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infer.py
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infer.py
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# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import argparse
import datetime
import numpy as np
import time
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import json
from tqdm import tqdm
from pathlib import Path
from timm.data import Mixup
from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.scheduler import create_scheduler
from timm.optim import create_optimizer
from timm.utils import NativeScaler, get_state_dict, ModelEma
from datasets import build_dataset
from engine import train_one_epoch, evaluate
from samplers import RASampler
from functools import partial
from models.dyvit import VisionTransformerDiffPruning
from models.dylvvit import LVViTDiffPruning
from models.dyconvnext import AdaConvNeXt
from models.dyswin import AdaSwinTransformer
import utils
from typing import Any, List, Dict, Tuple
import pandas
import torch.utils.benchmark as bench
def get_args_parser():
parser = argparse.ArgumentParser('DeiT training and evaluation script', add_help=False)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--model', default='deit_small', type=str, help='Name of model to train')
parser.add_argument('--input_size', default=224, type=int, help='images input size')
parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
help='dataset path')
parser.add_argument('--data_set', default='IMNET', choices=['CIFAR', 'IMNET', 'INAT', 'INAT19'],
type=str, help='Image Net dataset path')
parser.add_argument('--imagenet_default_mean_and_std', type=utils.str2bool, default=True)
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--model_path', default='', help='resume from checkpoint')
parser.add_argument('--crop_pct', type=float, default=None)
parser.add_argument('--num_workers', default=10, type=int)
parser.add_argument('--pin-mem', action='store_true',
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no-pin-mem', action='store_false', dest='pin_mem',
help='')
parser.set_defaults(pin_mem=True)
parser.add_argument('--base_rate', type=float, default=0.7)
parser.add_argument('--no-progress-bar', action='store_true')
parser.add_argument("--device", type=str, default="cuda:0")
###
### For Profiling
###
parser.add_argument('--pruning-loc-mask', nargs='+', default=None)
parser.add_argument("--output-filename-suffix", type=str, default="")
parser.add_argument("--evaluate-only-no-accuracy", action="store_true")
return parser
### Utility function for pruning location overriding
def list_to_int_list( list : List) -> List[int]:
return [int(k) for k in list]
def main(args):
### Set matmul precision
torch.set_float32_matmul_precision('high')
#cudnn.benchmark = True
dataset_val, _ = build_dataset(is_train=False, args=args)
data_loader_val = torch.utils.data.DataLoader(
dataset_val,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False
)
base_rate = args.base_rate
KEEP_RATE1 = [base_rate, base_rate ** 2, base_rate ** 3]
KEEP_RATE2 = [base_rate, base_rate - 0.2, base_rate - 0.4]
print(f"Creating model: {args.model}")
###
### Initialize empty pruning location mask
###
PRUNING_LOC_MASK = None
if args.model == 'deit-s':
PRUNING_LOC = [3,6,9]
### Check if we want to override pruning loc
if args.pruning_loc_mask is not None:
PRUNING_LOC_MASK = list_to_int_list(args.pruning_loc_mask)
print('infer.py: WARNING, masking all pruning locations except {}'.format(PRUNING_LOC_MASK))
print('token_ratio =', KEEP_RATE1, 'at layer', PRUNING_LOC)
model = VisionTransformerDiffPruning(
patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
pruning_loc=PRUNING_LOC, token_ratio=KEEP_RATE1, pruning_loc_mask=PRUNING_LOC_MASK
)
elif args.model == 'deit-256':
PRUNING_LOC = [3,6,9]
print('token_ratio =', KEEP_RATE1, 'at layer', PRUNING_LOC)
model = VisionTransformerDiffPruning(
patch_size=16, embed_dim=256, depth=12, num_heads=4, mlp_ratio=4, qkv_bias=True,
pruning_loc=PRUNING_LOC, token_ratio=KEEP_RATE1
)
elif args.model == 'deit-b':
PRUNING_LOC = [3,6,9]
### Check if we want to override pruning loc
if args.pruning_loc_mask is not None:
PRUNING_LOC_MASK = list_to_int_list(args.pruning_loc_mask)
print('infer.py: WARNING, masking all pruning locations except {}'.format(PRUNING_LOC_MASK))
print('token_ratio =', KEEP_RATE1, 'at layer', PRUNING_LOC)
model = VisionTransformerDiffPruning(
patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
pruning_loc=PRUNING_LOC, token_ratio=KEEP_RATE1, pruning_loc_mask=PRUNING_LOC_MASK
)
elif args.model == 'lvvit-s':
PRUNING_LOC = [4,8,12]
print('token_ratio =', KEEP_RATE1, 'at layer', PRUNING_LOC)
model = LVViTDiffPruning(
patch_size=16, embed_dim=384, depth=16, num_heads=6, mlp_ratio=3.,
p_emb='4_2',skip_lam=2., return_dense=True,mix_token=True,
pruning_loc=PRUNING_LOC, token_ratio=KEEP_RATE1
)
elif args.model == 'lvvit-m':
PRUNING_LOC = [5,10,15]
print('token_ratio =', KEEP_RATE1, 'at layer', PRUNING_LOC)
model = LVViTDiffPruning(
patch_size=16, embed_dim=512, depth=20, num_heads=8, mlp_ratio=3.,
p_emb='4_2',skip_lam=2., return_dense=True,mix_token=True,
pruning_loc=PRUNING_LOC, token_ratio=KEEP_RATE1
)
elif args.model == 'convnext-t':
PRUNING_LOC = [1,2,3]
print('token_ratio =', KEEP_RATE2, 'at layer', PRUNING_LOC)
model = AdaConvNeXt(
sparse_ratio=KEEP_RATE2, pruning_loc=PRUNING_LOC
)
elif args.model == 'convnext-s':
PRUNING_LOC = [3,6,9]
print('token_ratio =', KEEP_RATE2, 'at layer', PRUNING_LOC)
model = AdaConvNeXt(
sparse_ratio=KEEP_RATE2, pruning_loc=PRUNING_LOC,
depths=[3, 3, 27, 3]
)
elif args.model == 'convnext-b':
PRUNING_LOC = [3,6,9]
print('token_ratio =', KEEP_RATE2, 'at layer', PRUNING_LOC)
model = AdaConvNeXt(
sparse_ratio=KEEP_RATE2, pruning_loc=PRUNING_LOC,
depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024]
)
elif args.model == 'swin-t':
PRUNING_LOC = [1,2,3]
print('token_ratio =', KEEP_RATE2, 'at layer', PRUNING_LOC)
model = AdaSwinTransformer(
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
drop_rate=0.0,
pruning_loc=[1,2,3], sparse_ratio=KEEP_RATE2
)
elif args.model == 'swin-s':
PRUNING_LOC = [2,4,6]
print('token_ratio =', KEEP_RATE2, 'at layer', PRUNING_LOC)
model = AdaSwinTransformer(
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
drop_rate=0.0,
drop_path_rate=args.drop_path,
pruning_loc=[2,4,6], sparse_ratio=KEEP_RATE2
)
elif args.model == 'swin-b':
PRUNING_LOC = [2,4,6]
print('token_ratio =', KEEP_RATE2, 'at layer', PRUNING_LOC)
model = AdaSwinTransformer(
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=7,
drop_rate=0.0,
drop_path_rate=args.drop_path,
pruning_loc=[2,4,6], sparse_ratio=KEEP_RATE2
)
else:
raise NotImplementedError
model_path = args.model_path
checkpoint = torch.load(model_path, map_location="cpu")
model.load_state_dict(checkpoint["model"])
print('## model has been successfully loaded')
model = model.cuda()
n_parameters = sum(p.numel() for p in model.parameters())
print('number of params:', n_parameters)
criterion = torch.nn.CrossEntropyLoss().cuda()
validate(args, data_loader_val, model, criterion)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
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 __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
class ProgressMeter(object):
def __init__(self, num_batches, meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch):
entries = [self.prefix + self.batch_fmtstr.format(batch)]
entries += [str(meter) for meter in self.meters]
print('\t'.join(entries))
def _get_batch_fmtstr(self, num_batches):
num_digits = len(str(num_batches // 1))
fmt = '{:' + str(num_digits) + 'd}'
return '[' + fmt + '/' + fmt.format(num_batches) + ']'
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
###
### Benchmark function for profiling wrapped models
###
def benchmark_milliseconds_wrapped(
args : argparse.Namespace,
x : torch.Tensor,
model : torch.nn.Module
) -> bench.Measurement:
### Set a minimum runtime
MIN_RUNTIME = 32.0
t0 = bench.Timer(
stmt=f"model(x)",
globals={
"x" : x,
"model" : model,
}
)
return t0.blocked_autorange(min_run_time=MIN_RUNTIME)
def validate(args, val_loader, model, criterion):
### Port to device
model.eval().to(args.device)
### Latency measurment data
latency_measurement = None
### Accuracy computation
running_accuracy = 0.0
### Use TQDM instead
dataloader_object = tqdm(val_loader)
with torch.no_grad():
for batch_index, (images, target) in enumerate(dataloader_object):
### To Device
images = images.to(args.device)
target = target.to(args.device)
### Benchmark timing
if batch_index == 0:
dataloader_object.set_description(desc="Benchmarking (potentially wrapped) model...")
latency_measurement = benchmark_milliseconds_wrapped(args, images, model)
if args.evaluate_only_no_accuracy:
break
# compute output
output = model(images)
predictions = torch.argmax(output, dim=1)
### Append running accuracy
running_accuracy += (
predictions == target
).sum().item() / target.shape[0]
dataloader_object.set_description(desc=f"Recording Accuracy...", refresh=False)
accuracy = 100.0 * running_accuracy / len(dataloader_object) if not args.evaluate_only_no_accuracy else -1.0
latency_mean = latency_measurement.mean * 1e3
latency_median = latency_measurement.median * 1e3
latency_iqr = latency_measurement.iqr * 1e3
### Print Accuracy
print("gpu_tail_measure.py: Accuracy is {:.3f}".format(accuracy))
### Print Latency Data
print("gpu_tail_measure.py: Latency (ms) stats Mean/Median/IQR are {:.3f} / {:.3f} / {:.3f}".format(latency_mean, latency_median, latency_iqr))
### Save as .CSV data
eval_dataframe = pandas.DataFrame(
data={
"Accuracy": [accuracy],
"Avg. Latency (ms)": [latency_mean],
"Median Latency (ms)": [latency_median],
"Latency IQR (ms)": [latency_iqr],
}
).to_csv(
f"bin/dynamic_vit_r{args.base_rate}_{args.output_filename_suffix}_inference_data.csv",
float_format="{:.2f}".format,
)
if __name__ == '__main__':
parser = argparse.ArgumentParser('Dynamic evaluation script', parents=[get_args_parser()])
args = parser.parse_args()
main(args)