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
|
"""Training script for PyTorch models."""
from pathlib import Path
import time
from typing import Dict, List, Optional, Tuple, Type
from loguru import logger
import numpy as np
import torch
from torch import Tensor
from training.trainer.callbacks import Callback, CallbackList
from training.trainer.util import RunningAverage
import wandb
from text_recognizer.models import Model
torch.backends.cudnn.benchmark = True
np.random.seed(4711)
torch.manual_seed(4711)
torch.cuda.manual_seed(4711)
class Trainer:
"""Trainer for training PyTorch models."""
def __init__(
self,
model: Type[Model],
model_dir: Path,
train_args: Dict,
callbacks: CallbackList,
checkpoint_path: Optional[Path] = None,
) -> None:
"""Initialization of the Trainer.
Args:
model (Type[Model]): A model object.
model_dir (Path): Path to the model directory.
train_args (Dict): The training arguments.
callbacks (CallbackList): List of callbacks to be called.
checkpoint_path (Optional[Path]): The path to a previously trained model. Defaults to None.
"""
self.model = model
self.model_dir = model_dir
self.checkpoint_path = checkpoint_path
self.start_epoch = 1
self.epochs = train_args["epochs"]
self.callbacks = callbacks
if self.checkpoint_path is not None:
self.start_epoch = self.model.load_checkpoint(self.checkpoint_path)
# Parse the name of the experiment.
experiment_dir = str(self.model_dir.parents[1]).split("/")
self.experiment_name = experiment_dir[-2] + "/" + experiment_dir[-1]
def training_step(
self,
batch: int,
samples: Tuple[Tensor, Tensor],
loss_avg: Type[RunningAverage],
) -> Dict:
"""Performs the training step."""
# Pass the tensor to the device for computation.
data, targets = samples
data, targets = (
data.to(self.model.device),
targets.to(self.model.device),
)
# Forward pass.
# Get the network prediction.
output = self.model.network(data)
# Compute the loss.
loss = self.model.criterion(output, targets)
# Backward pass.
# Clear the previous gradients.
self.model.optimizer.zero_grad()
# Compute the gradients.
loss.backward()
# Perform updates using calculated gradients.
self.model.optimizer.step()
# Compute metrics.
loss_avg.update(loss.item())
output = output.data.cpu()
targets = targets.data.cpu()
metrics = {
metric: self.model.metrics[metric](output, targets)
for metric in self.model.metrics
}
metrics["loss"] = loss_avg()
return metrics
def train(self) -> None:
"""Runs the training loop for one epoch."""
# Set model to traning mode.
self.model.train()
# Running average for the loss.
loss_avg = RunningAverage()
data_loader = self.model.data_loaders["train"]
for batch, samples in enumerate(data_loader):
self.callbacks.on_train_batch_begin(batch)
metrics = self.training_step(batch, samples, loss_avg)
self.callbacks.on_train_batch_end(batch, logs=metrics)
@torch.no_grad()
def validation_step(
self,
batch: int,
samples: Tuple[Tensor, Tensor],
loss_avg: Type[RunningAverage],
) -> Dict:
"""Performs the validation step."""
# Pass the tensor to the device for computation.
data, targets = samples
data, targets = (
data.to(self.model.device),
targets.to(self.model.device),
)
# Forward pass.
# Get the network prediction.
output = self.model.network(data)
# Compute the loss.
loss = self.model.criterion(output, targets)
# Compute metrics.
loss_avg.update(loss.item())
output = output.data.cpu()
targets = targets.data.cpu()
metrics = {
metric: self.model.metrics[metric](output, targets)
for metric in self.model.metrics
}
metrics["loss"] = loss.item()
return metrics
def _log_val_metric(self, metrics_mean: Dict, epoch: Optional[int] = None) -> None:
log_str = "Validation metrics " + (f"at epoch {epoch} - " if epoch else " - ")
logger.debug(
log_str + " - ".join(f"{k}: {v:.4f}" for k, v in metrics_mean.items())
)
def validate(self, epoch: Optional[int] = None) -> Dict:
"""Runs the validation loop for one epoch."""
# Set model to eval mode.
self.model.eval()
# Running average for the loss.
data_loader = self.model.data_loaders["val"]
# Running average for the loss.
loss_avg = RunningAverage()
# Summary for the current eval loop.
summary = []
for batch, samples in enumerate(data_loader):
self.callbacks.on_validation_batch_begin(batch)
metrics = self.validation_step(batch, samples, loss_avg)
self.callbacks.on_validation_batch_end(batch, logs=metrics)
summary.append(metrics)
# Compute mean of all metrics.
metrics_mean = {
"val_" + metric: np.mean([x[metric] for x in summary])
for metric in summary[0]
}
self._log_val_metric(metrics_mean, epoch)
return metrics_mean
def fit(self) -> None:
"""Runs the training and evaluation loop."""
logger.debug(f"Running an experiment called {self.experiment_name}.")
# Set start time.
t_start = time.time()
self.callbacks.on_fit_begin()
# Run the training loop.
for epoch in range(self.start_epoch, self.epochs + 1):
self.callbacks.on_epoch_begin(epoch)
# Perform one training pass over the training set.
self.train()
# Evaluate the model on the validation set.
val_metrics = self.validate(epoch)
self.callbacks.on_epoch_end(epoch, logs=val_metrics)
if self.model.stop_training:
break
# Calculate the total training time.
t_end = time.time()
t_training = t_end - t_start
self.callbacks.on_fit_end()
logger.info(f"Training took {t_training:.2f} s.")
|
