Python roi_data_layer.roidb.add_bbox_regression_targets() Examples

The following are 8 code examples of roi_data_layer.roidb.add_bbox_regression_targets(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module roi_data_layer.roidb , or try the search function .
Example #1
Source File: train.py    From rgz_rcnn with MIT License 6 votes vote down vote up 
def __init__(self, sess, saver, network, imdb, roidb, output_dir, pretrained_model=None):
        """Initialize the SolverWrapper."""
        self.net = network
        self.imdb = imdb
        self.roidb = roidb
        self.output_dir = output_dir
        self.pretrained_model = pretrained_model

        print 'Computing bounding-box regression targets...'
        if cfg.TRAIN.BBOX_REG:
            self.bbox_means, self.bbox_stds = rdl_roidb.add_bbox_regression_targets(
                roidb)
        print 'done'

        # For checkpoint
        self.saver = saver
Example #2
Source File: train.py    From SubCNN with MIT License 6 votes vote down vote up 
def __init__(self, solver_prototxt, roidb, output_dir, pretrained_model=None):
        """Initialize the SolverWrapper."""
        self.output_dir = output_dir

        print 'Computing bounding-box regression targets...'
        if cfg.TRAIN.BBOX_REG:
            if cfg.IS_RPN:
                self.bbox_means, self.bbox_stds = gdl_roidb.add_bbox_regression_targets(roidb)
            else:
                self.bbox_means, self.bbox_stds = rdl_roidb.add_bbox_regression_targets(roidb)
        print 'done'

        self.solver = caffe.SGDSolver(solver_prototxt)
        if pretrained_model is not None:
            print ('Loading pretrained model '
                   'weights from {:s}').format(pretrained_model)
            self.solver.net.copy_from(pretrained_model)

        self.solver_param = caffe_pb2.SolverParameter()
        with open(solver_prototxt, 'rt') as f:
            pb2.text_format.Merge(f.read(), self.solver_param)

        self.solver.net.layers[0].set_roidb(roidb)
Example #3
Source File: train.py    From face-py-faster-rcnn with MIT License 5 votes vote down vote up 
def __init__(self, solver_prototxt, roidb, output_dir,
                 pretrained_model=None):
        """Initialize the SolverWrapper."""
        self.output_dir = output_dir

        if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
            cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
            # RPN can only use precomputed normalization because there are no
            # fixed statistics to compute a priori
            assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED

        if cfg.TRAIN.BBOX_REG:
            print 'Computing bounding-box regression targets...'
            self.bbox_means, self.bbox_stds = \
                    rdl_roidb.add_bbox_regression_targets(roidb)
            print 'done'

        self.solver = caffe.SGDSolver(solver_prototxt)
        if pretrained_model is not None:
            print ('Loading pretrained model '
                   'weights from {:s}').format(pretrained_model)
            self.solver.net.copy_from(pretrained_model)

        self.solver_param = caffe_pb2.SolverParameter()
        with open(solver_prototxt, 'rt') as f:
            pb2.text_format.Merge(f.read(), self.solver_param)

        self.solver.net.layers[0].set_roidb(roidb)
Example #4
Source File: train.py    From faster-rcnn-resnet with MIT License 5 votes vote down vote up 
def __init__(self, solver_prototxt, roidb, output_dir,
                 pretrained_model=None):
        """Initialize the SolverWrapper."""
        self.output_dir = output_dir

        if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
            cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
            # RPN can only use precomputed normalization because there are no
            # fixed statistics to compute a priori
            assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED

        if cfg.TRAIN.BBOX_REG:
            print 'Computing bounding-box regression targets...'
            self.bbox_means, self.bbox_stds = \
                    rdl_roidb.add_bbox_regression_targets(roidb)
            print 'done'

        self.solver = caffe.SGDSolver(solver_prototxt)
        if pretrained_model is not None:
            print ('Loading pretrained model '
                   'weights from {:s}').format(pretrained_model)
            self.solver.net.copy_from(pretrained_model)

        self.solver_param = caffe_pb2.SolverParameter()
        with open(solver_prototxt, 'rt') as f:
            pb2.text_format.Merge(f.read(), self.solver_param)

        self.solver.net.layers[0].set_roidb(roidb)
Example #5
Source File: train.py    From Faster-RCNN_TF with MIT License 5 votes vote down vote up 
def __init__(self, sess, saver, network, imdb, roidb, output_dir, pretrained_model=None):
        """Initialize the SolverWrapper."""
        self.net = network
        self.imdb = imdb
        self.roidb = roidb
        self.output_dir = output_dir
        self.pretrained_model = pretrained_model

        print 'Computing bounding-box regression targets...'
        if cfg.TRAIN.BBOX_REG:
            self.bbox_means, self.bbox_stds = rdl_roidb.add_bbox_regression_targets(roidb)
        print 'done'

        # For checkpoint
        self.saver = saver
Example #6
Source File: train.py    From uai-sdk with Apache License 2.0 5 votes vote down vote up 
def __init__(self, solver_prototxt, roidb, output_dir,
                 pretrained_model=None):
        """Initialize the SolverWrapper."""
        self.output_dir = output_dir

        if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
            cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
            # RPN can only use precomputed normalization because there are no
            # fixed statistics to compute a priori
            assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED

        if cfg.TRAIN.BBOX_REG:
            print 'Computing bounding-box regression targets...'
            self.bbox_means, self.bbox_stds = \
                    rdl_roidb.add_bbox_regression_targets(roidb)
            print 'done'

        self.solver = caffe.SGDSolver(solver_prototxt)
        if pretrained_model is not None:
            print ('Loading pretrained model '
                   'weights from {:s}').format(pretrained_model)
            self.solver.net.copy_from(pretrained_model)

        self.solver_param = caffe_pb2.SolverParameter()
        with open(solver_prototxt, 'rt') as f:
            pb2.text_format.Merge(f.read(), self.solver_param)

        self.solver.net.layers[0].set_roidb(roidb)
Example #7
Source File: train.py    From caffe-faster-rcnn-resnet-fpn with MIT License 5 votes vote down vote up 
def __init__(self, solver_prototxt, roidb, output_dir,
                 pretrained_model=None):
        """Initialize the SolverWrapper."""
        self.output_dir = output_dir

        if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
            cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
            # RPN can only use precomputed normalization because there are no
            # fixed statistics to compute a priori
            assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED

        if cfg.TRAIN.BBOX_REG:
            print 'Computing bounding-box regression targets...'
            self.bbox_means, self.bbox_stds = \
                    rdl_roidb.add_bbox_regression_targets(roidb)
            print 'done'

        self.solver = caffe.SGDSolver(solver_prototxt)
        if pretrained_model is not None:
            print ('Loading pretrained model '
                   'weights from {:s}').format(pretrained_model)
            self.solver.net.copy_from(pretrained_model)

        self.solver_param = caffe_pb2.SolverParameter()
        with open(solver_prototxt, 'rt') as f:
            pb2.text_format.Merge(f.read(), self.solver_param)

        self.solver.net.layers[0].set_roidb(roidb)
Example #8
Source File: train.py    From py-R-FCN with MIT License 5 votes vote down vote up 
def __init__(self, solver_prototxt, roidb, output_dir,
                 pretrained_model=None):
        """Initialize the SolverWrapper."""
        self.output_dir = output_dir

        if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
            cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
            # RPN can only use precomputed normalization because there are no
            # fixed statistics to compute a priori
            assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED

        if cfg.TRAIN.BBOX_REG:
            print 'Computing bounding-box regression targets...'
            self.bbox_means, self.bbox_stds = \
                    rdl_roidb.add_bbox_regression_targets(roidb)
            print 'done'

        self.solver = caffe.SGDSolver(solver_prototxt)
        if pretrained_model is not None:
            print ('Loading pretrained model '
                   'weights from {:s}').format(pretrained_model)
            self.solver.net.copy_from(pretrained_model)

        self.solver_param = caffe_pb2.SolverParameter()
        with open(solver_prototxt, 'rt') as f:
            pb2.text_format.Merge(f.read(), self.solver_param)

        self.solver.net.layers[0].set_roidb(roidb)