Python 使用实例

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]'{}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')
        raise ValueError("Number of image channels should be 1 or 3. Got: {}".format(arr.shape[3]))

    f = io.BytesIO(), "JPEG")
    return f.getvalue() 

Example 3

def visualize(self, zv, path):
        z, v = zv
        if 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
        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},
        # if not, try loading from the loading location
        if not load and not self.sameloc:
            load = self.load_from_db(self.load_query,
            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), 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 =[model.attention, model.mean_topic], {model.doc: x, model.tag: t})

        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, "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

        counter = 0
        with tf.Session() as sess:
            self.sess = sess
            for imgs,imgnames in self.DATA.test_generator():
                pred =,feed_dict={inputs: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'):
        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
            summary_writer = tf.summary.FileWriter(self.flags.log_path, sess.graph)
            saver = tf.train.Saver()
  , 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
    img_list = ["%s/%s"%(path,img) for img in os.listdir(path)]
    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],dic)
    return dic 

Example 10

def save(self, filename):
        """Saves the collection to a file.

        filename : :obj:`str`
            The file to save the collection to.

            If the file extension is not .npy or .npz.
        file_root, file_ext = os.path.splitext(filename)
        if file_ext == '.npy':
  , self._data)
        elif file_ext == '.npz':
            np.savez_compressed(filename, self._data)
            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), 'wb'), train_x1), 'wb'), train_x2), 'wb'), test_x1), 'wb'), test_x2), '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), '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
   + 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
                raise ValueError(
                    "You asked to save catched params," +
                    "but you didn't catch any!!!!!!!")
            for param in self.params:
        pkl.dump(params_vls, f_dump, protocol=pkl.HIGHEST_PROTOCOL)

Example 15

def main():
    args = docopt("""
    Usage: <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] + '.npy', new_matrix)
    save_vocabulary(path + '.vocab', iw) 

Example 16

def main():
    args = docopt("""
    Usage: [options] <pmi_path> <output_path>
        --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) + '.ut.npy', ut) + '.s.npy', s) + '.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():
        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 + OUT_FORMAT).format(year=year, dim=dim) + "-u.npy", u) + OUT_FORMAT).format(year=year, dim=dim) + "-v.npy", v) + 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)
        print "Aligning year:", year
        if first_iter:
            aligned_embed = year_embed
            first_iter = False
            aligned_embed = alignment.smart_procrustes_align(base_embed, year_embed)
        base_embed = aligned_embed
        print "Writing year:", year
        foutname = out_dir + str(year) + "-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):
    elif os.path.isdir(outputfile):
    if dvrdump:
	rdd_list = rdd.collect()
	with open(outputfile,'wb') as f:
	    count = 0
	    for item in rdd_list:
	        count = count+1
	        if count < len(rdd_list):

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:
        if self.Y is not None: self.Y.flush() 

Example 21

def stop(self):
        audio.say("Stopping Accuracy Test")'Stopping Accuracy_Test')
        self.screen_marker_state = 0 = False

        matched_data = calibrate.closest_matches_monocular(self.gaze_list,self.ref_list)
        pt_cloud = calibrate.preprocess_2d_data_monocular(matched_data)"Collected {} data points.".format(len(pt_cloud)))

        if len(pt_cloud) < 20:
            logger.warning("Did not collect enough data.")

        pt_cloud = np.array(pt_cloud),'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())

    fet = np.vstack(all_fet), 'c3d.npy'), fet)
    with open(osp.join(save_dir, 'c3d.list'), 'w') as writer:
        for img in all_imgs:
            writer.write('%s\n' % img)


Example 23

def convert(def_path, caffemodel_path, data_output_path, code_output_path, phase):
        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:
      , data)
        if code_output_path:
            print_stderr('Saving source...')
            with open(code_output_path, 'wb') as src_out:
    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) : ]
        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):
        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))
  , 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):

            for file_name in os.listdir(full_folder_name):

        vectorizer = CountVectorizer(input='filename')
        vector = vectorizer.fit_transform(all_file_names),vector)'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.path.join(
                directory, name + '.pkl'))
            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')

Example 27

def export_histories(self, path):
        if not os.path.exists(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),

        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') + 'loss', self.loss_history) + 'train_acc_history', self.train_acc_history) + 'val_acc_history', self.val_acc_history) 

Example 28

def main(args):
    with tf.Graph().as_default() as graph:
        # Create dataset'Create data flow from %s' %
        caffe_dataset = CaffeDataset(, 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.local_variables_initializer())
        # Start session
        with tf.Session(config=config) as sess:
            i = 0
            for s, a in caffe_dataset(5):
                pred_data =[op], feed_dict={x: [s]})[0]
                print pred_data.shape
      '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')
        # 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])
        raise SkipTest("Could not create 5GB large file")
    # write a small array to the end
    with open(tf_name, "wb") as f:
        d = np.arange(5), d)
    # read it back
    with open(tf_name, "rb") as f:
        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)
	# Create a label file
	lbl_file = path.join(tmp_dir, image_set+"_images.lbl")
	if not path.exists( 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'Create data flow from %s' %
        caffe_dataset = CaffeDataset(, 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.local_variables_initializer())
        # Start session
        with tf.Session(config=config) as sess:
            i = 0
            for s, a in caffe_dataset(5):
                pred_data =[op], feed_dict={x: [s]})[0]
                print pred_data.shape
      'tf-%03d.npy' % i, pred_data)
                i += 1 

Example 32

def save_mask(data, out_path):
    '''Save mask of data.

        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)), mask) 

Example 33

def main():
    for i in list(range(4))[::-1]:
    last_time = time.time()
    while True:
        if c%10==0:
            print('Recording at ' + str((10 / (time.time() - last_time)))+' fps')
            last_time = time.time()

        if len(training_data) % 500 == 0:

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.

        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.npy', weightMatrix)
        # print(weightMatrix.shape)
        i = 0
        for layer in backup.layers:
            weights = weightMatrix[i]
            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.

    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:, z_save_val)"Z_Save:{}".format(z_save_val.shape))"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)], ideal_data)
        rabi_cal = cal.RabiAmpCalibration(self.q.label, num_steps = len(ideal_data[0])/(2*self.nbr_round_robins))
        #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'
        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('', "{0}-{1}-data.gz".format(data_source, which))
    with gzip.GzipFile(fname, mode='wb') as f:, X)

    # Write labels
    fname = resource_filename('', "{0}-{1}-labels.gz".format(data_source, which))
    with gzip.GzipFile(fname, mode='wb') as 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)
	glove = GloveDictionary.Glove(d)

    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:

    return np.array(train_confidence)
#save pos scores 

Example 41

def get_dataset(dataset_path='Data/Train_Data'):
    # Getting all data from data path:
        X = np.load('Data/npy_train_data/X.npy')
        Y = np.load('Data/npy_train_data/Y.npy')
        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 = np.array(X)
        Y = np.array(Y)
        # Create dateset:
        if not os.path.exists('Data/npy_train_data/'):
            os.makedirs('Data/npy_train_data/')'Data/npy_train_data/X.npy', X)'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):"%s.model" % model_filename)"%s.tvocab" % model_filename, np.asarray(self.__trigrams))"%s.cvocab" % model_filename, np.asarray(self.__chars))"%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):, 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
   + '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 + '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.

        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.
        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],
    elif file_format=='numpy':, data_label)
        print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \

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.**
        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
        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 = 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],

Example 48

def save_checkpoint(self, session, step):, 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), 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)'data_%d' % GRID_SIZE, np.array(res)) 


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