def recon_kernel(sinogram, detec):
sinogram = np.maximum(sinogram, 0.0)
if recon_method == 'fbp':
recon = reconstruction2d(sinogram, detec, phan_spec)
elif recon_method == 'sart':
from dxpy.debug.utils import dbgmsg
dbgmsg('USING SART !!!!!!!!!!')
recon = reconstruction2d(sinogram,
detec,
phan_spec,
method='SART_CUDA',
iterations=500)
recon = np.maximum(recon, 0.0)
recon = recon / np.sum(recon) * 1e6
return recon
def dataset_generator(fields=('sinogram', ), ids=None):
if ids is None:
ids = range(0, int(NB_IMAGES * 0.8))
ids = list(ids)
import random
random.shuffle(ids)
if isinstance(fields, str):
fields = (fields, )
from dxpy.debug.utils import dbgmsg
dbgmsg(ids[0], ids[1], ids[10], ids[-1])
fn_sino, fn_recon, fn_recon_ms = _h5files()
with open_file(fn_sino) as h5sino, open_file(
fn_recon) as h5recon, open_file(fn_recon_ms) as h5recon_ms:
for idx in ids:
result = _get_example(idx, fields, h5sino, h5recon, h5recon_ms)
result = _post_processing(result)
yield result
def _processing(self, tensors):
from dxpy.learn.model.metrics import mse
with tf.name_scope("processing"):
result = {k: tensors[k] for k in tensors}
res_inf = tf.abs(result['label'] - result['infer'])
res_itp = tf.abs(result['label'] - result['interp'])
dif_inf_itp = tf.abs(result['infer'] - result['interp'])
result['res_inf'] = res_inf
result['res_itp'] = res_itp
result['dif_inf_itp'] = dif_inf_itp
result['mse_inf'] = mse(result['label'], result['infer'])
result['mse_itp'] = mse(result['label'], result['interp'])
result['mse_inf_to_itp_ratio'] = result['mse_inf'] / \
result['mse_itp']
from dxpy.debug.utils import dbgmsg
dbgmsg(result)
return super()._processing(result)
def _kernel(self, feeds):
from dxpy.debug.utils import dbgmsg
dbgmsg(self.param('mean'))
dbgmsg(self.param('std'))
label = feeds[NodeKeys.LABEL]
infer = feeds[NodeKeys.INPUT]
with tf.name_scope('denorm_white'):
label = label * \
tf.constant(self.param('std')) + \
tf.constant(self.param('mean'))
infer = infer * \
tf.constant(self.param('std')) + \
tf.constant(self.param('mean'))
if self.param('with_log'):
with tf.name_scope('denorm_log_for_data'):
infer = tf.exp(infer)
with tf.name_scope('loss'):
loss = log_possion_loss(label, infer)
else:
with tf.name_scope('loss'):
loss = poission_loss(label, infer)
return {NodeKeys.MAIN: loss}
def train_with_monitored_session(network, is_chief=True, target=None, steps=10000000000000):
from dxpy.learn.utils.general import pre_work
from dxpy.learn.session import set_default_session
from tqdm import tqdm
import time
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
hooks = []
# hooks.append(tf.train.StepCounterHook())
trainer = network['trainer']
if 'sync_hook' in trainer.nodes:
sync_hook = trainer['sync_hook']
hooks.append(sync_hook)
from dxpy.debug.utils import dbgmsg
dbgmsg(hooks)
with tf.train.MonitoredTrainingSession(master=target, config=config, checkpoint_dir='./save', hooks=hooks, is_chief=is_chief) as sess:
dbgmsg('SESS CREATED')
set_default_session(sess)
dbgmsg('BEFORE RESET')
network.nodes['trainer'].run('set_learning_rate')
dbgmsg('LR RESET')
for _ in tqdm(range(steps)):
network.train()
def grid_view(images,
windows=None,
scale=1.0,
cmap=None,
*,
max_columns=None,
max_rows=None,
hide_axis=True,
hspace=0.1,
wspace=0.1,
return_figure=False,
dpi=None,
adjust_figure_size=True,
save_filename=None,
invert_row_column=False,
scale_factor=2.0,
_top=None,
_right=None):
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.figure import SubplotParams
""" subplot list of images of multiple categories into grid subplots
Args:
image_lists: list of [list of images or 4D tensor]
windows: list of windows
nb_column: columns of images
Returns:
Return figure if return_figure is true, else None.
"""
images = _unified_images(images, invert_row_column)
windows = _unified_windows(images, windows)
images, windows = _adjust_images_to_fit_nb_columns(images, windows,
max_columns, max_rows)
figsize, default_dpi = _adjust_figure_size(images, scale, scale_factor)
from dxpy.debug.utils import dbgmsg
dbgmsg(figsize, default_dpi)
dbgmsg(images.shape)
if dpi is None:
dpi = default_dpi
dpi = dpi * scale
fig = plt.figure(figsize=figsize,
dpi=dpi,
subplotpars=SubplotParams(left=0.0,
right=1.0,
bottom=0.0,
top=1.0,
wspace=0.0,
hspace=0.0))
# fig.subplots_adjust(hspace=hspace, wspace=wspace)
nr, nc = images.shape
if _top is None:
# _top = figsize[1] / nr * nc
_top = scale
if _right is None:
_right = figsize[0] / nc * nr
_right = scale
gs = gridspec.GridSpec(nr,
nc,
wspace=wspace,
hspace=hspace,
top=_top,
bottom=0.0,
left=0.0,
right=_right)
for ir in range(nr):
for ic in range(nc):
if images[ir, ic] is None:
continue
# ax = plt.subplot(nr, nc, ir * nc + ic + 1)
ax = plt.subplot(gs[ir, ic])
ax.imshow(images[ir, ic],
cmap=cmap,
vmin=windows[ir, ic, 0],
vmax=windows[ir, ic, 1])
if hide_axis:
plt.axis('off')
else:
ax.set_xticklabels([])
ax.set_yticklabels([])
if save_filename is not None:
fig.savefig(save_filename)
if return_figure:
return fig
def infer_mice(dataset, nb_samples, output):
import numpy as np
import tensorflow as tf
from dxpy.learn.dataset.api import get_dataset
from dxpy.learn.net.api import get_network
from dxpy.learn.utils.general import load_yaml_config, pre_work
from dxpy.learn.session import Session
from dxpy.numpy_extension.visual import grid_view
from tqdm import tqdm
pre_work()
input_data = np.load(
'/home/hongxwing/Workspace/NetInference/Mice/mice_test_data.npz')
input_data = {k: np.array(input_data[k]) for k in input_data}
dataset_origin = get_dataset('dataset/srms')
is_low_dose = dataset_origin.param('low_dose')
from dxpy.debug.utils import dbgmsg
dbgmsg('IS LOW DOSE: ', is_low_dose)
for k in input_data:
print(k, input_data[k].shape)
input_keys = ['input/image{}x'.format(2**i) for i in range(4)]
label_keys = ['label/image{}x'.format(2**i) for i in range(4)]
shapes = [[1] + list(input_data['clean/image{}x'.format(2**i)].shape)[1:] +
[1] for i in range(4)]
inputs = {
input_keys[i]: tf.placeholder(tf.float32,
shapes[i],
name='input{}x'.format(2**i))
for i in range(4)
}
labels = {
label_keys[i]: tf.placeholder(tf.float32,
shapes[i],
name='label{}x'.format(2**i))
for i in range(4)
}
dataset = dict(inputs)
dataset.update(labels)
network = get_network('network/srms', dataset=dataset)
nb_down_sample = network.param('nb_down_sample')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.train.MonitoredTrainingSession(checkpoint_dir='./save',
config=config,
save_checkpoint_secs=None)
if not is_low_dose:
prefix = 'clean/image'
else:
prefix = 'noise/image'
def get_feed(idx):
# return dict()
data_raw = input_data['{}{}x'.format(prefix, 2**nb_down_sample)][idx,
...]
data_raw = np.reshape(data_raw, [1] + list(data_raw.shape) + [1])
data_label = input_data['{}1x'.format(prefix)][idx, ...]
data_label = np.reshape(data_label, [1] + list(data_label.shape) + [1])
return {
dataset['input/image{}x'.format(2**nb_down_sample)]: data_raw,
dataset['input/image1x'.format(2**nb_down_sample)]: data_label,
dataset['label/image1x'.format(2**nb_down_sample)]: data_label,
}
to_run = {
'inf': network['outputs/inference'],
'itp': network['outputs/interp'],
'high': network['input/image1x'],
# 'li': network['outputs/loss_itp'],
# 'ls': network['outputs/loss'],
# 'la': network['outputs/aligned_label']
}
def crop(data, target):
if len(data.shape) == 4:
data = data[0, :, :, 0]
o1 = data.shape[0] // 2
o2 = (data.shape[1] - target[1]) // 2
return data[o1:o1 + target[0], o2:-o2]
MEAN = 100.0
STD = 150.0
if is_low_dose:
MEAN /= dataset_origin.param('low_dose_ratio')
STD /= dataset_origin.param('low_dose_ratio')
NB_IMAGES = nb_samples
def get_infer(idx):
result = sess.run(to_run, feed_dict=get_feed(idx))
inf = crop(result['inf'], [320, 64])
itp = crop(result['itp'], [320, 64])
high = crop(input_data['{}1x'.format(prefix)][idx, ...], [320, 64])
low = crop(
input_data['{}{}x'.format(prefix, 2**nb_down_sample)][idx, ...],
[320 // (2**nb_down_sample), 64 // (2**nb_down_sample)])
high = high * STD + MEAN
low = low * STD + MEAN
inf = inf * STD + MEAN
itp = itp * STD + MEAN
high = np.pad(high, [[0, 0], [32, 32]], mode='constant')
low = np.pad(low, [[0, 0], [32 // (2**nb_down_sample)] * 2],
mode='constant')
inf = np.pad(inf, [[0, 0], [32, 32]], mode='constant')
# inf = np.maximum(inf, 0.0)
itp = np.pad(itp, [[0, 0], [32, 32]], mode='constant')
return high, low, inf, itp
results = {'high': [], 'low': [], 'inf': [], 'itp': []}
for i in tqdm(range(NB_IMAGES)):
high, low, inf, itp = get_infer(i)
results['high'].append(high)
results['low'].append(low)
results['inf'].append(inf)
results['itp'].append(itp)
np.savez(output, **results)
def infer_sino_sr(dataset, nb_samples, output):
"""
Use network in current directory as input for inference
"""
import tensorflow as tf
from dxpy.learn.dataset.api import get_dataset
from dxpy.learn.net.api import get_network
from dxpy.configs import ConfigsView
from dxpy.learn.config import config
import numpy as np
import yaml
from dxpy.debug.utils import dbgmsg
dbgmsg(dataset)
data_raw = np.load(dataset)
data_raw = {k: np.array(data_raw[k]) for k in data_raw.keys()}
config_view = ConfigsView(config)
def tensor_shape(key):
shape_origin = data_raw[key].shape
return [1] + list(shape_origin[1:3]) + [1]
with tf.name_scope('inputs'):
keys = ['input/image{}x'.format(2**i) for i in range(4)]
keys += ['label/image{}x'.format(2**i) for i in range(4)]
dataset = {
k: tf.placeholder(tf.float32, tensor_shape(k))
for k in keys
}
network = get_network('network/srms', dataset=dataset)
nb_down_sample = network.param('nb_down_sample')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.train.MonitoredTrainingSession(checkpoint_dir='./save',
config=config,
save_checkpoint_secs=None)
STAT_STD = 9.27
STAT_MEAN = 9.76
BASE_SHAPE = (640, 320)
dataset_configs = config_view['dataset']['srms']
with_noise = dataset_configs['with_poission_noise']
if with_noise:
PREFIX = 'input'
else:
PREFIX = 'label'
def crop_sinogram(tensor, target_shape=None):
if target_shape is None:
target_shape = BASE_SHAPE
if len(tensor.shape) == 4:
tensor = tensor[0, :, :, 0]
o1 = (tensor.shape[0] - target_shape[0]) // 2
o2 = (tensor.shape[1] - target_shape[1]) // 2
return tensor[o1:-o1, o2:-o2]
def run_infer(idx):
input_key = '{}/image{}x'.format(PREFIX, 2**nb_down_sample)
low_sino = np.reshape(data_raw[input_key][idx, :, :],
tensor_shape(input_key))
low_sino = (low_sino - STAT_MEAN) / STAT_STD
feeds = {dataset['input/image{}x'.format(2**nb_down_sample)]: low_sino}
inf, itp = sess.run(
[network['outputs/inference'], network['outputs/interp']],
feed_dict=feeds)
infc = crop_sinogram(inf)
itpc = crop_sinogram(itp)
infc = infc * STAT_STD + STAT_MEAN
itpc = itpc * STAT_STD + STAT_MEAN
return infc, itpc
phans = []
sino_highs = []
sino_lows = []
sino_itps = []
sino_infs = []
NB_MAX = data_raw['phantom'].shape[0]
for idx in tqdm(range(nb_samples), ascii=True):
if idx > NB_MAX:
import sys
print(
'Index {} out of range {}, stop running and store current result...'
.format(idx, NB_MAX),
file=sys.stderr)
break
phans.append(data_raw['phantom'][idx, ...])
sino_highs.append(
crop_sinogram(data_raw['{}/image1x'.format(PREFIX)][idx, :, :]))
sino_lows.append(
crop_sinogram(
data_raw['{}/image{}x'.format(PREFIX, 2**nb_down_sample)][idx,
...],
[s // (2**nb_down_sample) for s in BASE_SHAPE]))
sino_inf, sino_itp = run_infer(idx)
sino_infs.append(sino_inf)
sino_itps.append(sino_itp)
results = {
'phantom': phans,
'sino_itps': sino_itps,
'sino_infs': sino_infs,
'sino_highs': sino_highs,
'sino_lows': sino_lows
}
np.savez(output, **results)
def infer_mct(dataset, nb_samples, output):
"""
Use network in current directory as input for inference
"""
import tensorflow as tf
from dxpy.learn.dataset.api import get_dataset
from dxpy.learn.net.api import get_network
from dxpy.configs import ConfigsView
from dxpy.learn.config import config
import numpy as np
import yaml
from dxpy.debug.utils import dbgmsg
print('Using dataset file:', dataset)
data_raw = np.load(dataset)
data_raw = {k: np.array(data_raw[k]) for k in data_raw.keys()}
config_view = ConfigsView(config)
def data_key(nd):
return 'image{}x'.format(2**nd)
def tensor_shape(key):
shape_origin = data_raw[key].shape
return [1] + list(shape_origin[1:3]) + [1]
with tf.name_scope('inputs'):
keys = ['input/image{}x'.format(2**i) for i in range(4)]
keys += ['label/image{}x'.format(2**i) for i in range(4)]
dataset = {
k: tf.placeholder(tf.float32, tensor_shape(data_key(i % 4)))
for i, k in enumerate(keys)
}
network = get_network('network/srms', dataset=dataset)
nb_down_sample = network.param('nb_down_sample')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.train.MonitoredTrainingSession(checkpoint_dir='./save',
config=config,
save_checkpoint_secs=None)
STAT_MEAN = 9.93
STAT_STD = 7.95
STAT_MEAN_LOW = 9.93 * (4.0**nb_down_sample)
STAT_STD_LOW = 7.95 * (4.0**nb_down_sample)
BASE_SHAPE = (384, 384)
def crop_image(tensor, target_shape=None):
if target_shape is None:
target_shape = BASE_SHAPE
if len(tensor.shape) == 4:
tensor = tensor[0, :, :, 0]
# o1 = (tensor.shape[0] - target_shape[0]) // 2
o1 = tensor.shape[0] // 2
o2 = (tensor.shape[1] - target_shape[1]) // 2
return tensor[o1:o1 + target_shape[0], o2:-o2]
input_key = data_key(nb_down_sample)
dbgmsg('input_key:', input_key)
def run_infer(idx):
low_phan = np.reshape(data_raw[input_key][idx, :, :],
tensor_shape(input_key))
# low_phan = (low_phan - STAT_MEAN) / STAT_STD
feeds = {dataset['input/image{}x'.format(2**nb_down_sample)]: low_phan}
inf, itp = sess.run(
[network['outputs/inference'], network['outputs/interp']],
feed_dict=feeds)
infc = crop_image(inf)
itpc = crop_image(itp)
infc = infc * STAT_STD_LOW + STAT_MEAN_LOW
itpc = itpc * STAT_STD_LOW + STAT_MEAN_LOW
return infc, itpc
phans = []
img_highs = []
img_lows = []
img_itps = []
img_infs = []
NB_MAX = data_raw['phantom'].shape[0]
for idx in tqdm(range(nb_samples), ascii=True):
if idx > NB_MAX:
import sys
print(
'Index {} out of range {}, stop running and store current result...'
.format(idx, NB_MAX),
file=sys.stderr)
break
phans.append(data_raw['phantom'][idx, ...])
img_high = crop_image(data_raw[data_key(0)][idx, :, :])
img_high = img_high * STAT_STD + STAT_MEAN
# img_high = img_high * STAT_STD / \
# (4.0**nb_down_sample) + STAT_MEAN / (4.0**nb_down_sample)
img_highs.append(img_high)
img_low = crop_image(data_raw[data_key(nb_down_sample)][idx, ...],
[s // (2**nb_down_sample) for s in BASE_SHAPE])
img_low = img_low * STAT_STD_LOW + STAT_MEAN_LOW
img_lows.append(img_low)
img_inf, img_itp = run_infer(idx)
img_infs.append(img_inf)
img_itps.append(img_itp)
img_highs = np.array(img_highs)
img_infs = np.array(img_infs) / (4.0**nb_down_sample)
img_itps = np.array(img_itps) / (4.0**nb_down_sample)
img_lows = np.array(img_lows) / (4.0**nb_down_sample)
results = {
'phantom': phans,
'sino_itps': img_itps,
'sino_infs': img_infs,
'sino_highs': img_highs,
'sino_lows': img_lows
}
np.savez(output, **results)