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# flake8: noqa E501
# pylint: disable=not-callable
# E501: line too long
from collections import defaultdict
from datetime import datetime
import glob
import os
import tempfile
from boltons.cacheutils import cached, LRU
from boltons.fileutils import atomic_save, mkdir_p
from boltons.iterutils import windowed
from IPython import get_ipython
from IPython.display import display
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pysaliency
from pysaliency.filter_datasets import iterate_crossvalidation
from pysaliency.plotting import visualize_distribution
import torch
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
import yaml
from .data import ImageDataset, FixationDataset, ImageDatasetSampler, FixationMaskTransform
#from .loading import import_class, build_model, DeepGazeCheckpointModel, SharedPyTorchModel, _get_from_config
from .metrics import log_likelihood, nss, auc
from .modules import DeepGazeII
baseline_performance = cached(LRU(max_size=3))(lambda model, *args, **kwargs: model.information_gain(*args, **kwargs))
def eval_epoch(model, dataset, baseline_information_gain, device, metrics=None):
model.eval()
if metrics is None:
metrics = ['LL', 'IG', 'NSS', 'AUC']
metric_scores = {}
metric_functions = {
'LL': log_likelihood,
'NSS': nss,
'AUC': auc,
}
batch_weights = []
with torch.no_grad():
pbar = tqdm(dataset)
for batch in pbar:
image = batch.pop('image').to(device)
centerbias = batch.pop('centerbias').to(device)
fixation_mask = batch.pop('fixation_mask').to(device)
x_hist = batch.pop('x_hist', torch.tensor([])).to(device)
y_hist = batch.pop('y_hist', torch.tensor([])).to(device)
weights = batch.pop('weight').to(device)
durations = batch.pop('durations', torch.tensor([])).to(device)
kwargs = {}
for key, value in dict(batch).items():
kwargs[key] = value.to(device)
if isinstance(model, DeepGazeII):
log_density = model(image, centerbias, **kwargs)
else:
log_density = model(image, centerbias, x_hist=x_hist, y_hist=y_hist, durations=durations, **kwargs)
for metric_name, metric_fn in metric_functions.items():
if metric_name not in metrics:
continue
metric_scores.setdefault(metric_name, []).append(metric_fn(log_density, fixation_mask, weights=weights).detach().cpu().numpy())
batch_weights.append(weights.detach().cpu().numpy().sum())
for display_metric in ['LL', 'NSS', 'AUC']:
if display_metric in metrics:
pbar.set_description('{} {:.05f}'.format(display_metric, np.average(metric_scores[display_metric], weights=batch_weights)))
break
data = {metric_name: np.average(scores, weights=batch_weights) for metric_name, scores in metric_scores.items()}
if 'IG' in metrics:
data['IG'] = data['LL'] - baseline_information_gain
return data
def train_epoch(model, dataset, optimizer, device):
model.train()
losses = []
batch_weights = []
pbar = tqdm(dataset)
for batch in pbar:
optimizer.zero_grad()
image = batch.pop('image').to(device)
centerbias = batch.pop('centerbias').to(device)
fixation_mask = batch.pop('fixation_mask').to(device)
x_hist = batch.pop('x_hist', torch.tensor([])).to(device)
y_hist = batch.pop('y_hist', torch.tensor([])).to(device)
weights = batch.pop('weight').to(device)
durations = batch.pop('durations', torch.tensor([])).to(device)
kwargs = {}
for key, value in dict(batch).items():
kwargs[key] = value.to(device)
if isinstance(model, DeepGazeII):
log_density = model(image, centerbias, **kwargs)
else:
log_density = model(image, centerbias, x_hist=x_hist, y_hist=y_hist, durations=durations, **kwargs)
loss = -log_likelihood(log_density, fixation_mask, weights=weights)
losses.append(loss.detach().cpu().numpy())
batch_weights.append(weights.detach().cpu().numpy().sum())
pbar.set_description('{:.05f}'.format(np.average(losses, weights=batch_weights)))
loss.backward()
optimizer.step()
return np.average(losses, weights=batch_weights)
def restore_from_checkpoint(model, optimizer, scheduler, path):
print("Restoring from", path)
data = torch.load(path)
if 'optimizer' in data:
# checkpoint contains training progress
model.load_state_dict(data['model'])
optimizer.load_state_dict(data['optimizer'])
scheduler.load_state_dict(data['scheduler'])
torch.set_rng_state(data['rng_state'])
return data['step'], data['loss']
else:
# checkpoint contains just a model
missing_keys, unexpected_keys = model.load_state_dict(data, strict=False)
if missing_keys:
print("WARNING! missing keys", missing_keys)
if unexpected_keys:
print("WARNING! Unexpected keys", unexpected_keys)
def save_training_state(model, optimizer, scheduler, step, loss, path):
data = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'rng_state': torch.get_rng_state(),
'step': step,
'loss': loss,
}
with atomic_save(path, text_mode=False, overwrite_part=True) as f:
torch.save(data, f)
def _train(this_directory,
model,
train_loader, train_baseline_log_likelihood,
val_loader, val_baseline_log_likelihood,
optimizer, lr_scheduler,
#optimizer_config, lr_scheduler_config,
minimum_learning_rate,
#initial_learning_rate, learning_rate_scheduler, learning_rate_decay, learning_rate_decay_epochs, learning_rate_backlook, learning_rate_reset_strategy, minimum_learning_rate,
validation_metric='IG',
validation_metrics=['IG', 'LL', 'AUC', 'NSS'],
validation_epochs=1,
startwith=None,
device=None):
mkdir_p(this_directory)
if os.path.isfile(os.path.join(this_directory, 'final.pth')):
print("Training Already finished")
return
if device is None:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using device", device)
model.to(device)
val_metrics = defaultdict(lambda: [])
if startwith is not None:
restore_from_checkpoint(model, optimizer, lr_scheduler, startwith)
writer = SummaryWriter(os.path.join(this_directory, 'log'), flush_secs=30)
columns = ['epoch', 'timestamp', 'learning_rate', 'loss']
print("validation metrics", validation_metrics)
for metric in validation_metrics:
columns.append(f'validation_{metric}')
progress = pd.DataFrame(columns=columns)
step = 0
last_loss = np.nan
def save_step():
save_training_state(
model, optimizer, lr_scheduler, step, last_loss,
'{}/step-{:04d}.pth'.format(this_directory, step),
)
#f = visualize(model, vis_data_loader)
#display_if_in_IPython(f)
#writer.add_figure('prediction', f, step)
writer.add_scalar('training/loss', last_loss, step)
writer.add_scalar('training/learning_rate', optimizer.state_dict()['param_groups'][0]['lr'], step)
writer.add_scalar('parameters/sigma', model.finalizer.gauss.sigma.detach().cpu().numpy(), step)
writer.add_scalar('parameters/center_bias_weight', model.finalizer.center_bias_weight.detach().cpu().numpy()[0], step)
if step % validation_epochs == 0:
_val_metrics = eval_epoch(model, val_loader, val_baseline_log_likelihood, device, metrics=validation_metrics)
else:
print("Skipping validation")
_val_metrics = {}
for key, value in _val_metrics.items():
val_metrics[key].append(value)
for key, value in _val_metrics.items():
writer.add_scalar(f'validation/{key}', value, step)
new_row = {
'epoch': step,
'timestamp': datetime.utcnow(),
'learning_rate': optimizer.state_dict()['param_groups'][0]['lr'],
'loss': last_loss,
#'validation_ig': val_igs[-1]
}
for key, value in _val_metrics.items():
new_row['validation_{}'.format(key)] = value
progress.loc[step] = new_row
print(progress.tail(n=2))
print(progress[['validation_{}'.format(key) for key in val_metrics]].idxmax(axis=0))
with atomic_save('{}/log.csv'.format(this_directory), text_mode=True, overwrite_part=True) as f:
progress.to_csv(f)
for old_step in range(1, step):
# only check if we are computing validation metrics...
if validation_metric in val_metrics and val_metrics[validation_metric] and old_step == np.argmax(val_metrics[validation_metric]):
continue
for filename in glob.glob('{}/step-{:04d}.pth'.format(this_directory, old_step)):
print("removing", filename)
os.remove(filename)
old_checkpoints = sorted(glob.glob(os.path.join(this_directory, 'step-*.pth')))
if old_checkpoints:
last_checkpoint = old_checkpoints[-1]
print("Found old checkpoint", last_checkpoint)
step, last_loss = restore_from_checkpoint(model, optimizer, lr_scheduler, last_checkpoint)
print("Setting step to", step)
if step == 0:
print("Beginning training")
save_step()
else:
print("Continuing from step", step)
progress = pd.read_csv(os.path.join(this_directory, 'log.csv'), index_col=0)
val_metrics = {}
for column_name in progress.columns:
if column_name.startswith('validation_'):
val_metrics[column_name.split('validation_', 1)[1]] = list(progress[column_name])
if step not in progress.epoch.values:
print("Epoch not yet evaluated, evaluating...")
save_step()
# We have to make one scheduler step here, since we make the
# scheduler step _after_ saving the checkpoint
lr_scheduler.step()
print(progress)
while optimizer.state_dict()['param_groups'][0]['lr'] >= minimum_learning_rate:
step += 1
last_loss = train_epoch(model, train_loader, optimizer, device)
save_step()
lr_scheduler.step()
#if learning_rate_reset_strategy == 'validation':
# best_step = np.argmax(val_metrics[validation_metric])
# print("Best previous validation in step {}, saving as final result".format(best_step))
# restore_from_checkpoint(model, optimizer, scheduler, os.path.join(this_directory, 'step-{:04d}.pth'.format(best_step)))
#else:
# print("Not resetting to best validation epoch")
torch.save(model.state_dict(), '{}/final.pth'.format(this_directory))
for filename in glob.glob(os.path.join(this_directory, 'step-*')):
print("removing", filename)
os.remove(filename) |