def infer_outputs(self): # sampled rois (0, x1, y1, x2, y2) # for CNTK the proposal shape is [4 x roisPerImage], and mirrored in Python rois_shape = (FreeDimension, 4) labels_shape = (FreeDimension, self._num_classes) bbox_targets_shape = (FreeDimension, self._num_classes * 4) bbox_inside_weights_shape = (FreeDimension, self._num_classes * 4) return [ output_variable(rois_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_target_rois_raw", needs_gradient=False), output_variable(labels_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="label_targets_raw", needs_gradient=False), output_variable(bbox_targets_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="bbox_targets_raw", needs_gradient=False), output_variable(bbox_inside_weights_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="bbox_inside_w_raw", needs_gradient=False) ]
def infer_outputs(self): return [ C.output_variable(self.inputs[0].shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes), C.output_variable(self.inputs[0].shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes) ]
def infer_outputs(self): return [ C.output_variable(C.FreeDimension, self.inputs[0].dtype, self.inputs[0].dynamic_axes), C.output_variable(C.FreeDimension, self.inputs[0].dtype, self.inputs[0].dynamic_axes) ]
def infer_outputs(self): # sampled rois (0, x1, y1, x2, y2) # for CNTK the proposal shape is [4 x roisPerImage], and mirrored in Python rois_shape = (cfg["TRAIN"].RPN_POST_NMS_TOP_N, 4) #rois_shape = (FreeDimension, 4) # labels # for CNTK the labels shape is [1 x roisPerImage], and mirrored in Python labels_shape = (cfg["TRAIN"].RPN_POST_NMS_TOP_N, self._num_classes) #labels_shape = (FreeDimension, self._num_classes) # bbox_targets bbox_targets_shape = (cfg["TRAIN"].RPN_POST_NMS_TOP_N, self._num_classes * 4) #bbox_targets_shape = (FreeDimension, self._num_classes * 4) # bbox_inside_weights bbox_inside_weights_shape = (cfg["TRAIN"].RPN_POST_NMS_TOP_N, self._num_classes * 4) #bbox_inside_weights_shape = (FreeDimension, self._num_classes * 4) return [output_variable(rois_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_target_rois_raw", needs_gradient=False), output_variable(labels_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="label_targets_raw", needs_gradient=False), output_variable(bbox_targets_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="bbox_targets_raw", needs_gradient=False), output_variable(bbox_inside_weights_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="bbox_inside_w_raw", needs_gradient=False)]
def infer_outputs(self): # This is a necessary work around since after cloning the cloned inputs are just place holders without the proper shape if self._cfm_shape is None: self._cfm_shape = self.inputs[0].shape height, width = self._cfm_shape[-2:] if DEBUG: print('AnchorTargetLayer: height', height, 'width', width) A = self._num_anchors # labels labelShape = (1, A, height, width) # Comment: this layer uses encoded labels, while in CNTK we mostly use one hot labels # bbox_targets bbox_target_shape = (1, A * 4, height, width) # bbox_inside_weights bbox_inside_weights_shape = (1, A * 4, height, width) return [ output_variable(labelShape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="objectness_target", needs_gradient=False), output_variable(bbox_target_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_bbox_target", needs_gradient=False), output_variable(bbox_inside_weights_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_bbox_inside_w", needs_gradient=False), ]
def infer_outputs(self): height, width = self.inputs[0].shape[-2:] if DEBUG: print('AnchorTargetLayer: height', height, 'width', width) A = self._num_anchors # labels labelShape = (1, A, height, width) # Comment: this layer uses encoded labels, while in CNTK we mostly use one hot labels # bbox_targets bbox_target_shape = (1, A * 4, height, width) # bbox_inside_weights bbox_inside_weights_shape = (1, A * 4, height, width) return [ output_variable(labelShape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="objectness_target", needs_gradient=False), output_variable(bbox_target_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_bbox_target", needs_gradient=False), output_variable(bbox_inside_weights_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_bbox_inside_w", needs_gradient=False), ]
def infer_outputs(self): output_vars = [C.output_variable(self.inputs[0].shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes)] self.action, self.actionArg = self.multiFunc(self.inputs[0]) self.grad, self.gradArg, self.gradRoot = self.gradFunc(self.inputs[0]) return output_vars
def infer_outputs(self): return [ C.output_variable(self.inputs[0].shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, needs_gradient=False) ]
def infer_outputs(self): outputVar = [ C.output_variable(self.inputs[idx].shape, self.inputs[idx].dtype, self.inputs[idx].dynamic_axes, name='outSimpleUdf') for idx in range(len(self.inputs)) ] return outputVar
def infer_outputs(self): self.count = self.count + 1 outputVar = [ C.output_variable(self.inputs[1].shape, self.inputs[1].dtype, self.inputs[1].dynamic_axes, name='outDummyLayer') ] return outputVar
def infer_outputs(self): # rois blob: holds R regions of interest, each is a 5-tuple # (n, x1, y1, x2, y2) specifying an image batch index n and a # rectangle (x1, y1, x2, y2) # for CNTK the proposal shape is [4 x roisPerImage], and mirrored in Python proposalShape = (FreeDimension, 4) return [output_variable(proposalShape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_rois_raw", needs_gradient=False)]
def infer_outputs(self): # rois blob: holds R regions of interest, each is a 5-tuple # (n, x1, y1, x2, y2) specifying an image batch index n and a # rectangle (x1, y1, x2, y2) # for CNTK the proposal shape is [4 x roisPerImage], and mirrored in Python proposalShape = (FreeDimension, 4) return [output_variable(proposalShape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_rois_raw", needs_gradient=False)]
def infer_outputs(self): if not self.shape: self.shape = self.inputs[0].shape[:-1] + ( self.inputs[1].shape[-1], ) return [ ct.output_variable(self.shape, np.float32, self.inputs[1].dynamic_axes, name=self.name + '_output_shape') ]
def infer_outputs(self): output_vars = [ C.output_variable(self.inputs[0].shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes) ] self.action, self.actionArg = self.multiFunc(self.inputs[0]) self.grad, self.gradArg, self.gradRoot = self.gradFunc(self.inputs[0]) return output_vars
def infer_outputs(self): height, width = self.inputs[0].shape[-2:] if DEBUG: print('AnchorTargetLayer: height', height, 'width', width) A = self._num_anchors # labels labelShape = (1, A, height, width) # Comment: this layer uses encoded labels, while in CNTK we mostly use one hot labels # bbox_targets bbox_target_shape = (1, A * 4, height, width) # bbox_inside_weights bbox_inside_weights_shape = (1, A * 4, height, width) return [output_variable(labelShape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="objectness_target", needs_gradient=False), output_variable(bbox_target_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_bbox_target", needs_gradient=False), output_variable(bbox_inside_weights_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_bbox_inside_w", needs_gradient=False),]
def infer_outputs(self): # rois blob: holds R regions of interest, each is a 5-tuple # (n, x1, y1, x2, y2) specifying an image batch index n and a # rectangle (x1, y1, x2, y2) # for CNTK the proposal shape is [4 x roisPerImage], and mirrored in Python # cfg_key = str(self.phase) # either 'TRAIN' or 'TEST' --> use FreeDimension and set output size in fwd proposalShape = (cfg["TRAIN"].RPN_POST_NMS_TOP_N, 4) #proposalShape = (FreeDimension, 4) return [output_variable(proposalShape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_rois_raw", needs_gradient=False)] # , name="rpn_rois" | name="rpn_rois_raw"
def infer_outputs(self): # This is a necessary work around since anfter cloning the cloned inputs are just place holders without the proper shape if self._cfm_shape is None: self._cfm_shape = self.inputs[0].shape height, width = self._cfm_shape[-2:] if DEBUG: print('AnchorTargetLayer: height', height, 'width', width) A = self._num_anchors # labels labelShape = (1, A, height, width) # Comment: this layer uses encoded labels, while in CNTK we mostly use one hot labels # bbox_targets bbox_target_shape = (1, A * 4, height, width) # bbox_inside_weights bbox_inside_weights_shape = (1, A * 4, height, width) return [output_variable(labelShape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="objectness_target", needs_gradient=False), output_variable(bbox_target_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_bbox_target", needs_gradient=False), output_variable(bbox_inside_weights_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_bbox_inside_w", needs_gradient=False),]
def infer_outputs(self): # rois blob: holds R regions of interest, each is a 5-tuple # (n, x1, y1, x2, y2) specifying an image batch index n and a # rectangle (x1, y1, x2, y2) # for CNTK the proposal shape is [4 x roisPerImage], and mirrored in Python # cfg_key = str(self.phase) # either 'TRAIN' or 'TEST' --> use FreeDimension and set output size in fwd proposalShape = (cfg["TRAIN"].RPN_POST_NMS_TOP_N, 4) #proposalShape = (FreeDimension, 4) return [ output_variable(proposalShape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name="rpn_rois_raw", needs_gradient=False) ] # , name="rpn_rois" | name="rpn_rois_raw"
def infer_outputs(self): impl_func_output = self.impl_func.output return [C.output_variable(impl_func_output.shape, impl_func_output.dtype, impl_func_output.dynamic_axes)]
def infer_outputs(self): impl_func_output = self.impl_func.output return [ C.output_variable(impl_func_output.shape, impl_func_output.dtype, impl_func_output.dynamic_axes) ]
def infer_outputs(self): conversation_batch_axis = C.Axis.default_batch_axis() return [ C.output_variable((C.FreeDimension, ) + self.inputs[0].shape, self.inputs[0].dtype, [conversation_batch_axis]) ]
def infer_outputs(self): return [C.output_variable(C.FreeDimension, self.inputs[0].dtype, self.inputs[0].dynamic_axes), C.output_variable(C.FreeDimension, self.inputs[0].dtype, self.inputs[0].dynamic_axes)]
def infer_outputs(self): return [output_variable(self.inputs[0].shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, name='rpn_obj_prob'), output_variable(self.inputs[1].shape, self.inputs[1].dtype, self.inputs[1].dynamic_axes, name='rpn_obj_targets', needs_gradient=False)]
def infer_outputs(self): return [ output_variable((), self.inputs[0].dtype, self.inputs[0].dynamic_axes) ]
def infer_outputs(self): self.count = self.count + 1 outputVar = [C.output_variable(self.inputs[1].shape, self.inputs[1].dtype, self.inputs[1].dynamic_axes, name='outDummyLayer')] return outputVar
def infer_outputs(self): conversation_batch_axis = C.Axis.default_batch_axis() return [C.output_variable((C.FreeDimension,) + self.inputs[0].shape, self.inputs[0].dtype, [conversation_batch_axis])]
def infer_outputs(self): return [C.output_variable(self.inputs[0].shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes, needs_gradient=False)]
def infer_outputs(self): outputVar = [C.output_variable(self.inputs[idx].shape, self.inputs[idx].dtype, self.inputs[idx].dynamic_axes, name='outDummyLayer') for idx in range(len(self.inputs))] return outputVar
def infer_outputs(self): return [output_variable(self.inputs[0].shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes)]