def __init__(self):
self.aug = iaa.Sequential([
iaa.Scale((224, 224)),
iaa.Sometimes(0.30, iaa.GaussianBlur(sigma=(0, 3.0))),
iaa.Sometimes(0.25, iaa.Multiply((0.5, 1.5), per_channel=0.5)),
iaa.Sometimes(0.20, iaa.Invert(0.25, per_channel=0.5)),
iaa.Sometimes(
0.25,
iaa.ReplaceElementwise(iap.FromLowerResolution(
iap.Binomial(0.1), size_px=8),
iap.Normal(128, 0.4 * 128),
per_channel=0.5)),
iaa.Sometimes(0.30, iaa.AdditivePoissonNoise(40)),
iaa.Fliplr(0.5),
iaa.Affine(rotate=(-20, 20), mode='symmetric'),
iaa.Sometimes(
0.30,
iaa.OneOf([
iaa.Dropout(p=(0, 0.1)),
iaa.CoarseDropout(0.1, size_percent=0.5)
])),
iaa.AddToHueAndSaturation(value=(-10, 10), per_channel=True)
])
def chapter_augmenters_replaceelementwise():
aug_cls = iaa.ReplaceElementwise
fn_start = "arithmetic/replaceelementwise"
aug = aug_cls(0.05, [0, 255])
run_and_save_augseq(fn_start + ".jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2,
quality=95,
seed=2)
aug = aug_cls(0.05, [0, 255], per_channel=0.5)
run_and_save_augseq(fn_start + "_per_channel_050.jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2,
quality=95,
seed=2)
aug = aug_cls(0.1, iap.Normal(128, 0.4 * 128), per_channel=0.5)
run_and_save_augseq(fn_start + "_gaussian_noise.jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2,
quality=95,
seed=2)
aug = aug_cls(iap.FromLowerResolution(iap.Binomial(0.1), size_px=8),
iap.Normal(128, 0.4 * 128),
per_channel=0.5)
run_and_save_augseq(fn_start + "_gaussian_noise_coarse.jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2,
quality=95,
seed=2)
def main():
"""Function that initializes the training (e.g. models)
and runs the batches."""
parser = argparse.ArgumentParser(description="Train steering wheel tracker")
parser.add_argument('--nocontinue', default=False, action="store_true", help="Whether to NOT continue the previous experiment", required=False)
args = parser.parse_args()
if os.path.isfile("steering_wheel.tar") and not args.nocontinue:
checkpoint = torch.load("steering_wheel.tar")
else:
checkpoint = None
if checkpoint is not None:
history = plotting.History.from_string(checkpoint["history"])
else:
history = plotting.History()
history.add_group("loss", ["train", "val"], increasing=False)
history.add_group("acc", ["train", "val"], increasing=True)
loss_plotter = plotting.LossPlotter(
history.get_group_names(),
history.get_groups_increasing(),
save_to_fp="train_plot.jpg"
)
loss_plotter.start_batch_idx = 100
tracker_cnn = models.SteeringWheelTrackerCNNModel()
tracker_cnn.train()
optimizer = optim.Adam(tracker_cnn.parameters())
criterion = nn.CrossEntropyLoss()
#criterion = nn.BCELoss()
if checkpoint is not None:
tracker_cnn.load_state_dict(checkpoint["tracker_cnn_state_dict"])
if Config.GPU >= 0:
tracker_cnn.cuda(Config.GPU)
criterion.cuda(Config.GPU)
# initialize image augmentation cascade
rarely = lambda aug: iaa.Sometimes(0.1, aug)
sometimes = lambda aug: iaa.Sometimes(0.2, aug)
often = lambda aug: iaa.Sometimes(0.4, aug)
augseq = iaa.Sequential([
sometimes(iaa.Crop(percent=(0, 0.025))),
rarely(iaa.GaussianBlur((0, 1.0))), # blur images with a sigma between 0 and 3.0
rarely(iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.02*255), per_channel=0.5)), # add gaussian noise to images
often(iaa.Dropout(
iap.FromLowerResolution(
other_param=iap.Binomial(1 - 0.2),
size_px=(2, 16)
),
per_channel=0.2
)),
often(iaa.Add((-20, 20), per_channel=0.5)), # change brightness of images (by -10 to 10 of original value)
often(iaa.Multiply((0.8, 1.2), per_channel=0.25)), # change brightness of images (50-150% of original value)
often(iaa.ContrastNormalization((0.8, 1.2), per_channel=0.5)), # improve or worsen the contrast
often(iaa.Affine(
scale={"x": (0.8, 1.3), "y": (0.8, 1.3)},
translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)},
rotate=(-0, 0),
shear=(-0, 0),
order=[0, 1],
cval=(0, 255),
mode=["constant", "edge"]
)),
rarely(iaa.Grayscale(alpha=(0.0, 1.0)))
],
random_order=True # do all of the above in random order
)
#memory = replay_memory.ReplayMemory.get_instance_supervised()
batch_loader_train = BatchLoader(val=False, augseq=augseq, queue_size=15, nb_workers=4)
batch_loader_val = BatchLoader(val=True, augseq=iaa.Noop(), queue_size=NB_VAL_BATCHES, nb_workers=2)
start_batch_idx = 0 if checkpoint is None else checkpoint["batch_idx"] + 1
for batch_idx in xrange(start_batch_idx, NB_BATCHES):
run_batch(batch_idx, False, batch_loader_train, tracker_cnn, criterion, optimizer, history, (batch_idx % 20) == 0)
if (batch_idx+1) % VAL_EVERY == 0:
for i in xrange(NB_VAL_BATCHES):
run_batch(batch_idx, True, batch_loader_val, tracker_cnn, criterion, optimizer, history, i == 0)
if (batch_idx+1) % PLOT_EVERY == 0:
loss_plotter.plot(history)
# every N batches, save a checkpoint
if (batch_idx+1) % SAVE_EVERY == 0:
torch.save({
"batch_idx": batch_idx,
"history": history.to_string(),
"tracker_cnn_state_dict": tracker_cnn.state_dict()
}, "steering_wheel.tar")