db_resnet50 / tensorflow_train.py
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Update tensorflow_train.py
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import os
os.environ["USE_TF"] = "1"
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import datetime
import hashlib
import multiprocessing as mp
import time
import numpy as np
import psutil
import tensorflow as tf
from tensorflow.keras import mixed_precision
from tqdm.auto import tqdm
from doctr.models import login_to_hub, push_to_hf_hub
gpu_devices = tf.config.experimental.list_physical_devices("GPU")
if any(gpu_devices):
tf.config.experimental.set_memory_growth(gpu_devices[0], True)
from doctr import transforms as T
from doctr.datasets import DataLoader, DetectionDataset
from doctr.models import detection
from doctr.utils.metrics import LocalizationConfusion
from utils import EarlyStopper, load_backbone, plot_recorder, plot_samples
def record_lr(
model: tf.keras.Model,
train_loader: DataLoader,
batch_transforms,
optimizer,
start_lr: float = 1e-7,
end_lr: float = 1,
num_it: int = 100,
amp: bool = False,
):
"""Gridsearch the optimal learning rate for the training.
Adapted from https://github.com/frgfm/Holocron/blob/master/holocron/trainer/core.py
"""
if num_it > len(train_loader):
raise ValueError("the value of `num_it` needs to be lower than the number of available batches")
# Update param groups & LR
gamma = (end_lr / start_lr) ** (1 / (num_it - 1))
optimizer.learning_rate = start_lr
lr_recorder = [start_lr * gamma**idx for idx in range(num_it)]
loss_recorder = []
for batch_idx, (images, targets) in enumerate(train_loader):
images = batch_transforms(images)
# Forward, Backward & update
with tf.GradientTape() as tape:
train_loss = model(images, targets, training=True)["loss"]
grads = tape.gradient(train_loss, model.trainable_weights)
if amp:
grads = optimizer.get_unscaled_gradients(grads)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
optimizer.learning_rate = optimizer.learning_rate * gamma
# Record
train_loss = train_loss.numpy()
if np.any(np.isnan(train_loss)):
if batch_idx == 0:
raise ValueError("loss value is NaN or inf.")
else:
break
loss_recorder.append(train_loss.mean())
# Stop after the number of iterations
if batch_idx + 1 == num_it:
break
return lr_recorder[: len(loss_recorder)], loss_recorder
def fit_one_epoch(model, train_loader, batch_transforms, optimizer, amp=False):
train_iter = iter(train_loader)
# Iterate over the batches of the dataset
pbar = tqdm(train_iter, position=1)
for images, targets in pbar:
images = batch_transforms(images)
with tf.GradientTape() as tape:
train_loss = model(images, targets, training=True)["loss"]
grads = tape.gradient(train_loss, model.trainable_weights)
if amp:
grads = optimizer.get_unscaled_gradients(grads)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
pbar.set_description(f"Training loss: {train_loss.numpy():.6}")
def evaluate(model, val_loader, batch_transforms, val_metric):
# Reset val metric
val_metric.reset()
# Validation loop
val_loss, batch_cnt = 0, 0
val_iter = iter(val_loader)
for images, targets in tqdm(val_iter):
images = batch_transforms(images)
out = model(images, targets, training=False, return_preds=True)
# Compute metric
loc_preds = out["preds"]
for target, loc_pred in zip(targets, loc_preds):
for boxes_gt, boxes_pred in zip(target.values(), loc_pred.values()):
if args.rotation and args.eval_straight:
# Convert pred to boxes [xmin, ymin, xmax, ymax] N, 4, 2 --> N, 4
boxes_pred = np.concatenate((boxes_pred.min(axis=1), boxes_pred.max(axis=1)), axis=-1)
val_metric.update(gts=boxes_gt, preds=boxes_pred[:, :4])
val_loss += out["loss"].numpy()
batch_cnt += 1
val_loss /= batch_cnt
recall, precision, mean_iou = val_metric.summary()
return val_loss, recall, precision, mean_iou
def main(args):
print(args)
if args.push_to_hub:
login_to_hub()
if not isinstance(args.workers, int):
args.workers = min(16, mp.cpu_count())
system_available_memory = int(psutil.virtual_memory().available / 1024**3)
# AMP
if args.amp:
mixed_precision.set_global_policy("mixed_float16")
st = time.time()
val_set = DetectionDataset(
img_folder=os.path.join(args.val_path, "images"),
label_path=os.path.join(args.val_path, "labels.json"),
sample_transforms=T.SampleCompose(
(
[T.Resize((args.input_size, args.input_size), preserve_aspect_ratio=True, symmetric_pad=True)]
if not args.rotation or args.eval_straight
else []
)
+ (
[
T.Resize(args.input_size, preserve_aspect_ratio=True), # This does not pad
T.RandomApply(T.RandomRotate(90, expand=True), 0.5),
T.Resize((args.input_size, args.input_size), preserve_aspect_ratio=True, symmetric_pad=True),
]
if args.rotation and not args.eval_straight
else []
)
),
use_polygons=args.rotation and not args.eval_straight,
)
val_loader = DataLoader(
val_set,
batch_size=args.batch_size,
shuffle=False,
drop_last=False,
num_workers=args.workers,
)
print(
f"Validation set loaded in {time.time() - st:.4}s ({len(val_set)} samples in "
f"{val_loader.num_batches} batches)"
)
with open(os.path.join(args.val_path, "labels.json"), "rb") as f:
val_hash = hashlib.sha256(f.read()).hexdigest()
batch_transforms = T.Compose([
T.Normalize(mean=(0.798, 0.785, 0.772), std=(0.264, 0.2749, 0.287)),
])
# Load doctr model
model = detection.__dict__[args.arch](
pretrained=args.pretrained,
input_shape=(args.input_size, args.input_size, 3),
assume_straight_pages=not args.rotation,
class_names=val_set.class_names,
)
# Resume weights
if isinstance(args.resume, str):
model.load_weights(args.resume)
if isinstance(args.pretrained_backbone, str):
print("Loading backbone weights.")
model = load_backbone(model, args.pretrained_backbone)
print("Done.")
# Metrics
val_metric = LocalizationConfusion(
use_polygons=args.rotation and not args.eval_straight,
mask_shape=(args.input_size, args.input_size),
use_broadcasting=True if system_available_memory > 62 else False,
)
if args.test_only:
print("Running evaluation")
val_loss, recall, precision, mean_iou = evaluate(model, val_loader, batch_transforms, val_metric)
print(
f"Validation loss: {val_loss:.6} (Recall: {recall:.2%} | Precision: {precision:.2%} | "
f"Mean IoU: {mean_iou:.2%})"
)
return
st = time.time()
# Load both train and val data generators
train_set = DetectionDataset(
img_folder=os.path.join(args.train_path, "images"),
label_path=os.path.join(args.train_path, "labels.json"),
img_transforms=T.Compose([
# Augmentations
T.RandomApply(T.ColorInversion(), 0.1),
T.RandomJpegQuality(60),
T.RandomApply(T.GaussianNoise(mean=0.1, std=0.1), 0.1),
T.RandomApply(T.RandomShadow(), 0.4),
T.RandomApply(T.GaussianBlur(kernel_shape=3, std=(0.1, 0.1)), 0.3),
T.RandomSaturation(0.3),
T.RandomContrast(0.3),
T.RandomBrightness(0.3),
T.RandomApply(T.ToGray(num_output_channels=3), 0.1),
]),
sample_transforms=T.SampleCompose(
(
[T.Resize((args.input_size, args.input_size), preserve_aspect_ratio=True, symmetric_pad=True)]
if not args.rotation
else []
)
+ (
[
T.Resize(args.input_size, preserve_aspect_ratio=True), # This does not pad
T.RandomApply(T.RandomRotate(90, expand=True), 0.5),
T.Resize((args.input_size, args.input_size), preserve_aspect_ratio=True, symmetric_pad=True),
]
if args.rotation
else []
)
),
use_polygons=args.rotation,
)
train_loader = DataLoader(
train_set,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
num_workers=args.workers,
)
print(
f"Train set loaded in {time.time() - st:.4}s ({len(train_set)} samples in "
f"{train_loader.num_batches} batches)"
)
with open(os.path.join(args.train_path, "labels.json"), "rb") as f:
train_hash = hashlib.sha256(f.read()).hexdigest()
if args.show_samples:
x, target = next(iter(train_loader))
plot_samples(x, target)
return
# Optimizer
scheduler = tf.keras.optimizers.schedules.ExponentialDecay(
args.lr,
decay_steps=args.epochs * len(train_loader),
decay_rate=1 / (25e4), # final lr as a fraction of initial lr
staircase=False,
name="ExponentialDecay",
)
optimizer = tf.keras.optimizers.Adam(learning_rate=scheduler, beta_1=0.95, beta_2=0.99, epsilon=1e-6, clipnorm=5)
if args.amp:
optimizer = mixed_precision.LossScaleOptimizer(optimizer)
# LR Finder
if args.find_lr:
lrs, losses = record_lr(model, train_loader, batch_transforms, optimizer, amp=args.amp)
plot_recorder(lrs, losses)
return
# Tensorboard to monitor training
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
exp_name = f"{args.arch}_{current_time}" if args.name is None else args.name
config = {
"learning_rate": args.lr,
"epochs": args.epochs,
"batch_size": args.batch_size,
"architecture": args.arch,
"input_size": args.input_size,
"optimizer": optimizer.name,
"framework": "tensorflow",
"scheduler": scheduler.name,
"train_hash": train_hash,
"val_hash": val_hash,
"pretrained": args.pretrained,
"rotation": args.rotation,
}
# W&B
if args.wb:
import wandb
run = wandb.init(name=exp_name, project="text-detection", config=config)
# ClearML
if args.clearml:
from clearml import Task
task = Task.init(project_name="docTR/text-detection", task_name=exp_name, reuse_last_task_id=False)
task.upload_artifact("config", config)
if args.freeze_backbone:
for layer in model.feat_extractor.layers:
layer.trainable = False
min_loss = np.inf
if args.early_stop:
early_stopper = EarlyStopper(patience=args.early_stop_epochs, min_delta=args.early_stop_delta)
# Training loop
for epoch in range(args.epochs):
fit_one_epoch(model, train_loader, batch_transforms, optimizer, args.amp)
# Validation loop at the end of each epoch
val_loss, recall, precision, mean_iou = evaluate(model, val_loader, batch_transforms, val_metric)
if val_loss < min_loss:
print(f"Validation loss decreased {min_loss:.6} --> {val_loss:.6}: saving state...")
model.save_weights(f"./{exp_name}/weights")
min_loss = val_loss
log_msg = f"Epoch {epoch + 1}/{args.epochs} - Validation loss: {val_loss:.6} "
if any(val is None for val in (recall, precision, mean_iou)):
log_msg += "(Undefined metric value, caused by empty GTs or predictions)"
else:
log_msg += f"(Recall: {recall:.2%} | Precision: {precision:.2%} | Mean IoU: {mean_iou:.2%})"
print(log_msg)
# W&B
if args.wb:
wandb.log({
"val_loss": val_loss,
"recall": recall,
"precision": precision,
"mean_iou": mean_iou,
})
# ClearML
if args.clearml:
from clearml import Logger
logger = Logger.current_logger()
logger.report_scalar(title="Validation Loss", series="val_loss", value=val_loss, iteration=epoch)
logger.report_scalar(title="Precision Recall", series="recall", value=recall, iteration=epoch)
logger.report_scalar(title="Precision Recall", series="precision", value=precision, iteration=epoch)
logger.report_scalar(title="Mean IoU", series="mean_iou", value=mean_iou, iteration=epoch)
if args.early_stop and early_stopper.early_stop(val_loss):
print("Training halted early due to reaching patience limit.")
break
if args.wb:
run.finish()
if args.push_to_hub:
push_to_hf_hub(model, exp_name, task="detection", run_config=args)
def parse_args():
import argparse
parser = argparse.ArgumentParser(
description="DocTR training script for text detection (TensorFlow)",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("arch", type=str, help="text-detection model to train")
parser.add_argument("--train_path", type=str, required=True, help="path to training data folder")
parser.add_argument("--val_path", type=str, help="path to validation data folder")
parser.add_argument("--name", type=str, default=None, help="Name of your training experiment")
parser.add_argument("--epochs", type=int, default=10, help="number of epochs to train the model on")
parser.add_argument("-b", "--batch_size", type=int, default=2, help="batch size for training")
parser.add_argument("--input_size", type=int, default=1024, help="model input size, H = W")
parser.add_argument("--lr", type=float, default=0.001, help="learning rate for the optimizer (Adam)")
parser.add_argument("-j", "--workers", type=int, default=None, help="number of workers used for dataloading")
parser.add_argument("--resume", type=str, default=None, help="Path to your checkpoint")
parser.add_argument("--pretrained-backbone", type=str, default=None, help="Path to your backbone weights")
parser.add_argument("--test-only", dest="test_only", action="store_true", help="Run the validation loop")
parser.add_argument(
"--freeze-backbone", dest="freeze_backbone", action="store_true", help="freeze model backbone for fine-tuning"
)
parser.add_argument(
"--show-samples", dest="show_samples", action="store_true", help="Display unormalized training samples"
)
parser.add_argument("--wb", dest="wb", action="store_true", help="Log to Weights & Biases")
parser.add_argument("--clearml", dest="clearml", action="store_true", help="Log to ClearML")
parser.add_argument("--push-to-hub", dest="push_to_hub", action="store_true", help="Push to Huggingface Hub")
parser.add_argument(
"--pretrained",
dest="pretrained",
action="store_true",
help="Load pretrained parameters before starting the training",
)
parser.add_argument("--rotation", dest="rotation", action="store_true", help="train with rotated documents")
parser.add_argument(
"--eval-straight",
action="store_true",
help="metrics evaluation with straight boxes instead of polygons to save time + memory",
)
parser.add_argument("--amp", dest="amp", help="Use Automatic Mixed Precision", action="store_true")
parser.add_argument("--find-lr", action="store_true", help="Gridsearch the optimal LR")
parser.add_argument("--early-stop", action="store_true", help="Enable early stopping")
parser.add_argument("--early-stop-epochs", type=int, default=5, help="Patience for early stopping")
parser.add_argument("--early-stop-delta", type=float, default=0.01, help="Minimum Delta for early stopping")
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
main(args)