from utils import arr, TRAIN, VALID, invert_dict
import paths
from utils.plt import show_animate
from scripts.prep import *
from scripts.load import LoaderCon
n_frames = 32
n_channels = 1
output_shape = n_frames, 128, 128 #240, 320
l = LoaderCon(
data_path=paths.CON_PREP2,
inputs={
# "target": arr((1,), "int32"),
"target": arr((n_frames, ), "int32"),
# "video": arr(output_shape+(n_channels,), "float32"),
"video": arr(output_shape, "float32"),
"path": arr((), "S128"),
},
preprocessors=[
LabelsCon(),
VideoLoadPrep(n_frames=n_frames,
rand_middle_frame=True,
rgb=True,
depth=False,
use_bcolz=True,
tolerance=0,
rgbbias=-127.5),
Augment3D(
output_shape=output_shape,
import paths
# INPUT DATA
############
batch_size = 24
batches_per_chunk = 1
chunk_size = batch_size*batches_per_chunk
im_shp = 128, 128
n_channels = 2
n_frames = 32
voc_size = 249 # n_classes
data_tags = OrderedDict(
target=arr((n_frames,), "int32"),
video=arr((n_frames,) + im_shp + (n_channels,), "float32"),
)
if n_channels == 1:
data_tags["video"] = arr((n_frames,) + im_shp, "float32")
augm_params={
"translation": [0, 16, 16],
"rotation": [8, 0, 0],
"shear": [0, 0, 0],
"scale": [1, 1.5, 1.5],
"reflection": [0, 0, .5] # Bernoulli p
}
print "augm_params", augm_params
data_loader = Loader(
