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utils.py
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utils.py
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import os
from jax import numpy as jnp
import numpy as np
from jax import jit
def cond_mkdir(path):
"""
create directory if it does not already exist
"""
if not os.path.exists(path):
os.makedirs(path)
def str2bool(v):
""" Simple query parser for configArgParse (which doesn't support native bool from cmd)
Ref: https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
"""
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise ValueError('Boolean value expected.')
def im2float(im, dtype=np.float32):
"""convert uint16 or uint8 image to float32, with range scaled to 0-1
:param im: image
:param dtype: default jnp.float32
:return im: image converted to specified dtype
"""
if issubclass(im.dtype.type, np.floating):
return im.astype(dtype)
elif issubclass(im.dtype.type, np.integer):
return im / dtype(np.iinfo(im.dtype).max)
else:
raise ValueError(f'Unsupported data type {im.dtype}')
def pad_image(field, target_shape, padval=0):
"""
:param field: input field (N, H, W, C)
:param target_shape: desired shape for output (H', W')
:param padval: value to pad the input field with
:return field: output field with desired shape (N, H', W', C)
"""
size_diff = jnp.array(target_shape) - jnp.array(field.shape[-3:-1])
odd_dim = jnp.array(field.shape[-3:-1]) % 2
# pad the dimensions that need to increase in size
if (size_diff > 0).any():
pad_total = jnp.maximum(size_diff, 0)
pad_front = (pad_total + odd_dim) // 2
pad_end = (pad_total + 1 - odd_dim) // 2
pad_front = jnp.array([0, *pad_front, 0])
pad_end = jnp.array([0, *pad_end, 0])
return jnp.pad(field,
tuple(zip(pad_front, pad_end)),
'constant',
constant_values=padval)
else:
return field
def crop_image(field, target_shape):
"""
:param field: input field (N, H, W, C)
:param target_shape: desired shape for output (H', W')
:return field: output field with desired shape (N, H', W', C)
"""
size_diff = jnp.array(field.shape[-3:-1]) - jnp.array(target_shape)
odd_dim = jnp.array(field.shape[-3:-1]) % 2
# crop dimensions that need to decrease in size
if (size_diff > 0).any():
crop_total = jnp.maximum(size_diff, 0)
crop_front = (crop_total + 1 - odd_dim) // 2
crop_end = (crop_total + odd_dim) // 2
return field[:, crop_front[0]:-crop_end[0],
crop_front[1]:-crop_end[1], :]
else:
return field