The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def gen_pruned_features(name):
print name
feature_dir = 'data/feature_' + args.domain + \
'_' + str(args.n_boxes) + 'boxes/' + name + '/'
n_clips = len(glob.glob(feature_dir + BOX_FEATURE + '*.npy'))
for clip in xrange(1, n_clips+1):
pruned_boxes = np.load(feature_dir + BOX_FEATURE + '{:04d}.npy'.format(clip)) # (50, args.n_boxes, 4)
roisavg = np.load(feature_dir + 'roisavg{:04d}.npy'.format(clip)) # (50, args.n_boxes, 512)
pruned_roisavg = np.zeros((50, args.n_boxes, 512))
for frame in xrange(50):
for box_id in xrange(args.n_boxes):
if not np.array_equal(pruned_boxes[frame][box_id], np.zeros((4))):
pruned_roisavg[frame][box_id] = roisavg[frame][box_id]
np.save('{}pruned_roisavg{:04d}'.format(feature_dir, clip), pruned_roisavg) Example 2
def encode_jpeg(arr):
assert arr.dtype == np.uint8
# simulate multi-channel array for single channel arrays
if len(arr.shape) == 3:
arr = np.expand_dims(arr, 3) # add channels to end of x,y,z
arr = arr.transpose((3,2,1,0)) # channels, z, y, x
reshaped = arr.reshape(arr.shape[3] * arr.shape[2], arr.shape[1] * arr.shape[0])
if arr.shape[0] == 1:
img = Image.fromarray(reshaped, mode='L')
elif arr.shape[0] == 3:
img = Image.fromarray(reshaped, mode='RGB')
else:
raise ValueError("Number of image channels should be 1 or 3. Got: {}".format(arr.shape[3]))
f = io.BytesIO()
img.save(f, "JPEG")
return f.getvalue() Example 3
def visualize(self, zv, path):
self.ax1.clear()
self.ax2.clear()
z, v = zv
if path:
np.save(path + '/trajectory.npy', z)
z = np.reshape(z, [-1, 2])
self.ax1.hist2d(z[:, 0], z[:, 1], bins=400)
self.ax1.set(xlim=self.xlim(), ylim=self.ylim())
v = np.reshape(v, [-1, 2])
self.ax2.hist2d(v[:, 0], v[:, 1], bins=400)
self.ax2.set(xlim=self.xlim(), ylim=self.ylim())
if self.display:
import matplotlib.pyplot as plt
plt.show()
plt.pause(0.1)
elif path:
self.fig.savefig(path + '/visualize.png') Example 4
def load_rec(self):
# first try and see if anything with the save data exists, since obviously
# we dont' want to keep loading from the original load location if some work has
# already been done
load = self.load_from_db({'exp_id': self.exp_id},
cache_filters=True)
# if not, try loading from the loading location
if not load and not self.sameloc:
load = self.load_from_db(self.load_query,
cache_filters=True,
collfs=self.load_collfs,
collfs_recent=self.load_collfs_recent)
if load is None:
raise Exception('You specified load parameters but no '
'record was found with the given spec.')
self.load_data = load Example 5
def get_feature_mat_from_video(video_filename, output_dir='output'):
yt_vid, extension = video_filename.split('/')[-1].split('.')
assert extension in ['webm', 'mp4', '3gp']
mkdir_if_not_exist(output_dir, False)
output_filename = output_dir + '/' + yt_vid + '.npy'
vid_reader = imageio.get_reader(video_filename, 'ffmpeg')
img_list = get_img_list_from_vid_reader(vid_reader, extension)
base_model = InceptionV3(include_top=True, weights='imagenet')
model = Model(inputs=base_model.input, outputs=base_model.get_layer('avg_pool').output)
feature_mat = get_feature_mat(model, img_list)
np.save(output_filename, feature_mat)
return feature_mat Example 6
def compute_dt_dist(docs, labels, tags, model, max_len, batch_size, pad_id, idxvocab, output_file):
#generate batches
num_batches = int(math.ceil(float(len(docs)) / batch_size))
dt_dist = []
t = []
combined = []
docid = 0
for i in xrange(num_batches):
x, _, _, t, s = get_batch_doc(docs, labels, tags, i, max_len, cf.tag_len, batch_size, pad_id)
attention, mean_topic = sess.run([model.attention, model.mean_topic], {model.doc: x, model.tag: t})
dt_dist.extend(attention[:s])
if debug:
for si in xrange(s):
d = x[si]
print "\n\nDoc", docid, "=", " ".join([idxvocab[item] for item in d if (item != pad_id)])
sorted_dist = matutils.argsort(attention[si], reverse=True)
for ti in sorted_dist:
print "Topic", ti, "=", attention[si][ti]
docid += 1
np.save(open(output_file, "w"), dt_dist) Example 7
def predictPL(self):
B = self.flags.batch_size
W,H,C = self.flags.width, self.flags.height, self.flags.color
inputs = tf.placeholder(dtype=tf.float32,shape=[None,H,W,C])
#with open(self.flags.pred_path,'w') as f:
# pass
self._build(inputs,resize=False)
counter = 0
with tf.Session() as sess:
self.sess = sess
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
for imgs,imgnames in self.DATA.test_generator():
pred = sess.run(self.logit,feed_dict={inputs:imgs})
np.save("%s/%d.npy"%(self.flags.pred_path,counter),{"pred":pred,"name":imgnames})
counter+=len(imgs)
if counter/B%10 ==0:
print_mem_time("%d images predicted"%counter)
# train with placeholders Example 8
def show_embedding(self,name,save_model="model.ckpt",meta_path='metadata.tsv'):
self._build()
self._write_meta()
from tensorflow.contrib.tensorboard.plugins import projector
# Use the same LOG_DIR where you stored your checkpoint.
with tf.Session() as sess:
self.sess = sess
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
summary_writer = tf.summary.FileWriter(self.flags.log_path, sess.graph)
saver = tf.train.Saver()
saver.save(sess, os.path.join(self.flags.log_path, save_model), 0)
# Format: tensorflow/contrib/tensorboard/plugins/projector/projector_config.proto
config = projector.ProjectorConfig()
# You can add multiple embeddings. Here we add only one.
embedding = config.embeddings.add()
embedding.tensor_name = name
# Link this tensor to its metadata file (e.g. labels).
embedding.metadata_path = os.path.join(self.flags.log_path, meta_path)
# Saves a configuration file that TensorBoard will read during startup.
projector.visualize_embeddings(summary_writer, config) Example 9
def split(flags):
if os.path.exists(flags.split_path):
return np.load(flags.split_path).item()
folds = flags.folds
path = flags.input_path
random.seed(6)
img_list = ["%s/%s"%(path,img) for img in os.listdir(path)]
random.shuffle(img_list)
dic = {}
n = len(img_list)
num = (n+folds-1)//folds
for i in range(folds):
s,e = i*num,min(i*num+num,n)
dic[i] = img_list[s:e]
np.save(flags.split_path,dic)
return dic Example 10
def save(self, filename):
"""Saves the collection to a file.
Parameters
----------
filename : :obj:`str`
The file to save the collection to.
Raises
------
ValueError
If the file extension is not .npy or .npz.
"""
file_root, file_ext = os.path.splitext(filename)
if file_ext == '.npy':
np.save(filename, self._data)
elif file_ext == '.npz':
np.savez_compressed(filename, self._data)
else:
raise ValueError('Extension %s not supported for point saves.' %(file_ext)) Example 11
def get_word_seq(train_ori1, train_ori2, test_ori1, test_ori2):
# fit tokenizer
tk = Tokenizer(num_words=TrainConfig.MAX_NB_WORDS)
tk.fit_on_texts(train_ori1 + train_ori2 + test_ori1 + test_ori2)
word_index = tk.word_index
# q1, q2 training text sequence
# (sentence_len, MAX_SEQUENCE_LENGTH)
train_x1 = tk.texts_to_sequences(train_ori1)
train_x1 = pad_sequences(train_x1, maxlen=TrainConfig.MAX_SEQUENCE_LENGTH)
train_x2 = tk.texts_to_sequences(train_ori2)
train_x2 = pad_sequences(train_x2, maxlen=TrainConfig.MAX_SEQUENCE_LENGTH)
# q1, q2 testing text sequence
test_x1 = tk.texts_to_sequences(test_ori1)
test_x1 = pad_sequences(test_x1, maxlen=TrainConfig.MAX_SEQUENCE_LENGTH)
test_x2 = tk.texts_to_sequences(test_ori2)
test_x2 = pad_sequences(test_x2, maxlen=TrainConfig.MAX_SEQUENCE_LENGTH)
np.save(open(DirConfig.Q1_CACHE_TRAIN, 'wb'), train_x1)
np.save(open(DirConfig.Q2_CACHE_TRAIN, 'wb'), train_x2)
np.save(open(DirConfig.Q1_CACHE_TEST, 'wb'), test_x1)
np.save(open(DirConfig.Q2_CACHE_TEST, 'wb'), test_x2)
np.save(open(DirConfig.WORD_INDEX_CACHE, 'wb'), word_index)
return train_x1, train_x2, test_x1, test_x2, word_index Example 12
def load_word2vec_matrix(vec_file, word_index, config):
if os.path.isfile(DirConfig.W2V_CACHE):
print('---- Load word vectors from cache.')
embedding_matrix = np.load(open(DirConfig.W2V_CACHE, 'rb'))
return embedding_matrix
print('---- loading word2vec ...')
word2vec = KeyedVectors.load_word2vec_format(
vec_file, binary=True)
print('Found %s word vectors of word2vec' % len(word2vec.vocab))
nb_words = min(config.MAX_NB_WORDS, len(word_index)) + 1
embedding_matrix = np.zeros((nb_words, config.WORD_EMBEDDING_DIM))
for word, i in word_index.items():
if word in word2vec.vocab:
embedding_matrix[i] = word2vec.word_vec(word)
print('Null word embeddings: %d' % \
np.sum(np.sum(embedding_matrix, axis=1) == 0))
# check the words which not in embedding vectors
not_found_words = []
for word, i in word_index.items():
if word not in word2vec.vocab:
not_found_words.append(word)
np.save(open(DirConfig.W2V_CACHE, 'wb'), embedding_matrix)
return embedding_matrix Example 13
def af_h5_to_np(input_path, outpath):
files = tables.open_file(input_path, mode = 'r+')
speaker_nodes = files.root._f_list_nodes()
for spk in speaker_nodes:
file_nodes = spk._f_list_nodes()
for fls in file_nodes:
file_name = fls._v_name
af_nodes = fls._f_list_nodes()
af_list = []
for fts in af_nodes:
features = fts[:]
mean = numpy.mean(features,1)
normalised_feats = list(numpy.transpose(features)/mean)
af_list += normalised_feats
numpy.save(outpath + file_name, numpy.array(af_list)) Example 14
def save_params(self, weights_file, catched=False):
"""Save the model's parameters."""
f_dump = open(weights_file, "w")
params_vls = []
if catched:
if self.catched_params != []:
params_vls = self.catched_params
else:
raise ValueError(
"You asked to save catched params," +
"but you didn't catch any!!!!!!!")
else:
for param in self.params:
params_vls.append(param.get_value())
pkl.dump(params_vls, f_dump, protocol=pkl.HIGHEST_PROTOCOL)
f_dump.close() Example 15
def main():
args = docopt("""
Usage:
text2numpy.py <path>
""")
path = args['<path>']
matrix = read_vectors(path)
iw = sorted(matrix.keys())
new_matrix = np.zeros(shape=(len(iw), len(matrix[iw[0]])), dtype=np.float32)
for i, word in enumerate(iw):
if word in matrix:
new_matrix[i, :] = matrix[word]
np.save(path + '.npy', new_matrix)
save_vocabulary(path + '.vocab', iw) Example 16
def main():
args = docopt("""
Usage:
pmi2svd.py [options] <pmi_path> <output_path>
Options:
--dim NUM Dimensionality of eigenvectors [default: 500]
--neg NUM Number of negative samples; subtracts its log from PMI [default: 1]
""")
pmi_path = args['<pmi_path>']
output_path = args['<output_path>']
dim = int(args['--dim'])
neg = int(args['--neg'])
explicit = PositiveExplicit(pmi_path, normalize=False, neg=neg)
ut, s, vt = sparsesvd(explicit.m.tocsc(), dim)
np.save(output_path + '.ut.npy', ut)
np.save(output_path + '.s.npy', s)
np.save(output_path + '.vt.npy', vt)
save_vocabulary(output_path + '.words.vocab', explicit.iw)
save_vocabulary(output_path + '.contexts.vocab', explicit.ic) Example 17
def worker(proc_num, queue, out_dir, in_dir, count_dir, words, dim, num_words, min_count=100):
while True:
if queue.empty():
break
year = queue.get()
print "Loading embeddings for year", year
time.sleep(random.random() * 120)
valid_words = set(words_above_count(count_dir, year, min_count))
print len(valid_words)
words = list(valid_words.intersection(words[year][:num_words]))
print len(words)
base_embed = Explicit.load((in_dir + INPUT_FORMAT).format(year=year), normalize=False)
base_embed = base_embed.get_subembed(words, restrict_context=True)
print "SVD for year", year
u, s, v = randomized_svd(base_embed.m, n_components=dim, n_iter=5)
print "Saving year", year
np.save((out_dir + OUT_FORMAT).format(year=year, dim=dim) + "-u.npy", u)
np.save((out_dir + OUT_FORMAT).format(year=year, dim=dim) + "-v.npy", v)
np.save((out_dir + OUT_FORMAT).format(year=year, dim=dim) + "-s.npy", s)
write_pickle(base_embed.iw, (out_dir + OUT_FORMAT).format(year=year, dim=dim) + "-vocab.pkl") Example 18
def align_years(years, rep_type, in_dir, out_dir, count_dir, min_count, **rep_args):
first_iter = True
base_embed = None
for year in years:
print "Loading year:", year
year_embed = create_representation(rep_type, in_dir + str(year), **rep_args)
year_words = words_above_count(count_dir, year, min_count)
year_embed.get_subembed(year_words)
print "Aligning year:", year
if first_iter:
aligned_embed = year_embed
first_iter = False
else:
aligned_embed = alignment.smart_procrustes_align(base_embed, year_embed)
base_embed = aligned_embed
print "Writing year:", year
foutname = out_dir + str(year)
np.save(foutname + "-w.npy",aligned_embed.m)
write_pickle(aligned_embed.iw, foutname + "-vocab.pkl") Example 19
def safeWrite(rdd,outputfile,dvrdump=False):
"""Save the rdd in the given directory.
Keyword arguments:
--rdd: given rdd to be saved
--outputfile: desired directory to save rdd
"""
if os.path.isfile(outputfile):
os.remove(outputfile)
elif os.path.isdir(outputfile):
shutil.rmtree(outputfile)
if dvrdump:
rdd_list = rdd.collect()
with open(outputfile,'wb') as f:
count = 0
for item in rdd_list:
f.write(str(item))
count = count+1
if count < len(rdd_list):
f.write("\n")
else:
rdd.saveAsTextFile(outputfile) Example 20
def save(self):
if self.index is None:
self.index = np.array(range(self.X.shape[0]))
metadata = {
"index": self.index.tolist(),
"x_shape": self.X.shape,
"x_type": str(self.X.dtype),
"running_mean": self.running_mean.tolist(),
"running_dev": self.running_dev.tolist(),
"running_min": self.running_min.tolist(),
"running_max": self.running_max.tolist(),
}
if self.Y is not None:
metadata["y_shape"] = self.Y.shape
metadata["y_type"] = str(self.Y.dtype)
with open(self.path+"/dataset.json", "wt") as f:
f.write(json.dumps(metadata))
self.X.flush()
if self.Y is not None: self.Y.flush() Example 21
def stop(self):
audio.say("Stopping Accuracy Test")
logger.info('Stopping Accuracy_Test')
self.screen_marker_state = 0
self.active = False
self.close_window()
matched_data = calibrate.closest_matches_monocular(self.gaze_list,self.ref_list)
pt_cloud = calibrate.preprocess_2d_data_monocular(matched_data)
logger.info("Collected {} data points.".format(len(pt_cloud)))
if len(pt_cloud) < 20:
logger.warning("Did not collect enough data.")
return
pt_cloud = np.array(pt_cloud)
np.save(os.path.join(self.g_pool.user_dir,'accuracy_test_pt_cloud.npy'),pt_cloud)
gaze,ref = pt_cloud[:,0:2],pt_cloud[:,2:4]
error_lines = np.array([[g,r] for g,r in zip(gaze,ref)])
self.error_lines = error_lines.reshape(-1,2)
self.pt_cloud = pt_cloud Example 22
def main():
import sys
save_dir = sys.argv[1]
all_imgs = []
all_fet = []
for line in sys.stdin:
fet = load_np(line.strip())
all_fet.append(fet)
all_imgs.append(line.strip())
fet = np.vstack(all_fet)
np.save(osp.join(save_dir, 'c3d.npy'), fet)
with open(osp.join(save_dir, 'c3d.list'), 'w') as writer:
for img in all_imgs:
writer.write('%s\n' % img)
pass Example 23
def convert(def_path, caffemodel_path, data_output_path, code_output_path, phase):
try:
transformer = TensorFlowTransformer(def_path, caffemodel_path, phase=phase)
print_stderr('Converting data...')
if caffemodel_path is not None:
data = transformer.transform_data()
print_stderr('Saving data...')
with open(data_output_path, 'wb') as data_out:
np.save(data_out, data)
if code_output_path:
print_stderr('Saving source...')
with open(code_output_path, 'wb') as src_out:
src_out.write(transformer.transform_source())
print_stderr('Done.')
except KaffeError as err:
fatal_error('Error encountered: {}'.format(err)) Example 24
def batch_works(k):
if k == n_processes - 1:
paths = all_paths[k * int(len(all_paths) / n_processes) : ]
else:
paths = all_paths[k * int(len(all_paths) / n_processes) : (k + 1) * int(len(all_paths) / n_processes)]
for path in paths:
o_path = os.path.join(output_path, os.path.basename(path))
if not os.path.exists(o_path):
os.makedirs(o_path)
x, y, z = perturb_patch_locations(base_locs, patch_size / 16)
probs = generate_patch_probs(path, (x, y, z), patch_size, image_size)
selections = np.random.choice(range(len(probs)), size=patches_per_image, replace=False, p=probs)
image = read_image(path)
for num, sel in enumerate(selections):
i, j, k = np.unravel_index(sel, (len(x), len(y), len(z)))
patch = image[int(x[i] - patch_size / 2) : int(x[i] + patch_size / 2),
int(y[j] - patch_size / 2) : int(y[j] + patch_size / 2),
int(z[k] - patch_size / 2) : int(z[k] + patch_size / 2), :]
f = os.path.join(o_path, str(num))
np.save(f, patch) Example 25
def run(self):
all_file_names = []
all_labels = []
for n, folder_name in enumerate(os.listdir(self.in_txtdir().path)):
full_folder_name = self.in_txtdir().path+'/'+folder_name
if os.path.isfile(full_folder_name):
continue
for file_name in os.listdir(full_folder_name):
all_labels.append(n)
all_file_names.append(full_folder_name+'/'+file_name)
vectorizer = CountVectorizer(input='filename')
vector = vectorizer.fit_transform(all_file_names)
numpy.save(self.out_npy().path,vector)
numpy.save('labels',numpy.array(all_labels)) #Where and how do we want to save this?
#This is just to test the tasks above Example 26
def make_check_point(self):
'''
Save the solver's current status
'''
checkpoints = {
'model': self.model,
'epoch': self.epoch,
'best_params': self.best_params,
'best_val_acc': self.best_val_acc,
'loss_history': self.loss_history,
'val_acc_history': self.val_acc_history,
'train_acc_history': self.train_acc_history}
name = 'check_' + str(self.epoch)
directory = os.path.join(self.path_checkpoints, name)
if not os.path.exists(directory):
os.makedirs(directory)
try:
np.save(checkpoints, os.path.join(
directory, name + '.pkl'))
except:
print('sorry, I haven\'t fixed this line, but it should be easy to fix, if you want you can try now and make a pull request')
raise() Example 27
def export_histories(self, path):
if not os.path.exists(path):
os.makedirs(path)
i = np.arange(len(self.loss_history)) + 1
z = np.array(zip(i, i*self.batch_size, self.loss_history))
np.savetxt(path + 'loss_history.csv', z, delimiter=',', fmt=[
'%d', '%d', '%f'], header='iteration, n_images, loss')
i = np.arange(len(self.train_acc_history), dtype=np.int)
z = np.array(zip(i, self.train_acc_history))
np.savetxt(path + 'train_acc_history.csv', z, delimiter=',', fmt=[
'%d', '%f'], header='epoch, train_acc')
z = np.array(zip(i, self.val_acc_history))
np.savetxt(path + 'val_acc_history.csv', z, delimiter=',', fmt=[
'%d', '%f'], header='epoch, val_acc')
np.save(path + 'loss', self.loss_history)
np.save(path + 'train_acc_history', self.train_acc_history)
np.save(path + 'val_acc_history', self.val_acc_history) Example 28
def main(args):
with tf.Graph().as_default() as graph:
# Create dataset
logging.info('Create data flow from %s' % args.data)
caffe_dataset = CaffeDataset(dir=args.data, num_act=args.num_act, mean_path=args.mean)
# Config session
config = get_config(args)
x = tf.placeholder(dtype=tf.float32, shape=[None, 84, 84, 12])
op = load_caffe_model(x, args.load)
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
# Start session
with tf.Session(config=config) as sess:
sess.run(init)
i = 0
for s, a in caffe_dataset(5):
pred_data = sess.run([op], feed_dict={x: [s]})[0]
print pred_data.shape
np.save('tf-%03d.npy' % i, pred_data)
i += 1 Example 29
def test_large_file_support():
if (sys.platform == 'win32' or sys.platform == 'cygwin'):
raise SkipTest("Unknown if Windows has sparse filesystems")
# try creating a large sparse file
tf_name = os.path.join(tempdir, 'sparse_file')
try:
# seek past end would work too, but linux truncate somewhat
# increases the chances that we have a sparse filesystem and can
# avoid actually writing 5GB
import subprocess as sp
sp.check_call(["truncate", "-s", "5368709120", tf_name])
except:
raise SkipTest("Could not create 5GB large file")
# write a small array to the end
with open(tf_name, "wb") as f:
f.seek(5368709120)
d = np.arange(5)
np.save(f, d)
# read it back
with open(tf_name, "rb") as f:
f.seek(5368709120)
r = np.load(f)
assert_array_equal(r, d) Example 30
def dataAsImageDataLayer(voc_dir, tmp_dir, image_set='train', **kwargs):
from caffe_all import L
from os import path
from python_layers import PY
Py = PY('data')
voc_data = VOCData(voc_dir, image_set)
# Create a text file with all the paths
source_file = path.join(tmp_dir, image_set+"_images.txt")
if not path.exists( source_file ):
f = open(source_file, 'w')
for n in voc_data.image_names:
print('%s 0'% voc_data.image_path[n], file=f)
f.close()
# Create a label file
lbl_file = path.join(tmp_dir, image_set+"_images.lbl")
if not path.exists( lbl_file ):
np.save(open(lbl_file, 'wb'), [voc_data.labels[n] for n in voc_data.image_names])
cs = kwargs.get('transform_param',{}).get('crop_size',0)
return L.ImageData(source=source_file, ntop=2, new_width=cs, new_height=cs, **kwargs)[0], Py.LabelData(label=lbl_file, batch_size=kwargs.get('batch_size',1)) Example 31
def main(args):
with tf.Graph().as_default() as graph:
# Create dataset
logging.info('Create data flow from %s' % args.data)
caffe_dataset = CaffeDataset(dir=args.data, num_act=args.num_act, mean_path=args.mean)
# Config session
config = get_config(args)
x = tf.placeholder(dtype=tf.float32, shape=[None, 84, 84, 12])
op = load_caffe_model(x, args.load)
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
# Start session
with tf.Session(config=config) as sess:
sess.run(init)
i = 0
for s, a in caffe_dataset(5):
pred_data = sess.run([op], feed_dict={x: [s]})[0]
print pred_data.shape
np.save('tf-%03d.npy' % i, pred_data)
i += 1 Example 32
def save_mask(data, out_path):
'''Save mask of data.
Args:
data (numpy.array): Data to mask
out_path (str): Output path for mask.
'''
print 'Getting mask'
s, n, x, y, z = data.shape
mask = np.zeros((x, y, z))
_data = data.reshape((s * n, x, y, z))
mask[np.where(_data.mean(axis=0) > _data.mean())] = 1
print 'Masked out %d out of %d voxels' % ((mask == 0).sum(), reduce(
lambda x_, y_: x_ * y_, mask.shape))
np.save(out_path, mask) Example 33
def main():
for i in list(range(4))[::-1]:
print(i+1)
time.sleep(1)
c=0
last_time = time.time()
while True:
c+=1
screen=grab_screen(title='')
screenG=cv2.cvtColor(screen,cv2.COLOR_BGR2GRAY)
screenG=cv2.resize(screenG,(80,60))
keys=key_check()
output=keys_to_output(keys)
training_data.append([screenG,output])
if c%10==0:
print('Recording at ' + str((10 / (time.time() - last_time)))+' fps')
last_time = time.time()
if len(training_data) % 500 == 0:
print(len(training_data))
np.save(file_name,training_data) Example 34
def savez(file, *args, **kwds):
"""Saves one or more arrays into a file in uncompressed ``.npz`` format.
Arguments without keys are treated as arguments with automatic keys named
``arr_0``, ``arr_1``, etc. corresponding to the positions in the argument
list. The keys of arguments are used as keys in the ``.npz`` file, which
are used for accessing NpzFile object when the file is read by
:func:`cupy.load` function.
Args:
file (file or str): File or filename to save.
*args: Arrays with implicit keys.
**kwds: Arrays with explicit keys.
.. seealso:: :func:`numpy.savez`
"""
args = map(cupy.asnumpy, args)
for key in kwds:
kwds[key] = cupy.asnumpy(kwds[key])
numpy.savez(file, *args, **kwds) Example 35
def backupNetwork(self, model, backup):
weightMatrix = []
for layer in model.layers:
weights = layer.get_weights()
weightMatrix.append(weights)
# np.save('weightMatrix.npy', weightMatrix)
# print(weightMatrix.shape)
i = 0
for layer in backup.layers:
weights = weightMatrix[i]
layer.set_weights(weights)
i += 1
# def loadWeights(self,path):
# self.model.set_weights(load_model(path).get_weights()) Example 36
def save_arrays(savedir, hparams, z_val):
"""Save arrays as npy files.
Args:
savedir: Directory where arrays are saved.
hparams: Hyperparameters.
z_val: Array to save.
"""
z_save_val = np.array(z_val).reshape(-1, hparams.num_latent)
name = FLAGS.tfrecord_path.split("/")[-1].split(".tfrecord")[0]
save_name = os.path.join(savedir, "{}_%s.npy".format(name))
with tf.gfile.Open(save_name % "z", "w") as f:
np.save(f, z_save_val)
tf.logging.info("Z_Save:{}".format(z_save_val.shape))
tf.logging.info("Successfully saved to {}".format(save_name % "")) Example 37
def test_rabi_amp(self):
"""
Test RabiAmpCalibration. Ideal data generated by simulate_rabiAmp.
"""
ideal_data = [np.tile(simulate_rabiAmp(), self.nbr_round_robins)]
np.save(self.filename, ideal_data)
rabi_cal = cal.RabiAmpCalibration(self.q.label, num_steps = len(ideal_data[0])/(2*self.nbr_round_robins))
cal.calibrate([rabi_cal])
os.remove(self.filename)
self.assertAlmostEqual(rabi_cal.pi_amp,1,places=2)
self.assertAlmostEqual(rabi_cal.pi2_amp,0.5,places=2)
#test update_settings
new_settings = auspex.config.load_meas_file(cfg_file)
self.assertAlmostEqual(rabi_cal.pi_amp, new_settings['qubits'][self.q.label]['control']['pulse_params']['piAmp'], places=4)
self.assertAlmostEqual(rabi_cal.pi2_amp, new_settings['qubits'][self.q.label]['control']['pulse_params']['pi2Amp'], places=4)
#restore original settings
auspex.config.dump_meas_file(self.test_settings, cfg_file) Example 38
def _save_data(which, X, y, data_source):
if data_source.lower() == 'mnist':
data_source = 'mnist'
else:
data_source = 'se'
if X.shape[0] != len(y):
raise TypeError("Length of data samples ({0}) was not identical "
"to length of labels ({1})".format(X.shape[0], len(y)))
# Convert to numpy array.
if not isinstance(X, np.ndarray):
X = np.array(X)
if not isinstance(y, np.ndarray):
y = np.array(y)
# Write feature_data
fname = resource_filename('sudokuextract.data', "{0}-{1}-data.gz".format(data_source, which))
with gzip.GzipFile(fname, mode='wb') as f:
np.save(f, X)
# Write labels
fname = resource_filename('sudokuextract.data', "{0}-{1}-labels.gz".format(data_source, which))
with gzip.GzipFile(fname, mode='wb') as f:
np.save(f, y) Example 39
def loadGlove(d=200):
start = time.time()
f1 = 'resources/words.pkl'
f2 = 'resources/embeddings.npy'
if (os.path.isfile(f1) and os.path.isfile(f2)):
with open(f1, 'rb') as input:
w = pickle.load(input)
e = np.load(f2)
glove = GloveDictionary.Glove(words=w, emb=e)
else:
glove = GloveDictionary.Glove(d)
saveGlove(glove)
end=time.time()
return glove
#save trained moleds Example 40
def getConfidenceScores(features_train, labels_train, C):
train_confidence = []
#confidence scores for training data are computed using K-fold cross validation
kfold = KFold(features_train.shape[0], n_folds=10)
for train_index,test_index in kfold:
X_train, X_test = features_train[train_index], features_train[test_index]
y_train, y_test = labels_train[train_index], labels_train[test_index]
#train classifier for the subset of train data
m = SVM.train(X_train,y_train,c=C,k="linear")
#predict confidence for test data and append it to list
conf = m.decision_function(X_test)
for x in conf:
train_confidence.append(x)
return np.array(train_confidence)
#save pos scores Example 41
def get_dataset(dataset_path='Data/Train_Data'):
# Getting all data from data path:
try:
X = np.load('Data/npy_train_data/X.npy')
Y = np.load('Data/npy_train_data/Y.npy')
except:
inputs_path = dataset_path+'/input'
images = listdir(inputs_path) # Geting images
X = []
Y = []
for img in images:
img_path = inputs_path+'/'+img
x_img = get_img(img_path).astype('float32').reshape(64, 64, 3)
x_img /= 255.
y_img = get_img(img_path.replace('input/', 'mask/mask_')).astype('float32').reshape(64, 64, 1)
y_img /= 255.
X.append(x_img)
Y.append(y_img)
X = np.array(X)
Y = np.array(Y)
# Create dateset:
if not os.path.exists('Data/npy_train_data/'):
os.makedirs('Data/npy_train_data/')
np.save('Data/npy_train_data/X.npy', X)
np.save('Data/npy_train_data/Y.npy', Y)
X, X_test, Y, Y_test = train_test_split(X, Y, test_size=0.1, random_state=42)
return X, X_test, Y, Y_test Example 42
def save(self, model_filename):
self.__model.save("%s.model" % model_filename)
np.save("%s.tvocab" % model_filename, np.asarray(self.__trigrams))
np.save("%s.cvocab" % model_filename, np.asarray(self.__chars))
np.save("%s.classes" % model_filename, np.asarray(self.__classes)) Example 43
def save_pkl(path, obj):
with open(path, 'w') as f:
cPickle.dump(obj, f)
print(" [*] save %s" % path) Example 44
def save_npy(path, obj):
np.save(path, obj)
print(" [*] save %s" % path) Example 45
def get_hof(name):
print name
FLOW_DIR = 'data/of_' + args.domain + '/' + name + '/'
BOXES_DIR = 'data/feature_' + args.domain + \
'_' + str(args.n_boxes) + 'boxes/' + name + '/'
n_frames = len(glob.glob(FLOW_DIR + '*.png'))
# init boxes
clip_boxes_index = 1
clip_boxes = np.load(BOXES_DIR + 'roislist{:04d}.npy'.format(clip_boxes_index))
# init hof
hof_shape = (50, args.n_boxes, 12)
hof = np.zeros(hof_shape)
for i in xrange(1, n_frames+1):
print "{}, Flow {}, ".format(name, i)
# boxes
new_clip_boxes_index = (i-1) / 50 + 1
if clip_boxes_index != new_clip_boxes_index:
# 1.1 save hof and init a new one
np.save(BOXES_DIR + 'hof{:04d}.npy'.format(clip_boxes_index), hof)
hof = np.zeros(hof_shape)
# 2.1 update clip_boxes
clip_boxes_index = new_clip_boxes_index
clip_boxes = np.load(BOXES_DIR + 'roislist{:04d}.npy'.format(clip_boxes_index))
flow_img = np.array(cv2.imread(FLOW_DIR + '{:06d}.png'.format(i)), dtype=np.float32)
frame_boxes = clip_boxes[(i-1) % 50].astype(int)
for box_id, (xmin, ymin, xmax, ymax) in enumerate(frame_boxes):
xmin, ymin, xmax, ymax = preprocess_box(xmin, ymin, xmax, ymax)
box_flow_img = flow_img[ymin:ymax, xmin:xmax, :]
hof[(i-1) % 50][box_id], _ = flow_to_hist(box_flow_img)
# save latest hof
np.save(BOXES_DIR + 'hof{:04d}.npy'.format(clip_boxes_index), hof) Example 46
def collect_point_label(anno_path, out_filename, file_format='txt'):
""" Convert original dataset files to data_label file (each line is XYZRGBL).
We aggregated all the points from each instance in the room.
Args:
anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
out_filename: path to save collected points and labels (each line is XYZRGBL)
file_format: txt or numpy, determines what file format to save.
Returns:
None
Note:
the points are shifted before save, the most negative point is now at origin.
"""
points_list = []
for f in glob.glob(os.path.join(anno_path, '*.txt')):
cls = os.path.basename(f).split('_')[0]
if cls not in g_classes: # note: in some room there is 'staris' class..
cls = 'clutter'
points = np.loadtxt(f)
labels = np.ones((points.shape[0],1)) * g_class2label[cls]
points_list.append(np.concatenate([points, labels], 1)) # Nx7
data_label = np.concatenate(points_list, 0)
xyz_min = np.amin(data_label, axis=0)[0:3]
data_label[:, 0:3] -= xyz_min
if file_format=='txt':
fout = open(out_filename, 'w')
for i in range(data_label.shape[0]):
fout.write('%f %f %f %d %d %d %d\n' % \
(data_label[i,0], data_label[i,1], data_label[i,2],
data_label[i,3], data_label[i,4], data_label[i,5],
data_label[i,6]))
fout.close()
elif file_format=='numpy':
np.save(out_filename, data_label)
else:
print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \
(file_format))
exit() Example 47
def collect_bounding_box(anno_path, out_filename):
""" Compute bounding boxes from each instance in original dataset files on
one room. **We assume the bbox is aligned with XYZ coordinate.**
Args:
anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
out_filename: path to save instance bounding boxes for that room.
each line is x1 y1 z1 x2 y2 z2 label,
where (x1,y1,z1) is the point on the diagonal closer to origin
Returns:
None
Note:
room points are shifted, the most negative point is now at origin.
"""
bbox_label_list = []
for f in glob.glob(os.path.join(anno_path, '*.txt')):
cls = os.path.basename(f).split('_')[0]
if cls not in g_classes: # note: in some room there is 'staris' class..
cls = 'clutter'
points = np.loadtxt(f)
label = g_class2label[cls]
# Compute tightest axis aligned bounding box
xyz_min = np.amin(points[:, 0:3], axis=0)
xyz_max = np.amax(points[:, 0:3], axis=0)
ins_bbox_label = np.expand_dims(
np.concatenate([xyz_min, xyz_max, np.array([label])], 0), 0)
bbox_label_list.append(ins_bbox_label)
bbox_label = np.concatenate(bbox_label_list, 0)
room_xyz_min = np.amin(bbox_label[:, 0:3], axis=0)
bbox_label[:, 0:3] -= room_xyz_min
bbox_label[:, 3:6] -= room_xyz_min
fout = open(out_filename, 'w')
for i in range(bbox_label.shape[0]):
fout.write('%f %f %f %f %f %f %d\n' % \
(bbox_label[i,0], bbox_label[i,1], bbox_label[i,2],
bbox_label[i,3], bbox_label[i,4], bbox_label[i,5],
bbox_label[i,6]))
fout.close() Example 48
def save_checkpoint(self, session, step):
self.saver.save(session, self.logdir + "/model.ckpt", step)
#------------------------------------------------------------------------------- Example 49
def encode_npz(subvol):
"""
This file format is unrelated to np.savez
We are just saving as .npy and the compressing
using zlib.
The .npy format contains metadata indicating
shape and dtype, instead of np.tobytes which doesn't
contain any metadata.
"""
fileobj = io.BytesIO()
if len(subvol.shape) == 3:
subvol = np.expand_dims(subvol, 0)
np.save(fileobj, subvol)
cdz = zlib.compress(fileobj.getvalue())
return cdz Example 50
def generate_data():
"""Generate grid of data for interpolation."""
res = []
for hopping in np.linspace(0.0, 0.12, GRID_SIZE):
for mu in np.linspace(2.0, 3.0, GRID_SIZE):
print(hopping, mu)
res.append(np.concatenate([[hopping, mu], optimize(hopping, mu)]))
res = np.array(res)
np.save(r'data_%d' % GRID_SIZE, np.array(res)) 