr(FLAGS, 'target_bitops', False)
if target_bitops:
if getattr(FLAGS, 'relu_loss', False):
loss = torch.relu(loss - target_bitops)
else:
loss = torch.abs(loss - target_bitops)
return loss
# NEW loss : bitwidth regularizer
def get_bitwidth_loss(model):
loss = 0.0
for name, m in model.named_modules():
if hasattr(m, 'lamda_w'):
if getattr(FLAGS, "gamma_type", 0) == 1: #L1
loss += torch.abs(torch.round(m.lamda_w) - m.lamda_w)
loss += torch.abs(torch.round(m.lamda_a) - m.lamda_a)
elif getattr(FLAGS, "gamma_type", 0) == 2: #L2
loss += 2 * torch.square(torch.abs(torch.round(m.lamda_w) - m.lamda_w))
loss += 2 * torch.square(torch.abs(torch.round(m.lamda_a) - m.lamda_a))
return loss
def get_model_size_loss(model):
loss = 0.0
for name, m in model.named_modules():
loss += getattr(m, 'model_size_loss', 0.0)
target_size = getattr(FLAGS, 'target_size', False)
if target_size:
loss = torch.abs(loss - target_size)
return loss
@timing
def run_one_epoch(
epoch, loader, model, criterion, optimizer, meters, phase='train', ema=None, scheduler=None, scaler=None, kappa=None, gamma=None):
"""run one epoch for train/val/test/cal"""
t_start = time.time()
assert phase in ['train', 'val', 'test', 'cal'], "phase not be in train/val/test/cal."
train = phase == 'train'
log_dir = FLAGS.log_dir
if train:
model.train()
else:
model.eval()
bitwidth_learning = epoch >= FLAGS.warmup_epochs and not getattr(FLAGS,'hard_assignment', False)
eval_acc_loss = AverageMeter()
eval_cost_loss = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
n_layer = 53
lamda_w_list = []
lamda_a_list = []
ema_lamda_w_list = []
ema_lamda_a_list = []
loss_acc_list = []
acc1_iter_list = []
acc1_avg_list = []
for batch_idx, (inputs, targets) in enumerate(loader):
######### FAST TEST ###########
if getattr(FLAGS, 'debug_cut_batch', False):
if batch_idx == FLAGS.debug_cut_batch:
break
######### FAST TEST ###########
if phase == 'cal':
if batch_idx == getattr(FLAGS, 'bn_cal_batch_num', -1):
break
targets = targets.cuda(non_blocking=True)
if train:
if FLAGS.lr_scheduler == 'linear_decaying':
linear_decaying_per_step = (
FLAGS.lr/FLAGS.num_epochs/len(loader.dataset)*FLAGS.batch_size)
for param_group in optimizer.param_groups:
param_group['lr'] -= linear_decaying_per_step
space = '\n\n\n\n\n\n\n'
if getattr(FLAGS, 'normalize', False):
inputs = inputs #(128 * inputs).round_().clamp_(-128, 127)
else:
inputs = (255 * inputs).round_()
optimizer.zero_grad()
if getattr(FLAGS, 'amp', False):
with amp.autocast():
outputs = model(inputs)
loss_acc = torch.mean(criterion(outputs, targets))
loss_acc_list.append(loss_acc.item())
if bitwidth_learning:
if getattr(FLAGS,'weight_only', False):
loss_cost = kappa * get_model_size_loss(model)
else:
loss_cost = kappa * get_comp_cost_loss(model)
loss = loss_acc + loss_cost #getattr(FLAGS, 'kappa', 1.0) * loss_cost
if epoch+1 > getattr(FLAGS, 'bitwidth_regularize_start_epoch', 9999):
loss += gamma * get_bitwidth_loss(model)
else:
loss = loss_acc
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
outputs = model(inputs)
loss_acc = torch.mean(criterion(outputs, targets))
loss_acc_list.append(loss_acc.item())
loss_cost = 0.0 ## me!! ##
if bitwidth_learning:
if getattr(FLAGS,'weight_only', False):
loss_cost = kappa * get_model_size_loss(model)
else:
loss_cost = kappa * get_comp_cost_loss(model)
loss = loss_acc + loss_cost #getattr(FLAGS, 'kappa', 1.0) * loss_cost
if epoch+1 > getattr(FLAGS, 'bitwidth_regularize_start_epoch', 9999):
loss += gamma * get_bitwidth_loss(model)
else:
loss = loss_acc
loss.backward()
optimizer.step()
if FLAGS.lr_scheduler in ['exp_decaying_iter', 'gaussian_iter', 'cos_annealing_iter', 'butterworth_iter', 'mixed_iter']:
try:
scheduler.step()
except:
pass
acc1, acc5 = accuracy(outputs.data, targets.data, top_k=(1,5))
eval_acc_loss.update(loss_acc.item(), inputs.size(0))
if bitwidth_learning:
eval_cost_loss.update(loss_cost.item(), inputs.size(0))
top1.update(acc1[0], inputs.size(0))
top5.update(acc5[0], inputs.size(0))
acc1_iter_list.append(acc1.item())
acc1_avg_list.append(top1.avg.item())
lamda_w_temp = []
lamda_a_temp = []
ema_lamda_w_temp = []
ema_lamda_a_temp = []
if getattr(FLAGS, 'log_bitwidth', False):
for name, m in model.named_modules():
if hasattr(m, 'lamda_w'):
lamda_w_temp.append(m.lamda_w.item())
lamda_a_temp.append(m.lamda_a.item())
if getattr(FLAGS, 'grad_ema_alpha', False):
if m.lamda_w.grad is not None:
temp1 = torch.abs(m.lamda_w.grad) - m.ema_lamda_w_grad
m.ema_lamda_w_grad.data = m.ema_lamda_w_grad + FLAGS.grad_ema_alpha * temp1
ema_lamda_w_temp.append(m.ema_lamda_w_grad.item())
if m.lamda_a.grad is not None:
temp1 = torch.abs(m.lamda_a.grad) - m.ema_lamda_a_grad
m.ema_lamda_a_grad.data = m.ema_lamda_a_grad + FLAGS.grad_ema_alpha * temp1
ema_lamda_a_temp.append(m.ema_lamda_a_grad.item())
lamda_w_list.append(lamda_w_temp)
lamda_a_list.append(lamda_a_temp)
ema_lamda_w_list.append(ema_lamda_w_temp)
ema_lamda_a_list.append(ema_lamda_a_temp)
if (batch_idx) % FLAGS.log_interval == 0:
if getattr(FLAGS, 'log_wandb', False):
log_dict = {'acc1_iter': acc1.item(),
'acc1_avg': top1.avg,
'acc5_avg': top5.avg,
'loss': loss.item(),
'lamda_w': np.array(lamda_w_temp),
'lamda_a': np.array(lamda_a_temp)}
wandb.log(log_dict)
curr = batch_idx * len(inputs)
total = len(loader.dataset)
if bitwidth_learning:
loss_sentence = f'Loss_acc: {eval_acc_loss.avg:.3f} | Loss_cost: {eval_cost_loss.avg:.3f} | '
if epoch+1 > getattr(FLAGS, 'bitwidth_regularize_start_epoch', 9999):
loss_sentence = loss_sentence + f'Loss_bit: {gamma * get_bitwidth_loss(model):.3f} | '
else:
loss_sentence = f'Loss_acc: {eval_acc_loss.avg:5.3f} | '
print(f'[{datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}] Train Epoch: '\
f'{epoch:3d} Phase: {phase} Process: {curr:5d}/{total:5d} '\
+ loss_sentence + \
f'top1.avg: {top1.avg:.3f} % | '\
f'top5.avg: {top5.avg:.3f} % | ') ## me!! eval_loss -> eval_acc_loss ##
else: #not train
if ema:
print('ema apply')
ema.shadow_apply(model)
forward_loss(model, criterion, inputs, targets, meters)
outputs = model(inputs)
if ema:
print('ema recover')
ema.weight_recover(model)
acc1, acc5 = accuracy(outputs.data, targets.data, top_k=(1,5))
top1.update(acc1[0], inputs.size(0))
top5.update(acc5[0], inputs.size(0))
if train:
print(np.array(lamda_w_list).shape)
print(np.array(lamda_a_list).shape)
np.save(f'{FLAGS.log_dir}/lamda_w_ep{epoch}.npy', np.array(lamda_w_list))
np.save(f'{FLAGS.log_dir}/lamda_a_ep{epoch}.npy', np.array(lamda_a_list))
if getattr(FLAGS, 'grad_ema_alpha', False):
np.save(f'{FLAGS.log_dir}/ema_lamda_w_ep{epoch}.npy', np.array(ema_lamda_w_list))
np.save(f'{FLAGS.log_dir}/ema_lamda_a_ep{epoch}.npy', np.array(ema_lamda_a_list))
np.save(f'{FLAGS.log_dir}/acc1_iter_ep{epoch}.npy', np.array(acc1_iter_list))
np.save(f'{FLAGS.log_dir}/acc1_avg_ep{epoch}.npy', np.array(acc1_avg_list))
np.save(f'{FLAGS.log_dir}/loss_acc_ep{epoch}.npy', np.array(loss_acc_list))
print('bitwidth, acc, and loss numpy file saved!!')
print('\ncurrent bitwidth (weight):')
lamda_temp = []
for name, m in model.named_modules():
if hasattr(m, 'lamda_w'):
lamda_temp.append(m.lamda_w.item())
for idx, value in enumerate(lamda_temp):
print(f'{value:.4f} ', end='')
if idx % 10 == 0:
print()
print('\ncurrent bitwidth (activation):')
lamda_temp = []
for name, m in model.named_modules():
if hasattr(m, 'lamda_a'):
lamda_temp.append(m.lamda_a.item())
for idx, value in enumerate(lamda_temp):
print(f'{value:.4f} ', end='')
if idx % 10 == 0:
print()
val_top1 = None
try:
print('{:.1f}s\t{}\t{}: '.format(
time.time() - t_start, phase, epoch, FLAGS.num_epochs)) # +
#', '.join('{}: {}'.format(k, v) for k, v in results.items()))
val_top1 = top1.avg
#val_top1 = results['top1_error']
except:
val_top1 = top1.avg
if phase == 'val':
wandb.log({'eval_top1': top1.avg,
'eval_top5': top5.avg})
return val_top1
@timing
def train_val_test():
if getattr(FLAGS, 'amp', False):
print('\n--------------------------------------')
print('==> AUTOMATIC MIXED PRECISION Training')
print('--------------------------------------\n')
"""train and val"""
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
scaler = torch.cuda.amp.GradScaler()
set_random_seed()
####### DEBUG MSG
interp_method = 'simple_interpolation (ours)' if getattr(FLAGS, 'simple_interpolation', False) else 'fracbits_original'
print(f'\n==> Interpolation method: {interp_method}\n')
if getattr(FLAGS, 'bitwidth_direct', False):
print('==> Direct learning of bitwidth (This should be shown)\n')
# experiment setting
experiment_setting = get_experiment_setting()
# model
model, model_wrapper = get_model()
print(model)
criterion = torch.nn.CrossEntropyLoss(reduction='none').cuda()
if getattr(FLAGS, 'profiling_only', False):
if 'gpu' in FLAGS.profiling:
profiling(model, use_cuda=True)
if 'cpu' in FLAGS.profiling:
profiling(model, use_cuda=False)
return
# ema_decay : not used
ema_decay = getattr(FLAGS, 'ema_decay', None)
if ema_decay:
ema = EMA(ema_decay)
ema.shadow_register(model_wrapper)
else:
ema = None
# data
train_transforms, val_transforms, test_transforms = data_transforms()
train_set, val_set, test_set = dataset(
train_transforms, val_transforms, test_transforms)
train_loader, val_loader, test_loader = data_loader(
train_set, val_set, test_set)
log_dir = FLAGS.log_dir
log_dir = os.path.join(log_dir, experiment_setting)
model_link = {'models.q_mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
'models.q_resnet': 'https://download.pytorch.org/models/resnet18-f37072fd.pth'}
# full precision pretrained
if getattr(FLAGS, 'fp_pretrained_file', None): ## me!! ##
if not os.path.isfile(FLAGS.fp_pretrained_file):
pretrain_dir = os.path.dirname(FLAGS.fp_pretrained_file)
print(FLAGS.fp_pretrained_file)
os.system(f"wget -P {pretrain_dir} {model_link[FLAGS.model]}")
checkpoint = torch.load(FLAGS.fp_pretrained_file)
# update keys from external models
if type(checkpoint) == dict and 'model' in checkpoint:
checkpoint = checkpoint['model']
if getattr(FLAGS, 'pretrained_model_remap_keys', False):
new_checkpoint = {}
new_keys = list(model_wrapper.state_dict().keys())
old_keys = list(checkpoint.keys())
for key_new in new_keys:
for i, key_old in enumerate(old_keys):
if key_old.split('.')[-1] in key_new:
new_checkpoint[key_new] = checkpoint[key_old]
print('remap {} to {}'.format(key_new, key_old))
old_keys.pop(i)
break
'''
for key_new, key_old in zip(new_keys, old_keys):
new_checkpoint[key_new] = checkpoint[key_old]
print('remap {} to {}'.format(key_new, key_old))
'''
checkpoint = new_checkpoint
model_dict = model_wrapper.state_dict()
checkpoint = {k: v for k, v in checkpoint.items() if k in model_dict}
# remove unexpected keys
for k in list(checkpoint.keys()):
if k not in model_dict.keys():
checkpoint.pop(k)
#print(checkpoint.keys())
model_dict.update(checkpoint)
model_wrapper.load_state_dict(model_dict)
print('Loaded full precision model {}.'.format(FLAGS.fp_pretrained_file))
else:
print('Loaded random value model')
# check pretrained ----------------------------------
if FLAGS.pretrained_file and FLAGS.pretrained_dir:
pretrained_dir = FLAGS.pretrained_dir
#pretrained_dir = os.path.join(pretrained_dir, experiment_setting)
pretrained_file = os.path.join(pretrained_dir, FLAGS.pretrained_file)
#checkpoint = io.torch_load(
# pretrained_file, map_location=lambda storage, loc: storage)
checkpoint = torch.load(pretrained_file)
# update keys from external models
#if type(checkpoint) == dict and 'model' in checkpoint:
# checkpoint = checkpoint['model']
if getattr(FLAGS, 'pretrained_model_remap_keys', False):
new_checkpoint = {}
new_keys = list(model_wrapper.state_dict().keys())
old_keys = list(checkpoint.keys())
for key_new, key_old in zip(new_keys, old_keys):
new_checkpoint[key_new] = checkpoint[key_old]
print('remap {} to {}'.format(key_new, key_old))
checkpoint = new_checkpoint
# filter lamda_w and lamda_a args:
pretrained_dict = {}
for k,v in checkpoint['model'].items():
if 'lamda_w' in k or 'lamda_a' in k:
checkpoint['model'][k] = v.repeat(model_wrapper.state_dict()[k].size())
model_wrapper.load_state_dict(checkpoint['model'])
print('Loaded model {}.'.format(pretrained_file))
optimizer = get_optimizer(model_wrapper)
if FLAGS.test_only and (test_loader is not None):
print('Start testing.')
ema = checkpoint.get('ema', None)
test_meters = get_meters('test')
with torch.no_grad():
run_one_epoch(
-1, test_loader,
model_wrapper, criterion, optimizer,
test_meters, phase='test', ema=ema, scaler=scaler)
return
# check resume training ------------------------------
if os.path.isfile(os.path.join(log_dir, 'latest_checkpoint.pt')):
checkpoint = torch.load(os.path.join(log_dir, 'latest_checkpoint.pt'))
model_wrapper.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
last_epoch = checkpoint['last_epoch']
if FLAGS.lr_scheduler in ['exp_decaying_iter', 'gaussian_iter', 'cos_annealing_iter', 'butterworth_iter', 'mixed_iter']:
lr_scheduler = get_lr_scheduler(optimizer, len(train_loader))
lr_scheduler.last_epoch = last_epoch * len(train_loader)
else:
lr_scheduler = get_lr_scheduler(optimizer)
lr_scheduler.last_epoch = last_epoch
best_val = checkpoint['best_val']
train_meters, val_meters = checkpoint['meters']
ema = checkpoint.get('ema', None)
print('Loaded checkpoint {} at epoch {}.'.format(
log_dir, last_epoch))
else:
if FLAGS.lr_scheduler in ['exp_decaying_iter', 'gaussian_iter', 'cos_annealing_iter', 'butterworth_iter', 'mixed_iter']:
lr_scheduler = get_lr_scheduler(optimizer, len(train_loader))
else:
lr_scheduler = get_lr_scheduler(optimizer)
last_epoch = lr_scheduler.last_epoch
best_val = 0
train_meters = get_meters('train')