File size: 8,748 Bytes
2508db9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | import gc
import argparse
import datetime
import os
import logging
import torch
from torch_geometric.loader import DataLoader
from torch_geometric.data import Batch
from tqdm.autonotebook import tqdm
from omegaconf import OmegaConf
from swomo.data.dataset import SurgicalDataset
from swomo.graph_encoder.graph_segclip_masked import *
from swomo.utils.get_scene_graph import GraphConstructor
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group["lr"]
def create_logger(log_file_path):
logger = logging.getLogger('logger')
logger.setLevel(logging.DEBUG)
file_handler = logging.FileHandler(log_file_path)
file_handler.setLevel(logging.DEBUG)
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler.setFormatter(formatter)
console_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.addHandler(console_handler)
return logger
def build_loaders(video_folder, mode, dataset_name, class_size, ignore_index, size, num_graph_in_3d, overlap_size, augmentation, batch_size, num_workers):
dataset = SurgicalDataset(
video_folder=video_folder,
split_mode=mode,
dataset_name=dataset_name,
class_size=class_size,
ignore_index=ignore_index,
sample_size=size,
sample_n_frames=num_graph_in_3d,
overlap_size=overlap_size,
apply_augmentation=augmentation if mode == "train" else False,
train_graph_encoder=True,
)
dataloader = DataLoader(
dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=True if mode == "train" else False,
)
return dataloader
def train_epoch(model, train_loader, optimizer, device, graphconstr=None):
loss_meter = AvgMeter()
tqdm_object = tqdm(train_loader, total=len(train_loader))
for batch in tqdm_object:
batch = {k: v.to(device) for k, v in batch.items() if not k.endswith("path")}
# Outside the dataset, bcs need cuda for optical flow info for graph construction
if train_loader.dataset.apply_augmentation:
graphs = []
for n_b in range(batch['image'].shape[0]):
graph = graphconstr.create_scene_graph(batch['graph_cond'][n_b], batch['graph_segmentation'][n_b])
graphs.append(graph)
graphs = Batch.from_data_list(graphs)
batch["graph"] = graphs.to(device)
loss = model(batch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
count = batch["image"].size(0)
loss_meter.update(loss.item(), count)
tqdm_object.set_postfix(train_loss=loss_meter.avg, lr=get_lr(optimizer))
return loss_meter
def valid_epoch(model, valid_loader, device):
loss_meter = AvgMeter()
tqdm_object = tqdm(valid_loader, total=len(valid_loader))
for batch in tqdm_object:
batch = {k: v.to(device) for k, v in batch.items() if not k.endswith("path")}
loss = model(batch)
count = batch["image"].size(0)
loss_meter.update(loss.item(), count)
tqdm_object.set_postfix(valid_loss=loss_meter.avg)
return loss_meter
def train(name,
dataset_name="cataract",
class_size=18,
ignore_index="first",
num_graph_in_3d=16,
overlap_size=1,
size=128,
batch_size=32,
num_workers=8,
weight_decay=1e-5,
patience=1,
factor=0.8,
epochs=200,
graph_encoder_lr=1e-5,
graph_input_dim=21,
graph_hidden_dim=256,
graph_embedding_dim=256,
graph_conv_type="GCNConv",
graph_norm_type="GroupNorm",
graph_encoder_ckpt=None,
image_encoder_lr=1e-6,
image_embedding_dim=256,
image_encoder_config="",
image_encoder_ckpt="",
segmentation_encoder_lr=1e-7,
segmentation_embedding_dim=256,
segmentation_encoder_config="",
segmentation_encoder_ckpt="",
augmentation=False,
trainable=True,
temperature=1.0,
dropout=0.25,
data_root="",
log_dir="",
device="cuda",
**kwargs):
slurm_job_id = os.environ.get("SLURM_JOB_ID", None)
timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
if slurm_job_id is not None:
exp_dir = f"{log_dir}/graphencoder_{name}_{dataset_name}_{slurm_job_id}-{timestamp}"
else:
exp_dir = f"{log_dir}/graphencoder_{name}_{dataset_name}-{timestamp}"
checkpoint_dir = f"{exp_dir}/checkpoints"
os.makedirs(exp_dir, exist_ok=True)
os.makedirs(checkpoint_dir, exist_ok=True)
OmegaConf.save(config, os.path.join(exp_dir, 'config.yaml'))
log = create_logger(f"{exp_dir}/logfile.log")
log.info(f"{torch.cuda.is_available()=}")
train_loader = build_loaders(video_folder=data_root, mode="train", dataset_name=dataset_name, class_size=class_size, ignore_index=ignore_index, size=size, num_graph_in_3d=num_graph_in_3d, overlap_size=overlap_size, augmentation=augmentation, batch_size=batch_size, num_workers=num_workers)
valid_loader = build_loaders(video_folder=data_root, mode="val", dataset_name=dataset_name, class_size=class_size, ignore_index=ignore_index, size=size, num_graph_in_3d=num_graph_in_3d, overlap_size=overlap_size, augmentation=augmentation, batch_size=batch_size, num_workers=num_workers)
if name == "segclip":
graph_encoder = GraphEncoder(graph_input_dim, graph_hidden_dim, graph_embedding_dim, trainable, device, dropout, graph_conv_type, graph_norm_type, graph_encoder_ckpt)
segmentation_encoder = SegmentationEncoder(device, segmentation_encoder_config, segmentation_encoder_ckpt)
model = SegClipModel(temperature, segmentation_embedding_dim, segmentation_encoder, graph_encoder).to(device)
params = [{"params": model.graph_encoder.parameters(), "lr": graph_encoder_lr},
# {"params": model.segmentation_encoder.parameters(), "lr": segmentation_encoder_lr}
]
elif name == "masked":
graph_encoder = GraphEncoder(graph_input_dim, graph_hidden_dim, graph_embedding_dim, trainable, device, dropout, graph_conv_type, graph_norm_type, graph_encoder_ckpt)
image_encoder = ImageEncoder(device, image_encoder_config, image_encoder_ckpt)
model = MaskedLocalModel(ignore_index, dropout, image_embedding_dim, image_encoder, graph_embedding_dim, graph_encoder).to(device)
params = [{"params": model.basic_transformer.parameters(), "lr": image_encoder_lr},
{"params": model.graph_encoder.parameters(), "lr": graph_encoder_lr}
]
#TODO: add beta, eps
optimizer = torch.optim.AdamW(params, weight_decay=weight_decay)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", patience=patience, factor=factor)
if augmentation:
log.info("### Using data augmentation ###")
log.info("### Graphs will be formed on the fly and runs much slower ###")
graphconstr = GraphConstructor(num_graph_in_3d=num_graph_in_3d, device=device, num_classes=class_size, background_label=kwargs['background_label'], anatomy_label=kwargs['anatomy_label'], tool_label=kwargs['tool_label'])
best_loss = float('inf')
for epoch in range(epochs):
log.info(f"Epoch: {epoch + 1}")
model.train()
train_loss = train_epoch(model, train_loader, optimizer, device, graphconstr if augmentation else None)
model.eval()
with torch.no_grad():
valid_loss = valid_epoch(model, valid_loader, device)
log.info(f"### Training Loss: {train_loss} ###")
log.info(f"### Validation Loss: {valid_loss} ###")
torch.save(model.state_dict(), os.path.join(checkpoint_dir, 'latest_val_loss.pth'))
if valid_loss.avg < best_loss:
best_loss = valid_loss.avg
log.info("### New best validation loss ###")
torch.save(model.state_dict(), os.path.join(checkpoint_dir, 'best_val_loss.pth'))
lr_scheduler.step(valid_loss.avg)
gc.collect()
torch.cuda.empty_cache()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--name", type=str, required=True, choices=["segclip", "masked"])
parser.add_argument("--config", type=str, required=True)
args = parser.parse_args()
config = OmegaConf.load(args.config)
train(name=args.name, **config) |