Remove py2 compatibility (tusen-ai#109)

Author: RogerChern

core/detection_input.py

@@ -29,7 +29,7 @@ class ReadRoiRecord(DetectionAugmentation):
     """
 
     def __init__(self, gt_select):
-        super(ReadRoiRecord, self).__init__()
+        super().__init__()
         self.gt_select = gt_select
 
     def apply(self, input_record):
@@ -52,7 +52,7 @@ class Norm2DImage(DetectionAugmentation):
     """
 
     def __init__(self, pNorm):
-        super(Norm2DImage, self).__init__()
+        super().__init__()
         self.p = pNorm  # type: NormParam
 
     def apply(self, input_record):
@@ -76,7 +76,7 @@ class Resize2DImageBbox(DetectionAugmentation):
     """
 
     def __init__(self, pResize):
-        super(Resize2DImageBbox, self).__init__()
+        super().__init__()
         self.p = pResize  # type: ResizeParam
 
     def apply(self, input_record):
@@ -116,7 +116,7 @@ class Resize2DImageBboxByRoidb(DetectionAugmentation):
     """
 
     def __init__(self):
-        super(Resize2DImageBboxByRoidb, self).__init__()
+        super().__init__()
         class ResizeParam:
             long = None
             short = None
@@ -139,7 +139,7 @@ class RandResize2DImageBbox(DetectionAugmentation):
     """
 
     def __init__(self, pRandResize):
-        super(RandResize2DImageBbox, self).__init__()
+        super().__init__()
         self.p = pRandResize
         class ResizeParam:
             long = None
@@ -164,7 +164,7 @@ class Flip2DImageBbox(DetectionAugmentation):
     """
 
     def __init__(self):
-        super(Flip2DImageBbox, self).__init__()
+        super().__init__()
 
     def apply(self, input_record):
         if input_record["flipped"]:
@@ -181,7 +181,7 @@ def apply(self, input_record):
 
 class RandCrop2DImageBbox(DetectionAugmentation):
     def __init__(self, pCrop):
-        super(RandCrop2DImageBbox, self).__init__()
+        super().__init__()
         self.p = pCrop
         assert pCrop.mode in ["center", "random"], "The {} crop mode is not supported".format(pCrop.mode)
 
@@ -265,7 +265,7 @@ class Pad2DImageBbox(DetectionAugmentation):
     """
 
     def __init__(self, pPad):
-        super(Pad2DImageBbox, self).__init__()
+        super().__init__()
         self.p = pPad  # type: PadParam
 
     def apply(self, input_record):
@@ -289,7 +289,7 @@ def apply(self, input_record):
 
 class ConvertImageFromHwcToChw(DetectionAugmentation):
     def __init__(self):
-        super(ConvertImageFromHwcToChw, self).__init__()
+        super().__init__()
 
     def apply(self, input_record):
         input_record["image"] = input_record["image"].transpose((2, 0, 1))
@@ -305,7 +305,7 @@ class AnchorTarget2D(DetectionAugmentation):
     """
 
     def __init__(self, pAnchor):
-        super(AnchorTarget2D, self).__init__()
+        super().__init__()
         self.p = pAnchor  # type: AnchorTarget2DParam
 
         self.__base_anchor = None
@@ -512,7 +512,7 @@ def apply(self, input_record):
 
 class RenameRecord(DetectionAugmentation):
     def __init__(self, mapping):
-        super(RenameRecord, self).__init__()
+        super().__init__()
         self.mapping = mapping
 
     def apply(self, input_record):
@@ -539,7 +539,7 @@ def __init__(self, roidb, transform, data_name, label_name, batch_size=1,
         :param shuffle: bool
         :return: Loader
         """
-        super(Loader, self).__init__(batch_size=batch_size)
+        super().__init__(batch_size=batch_size)
 
         if kv:
             (self.rank, self.num_worker) = (kv.rank, kv.num_workers)
@@ -674,7 +674,7 @@ def collector(self):
 
 class SequentialLoader(mx.io.DataIter):
     def __init__(self, iters):
-        super(SequentialLoader, self).__init__()
+        super().__init__()
         self.iters = iters
         self.exhausted = [False] * len(iters)
 
@@ -714,7 +714,7 @@ class AnchorLoader(mx.io.DataIter):
     def __init__(self, roidb, transform, data_name, label_name, batch_size=1,
                  shuffle=False, num_worker=12, num_collector=4, worker_queue_depth=4,
                  collector_queue_depth=4, kv=None):
-        super(AnchorLoader, self).__init__(batch_size=batch_size)
+        super().__init__(batch_size=batch_size)
 
         v_roidb, h_roidb = self.roidb_aspect_group(roidb)
 

core/detection_metric.py

@@ -4,7 +4,7 @@
 
 class LossWithIgnore(mx.metric.EvalMetric):
     def __init__(self, name, output_names, label_names, ignore_label=-1):
-        super(LossWithIgnore, self).__init__(name, output_names, label_names)
+        super().__init__(name, output_names, label_names)
         self.ignore_label = ignore_label
 
     def update(self, labels, preds):
@@ -13,7 +13,7 @@ def update(self, labels, preds):
 
 class FgLossWithIgnore(LossWithIgnore):
     def __init__(self, name, output_names, label_names, bg_label=0, ignore_label=-1):
-        super(FgLossWithIgnore, self).__init__(name, output_names, label_names, ignore_label)
+        super().__init__(name, output_names, label_names, ignore_label)
         self.bg_label = bg_label
 
     def update(self, labels, preds):
@@ -22,7 +22,7 @@ def update(self, labels, preds):
 
 class AccWithIgnore(LossWithIgnore):
     def __init__(self, name, output_names, label_names, ignore_label=-1):
-        super(AccWithIgnore, self).__init__(name, output_names, label_names, ignore_label)
+        super().__init__(name, output_names, label_names, ignore_label)
 
     def update(self, labels, preds):
         if len(preds) == 1 and len(labels) == 1:
@@ -51,7 +51,7 @@ def update(self, labels, preds):
 
 class FgAccWithIgnore(FgLossWithIgnore):
     def __init__(self, name, output_names, label_names, bg_label=0, ignore_label=-1):
-        super(FgAccWithIgnore, self).__init__(name, output_names, label_names, bg_label, ignore_label)
+        super().__init__(name, output_names, label_names, bg_label, ignore_label)
 
     def update(self, labels, preds):
         pred = preds[0]
@@ -70,7 +70,7 @@ def update(self, labels, preds):
 
 class CeWithIgnore(LossWithIgnore):
     def __init__(self, name, output_names, label_names, ignore_label=-1):
-        super(CeWithIgnore, self).__init__(name, output_names, label_names, ignore_label)
+        super().__init__(name, output_names, label_names, ignore_label)
 
     def update(self, labels, preds):
         pred = preds[0]
@@ -93,7 +93,7 @@ def update(self, labels, preds):
 
 class FgCeWithIgnore(FgLossWithIgnore):
     def __init__(self, name, output_names, label_names, bg_label=0, ignore_label=-1):
-        super(FgCeWithIgnore, self).__init__(name, output_names, label_names, bg_label, ignore_label)
+        super().__init__(name, output_names, label_names, bg_label, ignore_label)
 
     def update(self, labels, preds):
         pred = preds[0]
@@ -116,7 +116,7 @@ def update(self, labels, preds):
 
 class L1(FgLossWithIgnore):
     def __init__(self, name, output_names, label_names, bg_label=0, ignore_label=-1):
-        super(L1, self).__init__(name, output_names, label_names, bg_label, ignore_label)
+        super().__init__(name, output_names, label_names, bg_label, ignore_label)
 
     def update(self, labels, preds):
         if len(preds) == 1 and len(labels) == 1:
@@ -141,7 +141,7 @@ def update(self, labels, preds):
 
 class SigmoidCrossEntropy(mx.metric.EvalMetric):
     def __init__(self, name, output_names, label_names):
-        super(SigmoidCrossEntropy, self).__init__(name, output_names, label_names)
+        super().__init__(name, output_names, label_names)
 
     def update(self, labels, preds):
         x = preds[0].reshape(-1)  # logit

core/detection_module.py

@@ -78,7 +78,7 @@ class DetModule(BaseModule):
     """
     def __init__(self, symbol, data_names=None, label_names=None, logger=logging, context=ctx.cpu(),
                  fixed_param=None, excluded_param=None):
-        super(DetModule, self).__init__(logger=logger)
+        super().__init__(logger=logger)
 
         if isinstance(context, ctx.Context):
             context = [context]

models/FPN/assign_layer_fpn.py

@@ -9,7 +9,7 @@
 
 class AssignLayerFPNOperator(mx.operator.CustomOp):
     def __init__(self, rcnn_stride, roi_canonical_scale, roi_canonical_level):
-        super(AssignLayerFPNOperator, self).__init__()
+        super().__init__()
         self.rcnn_stride = rcnn_stride
         self.roi_canonical_scale = roi_canonical_scale
         self.roi_canonical_level = roi_canonical_level
@@ -46,7 +46,7 @@ def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
 @mx.operator.register('assign_layer_fpn')
 class AssignLayerFPNProp(mx.operator.CustomOpProp):
     def __init__(self, rcnn_stride, roi_canonical_scale, roi_canonical_level):
-        super(AssignLayerFPNProp, self).__init__(need_top_grad=False)
+        super().__init__(need_top_grad=False)
         self.rcnn_stride = eval(rcnn_stride)
         self.roi_canonical_scale = int(roi_canonical_scale)
         self.roi_canonical_level = int(roi_canonical_level)

models/FPN/builder.py

@@ -9,7 +9,7 @@
 
 class FPNBbox2fcHead(BboxHead):
     def __init__(self, pBbox):
-        super(FPNBbox2fcHead, self).__init__(pBbox)
+        super().__init__(pBbox)
 
     def add_norm(self, sym):
         p = self.p
@@ -42,7 +42,7 @@ def _get_bbox_head_logit(self, conv_feat):
 
 class FPNRpnHead(RpnHead):
     def __init__(self, pRpn):
-        super(RpnHead, self).__init__(pRpn)
+        super().__init__(pRpn)
 
         self.cls_logit_dict         = None
         self.bbox_delta_dict        = None
@@ -304,7 +304,7 @@ def get_sampled_proposal(self, conv_fpn_feat, gt_bbox, im_info):
 
 class MSRAResNet50V1FPN(Backbone):
     def __init__(self, pBackbone):
-        super(MSRAResNet50V1FPN, self).__init__(pBackbone)
+        super().__init__(pBackbone)
         from mxnext.backbone.resnet_v1 import Builder
         b = Builder()
         self.symbol = b.get_backbone("msra", 50, "fpn", pBackbone.normalizer, pBackbone.fp16)
@@ -318,7 +318,7 @@ def get_rcnn_feature(self):
 
 class MSRAResNet101V1FPN(Backbone):
     def __init__(self, pBackbone):
-        super(MSRAResNet101V1FPN, self).__init__(pBackbone)
+        super().__init__(pBackbone)
         from mxnext.backbone.resnet_v1 import Builder
         b = Builder()
         self.symbol = b.get_backbone("msra", 101, "fpn", pBackbone.normalizer, pBackbone.fp16)
@@ -332,7 +332,7 @@ def get_rcnn_feature(self):
 
 class FPNNeck(Neck):
     def __init__(self, pNeck):
-        super(FPNNeck, self).__init__(pNeck)
+        super().__init__(pNeck)
         self.fpn_feat = None
 
     def add_norm(self, sym):
@@ -495,7 +495,7 @@ def get_rcnn_feature(self, rcnn_feat):
 
 class FPNRoiAlign(RoiAlign):
     def __init__(self, pRoi):
-        super(FPNRoiAlign, self).__init__(pRoi)
+        super().__init__(pRoi)
 
     def get_roi_feature(self, conv_fpn_feat, proposal):
         p = self.p

models/FPN/get_top_proposal.py

@@ -9,7 +9,7 @@
 
 class GetTopProposalOperator(mx.operator.CustomOp):
     def __init__(self, top_n):
-        super(GetTopProposalOperator, self).__init__()
+        super().__init__()
         self.top_n = top_n
 
     def forward(self, is_train, req, in_data, out_data, aux):
@@ -47,7 +47,7 @@ def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
 @mx.operator.register('get_top_proposal')
 class GetTopProposalProp(mx.operator.CustomOpProp):
     def __init__(self, top_n):
-        super(GetTopProposalProp, self).__init__(need_top_grad=False)
+        super().__init__(need_top_grad=False)
         self.top_n = int(top_n)
 
     def list_arguments(self):

models/FPN/input.py

@@ -62,7 +62,7 @@ class PyramidAnchorTarget2D(PyramidAnchorTarget2DBase):
     """
 
     def __init__(self, pAnchor):
-        super(PyramidAnchorTarget2D, self).__init__(pAnchor)
+        super().__init__(pAnchor)
 
         self.pyramid_levels = len(self.p.generate.stride)
         self.p_list = [copy.deepcopy(self.p) for _ in range(self.pyramid_levels)]

models/cascade_rcnn/builder.py

@@ -167,7 +167,7 @@ def get_refined_proposal(backbone, neck, rpn_head, roi_extractor, bbox_head, bbo
 
 class CascadeNeck(Neck):
     def __init__(self, pNeck):
-        super(CascadeNeck, self).__init__(pNeck)
+        super().__init__(pNeck)
 
     def get_rcnn_feature(self, rcnn_feat):
         p = self.p
@@ -189,7 +189,7 @@ def get_rcnn_feature(self, rcnn_feat):
 """
 class CascadeRoiAlign(RoiAlign):
     def __init__(self, pRoi):
-        super(CascadeRoiAlign, self).__init__(pRoi)
+        super().__init__(pRoi)
 
     def get_roi_feature(self, rcnn_feat, proposal, stage):
         p = self.p
@@ -220,7 +220,7 @@ def get_roi_feature(self, rcnn_feat, proposal, stage):
 """
 class CascadeBbox2fcHead(Bbox2fcHead):
     def __init__(self, pBbox):
-        super(CascadeBbox2fcHead, self).__init__(pBbox)
+        super().__init__(pBbox)
 
         self.stage                  = pBbox.stage
         self._cls_logit             = None

models/maskrcnn/bbox_post_processing.py

@@ -34,7 +34,7 @@ def multiclass_nms(nms, cls_score, bbox_xyxy, min_det_score, max_det_per_image):
 
 class BboxPostProcessingOperator(mx.operator.CustomOp):
     def __init__(self, max_det_per_image, min_det_score, nms_type, nms_thr):
-        super(BboxPostProcessingOperator, self).__init__()
+        super().__init__()
         self.max_det_per_image = max_det_per_image
         self.min_det_score = min_det_score
         self.nms_type = nms_type
@@ -79,7 +79,7 @@ def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
 @mx.operator.register("BboxPostProcessing")
 class BboxPostProcessingProp(mx.operator.CustomOpProp):
     def __init__(self, max_det_per_image, min_det_score, nms_type, nms_thr):
-        super(BboxPostProcessingProp, self).__init__(need_top_grad=False)
+        super().__init__(need_top_grad=False)
         self.max_det_per_image = int(max_det_per_image)
         self.min_det_score = float(min_det_score)
         self.nms_type = str(nms_type)

models/maskrcnn/builder.py

@@ -85,8 +85,8 @@ def get_post_processing(self, cls_score, bbox_xyxy):
 
 class MaskRpnHead(RpnHead):
     def __init__(self, pRpn, pMask):
-        super(MaskRpnHead, self).__init__(pRpn)
-        self.pMask = patch_config_as_no_throw(pMask)
+        super().__init__(pRpn)
+        self.pMask = patch_config_as_nothrow(pMask)
 
     def get_sampled_proposal(self, conv_fpn_feat, gt_bbox, gt_poly, im_info):
         p = self.p
@@ -149,8 +149,8 @@ def get_sampled_proposal(self, conv_fpn_feat, gt_bbox, gt_poly, im_info):
 
 class MaskFPNRpnHead(FPNRpnHead):
     def __init__(self, pRpn, pMask):
-        super(MaskFPNRpnHead, self).__init__(pRpn)
-        self.pMask = patch_config_as_no_throw(pMask)
+        super().__init__(pRpn)
+        self.pMask = patch_config_as_nothrow(pMask)
 
     def get_sampled_proposal(self, conv_fpn_feat, gt_bbox, gt_poly, im_info):
         p = self.p
@@ -213,9 +213,9 @@ def get_sampled_proposal(self, conv_fpn_feat, gt_bbox, gt_poly, im_info):
 
 class MaskFasterRcnnHead(object):
     def __init__(self, pBbox, pMask, pMaskRoi):
-        self.pBbox = patch_config_as_no_throw(pBbox)
-        self.pMask = patch_config_as_no_throw(pMask)
-        self.pMaskRoi = patch_config_as_no_throw(pMaskRoi)
+        self.pBbox = patch_config_as_nothrow(pBbox)
+        self.pMask = patch_config_as_nothrow(pMask)
+        self.pMaskRoi = patch_config_as_nothrow(pMaskRoi)
 
         self._head_feat = None
 
@@ -284,7 +284,7 @@ def get_loss(self, conv_feat, mask_target):
 
 class MaskFasterRcnn4ConvHead(MaskFasterRcnnHead):
     def __init__(self, pBbox, pMask, pMaskRoi):
-        super(MaskFasterRcnn4ConvHead, self).__init__(pBbox, pMask, pMaskRoi)
+        super().__init__(pBbox, pMask, pMaskRoi)
 
     def _get_mask_head_logit(self, conv_feat):
         if self._head_feat is not None: