python类save()的实例源码

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

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)

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)

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)

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())

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 % ""))

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)

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)

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

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

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

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))

def save_pkl(path, obj):
  with open(path, 'w') as f:
    cPickle.dump(obj, f)
    print(" [*] save %s" % path)

def save_npy(path, obj):
  np.save(path, obj)
  print(" [*] save %s" % path)

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)

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()

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()

def save_checkpoint(self, session, step):
        self.saver.save(session, self.logdir + "/model.ckpt", step)

#-------------------------------------------------------------------------------

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

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))