def save_posterior_patches(self,
full_datasets,
cascade_no,
cropped_patches_dataset=None,
face_tables=None,
size_of_rf=None):
"""
Save the information about the cascade and the fprop to here.
"""
if cascade_no > 1:
assert cropped_patches_dataset is not None
if face_tables is None:
face_tables = []
for dataset in datasets:
face_tables.append(dataset.face_table)
mode = SequentialSubsetIterator
targets = True
count_patches = 0
out_files = []
ms_model = self.predictors[cascade_no]
n_nonface_patches = 100
for i in xrange(self.nlevels):
name = "cascade_%d_lvl_%d" % (cascade_no, i)
input_space = ms_model.models[i].get_input_space()
models = ms_model.models
X = input_space.make_theano_batch()
# doesn't support topo yets
if X.ndim > 2:
assert False
dataset = datasets[i]
if dataset.iter_mode == "train":
dataset.set_iter_mode("fprop")
outputs = models[i].fprop(X, cascade_no)
batch_size = models[i].batch_size
fn = theano.function([X], outputs)
n_examples = X.shape[0]
model_file_path = ms_model.model_file_path
receptive_field_size = models[i].receptive_field_size
out_file, gcols = OutputMapFile.create_file(
model_file_path,
name,
n_examples=n_examples,
out_shape=(n_examples, size_of_rf[0], size_of_rf[1]))
outmap = OutputMap(receptive_field_size, self.img_shape,
models.stride)
random_patch_extractor = ExtractRandomPatches(
patch_shape=size_of_rf, num_patches=n_nonface_patches)
grid_patch_extractor = ExtractGridPatches(
patch_shape=size_of_rf, patch_stride=models.stride)
count_patches = 0
if cascade_no == 0 or models.isconv:
dataset = full_datasets
else:
dataset = cropped_patches_ds
#This is a counter for the cascade i where i > 0.
for data in dataset.iterator(batch_size=batch_size,
mode=mode,
targets=True):
if cascade_no == 0 or models.isconv:
#Extract the patches from the full size image according to the convolution
#operation with respect to a specific sized receptive fields
#and stride.
minibatch_images = data[0]
conv_targets = data[1]
imgnos = data[2]
batch_act = fn(minibatch_images)
batch_act = np.concatenate(
[elem.reshape(elem.size) for elem in batch_act],
axis=0)
new_preds = self._check_threshold_non_max_suppression(
batch_act, self.reject_probabilities[cascade_no],
self.non_max_radius)
#Remove the nonface entries:
self.remove_nonfaces(new_preds,
face_tables[i],
imgnos=imgnos,
images=minibatch_images)
start = count_patches
patches, targets, patch_locs, img_nos = outmap.extract_patches_batch(
minibatch_images, new_preds, start, conv_targets)
new_patches, new_targets, new_img_nos, new_patch_locs = self.balance_face_table(
dataset, i, face_tables[i], random_patch_extractor,
grid_patch_extractor)
patches = list_alternator(new_patches, patches)
targets = list_alternator(new_targets, targets)
imgnos = list_alternator(new_img_nos, img_nos)
patch_locs = list_alternator(new_patch_locs, patch_locs)
stop = count_patches + patches.shape[0]
OutputMapFile.save_output_map(h5file=out_file,
patches=patches,
imgnos=imgnos,
ilocs=patch_locs,
targets=targets,
start=start,
stop=stop)
count_patches = stop
else:
# Do the patchwise patch extraction
# Decide with patches to send to the next cascade member
minibatch_patches = data[0]
minibatch_targets = data[1]
minibatch_imgnos = data[2]
minibatch_imglocs = data[3]
assert receptive_field_size == cropped_patches_ds.img_shape, "Size of receptive field of the model is different from the size of the patches!"
batch_act = fn(minibatch_patches)
batch_act = np.concatenate(
[elem.reshape(elem.size) for elem in batch_act],
axis=0)
output_map = self.create_output_map(
minibatch_targets, minibatch_imglocs)
new_preds = self._check_threshold_non_max_suppression(
output_map, self.reject_probabilities[cascade_no],
self.non_max_radius)
#Remove the dead patches
self.remove_nonfaces(new_preds,
face_tables[i],
imgnos=minibatch_imgnos,
images=minibatch_patches,
imglocs=minibatch_imglocs,
img_targets=minibatch_targets)
start = count_patches
stop = count_patches + patches.shape[0]
new_patches, new_targets, new_img_nos, new_patch_locs = self.balance_face_table(
dataset, i, face_tables[i], random_patch_extractor,
grid_patch_extractor)
patches = list_alternator(new_patches, patches)
targets = list_alternator(new_targets, targets)
imgnos = list_alternator(new_img_nos, img_nos)
patch_locs = list_alternator(new_patch_locs, patch_locs)
OutputMapFile.save_output_map(out_file,
minibatch_patches,
new_preds,
img_nos,
targets=targets,
start=start,
stop=stop)
count_patches = stop
h5_path = os.path.join(model_file_path, name)
out_files.append(h5_path)
return out_files
def save_posterior_patches(self,
full_datasets,
cascade_no,
cropped_patches_dataset=None,
face_tables=None,
size_of_rf=None):
"""
Save the information about the cascade and the fprop to here.
"""
if cascade_no > 1:
assert cropped_patches_dataset is not None
if face_tables is None:
face_tables = []
for dataset in datasets:
face_tables.append(dataset.face_table)
mode = SequentialSubsetIterator
targets = True
count_patches = 0
out_files = []
ms_model = self.predictors[cascade_no]
n_nonface_patches = 100
for i in xrange(self.nlevels):
name = "cascade_%d_lvl_%d" % (cascade_no, i)
input_space = ms_model.models[i].get_input_space()
models = ms_model.models
X = input_space.make_theano_batch()
# doesn't support topo yets
if X.ndim > 2:
assert False
dataset = datasets[i]
if dataset.iter_mode == "train":
dataset.set_iter_mode("fprop")
outputs = models[i].fprop(X, cascade_no)
batch_size = models[i].batch_size
fn = theano.function([X], outputs)
n_examples = X.shape[0]
model_file_path = ms_model.model_file_path
receptive_field_size = models[i].receptive_field_size
out_file, gcols = OutputMapFile.create_file(model_file_path,
name,
n_examples=n_examples,
out_shape=(n_examples, size_of_rf[0], size_of_rf[1]))
outmap = OutputMap(receptive_field_size, self.img_shape, models.stride)
random_patch_extractor = ExtractRandomPatches(patch_shape=size_of_rf, num_patches=n_nonface_patches)
grid_patch_extractor = ExtractGridPatches(patch_shape=size_of_rf,
patch_stride=models.stride)
count_patches = 0
if cascade_no == 0 or models.isconv:
dataset = full_datasets
else:
dataset = cropped_patches_ds
#This is a counter for the cascade i where i > 0.
for data in dataset.iterator(batch_size=batch_size, mode=mode, targets=True):
if cascade_no == 0 or models.isconv:
#Extract the patches from the full size image according to the convolution
#operation with respect to a specific sized receptive fields
#and stride.
minibatch_images = data[0]
conv_targets = data[1]
imgnos = data[2]
batch_act = fn(minibatch_images)
batch_act = np.concatenate([elem.reshape(elem.size) for elem in batch_act], axis=0)
new_preds = self._check_threshold_non_max_suppression(batch_act,
self.reject_probabilities[cascade_no],
self.non_max_radius)
#Remove the nonface entries:
self.remove_nonfaces(new_preds,
face_tables[i],
imgnos=imgnos,
images=minibatch_images)
start = count_patches
patches, targets, patch_locs, img_nos = outmap.extract_patches_batch(minibatch_images,
new_preds,
start,
conv_targets)
new_patches, new_targets, new_img_nos, new_patch_locs = self.balance_face_table(dataset,
i, face_tables[i],
random_patch_extractor,
grid_patch_extractor)
patches = list_alternator(new_patches, patches)
targets = list_alternator(new_targets, targets)
imgnos = list_alternator(new_img_nos, img_nos)
patch_locs = list_alternator(new_patch_locs, patch_locs)
stop = count_patches + patches.shape[0]
OutputMapFile.save_output_map(h5file=out_file,
patches=patches,
imgnos=imgnos,
ilocs=patch_locs,
targets=targets,
start=start,
stop=stop)
count_patches = stop
else:
# Do the patchwise patch extraction
# Decide with patches to send to the next cascade member
minibatch_patches = data[0]
minibatch_targets = data[1]
minibatch_imgnos = data[2]
minibatch_imglocs = data[3]
assert receptive_field_size == cropped_patches_ds.img_shape, "Size of receptive field of the model is different from the size of the patches!"
batch_act = fn(minibatch_patches)
batch_act = np.concatenate([elem.reshape(elem.size) for elem in batch_act], axis=0)
output_map = self.create_output_map(minibatch_targets, minibatch_imglocs)
new_preds = self._check_threshold_non_max_suppression(output_map,
self.reject_probabilities[cascade_no],
self.non_max_radius)
#Remove the dead patches
self.remove_nonfaces(new_preds, face_tables[i],
imgnos=minibatch_imgnos,
images=minibatch_patches,
imglocs=minibatch_imglocs,
img_targets=minibatch_targets)
start = count_patches
stop = count_patches + patches.shape[0]
new_patches, new_targets, new_img_nos, new_patch_locs = self.balance_face_table(dataset,
i, face_tables[i],
random_patch_extractor,
grid_patch_extractor)
patches = list_alternator(new_patches, patches)
targets = list_alternator(new_targets, targets)
imgnos = list_alternator(new_img_nos, img_nos)
patch_locs = list_alternator(new_patch_locs, patch_locs)
OutputMapFile.save_output_map(out_file,
minibatch_patches,
new_preds,
img_nos,
targets=targets,
start=start,
stop=stop)
count_patches = stop
h5_path = os.path.join(model_file_path, name)
out_files.append(h5_path)
return out_files
def balance_face_table(self,
dataset,
cascade_no,
random_patch_extractor,
grid_patch_extractor,
face_table=None,
size_of_rf=None):
"""
TODO: Check the dead faces and replace them with new ones.
"""
assert face_table is not None
last_face_no = face_table.get_last_face_no()
last_nonface_no = face_table.get_last_nonface_no()
dead_faces = face_table.get_dead_faces()
dead_nonfaces = face_table.get_dead_nonfaces()
face_imgnos = []
nonface_imgnos = []
non_face_targets = []
face_targets = []
new_nonface_patches = []
new_face_patches = []
nonface_plocs = []
face_plocs = []
###Replace the dead faces with the new ones####
for dead_face in dead_faces:
last_face_no += 1
face_row = face_table.get_row(dead_face)
face_table.simple_update_face_table(face_row, last_face_no, 1, 1)
face_patches, face_targets_, patch_locs = grid_patch_extractor.apply(
dataset, start=last_face_no)
new_face_patches.append(face_patches)
face_plocs.append(patch_locs)
face_targets.extend(face_targets_.flatten())
face_imgno = np.array(
list(its.repeat(last_face_no, face_targets_.shape[0])))
face_imgnos.extend(face_imgno)
for dead_nonface in dead_nonfaces:
last_nonface_no += 1
face_row = face_table.get_row(dead_nonface)
face_table.simple_update_face_table(face_row, last_nonface_no, 0,
1)
nonface_patches, patch_targets, patch_locs = grid_patch_extractor.apply(
dataset, start=last_nonface_no)
new_nonface_patches.append(nonface_patches.flatten())
non_face_targets.append(patch_targets.flatten())
nonface_plocs.append(patch_locs)
nonface_imgno = np.array(
list(its.repeat(last_nonface_no, patch_targets.shape[0])))
nonface_imgnos.extend(nonface_imgno)
patches = list_alternator(new_face_patches, new_nonface_patches)
targets = list_alternator(face_targets, non_face_targets)
imgnos = list_alternator(face_imgnos, nonface_imgnos)
plocs = list_alternator(face_plocs, nonface_plocs)
return patches, targets, imgnos, plocs
def balance_face_table(self,
dataset,
cascade_no,
random_patch_extractor,
grid_patch_extractor,
face_table=None,
size_of_rf=None):
"""
TODO: Check the dead faces and replace them with new ones.
"""
assert face_table is not None
last_face_no = face_table.get_last_face_no()
last_nonface_no = face_table.get_last_nonface_no()
dead_faces = face_table.get_dead_faces()
dead_nonfaces = face_table.get_dead_nonfaces()
face_imgnos = []
nonface_imgnos = []
non_face_targets = []
face_targets = []
new_nonface_patches = []
new_face_patches = []
nonface_plocs = []
face_plocs = []
###Replace the dead faces with the new ones####
for dead_face in dead_faces:
last_face_no += 1
face_row = face_table.get_row(dead_face)
face_table.simple_update_face_table(face_row, last_face_no, 1, 1)
face_patches, face_targets_, patch_locs = grid_patch_extractor.apply(dataset, start=last_face_no)
new_face_patches.append(face_patches)
face_plocs.append(patch_locs)
face_targets.extend(face_targets_.flatten())
face_imgno = np.array(list(its.repeat(last_face_no, face_targets_.shape[0])))
face_imgnos.extend(face_imgno)
for dead_nonface in dead_nonfaces:
last_nonface_no += 1
face_row = face_table.get_row(dead_nonface)
face_table.simple_update_face_table(face_row, last_nonface_no, 0, 1)
nonface_patches, patch_targets, patch_locs = grid_patch_extractor.apply(dataset, start=last_nonface_no)
new_nonface_patches.append(nonface_patches.flatten())
non_face_targets.append(patch_targets.flatten())
nonface_plocs.append(patch_locs)
nonface_imgno = np.array(list(its.repeat(last_nonface_no, patch_targets.shape[0])))
nonface_imgnos.extend(nonface_imgno)
patches = list_alternator(new_face_patches, new_nonface_patches)
targets = list_alternator(face_targets, non_face_targets)
imgnos = list_alternator(face_imgnos, nonface_imgnos)
plocs = list_alternator(face_plocs, nonface_plocs)
return patches, targets, imgnos, plocs
