Python json_dataset.add_proposals方法代码示例

# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import add_proposals [as 别名]
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 = roi_data.fast_rcnn.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)
        blobs = {k: [] for k in output_blob_names}
        roi_data.fast_rcnn.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]) 

# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import add_proposals [as 别名]
def forward(self, inputs, outputs):
        # inputs is
        # [rpn_rois_fpn2, ..., rpn_rois_fpn6,
        #  rpn_roi_probs_fpn2, ..., rpn_roi_probs_fpn6]
        # If training with Faster R-CNN, then inputs will additionally include
        #  + [roidb, im_info]
        rois = collect(inputs, self._train)
        if self._train:
            # 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], ...]
            im_info = inputs[-1].data
            im_scales = im_info[:, 2]
            roidb = blob_utils.deserialize(inputs[-2].data)
            output_blob_names = roi_data.fast_rcnn.get_fast_rcnn_blob_names()
            json_dataset.add_proposals(roidb, rois, im_scales)
            blobs = {k: [] for k in output_blob_names}
            roi_data.fast_rcnn.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])
        else:
            # For inference we have a special code path that avoids some data
            # loader overhead
            distribute(rois, None, outputs, self._train) 

# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import add_proposals [as 别名]
def forward(self, inputs, roidb, im_info):
        """
        Args:
            inputs: a list of [rpn_rois_fpn2, ..., rpn_rois_fpn6,
                               rpn_roi_probs_fpn2, ..., rpn_roi_probs_fpn6]
            im_info: [[im_height, im_width, im_scale], ...]
        """
        rois = collect(inputs, self.training)
        if self.training:
            # During training we reuse the data loader code. We populate roidb
            # entries on the fly using the rois generated by RPN.
            im_scales = im_info.data.numpy()[:, 2]
            # 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)
            # Compute training labels for the RPN proposals; also handles
            # distributing the proposals over FPN levels
            output_blob_names = roi_data.fast_rcnn.get_fast_rcnn_blob_names()
            blobs = {k: [] for k in output_blob_names}
            roi_data.fast_rcnn.add_fast_rcnn_blobs(blobs, im_scales, roidb)
        else:
            # For inference we have a special code path that avoids some data
            # loader overhead
            blobs = distribute(rois, None)

        return blobs 

# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import add_proposals [as 别名]
def forward(self, rpn_rois, roidb, im_info):
        """Op for generating training labels for RPN proposals. This is used
        when training RPN jointly with Fast/Mask R-CNN (as in end-to-end
        Faster R-CNN training).

        blobs_in:
          - 'rpn_rois': 2D tensor of RPN proposals output by GenerateProposals
          - 'roidb': roidb entries that will be labeled
          - 'im_info': See GenerateProposals doc.

        blobs_out:
          - (variable set of blobs): returns whatever blobs are required for
            training the model. It does this by querying the data loader for
            the list of blobs that are needed.
        """
        im_scales = im_info.data.numpy()[:, 2]

        output_blob_names = roi_data.fast_rcnn.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).
        # Note: crowd_thresh=0 will ignore _filter_crowd_proposals
        json_dataset.add_proposals(roidb, rpn_rois, im_scales, crowd_thresh=0)
        blobs = {k: [] for k in output_blob_names}
        roi_data.fast_rcnn.add_fast_rcnn_blobs(blobs, im_scales, roidb)

        return blobs 

# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import add_proposals [as 别名]
def forward(self, inputs, roidb, im_info, stage=0):
        """
        Args:
            inputs: a list of [rpn_rois_fpn2, ..., rpn_rois_fpn6,
                               rpn_roi_probs_fpn2, ..., rpn_roi_probs_fpn6]
            im_info: [[im_height, im_width, im_scale], ...]
        """
        if stage == 0:
            rois = collect(inputs, self.training)
        else:
            rois = inputs
        if self.training:
            # During training we reuse the data loader code. We populate roidb
            # entries on the fly using the rois generated by RPN.
            im_scales = im_info.data.numpy()[:, 2]
            # 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)
            # Compute training labels for the RPN proposals; also handles
            # distributing the proposals over FPN levels
            output_blob_names = roi_data.fast_rcnn.get_fast_rcnn_blob_names()
            blobs = {k: [] for k in output_blob_names}
            roi_data.fast_rcnn.add_fast_rcnn_blobs(blobs, im_scales, roidb, stage)
        else:
            # For inference we have a special code path that avoids some data
            # loader overhead
            blobs = distribute(rois, None)

        return blobs 

# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import add_proposals [as 别名]
def forward(self, inputs, outputs):
        """See modeling.detector.CollectAndDistributeFpnRpnProposals for
        inputs/outputs documentation.
        """
        # inputs is
        # [rpn_rois_fpn2, ..., rpn_rois_fpn6,
        #  rpn_roi_probs_fpn2, ..., rpn_roi_probs_fpn6]
        # If training with Faster R-CNN, then inputs will additionally include
        #  + [roidb, im_info]
        rois = collect(inputs, self._train)
        if self._train:
            # 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], ...]
            im_info = inputs[-1].data
            im_scales = im_info[:, 2]
            roidb = blob_utils.deserialize(inputs[-2].data)
            # 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)
            # Compute training labels for the RPN proposals; also handles
            # distributing the proposals over FPN levels
            output_blob_names = roi_data.fast_rcnn.get_fast_rcnn_blob_names()
            blobs = {k: [] for k in output_blob_names}
            roi_data.fast_rcnn.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])
        else:
            # For inference we have a special code path that avoids some data
            # loader overhead
            distribute(rois, None, outputs, self._train) 

# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import add_proposals [as 别名]
def forward(self, inputs, outputs):
        """See modeling.detector.CollectAndDistributeFpnRpnProposalsRec for
        inputs/outputs documentation.
        """
        # inputs is
        # [rpn_rois_fpn2, ..., rpn_rois_fpn6,
        #  rpn_roi_probs_fpn2, ..., rpn_roi_probs_fpn6]
        # If training with Faster R-CNN, then inputs will additionally include
        #  + [roidb, im_info]
        rois = collect(inputs, self._train)
        if self._train:
            # 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], ...]
            im_info = inputs[-1].data
            im_scales = im_info[:, 2]
            roidb = blob_utils.deserialize(inputs[-2].data)
            # 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)
            # Compute training labels for the RPN proposals; also handles
            # distributing the proposals over FPN levels
            output_blob_names = roi_data.fast_rcnn.get_fast_rcnn_blob_names()
            blobs = {k: [] for k in output_blob_names}
            roi_data.fast_rcnn.add_fast_rcnn_blobs_rec(blobs, im_scales, roidb)
            for i, k in enumerate(output_blob_names):
                blob_utils.py_op_copy_blob(blobs[k], outputs[i])
        else:
            # For inference we have a special code path that avoids some data
            # loader overhead
            distribute(rois, None, outputs, self._train) 

# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import add_proposals [as 别名]
def forward(self, inputs, outputs):
        # 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 = roi_data.fast_rcnn.get_fast_rcnn_blob_names()
        json_dataset.add_proposals(roidb, rois, im_scales)
        blobs = {k: [] for k in output_blob_names}
        roi_data.fast_rcnn.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])