File size: 16,479 Bytes
747451d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 |
# /*---------------------------------------------------------------------------------------------
# * Copyright (c) 2022-2023 STMicroelectronics.
# * All rights reserved.
# *
# * This software is licensed under terms that can be found in the LICENSE file in
# * the root directory of this software component.
# * If no LICENSE file comes with this software, it is provided AS-IS.
# *--------------------------------------------------------------------------------------------*/
# Import necessary libraries
import os
import sys
from timeit import default_timer as timer
from datetime import timedelta
from typing import Tuple, List, Dict, Optional
import mlflow
from hydra.core.hydra_config import HydraConfig
from munch import DefaultMunch
from omegaconf import DictConfig
import numpy as np
import tensorflow as tf
# Suppress TensorFlow warnings to reduce log clutter
import logging
logging.getLogger('mlflow.tensorflow').setLevel(logging.ERROR)
logging.getLogger('tensorflow').setLevel(logging.ERROR)
# Import utility functions and modules
from common.utils import (
log_to_file, log_last_epoch_history, LRTensorBoard, check_training_determinism,
model_summary, collect_callback_args, vis_training_curves
)
from common.training import (
set_frozen_layers, set_dropout_rate, get_optimizer, lr_schedulers,
set_all_layers_trainable_parameter
)
from image_classification.tf.src.utils import get_loss, change_model_number_of_classes, change_model_input_shape
from image_classification.tf.src.data_augmentation import DataAugmentationLayer
# Define a custom callback for multi-resolution training
class MultiResCallback(tf.keras.callbacks.Callback):
"""
A custom Keras callback to dynamically change the input resolution
of the model during training.
Args:
image_sizes (List[int]): List of resolutions to cycle through.
period (int): Number of batches before changing resolution.
name (str, optional): Name of the callback.
"""
def __init__(self, image_sizes, period, name=None):
super().__init__()
self.resolutions = image_sizes
self.period = period
def on_train_batch_begin(self, batch, logs=None):
# Change the resolution of the input layer based on the batch number
res = self.resolutions[((batch - 1) // self.period) % len(self.resolutions)]
self.model.layers[0].change_res(res)
# Function to add preprocessing layers to the model
def _add_preprocessing_layers(
model: tf.keras.Model,
input_shape: Tuple = None,
scale: float = None,
offset: float = None,
mean: float = None,
std: float = None,
data_augmentation: Dict = None,
batches_per_epoch: float = None):
"""
Adds preprocessing layers (rescaling and data augmentation) to the model.
Args:
model (tf.keras.Model): The base model.
input_shape (Tuple): Input shape of the model.
scale (float): Scaling factor for rescaling.
offset (float): Offset for rescaling.
mean (float): Mean for normalization.
std (float): Standard deviation for normalization.
data_augmentation (Dict): Data augmentation configuration.
batches_per_epoch (float): Number of training batches per epoch.
Returns:
tf.keras.Model: The augmented model with preprocessing layers.
"""
data_aug_args = DefaultMunch.fromDict(data_augmentation.config)
if data_aug_args.random_periodic_resizing is not None:
model, _ = change_model_input_shape(model, (None, None, None, 3))
model_layers = []
model_layers.append(tf.keras.Input(shape=input_shape))
# Add data augmentation layer if specified
if data_augmentation:
# defining rescaling and normalization in case the three values are provided for std and mean
if isinstance(std, float) and isinstance(mean, float):
pixels_range = ((offset - mean) / std, (scale * 255 + offset - mean) / std)
elif isinstance(std, list) and isinstance(mean, list):
if len(std) != 3 or len(mean) != 3:
raise ValueError("If std and mean are lists, they must have three elements each.")
pixel_range_min = [(offset - m) / s for m, s in zip(mean, std)]
pixel_range_max = [(scale * 255 + offset - m) / s for m, s in zip(mean, std)]
pixels_range = (min(pixel_range_min), max(pixel_range_max))
else:
raise TypeError("std and mean must be either floats or lists of length 3.")
model_layers.append(
DataAugmentationLayer(
data_augmentation_fn=data_augmentation.function_name,
config=data_augmentation.config,
pixels_range=pixels_range,
batches_per_epoch=batches_per_epoch
)
)
model_layers.append(model)
augmented_model = tf.keras.Sequential(model_layers, name="augmented_model")
return augmented_model
# Function to create Keras callbacks
def _get_callbacks(callbacks_dict: DictConfig, output_dir: str = None, logs_dir: str = None,
saved_models_dir: str = None) -> List[tf.keras.callbacks.Callback]:
"""
Creates a list of Keras callbacks for training.
Args:
callbacks_dict (DictConfig): Configuration for callbacks.
output_dir (str): Directory for saving outputs.
logs_dir (str): Directory for saving logs.
saved_models_dir (str): Directory for saving models.
For each callback, the attributes and their values used in the config
file are used to create a string that is the callback instantiation as
it would be written in a Python script. Then, the string is evaluated.
If the evaluation succeeds, the callback object is returned. If it fails,
an error is thrown with a message saying that the name and/or arguments
of the callback are incorrect.
Returns:
List[tf.keras.callbacks.Callback]: List of callbacks.
"""
message = "\nPlease check the 'training.callbacks' section of your configuration file."
lr_scheduler_names = lr_schedulers.get_scheduler_names()
num_lr_schedulers = 0
# Generate the callbacks used in the config file (there may be none)
callback_list = []
if callbacks_dict is not None:
if type(callbacks_dict) != DefaultMunch:
raise ValueError(f"\nInvalid callbacks syntax{message}")
for name in callbacks_dict.keys():
if name in ("ModelCheckpoint", "TensorBoard", "CSVLogger"):
raise ValueError(f"\nThe `{name}` callback is built-in and can't be redefined.{message}")
elif name in lr_scheduler_names:
text = f"lr_schedulers.{name}"
elif name == 'MultiResCallback':
text = f"{name}"
else:
text = f"tf.keras.callbacks.{name}"
# Add the arguments to the callback string
# and evaluate it to get the callback object
text += collect_callback_args(name, args=callbacks_dict[name], message=message)
try:
callback = eval(text)
except ValueError as error:
raise ValueError(f"\nThe callback name `{name}` is unknown, or its arguments are incomplete "
f"or invalid\nReceived: {text}{message}") from error
callback_list.append(callback)
if name in lr_scheduler_names + ["ReduceLROnPlateau", "LearningRateScheduler"]:
num_lr_schedulers += 1
# Check that there is only one scheduler
if num_lr_schedulers > 1:
raise ValueError(f"\nFound more than one learning rate scheduler{message}")
# Add built-in callbacks that saves the best model obtained so far
callback_list.append(tf.keras.callbacks.ModelCheckpoint(
filepath=os.path.join(output_dir, saved_models_dir, "best_augmented_model.keras"),
save_best_only=True,
monitor="val_accuracy",
mode="max"
))
# Add the Keras callback that saves the model at the end of the epoch
callback_list.append(tf.keras.callbacks.ModelCheckpoint(
filepath=os.path.join(output_dir, saved_models_dir, "last_augmented_model.keras"),
save_best_only=False,
monitor="val_accuracy",
mode="max"
))
# Add the TensorBoard callback
callback_list.append(LRTensorBoard(log_dir=os.path.join(output_dir, logs_dir)))
# Add the CVSLogger callback (must be last in the list
# of callbacks to make sure it records the learning rate)
callback_list.append(tf.keras.callbacks.CSVLogger(os.path.join(output_dir, logs_dir, "metrics", "train_metrics.csv")))
return callback_list
# Main class for training image classification models
class ICTrainer:
def __init__(self, cfg, model=None, dataloaders=None):
"""
Initializes the trainer with configuration, model, and datasets.
Args:
cfg: Configuration object.
model: TensorFlow model.
dataloaders: Dictionary containing training, validation, and test datasets.
"""
self.cfg = cfg
self.model = model
self.train_ds = dataloaders['train']
self.valid_ds = dataloaders['valid']
self.test_ds = dataloaders['test']
self.output_dir = HydraConfig.get().runtime.output_dir
self.saved_models_dir = cfg.general.saved_models_dir
self.class_names = cfg.dataset.class_names
self.num_classes = len(self.class_names)
self.augmented_model = None
self.callbacks = None
self.history = None
def prepare(self):
"""
Prepares the model, datasets, and callbacks for training.
"""
# Print dataset statistics
print("Dataset stats:")
train_size = sum([x.shape[0] for x, _ in self.train_ds])
valid_size = sum([x.shape[0] for x, _ in self.valid_ds])
if self.test_ds:
test_size = sum([x.shape[0] for x, _ in self.test_ds])
print(" classes:", self.num_classes)
print(" training set size:", train_size)
print(" validation set size:", valid_size)
if self.test_ds:
print(" test set size:", test_size)
else:
print(" no test set")
# Log dataset information
if self.cfg.dataset.dataset_name:
log_to_file(self.output_dir, f"Dataset : {self.cfg.dataset.dataset_name}")
# Prepare the model
if self.cfg.model:
cfm = self.cfg.model
print(f"[INFO] : Using `{cfm.model_name}` model")
log_to_file(self.cfg.output_dir, (f"Model name : {cfm.model_name}"))
elif self.cfg.model.model_path:
self.model = change_model_number_of_classes(self.model, self.num_classes)
print(f"[INFO] : Initialized model with weights from model file {self.cfg.model.model_path}")
log_to_file(self.cfg.output_dir, (f"Weights from model file : {self.cfg.model.model_path}"))
# Add preprocessing layers if not resuming training
if self.cfg.training.resume_training_from:
model_summary(self.model)
self.augmented_model = self.model
else:
model_summary(self.model)
input_shape = tuple(self.model.inputs[0].shape[1:])
self.augmented_model = _add_preprocessing_layers(
self.model,
input_shape=input_shape,
scale=self.cfg.preprocessing.rescaling.scale,
offset=self.cfg.preprocessing.rescaling.offset,
mean=getattr(self.cfg.preprocessing.normalization, 'mean', 0.0),
std=getattr(self.cfg.preprocessing.normalization, 'std', 1.0),
data_augmentation=self.cfg.data_augmentation,
batches_per_epoch=len(self.train_ds)
)
self.augmented_model.compile(
loss=get_loss(num_classes=self.num_classes),
metrics=['accuracy'],
optimizer=get_optimizer(cfg=self.cfg.training.optimizer)
)
# Configure MultiResCallback if applicable
data_aug_args = DefaultMunch.fromDict(self.cfg.data_augmentation.config)
if data_aug_args.random_periodic_resizing is not None:
rpr = DefaultMunch.fromDict(data_aug_args.random_periodic_resizing)
if rpr.image_sizes is not None:
self.cfg.training.callbacks['MultiResCallback'] = DefaultMunch.fromDict({
'image_sizes': rpr.image_sizes,
'period': rpr.period if rpr.period is not None else 10
})
else:
print("[WARNING]: 'random_periodic_resizing' can't be used because [image_sizes] argument is missing.")
# Generate callbacks
self.callbacks = _get_callbacks(
callbacks_dict=self.cfg.training.callbacks,
output_dir=self.output_dir,
saved_models_dir=self.saved_models_dir,
logs_dir=self.cfg.general.logs_dir
)
def enable_determinism(self):
"""
Enables deterministic operations for reproducibility.
"""
if self.cfg.general.deterministic_ops:
sample_ds = self.train_ds.take(1)
tf.config.experimental.enable_op_determinism()
if not check_training_determinism(self.augmented_model, sample_ds):
print("[WARNING]: Some operations cannot be run deterministically. Setting deterministic_ops to False.")
tf.config.experimental.enable_op_determinism.__globals__["_pywrap_determinism"].enable(False)
def fit(self):
"""
Trains the model using the training dataset.
"""
print("Starting training...")
start_time = timer()
steps_per_epoch = self.cfg.training.dryrun if self.cfg.training.dryrun else None
self.history = self.augmented_model.fit(
self.train_ds,
validation_data=self.valid_ds,
epochs=self.cfg.training.epochs,
steps_per_epoch=steps_per_epoch,
callbacks=self.callbacks
)
last_epoch = log_last_epoch_history(self.cfg, self.output_dir)
end_time = timer()
fit_run_time = int(end_time - start_time)
average_time_per_epoch = round(fit_run_time / (int(last_epoch) + 1), 2)
print("Training runtime: " + str(timedelta(seconds=fit_run_time)))
log_to_file(self.cfg.output_dir, (
f"Training runtime : {fit_run_time} s\n" +
f"Average time per epoch : {average_time_per_epoch} s"
))
vis_training_curves(history=self.history, output_dir=self.output_dir)
def save_and_evaluate(self):
"""
Saves the best model and evaluates it on validation and test datasets.
"""
# Load the best model checkpoint
models_dir = os.path.join(self.output_dir, self.saved_models_dir)
checkpoint_filepath = os.path.join(models_dir, "best_augmented_model.keras")
checkpoint_model = tf.keras.models.load_model(
checkpoint_filepath,
custom_objects={'DataAugmentationLayer': DataAugmentationLayer}
)
output_model_input_shape = tuple(self.model.inputs[0].shape)
best_model = checkpoint_model.layers[-1]
best_model, _ = change_model_input_shape(best_model, output_model_input_shape)
best_model.compile(loss=get_loss(self.num_classes), metrics=['accuracy'])
best_model_path = os.path.join(self.output_dir, f"{self.saved_models_dir}/best_model.keras")
best_model.save(best_model_path)
setattr(best_model, 'model_path', best_model_path)
print('[INFO] : Training complete.')
return best_model
def train(self):
"""
Executes the full training pipeline: prepare, train, save, and evaluate.
"""
self.prepare()
self.enable_determinism()
self.fit()
return self.save_and_evaluate()
|