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main.py
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# Adapted from
# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import datetime
import json
import os
import random
import time
from collections import namedtuple
from copy import deepcopy
from functools import partial
from pathlib import Path
import numpy as np
import torch
import torch.utils
import math
from torch.utils.data import ConcatDataset, DataLoader, DistributedSampler
import util.dist as dist
import util.misc as utils
from datasets import build_dataset
from datasets.vidstg_eval import VidSTGEvaluator
from datasets.hcstvg_eval import HCSTVGEvaluator
from engine import evaluate, train_one_epoch
from models import build_model
from models.postprocessors import build_postprocessors
from torch.utils.tensorboard import SummaryWriter
def get_args_parser():
parser = argparse.ArgumentParser("Set TubeDETR", add_help=False)
parser.add_argument("--run_name", default="", type=str)
# Dataset specific
parser.add_argument("--dataset_config", default=None, required=True)
parser.add_argument(
"--combine_datasets",
nargs="+",
help="List of datasets to combine for training",
required=True,
)
parser.add_argument(
"--combine_datasets_val",
nargs="+",
help="List of datasets to combine for eval",
required=True,
)
parser.add_argument(
"--v2",
action="store_true",
help="whether to use the second version of HC-STVG or not",
)
parser.add_argument(
"--tb_dir", type=str, default="", help="eventual path to tensorboard directory"
)
# Training hyper-parameters
parser.add_argument("--lr", default=5e-5, type=float)
parser.add_argument("--lr_backbone", default=1e-5, type=float)
parser.add_argument("--text_encoder_lr", default=5e-5, type=float)
parser.add_argument("--batch_size", default=1, type=int)
parser.add_argument("--weight_decay", default=1e-4, type=float)
parser.add_argument("--epochs", default=10, type=int)
parser.add_argument("--lr_drop", default=10, type=int)
parser.add_argument(
"--epoch_chunks",
default=-1,
type=int,
help="If greater than 0, will split the training set into chunks and validate/checkpoint after each chunk",
)
parser.add_argument("--optimizer", default="adam", type=str)
parser.add_argument(
"--clip_max_norm", default=0.1, type=float, help="gradient clipping max norm"
)
parser.add_argument(
"--eval_skip",
default=1,
type=int,
help='do evaluation every "eval_skip" epochs',
)
parser.add_argument(
"--schedule",
default="linear_with_warmup",
type=str,
choices=("step", "multistep", "linear_with_warmup", "all_linear_with_warmup"),
)
parser.add_argument("--ema", action="store_true")
parser.add_argument("--ema_decay", type=float, default=0.9998)
parser.add_argument(
"--fraction_warmup_steps",
default=0.01,
type=float,
help="Fraction of total number of steps",
)
# Model parameters
parser.add_argument(
"--freeze_text_encoder",
action="store_true",
help="Whether to freeze the weights of the text encoder",
)
parser.add_argument(
"--freeze_backbone",
action="store_true",
help="Whether to freeze the weights of the visual encoder",
)
parser.add_argument(
"--text_encoder_type",
default="roberta-base",
choices=("roberta-base", "distilroberta-base", "roberta-large"),
)
# Backbone
parser.add_argument(
"--backbone",
default="resnet101",
type=str,
help="Name of the convolutional backbone to use such as resnet50 resnet101 timm_tf_efficientnet_b3_ns",
)
parser.add_argument(
"--dilation",
action="store_true",
help="If true, we replace stride with dilation in the last convolutional block (DC5)",
)
parser.add_argument(
"--position_embedding",
default="sine",
type=str,
choices=("sine", "learned"),
help="Type of positional embedding to use on top of the image features",
)
# Transformer
parser.add_argument(
"--enc_layers",
default=6,
type=int,
help="Number of encoding layers in the transformer",
)
parser.add_argument(
"--dec_layers",
default=6,
type=int,
help="Number of decoding layers in the transformer",
)
parser.add_argument(
"--dim_feedforward",
default=2048,
type=int,
help="Intermediate size of the feedforward layers in the transformer blocks",
)
parser.add_argument(
"--hidden_dim",
default=256,
type=int,
help="Size of the embeddings (dimension of the transformer)",
)
parser.add_argument(
"--dropout", default=0.1, type=float, help="Dropout applied in the transformer"
)
parser.add_argument(
"--nheads",
default=8,
type=int,
help="Number of attention heads inside the transformer's attentions",
)
parser.add_argument(
"--num_queries",
default=1,
type=int,
help="Number of object query slots per image",
)
parser.add_argument(
"--no_pass_pos_and_query",
dest="pass_pos_and_query",
action="store_false",
help="Disables passing the positional encodings to each attention layers",
)
# Loss
parser.add_argument(
"--no_aux_loss",
dest="aux_loss",
action="store_false",
help="Disables auxiliary decoding losses (loss at each layer)",
)
parser.add_argument(
"--sigma",
type=int,
default=1,
help="standard deviation for the quantized gaussian law used for the kullback leibler divergence loss",
)
parser.add_argument(
"--no_guided_attn",
dest="guided_attn",
action="store_false",
help="whether to use the guided attention loss",
)
parser.add_argument(
"--no_sted",
dest="sted",
action="store_false",
help="whether to use start end KL loss",
)
# Loss coefficients
parser.add_argument("--bbox_loss_coef", default=5, type=float)
parser.add_argument("--giou_loss_coef", default=2, type=float)
parser.add_argument("--sted_loss_coef", default=10, type=float)
parser.add_argument("--guided_attn_loss_coef", default=1, type=float)
# Run specific
parser.add_argument(
"--test",
action="store_true",
help="Whether to run evaluation on val or test set",
)
parser.add_argument(
"--output-dir", default="", help="path where to save, empty for no saving"
)
parser.add_argument(
"--device", default="cuda", help="device to use for training / testing"
)
parser.add_argument("--seed", default=42, type=int)
parser.add_argument("--resume", default="", help="resume from checkpoint")
parser.add_argument(
"--load",
default="",
help="resume from checkpoint",
)
parser.add_argument(
"--start-epoch", default=0, type=int, metavar="N", help="start epoch"
)
parser.add_argument("--eval", action="store_true", help="Only run evaluation")
parser.add_argument("--num_workers", default=3, type=int)
# Distributed training parameters
parser.add_argument(
"--world-size", default=1, type=int, help="number of distributed processes"
)
parser.add_argument(
"--dist-url", default="env://", help="url used to set up distributed training"
)
# Video parameters
parser.add_argument(
"--resolution", type=int, default=224, help="spatial resolution of the images"
)
parser.add_argument(
"--video_max_len",
type=int,
default=200,
help="maximum number of frames for a video",
)
parser.add_argument(
"--video_max_len_train",
type=int,
default=200,
help="maximum number of frames used by the model - may it differ from video_max_len, the model ensembles start-end probability predictions at eval time",
)
parser.add_argument("--stride", type=int, default=5, help="temporal stride k")
parser.add_argument(
"--fps",
type=int,
default=5,
help="number of frames per second extracted from videos",
)
parser.add_argument(
"--no_tmp_crop",
dest="tmp_crop",
action="store_false",
help="whether to use random temporal cropping during training",
)
# Baselines
parser.add_argument(
"--no_fast",
dest="fast",
action="store_false",
help="whether to use the fast branch in the encoder",
)
parser.add_argument(
"--learn_time_embed",
action="store_true",
help="whether to learn time embeddings or use frozen sinusoidal ones",
)
parser.add_argument(
"--no_time_embed",
action="store_true",
help="whether to deactivate the time encodings or not",
)
parser.add_argument(
"--no_tsa",
action="store_true",
help="whether to deactivate the temporal self-attention in the decoder",
)
parser.add_argument(
"--rd_init_tsa",
action="store_true",
help="whether to randomly initialize the temporal self-attention in the decoder",
)
parser.add_argument(
"--fast_mode",
type=str,
default="",
choices=["", "gating", "transformer", "pool", "noslow"],
help="alternative implementations for the fast and aggregation modules",
)
parser.add_argument(
"--caption_example", default="", type=str, help="caption example for STVG demo"
)
parser.add_argument(
"--video_example",
default="",
type=str,
help="path to a video example for STVG demo",
)
parser.add_argument(
"--start_example",
default=-1,
type=int,
help="potential start (seconds) for STVG demo, =0s if <0",
)
parser.add_argument(
"--end_example",
default=-1,
type=int,
help="potential start (seconds) for STVG demo, =end of the video if <0",
)
parser.add_argument(
"--port", default=80, type=int, help="port for the STVG online demo"
)
return parser
def main(args):
# Init distributed mode
dist.init_distributed_mode(args)
# Update dataset specific configs
if args.dataset_config is not None:
# https://stackoverflow.com/a/16878364
d = vars(args)
with open(args.dataset_config, "r") as f:
cfg = json.load(f)
d.update(cfg)
print("git:\n {}\n".format(utils.get_sha()))
print(args)
device = torch.device(args.device)
output_dir = Path(args.output_dir)
# fix the seed for reproducibility
seed = args.seed + dist.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
# torch.set_deterministic(True)
torch.use_deterministic_algorithms(True)
# Build the model
model, criterion, weight_dict = build_model(args)
model.to(device)
# Get a copy of the model for exponential moving averaged version of the model
model_ema = deepcopy(model) if args.ema else None
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu], find_unused_parameters=True
)
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print("number of params:", n_parameters)
# Set up optimizers
param_dicts = [
{
"params": [
p
for n, p in model_without_ddp.named_parameters()
if "backbone" not in n and "text_encoder" not in n and p.requires_grad
]
},
{
"params": [
p
for n, p in model_without_ddp.named_parameters()
if "backbone" in n and p.requires_grad
],
"lr": args.lr_backbone,
},
{
"params": [
p
for n, p in model_without_ddp.named_parameters()
if "text_encoder" in n and p.requires_grad
],
"lr": args.text_encoder_lr,
},
]
if args.optimizer == "sgd":
optimizer = torch.optim.SGD(
param_dicts, lr=args.lr, momentum=0.9, weight_decay=args.weight_decay
)
elif args.optimizer in ["adam", "adamw"]:
optimizer = torch.optim.AdamW(
param_dicts, lr=args.lr, weight_decay=args.weight_decay
)
else:
raise RuntimeError(f"Unsupported optimizer {args.optimizer}")
"""import ipdb;
ipdb.set_trace()"""
# Train dataset
if len(args.combine_datasets) == 0 and not args.eval:
raise RuntimeError("Please provide at least one training dataset")
dataset_train, sampler_train, data_loader_train = None, None, None
if not args.eval:
dataset_train = ConcatDataset(
[
build_dataset(name, image_set="train", args=args)
for name in args.combine_datasets
]
)
# To handle very big datasets, we chunk it into smaller parts.
if args.epoch_chunks > 0:
print(
"Splitting the training set into {args.epoch_chunks} of size approximately "
f" {len(dataset_train) // args.epoch_chunks}"
)
chunks = torch.chunk(torch.arange(len(dataset_train)), args.epoch_chunks)
datasets = [
torch.utils.data.Subset(dataset_train, chunk.tolist())
for chunk in chunks
]
if args.distributed:
samplers_train = [DistributedSampler(ds) for ds in datasets]
else:
samplers_train = [torch.utils.data.RandomSampler(ds) for ds in datasets]
batch_samplers_train = [
torch.utils.data.BatchSampler(
sampler_train, args.batch_size, drop_last=True
)
for sampler_train in samplers_train
]
assert len(batch_samplers_train) == len(datasets)
data_loaders_train = [
DataLoader(
ds,
batch_sampler=batch_sampler_train,
collate_fn=partial(utils.video_collate_fn, False, 0),
num_workers=args.num_workers,
)
for ds, batch_sampler_train in zip(datasets, batch_samplers_train)
]
else:
if args.distributed:
sampler_train = DistributedSampler(dataset_train, shuffle=True)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
batch_sampler_train = torch.utils.data.BatchSampler(
sampler_train, args.batch_size, drop_last=True
)
data_loader_train = DataLoader(
dataset_train,
batch_sampler=batch_sampler_train,
collate_fn=partial(utils.video_collate_fn, False, 0),
num_workers=args.num_workers,
)
# Val dataset
if len(args.combine_datasets_val) == 0:
raise RuntimeError("Please provide at least one validation dataset")
Val_all = namedtuple(
typename="val_data",
field_names=["dataset_name", "dataloader", "evaluator_list"],
)
val_tuples = []
for dset_name in args.combine_datasets_val:
dset = build_dataset(dset_name, image_set="val", args=args)
sampler = (
DistributedSampler(dset, shuffle=False)
if args.distributed
else torch.utils.data.SequentialSampler(dset)
)
dataloader = DataLoader(
dset,
math.ceil(
(args.batch_size * args.video_max_len_train) / args.video_max_len
),
sampler=sampler,
drop_last=False,
collate_fn=partial(
utils.video_collate_fn,
False,
args.video_max_len_train
if args.video_max_len_train != args.video_max_len
else 0,
),
num_workers=args.num_workers,
)
val_tuples.append(
Val_all(dataset_name=dset_name, dataloader=dataloader, evaluator_list=None)
)
# Used for loading weights from another model and starting a training from scratch. Especially useful if
# loading into a model with different functionality.
if args.load:
print("loading from", args.load)
checkpoint = torch.load(args.load, map_location="cpu")
if "model_ema" in checkpoint:
if (
args.num_queries < 100
and "query_embed.weight" in checkpoint["model_ema"]
): # initialize from the first object queries
checkpoint["model_ema"]["query_embed.weight"] = checkpoint["model_ema"][
"query_embed.weight"
][: args.num_queries]
if "transformer.time_embed.te" in checkpoint["model_ema"]:
del checkpoint["model_ema"]["transformer.time_embed.te"]
model_without_ddp.load_state_dict(checkpoint["model_ema"], strict=False)
else:
if (
args.num_queries < 100 and "query_embed.weight" in checkpoint["model"]
): # initialize from the first object queries
checkpoint["model"]["query_embed.weight"] = checkpoint["model"][
"query_embed.weight"
][: args.num_queries]
if "transformer.time_embed.te" in checkpoint["model"]:
del checkpoint["model"]["transformer.time_embed.te"]
model_without_ddp.load_state_dict(checkpoint["model"], strict=False)
if "pretrained_resnet101_checkpoint.pth" in args.load:
model_without_ddp.transformer._reset_temporal_parameters()
if args.ema:
model_ema = deepcopy(model_without_ddp)
# Used for resuming training from the checkpoint of a model. Used when training times-out or is pre-empted.
if args.resume:
if args.resume.startswith("https"):
checkpoint = torch.hub.load_state_dict_from_url(
args.resume, map_location="cpu", check_hash=True
)
else:
checkpoint = torch.load(args.resume, map_location="cpu")
model_without_ddp.load_state_dict(checkpoint["model"])
if not args.eval and "optimizer" in checkpoint and "epoch" in checkpoint:
optimizer.load_state_dict(checkpoint["optimizer"])
args.start_epoch = checkpoint["epoch"] + 1
if args.ema:
if "model_ema" not in checkpoint:
print(
"WARNING: ema model not found in checkpoint, resetting to current model"
)
model_ema = deepcopy(model_without_ddp)
else:
model_ema.load_state_dict(checkpoint["model_ema"])
def build_evaluator_list(dataset_name):
"""Helper function to build the list of evaluators for a given dataset"""
evaluator_list = []
if "vidstg" in dataset_name:
evaluator_list.append(
VidSTGEvaluator(
args.vidstg_ann_path,
"test" if args.test else "val",
iou_thresholds=[0.3, 0.5],
fps=args.fps,
video_max_len=args.video_max_len,
save_pred=args.test,
tmp_loc=args.sted,
)
)
if "hcstvg" in dataset_name:
evaluator_list.append(
HCSTVGEvaluator(
args.hcstvg_ann_path,
"test"
if not args.v2
else "val", # no val set in v1, no test set in v2
iou_thresholds=[0.3, 0.5],
fps=args.fps,
video_max_len=args.video_max_len,
v2=args.v2,
save_pred=args.test,
tmp_loc=args.sted,
)
)
return evaluator_list
if args.tb_dir and dist.is_main_process():
writer = SummaryWriter(args.tb_dir)
else:
writer = None
# Runs only evaluation, by default on the validation set unless --test is passed.
if args.eval:
test_stats = {}
test_model = model_ema if model_ema is not None else model
for i, item in enumerate(val_tuples):
evaluator_list = build_evaluator_list(item.dataset_name)
postprocessors = build_postprocessors(args, item.dataset_name)
item = item._replace(evaluator_list=evaluator_list)
print(f"Evaluating {item.dataset_name}")
curr_test_stats = evaluate(
model=test_model,
criterion=criterion,
postprocessors=postprocessors,
weight_dict=weight_dict,
data_loader=item.dataloader,
evaluator_list=item.evaluator_list,
device=device,
args=args,
)
test_stats.update(
{item.dataset_name + "_" + k: v for k, v in curr_test_stats.items()}
)
log_stats = {
**{f"test_{k}": v for k, v in test_stats.items()},
"n_parameters": n_parameters,
}
if args.output_dir and dist.is_main_process():
json.dump(
log_stats, open(os.path.join(args.output_dir, "log_stats.json"), "w")
)
return
# Runs training and evaluates after every --eval_skip epochs
print("Start training")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.epoch_chunks > 0:
sampler_train = samplers_train[epoch % len(samplers_train)]
data_loader_train = data_loaders_train[epoch % len(data_loaders_train)]
print(
f"Starting epoch {epoch // len(data_loaders_train)}, sub_epoch {epoch % len(data_loaders_train)}"
)
else:
print(f"Starting epoch {epoch}")
if args.distributed:
sampler_train.set_epoch(epoch)
train_stats = train_one_epoch(
model=model,
criterion=criterion,
data_loader=data_loader_train,
weight_dict=weight_dict,
optimizer=optimizer,
device=device,
epoch=epoch,
args=args,
max_norm=args.clip_max_norm,
model_ema=model_ema,
writer=writer,
)
if args.output_dir:
checkpoint_paths = [output_dir / "checkpoint.pth"]
# extra checkpoint before LR drop and every 2 epochs
if (
(epoch + 1) % args.lr_drop == 0
or (epoch + 1) % 2 == 0
or (args.combine_datasets_val[0] == "vidstg")
):
checkpoint_paths.append(output_dir / f"checkpoint{epoch:04}.pth")
for checkpoint_path in checkpoint_paths:
dist.save_on_master(
{
"model": model_without_ddp.state_dict(),
"model_ema": model_ema.state_dict() if args.ema else None,
"optimizer": optimizer.state_dict(),
"epoch": epoch,
"args": args,
},
checkpoint_path,
)
if (epoch + 1) % args.eval_skip == 0:
test_stats = {}
test_model = model_ema if model_ema is not None else model
for i, item in enumerate(val_tuples):
evaluator_list = build_evaluator_list(item.dataset_name)
item = item._replace(evaluator_list=evaluator_list)
postprocessors = build_postprocessors(args, item.dataset_name)
print(f"Evaluating {item.dataset_name}")
curr_test_stats = evaluate(
model=test_model,
criterion=criterion,
postprocessors=postprocessors,
weight_dict=weight_dict,
data_loader=item.dataloader,
evaluator_list=item.evaluator_list,
device=device,
args=args,
)
test_stats.update(
{item.dataset_name + "_" + k: v for k, v in curr_test_stats.items()}
)
else:
test_stats = {}
log_stats = {
**{f"train_{k}": v for k, v in train_stats.items()},
**{f"test_{k}": v for k, v in test_stats.items()},
"epoch": epoch,
"n_parameters": n_parameters,
}
if args.output_dir and dist.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print("Training time {}".format(total_time_str))
if writer is not None:
writer.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(
"TubeDETR training and evaluation script", parents=[get_args_parser()]
)
args = parser.parse_args()
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
main(args)