def evaluate_one_percent(self):
"""
evaluate one percent of all fonts
"""
if self.percent >= len(self.df_percents):
self.got_results = True
return False
norm_user_img = utils.normalize(self.user_image)
fonts = self.df_percents[self.percent]['aws_bucket_key']
indices = [(self.font_index_map[font]) for font in fonts]
imgs = self.char_array[indices]
norm_imgs = np.zeros(imgs.shape)
for i in range(imgs.shape[0]):
norm_imgs[i] = utils.normalize(imgs[i])
norm_imgs.shape = (norm_imgs.shape[0], norm_imgs.shape[1]*norm_imgs.shape[2])
predictions = self.nn.predict_proba(norm_imgs)
scores = np.divide(predictions[:,0], np.max(predictions[:,1:], axis=1))
self.scores.update(dict(zip(fonts, scores)))
self.percent += 1
if self.percent % 10 == 0:
print '{0}% fonts evaluated'.format(self.percent)
return True
def add_doc_attributes(doc):
doc_json = proposal_utils.doc_info(doc)
properties = extract.get_properties(doc_json)
for name, value in properties.items():
logger.info("Adding %s attribute", name)
published = doc.published or datetime.now()
handle = normalize(name)
try:
attr = Attribute.objects.get(proposal=doc.proposal,
handle=handle)
except Attribute.DoesNotExist:
attr = Attribute(proposal=doc.proposal,
name=name,
handle=normalize(name),
published=published)
attr.set_value(value)
else:
# TODO: Either mark the old attribute as stale and create a
# new one or create a record that the value has changed
if published > attr.published:
attr.clear_value()
attr.set_value(value)
attr.save()
add_doc_events(doc, properties)
return doc
def album(self, album):
title = normalize(album.name)
# TODO album years
#if Prefs["displayAlbumYear"] and album.getYear() != 0:
# title = "%s (%s)" % (title, album.getYear())
cover_url = self.image(album.covers)
track_count = None
if album.discs:
track_count = len(album.discs[0].tracks)
return DirectoryObject(
key=route_path('album', album.uri),
#rating_key=album.uri,
title=title,
tagline=', '.join([normalize(ar.name) for ar in album.artists]),
#track_count=track_count,
art=cover_url,
thumb=cover_url,
)
def triangle(i, amp, phase=0):
phase = normalize(phase, 360)
i = normalize(i + phase/360.)
if i < 0.5:
return amp * 2 * i
else:
return amp * 2 * (1 - i)
def square(i, amp, phase=0):
phase = normalize(phase, 360)
i = normalize(i + phase/360.)
if i < 0.5:
return 0
else:
return amp
def get_left_elbow_yaw(kinect_pos, shoulder_roll=None, shoulder_pitch=None, world=None):
if world is None:
world = get_robot_world(kinect_pos)
if shoulder_roll is None:
shoulder_roll = get_left_shoulder_roll(kinect_pos, world)
if shoulder_pitch is None:
shoulder_pitch = get_left_shoulder_pitch(kinect_pos, world)
shoulder = kinect_pos[kinecthandler.joints_map[joints.SHOULDER_LEFT]]
elbow = kinect_pos[kinecthandler.joints_map[joints.ELBOW_LEFT]]
wrist = kinect_pos[kinecthandler.joints_map[joints.WRIST_LEFT]]
pitch_matrix = np.matrix([[1, 0, 0],
[0, np.cos(shoulder_pitch), -np.sin(shoulder_pitch)],
[0, np.sin(shoulder_pitch), np.cos(shoulder_pitch)]])
roll_matrix = np.matrix([[np.cos(shoulder_roll), 0, np.sin(shoulder_roll)],
[0, 1, 0],
[-np.sin(shoulder_roll), 0, np.cos(shoulder_roll)]])
transform = world[0] * pitch_matrix * roll_matrix
elbow_shoulder = utils.get_vector(shoulder, elbow, transform=transform)
elbow_shoulder = utils.normalize(elbow_shoulder)
modified_elbow = [elbow[0], elbow[1] + 2, elbow[2]]
elbow_vertical = utils.get_vector(modified_elbow, elbow, transform=transform)
elbow_wrist = utils.get_vector(wrist, elbow, transform=transform)
elbow_wrist = utils.normalize([elbow_wrist[0], elbow_wrist[1]])
cross_arm = np.cross(elbow_vertical, elbow_shoulder)
cross_arm = utils.normalize([cross_arm[0], cross_arm[1]])
# cross_arm = np.array([cross_arm[0], cross_arm[1]])
# elbow_wrist = np.array([elbow_wrist[0], elbow_wrist[1]])
sign = -1
if elbow_wrist[1] > 0:
sign = 1
dot = utils.normalized_dot(elbow_wrist, cross_arm)
return sign * (np.arccos(dot))
def _tick(self):
if self.has_rock:
# Try to drop at base.
if self._drop_available():
self.has_rock = False
self.world.rock_collected()
return
# Call for a carrier to pick up.
self._broadcast_come_message()
# Head towards base if carriers not available.
if not self.world.carriers:
self.dx, self.dy = normalize(self.world.mars_base.x - self.x,
self.world.mars_base.y - self.y)
else:
return
else:
# Pick up.
rock = self._rock_available()
if rock:
self.has_rock = True
self.world.remove_entity(rock)
return
# Head towards rock.
rock = self._sense_rock()
if rock:
self.dx, self.dy = normalize(rock.x - self.x, rock.y - self.y)
# Keep walkin'.
while not self._can_move():
self.dx, self.dy = self._get_new_direction()
self._move()
def random_rhyme(self):
c1 = 'a'
c2 = 'a'
while not rhymes_with(c1, c2, self.span):
c1 = normalize(random.choice(self.words))
c2 = normalize(random.choice(self.words))
return (c1, c2)
def generate_smooth_normals(vertices, faces):
print "generating normals for", vertices.shape[0], "vertices"
vertex_normals = [[] for _ in xrange(vertices.shape[0])]
print len(vertex_normals), "vertices"
normalize = lambda n: n / numpy.sqrt(numpy.sum(n ** 2))
for i in range(faces.shape[0]):
face_vertices = faces[i, :]
v1, v2, v3 = [vertices[face_vertices[j], :] for j in range(3)]
n = normalize(numpy.cross(v2 - v1, v3 - v1))
for v in face_vertices:
vertex_normals[v].append(n)
normals = numpy.ones(vertices.shape)
for i in range(normals.shape[0]):
if len(vertex_normals[i]) == 0:
print "WARNING: no normal for vertex", i
continue
avg_normal = numpy.mean(numpy.vstack(vertex_normals[i]), 0)
normals[i, :] = normalize(avg_normal)
return normals
def write_xml(matching, f):
""" `matching` contains a list of pairs (tagged_string, its_superstring)
Tagged superstrings are written to the file `f'
"""
for (tagged, raw) in matching:
print >>f, '<aff>'
#print tagged, raw
i = 0
tag_to_write = None
for c in tagged:
if not normalize(c):
continue
if len(c) > 1 and c[1] == '/': # closing tag
f.write(c)
elif len(c) > 1: # opening tag
if tag_to_write:
f.write(tag_to_write)
tag_to_write = None
tag_to_write = c
else:
while normalize(raw[i]) != normalize(c):
f.write(xml_escape(raw[i]))
i += 1
if tag_to_write:
f.write(tag_to_write)
tag_to_write = None
f.write(xml_escape(raw[i]))
i += 1
f.write(''.join(xml_escape(c) for c in raw[i:]))
print >>f
print >>f, '</aff>'
def normalize_dataset(dataset):
fiveMinuteMean = dataset['fiveMinuteMean']
trafficVolume = dataset['trafficVolume']
actualTravelTime = dataset['actualTravelTime']
dataset['fiveMinuteMean'] = normalize(fiveMinuteMean, min(fiveMinuteMean), max(fiveMinuteMean))
dataset['trafficVolume'] = normalize(trafficVolume, min(trafficVolume), max(trafficVolume))
dataset['actualTravelTime'] = normalize(actualTravelTime, min(actualTravelTime), max(actualTravelTime))
def SIM(saliency_map1, saliency_map2):
'''
Similarity between two different saliency maps when viewed as distributions
(SIM=1 means the distributions are identical).
This similarity measure is also called **histogram intersection**.
Parameters
----------
saliency_map1 : real-valued matrix
If the two maps are different in shape, saliency_map1 will be resized to match saliency_map2.
saliency_map2 : real-valued matrix
Returns
-------
SIM : float, between [0,1]
'''
map1 = np.array(saliency_map1, copy=False)
map2 = np.array(saliency_map2, copy=False)
if map1.shape != map2.shape:
map1 = resize(map1, map2.shape, order=3, mode='nearest') # bi-cubic/nearest is what Matlab imresize() does by default
# Normalize the two maps to have values between [0,1] and sum up to 1
map1 = normalize(map1, method='range')
map2 = normalize(map2, method='range')
map1 = normalize(map1, method='sum')
map2 = normalize(map2, method='sum')
# Compute histogram intersection
intersection = np.minimum(map1, map2)
return np.sum(intersection)
def track(self, track, index=None):
rating_key = track.uri
if index is not None:
rating_key = '%s::%s' % (track.uri, index)
cover_url = self.image(track.album.covers)
return TrackObject(
items=[
MediaObject(
parts=[PartObject(
key=self.client.track_url(track),
duration=int(track.duration)
)],
duration=int(track.duration),
container=Container.MP3,
audio_codec=AudioCodec.MP3
)
],
key=route_path('metadata', str(track.uri)),
rating_key=quote(rating_key),
title=normalize(track.name),
album=normalize(track.album.name),
artist=', '.join([normalize(ar.name) for ar in track.artists]),
index=int(track.number),
duration=int(track.duration),
art=cover_url,
thumb=cover_url
)
def generate_features(self):
# prepare variables
img_lab = rgb2lab(self._img)
segments = slic(img_lab, n_segments=500, compactness=30.0, convert2lab=False)
max_segments = segments.max() + 1
# create x,y feather
shape = self._img.shape
a = shape[0]
b = shape[1]
x_axis = np.linspace(0, b - 1, num=b)
y_axis = np.linspace(0, a - 1, num=a)
x_coordinate = np.tile(x_axis, (a, 1,)) # 创建X轴的坐标表
y_coordinate = np.tile(y_axis, (b, 1,)) # 创建y轴的坐标表
y_coordinate = np.transpose(y_coordinate)
coordinate_segments_mean = np.zeros((max_segments, 2))
# create lab feather
img_l = img_lab[:, :, 0]
img_a = img_lab[:, :, 1]
img_b = img_lab[:, :, 2]
img_segments_mean = np.zeros((max_segments, 3))
for i in xrange(max_segments):
segments_i = segments == i
coordinate_segments_mean[i, 0] = x_coordinate[segments_i].mean()
coordinate_segments_mean[i, 1] = y_coordinate[segments_i].mean()
img_segments_mean[i, 0] = img_l[segments_i].mean()
img_segments_mean[i, 1] = img_a[segments_i].mean()
img_segments_mean[i, 2] = img_b[segments_i].mean()
# element distribution
wc_ij = np.exp(-cdist(img_segments_mean, img_segments_mean) ** 2 / (2 * self._sigma_distribution ** 2))
wc_ij = wc_ij / wc_ij.sum(axis=1)[:, None]
mu_i = np.dot(wc_ij, coordinate_segments_mean)
distribution = np.dot(wc_ij, np.linalg.norm(coordinate_segments_mean - mu_i, axis=1) ** 2)
distribution = normalize(distribution)
distribution = np.array([distribution]).T
# element uniqueness feature
wp_ij = np.exp(
-cdist(coordinate_segments_mean, coordinate_segments_mean) ** 2 / (2 * self._sigma_uniqueness ** 2))
wp_ij = wp_ij / wp_ij.sum(axis=1)[:, None]
uniqueness = np.sum(cdist(img_segments_mean, img_segments_mean) ** 2 * wp_ij, axis=1)
uniqueness = normalize(uniqueness)
uniqueness = np.array([uniqueness]).T
# save features and variables
self.img_lab = img_lab
self.segments = segments
self.img_segments_mean = img_segments_mean
self.coordinate_segments_mean = coordinate_segments_mean
self.uniqueness = uniqueness
self.distribution = distribution
def rhymes_with(c1, c2, span):
w1 = normalize(c1)
w2 = normalize(c2)
if len(w1) < 3 or len(w2) < 3:
return False
if w1 in w2 or w2 in w1:
return False
return get_end(w1, span) == get_end(w2, span)
def __computeNewMask__(self,frame_feat,shape,models): assert(shape[0]*shape[1] == frame_feat.__len__()) (fg_eigt,fg_mean) = models[0] fg_score = normalize(np.sum(np.dot(frame_feat-fg_mean,fg_eigt.transpose()),1)) (bg_eigt,bg_mean) = models[1] bg_score = normalize(np.sum(np.dot(frame_feat-bg_mean,bg_eigt.transpose()),1)) frames_mask = (fg_score>bg_score+0.2).reshape((shape[0],shape[1])) return frames_mask;
def generate_training_sample(self):
"""
A function to generate the training samples
"""
random.seed(42)
## normalize the image
norm_img = utils.normalize(self.user_image)
## Get image dimensions
w,h = norm_img.shape
assert w == h, 'Character image should be square'
## Obtain similar enough random fonts
random_fonts = []
n_fonts = self.char_array.shape[0]
endloop = 0
n_random = self.n_random
while len(random_fonts) < n_random:
rdn_img = self.char_array[random.randint(0, n_fonts)]
rdn_norm_img = utils.normalize(rdn_img)
pbp = utils.pixbypix_similarity(rdn_norm_img, norm_img)
if (pbp < 0.9999):
random_fonts.append(np.ravel(rdn_norm_img))
## Bail out of the loop if not enough similar fonts are found
if endloop > 20000:
n_random = len(random_fonts)
break
endloop += 1
print 'Found {0} fonts for the random sample'.format(n_random)
## Put together the different types of training samples
n_signal = n_random
n_variations = n_signal//4
variations = []
variations += utils.scale_variations(norm_img, scale_factors=np.linspace(0.95, 0.99, n_variations))
variations += utils.skew_variations(norm_img, vertical_shear=np.linspace(-0.02, 0.02, math.ceil(math.sqrt(n_variations))), horizontal_shear=np.linspace(-0.02, 0.02, math.ceil(math.sqrt(n_variations))))
variations += utils.rotate_variations(norm_img, angles=np.linspace(-5,5, n_variations))
variations += [norm_img]*n_variations
signal = [np.ravel(var) for var in variations]
self.X = np.stack(signal + random_fonts, axis=0)
self.y = np.array([0]*len(signal) + range(1, n_random+1))
def do_mstep_b(d):
result = np.zeros( [ number_of_topics ])
for z in range(number_of_topics):
s = 0
for w_index in range(vocabulary_size):
count = term_doc_matrix[d][w_index]
s = s + count * topic_prob[d, w_index, z]
result[z] = s
normalize(result)
return result
def do_mstep_a(t):
result = np.zeros([ vocabulary_size ])
for w_index in range(vocabulary_size):
s = 0
for d_index in range(number_of_documents):
count = term_doc_matrix[d_index][w_index]
s = s + count * topic_prob[d_index, w_index, t]
result[w_index] = s
normalize(result)
return result
def do_estep(d):
result = np.zeros([vocabulary_size, number_of_topics])
for w in range(vocabulary_size):
prob = document_topic_prob[d, :] * topic_word_prob[:, w]
if sum(prob) == 0.0:
print 'exit'
else:
normalize(prob)
result[w] = prob
return result
def eigenBasedFeats(_input): assert(_input.ndim==1|_input.ndim==3) if(_input.ndim == 3): _input = cv2.cvtColor(_input,cv2.COLOR_RGB2GRAY) eigen = cv2.cornerEigenValsAndVecs(_input,15,3); eigen = eigen.reshape(_input.shape[0], _input.shape[1], 3, 2) texture_mag = normalize(np.sqrt(eigen[:,:,0,0]**2 + eigen[:,:,0,1]**2)) texture_dir1 = normalize(np.arctan2(eigen[:,:,1,1],eigen[:,:,1,0])) texture_dir2 = normalize(np.arctan2(eigen[:,:,2,1],eigen[:,:,2,0])) texture_prop = np.dstack((texture_mag,texture_dir1,texture_dir2)); return texture_prop;
def set_fsb(self, preset):
if preset not in self._presets.keys():
return False
preset_data = self._presets[preset]
pll_data = self.smbus_read_block(I2cDev.PLL_ADDR, 0)
pll_data[1] |= (1<<0) | (1<<4) | (1<<6)
if len(pll_data) < 17:
log("we didn't get enough pll data!")
return False
# M is always 24 for simplicity
# Adjust N for the new M values
pll_data[12] = int(24.0/(pll_data[11] & 0x3f) * pll_data[12])
pll_data[16] = int(24.0/(pll_data[15] & 0x3f) * pll_data[16])
pll_data[11] = 24
pll_data[15] = 24
curr_fsb = pll_data[12]
curr_pci = pll_data[16]
target_fsb, target_pci, voltage_flag = preset_data
if curr_fsb == target_fsb and curr_pci == target_pci: return True
log("adjustment from %d/%d to %d/%d" %
(curr_fsb, curr_pci, target_fsb, target_pci))
# calculate fsb steps
fsb_dir = self._stepwidth
pci_dir = self._stepwidth
# direction
if curr_fsb > target_fsb: fsb_dir = -fsb_dir
if curr_pci > target_pci: pci_dir = -pci_dir
fsb_steps = range(curr_fsb, target_fsb, fsb_dir)
pci_steps = range(curr_pci, target_pci, pci_dir)
# add target
fsb_steps.append(int(target_fsb))
pci_steps.append(int(target_pci))
# normalize lists to the same length
utils.normalize(fsb_steps, pci_steps)
log("FSB steps: %s" %fsb_steps)
log("PCI steps: %s" %pci_steps)
# apply steps
# set voltage to high during transition
utils.ec_gpio_set(utils.EC_VOLTAGE, 1)
for fsb, pci in zip(fsb_steps, pci_steps):
log("applying step %d/%d" %(fsb, pci))
time.sleep(0.05)
pll_data[12] = fsb
self.smbus_write_block(I2cDev.PLL_ADDR, 0, pll_data)
pll_data[16] = pci
time.sleep(0.05)
self.smbus_write_block(I2cDev.PLL_ADDR, 0, pll_data)
# restore voltage flag
utils.ec_gpio_set(utils.EC_VOLTAGE, voltage_flag)
self._callback(preset)
return True
def from_file(words, length):
g = Graph(set(normalize(w) for w in words), length)
for i in range(len(words)):
w = normalize(words[i])
prev = tuple(normalize(p) for p in words[max(0,i-length):i])
for j in range(len(prev) + 1):
if not any(p[-1] == '.' for p in prev[j:]):
g.add_edge(prev[j:], w)
#g.add_edge(prev[-1], w)
g.count_probs()
for ((u,v), val) in g.edges.items():
if (len(u) >= 1 and val > 5) or (len(u) >= 2 and val > 1):
print(u, v, g.probs[u, v], g.degs[u, len(u)], file=sys.stderr)
return g
def process_extract(self, name, extra):
native_result = self.extractor.from_native(self.db, name)
try:
name = self.pypi.real_name(name)
except urllib2.HTTPError:
logging.warning("PyPi error for {}".format(name))
return
versions = self.pypi.package_releases(name)
if not versions and not native_result:
logging.warn("No versions found for {}".format(name))
return
for version in versions:
data = self.db.get(name, version)
if data:
logging.info("Cached {}:{}".format(utils.normalize(name), utils.normalize(version)))
elif self.is_version_blacklisted(name, version):
logging.info("Blacklisted {}:{}".format(name, version))
else:
try:
logging.info(
"Fetching {}:{}".format(
utils.normalize(name),
utils.normalize(version)
)
)
data = self.extractor.from_pypi(self.db, name, version)
# did we get something useful?
if not data:
self.blacklist_version(name, version)
except Exception as e:
logging.warn(
"Unhandled exception while processing {}:{} - {}".format(
name,
version,
e
)
)
self.blacklist_version(name, version)
# register
data = self.db.get(name, version)
if data:
self.add_todos_from_db(data['name'], data['version'], extra)
self.done_with_all_versions(name, extra)
def do_mstep_b(d, vocabulary_size, number_of_topics, term_doc_matrix, topic_prob):
# number_of_topics = getNumberOfTopics()
# vocabulary_size = getVocabularySize()
# term_doc_matrix = getTermDocMatrix()
# topic_word_prob = getTopicWordProb()
print 'document %d' %d
result = np.zeros([number_of_topics])
for z in range(number_of_topics):
s = 0
for w_index in range(vocabulary_size):
count = term_doc_matrix[d][w_index]
s = s + count * topic_prob[d, w_index, z]
result[z] = s
normalize(result)
return result
def __init__(self, *args, **kwargs):
app.Canvas.__init__(self, *args, **kwargs)
self.program = gloo.Program(self.read_shader('1.vert'), self.read_shader('3.frag'))
# Fill screen with single quad, fragment shader does all the real work
self.program["position"] = [(-1, -1), (-1, 1), (1, 1),
(-1, -1), (1, 1), (1, -1)]
self._starttime = time.time()
self.program['time'] = 0
self.program['cameraPos'] = (0.0, 3.0, -6.0)
self.program['cameraLookat'] = (0.0, -0.85, 0.5)
self.program['lightDir'] = normalize(np.array((-1, -1, -1))) # needs to be normalized
self.program['lightColour'] = (1.4, 3.0, 0.3)
self.program['diffuse'] = (0.27, 0.27, 0.27)
self.program['ambientFactor'] = 0.45
self.program['rotateWorld'] = True
self.program['scale'] = 1.5
self.program['offset'] = 1.8
self.program['cubeWidth'] = 1
self.program['angleA'] = 1
self.program['angleB'] = 1
self.apply_zoom()
gloo.set_clear_color(color='black')
self._timer = app.Timer('auto', connect=self.update, start=True)
self.show()
def __of__(self, parent):
dirpath = self._dirpath
info = _dirreg.getDirectoryInfo(dirpath)
if info is None:
# for DirectoryViews created with CMF versions before 1.5
# this is basically the old minimalpath() code
dirpath = normalize(dirpath)
index = dirpath.rfind('Products')
if index == -1:
index = dirpath.rfind('products')
if index != -1:
dirpath = dirpath[index+len('products/'):]
info = _dirreg.getDirectoryInfo(dirpath)
if info is not None:
# update the directory view with a corrected path
self._dirpath = dirpath
elif self._dirpath:
warn('DirectoryView %s refers to a non-existing path %s'
% (self.id, dirpath), UserWarning)
if info is None:
data = {}
objects = ()
else:
data, objects = info.getContents(_dirreg)
s = DirectoryViewSurrogate(self, data, objects)
res = s.__of__(parent)
return res
def fixed_lag_smoothing(e_t, HMM, d, ev, t):
"""[Figure 15.6]
Smoothing algorithm with a fixed time lag of 'd' steps.
Online algorithm that outputs the new smoothed estimate if observation
for new time step is given."""
ev.insert(0, None)
T_model = HMM.transition_model
f = HMM.prior
B = [[1, 0], [0, 1]]
evidence = []
evidence.append(e_t)
O_t = vector_to_diagonal(HMM.sensor_dist(e_t))
if t > d:
f = forward(HMM, f, e_t)
O_tmd = vector_to_diagonal(HMM.sensor_dist(ev[t- d]))
B = matrix_multiplication(inverse_matrix(O_tmd), inverse_matrix(T_model), B, T_model, O_t)
else:
B = matrix_multiplication(B, T_model, O_t)
t = t + 1
if t > d:
# always returns a 1x2 matrix
return([normalize(i) for i in matrix_multiplication([f], B)][0])
else:
return None
def ps_from_copies(sigma,Ne,L,copies,approx=True):
#print "ps from copies:", sigma, Ne, L, copies
if approx:
mu = approx_mu(G, sigma, L, copies)
else:
mu = mu_from(G,sigma,L,copies)
return normalize([phat(k,sigma,mu,Ne,L) for k in range(L+1)])
def parse(st):
"""
:param st:
:return:
"""
st = normalize(st)
s = st.split()
if NUMBERS.has_key(s[0].lower()):
s[0] = str(NUMBERS[s[0].lower()])
st = ' '.join(s)
res = PARSER_RE.match(st)
parsed = {}
u = res.group('unit')
if u:
for k,v in UNITS.items():
if u.lower().strip() in v:
#print "Updating unit: was: {} now: {}".format(u,k)
parsed['unit'] = k
if not parsed.has_key('unit'):
parsed['unit'] = res.group('unit') or ''
parsed['quantity'] = res.group('quantity') or ''
parsed['name'] = (res.group('name') or '').strip()
#print parsed
return parsed
def forward_fc(self, inp, weights, reuse=False):
hidden = normalize(tf.tensordot(inp, weights['w1'],1) + weights['b1'], activation=tf.nn.relu, reuse=reuse, scope='0', is_training = self.train_phase)
for i in range(1,len(self.dim_hidden)):
hidden = normalize(tf.tensordot(hidden, weights['w'+str(i+1)], 1) + weights['b'+str(i+1)], activation=tf.nn.relu, reuse=reuse, scope=str(i+1), is_training = self.train_phase)
return tf.tensordot(hidden, weights['w'+str(len(self.dim_hidden)+1)], 1) + weights['b'+str(len(self.dim_hidden)+1)]
def plsa(self, number_of_topics, max_iter, lambda_b=0):
'''
Topic Modeling
'''
print("EM iteration begins...")
# Get vocabulary and number of documents.
self.build_vocabulary()
number_of_documents = len(self.documents)
vocabulary_size = len(self.vocabulary)
# build term-doc matrix
self.term_doc_matrix = np.zeros([number_of_documents, vocabulary_size],
dtype=np.int)
for d_index, doc in enumerate(self.documents):
term_count = np.zeros(vocabulary_size, dtype=np.int)
for word in doc:
if word in self.vocabulary:
w_index = self.vocabulary.index(word)
term_count[w_index] += 1
self.term_doc_matrix[d_index] = term_count
# Create the counter arrays.
self.document_topic_prob = np.zeros(
[number_of_documents, number_of_topics],
dtype=np.float) # P(z | d)
self.topic_word_prob = np.zeros(
[number_of_topics, len(self.vocabulary)],
dtype=np.float) # P(w | z)
self.topic_prob = np.zeros(
[number_of_documents,
len(self.vocabulary), number_of_topics],
dtype=np.float) # P(z | d, w)
self.background_word_prob = np.sum(
self.term_doc_matrix, axis=0) / np.sum(
self.term_doc_matrix) # Background words probability (1 X W)
self.background_prob = np.zeros(
[number_of_documents, len(self.vocabulary)],
dtype=np.float) # initialize background probability
# Initialize
print("Initializing...")
# randomly assign values
self.document_topic_prob = np.random.random(size=(number_of_documents,
number_of_topics))
for d_index in range(len(self.documents)):
normalize(self.document_topic_prob[d_index]
) # normalize for each document
self.topic_word_prob = np.random.random(size=(number_of_topics,
len(self.vocabulary)))
for z in range(number_of_topics):
normalize(self.topic_word_prob[z]) # normalize for each topic
# Run the EM algorithm
temp = 0
for iteration in range(max_iter):
print("Iteration #" + str(iteration + 1) + "...")
#print("===E step===")
for d_index, document in enumerate(self.documents):
for w_index in range(vocabulary_size):
denominator = (
(lambda_b * self.background_word_prob[w_index]) +
((1 - lambda_b) *
np.sum(self.document_topic_prob[d_index, :] *
self.topic_word_prob[:, w_index])))
prob = self.document_topic_prob[
d_index, :] * self.topic_word_prob[:, w_index]
if sum(prob) == 0.0:
print("d_index = " + str(d_index) + ", w_index = " +
str(w_index))
print("self.document_topic_prob[d_index, :] = " +
str(self.document_topic_prob[d_index, :]))
print("self.topic_word_prob[:, w_index] = " +
str(self.topic_word_prob[:, w_index]))
print("topic_prob[d_index][w_index] = " + str(prob))
exit(0)
else:
normalize(prob)
self.topic_prob[d_index][w_index] = prob
self.background_prob[d_index][w_index] = (
lambda_b *
self.background_word_prob[w_index]) / denominator
#print("===M step===")
# update P(w | z); word-distribution
for z in range(number_of_topics):
for w_index in range(vocabulary_size):
s = 0
for d_index in range(len(self.documents)):
count = self.term_doc_matrix[d_index][w_index]
s = s + count * (
1 - self.background_prob[d_index][w_index]
) * self.topic_prob[d_index, w_index, z]
self.topic_word_prob[z][w_index] = s
normalize(self.topic_word_prob[z])
# update P(z | d); lamda(coverage)
for d_index in range(len(self.documents)):
for z in range(number_of_topics):
s = 0
for w_index in range(vocabulary_size):
count = self.term_doc_matrix[d_index][w_index]
s = s + count * (
1 - self.background_prob[d_index][w_index]
) * self.topic_prob[d_index, w_index, z]
self.document_topic_prob[d_index][z] = s
normalize(self.document_topic_prob[d_index])
self.para = lambda_b
if abs(self.loglikelihood() - temp) < 0.0001:
break
else:
temp = self.loglikelihood()
self.listLoglikelihood.append(temp)
return self.para, self.listLoglikelihood, self.topic_word_prob, self.document_topic_prob
def generate_pva(feature_name='sk_pva_99', labels_name='labels_raw'):
print("Extracting the training set of position, velocity and acceleration")
data = os.path.join("E:\\program\\Chalearn\\rawdata\\train\\")
# Get the list of training samples
samples = os.listdir(data)
target_dir = 'E:\\program\\Chalearn\\Chalearn_LSTM\\target\\'
output_dir = 'E:\\program\\Chalearn\\Chalearn_LSTM\\feature\\' + feature_name
if not os.path.exists(output_dir):
os.makedirs(output_dir)
used_joints = [
'ElbowLeft', 'WristLeft', 'ShoulderLeft', 'HandLeft', 'ElbowRight',
'WristRight', 'ShoulderRight', 'HandRight', 'Head', 'Spine',
'HipCenter'
]
njoints = len(used_joints)
# f = open('SK_normalization.pkl','r')
# normal_params = pickle.load(f)
# f.close()
# Mean = normal_params['Mean1']
# Std = normal_params['Std1']
count = 0
# target_category = 21
Target_all = []
#Feature_all = numpy.zeros(shape=(400000, (njoints*(njoints-1)/2 + njoints**2)*3),dtype=numpy.float32)
for file_count, file in enumerate(samples):
if int(file[-8:-4]) != 417 and int(file[-8:-4]) != 675:
print("\t Processing file " + file)
# Create the object to access the sample
smp = GestureSample(os.path.join(data, file))
# ###############################################
# USE Ground Truth information to learn the model
# ###############################################
# Get the list of actions for this frame
gesturesList = smp.getGestures()
frame_num = smp.getNumFrames()
Feature_Array = np.zeros(shape=(frame_num, 3 * 3 * njoints),
dtype=np.float32)
# Target = np.zeros( shape=(frame_num, target_category), dtype=np.uint8)
#feature generate
Skeleton_matrix, valid_skel = Extract_feature_normalized_ALL(
smp, used_joints, 1, frame_num)
# Feature_Array = Extract_feature_Realtime(Skeleton_matrix, njoints)
# Skeleton_matrix = Smooth_Skeleton(Skeleton_matrix, window_len = 5, smooth_mode = 'gaussian')
Feature_Array = Extract_feature_pva(Skeleton_matrix, njoints)
Mean = np.mean(Feature_Array, axis=0)
Std = np.std(Feature_Array, axis=0)
Feature_Array = normalize(Feature_Array, Mean, Std)
#save sample sk features
output_name = '%04d.npy' % count
# output_name = file[-8:-4]+'.npy'
np.save(os.path.join(output_dir, output_name), Feature_Array)
count += 1
#target generate
labels = np.zeros(frame_num, np.uint8)
for row in gesturesList:
labels[int(row[1]) - 1:int(row[2]) - 1] = int(row[0])
Target_all.append(labels)
del smp
np.save(target_dir + '%s.npy' % labels_name, Target_all)
def run():
if not plugin_conf["enabled"]: return
log.debug("[" + __name__ + "] listening for UDP datagrams on port " +
str(plugin_conf['port_listen']))
# bind to the network
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)
sock.bind(("", plugin_conf['port_listen']))
while True:
try:
# new data arrives
data, addr = sock.recvfrom(1024)
log.debug("[" + __name__ + "] received " + data)
data = json.loads(data)
if data["type"] != "WirelessMessage": continue
# check if it belongs to a registered sensor
if data["id"] not in nodes: continue
node = nodes[data["id"]]
# for each measure
for message in data["data"]:
if message == "STARTED":
if default_measure not in node: continue
sensor = node[default_measure]
log.info("[" + sensor["module_id"] + "][" +
sensor["sensor_id"] + "] has just started")
# ACK a started message
tx(sensor, "ACK", True)
# initialize
init(sensor)
if message == "AWAKE":
if default_measure not in node: continue
sensor = node[default_measure]
# send a message if there is something in the queue
if data["id"] in queue and len(queue[data["id"]]) > 0:
tx(sensor, queue[data["id"]])
queue[data["id"]] = []
# put it to sleep again
sleep(sensor)
# other messages can be a measure from the sensor
measures = []
# for each registered measure for this node_id
for measure, sensor in node.iteritems():
# skip if not a registered measure
if not message.startswith(measure): continue
measure_data = {}
# generate the timestamp
date = datetime.datetime.strptime(
data["timestamp"], "%d %b %Y %H:%M:%S +0000")
measure_data["timestamp"] = utils.timezone(
utils.timezone(int(time.mktime(date.timetuple()))))
measure_data["key"] = sensor["sensor_id"]
# strip out the measure from the value
measure_data["value"] = utils.normalize(
message.replace(measure, ""), conf["constants"]
["formats"][sensor["format"]]["formatter"])
measures.append(measure_data)
sensors.store(sensor, measures)
except Exception, e:
log.warning("unable to parse " + str(data) + ": " +
utils.get_exception(e))
return parameters
nll_history = {}
nll_history['teacher'] = list()
nll_history['student'] = list()
rmse_history = {}
rmse_history['teacher'] = list()
rmse_history['student'] = list()
dataset_name = 'kin8nm'
for i in range(20):
print("\nIteration: ", i)
# -- Load training and test data --
(x_train, y_train), (x_test, y_test) = load_data(i, 0.1, dataset_name)
(x_train, y_train), (x_test, y_test) = normalize(x_train,y_train,x_test,y_test)
network_shape = get_network_shape(dataset_name)
#print("x_train.s: ", x_train.shape, ", y_train.shape: ", y_train.shape,
# ", x_test.s: ", x_test.shape, ", y_test.shape: ", y_test.shape)
#print("network_shape: ", network_shape)
# -- Create and train teacher --
teacher_parameters = initialize_teacher_parameters(network_shape)
teacher = t.RegressionTeacherModel(teacher_parameters)
teacher_history = teacher.train(x_train, y_train, x_test, y_test)
# -- Create and train student(using all nets of the teacher) --
M = teacher_parameters['ensemble_nets']
student_parameters = initialize_student_parameters(teacher_parameters)
student = s.RegressionStudentModel(teacher, student_parameters)
def plsa(self, nb_of_topics, max_iter):
"""
EM
:param nb_of_topics: number of topic
:param max_iter: max number of iterations
:return:
"""
print("EM iteration begins....")
self.build_vocabulary()
self.generate_term_doc_matrix()
# 构造counter arrays
# p(zk|di) shape : (nb_of_document, nb_of_topics)
self.doc_topic_prob = np.zeros([self.nb_of_documents, nb_of_topics], dtype=np.float)
# p(wj|zk) shape: (nb_of_topics, vocab_size)
self.topic_word_prob = np.zeros([nb_of_topics, self.vocab_size], dtype=np.float)
# p(zk|di, wj) shape: (nb_of_documents, vocab_size, nb_of_topics)
self.topic_prob = np.zeros([self.nb_of_documents, self.vocab_size, nb_of_topics], dtype=np.float)
# Initialize
print("Initializing ... ")
# 随机初始
self.doc_topic_prob = np.random.random(size=(self.nb_of_documents, nb_of_topics))
for d_index in range(self.nb_of_documents):
# 归一化每个文档
normalize(self.doc_topic_prob[d_index])
self.topic_word_prob = np.random.random(size=(nb_of_topics, self.vocab_size))
for z in range(nb_of_topics):
# 归一化每个主题
normalize(self.topic_word_prob[z])
# run EM algorithm
# E-Step:
# p(zk|di ,wj) = (p(wj|zk) * p(zk|di)) / (Σl=1,K p(wj|zl) * p(zl|di))
# M-Step:
# p(wj|zk) = Σi n(di, wj) * p(zk|di, wj) / (Σm=1,M Σi n(di, wj)p(zk|di, wj))
# p(zk|di) = Σj n(di, wj) * p(zk|di, wj) / (Σk=1,K Σj n(di, wj)p(zk|di, wj))
for iter in range(max_iter):
print("Iteration #" + str(iter + 1) + "...")
print("E step : ")
for d_index, document in enumerate(self.documents):
for w_index in range(self.vocab_size):
# p(zk|di) * p(wj|zk)
# shape : (nb_of_topics), prob是个数组，长度为nb_of_topics
prob = self.doc_topic_prob[d_index, :] * self.topic_word_prob[:, w_index]
if sum(prob) == 0.0:
print("d_index = " + str(d_index) + ", w_index = " + str(w_index))
print("self.document_topic_prob[d_index, :] = " + str(self.doc_topic_prob[w_index, :]))
print("self.topic_word_prob[:, w_index] = " + str(self.topic_word_prob[:, w_index]))
print("topic_prob[d_index][w_index] = " + str(prob))
exit(0)
else:
normalize(prob)
self.topic_prob[d_index][w_index] = prob
print("M step : ")
# update p(wj|zk)
for z in range(nb_of_topics):
for w_index in range(self.vocab_size):
numer = 0
for d_index in range(self.nb_of_documents):
# n(di, wj)
count = self.term_doc_matrix[d_index][w_index]
# Σi n(di, wj) * p(zk|di, wj)
numer += count * self.topic_prob[d_index, w_index, z]
self.topic_word_prob[z][w_index] = numer
normalize(self.topic_word_prob)
# update p(zk|di)
for d_index in range(self.nb_of_documents):
for z in range(nb_of_topics):
numer = 0
for w_index in range(self.vocab_size):
# n(di, wj)
count = self.term_doc_matrix[d_index][w_index]
numer += count * self.topic_prob[d_index, w_index, z]
self.doc_topic_prob[d_index][z] = numer
normalize(self.doc_topic_prob)
shape=[None, 1,
20], dtype=tf.float32) # enrollment batch (time x batch x n_mel)
# embedding lstm (3-layer default)
with tf.variable_scope("lstm"):
lstm_cells = [
tf.contrib.rnn.LSTMCell(num_units=config.hidden, num_proj=config.proj)
for i in range(config.num_layer)
]
lstm = tf.contrib.rnn.MultiRNNCell(
lstm_cells) # make lstm op and variables
outputs, _ = tf.nn.dynamic_rnn(
cell=lstm, inputs=inputtensor, dtype=tf.float32,
time_major=True) # for TI-VS must use dynamic rnn
embedded = outputs[-1] # the last ouput is the embedded d-vector
embedded = normalize(embedded) # normalize
outputfun = 1 * embedded
saver = tf.train.Saver(var_list=tf.global_variables())
with tf.Session() as sess:
tf.global_variables_initializer().run()
# load model
# print("model path :", config.model_path)
ckpt = tf.train.get_checkpoint_state(
checkpoint_dir=os.path.join(config.model_path, "Check_Point"))
ckpt_list = ckpt.all_model_checkpoint_paths
loaded = 0
for model in ckpt_list:
if config.model_num == int(
def evaluate(self,
eval_batches,
result_dir=None,
result_prefix=None,
save_full_info=False):
"""
Evaluates the model performance on eval_batches and results are saved if specified
Args:
eval_batches: iterable batch data
result_dir: directory to save predicted answers, answers will not be saved if None
result_prefix: prefix of the file for saving predicted answers,
answers will not be saved if None
save_full_info: if True, the pred_answers will be added to raw sample and saved
"""
pred_answers, ref_answers = [], []
total_loss, total_num = 0, 0
for b_itx, batch in enumerate(eval_batches):
feed_dict = {
self.p: batch['passage_token_ids'],
self.q: batch['question_token_ids'],
self.p_length: batch['passage_length'],
self.q_length: batch['question_length'],
self.start_label: batch['start_id'],
self.end_label: batch['end_id'],
self.dropout_keep_prob: 1.0
}
start_probs, end_probs, loss = self.sess.run(
[self.start_probs, self.end_probs, self.loss], feed_dict)
total_loss += loss * len(batch['raw_data'])
total_num += len(batch['raw_data'])
padded_p_len = len(batch['passage_token_ids'][0])
for sample, start_prob, end_prob in zip(batch['raw_data'],
start_probs, end_probs):
best_answer = self.find_best_answer(sample, start_prob,
end_prob, padded_p_len)
if save_full_info:
sample['pred_answers'] = [best_answer]
pred_answers.append(sample)
else:
pred_answers.append({
'question_id':
sample['question_id'],
'question_type':
sample['question_type'],
'answers': [best_answer],
'entity_answers': [[]],
'yesno_answers': []
})
if 'answers' in sample:
ref_answers.append({
'question_id': sample['question_id'],
'question_type': sample['question_type'],
'answers': sample['answers'],
'entity_answers': [[]],
'yesno_answers': []
})
if result_dir is not None and result_prefix is not None:
result_file = os.path.join(result_dir, result_prefix + '.json')
with open(result_file, 'w') as fout:
for pred_answer in pred_answers:
fout.write(
json.dumps(pred_answer, ensure_ascii=False) + '\n')
self.logger.info('Saving {} results to {}'.format(
result_prefix, result_file))
# this average loss is invalid on test set, since we don't have true start_id and end_id
ave_loss = 1.0 * total_loss / total_num
# compute the bleu and rouge scores if reference answers is provided
if len(ref_answers) > 0:
pred_dict, ref_dict = {}, {}
for pred, ref in zip(pred_answers, ref_answers):
question_id = ref['question_id']
if len(ref['answers']) > 0:
pred_dict[question_id] = normalize(pred['answers'])
ref_dict[question_id] = normalize(ref['answers'])
bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)
else:
bleu_rouge = None
return ave_loss, bleu_rouge
def fit(self,
path_real,
path_synth,
path_eval=None,
batch_size=30,
epochs=500,
save_model_every=10,
save_images_every=10,
use_roids=False,
rec_weight=10,
dis_weight=10,
cycle_weight=10,
attr_cycle_b3_weight=5,
attr_cycle_a_weight=3,
save_summary=True):
'''
The training function.
args:
path_real : the path containing the images from the real domain are stored
path_synth : the path where the images from the real domain are stored
img_size : the size of the images. It's used to deal with different datasets
batch_size : the size of the mini-batch
epochs : the number of epochs
save_model_every : every how many epochs to create a new checkpoint
save_images_every : every how many epochs to save the outputs of the model
use_roids : whether or not to use extra conditions, other than the ones of the paper
rec_weight : the weight of the reconstruction loss
dis_weight : the weight of the disentanglement loss
cycle_weight : the weight of the cycle loss
attr_cycle_b3_weight : the weight of the attribute cycle loss for b3
attr_cycle_a_weight : the weight of the attribute cycle loss for a
save_summary : whether or not to create a summary report for
the architecture and the hyperparameters
'''
if save_summary:
self.log_config(batch_size=batch_size,
roids=use_roids,
rec_weight=rec_weight,
dis_weight=dis_weight,
cycle_weight=cycle_weight,
attr_cycle_b3_weight=attr_cycle_b3_weight,
attr_cycle_a_weight=attr_cycle_a_weight)
losses = np.empty((0, 8), float)
cnt = 0
for epoch in range(epochs):
start = time()
print(f'\nEpoch: {epoch} / {epochs}')
epoch_losses = np.zeros([8])
# load the datasets
data_real = utils.get_batch_flow(path_real, self.img_size,
batch_size)
data_synth = utils.get_batch_flow(
path_synth, tuple([3 * self.img_size[0], self.img_size[1]]),
batch_size)
n_batches_real = len(data_real) if len(
data_real) % batch_size == 0 else len(data_real) - 1
data_real = list(islice(data_real, n_batches_real))
data_synth = list(islice(data_synth, n_batches_real))
for i, (a, b) in enumerate(zip(data_real, data_synth)):
print(f'\tBatch: {i+1} / {n_batches_real}\r', end='')
# normalize the input images
a = utils.normalize(a)
b = utils.normalize(b)
# split b to b1, b2 and b3
b1, b2, b3 = utils.split_to_attributes(b)
# if cnt == 0:
# print("####",np.shape(b1))
# for b_ in b1:
# b_ = np.array(b_)
# g1 = utils.denormalize(b_)
# plt.imsave('/content/sample_data/b1.png', g1)
# # g1 = utils.denormalize(b1)
# # g2 = utils.denormalize(b2)
# # g3 = utils.denormalize(b3)
# # plt.imsave('/content/sample_data/b1.png', g1)
# # plt.imsave('/content/sample_data/b2.png', g2)
# # plt.imsave('/content/sample_data/b3.png', g3)
# cnt+=1
batch_losses, generated_images = self.train_step(
a=a,
b1=b1,
b2=b2,
b3=b3,
use_roids=use_roids,
rec_weight=rec_weight,
dis_weight=dis_weight,
cycle_weight=cycle_weight,
attr_cycle_b3_weight=attr_cycle_b3_weight,
attr_cycle_a_weight=attr_cycle_a_weight)
batch_losses = [
batch_losses['reconstruction'],
batch_losses['disentanglement'], batch_losses['cycle'],
batch_losses['attribute cycle'],
batch_losses['generator real'],
batch_losses['generator synth'],
batch_losses['discriminator real'],
batch_losses['discriminator synth']
]
epoch_losses = np.add(epoch_losses, batch_losses)
# calculate the losses for the whole epoch
epoch_losses = epoch_losses / n_batches_real
losses = np.append(losses, [epoch_losses], axis=0)
# save only the images from the last batch to save space
if save_images_every:
if epoch % save_images_every == 0 or epoch + 1 == epochs:
if path_eval:
self.eval(base_path=path_eval, target_folder=epoch)
print(
f'\tSaved evaluation rows and gifs for epoch {epoch}!')
utils.plot_losses(losses)
utils.save(a,
b1,
b2,
b3,
generated_images,
i,
epoch,
remove_existing=False)
print(f'\tSaved losses and images for epoch {epoch}!')
if save_model_every:
if epoch % save_model_every == 0 or epoch + 1 == epochs:
ckpt_path = self.ckpt_manager.save()
print(
f'\tSaved checkpoint for epoch {epoch} at {ckpt_path}!\n'
)
utils.print_losses(epoch_losses)
print(f'\n\tTime taken for epoch {epoch}: {time()-start}sec.')
isodir = "/home/ondrej/data/isochrones/696/halo"
data = iso.readfits(isodir + "/datarr.fits")
isos = iso.readisos(isodir)
t = utils.frange(8, 10.25, 0.001)
p = parameters()
p.load()
m = fit.metallicity(t, p)
s = fit.sfr(t, p)
w = utils.calculateweights(t, s)
if gauss:
isow = iso.getisosweights_gauss(w, 10.**t, m, isos, p.sigma)
else:
isow = iso.getisosweights(w, 10.**t, m, isos)
model = iso.computeCMD(isow, isos)
#model=isos[0][2]*0.0+1.0
model = utils.normalize(model, sum(data.flat))
#model=model/sum(model.flat)
def plot_residuals(d, m):
import pylab
pylab.subplot(131)
pylab.imshow(d, origin='lower', interpolation="nearest")
pylab.subplot(132)
pylab.imshow(m, origin='lower', interpolation="nearest")
pylab.subplot(133)
#pylab.imshow((d-m)/m,origin='lower',interpolation="nearest")
pylab.imshow(d - m, origin='lower', interpolation="nearest")
# pylab.cool()
pylab.savefig("graph.eps")
def information_content(values):
"""Number of bits to represent the probability distribution in values."""
probabilities = normalize(removeall(0, values))
return sum(-p * math.log2(p) for p in probabilities)
def forward_fc(self, inp, weights, reuse=False):
hidden = normalize(tf.matmul(inp, weights['w1']) + weights['b1'], activation=tf.nn.relu, reuse=reuse, scope='0')
for i in range(1,len(self.dim_hidden)):
hidden = normalize(tf.matmul(hidden, weights['w'+str(i+1)]) + weights['b'+str(i+1)], activation=tf.nn.relu, reuse=reuse, scope=str(i+1))
return tf.matmul(hidden, weights['w'+str(len(self.dim_hidden)+1)]) + weights['b'+str(len(self.dim_hidden)+1)]
# compute the average loss
avg_loss_G.update(errG.item(), batch_size)
avg_loss_D.update(errD.item(), batch_size)
avg_loss_A.update(accuracy, batch_size)
avg_loss_M.update(mi.item(), batch_size)
print('[%d/%d][%d/%d] Loss_D: %.4f (%.4f) Loss_G: %.4f (%.4f) D(x): %.4f D(G(z)): %.4f / %.4f Acc: %.4f (%.4f) MI: %.4f (%.4f)'
% (epoch, opt.niter, i, len(dataloader),
errD.item(), avg_loss_D.avg,
errG.item(), avg_loss_G.avg,
D_x, D_G_z1, D_G_z2,
accuracy, avg_loss_A.avg,
mi.item(), avg_loss_M.avg))
if i % 100 == 0:
vutils.save_image(
utils.normalize(real_cpu), '%s/real_samples.png' % opt.outf)
# print('Label for eval = {}'.format(eval_label))
fake = netG(eval_noise, eval_label)
vutils.save_image(
utils.normalize(fake.data),
'%s/fake_samples_epoch_%03d.png' % (opt.outf, epoch)
)
# update eval_label
if opt.visualize_class_label >= 0 and opt.label_rotation:
eval_label_const = (eval_label_const + 1) % num_classes
eval_label.data.fill_(eval_label_const)
# compute metrics
is_mean, is_std, fid = get_metrics(sampler, num_inception_images=opt.num_inception_images, num_splits=10,
prints=True, use_torch=False)
def load_CodaLab_skel(ratio_train=0.9, ration_valid=0.1):
print '... loading data'
f = file('Feature_train_realtime.pkl','rb' )
Feature_train = cPickle.load(f)
f.close()
f = file('Feature_all_neutral_realtime.pkl','rb' )
Feature_train_neural = cPickle.load(f)
f.close()
#Because we have too much neural frames, we only need part of them
rand_num = numpy.random.permutation(Feature_train_neural['Feature_all_neutral'].shape[0])
F_neural = Feature_train_neural['Feature_all_neutral'][rand_num]
T_neural = Feature_train_neural['Targets_all_new'][rand_num]
Feature_all = numpy.concatenate((Feature_train['Feature_all'], F_neural))
Target_all = numpy.concatenate((Feature_train['Targets_all'], T_neural))
rand_num = numpy.random.permutation(Feature_all.shape[0])
Feature_all = Feature_all[rand_num]
Target_all = Target_all[rand_num]
Target_all_numeric = numpy.argmax(Target_all, axis=1)
#train_set, valid_set, test_set format: tuple(input, target)
#input is an numpy.ndarray of 2 dimensions (a matrix)
#witch row's correspond to an example. target is a
#numpy.ndarray of 1 dimensions (vector)) that have the same length as
#the number of rows in the input. It should give the target
#target to the example with the same index in the input.
# we separate the dataset into training: 80%, validation: 10%, testing: 10%
train_end = int(rand_num.shape[0]*ratio_train)
valid_end = int(rand_num.shape[0]*(ratio_train+ration_valid))
# Wudi made it a small set:
train_set_feature = Feature_all[0:train_end,:]
train_set_new_target = Target_all_numeric[0:train_end]
# Wudi added normalized data for GRBM
[train_set_feature_normalized, Mean1, Std1] = preprocessing.scale(train_set_feature)
import cPickle as pickle
f = open('SK_normalization.pkl','wb')
pickle.dump( {"Mean1": Mean1, "Std1": Std1 },f)
f.close()
train_set_x, train_set_y = shared_dataset( (train_set_feature_normalized, train_set_new_target))
valid_set_feature = Feature_all[train_end:valid_end,:]
valid_set_new_target = Target_all_numeric[train_end:valid_end]
valid_set_feature = normalize(valid_set_feature, Mean1, Std1)
valid_set_x, valid_set_y = shared_dataset((valid_set_feature,valid_set_new_target))
# test feature set
test_set_feature = Feature_all[valid_end:,:]
test_set_new_target = Target_all_numeric[valid_end:]
test_set_feature = normalize(test_set_feature, Mean1, Std1)
test_set_x, test_set_y = shared_dataset((test_set_feature,test_set_new_target))
rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y),
(test_set_x, test_set_y)]
return rval
def _init_params(self):
# Multinomial parameter beta: KxV
self._beta = np.random.gamma(100, 1. / 100, (self._K, self._V))
self._beta = normalize(self._beta, axis=1)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.dataset == "polblogs":
tmp_adj, features, labels, idx_train, idx_test = load_polblogs_data()
else:
_, features, labels, idx_train, idx_val, idx_test, tmp_adj = load_data(
args.dataset)
num_classes = labels.max().item() + 1
# tmp_adj = tmp_adj.toarray()
adj = tmp_adj
adj = np.eye(tmp_adj.shape[0]) + adj
adj, _ = normalize(adj)
adj = torch.from_numpy(adj.astype(np.float32))
# print (sum(features))
# print (labels.shape)
# print (idx_train.shape)
# print (idx_val.shape)
# print (idx_test)
# s = adj.shape[0]
# Model and optimizer
model = GCN(nfeat=features.shape[1],
nhid=args.hidden,
nclass=num_classes,
dropout=args.dropout)
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
if tube_id == -1:
pass
#pdb.set_trace()
if not (tube_id in ids_filter):
continue
material = objects[j]['material']
shape = objects[j]['shape']
attr = encode_attr(material, shape, bbox_size, args.attr_dim)
if args.box_only_flag:
xyhw_norm = (xyhw_exp - 0.5) / 0.5
s = [attr, torch.cat([xyhw_norm], 0).unsqueeze(0), tube_id]
elif args.new_mode == 1:
xyhw_norm = (xyhw_exp - 0.5) / 0.5
img_crop = normalize(crop(img, crop_box_v2, H, W), 0.5,
0.5).permute(2, 0, 1)
s = [
attr,
torch.cat([xyhw_norm, img_crop], 0).unsqueeze(0),
tube_id
]
else:
img_crop = normalize(crop(img, crop_box_v2, H, W), 0.5,
0.5).permute(2, 0, 1)
s = [
attr,
torch.cat([xyhw_exp, img_crop], 0).unsqueeze(0),
tube_id
]
frame_objs.append(s)
import librosa
import numpy as np
import soundfile
from utils import normalize
fullpath = '../audios/F001_001.wav'
sampling_rate = 22050
y, sr = librosa.core.load(fullpath, sampling_rate)
print(y.shape)
print('np.mean(y): ' + str(np.mean(y)))
print('np.max(y): ' + str(np.max(y)))
print('np.min(y): ' + str(np.min(y)))
soundfile.write('test.wav', y, sampling_rate)
y, sr = librosa.load('test.wav')
# Trim the beginning and ending silence
yt, index = librosa.effects.trim(y, top_db=20)
yt = normalize(yt)
print(index)
# Print the durations
print(librosa.get_duration(y), librosa.get_duration(yt))
print(len(y), len(yt))
soundfile.write('test2.wav', yt, sampling_rate)
def edge_decision(type, alphas, selected_idxs, candidate_flags, probs_history, epoch, model, args):
mat = F.softmax(torch.stack(alphas, dim=0), dim=-1).detach()
print(mat)
importance = torch.sum(mat[:, 1:], dim=-1)
# logging.info(type + " importance {}".format(importance))
probs = mat[:, 1:] / importance[:, None]
# print(type + " probs", probs)
entropy = cate.Categorical(probs=probs).entropy() / math.log(probs.size()[1])
# logging.info(type + " entropy {}".format(entropy))
if args.use_history: # SGAS Cri.2
# logging.info(type + " probs history {}".format(probs_history))
histogram_inter = histogram_average(probs_history, probs)
# logging.info(type + " histogram intersection average {}".format(histogram_inter))
probs_history.append(probs)
if len(probs_history) > args.history_size:
probs_history.pop(0)
score = utils.normalize(importance) * utils.normalize(
1 - entropy) * utils.normalize(histogram_inter)
# logging.info(type + " score {}".format(score))
else: # SGAS Cri.1
score = utils.normalize(importance) * utils.normalize(1 - entropy)
# logging.info(type + " score {}".format(score))
if torch.sum(candidate_flags.int()) > 0 and \
epoch >= args.warmup_dec_epoch and \
(epoch - args.warmup_dec_epoch) % args.decision_freq == 0:
masked_score = torch.min(score,
(2 * candidate_flags.float() - 1) * np.inf)
# cut strategy
# selected_edge_idx = torch.argmax(masked_score)
# selected_edge_idx = random.randint(0, 13)
if type == 'normal':
reduction = False
selected_edge_idx = random.choice(select_normal)
select_normal.remove(selected_edge_idx)
logging.info('select_list: {}, choice:{}'.format(select_normal, selected_edge_idx))
elif type == 'reduce':
reduction = True
selected_edge_idx = random.choice(select_reduce)
select_reduce.remove(selected_edge_idx)
logging.info('select_list: {}, choice:{}'.format(select_reduce, selected_edge_idx))
else:
raise Exception('Unknown Cell Type')
selected_op_idx = torch.argmax(probs[selected_edge_idx]) + 1 # add 1 since none op
selected_idxs[selected_edge_idx] = selected_op_idx
candidate_flags[selected_edge_idx] = False
alphas[selected_edge_idx].requires_grad = False
candidate_flags, selected_idxs = model.check_edges(candidate_flags,
selected_idxs,
reduction=reduction)
logging.info("#" * 30 + " Decision Epoch " + "#" * 30)
logging.info("epoch {}, {}_selected_idxs {}, added edge {} with op idx {}".format(epoch,
type,
selected_idxs,
selected_edge_idx,
selected_op_idx))
print(type + "_candidate_flags {}".format(candidate_flags))
score_image(type, score, epoch)
return True, selected_idxs, candidate_flags
else:
logging.info("#" * 30 + " Not a Decision Epoch " + "#" * 30)
logging.info("epoch {}, {}_selected_idxs {}".format(epoch,
type,
selected_idxs))
print(type + "_candidate_flags {}".format(candidate_flags))
score_image(type, score, epoch)
return False, selected_idxs, candidate_flags
def test_firstmodel(generator, opt, dataloader, writer, scale):
content_criterion = nn.MSELoss()
ones_const = Variable(torch.ones(1, 1))
if opt.cuda:
generator.cuda()
content_criterion.cuda()
curr_time = time.time()
for epoch in range(opt.nEpochs):
mean_generator_content_loss = 0.0
mean_generator_total_loss = 0.0
high_res_fake = 0
for batch_no, data in enumerate(dataloader['test']):
high_img, _ = data
generator.train(False)
input1 = high_img[0, :, :, :]
input2 = high_img[1, :, :, :]
input3 = high_img[2, :, :, :]
input4 = high_img[3, :, :, :]
# imshow(input3)
for j in range(opt.batchSize):
high_img[j] = normalize(high_img[j])
high_comb = torch.cat(
[high_img[0], high_img[1], high_img[2], high_img[3]], 0)
high_comb = Variable(high_comb[np.newaxis, :]).cuda()
# imshow(high_comb.cpu().data)
input_comb = torch.cat(
[scale(input1),
scale(input2),
scale(input3),
scale(input4)], 0)
input_comb = input_comb[np.newaxis, :]
if opt.cuda:
high_res_real = Variable(high_img.cuda())
high_res_fake = generator(Variable(input_comb).cuda())
outputs = torch.chunk(high_res_fake, 4, 1)
outputs = torch.cat(
[outputs[0], outputs[1], outputs[2], outputs[3]], 0)
# imshow(outputs[0])
generator_content_loss = content_criterion(
high_res_fake, high_comb)
mean_generator_content_loss += generator_content_loss.data[0]
generator_total_loss = generator_content_loss
mean_generator_total_loss += generator_total_loss.data[0]
if (batch_no % 10 == 0):
# # print("phase {} batch no. {} generator_content_loss {} discriminator_loss {}".format(phase, batch_no, generator_content_loss, discriminator_loss))
sys.stdout.write(
'\r epoch [%d/%d] batch no. [%d/%d] Generator_content_Loss: %.4f '
% (epoch, opt.nEpochs, batch_no, len(
dataloader['test']), generator_content_loss))
mssim = avg_msssim(high_res_real, outputs)
psnr_val = psnr(un_normalize(high_res_real), un_normalize(outputs))
writer.add_scalar("test per epoch/PSNR", psnr_val, epoch + 1)
# writer.add_scalar(phase+" per epoch/discriminator loss", mean_discriminator_loss/len(dataloader[phase]), epoch+1)
writer.add_scalar("test per epoch/generator loss",
mean_generator_total_loss / len(dataloader[phase]),
epoch + 1)
writer.add_scalar("per epoch/total time taken",
time.time() - curr_time, epoch + 1)
writer.add_scalar("test per epoch/avg_mssim", mssim, epoch + 1)
torch.save(generator.state_dict(),
'%s/generator_firstfinal.pth' % opt.out)
def generate_eigenjoint(feature_name='sk_eigenjoint_nor_528',
labels_name='labels_raw'):
# Data folder (Training data)
print("Extracting the training files")
data = os.path.join("E:\\program\\Chalearn\\rawdata\\train\\")
target_dir = 'E:\\program\\Chalearn\\Chalearn_LSTM\\target\\'
# Get the list of training samples
samples = os.listdir(data)
output_dir = 'E:\\program\\Chalearn\\Chalearn_LSTM\\feature\\' + feature_name
if not os.path.exists(output_dir):
os.makedirs(output_dir)
used_joints = [
'ElbowLeft', 'WristLeft', 'ShoulderLeft', 'HandLeft', 'ElbowRight',
'WristRight', 'ShoulderRight', 'HandRight', 'Head', 'Spine',
'HipCenter'
]
njoints = len(used_joints)
f = open('SK_normalization.pkl', 'r')
normal_params = pickle.load(f)
f.close()
Mean = normal_params['Mean1']
Std = normal_params['Std1']
count = 0
# target_category = 21
Target_all = []
#Feature_all = numpy.zeros(shape=(400000, (njoints*(njoints-1)/2 + njoints**2)*3),dtype=numpy.float32)
for file_count, file in enumerate(samples):
if int(file[-8:-4]) != 417 and int(file[-8:-4]) != 675:
print("\t Processing file " + file)
# Create the object to access the sample
smp = GestureSample(os.path.join(data, file))
# ###############################################
# USE Ground Truth information to learn the model
# ###############################################
# Get the list of actions for this frame
gesturesList = smp.getGestures()
frame_num = smp.getNumFrames()
Feature_Array = np.zeros(
shape=(frame_num,
(njoints * (njoints - 1) / 2 + njoints**2) * 3),
dtype=np.float32)
# Target = np.zeros( shape=(frame_num, target_category), dtype=np.uint8)
#feature generate
Skeleton_matrix, valid_skel = Extract_feature_UNnormalized(
smp, used_joints, 1, frame_num)
Feature_Array = Extract_feature_Realtime(Skeleton_matrix, njoints)
Feature_Array = normalize(Feature_Array, Mean, Std)
add_ = Feature_Array[-1].reshape((1, Feature_Array.shape[1]))
Feature_Array = np.concatenate((Feature_Array, add_), axis=0)
#save sample sk features
output_name = '%04d.npy' % count
count += 1
np.save(os.path.join(output_dir, output_name), Feature_Array)
#target generate
labels = np.zeros(frame_num, np.uint8)
for row in gesturesList:
labels[int(row[1]) - 1:int(row[2]) - 1] = int(row[0])
Target_all.append(labels)
del smp
np.save(target_dir + '%s.npy' % labels_name, Target_all)
def test_single(generator, discriminator, opt, dataloader, scale):
generator.load_state_dict(torch.load(opt.generatorWeights))
discriminator.load_state_dict(torch.load(opt.discriminatorWeights))
content_criterion = nn.MSELoss()
adversarial_criterion = nn.BCELoss()
ones_const = Variable(torch.ones(1, 1))
if opt.cuda:
generator.cuda()
discriminator.cuda()
content_criterion.cuda()
adversarial_criterion.cuda()
ones_const = ones_const.cuda()
curr_time = time.time()
# for epoch in range(opt.nEpochs):
mean_generator_content_loss = 0.0
mean_generator_adversarial_loss = 0.0
mean_generator_total_loss = 0.0
mean_discriminator_loss = 0.0
high_res_fake = 0
for batch_no, data in enumerate(dataloader['test']):
high_img, _ = data
generator.train(False)
discriminator.train(False)
print("batch no. {} shape of input {}".format(batch_no,
high_img.shape))
input1 = high_img[0, :, :, :]
input2 = high_img[1, :, :, :]
input3 = high_img[2, :, :, :]
input4 = high_img[3, :, :, :]
inputs = torch.FloatTensor(opt.batchSize, 3, opt.imageSize,
opt.imageSize)
# imshow(input3)
for j in range(opt.batchSize):
inputs[j] = scale(high_img[j])
high_img[j] = normalize(high_img[j])
high_comb = torch.cat(
[high_img[0], high_img[1], high_img[2], high_img[3]], 0)
high_comb = Variable(high_comb[np.newaxis, :]).cuda()
# imshow(high_comb.cpu().data)
input_comb = torch.cat(
[scale(input1),
scale(input2),
scale(input3),
scale(input4)], 0)
# inputs = [scale(input1), scale(input2), scale(input3), scale(input4)]
input_comb = input_comb[np.newaxis, :]
if opt.cuda:
# optimizer.zero_grad()
high_res_real = Variable(high_img.cuda())
high_res_fake = generator(Variable(input_comb).cuda())
target_real = Variable(torch.rand(1, 1) * 0.5 + 0.7).cuda()
target_fake = Variable(torch.rand(1, 1) * 0.3).cuda()
outputs = torch.chunk(high_res_fake, 4, 1)
outputs = torch.cat(
[outputs[0], outputs[1], outputs[2], outputs[3]], 0)
# imshow(outputs[0])
generator_content_loss = content_criterion(high_res_fake,
high_comb)
mean_generator_content_loss += generator_content_loss.data[0]
generator_adversarial_loss = adversarial_criterion(
discriminator(high_res_fake), ones_const)
mean_generator_adversarial_loss += generator_adversarial_loss.data[
0]
generator_total_loss = generator_content_loss + 1e-3 * generator_adversarial_loss
mean_generator_total_loss += generator_total_loss.data[0]
discriminator_loss = adversarial_criterion(discriminator(high_comb), target_real) + \
adversarial_criterion(discriminator(Variable(high_res_fake.data)), target_fake)
mean_discriminator_loss += discriminator_loss.data[0]
psnr_val = psnr(un_normalize(outputs), un_normalize(high_res_real))
print(psnr_val)
imsave(outputs.cpu().data,
train=False,
epoch=batch_no,
image_type='fake')
imsave(high_img, train=False, epoch=batch_no, image_type='real')
imsave(inputs, train=False, epoch=batch_no, image_type='low')
def _analysis(self, step , train_batches, dropout_keep_prob):
"""
Trains the model for a single epoch.
Args:
train_batches: iterable batch data for training
dropout_keep_prob: float value indicating dropout keep probability
"""
total_loss = 0
num_loss = 0
total_recall = [0.0,0.0,0.0,0.0]
num_recall =0
batch_start_time = 0
batch_start_time = time.time()
pred_answers, ref_answers = [], []
fake_answers = []
for fbitx, batch in enumerate(train_batches, 1):
step += 1
if fbitx % 1000 == 0:
print '------ Batch Question: ' + str(fbitx)
trees = []
batch_tree_set = []
batch_size = len(batch['question_ids'])
#print ('batch_size)', batch_size)
for bitx in range(batch_size):
tree = {'question_id': batch['question_ids'][bitx],
'question_token_ids': batch['question_token_ids'][bitx],
'q_length': batch['question_length'][bitx],
'passage_token_ids_list': batch['passage_token_ids_list'][bitx],
'passage_title_token_ids_list': batch['passage_title_token_ids_list'][bitx],
'passage_title_length_list': batch['passage_title_length_list'][bitx],
'passage_sentence_token_ids_list': batch['passage_sentence_token_ids_list'][bitx],
'passage_sen_length': batch['passage_sen_length_list'][bitx],
#'p_length': batch['passage_length'][bitx],
'passage_is_selected_list': batch['passage_is_selected_list'][bitx],
'question_type': batch['question_types'][bitx],
'ref_answers': batch['ref_answers'][bitx],
'fake_answers': batch['fake_answers'][bitx],
'segmented_answers': batch['segmented_answers'][bitx]
}
ref_answers.append({'question_id': tree['question_id'],
'question_type': tree['question_type'],
'answers': tree['ref_answers']})
trees.append(tree)
#print batch
batch_tree = SearchTree(self.tfg, tree, self.max_a_len, self.search_time, self.beta, self.m_value, dropout_keep_prob)
batch_tree_set.append(batch_tree)
# for every data in batch do training process
for idx, batch_tree in enumerate(batch_tree_set,1):
pred_answer, fake_answer, recall = batch_tree.train_analysis(step)
pred_answers.append(pred_answer)
fake_answers.append(fake_answer)
total_recall[0] += recall[0]
total_recall[1] += recall[1]
total_recall[2] += recall[2]
total_recall[3] += recall[3]
num_recall += 1
print('ave select recall', total_recall[0] / num_recall)
print('ave select f1', total_recall[1] / num_recall)
print('ave all recall', total_recall[2] / num_recall)
print('ave all f1', total_recall[3] / num_recall)
ii = 0
if len(ref_answers) > 0:
pred_dict, ref_dict = {}, {}
for pred, ref in zip(pred_answers, ref_answers):
ii += 1
question_id = ref['question_id']
#print('type', question_id)
if len(ref['answers']) > 0:
ref_dict[question_id] = normalize(ref['answers'])
pred_dict[question_id] = normalize(pred['answers'])
bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)
else:
bleu_rouge = None
value_with_mcts = bleu_rouge
print ('pre_scor',value_with_mcts)
#return 1.0 * total_loss / num_loss, step
return 0, step
def build_adj_mat(self):
adj = self.build_adj_original()
adj = normalize(adj + sp.eye(adj.shape[0]))
adj = sparse_mx_to_torch_sparse_tensor(adj)
return adj
def svm(training_file, development_file, test_file, counts):
twords, tlabels_true = hs.load_file(training_file)
dwords, dlabels_true = hs.load_file(development_file)
test_words = utils.load_test(test_file)
## Length
tlength_feature = hs.length_feature(twords)
tlength_normalized, tl_mean, tl_std = utils.normalize(tlength_feature)
dlength_feature = hs.length_feature(dwords)
dlength_normalized = utils.normalize_with_params(dlength_feature, tl_mean,
tl_std)
## Frequency
tfrequency_feature = hs.frequency_feature(twords, counts)
tfrequency_normalized, tf_mean, tf_std = utils.normalize(
tfrequency_feature)
dfrequency_feature = hs.frequency_feature(dwords, counts)
dfrequency_normalized = utils.normalize_with_params(
dfrequency_feature, tf_mean, tf_std)
## Syllables
tsyllables_feature = features.syllables_feature(twords)
tsyllables_normalized, tsy_mean, tsy_std = utils.normalize(
tsyllables_feature)
dsyllables_feature = features.syllables_feature(dwords)
dsyllables_normalized = utils.normalize_with_params(
dsyllables_feature, tsy_mean, tsy_std)
## Vowels
tvowels_feature = features.vowels_feature(twords)
tvowels_normalized, tv_mean, tv_std = utils.normalize(tvowels_feature)
dvowels_feature = features.vowels_feature(dwords)
dvowels_normalized = utils.normalize_with_params(dvowels_feature, tv_mean,
tv_std)
## Consonants
tconsonant_feature = features.vowels_feature(twords)
tconsonant_normalized, tc_mean, tc_std = utils.normalize(
tconsonant_feature)
dconsonant_feature = features.vowels_feature(dwords)
dconsonant_normalized = utils.normalize_with_params(
dconsonant_feature, tc_mean, tc_std)
## Senses
tsenses_feature = features.senses_feature(twords)
tsenses_normalized, tse_mean, tse_std = utils.normalize(tsenses_feature)
dsenses_feature = features.senses_feature(dwords)
dsenses_normalized = utils.normalize_with_params(dsenses_feature, tse_mean,
tse_std)
## Hypernyms
thypernyms_feature = features.hypernyms_feature(twords)
thypernyms_normalized, th_mean, th_std = utils.normalize(
thypernyms_feature)
dhypernyms_feature = features.hypernyms_feature(dwords)
dhypernyms_normalized = utils.normalize_with_params(
dhypernyms_feature, th_mean, th_std)
x_train = np.column_stack((tlength_normalized, tfrequency_normalized,
tsyllables_normalized, tsenses_normalized))
y = tlabels_true
x_dev = np.column_stack((dlength_normalized, dfrequency_normalized,
dsyllables_normalized, dsenses_normalized))
clf = SVC(C=48,
cache_size=200,
class_weight=None,
coef0=0.0,
decision_function_shape='ovr',
degree=3,
gamma='scale',
kernel='rbf',
max_iter=-1,
probability=False,
random_state=None,
shrinking=True,
tol=0.001,
verbose=False)
clf.fit(x_train, y)
y_pred = clf.predict(x_dev)
daccuracy = hs.get_accuracy(y_pred, dlabels_true)
dprecision = hs.get_precision(y_pred, dlabels_true)
drecall = hs.get_recall(y_pred, dlabels_true)
dfscore = hs.get_fscore(y_pred, dlabels_true)
# Test Set
# test_length_feature = hs.length_feature(test_words)
# test_frequency_feature = hs.frequency_feature(test_words, counts)
# test_syllables_feature = features.syllables_feature(test_words)
# test_senses_feature = features.senses_feature(test_words)
#
# test_length_normalized = utils.normalize_with_params(test_length_feature, tl_mean, tl_std)
# test_frequency_normalized = utils.normalize_with_params(test_frequency_feature, tf_mean, tf_std)
# test_syllables_normalized = utils.normalize_with_params(test_syllables_feature, tsy_mean, tsy_std)
# test_senses_normalized = utils.normalize_with_params(test_senses_feature, tse_mean, tse_std)
#
# x_test = np.column_stack((test_length_normalized, test_frequency_normalized,
# test_syllables_normalized, test_senses_normalized))
# y_pred_test = clf.predict(x_test)
#
# f = open('test_labels.txt', 'w')
# for item in y_pred_test:
# print(item, file=f)
# f.close()
# training_performance = (tprecision, trecall, tfscore)
development_performance = (daccuracy, dprecision, drecall, dfscore)
return development_performance
def create_non_intersecting():
pr = np.array([DEFAULT_POSITION[0], DEFAULT_POSITION[1], 0])
nr = utils.normalize(np.array([0, LARGE_DISTANCE, 1]))
return Ray(pr, nr)
def random_forest(training_file, development_file, test_file, counts):
twords, tlabels_true = hs.load_file(training_file)
dwords, dlabels_true = hs.load_file(development_file)
test_words = utils.load_test(test_file)
## Length
tlength_feature = hs.length_feature(twords)
tlength_normalized, tl_mean, tl_std = utils.normalize(tlength_feature)
dlength_feature = hs.length_feature(dwords)
dlength_normalized = utils.normalize_with_params(dlength_feature, tl_mean,
tl_std)
## Frequency
tfrequency_feature = hs.frequency_feature(twords, counts)
tfrequency_normalized, tf_mean, tf_std = utils.normalize(
tfrequency_feature)
dfrequency_feature = hs.frequency_feature(dwords, counts)
dfrequency_normalized = utils.normalize_with_params(
dfrequency_feature, tf_mean, tf_std)
## Syllables
tsyllables_feature = features.syllables_feature(twords)
tsyllables_normalized, tsy_mean, tsy_std = utils.normalize(
tsyllables_feature)
dsyllables_feature = features.syllables_feature(dwords)
dsyllables_normalized = utils.normalize_with_params(
dsyllables_feature, tsy_mean, tsy_std)
## Vowels
tvowels_feature = features.vowels_feature(twords)
tvowels_normalized, tv_mean, tv_std = utils.normalize(tvowels_feature)
dvowels_feature = features.vowels_feature(dwords)
dvowels_normalized = utils.normalize_with_params(dvowels_feature, tv_mean,
tv_std)
## Consonants
tconsonant_feature = features.vowels_feature(twords)
tconsonant_normalized, tc_mean, tc_std = utils.normalize(
tconsonant_feature)
dconsonant_feature = features.vowels_feature(dwords)
dconsonant_normalized = utils.normalize_with_params(
dconsonant_feature, tc_mean, tc_std)
## Senses
tsenses_feature = features.senses_feature(twords)
tsenses_normalized, tse_mean, tse_std = utils.normalize(tsenses_feature)
dsenses_feature = features.senses_feature(dwords)
dsenses_normalized = utils.normalize_with_params(dsenses_feature, tse_mean,
tse_std)
## Hypernyms
thypernyms_feature = features.hypernyms_feature(twords)
thypernyms_normalized, th_mean, th_std = utils.normalize(
thypernyms_feature)
dhypernyms_feature = features.hypernyms_feature(dwords)
dhypernyms_normalized = utils.normalize_with_params(
dhypernyms_feature, th_mean, th_std)
x_train = np.column_stack(
(tlength_normalized, tfrequency_normalized, tsyllables_normalized,
tsenses_normalized, thypernyms_normalized))
y = tlabels_true
x_dev = np.column_stack(
(dlength_normalized, dfrequency_normalized, dsyllables_normalized,
dsenses_normalized, dhypernyms_normalized))
clf = RandomForestClassifier(bootstrap=True,
class_weight=None,
criterion='gini',
max_depth=7,
max_features=3,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=8,
min_samples_split=50,
min_weight_fraction_leaf=0.0,
n_estimators=70,
n_jobs=None,
oob_score=False,
random_state=0,
verbose=0,
warm_start=False)
clf.fit(x_train, y)
y_pred = clf.predict(x_dev)
daccuracy = hs.get_accuracy(y_pred, dlabels_true)
dprecision = hs.get_precision(y_pred, dlabels_true)
drecall = hs.get_recall(y_pred, dlabels_true)
dfscore = hs.get_fscore(y_pred, dlabels_true)
# Test Set
test_length_feature = hs.length_feature(test_words)
test_frequency_feature = hs.frequency_feature(test_words, counts)
test_syllables_feature = features.syllables_feature(test_words)
test_vowels_feature = features.vowels_feature(test_words)
test_consonants_feature = features.consonants_feature(test_words)
test_senses_feature = features.senses_feature(test_words)
test_hypernyms_feature = features.hypernyms_feature(test_words)
test_length_normalized = utils.normalize_with_params(
test_length_feature, tl_mean, tl_std)
test_frequency_normalized = utils.normalize_with_params(
test_frequency_feature, tf_mean, tf_std)
test_syllables_normalized = utils.normalize_with_params(
test_syllables_feature, tsy_mean, tsy_std)
test_vowels_normalized = utils.normalize_with_params(
test_vowels_feature, tv_mean, tv_std)
test_consonants_normalized = utils.normalize_with_params(
test_consonants_feature, tc_mean, tc_std)
test_senses_normalized = utils.normalize_with_params(
test_senses_feature, tse_mean, tse_std)
test_hypernyms_normalized = utils.normalize_with_params(
test_hypernyms_feature, th_mean, th_std)
x_test = np.column_stack(
(test_length_normalized, test_frequency_normalized,
test_syllables_normalized, test_senses_normalized,
test_hypernyms_normalized))
y_pred_test = clf.predict(x_test)
f = open('test_labels.txt', 'w')
for item in y_pred_test:
print(item, file=f)
f.close()
# training_performance = (tprecision, trecall, tfscore)
development_performance = (daccuracy, dprecision, drecall, dfscore)
return development_performance
def universal_attack(attack_epoch, max_epoch):
model.eval()
delta = 0.1
fooling_rate = 0.0
overshoot = 0.02
# max_iter_df = 10
max_iter_df = 30
v = np.zeros(tmp_adj.shape[0]).astype(np.float32)
# stdv = 1./math.sqrt(tmp_adj.shape[0])
# v = np.random.uniform(-stdv, stdv, tmp_adj.shape[0])
cur_foolingrate = 0.0
epoch = 0
early_stop = 0
results = []
folder_path = op.join("./", "perturbation_results")
if not op.exists(folder_path):
os.mkdir(folder_path)
while fooling_rate < 1 - delta and epoch < max_epoch:
epoch += 1
train_idx = idx_train.cpu().numpy()
np.random.shuffle(train_idx)
###############################################
print('deepfooling...')
attack_time = time.time()
for k in train_idx:
print('deepfool node', k)
#add v to see if the attack succeeds
innormal_x_p = add_perturb(tmp_adj, k, v)
##################whether to use filtering
# innormal_x_p = np.where(innormal_x_p<0.5, 0, 1)
x_p, degree_p = normalize(innormal_x_p +
np.eye(tmp_adj.shape[0])) #A' = A + I
x_p = torch.from_numpy(x_p.astype(np.float32))
x_p = x_p.cuda()
output = model(features, x_p)
# print ('output', output[k])
if int(torch.argmax(output[k])) == int(torch.argmax(
ori_output[k])):
dr, iter = deepfool(innormal_x_p, x_p, k, num_classes,
degree_p[k], overshoot, max_iter_df)
# print ('dr', dr)
# print ('iter', iter)
# print ('the old perturbation matrix wass', v)
# print ('the old distance of perturbation is', np.linalg.norm(v.flatten(1)))
if iter < max_iter_df - 1:
v = v + dr
# Project on l_p ball
v = proj_lp(v)
# print ('L1 norm ov v', torch.norm(v, p=1))
# print ('L2 norm ov v', torch.norm(v, p=2))
else:
print('cant attack this node')
else:
print('attack succeed')
# print ('the prediction of k node', int(torch.argmax(output[k])))
# print ('the true label', int(labels[k]))
print('the deepfooling time cost is', time.time() - attack_time)
###################################################
# v = np.random.rand(tmp_adj.shape[0])
print('the perturbation matrix is', v)
print('testing the attack success rate')
res = []
#################
v = np.where(v > 0.5, 1, 0)
##################
for k in train_idx:
print('test node', k)
innormal_x_p = add_perturb(tmp_adj, k, v)
############
# innormal_x_p = np.where(innormal_x_p<0.5, 0, 1)
############
x_p, degree_p = normalize(innormal_x_p + np.eye(tmp_adj.shape[0]))
x_p = torch.from_numpy(x_p.astype(np.float32))
x_p = x_p.cuda()
output = model(features, x_p)
if int(torch.argmax(output[k])) == int(torch.argmax(
ori_output[k])):
res.append(0)
else:
res.append(1)
fooling_rate = float(sum(res) / len(res))
print('the current fooling rate is', fooling_rate)
# print ('the current fooling rate is', fooling_rate)
####################
# if fooling_rate > cur_foolingrate:
#####################
if fooling_rate >= cur_foolingrate:
cur_foolingrate = fooling_rate
file_path = op.join(
folder_path,
'{1}_xi{2}_epoch100/perturbation_{1}_{0}.txt'.format(
attack_epoch, args.dataset, args.radius))
with open(file_path, "w") as f:
for i in v:
f.write(str(i) + '\n')
#################
results.append(fooling_rate)
if epoch > 3:
if fooling_rate == results[-2]:
early_stop += 1
else:
early_stop = 0
if early_stop == 15:
break
#####################
return cur_foolingrate
def sobel(frame):
frame = GRAY(frame)
gx = cv2.Sobel(frame, cv2.CV_32F, 1, 0)
gy = cv2.Sobel(frame, cv2.CV_32F, 0, 1)
mag, ang = cv2.cartToPolar(gx, gy)
return normalize(mag)
def compute_fgbg_masks(saliency, bg_variation):
saliency_prob = normalize(6 * bg_variation + 4 * saliency)
_, fgmask = cv2.threshold(saliency_prob, 0.6, 1, cv2.THRESH_BINARY)
_, bgmask = cv2.threshold(bg_variation, 0.1, 1, cv2.THRESH_BINARY_INV)
return fgmask, bgmask
