summaryrefslogtreecommitdiff
path: root/src/training/run_experiment.py
blob: 2c9a19699ce926265bb1d2be8b4636bd8f432e02 (plain)
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
374
375
376
377
"""Script to run experiments."""
from datetime import datetime
from glob import glob
import importlib
import json
import os
from pathlib import Path
import re
from typing import Callable, Dict, List, Optional, Tuple, Type
import warnings

import click
from loguru import logger
import numpy as np
import torch
from torchsummary import summary
from tqdm import tqdm
from training.gpu_manager import GPUManager
from training.trainer.callbacks import CallbackList
from training.trainer.train import Trainer
import wandb
import yaml

import text_recognizer.models
from text_recognizer.models import Model
import text_recognizer.networks
from text_recognizer.networks.loss import loss as custom_loss_module

EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"


def _get_level(verbose: int) -> int:
    """Sets the logger level."""
    levels = {0: 40, 1: 20, 2: 10}
    verbose = verbose if verbose <= 2 else 2
    return levels[verbose]


def _create_experiment_dir(
    experiment_config: Dict, checkpoint: Optional[str] = None
) -> Path:
    """Create new experiment."""
    EXPERIMENTS_DIRNAME.mkdir(parents=True, exist_ok=True)
    experiment_dir = EXPERIMENTS_DIRNAME / (
        f"{experiment_config['model']}_"
        + f"{experiment_config['dataset']['type']}_"
        + f"{experiment_config['network']['type']}"
    )

    if checkpoint is None:
        experiment = datetime.now().strftime("%m%d_%H%M%S")
        logger.debug(f"Creating a new experiment called {experiment}")
    else:
        available_experiments = glob(str(experiment_dir) + "/*")
        available_experiments.sort()
        if checkpoint == "last":
            experiment = available_experiments[-1]
            logger.debug(f"Resuming the latest experiment {experiment}")
        else:
            experiment = checkpoint
            if not str(experiment_dir / experiment) in available_experiments:
                raise FileNotFoundError("Experiment does not exist.")
            logger.debug(f"Resuming the from experiment {checkpoint}")

    experiment_dir = experiment_dir / experiment

    # Create log and model directories.
    log_dir = experiment_dir / "log"
    model_dir = experiment_dir / "model"

    return experiment_dir, log_dir, model_dir


def _load_modules_and_arguments(experiment_config: Dict,) -> Tuple[Callable, Dict]:
    """Loads all modules and arguments."""
    # Load the dataset module.
    dataset_args = experiment_config.get("dataset", {})
    dataset_ = dataset_args["type"]

    # Import the model module and model arguments.
    model_class_ = getattr(text_recognizer.models, experiment_config["model"])

    # Import metrics.
    metric_fns_ = (
        {
            metric: getattr(text_recognizer.networks, metric)
            for metric in experiment_config["metrics"]
        }
        if experiment_config["metrics"] is not None
        else None
    )

    # Import network module and arguments.
    network_fn_ = experiment_config["network"]["type"]
    network_args = experiment_config["network"].get("args", {})

    # Criterion
    if experiment_config["criterion"]["type"] in custom_loss_module.__all__:
        criterion_ = getattr(custom_loss_module, experiment_config["criterion"]["type"])
    else:
        criterion_ = getattr(torch.nn, experiment_config["criterion"]["type"])
    criterion_args = experiment_config["criterion"].get("args", {}) or {}

    # Optimizers
    optimizer_ = getattr(torch.optim, experiment_config["optimizer"]["type"])
    optimizer_args = experiment_config["optimizer"].get("args", {})

    # Learning rate scheduler
    lr_scheduler_ = None
    lr_scheduler_args = None
    if "lr_scheduler" in experiment_config:
        lr_scheduler_ = getattr(
            torch.optim.lr_scheduler, experiment_config["lr_scheduler"]["type"]
        )
        lr_scheduler_args = experiment_config["lr_scheduler"].get("args", {}) or {}

    # SWA scheduler.
    if "swa_args" in experiment_config:
        swa_args = experiment_config.get("swa_args", {}) or {}
    else:
        swa_args = None

    model_args = {
        "dataset": dataset_,
        "dataset_args": dataset_args,
        "metrics": metric_fns_,
        "network_fn": network_fn_,
        "network_args": network_args,
        "criterion": criterion_,
        "criterion_args": criterion_args,
        "optimizer": optimizer_,
        "optimizer_args": optimizer_args,
        "lr_scheduler": lr_scheduler_,
        "lr_scheduler_args": lr_scheduler_args,
        "swa_args": swa_args,
    }

    return model_class_, model_args


def _configure_callbacks(experiment_config: Dict, model_dir: Path) -> CallbackList:
    """Configure a callback list for trainer."""
    if "Checkpoint" in experiment_config["callback_args"]:
        experiment_config["callback_args"]["Checkpoint"]["checkpoint_path"] = str(
            model_dir
        )

    # Initializes callbacks.
    callback_modules = importlib.import_module("training.trainer.callbacks")
    callbacks = []
    for callback in experiment_config["callbacks"]:
        args = experiment_config["callback_args"][callback] or {}
        callbacks.append(getattr(callback_modules, callback)(**args))

    return callbacks


def _configure_logger(log_dir: Path, verbose: int = 0) -> None:
    """Configure the loguru logger for output to terminal and disk."""
    # Have to remove default logger to get tqdm to work properly.
    logger.remove()

    # Fetch verbosity level.
    level = _get_level(verbose)

    logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True, level=level)
    logger.add(
        str(log_dir / "train.log"),
        format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}",
    )


def _save_config(experiment_dir: Path, experiment_config: Dict) -> None:
    """Copy config to experiment directory."""
    config_path = experiment_dir / "config.yml"
    with open(str(config_path), "w") as f:
        yaml.dump(experiment_config, f)


def _load_from_checkpoint(
    model: Type[Model], model_dir: Path, pretrained_weights: str = None,
) -> None:
    """If checkpoint exists, load model weights and optimizers from checkpoint."""
    # Get checkpoint path.
    if pretrained_weights is not None:
        logger.info(f"Loading weights from {pretrained_weights}.")
        checkpoint_path = (
            EXPERIMENTS_DIRNAME / Path(pretrained_weights) / "model" / "best.pt"
        )
    else:
        logger.info(f"Loading weights from {model_dir}.")
        checkpoint_path = model_dir / "last.pt"
    if checkpoint_path.exists():
        logger.info("Loading and resuming training from checkpoint.")
        model.load_from_checkpoint(checkpoint_path)


def evaluate_embedding(model: Type[Model]) -> Dict:
    """Evaluates the embedding space."""
    from pytorch_metric_learning import testers
    from pytorch_metric_learning.utils.accuracy_calculator import AccuracyCalculator

    accuracy_calculator = AccuracyCalculator(
        include=("mean_average_precision_at_r",), k=10
    )

    def get_all_embeddings(model: Type[Model]) -> Tuple:
        tester = testers.BaseTester()
        return tester.get_all_embeddings(model.test_dataset, model.network)

    embeddings, labels = get_all_embeddings(model)
    logger.info("Computing embedding accuracy")
    accuracies = accuracy_calculator.get_accuracy(
        embeddings, embeddings, np.squeeze(labels), np.squeeze(labels), True
    )
    logger.info(
        f"Test set accuracy (MAP@10) = {accuracies['mean_average_precision_at_r']}"
    )
    return accuracies


def run_experiment(
    experiment_config: Dict,
    save_weights: bool,
    device: str,
    use_wandb: bool,
    train: bool,
    test: bool,
    verbose: int = 0,
    checkpoint: Optional[str] = None,
    pretrained_weights: Optional[str] = None,
) -> None:
    """Runs an experiment."""
    logger.info(f"Experiment config: {json.dumps(experiment_config)}")

    # Create new experiment.
    experiment_dir, log_dir, model_dir = _create_experiment_dir(
        experiment_config, checkpoint
    )

    # Make sure the log/model directory exists.
    log_dir.mkdir(parents=True, exist_ok=True)
    model_dir.mkdir(parents=True, exist_ok=True)

    # Load the modules and model arguments.
    model_class_, model_args = _load_modules_and_arguments(experiment_config)

    # Initializes the model with experiment config.
    model = model_class_(**model_args, device=device)

    callbacks = _configure_callbacks(experiment_config, model_dir)

    # Setup logger.
    _configure_logger(log_dir, verbose)

    # Load from checkpoint if resuming an experiment.
    resume = False
    if checkpoint is not None or pretrained_weights is not None:
        # resume = True
        _load_from_checkpoint(model, model_dir, pretrained_weights)

    logger.info(f"The class mapping is {model.mapping}")

    # Initializes Weights & Biases
    if use_wandb:
        wandb.init(project="text-recognizer", config=experiment_config, resume=resume)

        # Lets W&B save the model and track the gradients and optional parameters.
        wandb.watch(model.network)

    experiment_config["experiment_group"] = experiment_config.get(
        "experiment_group", None
    )

    experiment_config["device"] = device

    # Save the config used in the experiment folder.
    _save_config(experiment_dir, experiment_config)

    # Prints a summary of the network in terminal.
    model.summary(experiment_config["train_args"]["input_shape"])

    # Load trainer.
    trainer = Trainer(
        max_epochs=experiment_config["train_args"]["max_epochs"],
        callbacks=callbacks,
        transformer_model=experiment_config["train_args"]["transformer_model"],
        max_norm=experiment_config["train_args"]["max_norm"],
        freeze_backbone=experiment_config["train_args"]["freeze_backbone"],
    )

    # Train the model.
    if train:
        trainer.fit(model)

    # Run inference over test set.
    if test:
        logger.info("Loading checkpoint with the best weights.")
        model.load_from_checkpoint(model_dir / "best.pt")

        logger.info("Running inference on test set.")
        if experiment_config["criterion"]["type"] == "EmbeddingLoss":
            logger.info("Evaluating embedding.")
            score = evaluate_embedding(model)
        else:
            score = trainer.test(model)

        logger.info(f"Test set evaluation: {score}")

        if use_wandb:
            wandb.log(
                {
                    experiment_config["test_metric"]: score[
                        experiment_config["test_metric"]
                    ]
                }
            )

    if save_weights:
        model.save_weights(model_dir)


@click.command()
@click.argument("experiment_config",)
@click.option("--gpu", type=int, default=0, help="Provide the index of the GPU to use.")
@click.option(
    "--save",
    is_flag=True,
    help="If set, the final weights will be saved to a canonical, version-controlled location.",
)
@click.option(
    "--nowandb", is_flag=False, help="If true, do not use wandb for this run."
)
@click.option("--test", is_flag=True, help="If true, test the model.")
@click.option("-v", "--verbose", count=True)
@click.option("--checkpoint", type=str, help="Path to the experiment.")
@click.option(
    "--pretrained_weights", type=str, help="Path to pretrained model weights."
)
@click.option(
    "--notrain", is_flag=False, help="Do not train the model.",
)
def run_cli(
    experiment_config: str,
    gpu: int,
    save: bool,
    nowandb: bool,
    notrain: bool,
    test: bool,
    verbose: int,
    checkpoint: Optional[str] = None,
    pretrained_weights: Optional[str] = None,
) -> None:
    """Run experiment."""
    if gpu < 0:
        gpu_manager = GPUManager(True)
        gpu = gpu_manager.get_free_gpu()
    device = "cuda:" + str(gpu)

    experiment_config = json.loads(experiment_config)
    os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu}"

    run_experiment(
        experiment_config,
        save,
        device,
        use_wandb=not nowandb,
        train=not notrain,
        test=test,
        verbose=verbose,
        checkpoint=checkpoint,
        pretrained_weights=pretrained_weights,
    )


if __name__ == "__main__":
    run_cli()