Python numpy.int32() Examples

def draw_heatmap(img, heatmap, alpha=0.5):
    """Draw a heatmap overlay over an image."""
    assert len(heatmap.shape) == 2 or \
        (len(heatmap.shape) == 3 and heatmap.shape[2] == 1)
    assert img.dtype in [np.uint8, np.int32, np.int64]
    assert heatmap.dtype in [np.float32, np.float64]

    if img.shape[0:2] != heatmap.shape[0:2]:
        heatmap_rs = np.clip(heatmap * 255, 0, 255).astype(np.uint8)
        heatmap_rs = ia.imresize_single_image(
            heatmap_rs[..., np.newaxis],
            img.shape[0:2],
            interpolation="nearest"
        )
        heatmap = np.squeeze(heatmap_rs) / 255.0

    cmap = plt.get_cmap('jet')
    heatmap_cmapped = cmap(heatmap)
    heatmap_cmapped = np.delete(heatmap_cmapped, 3, 2)
    heatmap_cmapped = heatmap_cmapped * 255
    mix = (1-alpha) * img + alpha * heatmap_cmapped
    mix = np.clip(mix, 0, 255).astype(np.uint8)
    return mix 

def load_keypoints(image_filepath, image_height, image_width):
    """Load facial keypoints of one image."""
    fp_keypoints = "%s.cat" % (image_filepath,)
    if not os.path.isfile(fp_keypoints):
        raise Exception("Could not find keypoint coordinates for image '%s'." \
                        % (image_filepath,))
    else:
        coords_raw = open(fp_keypoints, "r").readlines()[0].strip().split(" ")
        coords_raw = [abs(int(coord)) for coord in coords_raw]
        keypoints = []
        #keypoints_arr = np.zeros((9*2,), dtype=np.int32)
        for i in range(1, len(coords_raw), 2): # first element is the number of coords
            x = np.clip(coords_raw[i], 0, image_width-1)
            y = np.clip(coords_raw[i+1], 0, image_height-1)
            keypoints.append((x, y))

        return keypoints 

def in_top_k(predictions, targets, k):
    '''Returns whether the `targets` are in the top `k` `predictions`

    # Arguments
        predictions: A tensor of shape batch_size x classess and type float32.
        targets: A tensor of shape batch_size and type int32 or int64.
        k: An int, number of top elements to consider.

    # Returns
        A tensor of shape batch_size and type int. output_i is 1 if
        targets_i is within top-k values of predictions_i
    '''
    predictions_top_k = T.argsort(predictions)[:, -k:]
    result, _ = theano.map(lambda prediction, target: any(equal(prediction, target)), sequences=[predictions_top_k, targets])
    return result


# CONVOLUTIONS 

def ctc_path_probs(predict, Y, alpha=1e-4):
    smoothed_predict = (1 - alpha) * predict[:, Y] + alpha * np.float32(1.) / Y.shape[0]
    L = T.log(smoothed_predict)
    zeros = T.zeros_like(L[0])
    log_first = zeros

    f_skip_idxs = ctc_create_skip_idxs(Y)
    b_skip_idxs = ctc_create_skip_idxs(Y[::-1])  # there should be a shortcut to calculating this

    def step(log_f_curr, log_b_curr, f_active, log_f_prev, b_active, log_b_prev):
        f_active_next, log_f_next = ctc_update_log_p(f_skip_idxs, zeros, f_active, log_f_curr, log_f_prev)
        b_active_next, log_b_next = ctc_update_log_p(b_skip_idxs, zeros, b_active, log_b_curr, log_b_prev)
        return f_active_next, log_f_next, b_active_next, log_b_next

    [f_active, log_f_probs, b_active, log_b_probs], _ = theano.scan(
        step, sequences=[L, L[::-1, ::-1]], outputs_info=[np.int32(1), log_first, np.int32(1), log_first])

    idxs = T.arange(L.shape[1]).dimshuffle('x', 0)
    mask = (idxs < f_active.dimshuffle(0, 'x')) & (idxs < b_active.dimshuffle(0, 'x'))[::-1, ::-1]
    log_probs = log_f_probs + log_b_probs[::-1, ::-1] - L
    return log_probs, mask 

def create_roidb_from_box_list(self, box_list, gt_roidb):
    assert len(box_list) == self.num_images, \
            'Number of boxes must match number of ground-truth images'
    roidb = []

    if gt_roidb is not None:
        for i in range(self.num_images):
            boxes = box_list[i]

            real_label = gt_roidb[i]['labels']

            roidb.append({'boxes' : boxes,
                          'labels' : np.array([real_label], dtype=np.int32),
                          'flipped' : False})
    else:
        for i in range(self.num_images):
            boxes = box_list[i]

            roidb.append({'boxes' : boxes,
                          'labels' : np.zeros((1, 0), dtype=np.int32),
                          'flipped' : False})

    return roidb 

def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)):
  """ A wrapper function to generate anchors given different scales
    Also return the number of anchors in variable 'length'
  """
  anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
  A = anchors.shape[0]
  shift_x = np.arange(0, width) * feat_stride
  shift_y = np.arange(0, height) * feat_stride
  shift_x, shift_y = np.meshgrid(shift_x, shift_y)
  shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
  K = shifts.shape[0]
  # width changes faster, so here it is H, W, C
  anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
  anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
  length = np.int32(anchors.shape[0])

  return anchors, length 

def assemble_batch(story_fns, num_answer_words, format_spec):
    stories = []
    for sfn in story_fns:
        with gzip.open(sfn,'rb') as f:
            cvtd_story, _, _, _ = pickle.load(f)
        stories.append(cvtd_story)
    sents, graphs, queries, answers = zip(*stories)
    cvtd_sents = np.array(sents, np.int32)
    cvtd_queries = np.array(queries, np.int32)
    max_ans_len = max(len(a) for a in answers)
    cvtd_answers = np.stack([convert_answer(answer, num_answer_words, format_spec, max_ans_len) for answer in answers])
    num_new_nodes, new_node_strengths, new_node_ids, next_edges = zip(*graphs)
    num_new_nodes = np.stack(num_new_nodes)
    new_node_strengths = np.stack(new_node_strengths)
    new_node_ids = np.stack(new_node_ids)
    next_edges = np.stack(next_edges)
    return cvtd_sents, cvtd_queries, cvtd_answers, num_new_nodes, new_node_strengths, new_node_ids, next_edges 

def create_cifar100(tfrecord_dir, cifar100_dir):
    print('Loading CIFAR-100 from "%s"' % cifar100_dir)
    import pickle
    with open(os.path.join(cifar100_dir, 'train'), 'rb') as file:
        data = pickle.load(file, encoding='latin1')
    images = data['data'].reshape(-1, 3, 32, 32)
    labels = np.array(data['fine_labels'])
    assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
    assert labels.shape == (50000,) and labels.dtype == np.int32
    assert np.min(images) == 0 and np.max(images) == 255
    assert np.min(labels) == 0 and np.max(labels) == 99
    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
    onehot[np.arange(labels.size), labels] = 1.0

    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
        order = tfr.choose_shuffled_order()
        for idx in range(order.size):
            tfr.add_image(images[order[idx]])
        tfr.add_labels(onehot[order])

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

def __init__(self, resolution=1024, num_channels=3, dtype='uint8', dynamic_range=[0,255], label_size=0, label_dtype='float32'):
        self.resolution         = resolution
        self.resolution_log2    = int(np.log2(resolution))
        self.shape              = [num_channels, resolution, resolution]
        self.dtype              = dtype
        self.dynamic_range      = dynamic_range
        self.label_size         = label_size
        self.label_dtype        = label_dtype
        self._tf_minibatch_var  = None
        self._tf_lod_var        = None
        self._tf_minibatch_np   = None
        self._tf_labels_np      = None

        assert self.resolution == 2 ** self.resolution_log2
        with tf.name_scope('Dataset'):
            self._tf_minibatch_var = tf.Variable(np.int32(0), name='minibatch_var')
            self._tf_lod_var = tf.Variable(np.int32(0), name='lod_var') 

def _get_area_ratio(self, pos_proposals, pos_assigned_gt_inds, gt_masks):
        """Compute area ratio of the gt mask inside the proposal and the gt
        mask of the corresponding instance."""
        num_pos = pos_proposals.size(0)
        if num_pos > 0:
            area_ratios = []
            proposals_np = pos_proposals.cpu().numpy()
            pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy()
            # compute mask areas of gt instances (batch processing for speedup)
            gt_instance_mask_area = gt_masks.areas
            for i in range(num_pos):
                gt_mask = gt_masks[pos_assigned_gt_inds[i]]

                # crop the gt mask inside the proposal
                bbox = proposals_np[i, :].astype(np.int32)
                gt_mask_in_proposal = gt_mask.crop(bbox)

                ratio = gt_mask_in_proposal.areas[0] / (
                    gt_instance_mask_area[pos_assigned_gt_inds[i]] + 1e-7)
                area_ratios.append(ratio)
            area_ratios = torch.from_numpy(np.stack(area_ratios)).float().to(
                pos_proposals.device)
        else:
            area_ratios = pos_proposals.new_zeros((0, ))
        return area_ratios 

def _load_dataset_clipping(self, dataset_dir, epsilon):
    """Helper method which loads dataset and determines clipping range.

    Args:
      dataset_dir: location of the dataset.
      epsilon: maximum allowed size of adversarial perturbation.
    """
    self.dataset_max_clip = {}
    self.dataset_min_clip = {}
    self._dataset_image_count = 0
    for fname in os.listdir(dataset_dir):
      if not fname.endswith('.png'):
        continue
      image_id = fname[:-4]
      image = np.array(
          Image.open(os.path.join(dataset_dir, fname)).convert('RGB'))
      image = image.astype('int32')
      self._dataset_image_count += 1
      self.dataset_max_clip[image_id] = np.clip(image + epsilon,
                                                0,
                                                255).astype('uint8')
      self.dataset_min_clip[image_id] = np.clip(image - epsilon,
                                                0,
                                                255).astype('uint8') 

def load_images(input_dir, metadata_file_path, batch_shape):
    """Retrieve numpy arrays of images and labels, read from a directory."""
    num_images = batch_shape[0]
    with open(metadata_file_path) as input_file:
        reader = csv.reader(input_file)
        header_row = next(reader)
        rows = list(reader)

    row_idx_image_id = header_row.index('ImageId')
    row_idx_true_label = header_row.index('TrueLabel')
    images = np.zeros(batch_shape)
    labels = np.zeros(num_images, dtype=np.int32)
    for idx in xrange(num_images):
        row = rows[idx]
        filepath = os.path.join(input_dir, row[row_idx_image_id] + '.png')

        with tf.gfile.Open(filepath, 'rb') as f:
            image = np.array(
                Image.open(f).convert('RGB')).astype(np.float) / 255.0
        images[idx, :, :, :] = image
        labels[idx] = int(row[row_idx_true_label])
    return images, labels 

def read_from_tfrecord(filenames):
    tfrecord_file_queue = tf.train.string_input_producer(filenames, name='queue')
    reader = tf.TFRecordReader()
    _, tfrecord_serialized = reader.read(tfrecord_file_queue)

    tfrecord_features = tf.parse_single_example(tfrecord_serialized, features={
        'label': tf.FixedLenFeature([],tf.int64),
        'shape': tf.FixedLenFeature([],tf.string),
        'image': tf.FixedLenFeature([],tf.string),
    }, name='features')

    image = tf.decode_raw(tfrecord_features['image'], tf.uint8)
    shape = tf.decode_raw(tfrecord_features['shape'], tf.int32)

    image = tf.reshape(image, shape)
    label = tfrecord_features['label']
    return label, shape, image 

def batch_gen(download_url, expected_byte, vocab_size, batch_size,
              skip_window, visual_fld):
    local_dest = 'data/w2v/text8.zip'
    utils.download_one_file(download_url, local_dest, expected_byte)
    words = read_data(local_dest)
    dictionary, _ = build_vocab(words, vocab_size, visual_fld)
    index_words = convert_words_to_index(words, dictionary)
    del words  # to save memory
    single_gen = generate_sample(index_words, skip_window)

    while True:
        center_batch = np.zeros(batch_size, dtype=np.int32)
        target_batch = np.zeros([batch_size, 1])
        for index in range(batch_size):
            center_batch[index], target_batch[index] = next(single_gen)
        yield center_batch, target_batch 

def bonds_CH(ENGINE, rang=10, recur=10, refine=False, explore=True):
    groups = []
    for idx in range(0,ENGINE.pdb.numberOfAtoms, 13):
        groups.append( np.array([idx+1 ,idx+2 ], dtype=np.int32) ) # C1-H11
        groups.append( np.array([idx+1 ,idx+3 ], dtype=np.int32) ) # C1-H12
        groups.append( np.array([idx+4 ,idx+5 ], dtype=np.int32) ) # C2-H21
        groups.append( np.array([idx+4 ,idx+6 ], dtype=np.int32) ) # C2-H22
        groups.append( np.array([idx+7 ,idx+8 ], dtype=np.int32) ) # C3-H31
        groups.append( np.array([idx+7 ,idx+9 ], dtype=np.int32) ) # C3-H32
        groups.append( np.array([idx+10,idx+11], dtype=np.int32) ) # C4-H41
        groups.append( np.array([idx+10,idx+12], dtype=np.int32) ) # C4-H42
    ENGINE.set_groups(groups)
    [g.set_move_generator(DistanceAgitationGenerator(amplitude=0.2,agitate=(True,True))) for g in ENGINE.groups]
    # set selector
    if refine or explore:
        gs = RecursiveGroupSelector(RandomSelector(ENGINE), recur=recur, refine=refine, explore=explore)
        ENGINE.set_group_selector(gs)
    # number of steps
    nsteps = recur*len(ENGINE.groups)
    for stepIdx in range(rang):
        LOGGER.info("Running 'bonds_CH' mode step %i"%(stepIdx))
        ENGINE.run(numberOfSteps=nsteps, saveFrequency=nsteps)

# ############ RUN H-C-H ANGLES ############ # 

def angles_HCH(ENGINE, rang=5, recur=10, refine=False, explore=True):
    groups = []
    for idx in range(0,ENGINE.pdb.numberOfAtoms, 13):
        groups.append( np.array([idx+1 ,idx+2, idx+3 ], dtype=np.int32) ) # H11-C1-H12
        groups.append( np.array([idx+4 ,idx+5, idx+6 ], dtype=np.int32) ) # H21-C2-H22
        groups.append( np.array([idx+7 ,idx+8, idx+9 ], dtype=np.int32) ) # H31-C3-H32
        groups.append( np.array([idx+10,idx+11,idx+12], dtype=np.int32) ) # H41-C4-H42
    ENGINE.set_groups(groups)
    [g.set_move_generator(AngleAgitationGenerator(amplitude=5)) for g in ENGINE.groups]
    # set selector
    if refine or explore:
        gs = RecursiveGroupSelector(RandomSelector(ENGINE), recur=recur, refine=refine, explore=explore)
        ENGINE.set_group_selector(gs)
    # number of steps
    nsteps = recur*len(ENGINE.groups)
    for stepIdx in range(rang):
        LOGGER.info("Running 'angles_HCH' mode step %i"%(stepIdx))
        ENGINE.run(numberOfSteps=nsteps, saveFrequency=nsteps)

# ############ RUN ATOMS ############ # 

def tokenize(self, path):
        """Tokenizes a text file."""
        assert os.path.exists(path)
        # Add words to the dictionary
        with open(path, 'r') as f:
            tokens = 0
            for line in f:
                words = line.split() + ['<eos>']
                tokens += len(words)
                for word in words:
                    self.dictionary.add_word(word)

        # Tokenize file content
        with open(path, 'r') as f:
            ids = np.zeros((tokens,), dtype='int32')
            token = 0
            for line in f:
                words = line.split() + ['<eos>']
                for word in words:
                    ids[token] = self.dictionary.word2idx[word]
                    token += 1

        return mx.nd.array(ids, dtype='int32') 

def sample_mog(prob, mean, var, rng):
    """Sample from independent mixture of gaussian (MoG) distributions

    Each batch is an independent MoG distribution.

    Parameters
    ----------
    prob : numpy.ndarray
      mixture probability of each gaussian. Shape --> (batch_num, center_num)
    mean : numpy.ndarray
      mean of each gaussian. Shape --> (batch_num, center_num, sample_dim)
    var : numpy.ndarray
      variance of each gaussian. Shape --> (batch_num, center_num, sample_dim)
    rng : numpy.random.RandomState

    Returns
    -------
    ret : numpy.ndarray
      sampling result. Shape --> (batch_num, sample_dim)
    """
    gaussian_inds = sample_categorical(prob, rng).astype(numpy.int32)
    mean = mean[numpy.arange(mean.shape[0]), gaussian_inds, :]
    var = var[numpy.arange(mean.shape[0]), gaussian_inds, :]
    ret = sample_normal(mean=mean, var=var, rng=rng)
    return ret 

def dtype(self):
        """Data-type of the array's elements.

        Returns
        -------
        numpy.dtype
            This NDArray's data type.

        Examples
        --------
        >>> x = mx.nd.zeros((2,3))
        >>> x.dtype
        <type 'numpy.float32'>
        >>> y = mx.nd.zeros((2,3), dtype='int32')
        >>> y.dtype
        <type 'numpy.int32'>
        """
        mx_dtype = ctypes.c_int()
        check_call(_LIB.MXNDArrayGetDType(
            self.handle, ctypes.byref(mx_dtype)))
        return _DTYPE_MX_TO_NP[mx_dtype.value] 

def asnumpy(self):
        """Returns a ``numpy.ndarray`` object with value copied from this array.

        Examples
        --------
        >>> x = mx.nd.ones((2,3))
        >>> y = x.asnumpy()
        >>> type(y)
        <type 'numpy.ndarray'>
        >>> y
        array([[ 1.,  1.,  1.],
               [ 1.,  1.,  1.]], dtype=float32)
        >>> z = mx.nd.ones((2,3), dtype='int32')
        >>> z.asnumpy()
        array([[1, 1, 1],
               [1, 1, 1]], dtype=int32)
        """
        data = np.empty(self.shape, dtype=self.dtype)
        check_call(_LIB.MXNDArraySyncCopyToCPU(
            self.handle,
            data.ctypes.data_as(ctypes.c_void_p),
            ctypes.c_size_t(data.size)))
        return data 

def asscalar(self):
        """Returns a scalar whose value is copied from this array.

        This function is equivalent to ``self.asnumpy()[0]``. This NDArray must have shape (1,).

        Examples
        --------
        >>> x = mx.nd.ones((1,), dtype='int32')
        >>> x.asscalar()
        1
        >>> type(x.asscalar())
        <type 'numpy.int32'>
        """
        if self.shape != (1,):
            raise ValueError("The current array is not a scalar")
        return self.asnumpy()[0] 

def test_zero_prop2():
    x = mx.sym.Variable('x')
    idx = mx.sym.Variable('idx')
    y = mx.sym.batch_take(x, idx)
    z = mx.sym.stop_gradient(y)
    exe = z.simple_bind(ctx=mx.cpu(), x=(10, 10), idx=(10,),
                        type_dict={'x': np.float32, 'idx': np.int32})
    exe.forward()
    exe.backward()

    # The following bind() should throw an exception. We discard the expected stderr
    # output for this operation only in order to keep the test logs clean.
    with discard_stderr():
        try:
            y.simple_bind(ctx=mx.cpu(), x=(10, 10), idx=(10,),
                          type_dict={'x': np.float32, 'idx': np.int32})
        except:
            return

    assert False 

def setup_graph(self, input_audio_batch, target_phrase): 
        batch_size = input_audio_batch.shape[0]
        weird = (input_audio_batch.shape[1] - 1) // 320 
        logits_arg2 = np.tile(weird, batch_size)
        dense_arg1 = np.array(np.tile(target_phrase, (batch_size, 1)), dtype=np.int32)
        dense_arg2 = np.array(np.tile(target_phrase.shape[0], batch_size), dtype=np.int32)
        
        pass_in = np.clip(input_audio_batch, -2**15, 2**15-1)
        seq_len = np.tile(weird, batch_size).astype(np.int32)
        
        with tf.variable_scope('', reuse=tf.AUTO_REUSE):
            
            inputs = tf.placeholder(tf.float32, shape=pass_in.shape, name='a')
            len_batch = tf.placeholder(tf.float32, name='b')
            arg2_logits = tf.placeholder(tf.int32, shape=logits_arg2.shape, name='c')
            arg1_dense = tf.placeholder(tf.float32, shape=dense_arg1.shape, name='d')
            arg2_dense = tf.placeholder(tf.int32, shape=dense_arg2.shape, name='e')
            len_seq = tf.placeholder(tf.int32, shape=seq_len.shape, name='f')
            
            logits = get_logits(inputs, arg2_logits)
            target = ctc_label_dense_to_sparse(arg1_dense, arg2_dense, len_batch)
            ctcloss = tf.nn.ctc_loss(labels=tf.cast(target, tf.int32), inputs=logits, sequence_length=len_seq)
            decoded, _ = tf.nn.ctc_greedy_decoder(logits, arg2_logits, merge_repeated=True)
            
            sess = tf.Session()
            saver = tf.train.Saver(tf.global_variables())
            saver.restore(sess, "models/session_dump")
            
        func1 = lambda a, b, c, d, e, f: sess.run(ctcloss, 
            feed_dict={inputs: a, len_batch: b, arg2_logits: c, arg1_dense: d, arg2_dense: e, len_seq: f})
        func2 = lambda a, b, c, d, e, f: sess.run([ctcloss, decoded], 
            feed_dict={inputs: a, len_batch: b, arg2_logits: c, arg1_dense: d, arg2_dense: e, len_seq: f})
        return (func1, func2) 

def getctcloss(self, input_audio_batch, target_phrase, decode=False):
        batch_size = input_audio_batch.shape[0]
        weird = (input_audio_batch.shape[1] - 1) // 320 
        logits_arg2 = np.tile(weird, batch_size)
        dense_arg1 = np.array(np.tile(target_phrase, (batch_size, 1)), dtype=np.int32)
        dense_arg2 = np.array(np.tile(target_phrase.shape[0], batch_size), dtype=np.int32)
        
        pass_in = np.clip(input_audio_batch, -2**15, 2**15-1)
        seq_len = np.tile(weird, batch_size).astype(np.int32)

        if decode:
            return self.funcs[1](pass_in, batch_size, logits_arg2, dense_arg1, dense_arg2, seq_len)
        else:
            return self.funcs[0](pass_in, batch_size, logits_arg2, dense_arg1, dense_arg2, seq_len) 

def _convert(self, vals):
        res = {}
        for k, v in vals.items():
            if isinstance(v, (np.int, np.int8, np.int16, np.int32, np.int64)):
                v = int(v)
            elif isinstance(v, (np.float, np.float16, np.float32, np.float64)):
                v = float(v)
            elif isinstance(v, Labels):
                v = list(v)
            elif isinstance(v, np.ndarray):
                v = v.tolist()
            elif isinstance(v, dict):
                v = self._convert(v)
            res[k] = v
        return res 

def _toscalar(v):
    if isinstance(v, (np.float16, np.float32, np.float64,
                      np.uint8, np.uint16, np.uint32, np.uint64,
                      np.int8, np.int16, np.int32, np.int64)):
        return np.asscalar(v)
    else:
        return v 

def draw_on_image(self, img, color=[0, 255, 0], alpha=1.0, thickness=1, copy=copy):
        assert img.dtype in [np.uint8, np.float32, np.int32, np.int64]

        result = np.copy(img) if copy else img
        for i in range(thickness):
            y = [self.y1-i, self.y1-i, self.y2+i, self.y2+i]
            x = [self.x1-i, self.x2+i, self.x2+i, self.x1-i]
            rr, cc = draw.polygon_perimeter(y, x, shape=img.shape)
            if alpha >= 0.99:
                result[rr, cc, 0] = color[0]
                result[rr, cc, 1] = color[1]
                result[rr, cc, 2] = color[2]
            else:
                if result.dtype == np.float32:
                    result[rr, cc, 0] = (1 - alpha) * result[rr, cc, 0] + alpha * color[0]
                    result[rr, cc, 1] = (1 - alpha) * result[rr, cc, 1] + alpha * color[1]
                    result[rr, cc, 2] = (1 - alpha) * result[rr, cc, 2] + alpha * color[2]
                    result = np.clip(result, 0, 255)
                else:
                    result = result.astype(np.float32)
                    result[rr, cc, 0] = (1 - alpha) * result[rr, cc, 0] + alpha * color[0]
                    result[rr, cc, 1] = (1 - alpha) * result[rr, cc, 1] + alpha * color[1]
                    result[rr, cc, 2] = (1 - alpha) * result[rr, cc, 2] + alpha * color[2]
                    result = np.clip(result, 0, 255).astype(np.uint8)

        return result 

def evaluate(parameters, f_eval, raw_sentences, parsed_sentences,
             id_to_tag, dictionary_tags,filename,
             useAttend=True):
#{{{
    """
    Evaluate current model using CoNLL script.
    """
    n_tags = len(id_to_tag)
    predictions = []
    count = np.zeros((n_tags, n_tags), dtype=np.int32)

    for raw_sentence, data in zip(raw_sentences, parsed_sentences):
        input = create_input(data, parameters, False,useAttend=useAttend)
        if parameters['crf']:
            y_preds = np.array(f_eval(*input))
        else:
            y_preds = f_eval(*input).argmax(axis=1)
        y_reals = np.array(data['tags']).astype(np.int32)
        assert len(y_preds) == len(y_reals)
        p_tags = [id_to_tag[y_pred] for y_pred in y_preds]
        r_tags = [id_to_tag[y_real] for y_real in y_reals]
        if parameters['tag_scheme'] == 'iobes':
            p_tags = iobes_iob(p_tags)
            r_tags = iobes_iob(r_tags)
        for i, (y_pred, y_real) in enumerate(zip(y_preds, y_reals)):
            new_line = " ".join(raw_sentence[i][:-1] + [r_tags[i], p_tags[i]])
            predictions.append(new_line)
            count[y_real, y_pred] += 1
        predictions.append("")
    #write to file 
    with codecs.open(filename, 'w', 'utf8') as f:
        f.write("\n".join(predictions))
    return get_perf(filename) 
#}}} 

def modelScore(self,tag_ids,scores,s_len):
    #{{{
        """
            ATTENTATION THIS FUNCTION IS SYMBOL PROGRAMMING
            this function is to return the score of our model at a fixed sentence label 
        @param:
            scores:        the scores matrix ,the output of our model
            tag:           a numpy array, which represent one sentence label 
            sent_lens:     a scalar number, the length of sentence.
                because our sentence label will be expand to max sentence length,
                so we will use this to get the original sentence label. 
        @return: 
            a scalar number ,the score;
        """
    #{{{
        n_tags=self.output_dim;
        transitions=self.transitions;
        #score from tags_scores
        real_path_score = scores[T.arange(s_len), tag_ids].sum()

        # Score from transitions
        b_id = theano.shared(value=np.array([n_tags], dtype=np.int32))
        e_id = theano.shared(value=np.array([n_tags + 1], dtype=np.int32))
        padded_tags_ids = T.concatenate([b_id, tag_ids, e_id], axis=0)
        real_path_score += transitions[
                padded_tags_ids[T.arange(s_len + 1)],
                padded_tags_ids[T.arange(s_len + 1) + 1]
            ].sum()
        #to prevent T.exp(real_path_score) to be inf 
        #return real_path_score;
        return real_path_score/s_len;
    #}}}
    #}}} 

def arange(start, stop=None, step=1, dtype='int32'):
    '''Creates a 1-D tensor containing a sequence of integers.

    The function arguments use the same convention as
    Theano's arange: if only one argument is provided,
    it is in fact the "stop" argument.

    The default type of the returned tensor is 'int32' to
    match TensorFlow's default.
    '''
    return T.arange(start, stop=stop, step=step, dtype=dtype)