generate_proposal_labels.py

# Copyright (c) 2017-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import logging

from detectron.datasets import json_dataset
from detectron.datasets import roidb as roidb_utils
from detectron.utils import blob as blob_utils
import detectron.roi_data.fast_rcnn as fast_rcnn_roi_data

logger = logging.getLogger(__name__)


class GenerateProposalLabelsOp(object):

    def forward(self, inputs, outputs):
        """See modeling.detector.GenerateProposalLabels for inputs/outputs
        documentation.
        """
        # During training we reuse the data loader code. We populate roidb
        # entries on the fly using the rois generated by RPN.
        # im_info: [[im_height, im_width, im_scale], ...]
        rois = inputs[0].data
        roidb = blob_utils.deserialize(inputs[1].data)
        im_info = inputs[2].data
        im_scales = im_info[:, 2]
        output_blob_names = fast_rcnn_roi_data.get_fast_rcnn_blob_names()
        # For historical consistency with the original Faster R-CNN
        # implementation we are *not* filtering crowd proposals.
        # This choice should be investigated in the future (it likely does
        # not matter).
        json_dataset.add_proposals(roidb, rois, im_scales, crowd_thresh=0)
        roidb_utils.add_bbox_regression_targets(roidb)
        blobs = {k: [] for k in output_blob_names}
        fast_rcnn_roi_data.add_fast_rcnn_blobs(blobs, im_scales, roidb)
        for i, k in enumerate(output_blob_names):
            blob_utils.py_op_copy_blob(blobs[k], outputs[i])