Example #1
0
def _rezoom(hyp, pred_boxes, early_feat, early_feat_channels,
            w_offsets, h_offsets):
   
    #Rezoom into a feature map at multiple interpolation points
    #in a grid.

    #If the predicted object center is at X, len(w_offsets) == 3,
    #and len(h_offsets) == 5,
    #the rezoom grid will look as follows:

    #[o o o]
    #[o o o]
    #[o X o]
    #[o o o]
    #[o o o]

    #Where each letter indexes into the feature map with bilinear interpolation
    
	#grid size
    grid_size = hyp['grid_width'] * hyp['grid_height']
    outer_size = grid_size * hyp['batch_size']
    indices = []
    for w_offset in w_offsets:
        for h_offset in h_offsets:
            indices.append(train_utils.bilinear_select(hyp,
                                                       pred_boxes,
                                                       early_feat,
                                                       early_feat_channels,
                                                       w_offset, h_offset))
    #Rezoom into a feature map at multiple interpolation points
    interp_indices = tf.concat(axis=0, values=indices)
	rezoom_features = train_utils.interp(early_feat,
                                         interp_indices,
                                         early_feat_channels)
Example #2
0
def rezoom(H, pred_boxes, early_feat, early_feat_channels, w_offsets,
           h_offsets):
    '''
    Rezoom into a feature map at multiple interpolation points in a grid.
    If the predicted object center is at X, len(w_offsets) == 3, and len(h_offsets) == 5,
    the rezoom grid will look as follows:
    [o o o]
    [o o o]
    [o X o]
    [o o o]
    [o o o]
    Where each letter indexes into the feature map with bilinear interpolation
    '''
    grid_size = H['grid_width'] * H['grid_height']
    outer_size = grid_size * H['batch_size']
    indices = []
    for w_offset in w_offsets:
        for h_offset in h_offsets:
            indices.append(
                train_utils.bilinear_select(H, pred_boxes, early_feat,
                                            early_feat_channels, w_offset,
                                            h_offset))

    interp_indices = tf.concat(axis=0, values=indices)
    rezoom_features = train_utils.interp(early_feat, interp_indices,
                                         early_feat_channels)
    rezoom_features_r = tf.reshape(rezoom_features, [
        len(w_offsets) * len(h_offsets), outer_size, H['rnn_len'],
        early_feat_channels
    ])
    rezoom_features_t = tf.transpose(rezoom_features_r, [1, 2, 0, 3])
    return tf.reshape(rezoom_features_t, [
        outer_size, H['rnn_len'],
        len(w_offsets) * len(h_offsets) * early_feat_channels
    ])
Example #3
0
def rezoom(H, pred_boxes, early_feat, early_feat_channels, w_offsets, h_offsets):
    '''
    Rezoom into a feature map at multiple interpolation points in a grid. 

    If the predicted object center is at X, len(w_offsets) == 3, and len(h_offsets) == 5,
    the rezoom grid will look as follows:

    [o o o]
    [o o o]
    [o X o]
    [o o o]
    [o o o]

    Where each letter indexes into the feature map with bilinear interpolation
    '''


    grid_size = H['grid_width'] * H['grid_height']
    outer_size = grid_size * H['batch_size']
    indices = []
    for w_offset in w_offsets:
        for h_offset in h_offsets:
            indices.append(train_utils.bilinear_select(H,
                                                       pred_boxes,
                                                       early_feat,
                                                       early_feat_channels,
                                                       w_offset, h_offset))

    interp_indices = tf.concat(0, indices)
    rezoom_features = train_utils.interp(early_feat,
                                         interp_indices,
                                         early_feat_channels)
    rezoom_features_r = tf.reshape(rezoom_features,
                                   [len(w_offsets) * len(h_offsets),
                                    outer_size,
                                    H['rnn_len'],
                                    early_feat_channels])
    rezoom_features_t = tf.transpose(rezoom_features_r, [1, 2, 0, 3])
    return tf.reshape(rezoom_features_t,
                      [outer_size,
                       H['rnn_len'],
                       len(w_offsets) * len(h_offsets) * early_feat_channels])
Example #4
0
def rezoom(H, pred_boxes, early_feat, early_feat_channels, w_offsets,
           h_offsets):
    grid_size = H['grid_width'] * H['grid_height']
    outer_size = grid_size * H['batch_size']
    indices = []
    for w_offset in w_offsets:
        for h_offset in h_offsets:
            indices.append(
                train_utils.bilinear_select(H, pred_boxes, early_feat,
                                            early_feat_channels, w_offset,
                                            h_offset))

    interp_indices = tf.concat(axis=0, values=indices)
    rezoom_features = train_utils.interp(early_feat, interp_indices,
                                         early_feat_channels)
    rezoom_features_r = tf.reshape(rezoom_features, [
        len(w_offsets) * len(h_offsets), outer_size, H['rnn_len'],
        early_feat_channels
    ])
    rezoom_features_t = tf.transpose(rezoom_features_r, [1, 2, 0, 3])
    return tf.reshape(rezoom_features_t, [
        outer_size, H['rnn_len'],
        len(w_offsets) * len(h_offsets) * early_feat_channels
    ])