#!/usr/bin/env python
# encoding: utf-8
import copy
import os
import time
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.tensorboard import SummaryWriter
import bob.core
logger = bob.core.log.setup("bob.learn.pytorch")
[docs]class GenericTrainer(object):
"""
Class to train a generic NN; all the parameters are provided in configs
Attributes
----------
network: :py:class:`torch.nn.Module`
The network to train
optimizer: :py:class:`torch.optim.Optimizer`
Optimizer object to be used. Initialized in the config file.
device: str
Device which will be used for training the model
verbosity_level: int
The level of verbosity output to stdout
"""
def __init__(
self,
network,
optimizer,
compute_loss,
learning_rate=0.0001,
device="cpu",
verbosity_level=2,
tf_logdir="tf_logs",
do_crossvalidation=False,
save_interval=5,
):
""" Init function . The layers to be adapted in the network is selected and the gradients are set to `True`
for the layers which needs to be adapted.
Parameters
----------
network: :py:class:`torch.nn.Module`
The network to train
device: str
Device which will be used for training the model
verbosity_level: int
The level of verbosity output to stdout
do_crossvalidation: bool
If set to `True`, performs validation in each epoch and stores the best model based on validation loss.
"""
self.network = network
self.optimizer = optimizer
self.compute_loss = compute_loss
self.device = device
self.learning_rate = learning_rate
self.save_interval = save_interval
self.do_crossvalidation = do_crossvalidation
if self.do_crossvalidation:
phases = ["train", "val"]
else:
phases = ["train"]
self.phases = phases
# Move the network to device
self.network.to(self.device)
bob.core.log.set_verbosity_level(logger, verbosity_level)
self.tf_logger = SummaryWriter(log_dir=tf_logdir)
# Setting the gradients to true for the layers which needs to be adapted
[docs] def load_model(self, model_filename):
"""Loads an existing model
Parameters
----------
model_file: str
The filename of the model to load
Returns
-------
start_epoch: int
The epoch to start with
start_iteration: int
The iteration to start with
losses: list(float)
The list of losses from previous training
"""
cp = torch.load(model_filename)
self.network.load_state_dict(cp["state_dict"])
start_epoch = cp["epoch"]
start_iter = cp["iteration"]
losses = cp["loss"]
return start_epoch, start_iter, losses
[docs] def save_model(self, output_dir, epoch=0, iteration=0, losses=None):
"""Save the trained network
Parameters
----------
output_dir: str
The directory to write the models to
epoch: int
the current epoch
iteration: int
the current (last) iteration
losses: list(float)
The list of losses since the beginning of training
"""
saved_filename = "model_{}_{}.pth".format(epoch, iteration)
saved_path = os.path.join(output_dir, saved_filename)
logger.info("Saving model to {}".format(saved_path))
cp = {
"epoch": epoch,
"iteration": iteration,
"loss": losses,
"state_dict": self.network.cpu().state_dict(),
}
torch.save(cp, saved_path)
self.network.to(self.device)
[docs] def train(self, dataloader, n_epochs=25, output_dir="out", model=None):
"""Performs the training.
Parameters
----------
dataloader: :py:class:`torch.utils.data.DataLoader`
The dataloader for your data
n_epochs: int
The number of epochs you would like to train for
learning_rate: float
The learning rate for Adam optimizer.
output_dir: str
The directory where you would like to save models
model: str
The path to a pretrained model file to start training from; this is the PAD model; not the LightCNN model
"""
# if model exists, load it
if model is not None:
start_epoch, start_iter, losses = self.load_model(model)
logger.info(
"Starting training at epoch {}, iteration {} - last loss value is {}".format(
start_epoch, start_iter, losses[-1]
)
)
else:
start_epoch = 0
start_iter = 0
losses = []
logger.info("Starting training from scratch")
for name, param in self.network.named_parameters():
if param.requires_grad == True:
logger.info("Layer to be adapted from grad check : {}".format(name))
# setup optimizer
self.network.train(True)
best_model_wts = copy.deepcopy(self.network.state_dict())
best_loss = float("inf")
# let's go
for epoch in range(start_epoch, n_epochs):
# in the epoch
train_loss_history = []
val_loss_history = []
for phase in self.phases:
if phase == "train":
self.network.train() # Set model to training mode
else:
self.network.eval() # Set model to evaluate mode
for i, data in enumerate(dataloader[phase], 0):
if i >= start_iter:
start = time.time()
# get data from dataset
img, labels = data
self.optimizer.zero_grad()
with torch.set_grad_enabled(phase == "train"):
loss = self.compute_loss(
self.network, img, labels, self.device
)
if phase == "train":
loss.backward()
self.optimizer.step()
train_loss_history.append(loss.item())
else:
val_loss_history.append(loss.item())
end = time.time()
logger.info(
"[{}/{}][{}/{}] => Loss = {} (time spent: {}), Phase {}".format(
epoch,
n_epochs,
i,
len(dataloader[phase]),
loss.item(),
(end - start),
phase,
)
)
losses.append(loss.item())
epoch_train_loss = np.mean(train_loss_history)
logger.info("Train Loss : {} epoch : {}".format(epoch_train_loss, epoch))
if self.do_crossvalidation:
epoch_val_loss = np.mean(val_loss_history)
logger.info("Val Loss : {} epoch : {}".format(epoch_val_loss, epoch))
if phase == "val" and epoch_val_loss < best_loss:
logger.debug(
"New val loss : {} is better than old: {}, copying over the new weights".format(
epoch_val_loss, best_loss
)
)
best_loss = epoch_val_loss
best_model_wts = copy.deepcopy(self.network.state_dict())
######################################## <Logging> ###################################
if self.do_crossvalidation:
info = {"train_loss": epoch_train_loss, "val_loss": epoch_val_loss}
else:
info = {"train_loss": epoch_train_loss}
# scalar logs
for tag, value in info.items():
self.tf_logger.add_scalar(
tag=tag, scalar_value=value, global_step=epoch + 1
)
# Log values and gradients of the parameters (histogram summary)
for tag, value in self.network.named_parameters():
tag = tag.replace(".", "/")
try:
self.tf_logger.add_histogram(
tag=tag, values=value.data.cpu().numpy(), global_step=epoch + 1
)
self.tf_logger.add_histogram(
tag=tag + "/grad",
values=value.grad.data.cpu().numpy(),
global_step=epoch + 1,
)
except:
pass
######################################## </Logging> ###################################
# do stuff - like saving models
logger.info("EPOCH {} DONE".format(epoch + 1))
# comment it out after debugging
# save the last model, and the ones in the specified interval
if (epoch + 1) == n_epochs or epoch % self.save_interval == 0:
self.save_model(
output_dir, epoch=(epoch + 1), iteration=0, losses=losses
)
# load the best weights
self.network.load_state_dict(best_model_wts)
# best epoch is 0
self.save_model(output_dir, epoch=0, iteration=0, losses=losses)