def load_weights_from_hdf5(model_path, keras_model):
f = h5py.File(model_path, 'r')['model_weights']
layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model") \
else keras_model.layers
filtered_layers = []
for layer in layers:
weights = layer.weights
if weights:
filtered_layers.append(layer)
layer_names = load_attributes_from_hdf5_group(f, 'layer_names')
filtered_layer_names = []
for name in layer_names:
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
if weight_names:
filtered_layer_names.append(name)
layer_names = filtered_layer_names
if len(layer_names) != len(filtered_layers):
raise ValueError('You are trying to load a weight file '
'containing ' + str(len(layer_names)) +
' layers into a model with ' +
str(len(filtered_layers)) + ' layers.')
weight_dict = {}
for k, name in enumerate(layer_names):
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names]
weight_dict.update({name: weight_values})
return weight_dict
def _load_weights_from_hdf5_group(f,
layers):
"""
Implements topological (order-based) weight loading.
Parameters
----------
f : File
A pointer to a HDF5 group.
layers : list of np.array
List of target layers.
"""
filtered_layers = []
for layer in layers:
weights = layer.weights
if weights:
filtered_layers.append(layer)
layer_names = load_attributes_from_hdf5_group(f, "layer_names")
filtered_layer_names = []
for name in layer_names:
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, "weight_names")
if weight_names:
filtered_layer_names.append(name)
layer_names = filtered_layer_names
if len(layer_names) != len(filtered_layers):
raise ValueError("You are trying to load a weight file "
"containing " + str(len(layer_names)) +
" layers into a model with " +
str(len(filtered_layers)) + " layers.")
weight_value_tuples = []
for k, name in enumerate(layer_names):
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, "weight_names")
weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names]
layer = filtered_layers[k]
symbolic_weights = layer.weights
weight_values = _preprocess_weights_for_loading(
layer=layer,
weights=weight_values)
if len(weight_values) != len(symbolic_weights):
raise ValueError("Layer #" + str(k) +
" (named `" + layer.name +
"` in the current model) was found to "
"correspond to layer " + name +
" in the save file. "
"However the new layer " + layer.name +
" expects " + str(len(symbolic_weights)) +
" weights, but the saved weights have " +
str(len(weight_values)) +
" elements.")
weight_value_tuples += zip(symbolic_weights, weight_values)
K.batch_set_value(weight_value_tuples)
def _load_weights_from_hdf5_group_by_name(f, layers):
"""
Implements name-based weight loading.
Parameters
----------
f : File
A pointer to a HDF5 group.
layers : list of np.array
List of target layers.
"""
# New file format.
layer_names = load_attributes_from_hdf5_group(f, "layer_names")
# Reverse index of layer name to list of layers with name.
index = {}
for layer in layers:
if layer.name:
index.setdefault(layer.name, []).append(layer)
weight_value_tuples = []
for k, name in enumerate(layer_names):
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, "weight_names")
weight_values = [
np.asarray(g[weight_name]) for weight_name in weight_names
]
for layer in index.get(name, []):
symbolic_weights = layer.weights
weight_values = _preprocess_weights_for_loading(
layer=layer, weights=weight_values)
if len(weight_values) != len(symbolic_weights):
warnings.warn(
"Skipping loading of weights for layer {} due to mismatch in number of weights ({} vs"
" {}).".format(layer, len(symbolic_weights),
len(weight_values)))
continue
# Set values.
for i in range(len(weight_values)):
symbolic_shape = K.int_shape(symbolic_weights[i])
if symbolic_shape != weight_values[i].shape:
warnings.warn(
"Skipping loading of weights for layer {} due to mismatch in shape ({} vs"
" {}).".format(layer, symbolic_weights[i].shape,
weight_values[i].shape))
continue
else:
weight_value_tuples.append(
(symbolic_weights[i], weight_values[i]))
K.batch_set_value(weight_value_tuples)
def get_named_layer_weights_from_h5py(h5py_file):
"""decodes h5py for a given model downloaded by keras and gets layer weight name to value mapping"""
with h5py.File(h5py_file) as h5py_stream:
layer_names = load_attributes_from_hdf5_group(h5py_stream,
'layer_names')
weights_values = []
for name in layer_names:
layer = h5py_stream[name]
weight_names = load_attributes_from_hdf5_group(
layer, 'weight_names')
if weight_names:
weight_values = [
np.asarray(layer[weight_name])
for weight_name in weight_names
]
weights_values.append((name, weight_values))
return weights_values
import h5py
import os
from keras.engine import saving
weights = h5py.File("/proj/SegSRGAN/snapshot/SegSRGAN_epoch_99", 'r')
print(weights)
G = weights[list(weights.keys())[1]]
weight_names = saving.load_attributes_from_hdf5_group(G, 'weight_names')
print(weight_names)
for i in weight_names:
if 'gen_conv1' in i:
weight_values = G[i]
if 'gen_1conv' in i:
last_conv = G[i]
first_generator_kernel = weight_values.shape[4]
nb_out_kernel = last_conv.shape[4]
if nb_out_kernel > 2:
fit_mask = True
nb_classe_mask = nb_out_kernel - 2
print("The initialize network will fit mask")
else:
fit_mask = False
nb_classe_mask = 0
print("The initialize network won't fit mask")
def segmentation(input_file_path,
step,
new_resolution,
path_output_cortex,
path_output_hr,
weights_path,
patch=None,
spline_order=3,
by_batch=False):
"""
:param input_file_path: path of the image to be super resolved and segmented
:param step: the shifting step for the patches
:param new_resolution: the new z-resolution we want for the output image
:param path_output_cortex: output path of the segmented cortex
:param path_output_hr: output path of the super resolution output image
:param weights_path: the path of the file which contains the pre-trained weights for the neural network
:param patch: the size of the patches
:param spline_order: for the interpolation
:param by_batch: to enable the by-batch processing
:return:
"""
# TestFile = path de l'image en entree
# high_resolution = tuple des resolutions (par axe)
# Get the generator kernel from the weights we are going to use
weights = h5py.File(weights_path, 'r')
G = weights[list(weights.keys())[1]]
weight_names = saving.load_attributes_from_hdf5_group(G, 'weight_names')
for i in weight_names:
if 'gen_conv1' in i:
weight_values = G[i]
first_generator_kernel = weight_values.shape[4]
# Get the generator kernel from the weights we are going to use
D = weights[list(weights.keys())[0]]
weight_names = saving.load_attributes_from_hdf5_group(D, 'weight_names')
for i in weight_names:
if 'conv_dis_1/kernel' in i:
weight_values = D[i]
first_discriminator_kernel = weight_values.shape[4]
# Selection of the kind of network
if "_nn_residual" in list(weights.keys())[1]:
residual_string = "_nn_residual"
is_residual = False
else:
residual_string = ""
is_residual = True
if ('G_cond' + residual_string) == list(weights.keys())[1]:
is_conditional = True
u_net_gen = False
elif ('G_unet' + residual_string) == list(weights.keys())[1]:
is_conditional = False
u_net_gen = True
elif ('G_unet_cond' + residual_string) == list(weights.keys())[1]:
is_conditional = True
u_net_gen = True
else:
is_conditional = False
u_net_gen = False
# Check resolution
if np.isscalar(new_resolution):
new_resolution = (new_resolution, new_resolution, new_resolution)
else:
if len(new_resolution) != 3:
raise AssertionError('Resolution not supported!')
# Read low-resolution image
if input_file_path.endswith('.nii.gz'):
image_instance = NIFTIReader(input_file_path)
elif os.path.isdir(input_file_path):
image_instance = DICOMReader(input_file_path)
test_image = image_instance.get_np_array()
test_imageMinValue = float(np.min(test_image))
test_imageMaxValue = float(np.max(test_image))
test_imageNorm = test_image / test_imageMaxValue
resolution = image_instance.get_resolution()
itk_image = image_instance.itk_image
# Check scale factor type
up_scale = tuple(
itema / itemb
for itema, itemb in zip(itk_image.GetSpacing(), new_resolution))
# spline interpolation
interpolated_image = scipy.ndimage.zoom(test_imageNorm,
zoom=up_scale,
order=spline_order)
if patch is not None:
print("patch given")
patch1 = patch2 = patch3 = int(patch)
border = (int((interpolated_image.shape[0] - int(patch)) % step),
int((interpolated_image.shape[1] - int(patch)) % step),
int((interpolated_image.shape[2] - int(patch)) % step))
border_to_add = (step - border[0], step - border[1], step - border[2])
# padd border
padded_interpolated_image = pad3D(
interpolated_image, border_to_add) # remove border of the image
else:
border = (int(interpolated_image.shape[0] % 4),
int(interpolated_image.shape[1] % 4),
int(interpolated_image.shape[2] % 4))
border_to_add = (4 - border[0], 4 - border[1], 4 - border[2])
padded_interpolated_image = pad3D(
interpolated_image, border_to_add) # remove border of the image
height, width, depth = np.shape(padded_interpolated_image)
patch1 = height
patch2 = width
patch3 = depth
if ((step > patch1) | (step > patch2) |
(step > patch3)) & (patch is not None):
raise AssertionError('The step need to be smaller than the patch size')
if (np.shape(padded_interpolated_image)[0] <
patch1) | (np.shape(padded_interpolated_image)[1] < patch2) | (
np.shape(padded_interpolated_image)[2] < patch3):
raise AssertionError(
'The patch size need to be smaller than the interpolated image size'
)
# Loading weights
segsrgan_test_instance = SegSRGAN_test(weights_path, patch1, patch2,
patch3, is_conditional, u_net_gen,
is_residual, first_generator_kernel,
first_discriminator_kernel,
resolution)
# GAN
print("Testing : ")
estimated_hr_image, estimated_cortex = segsrgan_test_instance.test_by_patch(
padded_interpolated_image, step=step, by_batch=by_batch)
# parcours de l'image avec le patch
# Padding
# on fait l'operation de padding a l'envers
padded_estimated_hr_image = shave3D(estimated_hr_image, border_to_add)
estimated_cortex = shave3D(estimated_cortex, border_to_add)
# SR image
estimated_hr_imageInverseNorm = padded_estimated_hr_image * test_imageMaxValue
estimated_hr_imageInverseNorm[
estimated_hr_imageInverseNorm <=
test_imageMinValue] = test_imageMinValue # Clear negative value
output_image = sitk.GetImageFromArray(
np.swapaxes(estimated_hr_imageInverseNorm, 0, 2))
output_image.SetSpacing(new_resolution)
output_image.SetOrigin(itk_image.GetOrigin())
output_image.SetDirection(itk_image.GetDirection())
sitk.WriteImage(output_image, path_output_hr)
# Cortex segmentation
output_cortex = sitk.GetImageFromArray(np.swapaxes(estimated_cortex, 0, 2))
output_cortex.SetSpacing(new_resolution)
output_cortex.SetOrigin(itk_image.GetOrigin())
output_cortex.SetDirection(itk_image.GetDirection())
sitk.WriteImage(output_cortex, path_output_cortex)
return "Segmentation Done"
def load_weights_from_hdf5_group_by_name(f,
layers,
skip_mismatch=False,
reshape=False):
"""Implements name-based weight loading.
(instead of topological weight loading).
Layers that have no matching name are skipped.
# Arguments
f: A pointer to a HDF5 group.
layers: A list of target layers.
skip_mismatch: Boolean, whether to skip loading of layers
where there is a mismatch in the number of weights,
or a mismatch in the shape of the weights.
reshape: Reshape weights to fit the layer when the correct number
of values are present but the shape does not match.
# Raises
ValueError: in case of mismatch between provided layers
and weights file and skip_mismatch=False.
"""
if 'keras_version' in f.attrs:
original_keras_version = f.attrs['keras_version'].decode('utf8')
else:
original_keras_version = '1'
if 'backend' in f.attrs:
original_backend = f.attrs['backend'].decode('utf8')
else:
original_backend = None
# New file format.
layer_names = load_attributes_from_hdf5_group(f, 'layer_names')
# Reverse index of layer name to list of layers with name.
index = {}
for layer in layers:
if layer.name:
index.setdefault(layer.name, []).append(layer)
print(layer_names)
print(index.keys())
# We batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for k, name in enumerate(layer_names):
print(name)
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
weight_values = [
np.asarray(g[weight_name]) for weight_name in weight_names
]
for layer in index.get(name, []):
symbolic_weights = layer.weights
symbolic_weights_names = [w.name for w in symbolic_weights]
weight_values = preprocess_weights_for_loading(
layer,
weight_values,
original_keras_version,
original_backend,
reshape=reshape)
# if len(weight_values) != len(symbolic_weights):
# if skip_mismatch:
# warnings.warn('Skipping loading of weights for layer {}'.format(layer.name) +
# ' due to mismatch in number of weights' +
# ' ({} vs {}).'.format(len(symbolic_weights), len(weight_values)))
# continue
# else:
# raise ValueError('Layer #' + str(k) +
# ' (named "' + layer.name +
# '") expects ' +
# str(len(symbolic_weights)) +
# ' weight(s), but the saved weights' +
# ' have ' + str(len(weight_values)) +
# ' element(s).')
# Set values.
weight_names = [name.split('/')[-1]
for name in weight_names] #delete prefix of name
symbolic_weights_names = [
name.split('/')[-1] for name in symbolic_weights_names
]
print(weight_names)
print(symbolic_weights_names)
for i in range(len(weight_values)):
if weight_names[i] in symbolic_weights_names:
ii = symbolic_weights_names.index(weight_names[i])
if K.int_shape(
symbolic_weights[ii]) != weight_values[i].shape:
if skip_mismatch:
warnings.warn(
'Skipping loading of weights for layer {}'.
format(layer.name) +
' due to mismatch in shape' +
' ({} vs {}).'.format(
symbolic_weights[ii].shape,
weight_values[i].shape))
continue
else:
raise ValueError(
'Layer #' + str(k) + ' (named "' + layer.name +
'"), weight ' + str(symbolic_weights[ii]) +
' has shape {}'.format(
K.int_shape(symbolic_weights[ii])) +
', but the saved weight has shape ' +
str(weight_values[i].shape) + '.')
else:
weight_value_tuples.append(
(symbolic_weights[ii], weight_values[i]))
K.batch_set_value(weight_value_tuples)
def load_weights_from_hdf5_group_by_name(f,
layers,
skip_mismatch=False,
reshape=False,
consider_weight_name_match=False):
"""Implements name-based weight loading.
(instead of topological weight loading).
Layers that have no matching name are skipped.
# Arguments
f: A pointer to a HDF5 group.
layers: A list of target layers.
skip_mismatch: Boolean, whether to skip loading of layers
where there is a mismatch in the number of weights,
or a mismatch in the shape of the weights.
reshape: Reshape weights to fit the layer when the correct number
of values are present but the shape does not match.
consider_weight_name_match: Boolean, whether to consider loading of layers
even when there is a mismatch in the number of weights,
in this case loading any weights that have name and shape match,
only applicable when `skip_mismatch` = False
# Raises
ValueError: in case of mismatch between provided layers
and weights file and skip_mismatch=False.
"""
if 'keras_version' in f.attrs:
original_keras_version = f.attrs['keras_version'].decode('utf8')
else:
original_keras_version = '1'
if 'backend' in f.attrs:
original_backend = f.attrs['backend'].decode('utf8')
else:
original_backend = None
# New file format.
layer_names = load_attributes_from_hdf5_group(f, 'layer_names')
# Reverse index of layer name to list of layers with name.
index = {}
for layer in layers:
if layer.name:
index.setdefault(layer.name, []).append(layer)
# We batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for k, name in enumerate(layer_names):
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
weight_values = [
np.asarray(g[weight_name]) for weight_name in weight_names
]
for layer in index.get(name, []):
symbolic_weights = layer.weights
weight_values = preprocess_weights_for_loading(
layer,
weight_values,
original_keras_version,
original_backend,
reshape=reshape)
if len(weight_values) != len(symbolic_weights):
if skip_mismatch:
warnings.warn(
'Skipping loading of weights for '
'layer {}'.format(layer.name) + ' due to mismatch '
'in number of weights ({} vs {}).'.format(
len(symbolic_weights), len(weight_values)))
continue
else: #(thanhnt): Allows loading if variable name match (conditioned on variable shape match)
if not consider_weight_name_match:
raise ValueError(
'Layer #' + str(k) + ' (named "' + layer.name +
'") expects ' + str(len(symbolic_weights)) +
' weight(s), but the saved weights' + ' have ' +
str(len(weight_values)) + ' element(s).' +
'Consider set `consider_weight_name_match`' +
' to `True` to load weights by name match.')
else:
warnings.warn(
'Mismatch in '
'the number of weights ({} vs {}).'.format(
len(symbolic_weights), len(weight_values)) +
' Loading still continues for whichever model variable whose name matches that of the stored variables '
'(conditioned on variable shape match).')
warning_weights = []
for i in range(len(symbolic_weights)):
symbolic_shape = K.int_shape(symbolic_weights[i])
symbolic_name = symbolic_weights[i].name.split(
'/')[-1].split(':')[0]
# Look up for any weight name match
_check = [ weight_value_tuples.append((symbolic_weights[i], weight_value)) \
for weight_name, weight_value in zip(weight_names, weight_values) \
if weight_name.split('/')[-1].split(':')[0] == symbolic_name and \
weight_value.shape == symbolic_shape ]
if len(_check) == 0:
warning_weights.append(
symbolic_weights[i].name)
if len(warning_weights) > 0:
warnings.warn(
'Skipping loading of weights of some variables for '
'layer {}'.format(layer.name) +
' due to mismatch '
'in variable names or variable shapes. '
'The variables are {}.'.format(warning_weights)
+ 'The stored variables are {}.'.format(
weight_names))
else:
# Set values.
for i in range(len(weight_values)):
symbolic_shape = K.int_shape(symbolic_weights[i])
if symbolic_shape != weight_values[i].shape:
if skip_mismatch:
warnings.warn('Skipping loading of weights for '
'layer {}'.format(layer.name) +
' due to '
'mismatch in shape ({} vs {}).'.
format(symbolic_weights[i].shape,
weight_values[i].shape))
continue
else:
raise ValueError(
'Layer #' + str(k) + ' (named "' + layer.name +
'"), weight ' + str(symbolic_weights[i]) +
' has shape {}'.format(symbolic_shape) +
', but the saved weight has shape ' +
str(weight_values[i].shape) + '.')
else:
weight_value_tuples.append(
(symbolic_weights[i], weight_values[i]))
K.batch_set_value(weight_value_tuples)
def segmentation(input_file_path,
step,
new_resolution,
path_output_cortex,
path_output_hr,
path_output_mask,
weights_path,
interpolation_type,
patch=None,
spline_order=3,
by_batch=False,
interp='scipy'):
"""
:param input_file_path: path of the image to be super resolved and segmented
:param step: the shifting step for the patches
:param new_resolution: the new z-resolution we want for the output image
:param path_output_cortex: output path of the segmented cortex
:param path_output_hr: output path of the super resolution output image
:param path_output_mask: output path of the estimated mask. Only used if the weights have been obtain for also fit mask.
:param weights_path: the path of the file which contains the pre-trained weights for the neural network
:param patch: the size of the patches
:param spline_order: for the interpolation
:param by_batch: to enable the by-batch processing
:return:
"""
# TestFile = path de l'image en entree
# high_resolution = tuple des resolutions (par axe)
# Get the generator kernel from the weights we are going to use
weights = h5py.File(weights_path, 'r')
G = weights[list(weights.keys())[1]]
weight_names = saving.load_attributes_from_hdf5_group(G, 'weight_names')
for i in weight_names:
if 'gen_conv1' in i:
weight_values = G[i]
if 'gen_1conv' in i:
last_conv = G[i]
first_generator_kernel = weight_values.shape[4]
nb_out_kernel = last_conv.shape[4]
if nb_out_kernel > 2:
fit_mask = True
nb_classe_mask = nb_out_kernel - 2
print("The initialize network will fit mask")
else:
fit_mask = False
nb_classe_mask = 0
print("The initialize network won't fit mask")
# Get the generator kernel from the weights we are going to use
D = weights[list(weights.keys())[0]]
weight_names = saving.load_attributes_from_hdf5_group(D, 'weight_names')
for i in weight_names:
if 'conv_dis_1/kernel' in i:
weight_values = D[i]
first_discriminator_kernel = weight_values.shape[4]
# Selection of the kind of network
if "_nn_residual" in list(weights.keys())[1]:
residual_string = "_nn_residual"
is_residual = False
else:
residual_string = ""
is_residual = True
if ('G_cond' + residual_string) == list(weights.keys())[1]:
is_conditional = True
u_net_gen = False
elif ('G_unet' + residual_string) == list(weights.keys())[1]:
is_conditional = False
u_net_gen = True
elif ('G_unet_cond' + residual_string) == list(weights.keys())[1]:
is_conditional = True
u_net_gen = True
else:
is_conditional = False
u_net_gen = False
# Check resolution
if np.isscalar(new_resolution):
new_resolution = (new_resolution, new_resolution, new_resolution)
else:
if len(new_resolution) != 3:
raise AssertionError('Resolution not supported!')
# Read low-resolution image
if input_file_path.endswith('.nii.gz') or input_file_path.endswith('.hdr'):
image_instance = NIFTIReader(input_file_path)
elif os.path.isdir(input_file_path):
image_instance = DICOMReader(input_file_path)
test_image = image_instance.get_np_array()
test_imageMinValue = float(np.min(test_image))
norm_instance = Normalization(test_image)
test_imageNorm = norm_instance.get_normalized_image()[
0] #zero indice means get only the normalized LR
resolution = image_instance.get_resolution()
itk_image = image_instance.itk_image
# Check scale factor type
up_scale = tuple(
itema / itemb
for itema, itemb in zip(itk_image.GetSpacing(), new_resolution))
# spline interpolation
interpolated_image, up_scale = inter.Interpolation(test_imageNorm, up_scale, spline_order, interp,
interpolation_type). \
get_interpolated_image(image_instance)
if patch is not None:
print("patch given")
patch1 = patch2 = patch3 = int(patch)
border = (int((interpolated_image.shape[0] - int(patch)) % step),
int((interpolated_image.shape[1] - int(patch)) % step),
int((interpolated_image.shape[2] - int(patch)) % step))
border_to_add = (step - border[0], step - border[1], step - border[2])
# padd border
padded_interpolated_image = pad3D(
interpolated_image, border_to_add) # remove border of the image
else:
border = (int(interpolated_image.shape[0] % 4),
int(interpolated_image.shape[1] % 4),
int(interpolated_image.shape[2] % 4))
border_to_add = (4 - border[0], 4 - border[1], 4 - border[2])
padded_interpolated_image = pad3D(
interpolated_image, border_to_add) # remove border of the image
height, width, depth = np.shape(padded_interpolated_image)
patch1 = height
patch2 = width
patch3 = depth
if ((step > patch1) | (step > patch2) |
(step > patch3)) & (patch is not None):
raise AssertionError('The step need to be smaller than the patch size')
if (np.shape(padded_interpolated_image)[0] <
patch1) | (np.shape(padded_interpolated_image)[1] < patch2) | (
np.shape(padded_interpolated_image)[2] < patch3):
raise AssertionError(
'The patch size need to be smaller than the interpolated image size'
)
# Loading weights
segsrgan_test_instance = SegSRGAN_test(weights_path,
patch1,
patch2,
patch3,
is_conditional,
u_net_gen,
is_residual,
first_generator_kernel,
first_discriminator_kernel,
resolution,
fit_mask=fit_mask,
nb_classe_mask=nb_classe_mask)
# GAN
print("Testing : ")
estimated_hr_image, estimated_seg = segsrgan_test_instance.test_by_patch(
padded_interpolated_image,
step=step,
by_batch=by_batch,
nb_classe_mask=nb_classe_mask)
estimated_cortex = estimated_seg[0]
# parcours de l'image avec le patch
# shaving :
padded_estimated_hr_image = shave3D(estimated_hr_image, border_to_add)
# SR image
estimated_hr_imageInverseNorm = norm_instance.get_denormalized_result_image(
padded_estimated_hr_image)
estimated_hr_imageInverseNorm[
estimated_hr_imageInverseNorm <=
test_imageMinValue] = test_imageMinValue # Clear negative value
output_image = sitk.GetImageFromArray(
np.swapaxes(estimated_hr_imageInverseNorm, 0, 2))
output_image.SetSpacing(
tuple(
np.array(image_instance.itk_image.GetSpacing()) /
np.array(up_scale)))
output_image.SetOrigin(itk_image.GetOrigin())
output_image.SetDirection(itk_image.GetDirection())
sitk.WriteImage(output_image, path_output_hr)
# Cortex segmentation
estimated_cortex = shave3D(estimated_cortex, border_to_add)
output_cortex = sitk.GetImageFromArray(np.swapaxes(estimated_cortex, 0, 2))
output_cortex.SetSpacing(
tuple(
np.array(image_instance.itk_image.GetSpacing()) /
np.array(up_scale)))
output_cortex.SetOrigin(itk_image.GetOrigin())
output_cortex.SetDirection(itk_image.GetDirection())
sitk.WriteImage(output_cortex, path_output_cortex)
# Mask :
if fit_mask:
estimated_mask = estimated_seg[1:]
estimated_mask_discretized = np.full(estimated_mask.shape[1:], np.nan)
estimated_mask_discretized = np.argmax(estimated_mask,
axis=0).astype(np.float64)
estimated_mask_discretized = shave3D(estimated_mask_discretized,
border_to_add)
# label_connected_region = measure.label(estimated_mask_binary, connectivity=2)
# unique_elements, counts_elements = np.unique(label_connected_region[label_connected_region != 0], return_counts=
# True)
# label_max_element = unique_elements[np.argmax(counts_elements)]
# estimated_mask_binary[label_connected_region!=label_max_element] = 0
output_mask = sitk.GetImageFromArray(
np.swapaxes(estimated_mask_discretized, 0, 2))
output_mask.SetSpacing(
tuple(
np.array(image_instance.itk_image.GetSpacing()) /
np.array(up_scale)))
output_mask.SetOrigin(itk_image.GetOrigin())
output_mask.SetDirection(itk_image.GetDirection())
sitk.WriteImage(output_mask, path_output_mask)
return "Segmentation Done"
def load_weights_from_hdf5_group_new(f, layers, reshape=False):
"""Implements topological (order-based) weight loading.
# Arguments
f: A pointer to a HDF5 group.
layers: a list of target layers.
reshape: Reshape weights to fit the layer when the correct number
of values are present but the shape does not match.
# Raises
ValueError: in case of mismatch between provided layers
and weights file.
"""
if 'keras_version' in f.attrs:
original_keras_version = f.attrs['keras_version'].decode('utf8')
else:
original_keras_version = '1'
if 'backend' in f.attrs:
original_backend = f.attrs['backend'].decode('utf8')
else:
original_backend = None
#Problemas en la m.layers[180].weights = [] (attentive)
filtered_layers = [] #Recibe solo las layers que tienen pesos
for layer in layers:
weights = layer.weights
if weights:
filtered_layers.append(layer)
layer_names = saving.load_attributes_from_hdf5_group(f, 'layer_names')
filtered_layer_names = []
for name in layer_names:
g = f[name]
weight_names = saving.load_attributes_from_hdf5_group(g, 'weight_names')
if weight_names:
filtered_layer_names.append(name)
layer_names = filtered_layer_names
#print("||||||||||||||||||||||||||||||||||||||")
#for i in range(100,113):
# print(layer_names[i], "===", filtered_layers[i])
# print(" ")
#print(layer_names[107], "===", filtered_layers[107]) #Problema en la 5/22
#print("------------------------------------")
#layer = filtered_layers[107]
#symbolic_weights = layer.weights
#for nro in range(0,22):
# print(nro)
# print(symbolic_weights[nro].shape)
#print("||||||||||||||||||||||||||||||||||||||")
if len(layer_names) != len(filtered_layers):
raise ValueError('You are trying to load a weight file '
'containing ' + str(len(layer_names)) +
' layers into a model with ' +
str(len(filtered_layers)) + ' layers.')
# We batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for k, name in enumerate(layer_names):
g = f[name]
weight_names = saving.load_attributes_from_hdf5_group(g, 'weight_names')
weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names]
layer = filtered_layers[k]
symbolic_weights = layer.weights
weight_values = saving.preprocess_weights_for_loading(layer,
weight_values,
original_keras_version,
original_backend,
reshape=reshape)
#if(k == 107):
# for nro in range(0,21):
# print(weight_values[nro].shape)
if len(weight_values) != len(symbolic_weights):
raise ValueError('Layer #' + str(k) +
' (named "' + layer.name +
'" in the current model) was found to '
'correspond to layer ' + name +
' in the save file. '
'However the new layer ' + layer.name +
' expects ' + str(len(symbolic_weights)) +
' weights, but the saved weights have ' +
str(len(weight_values)) +
' elements.')
#Zona Hack
for i in range(len(symbolic_weights)):
if (symbolic_weights[i].shape != weight_values[i].shape):
weight_values[i] = np.moveaxis(weight_values[i], (0,1,2,3), (3,2,1,0))
weight_value_tuples += zip(symbolic_weights, weight_values)
#for i in range(len(weight_value_tuples)):
# print(i, weight_value_tuples[i][0].shape," = ",weight_value_tuples[i][1].shape)
K.batch_set_value(weight_value_tuples)
print("Procedimiento weights_proc.py finalizado")
