def __init__(self):
train_seed, val_seed, test_seed = 0, 1, 2
train = EmnistObjectDetectionDataset(
n_examples=int(cfg.n_train), example_range=cfg.train_example_range, seed=train_seed)
val = EmnistObjectDetectionDataset(
n_examples=int(cfg.n_val), example_range=cfg.val_example_range, seed=val_seed)
test = EmnistObjectDetectionDataset(
n_examples=int(cfg.n_val), example_range=cfg.test_example_range, seed=test_seed)
self.datasets = dict(train=train, val=val, test=test)
from dps.datasets import EmnistObjectDetectionDataset
"""
The main takeaway from this is that AffineGridWarper layers an axis (-1, 1) x (-1, 1) over the input
image, with y increasing downward and x increasing rightward.
"""
n_examples = 10
image_shape = (28, 28, 3)
crop_shape = (14, 14, 3)
cfg.batch_size = 10
with tf.Session().as_default():
_train = EmnistObjectDetectionDataset(
n_examples=n_examples,
min_chars=2,
max_chars=2,
patch_shape=crop_shape[:2],
characters=[0],
max_overlap=1000,
image_shape=image_shape[:2]).sample(n_examples)
images = _train["image"]
"""
A = [a, b, tx],
[c, d, ty]
"""
boxes = np.array([[.5, 0, .5, 0, .5, -.5]], dtype='f')
A = boxes.reshape(2, 3)
# top-left, bottom-right
corners = np.array([[-1, -1, 1], [1, 1, 1]], dtype='f').T
corners = A @ corners
config = yolo_rl.good_config.copy(
prepare_func=prepare_func,
patience=10000,
render_step=100000,
lr_schedule=1e-4,
max_overlap=40,
hooks=[],
n_val=16,
eval_step=1000,
max_steps=100000,
fixed_values=dict(),
curriculum=[
dict(fixed_values=dict(obj=1), max_steps=10000),
dict(obj_exploration=0.2,),
dict(obj_exploration=0.1,),
dict(obj_exploration=0.1, lr_schedule=1e-5),
dict(obj_exploration=0.1, lr_schedule=1e-6),
],
)
# Create the datasets if necessary.
with config:
train = EmnistObjectDetectionDataset(n_examples=int(config.n_train), shuffle=True, example_range=(0.0, 0.9))
val = EmnistObjectDetectionDataset(n_examples=int(config.n_val), shuffle=True, example_range=(0.9, 1.))
from dps.hyper import build_and_submit
clify.wrap_function(build_and_submit)(config=config, distributions=distributions)