增加conformer代码

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/dense_heads/ssd_head.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import xavier_init
+from mmcv.runner import force_fp32
+
+from mmdet.core import (build_anchor_generator, build_assigner,
+                        build_bbox_coder, build_sampler, multi_apply)
+from ..builder import HEADS
+from ..losses import smooth_l1_loss
+from .anchor_head import AnchorHead
+
+
+# TODO: add loss evaluator for SSD
+@HEADS.register_module()
+class SSDHead(AnchorHead):
+    """SSD head used in https://arxiv.org/abs/1512.02325.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        anchor_generator (dict): Config dict for anchor generator
+        bbox_coder (dict): Config of bounding box coder.
+        reg_decoded_bbox (bool): If true, the regression loss would be
+            applied directly on decoded bounding boxes, converting both
+            the predicted boxes and regression targets to absolute
+            coordinates format. Default False. It should be `True` when
+            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
+        train_cfg (dict): Training config of anchor head.
+        test_cfg (dict): Testing config of anchor head.
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_classes=80,
+                 in_channels=(512, 1024, 512, 256, 256, 256),
+                 anchor_generator=dict(
+                     type='SSDAnchorGenerator',
+                     scale_major=False,
+                     input_size=300,
+                     strides=[8, 16, 32, 64, 100, 300],
+                     ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]),
+                     basesize_ratio_range=(0.1, 0.9)),
+                 bbox_coder=dict(
+                     type='DeltaXYWHBBoxCoder',
+                     clip_border=True,
+                     target_means=[.0, .0, .0, .0],
+                     target_stds=[1.0, 1.0, 1.0, 1.0],
+                 ),
+                 reg_decoded_bbox=False,
+                 train_cfg=None,
+                 test_cfg=None):
+        super(AnchorHead, self).__init__()
+        self.num_classes = num_classes
+        self.in_channels = in_channels
+        self.cls_out_channels = num_classes + 1  # add background class
+        self.anchor_generator = build_anchor_generator(anchor_generator)
+        num_anchors = self.anchor_generator.num_base_anchors
+
+        reg_convs = []
+        cls_convs = []
+        for i in range(len(in_channels)):
+            reg_convs.append(
+                nn.Conv2d(
+                    in_channels[i],
+                    num_anchors[i] * 4,
+                    kernel_size=3,
+                    padding=1))
+            cls_convs.append(
+                nn.Conv2d(
+                    in_channels[i],
+                    num_anchors[i] * (num_classes + 1),
+                    kernel_size=3,
+                    padding=1))
+        self.reg_convs = nn.ModuleList(reg_convs)
+        self.cls_convs = nn.ModuleList(cls_convs)
+
+        self.bbox_coder = build_bbox_coder(bbox_coder)
+        self.reg_decoded_bbox = reg_decoded_bbox
+        self.use_sigmoid_cls = False
+        self.cls_focal_loss = False
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        # set sampling=False for archor_target
+        self.sampling = False
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            # SSD sampling=False so use PseudoSampler
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+        self.fp16_enabled = False
+
+    def init_weights(self):
+        """Initialize weights of the head."""
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                xavier_init(m, distribution='uniform', bias=0)
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple:
+                cls_scores (list[Tensor]): Classification scores for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_anchors * num_classes.
+                bbox_preds (list[Tensor]): Box energies / deltas for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_anchors * 4.
+        """
+        cls_scores = []
+        bbox_preds = []
+        for feat, reg_conv, cls_conv in zip(feats, self.reg_convs,
+                                            self.cls_convs):
+            cls_scores.append(cls_conv(feat))
+            bbox_preds.append(reg_conv(feat))
+        return cls_scores, bbox_preds
+
+    def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights,
+                    bbox_targets, bbox_weights, num_total_samples):
+        """Compute loss of a single image.
+
+        Args:
+            cls_score (Tensor): Box scores for eachimage
+                Has shape (num_total_anchors, num_classes).
+            bbox_pred (Tensor): Box energies / deltas for each image
+                level with shape (num_total_anchors, 4).
+            anchors (Tensor): Box reference for each scale level with shape
+                (num_total_anchors, 4).
+            labels (Tensor): Labels of each anchors with shape
+                (num_total_anchors,).
+            label_weights (Tensor): Label weights of each anchor with shape
+                (num_total_anchors,)
+            bbox_targets (Tensor): BBox regression targets of each anchor wight
+                shape (num_total_anchors, 4).
+            bbox_weights (Tensor): BBox regression loss weights of each anchor
+                with shape (num_total_anchors, 4).
+            num_total_samples (int): If sampling, num total samples equal to
+                the number of total anchors; Otherwise, it is the number of
+                positive anchors.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+
+        loss_cls_all = F.cross_entropy(
+            cls_score, labels, reduction='none') * label_weights
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        pos_inds = ((labels >= 0) &
+                    (labels < self.num_classes)).nonzero().reshape(-1)
+        neg_inds = (labels == self.num_classes).nonzero().view(-1)
+
+        num_pos_samples = pos_inds.size(0)
+        num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
+        if num_neg_samples > neg_inds.size(0):
+            num_neg_samples = neg_inds.size(0)
+        topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
+        loss_cls_pos = loss_cls_all[pos_inds].sum()
+        loss_cls_neg = topk_loss_cls_neg.sum()
+        loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
+
+        if self.reg_decoded_bbox:
+            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
+            # is applied directly on the decoded bounding boxes, it
+            # decodes the already encoded coordinates to absolute format.
+            bbox_pred = self.bbox_coder.decode(anchor, bbox_pred)
+
+        loss_bbox = smooth_l1_loss(
+            bbox_pred,
+            bbox_targets,
+            bbox_weights,
+            beta=self.train_cfg.smoothl1_beta,
+            avg_factor=num_total_samples)
+        return loss_cls[None], loss_bbox
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            gt_bboxes (list[Tensor]): each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.anchor_generator.num_levels
+
+        device = cls_scores[0].device
+
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=1,
+            unmap_outputs=False)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg) = cls_reg_targets
+
+        num_images = len(img_metas)
+        all_cls_scores = torch.cat([
+            s.permute(0, 2, 3, 1).reshape(
+                num_images, -1, self.cls_out_channels) for s in cls_scores
+        ], 1)
+        all_labels = torch.cat(labels_list, -1).view(num_images, -1)
+        all_label_weights = torch.cat(label_weights_list,
+                                      -1).view(num_images, -1)
+        all_bbox_preds = torch.cat([
+            b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
+            for b in bbox_preds
+        ], -2)
+        all_bbox_targets = torch.cat(bbox_targets_list,
+                                     -2).view(num_images, -1, 4)
+        all_bbox_weights = torch.cat(bbox_weights_list,
+                                     -2).view(num_images, -1, 4)
+
+        # concat all level anchors to a single tensor
+        all_anchors = []
+        for i in range(num_images):
+            all_anchors.append(torch.cat(anchor_list[i]))
+
+        # check NaN and Inf
+        assert torch.isfinite(all_cls_scores).all().item(), \
+            'classification scores become infinite or NaN!'
+        assert torch.isfinite(all_bbox_preds).all().item(), \
+            'bbox predications become infinite or NaN!'
+
+        losses_cls, losses_bbox = multi_apply(
+            self.loss_single,
+            all_cls_scores,
+            all_bbox_preds,
+            all_anchors,
+            all_labels,
+            all_label_weights,
+            all_bbox_targets,
+            all_bbox_weights,
+            num_total_samples=num_total_pos)
+        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/dense_heads/transformer_head.py

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import Conv2d, Linear, build_activation_layer
+from mmcv.runner import force_fp32
+
+from mmdet.core import (bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh,
+                        build_assigner, build_sampler, multi_apply,
+                        reduce_mean)
+from mmdet.models.utils import (FFN, build_positional_encoding,
+                                build_transformer)
+from ..builder import HEADS, build_loss
+from .anchor_free_head import AnchorFreeHead
+
+
+@HEADS.register_module()
+class TransformerHead(AnchorFreeHead):
+    """Implements the DETR transformer head.
+
+    See `paper: End-to-End Object Detection with Transformers
+    <https://arxiv.org/pdf/2005.12872>`_ for details.
+
+    Args:
+        num_classes (int): Number of categories excluding the background.
+        in_channels (int): Number of channels in the input feature map.
+        num_fcs (int, optional): Number of fully-connected layers used in
+            `FFN`, which is then used for the regression head. Default 2.
+        transformer (dict, optional): Config for transformer.
+        positional_encoding (dict, optional): Config for position encoding.
+        loss_cls (dict, optional): Config of the classification loss.
+            Default `CrossEntropyLoss`.
+        loss_bbox (dict, optional): Config of the regression loss.
+            Default `L1Loss`.
+        loss_iou (dict, optional): Config of the regression iou loss.
+            Default `GIoULoss`.
+        tran_cfg (dict, optional): Training config of transformer head.
+        test_cfg (dict, optional): Testing config of transformer head.
+
+    Example:
+        >>> import torch
+        >>> self = TransformerHead(80, 2048)
+        >>> x = torch.rand(1, 2048, 32, 32)
+        >>> mask = torch.ones(1, 32, 32).to(x.dtype)
+        >>> mask[:, :16, :15] = 0
+        >>> all_cls_scores, all_bbox_preds = self(x, mask)
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 num_fcs=2,
+                 transformer=dict(
+                     type='Transformer',
+                     embed_dims=256,
+                     num_heads=8,
+                     num_encoder_layers=6,
+                     num_decoder_layers=6,
+                     feedforward_channels=2048,
+                     dropout=0.1,
+                     act_cfg=dict(type='ReLU', inplace=True),
+                     norm_cfg=dict(type='LN'),
+                     num_fcs=2,
+                     pre_norm=False,
+                     return_intermediate_dec=True),
+                 positional_encoding=dict(
+                     type='SinePositionalEncoding',
+                     num_feats=128,
+                     normalize=True),
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     bg_cls_weight=0.1,
+                     use_sigmoid=False,
+                     loss_weight=1.0,
+                     class_weight=1.0),
+                 loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+                 loss_iou=dict(type='GIoULoss', loss_weight=2.0),
+                 train_cfg=dict(
+                     assigner=dict(
+                         type='HungarianAssigner',
+                         cls_cost=dict(type='ClassificationCost', weight=1.),
+                         reg_cost=dict(type='BBoxL1Cost', weight=5.0),
+                         iou_cost=dict(
+                             type='IoUCost', iou_mode='giou', weight=2.0))),
+                 test_cfg=dict(max_per_img=100),
+                 **kwargs):
+        # NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
+        # since it brings inconvenience when the initialization of
+        # `AnchorFreeHead` is called.
+        super(AnchorFreeHead, self).__init__()
+        use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
+        assert not use_sigmoid_cls, 'setting use_sigmoid_cls as True is ' \
+            'not supported in DETR, since background is needed for the ' \
+            'matching process.'
+        assert 'embed_dims' in transformer \
+            and 'num_feats' in positional_encoding
+        num_feats = positional_encoding['num_feats']
+        embed_dims = transformer['embed_dims']
+        assert num_feats * 2 == embed_dims, 'embed_dims should' \
+            f' be exactly 2 times of num_feats. Found {embed_dims}' \
+            f' and {num_feats}.'
+        assert test_cfg is not None and 'max_per_img' in test_cfg
+
+        class_weight = loss_cls.get('class_weight', None)
+        if class_weight is not None:
+            assert isinstance(class_weight, float), 'Expected ' \
+                'class_weight to have type float. Found ' \
+                f'{type(class_weight)}.'
+            # NOTE following the official DETR rep0, bg_cls_weight means
+            # relative classification weight of the no-object class.
+            bg_cls_weight = loss_cls.get('bg_cls_weight', class_weight)
+            assert isinstance(bg_cls_weight, float), 'Expected ' \
+                'bg_cls_weight to have type float. Found ' \
+                f'{type(bg_cls_weight)}.'
+            class_weight = torch.ones(num_classes + 1) * class_weight
+            # set background class as the last indice
+            class_weight[num_classes] = bg_cls_weight
+            loss_cls.update({'class_weight': class_weight})
+            if 'bg_cls_weight' in loss_cls:
+                loss_cls.pop('bg_cls_weight')
+            self.bg_cls_weight = bg_cls_weight
+
+        if train_cfg:
+            assert 'assigner' in train_cfg, 'assigner should be provided '\
+                'when train_cfg is set.'
+            assigner = train_cfg['assigner']
+            assert loss_cls['loss_weight'] == assigner['cls_cost']['weight'], \
+                'The classification weight for loss and matcher should be' \
+                'exactly the same.'
+            assert loss_bbox['loss_weight'] == assigner['reg_cost'][
+                'weight'], 'The regression L1 weight for loss and matcher ' \
+                'should be exactly the same.'
+            assert loss_iou['loss_weight'] == assigner['iou_cost']['weight'], \
+                'The regression iou weight for loss and matcher should be' \
+                'exactly the same.'
+            self.assigner = build_assigner(assigner)
+            # DETR sampling=False, so use PseudoSampler
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+        self.num_classes = num_classes
+        self.cls_out_channels = num_classes + 1
+        self.in_channels = in_channels
+        self.num_fcs = num_fcs
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        self.use_sigmoid_cls = use_sigmoid_cls
+        self.embed_dims = embed_dims
+        self.num_query = test_cfg['max_per_img']
+        self.fp16_enabled = False
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox = build_loss(loss_bbox)
+        self.loss_iou = build_loss(loss_iou)
+        self.act_cfg = transformer.get('act_cfg',
+                                       dict(type='ReLU', inplace=True))
+        self.activate = build_activation_layer(self.act_cfg)
+        self.positional_encoding = build_positional_encoding(
+            positional_encoding)
+        self.transformer = build_transformer(transformer)
+        self._init_layers()
+
+    def _init_layers(self):
+        """Initialize layers of the transformer head."""
+        self.input_proj = Conv2d(
+            self.in_channels, self.embed_dims, kernel_size=1)
+        self.fc_cls = Linear(self.embed_dims, self.cls_out_channels)
+        self.reg_ffn = FFN(
+            self.embed_dims,
+            self.embed_dims,
+            self.num_fcs,
+            self.act_cfg,
+            dropout=0.0,
+            add_residual=False)
+        self.fc_reg = Linear(self.embed_dims, 4)
+        self.query_embedding = nn.Embedding(self.num_query, self.embed_dims)
+
+    def init_weights(self, distribution='uniform'):
+        """Initialize weights of the transformer head."""
+        # The initialization for transformer is important
+        self.transformer.init_weights()
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        """load checkpoints."""
+        # NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
+        # since `AnchorFreeHead._load_from_state_dict` should not be
+        # called here. Invoking the default `Module._load_from_state_dict`
+        # is enough.
+        super(AnchorFreeHead,
+              self)._load_from_state_dict(state_dict, prefix, local_metadata,
+                                          strict, missing_keys,
+                                          unexpected_keys, error_msgs)
+
+    def forward(self, feats, img_metas):
+        """Forward function.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels.
+
+                - all_cls_scores_list (list[Tensor]): Classification scores \
+                    for each scale level. Each is a 4D-tensor with shape \
+                    [nb_dec, bs, num_query, cls_out_channels]. Note \
+                    `cls_out_channels` should includes background.
+                - all_bbox_preds_list (list[Tensor]): Sigmoid regression \
+                    outputs for each scale level. Each is a 4D-tensor with \
+                    normalized coordinate format (cx, cy, w, h) and shape \
+                    [nb_dec, bs, num_query, 4].
+        """
+        num_levels = len(feats)
+        img_metas_list = [img_metas for _ in range(num_levels)]
+        return multi_apply(self.forward_single, feats, img_metas_list)
+
+    def forward_single(self, x, img_metas):
+        """"Forward function for a single feature level.
+
+        Args:
+            x (Tensor): Input feature from backbone's single stage, shape
+                [bs, c, h, w].
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            all_cls_scores (Tensor): Outputs from the classification head,
+                shape [nb_dec, bs, num_query, cls_out_channels]. Note
+                cls_out_channels should includes background.
+            all_bbox_preds (Tensor): Sigmoid outputs from the regression
+                head with normalized coordinate format (cx, cy, w, h).
+                Shape [nb_dec, bs, num_query, 4].
+        """
+        # construct binary masks which used for the transformer.
+        # NOTE following the official DETR repo, non-zero values representing
+        # ignored positions, while zero values means valid positions.
+        batch_size = x.size(0)
+        input_img_h, input_img_w = img_metas[0]['batch_input_shape']
+        masks = x.new_ones((batch_size, input_img_h, input_img_w))
+        for img_id in range(batch_size):
+            img_h, img_w, _ = img_metas[img_id]['img_shape']
+            masks[img_id, :img_h, :img_w] = 0
+
+        x = self.input_proj(x)
+        # interpolate masks to have the same spatial shape with x
+        masks = F.interpolate(
+            masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1)
+        # position encoding
+        pos_embed = self.positional_encoding(masks)  # [bs, embed_dim, h, w]
+        # outs_dec: [nb_dec, bs, num_query, embed_dim]
+        outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight,
+                                       pos_embed)
+
+        all_cls_scores = self.fc_cls(outs_dec)
+        all_bbox_preds = self.fc_reg(self.activate(
+            self.reg_ffn(outs_dec))).sigmoid()
+        return all_cls_scores, all_bbox_preds
+
+    @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list'))
+    def loss(self,
+             all_cls_scores_list,
+             all_bbox_preds_list,
+             gt_bboxes_list,
+             gt_labels_list,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """"Loss function.
+
+        Only outputs from the last feature level are used for computing
+        losses by default.
+
+        Args:
+            all_cls_scores_list (list[Tensor]): Classification outputs
+                for each feature level. Each is a 4D-tensor with shape
+                [nb_dec, bs, num_query, cls_out_channels].
+            all_bbox_preds_list (list[Tensor]): Sigmoid regression
+                outputs for each feature level. Each is a 4D-tensor with
+                normalized coordinate format (cx, cy, w, h) and shape
+                [nb_dec, bs, num_query, 4].
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
+                with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image with shape (num_gts, ).
+            img_metas (list[dict]): List of image meta information.
+            gt_bboxes_ignore (list[Tensor], optional): Bounding boxes
+                which can be ignored for each image. Default None.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        # NOTE defaultly only the outputs from the last feature scale is used.
+        all_cls_scores = all_cls_scores_list[-1]
+        all_bbox_preds = all_bbox_preds_list[-1]
+        assert gt_bboxes_ignore is None, \
+            'Only supports for gt_bboxes_ignore setting to None.'
+
+        num_dec_layers = len(all_cls_scores)
+        all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)]
+        all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)]
+        all_gt_bboxes_ignore_list = [
+            gt_bboxes_ignore for _ in range(num_dec_layers)
+        ]
+        img_metas_list = [img_metas for _ in range(num_dec_layers)]
+
+        losses_cls, losses_bbox, losses_iou = multi_apply(
+            self.loss_single, all_cls_scores, all_bbox_preds,
+            all_gt_bboxes_list, all_gt_labels_list, img_metas_list,
+            all_gt_bboxes_ignore_list)
+
+        loss_dict = dict()
+        # loss from the last decoder layer
+        loss_dict['loss_cls'] = losses_cls[-1]
+        loss_dict['loss_bbox'] = losses_bbox[-1]
+        loss_dict['loss_iou'] = losses_iou[-1]
+        # loss from other decoder layers
+        num_dec_layer = 0
+        for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1],
+                                                       losses_bbox[:-1],
+                                                       losses_iou[:-1]):
+            loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i
+            loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i
+            loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i
+            num_dec_layer += 1
+        return loss_dict
+
+    def loss_single(self,
+                    cls_scores,
+                    bbox_preds,
+                    gt_bboxes_list,
+                    gt_labels_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None):
+        """"Loss function for outputs from a single decoder layer of a single
+        feature level.
+
+        Args:
+            cls_scores (Tensor): Box score logits from a single decoder layer
+                for all images. Shape [bs, num_query, cls_out_channels].
+            bbox_preds (Tensor): Sigmoid outputs from a single decoder layer
+                for all images, with normalized coordinate (cx, cy, w, h) and
+                shape [bs, num_query, 4].
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
+                with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image with shape (num_gts, ).
+            img_metas (list[dict]): List of image meta information.
+            gt_bboxes_ignore_list (list[Tensor], optional): Bounding
+                boxes which can be ignored for each image. Default None.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components for outputs from
+                a single decoder layer.
+        """
+        num_imgs = cls_scores.size(0)
+        cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
+        bbox_preds_list = [bbox_preds[i] for i in range(num_imgs)]
+        cls_reg_targets = self.get_targets(cls_scores_list, bbox_preds_list,
+                                           gt_bboxes_list, gt_labels_list,
+                                           img_metas, gt_bboxes_ignore_list)
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg) = cls_reg_targets
+        labels = torch.cat(labels_list, 0)
+        label_weights = torch.cat(label_weights_list, 0)
+        bbox_targets = torch.cat(bbox_targets_list, 0)
+        bbox_weights = torch.cat(bbox_weights_list, 0)
+
+        # classification loss
+        cls_scores = cls_scores.reshape(-1, self.cls_out_channels)
+        # construct weighted avg_factor to match with the official DETR repo
+        cls_avg_factor = num_total_pos * 1.0 + \
+            num_total_neg * self.bg_cls_weight
+        loss_cls = self.loss_cls(
+            cls_scores, labels, label_weights, avg_factor=cls_avg_factor)
+
+        # Compute the average number of gt boxes accross all gpus, for
+        # normalization purposes
+        num_total_pos = loss_cls.new_tensor([num_total_pos])
+        num_total_pos = torch.clamp(reduce_mean(num_total_pos), min=1).item()
+
+        # construct factors used for rescale bboxes
+        factors = []
+        for img_meta, bbox_pred in zip(img_metas, bbox_preds):
+            img_h, img_w, _ = img_meta['img_shape']
+            factor = bbox_pred.new_tensor([img_w, img_h, img_w,
+                                           img_h]).unsqueeze(0).repeat(
+                                               bbox_pred.size(0), 1)
+            factors.append(factor)
+        factors = torch.cat(factors, 0)
+
+        # DETR regress the relative position of boxes (cxcywh) in the image,
+        # thus the learning target is normalized by the image size. So here
+        # we need to re-scale them for calculating IoU loss
+        bbox_preds = bbox_preds.reshape(-1, 4)
+        bboxes = bbox_cxcywh_to_xyxy(bbox_preds) * factors
+        bboxes_gt = bbox_cxcywh_to_xyxy(bbox_targets) * factors
+
+        # regression IoU loss, defaultly GIoU loss
+        loss_iou = self.loss_iou(
+            bboxes, bboxes_gt, bbox_weights, avg_factor=num_total_pos)
+
+        # regression L1 loss
+        loss_bbox = self.loss_bbox(
+            bbox_preds, bbox_targets, bbox_weights, avg_factor=num_total_pos)
+        return loss_cls, loss_bbox, loss_iou
+
+    def get_targets(self,
+                    cls_scores_list,
+                    bbox_preds_list,
+                    gt_bboxes_list,
+                    gt_labels_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None):
+        """"Compute regression and classification targets for a batch image.
+
+        Outputs from a single decoder layer of a single feature level are used.
+
+        Args:
+            cls_scores_list (list[Tensor]): Box score logits from a single
+                decoder layer for each image with shape [num_query,
+                cls_out_channels].
+            bbox_preds_list (list[Tensor]): Sigmoid outputs from a single
+                decoder layer for each image, with normalized coordinate
+                (cx, cy, w, h) and shape [num_query, 4].
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
+                with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image with shape (num_gts, ).
+            img_metas (list[dict]): List of image meta information.
+            gt_bboxes_ignore_list (list[Tensor], optional): Bounding
+                boxes which can be ignored for each image. Default None.
+
+        Returns:
+            tuple: a tuple containing the following targets.
+
+                - labels_list (list[Tensor]): Labels for all images.
+                - label_weights_list (list[Tensor]): Label weights for all \
+                    images.
+                - bbox_targets_list (list[Tensor]): BBox targets for all \
+                    images.
+                - bbox_weights_list (list[Tensor]): BBox weights for all \
+                    images.
+                - num_total_pos (int): Number of positive samples in all \
+                    images.
+                - num_total_neg (int): Number of negative samples in all \
+                    images.
+        """
+        assert gt_bboxes_ignore_list is None, \
+            'Only supports for gt_bboxes_ignore setting to None.'
+        num_imgs = len(cls_scores_list)
+        gt_bboxes_ignore_list = [
+            gt_bboxes_ignore_list for _ in range(num_imgs)
+        ]
+
+        (labels_list, label_weights_list, bbox_targets_list,
+         bbox_weights_list, pos_inds_list, neg_inds_list) = multi_apply(
+             self._get_target_single, cls_scores_list, bbox_preds_list,
+             gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list)
+        num_total_pos = sum((inds.numel() for inds in pos_inds_list))
+        num_total_neg = sum((inds.numel() for inds in neg_inds_list))
+        return (labels_list, label_weights_list, bbox_targets_list,
+                bbox_weights_list, num_total_pos, num_total_neg)
+
+    def _get_target_single(self,
+                           cls_score,
+                           bbox_pred,
+                           gt_bboxes,
+                           gt_labels,
+                           img_meta,
+                           gt_bboxes_ignore=None):
+        """"Compute regression and classification targets for one image.
+
+        Outputs from a single decoder layer of a single feature level are used.
+
+        Args:
+            cls_score (Tensor): Box score logits from a single decoder layer
+                for one image. Shape [num_query, cls_out_channels].
+            bbox_pred (Tensor): Sigmoid outputs from a single decoder layer
+                for one image, with normalized coordinate (cx, cy, w, h) and
+                shape [num_query, 4].
+            gt_bboxes (Tensor): Ground truth bboxes for one image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (Tensor): Ground truth class indices for one image
+                with shape (num_gts, ).
+            img_meta (dict): Meta information for one image.
+            gt_bboxes_ignore (Tensor, optional): Bounding boxes
+                which can be ignored. Default None.
+
+        Returns:
+            tuple[Tensor]: a tuple containing the following for one image.
+
+                - labels (Tensor): Labels of each image.
+                - label_weights (Tensor]): Label weights of each image.
+                - bbox_targets (Tensor): BBox targets of each image.
+                - bbox_weights (Tensor): BBox weights of each image.
+                - pos_inds (Tensor): Sampled positive indices for each image.
+                - neg_inds (Tensor): Sampled negative indices for each image.
+        """
+
+        num_bboxes = bbox_pred.size(0)
+        # assigner and sampler
+        assign_result = self.assigner.assign(bbox_pred, cls_score, gt_bboxes,
+                                             gt_labels, img_meta,
+                                             gt_bboxes_ignore)
+        sampling_result = self.sampler.sample(assign_result, bbox_pred,
+                                              gt_bboxes)
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+
+        # label targets
+        labels = gt_bboxes.new_full((num_bboxes, ),
+                                    self.num_classes,
+                                    dtype=torch.long)
+        labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
+        label_weights = gt_bboxes.new_ones(num_bboxes)
+
+        # bbox targets
+        bbox_targets = torch.zeros_like(bbox_pred)
+        bbox_weights = torch.zeros_like(bbox_pred)
+        bbox_weights[pos_inds] = 1.0
+        img_h, img_w, _ = img_meta['img_shape']
+
+        # DETR regress the relative position of boxes (cxcywh) in the image.
+        # Thus the learning target should be normalized by the image size, also
+        # the box format should be converted from defaultly x1y1x2y2 to cxcywh.
+        factor = bbox_pred.new_tensor([img_w, img_h, img_w,
+                                       img_h]).unsqueeze(0)
+        pos_gt_bboxes_normalized = sampling_result.pos_gt_bboxes / factor
+        pos_gt_bboxes_targets = bbox_xyxy_to_cxcywh(pos_gt_bboxes_normalized)
+        bbox_targets[pos_inds] = pos_gt_bboxes_targets
+        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
+                neg_inds)
+
+    # over-write because img_metas are needed as inputs for bbox_head.
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels=None,
+                      gt_bboxes_ignore=None,
+                      proposal_cfg=None,
+                      **kwargs):
+        """Forward function for training mode.
+
+        Args:
+            x (list[Tensor]): Features from backbone.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+            proposal_cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        assert proposal_cfg is None, '"proposal_cfg" must be None'
+        outs = self(x, img_metas)
+        if gt_labels is None:
+            loss_inputs = outs + (gt_bboxes, img_metas)
+        else:
+            loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
+        losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
+        return losses
+
+    @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list'))
+    def get_bboxes(self,
+                   all_cls_scores_list,
+                   all_bbox_preds_list,
+                   img_metas,
+                   rescale=False):
+        """Transform network outputs for a batch into bbox predictions.
+
+        Args:
+            all_cls_scores_list (list[Tensor]): Classification outputs
+                for each feature level. Each is a 4D-tensor with shape
+                [nb_dec, bs, num_query, cls_out_channels].
+            all_bbox_preds_list (list[Tensor]): Sigmoid regression
+                outputs for each feature level. Each is a 4D-tensor with
+                normalized coordinate format (cx, cy, w, h) and shape
+                [nb_dec, bs, num_query, 4].
+            img_metas (list[dict]): Meta information of each image.
+            rescale (bool, optional): If True, return boxes in original
+                image space. Defalut False.
+
+        Returns:
+            list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \
+                The first item is an (n, 5) tensor, where the first 4 columns \
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the \
+                5-th column is a score between 0 and 1. The second item is a \
+                (n,) tensor where each item is the predicted class label of \
+                the corresponding box.
+        """
+        # NOTE defaultly only using outputs from the last feature level,
+        # and only the ouputs from the last decoder layer is used.
+        cls_scores = all_cls_scores_list[-1][-1]
+        bbox_preds = all_bbox_preds_list[-1][-1]
+
+        result_list = []
+        for img_id in range(len(img_metas)):
+            cls_score = cls_scores[img_id]
+            bbox_pred = bbox_preds[img_id]
+            img_shape = img_metas[img_id]['img_shape']
+            scale_factor = img_metas[img_id]['scale_factor']
+            proposals = self._get_bboxes_single(cls_score, bbox_pred,
+                                                img_shape, scale_factor,
+                                                rescale)
+            result_list.append(proposals)
+        return result_list
+
+    def _get_bboxes_single(self,
+                           cls_score,
+                           bbox_pred,
+                           img_shape,
+                           scale_factor,
+                           rescale=False):
+        """Transform outputs from the last decoder layer into bbox predictions
+        for each image.
+
+        Args:
+            cls_score (Tensor): Box score logits from the last decoder layer
+                for each image. Shape [num_query, cls_out_channels].
+            bbox_pred (Tensor): Sigmoid outputs from the last decoder layer
+                for each image, with coordinate format (cx, cy, w, h) and
+                shape [num_query, 4].
+            img_shape (tuple[int]): Shape of input image, (height, width, 3).
+            scale_factor (ndarray, optional): Scale factor of the image arange
+                as (w_scale, h_scale, w_scale, h_scale).
+            rescale (bool, optional): If True, return boxes in original image
+                space. Default False.
+
+        Returns:
+            tuple[Tensor]: Results of detected bboxes and labels.
+
+                - det_bboxes: Predicted bboxes with shape [num_query, 5], \
+                    where the first 4 columns are bounding box positions \
+                    (tl_x, tl_y, br_x, br_y) and the 5-th column are scores \
+                    between 0 and 1.
+                - det_labels: Predicted labels of the corresponding box with \
+                    shape [num_query].
+        """
+        assert len(cls_score) == len(bbox_pred)
+        # exclude background
+        scores, det_labels = F.softmax(cls_score, dim=-1)[..., :-1].max(-1)
+        det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred)
+        det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1]
+        det_bboxes[:, 1::2] = det_bboxes[:, 1::2] * img_shape[0]
+        det_bboxes[:, 0::2].clamp_(min=0, max=img_shape[1])
+        det_bboxes[:, 1::2].clamp_(min=0, max=img_shape[0])
+        if rescale:
+            det_bboxes /= det_bboxes.new_tensor(scale_factor)
+        det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(1)), -1)
+        return det_bboxes, det_labels

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/dense_heads/vfnet_head.py

+import numpy as np
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
+from mmcv.ops import DeformConv2d
+from mmcv.runner import force_fp32
+
+from mmdet.core import (bbox2distance, bbox_overlaps, build_anchor_generator,
+                        build_assigner, build_sampler, distance2bbox,
+                        multi_apply, multiclass_nms, reduce_mean)
+from ..builder import HEADS, build_loss
+from .atss_head import ATSSHead
+from .fcos_head import FCOSHead
+
+INF = 1e8
+
+
+@HEADS.register_module()
+class VFNetHead(ATSSHead, FCOSHead):
+    """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
+    Detector.<https://arxiv.org/abs/2008.13367>`_.
+
+    The VFNet predicts IoU-aware classification scores which mix the
+    object presence confidence and object localization accuracy as the
+    detection score. It is built on the FCOS architecture and uses ATSS
+    for defining positive/negative training examples. The VFNet is trained
+    with Varifocal Loss and empolys star-shaped deformable convolution to
+    extract features for a bbox.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
+            level points.
+        center_sampling (bool): If true, use center sampling. Default: False.
+        center_sample_radius (float): Radius of center sampling. Default: 1.5.
+        sync_num_pos (bool): If true, synchronize the number of positive
+            examples across GPUs. Default: True
+        gradient_mul (float): The multiplier to gradients from bbox refinement
+            and recognition. Default: 0.1.
+        bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
+            'stride'. Default: reg_denom
+        loss_cls_fl (dict): Config of focal loss.
+        use_vfl (bool): If true, use varifocal loss for training.
+            Default: True.
+        loss_cls (dict): Config of varifocal loss.
+        loss_bbox (dict): Config of localization loss, GIoU Loss.
+        loss_bbox (dict): Config of localization refinement loss, GIoU Loss.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: norm_cfg=dict(type='GN', num_groups=32,
+            requires_grad=True).
+        use_atss (bool): If true, use ATSS to define positive/negative
+            examples. Default: True.
+        anchor_generator (dict): Config of anchor generator for ATSS.
+
+    Example:
+        >>> self = VFNetHead(11, 7)
+        >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
+        >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
+        >>> assert len(cls_score) == len(self.scales)
+    """  # noqa: E501
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512),
+                                 (512, INF)),
+                 center_sampling=False,
+                 center_sample_radius=1.5,
+                 sync_num_pos=True,
+                 gradient_mul=0.1,
+                 bbox_norm_type='reg_denom',
+                 loss_cls_fl=dict(
+                     type='FocalLoss',
+                     use_sigmoid=True,
+                     gamma=2.0,
+                     alpha=0.25,
+                     loss_weight=1.0),
+                 use_vfl=True,
+                 loss_cls=dict(
+                     type='VarifocalLoss',
+                     use_sigmoid=True,
+                     alpha=0.75,
+                     gamma=2.0,
+                     iou_weighted=True,
+                     loss_weight=1.0),
+                 loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
+                 loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0),
+                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
+                 use_atss=True,
+                 anchor_generator=dict(
+                     type='AnchorGenerator',
+                     ratios=[1.0],
+                     octave_base_scale=8,
+                     scales_per_octave=1,
+                     center_offset=0.0,
+                     strides=[8, 16, 32, 64, 128]),
+                 **kwargs):
+        # dcn base offsets, adapted from reppoints_head.py
+        self.num_dconv_points = 9
+        self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
+        self.dcn_pad = int((self.dcn_kernel - 1) / 2)
+        dcn_base = np.arange(-self.dcn_pad,
+                             self.dcn_pad + 1).astype(np.float64)
+        dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
+        dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
+        dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
+            (-1))
+        self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
+
+        super(FCOSHead, self).__init__(
+            num_classes, in_channels, norm_cfg=norm_cfg, **kwargs)
+        self.regress_ranges = regress_ranges
+        self.reg_denoms = [
+            regress_range[-1] for regress_range in regress_ranges
+        ]
+        self.reg_denoms[-1] = self.reg_denoms[-2] * 2
+        self.center_sampling = center_sampling
+        self.center_sample_radius = center_sample_radius
+        self.sync_num_pos = sync_num_pos
+        self.bbox_norm_type = bbox_norm_type
+        self.gradient_mul = gradient_mul
+        self.use_vfl = use_vfl
+        if self.use_vfl:
+            self.loss_cls = build_loss(loss_cls)
+        else:
+            self.loss_cls = build_loss(loss_cls_fl)
+        self.loss_bbox = build_loss(loss_bbox)
+        self.loss_bbox_refine = build_loss(loss_bbox_refine)
+
+        # for getting ATSS targets
+        self.use_atss = use_atss
+        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
+        self.anchor_generator = build_anchor_generator(anchor_generator)
+        self.anchor_center_offset = anchor_generator['center_offset']
+        self.num_anchors = self.anchor_generator.num_base_anchors[0]
+        self.sampling = False
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        super(FCOSHead, self)._init_cls_convs()
+        super(FCOSHead, self)._init_reg_convs()
+        self.relu = nn.ReLU(inplace=True)
+        self.vfnet_reg_conv = ConvModule(
+            self.feat_channels,
+            self.feat_channels,
+            3,
+            stride=1,
+            padding=1,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            bias=self.conv_bias)
+        self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
+        self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
+
+        self.vfnet_reg_refine_dconv = DeformConv2d(
+            self.feat_channels,
+            self.feat_channels,
+            self.dcn_kernel,
+            1,
+            padding=self.dcn_pad)
+        self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
+        self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
+
+        self.vfnet_cls_dconv = DeformConv2d(
+            self.feat_channels,
+            self.feat_channels,
+            self.dcn_kernel,
+            1,
+            padding=self.dcn_pad)
+        self.vfnet_cls = nn.Conv2d(
+            self.feat_channels, self.cls_out_channels, 3, padding=1)
+
+    def init_weights(self):
+        """Initialize weights of the head."""
+        for m in self.cls_convs:
+            if isinstance(m.conv, nn.Conv2d):
+                normal_init(m.conv, std=0.01)
+        for m in self.reg_convs:
+            if isinstance(m.conv, nn.Conv2d):
+                normal_init(m.conv, std=0.01)
+        normal_init(self.vfnet_reg_conv.conv, std=0.01)
+        normal_init(self.vfnet_reg, std=0.01)
+        normal_init(self.vfnet_reg_refine_dconv, std=0.01)
+        normal_init(self.vfnet_reg_refine, std=0.01)
+        normal_init(self.vfnet_cls_dconv, std=0.01)
+        bias_cls = bias_init_with_prob(0.01)
+        normal_init(self.vfnet_cls, std=0.01, bias=bias_cls)
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple:
+                cls_scores (list[Tensor]): Box iou-aware scores for each scale
+                    level, each is a 4D-tensor, the channel number is
+                    num_points * num_classes.
+                bbox_preds (list[Tensor]): Box offsets for each
+                    scale level, each is a 4D-tensor, the channel number is
+                    num_points * 4.
+                bbox_preds_refine (list[Tensor]): Refined Box offsets for
+                    each scale level, each is a 4D-tensor, the channel
+                    number is num_points * 4.
+        """
+        return multi_apply(self.forward_single, feats, self.scales,
+                           self.scales_refine, self.strides, self.reg_denoms)
+
+    def forward_single(self, x, scale, scale_refine, stride, reg_denom):
+        """Forward features of a single scale level.
+
+        Args:
+            x (Tensor): FPN feature maps of the specified stride.
+            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
+                the bbox prediction.
+            scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
+                resize the refined bbox prediction.
+            stride (int): The corresponding stride for feature maps,
+                used to normalize the bbox prediction when
+                bbox_norm_type = 'stride'.
+            reg_denom (int): The corresponding regression range for feature
+                maps, only used to normalize the bbox prediction when
+                bbox_norm_type = 'reg_denom'.
+
+        Returns:
+            tuple: iou-aware cls scores for each box, bbox predictions and
+                refined bbox predictions of input feature maps.
+        """
+        cls_feat = x
+        reg_feat = x
+
+        for cls_layer in self.cls_convs:
+            cls_feat = cls_layer(cls_feat)
+
+        for reg_layer in self.reg_convs:
+            reg_feat = reg_layer(reg_feat)
+
+        # predict the bbox_pred of different level
+        reg_feat_init = self.vfnet_reg_conv(reg_feat)
+        if self.bbox_norm_type == 'reg_denom':
+            bbox_pred = scale(
+                self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
+        elif self.bbox_norm_type == 'stride':
+            bbox_pred = scale(
+                self.vfnet_reg(reg_feat_init)).float().exp() * stride
+        else:
+            raise NotImplementedError
+
+        # compute star deformable convolution offsets
+        # converting dcn_offset to reg_feat.dtype thus VFNet can be
+        # trained with FP16
+        dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
+                                          stride).to(reg_feat.dtype)
+
+        # refine the bbox_pred
+        reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
+        bbox_pred_refine = scale_refine(
+            self.vfnet_reg_refine(reg_feat)).float().exp()
+        bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
+
+        # predict the iou-aware cls score
+        cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
+        cls_score = self.vfnet_cls(cls_feat)
+
+        return cls_score, bbox_pred, bbox_pred_refine
+
+    def star_dcn_offset(self, bbox_pred, gradient_mul, stride):
+        """Compute the star deformable conv offsets.
+
+        Args:
+            bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
+            gradient_mul (float): Gradient multiplier.
+            stride (int): The corresponding stride for feature maps,
+                used to project the bbox onto the feature map.
+
+        Returns:
+            dcn_offsets (Tensor): The offsets for deformable convolution.
+        """
+        dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
+        bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
+            gradient_mul * bbox_pred
+        # map to the feature map scale
+        bbox_pred_grad_mul = bbox_pred_grad_mul / stride
+        N, C, H, W = bbox_pred.size()
+
+        x1 = bbox_pred_grad_mul[:, 0, :, :]
+        y1 = bbox_pred_grad_mul[:, 1, :, :]
+        x2 = bbox_pred_grad_mul[:, 2, :, :]
+        y2 = bbox_pred_grad_mul[:, 3, :, :]
+        bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
+            N, 2 * self.num_dconv_points, H, W)
+        bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1  # -y1
+        bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1  # -x1
+        bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1  # -y1
+        bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1  # -y1
+        bbox_pred_grad_mul_offset[:, 5, :, :] = x2  # x2
+        bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1  # -x1
+        bbox_pred_grad_mul_offset[:, 11, :, :] = x2  # x2
+        bbox_pred_grad_mul_offset[:, 12, :, :] = y2  # y2
+        bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1  # -x1
+        bbox_pred_grad_mul_offset[:, 14, :, :] = y2  # y2
+        bbox_pred_grad_mul_offset[:, 16, :, :] = y2  # y2
+        bbox_pred_grad_mul_offset[:, 17, :, :] = x2  # x2
+        dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
+
+        return dcn_offset
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             bbox_preds_refine,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute loss of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box iou-aware scores for each scale
+                level, each is a 4D-tensor, the channel number is
+                num_points * num_classes.
+            bbox_preds (list[Tensor]): Box offsets for each
+                scale level, each is a 4D-tensor, the channel number is
+                num_points * 4.
+            bbox_preds_refine (list[Tensor]): Refined Box offsets for
+                each scale level, each is a 4D-tensor, the channel
+                number is num_points * 4.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+                Default: None.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
+                                           bbox_preds[0].device)
+        labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
+            cls_scores, all_level_points, gt_bboxes, gt_labels, img_metas,
+            gt_bboxes_ignore)
+
+        num_imgs = cls_scores[0].size(0)
+        # flatten cls_scores, bbox_preds and bbox_preds_refine
+        flatten_cls_scores = [
+            cls_score.permute(0, 2, 3,
+                              1).reshape(-1,
+                                         self.cls_out_channels).contiguous()
+            for cls_score in cls_scores
+        ]
+        flatten_bbox_preds = [
+            bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
+            for bbox_pred in bbox_preds
+        ]
+        flatten_bbox_preds_refine = [
+            bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
+            for bbox_pred_refine in bbox_preds_refine
+        ]
+        flatten_cls_scores = torch.cat(flatten_cls_scores)
+        flatten_bbox_preds = torch.cat(flatten_bbox_preds)
+        flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
+        flatten_labels = torch.cat(labels)
+        flatten_bbox_targets = torch.cat(bbox_targets)
+        # repeat points to align with bbox_preds
+        flatten_points = torch.cat(
+            [points.repeat(num_imgs, 1) for points in all_level_points])
+
+        # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
+        bg_class_ind = self.num_classes
+        pos_inds = torch.where(
+            ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
+        num_pos = len(pos_inds)
+
+        pos_bbox_preds = flatten_bbox_preds[pos_inds]
+        pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
+        pos_labels = flatten_labels[pos_inds]
+
+        # sync num_pos across all gpus
+        if self.sync_num_pos:
+            num_pos_avg_per_gpu = reduce_mean(
+                pos_inds.new_tensor(num_pos).float()).item()
+            num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
+        else:
+            num_pos_avg_per_gpu = num_pos
+
+        if num_pos > 0:
+            pos_bbox_targets = flatten_bbox_targets[pos_inds]
+            pos_points = flatten_points[pos_inds]
+
+            pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds)
+            pos_decoded_target_preds = distance2bbox(pos_points,
+                                                     pos_bbox_targets)
+            iou_targets_ini = bbox_overlaps(
+                pos_decoded_bbox_preds,
+                pos_decoded_target_preds.detach(),
+                is_aligned=True).clamp(min=1e-6)
+            bbox_weights_ini = iou_targets_ini.clone().detach()
+            iou_targets_ini_avg_per_gpu = reduce_mean(
+                bbox_weights_ini.sum()).item()
+            bbox_avg_factor_ini = max(iou_targets_ini_avg_per_gpu, 1.0)
+            loss_bbox = self.loss_bbox(
+                pos_decoded_bbox_preds,
+                pos_decoded_target_preds.detach(),
+                weight=bbox_weights_ini,
+                avg_factor=bbox_avg_factor_ini)
+
+            pos_decoded_bbox_preds_refine = \
+                distance2bbox(pos_points, pos_bbox_preds_refine)
+            iou_targets_rf = bbox_overlaps(
+                pos_decoded_bbox_preds_refine,
+                pos_decoded_target_preds.detach(),
+                is_aligned=True).clamp(min=1e-6)
+            bbox_weights_rf = iou_targets_rf.clone().detach()
+            iou_targets_rf_avg_per_gpu = reduce_mean(
+                bbox_weights_rf.sum()).item()
+            bbox_avg_factor_rf = max(iou_targets_rf_avg_per_gpu, 1.0)
+            loss_bbox_refine = self.loss_bbox_refine(
+                pos_decoded_bbox_preds_refine,
+                pos_decoded_target_preds.detach(),
+                weight=bbox_weights_rf,
+                avg_factor=bbox_avg_factor_rf)
+
+            # build IoU-aware cls_score targets
+            if self.use_vfl:
+                pos_ious = iou_targets_rf.clone().detach()
+                cls_iou_targets = torch.zeros_like(flatten_cls_scores)
+                cls_iou_targets[pos_inds, pos_labels] = pos_ious
+        else:
+            loss_bbox = pos_bbox_preds.sum() * 0
+            loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
+            if self.use_vfl:
+                cls_iou_targets = torch.zeros_like(flatten_cls_scores)
+
+        if self.use_vfl:
+            loss_cls = self.loss_cls(
+                flatten_cls_scores,
+                cls_iou_targets,
+                avg_factor=num_pos_avg_per_gpu)
+        else:
+            loss_cls = self.loss_cls(
+                flatten_cls_scores,
+                flatten_labels,
+                weight=label_weights,
+                avg_factor=num_pos_avg_per_gpu)
+
+        return dict(
+            loss_cls=loss_cls,
+            loss_bbox=loss_bbox,
+            loss_bbox_rf=loss_bbox_refine)
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
+    def get_bboxes(self,
+                   cls_scores,
+                   bbox_preds,
+                   bbox_preds_refine,
+                   img_metas,
+                   cfg=None,
+                   rescale=None,
+                   with_nms=True):
+        """Transform network outputs for a batch into bbox predictions.
+
+        Args:
+            cls_scores (list[Tensor]): Box iou-aware scores for each scale
+                level with shape (N, num_points * num_classes, H, W).
+            bbox_preds (list[Tensor]): Box offsets for each scale
+                level with shape (N, num_points * 4, H, W).
+            bbox_preds_refine (list[Tensor]): Refined Box offsets for
+                each scale level with shape (N, num_points * 4, H, W).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used. Default: None.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before returning boxes.
+                Default: True.
+
+        Returns:
+            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is an (n, 5) tensor, where the first 4 columns
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
+                5-th column is a score between 0 and 1. The second item is a
+                (n,) tensor where each item is the predicted class label of
+                the corresponding box.
+        """
+        assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
+        num_levels = len(cls_scores)
+
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        mlvl_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
+                                      bbox_preds[0].device)
+        result_list = []
+        for img_id in range(len(img_metas)):
+            cls_score_list = [
+                cls_scores[i][img_id].detach() for i in range(num_levels)
+            ]
+            bbox_pred_list = [
+                bbox_preds_refine[i][img_id].detach()
+                for i in range(num_levels)
+            ]
+            img_shape = img_metas[img_id]['img_shape']
+            scale_factor = img_metas[img_id]['scale_factor']
+            det_bboxes = self._get_bboxes_single(cls_score_list,
+                                                 bbox_pred_list, mlvl_points,
+                                                 img_shape, scale_factor, cfg,
+                                                 rescale, with_nms)
+            result_list.append(det_bboxes)
+        return result_list
+
+    def _get_bboxes_single(self,
+                           cls_scores,
+                           bbox_preds,
+                           mlvl_points,
+                           img_shape,
+                           scale_factor,
+                           cfg,
+                           rescale=False,
+                           with_nms=True):
+        """Transform outputs for a single batch item into bbox predictions.
+
+        Args:
+            cls_scores (list[Tensor]): Box iou-aware scores for a single scale
+                level with shape (num_points * num_classes, H, W).
+            bbox_preds (list[Tensor]): Box offsets for a single scale
+                level with shape (num_points * 4, H, W).
+            mlvl_points (list[Tensor]): Box reference for a single scale level
+                with shape (num_total_points, 4).
+            img_shape (tuple[int]): Shape of the input image,
+                (height, width, 3).
+            scale_factor (ndarray): Scale factor of the image arrange as
+                (w_scale, h_scale, w_scale, h_scale).
+            cfg (mmcv.Config | None): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before returning boxes.
+                Default: True.
+
+        Returns:
+            tuple(Tensor):
+                det_bboxes (Tensor): BBox predictions in shape (n, 5), where
+                    the first 4 columns are bounding box positions
+                    (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
+                    between 0 and 1.
+                det_labels (Tensor): A (n,) tensor where each item is the
+                    predicted class label of the corresponding box.
+        """
+        cfg = self.test_cfg if cfg is None else cfg
+        assert len(cls_scores) == len(bbox_preds) == len(mlvl_points)
+        mlvl_bboxes = []
+        mlvl_scores = []
+        for cls_score, bbox_pred, points in zip(cls_scores, bbox_preds,
+                                                mlvl_points):
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+            scores = cls_score.permute(1, 2, 0).reshape(
+                -1, self.cls_out_channels).contiguous().sigmoid()
+            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).contiguous()
+
+            nms_pre = cfg.get('nms_pre', -1)
+            if 0 < nms_pre < scores.shape[0]:
+                max_scores, _ = scores.max(dim=1)
+                _, topk_inds = max_scores.topk(nms_pre)
+                points = points[topk_inds, :]
+                bbox_pred = bbox_pred[topk_inds, :]
+                scores = scores[topk_inds, :]
+            bboxes = distance2bbox(points, bbox_pred, max_shape=img_shape)
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+        mlvl_bboxes = torch.cat(mlvl_bboxes)
+        if rescale:
+            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
+        mlvl_scores = torch.cat(mlvl_scores)
+        padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
+        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
+        # BG cat_id: num_class
+        mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
+        if with_nms:
+            det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
+                                                    cfg.score_thr, cfg.nms,
+                                                    cfg.max_per_img)
+            return det_bboxes, det_labels
+        else:
+            return mlvl_bboxes, mlvl_scores
+
+    def _get_points_single(self,
+                           featmap_size,
+                           stride,
+                           dtype,
+                           device,
+                           flatten=False):
+        """Get points according to feature map sizes."""
+        h, w = featmap_size
+        x_range = torch.arange(
+            0, w * stride, stride, dtype=dtype, device=device)
+        y_range = torch.arange(
+            0, h * stride, stride, dtype=dtype, device=device)
+        y, x = torch.meshgrid(y_range, x_range)
+        # to be compatible with anchor points in ATSS
+        if self.use_atss:
+            points = torch.stack(
+                (x.reshape(-1), y.reshape(-1)), dim=-1) + \
+                     stride * self.anchor_center_offset
+        else:
+            points = torch.stack(
+                (x.reshape(-1), y.reshape(-1)), dim=-1) + stride // 2
+        return points
+
+    def get_targets(self, cls_scores, mlvl_points, gt_bboxes, gt_labels,
+                    img_metas, gt_bboxes_ignore):
+        """A wrapper for computing ATSS and FCOS targets for points in multiple
+        images.
+
+        Args:
+            cls_scores (list[Tensor]): Box iou-aware scores for each scale
+                level with shape (N, num_points * num_classes, H, W).
+            mlvl_points (list[Tensor]): Points of each fpn level, each has
+                shape (num_points, 2).
+            gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
+                each has shape (num_gt, 4).
+            gt_labels (list[Tensor]): Ground truth labels of each box,
+                each has shape (num_gt,).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+
+        Returns:
+            tuple:
+                labels_list (list[Tensor]): Labels of each level.
+                label_weights (Tensor/None): Label weights of all levels.
+                bbox_targets_list (list[Tensor]): Regression targets of each
+                    level, (l, t, r, b).
+                bbox_weights (Tensor/None): Bbox weights of all levels.
+        """
+        if self.use_atss:
+            return self.get_atss_targets(cls_scores, mlvl_points, gt_bboxes,
+                                         gt_labels, img_metas,
+                                         gt_bboxes_ignore)
+        else:
+            self.norm_on_bbox = False
+            return self.get_fcos_targets(mlvl_points, gt_bboxes, gt_labels)
+
+    def _get_target_single(self, *args, **kwargs):
+        """Avoid ambiguity in multiple inheritance."""
+        if self.use_atss:
+            return ATSSHead._get_target_single(self, *args, **kwargs)
+        else:
+            return FCOSHead._get_target_single(self, *args, **kwargs)
+
+    def get_fcos_targets(self, points, gt_bboxes_list, gt_labels_list):
+        """Compute FCOS regression and classification targets for points in
+        multiple images.
+
+        Args:
+            points (list[Tensor]): Points of each fpn level, each has shape
+                (num_points, 2).
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
+                each has shape (num_gt, 4).
+            gt_labels_list (list[Tensor]): Ground truth labels of each box,
+                each has shape (num_gt,).
+
+        Returns:
+            tuple:
+                labels (list[Tensor]): Labels of each level.
+                label_weights: None, to be compatible with ATSS targets.
+                bbox_targets (list[Tensor]): BBox targets of each level.
+                bbox_weights: None, to be compatible with ATSS targets.
+        """
+        labels, bbox_targets = FCOSHead.get_targets(self, points,
+                                                    gt_bboxes_list,
+                                                    gt_labels_list)
+        label_weights = None
+        bbox_weights = None
+        return labels, label_weights, bbox_targets, bbox_weights
+
+    def get_atss_targets(self,
+                         cls_scores,
+                         mlvl_points,
+                         gt_bboxes,
+                         gt_labels,
+                         img_metas,
+                         gt_bboxes_ignore=None):
+        """A wrapper for computing ATSS targets for points in multiple images.
+
+        Args:
+            cls_scores (list[Tensor]): Box iou-aware scores for each scale
+                level with shape (N, num_points * num_classes, H, W).
+            mlvl_points (list[Tensor]): Points of each fpn level, each has
+                shape (num_points, 2).
+            gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
+                each has shape (num_gt, 4).
+            gt_labels (list[Tensor]): Ground truth labels of each box,
+                each has shape (num_gt,).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4). Default: None.
+
+        Returns:
+            tuple:
+                labels_list (list[Tensor]): Labels of each level.
+                label_weights (Tensor): Label weights of all levels.
+                bbox_targets_list (list[Tensor]): Regression targets of each
+                    level, (l, t, r, b).
+                bbox_weights (Tensor): Bbox weights of all levels.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.anchor_generator.num_levels
+
+        device = cls_scores[0].device
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+
+        cls_reg_targets = ATSSHead.get_targets(
+            self,
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels,
+            unmap_outputs=True)
+        if cls_reg_targets is None:
+            return None
+
+        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
+         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
+
+        bbox_targets_list = [
+            bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
+        ]
+
+        num_imgs = len(img_metas)
+        # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
+        bbox_targets_list = self.transform_bbox_targets(
+            bbox_targets_list, mlvl_points, num_imgs)
+
+        labels_list = [labels.reshape(-1) for labels in labels_list]
+        label_weights_list = [
+            label_weights.reshape(-1) for label_weights in label_weights_list
+        ]
+        bbox_weights_list = [
+            bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
+        ]
+        label_weights = torch.cat(label_weights_list)
+        bbox_weights = torch.cat(bbox_weights_list)
+        return labels_list, label_weights, bbox_targets_list, bbox_weights
+
+    def transform_bbox_targets(self, decoded_bboxes, mlvl_points, num_imgs):
+        """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
+
+        Args:
+            decoded_bboxes (list[Tensor]): Regression targets of each level,
+                in the form of (x1, y1, x2, y2).
+            mlvl_points (list[Tensor]): Points of each fpn level, each has
+                shape (num_points, 2).
+            num_imgs (int): the number of images in a batch.
+
+        Returns:
+            bbox_targets (list[Tensor]): Regression targets of each level in
+                the form of (l, t, r, b).
+        """
+        # TODO: Re-implemented in Class PointCoder
+        assert len(decoded_bboxes) == len(mlvl_points)
+        num_levels = len(decoded_bboxes)
+        mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
+        bbox_targets = []
+        for i in range(num_levels):
+            bbox_target = bbox2distance(mlvl_points[i], decoded_bboxes[i])
+            bbox_targets.append(bbox_target)
+
+        return bbox_targets
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        """Override the method in the parent class to avoid changing para's
+        name."""
+        pass

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/dense_heads/yolact_head.py

+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule, xavier_init
+from mmcv.runner import force_fp32
+
+from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply
+from ..builder import HEADS, build_loss
+from .anchor_head import AnchorHead
+
+
+@HEADS.register_module()
+class YOLACTHead(AnchorHead):
+    """YOLACT box head used in https://arxiv.org/abs/1904.02689.
+
+    Note that YOLACT head is a light version of RetinaNet head.
+    Four differences are described as follows:
+
+    1. YOLACT box head has three-times fewer anchors.
+    2. YOLACT box head shares the convs for box and cls branches.
+    3. YOLACT box head uses OHEM instead of Focal loss.
+    4. YOLACT box head predicts a set of mask coefficients for each box.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        anchor_generator (dict): Config dict for anchor generator
+        loss_cls (dict): Config of classification loss.
+        loss_bbox (dict): Config of localization loss.
+        num_head_convs (int): Number of the conv layers shared by
+            box and cls branches.
+        num_protos (int): Number of the mask coefficients.
+        use_ohem (bool): If true, ``loss_single_OHEM`` will be used for
+            cls loss calculation. If false, ``loss_single`` will be used.
+        conv_cfg (dict): Dictionary to construct and config conv layer.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 anchor_generator=dict(
+                     type='AnchorGenerator',
+                     octave_base_scale=3,
+                     scales_per_octave=1,
+                     ratios=[0.5, 1.0, 2.0],
+                     strides=[8, 16, 32, 64, 128]),
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=False,
+                     reduction='none',
+                     loss_weight=1.0),
+                 loss_bbox=dict(
+                     type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
+                 num_head_convs=1,
+                 num_protos=32,
+                 use_ohem=True,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 **kwargs):
+        self.num_head_convs = num_head_convs
+        self.num_protos = num_protos
+        self.use_ohem = use_ohem
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        super(YOLACTHead, self).__init__(
+            num_classes,
+            in_channels,
+            loss_cls=loss_cls,
+            loss_bbox=loss_bbox,
+            anchor_generator=anchor_generator,
+            **kwargs)
+        if self.use_ohem:
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+            self.sampling = False
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.relu = nn.ReLU(inplace=True)
+        self.head_convs = nn.ModuleList()
+        for i in range(self.num_head_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.head_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+        self.conv_cls = nn.Conv2d(
+            self.feat_channels,
+            self.num_anchors * self.cls_out_channels,
+            3,
+            padding=1)
+        self.conv_reg = nn.Conv2d(
+            self.feat_channels, self.num_anchors * 4, 3, padding=1)
+        self.conv_coeff = nn.Conv2d(
+            self.feat_channels,
+            self.num_anchors * self.num_protos,
+            3,
+            padding=1)
+
+    def init_weights(self):
+        """Initialize weights of the head."""
+        for m in self.head_convs:
+            xavier_init(m.conv, distribution='uniform', bias=0)
+        xavier_init(self.conv_cls, distribution='uniform', bias=0)
+        xavier_init(self.conv_reg, distribution='uniform', bias=0)
+        xavier_init(self.conv_coeff, distribution='uniform', bias=0)
+
+    def forward_single(self, x):
+        """Forward feature of a single scale level.
+
+        Args:
+            x (Tensor): Features of a single scale level.
+
+        Returns:
+            tuple:
+                cls_score (Tensor): Cls scores for a single scale level \
+                    the channels number is num_anchors * num_classes.
+                bbox_pred (Tensor): Box energies / deltas for a single scale \
+                    level, the channels number is num_anchors * 4.
+                coeff_pred (Tensor): Mask coefficients for a single scale \
+                    level, the channels number is num_anchors * num_protos.
+        """
+        for head_conv in self.head_convs:
+            x = head_conv(x)
+        cls_score = self.conv_cls(x)
+        bbox_pred = self.conv_reg(x)
+        coeff_pred = self.conv_coeff(x).tanh()
+        return cls_score, bbox_pred, coeff_pred
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """A combination of the func:``AnchorHead.loss`` and
+        func:``SSDHead.loss``.
+
+        When ``self.use_ohem == True``, it functions like ``SSDHead.loss``,
+        otherwise, it follows ``AnchorHead.loss``. Besides, it additionally
+        returns ``sampling_results``.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): Class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
+                boxes can be ignored when computing the loss. Default: None
+
+        Returns:
+            tuple:
+                dict[str, Tensor]: A dictionary of loss components.
+                List[:obj:``SamplingResult``]: Sampler results for each image.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.anchor_generator.num_levels
+
+        device = cls_scores[0].device
+
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels,
+            unmap_outputs=not self.use_ohem,
+            return_sampling_results=True)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg, sampling_results) = cls_reg_targets
+
+        if self.use_ohem:
+            num_images = len(img_metas)
+            all_cls_scores = torch.cat([
+                s.permute(0, 2, 3, 1).reshape(
+                    num_images, -1, self.cls_out_channels) for s in cls_scores
+            ], 1)
+            all_labels = torch.cat(labels_list, -1).view(num_images, -1)
+            all_label_weights = torch.cat(label_weights_list,
+                                          -1).view(num_images, -1)
+            all_bbox_preds = torch.cat([
+                b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
+                for b in bbox_preds
+            ], -2)
+            all_bbox_targets = torch.cat(bbox_targets_list,
+                                         -2).view(num_images, -1, 4)
+            all_bbox_weights = torch.cat(bbox_weights_list,
+                                         -2).view(num_images, -1, 4)
+
+            # concat all level anchors to a single tensor
+            all_anchors = []
+            for i in range(num_images):
+                all_anchors.append(torch.cat(anchor_list[i]))
+
+            # check NaN and Inf
+            assert torch.isfinite(all_cls_scores).all().item(), \
+                'classification scores become infinite or NaN!'
+            assert torch.isfinite(all_bbox_preds).all().item(), \
+                'bbox predications become infinite or NaN!'
+
+            losses_cls, losses_bbox = multi_apply(
+                self.loss_single_OHEM,
+                all_cls_scores,
+                all_bbox_preds,
+                all_anchors,
+                all_labels,
+                all_label_weights,
+                all_bbox_targets,
+                all_bbox_weights,
+                num_total_samples=num_total_pos)
+        else:
+            num_total_samples = (
+                num_total_pos +
+                num_total_neg if self.sampling else num_total_pos)
+
+            # anchor number of multi levels
+            num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+            # concat all level anchors and flags to a single tensor
+            concat_anchor_list = []
+            for i in range(len(anchor_list)):
+                concat_anchor_list.append(torch.cat(anchor_list[i]))
+            all_anchor_list = images_to_levels(concat_anchor_list,
+                                               num_level_anchors)
+            losses_cls, losses_bbox = multi_apply(
+                self.loss_single,
+                cls_scores,
+                bbox_preds,
+                all_anchor_list,
+                labels_list,
+                label_weights_list,
+                bbox_targets_list,
+                bbox_weights_list,
+                num_total_samples=num_total_samples)
+
+        return dict(
+            loss_cls=losses_cls, loss_bbox=losses_bbox), sampling_results
+
+    def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels,
+                         label_weights, bbox_targets, bbox_weights,
+                         num_total_samples):
+        """"See func:``SSDHead.loss``."""
+        loss_cls_all = self.loss_cls(cls_score, labels, label_weights)
+
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        pos_inds = ((labels >= 0) &
+                    (labels < self.num_classes)).nonzero().reshape(-1)
+        neg_inds = (labels == self.num_classes).nonzero().view(-1)
+
+        num_pos_samples = pos_inds.size(0)
+        if num_pos_samples == 0:
+            num_neg_samples = neg_inds.size(0)
+        else:
+            num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
+            if num_neg_samples > neg_inds.size(0):
+                num_neg_samples = neg_inds.size(0)
+        topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
+        loss_cls_pos = loss_cls_all[pos_inds].sum()
+        loss_cls_neg = topk_loss_cls_neg.sum()
+        loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
+        if self.reg_decoded_bbox:
+            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
+            # is applied directly on the decoded bounding boxes, it
+            # decodes the already encoded coordinates to absolute format.
+            bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
+        loss_bbox = self.loss_bbox(
+            bbox_pred,
+            bbox_targets,
+            bbox_weights,
+            avg_factor=num_total_samples)
+        return loss_cls[None], loss_bbox
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'coeff_preds'))
+    def get_bboxes(self,
+                   cls_scores,
+                   bbox_preds,
+                   coeff_preds,
+                   img_metas,
+                   cfg=None,
+                   rescale=False):
+        """"Similiar to func:``AnchorHead.get_bboxes``, but additionally
+        processes coeff_preds.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                with shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            coeff_preds (list[Tensor]): Mask coefficients for each scale
+                level with shape (N, num_anchors * num_protos, H, W)
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            cfg (mmcv.Config | None): Test / postprocessing configuration,
+                if None, test_cfg would be used
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+
+        Returns:
+            list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is
+                a 3-tuple. The first item is an (n, 5) tensor, where the
+                first 4 columns are bounding box positions
+                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
+                between 0 and 1. The second item is an (n,) tensor where each
+                item is the predicted class label of the corresponding box.
+                The third item is an (n, num_protos) tensor where each item
+                is the predicted mask coefficients of instance inside the
+                corresponding box.
+        """
+        assert len(cls_scores) == len(bbox_preds)
+        num_levels = len(cls_scores)
+
+        device = cls_scores[0].device
+        featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
+        mlvl_anchors = self.anchor_generator.grid_anchors(
+            featmap_sizes, device=device)
+
+        det_bboxes = []
+        det_labels = []
+        det_coeffs = []
+        for img_id in range(len(img_metas)):
+            cls_score_list = [
+                cls_scores[i][img_id].detach() for i in range(num_levels)
+            ]
+            bbox_pred_list = [
+                bbox_preds[i][img_id].detach() for i in range(num_levels)
+            ]
+            coeff_pred_list = [
+                coeff_preds[i][img_id].detach() for i in range(num_levels)
+            ]
+            img_shape = img_metas[img_id]['img_shape']
+            scale_factor = img_metas[img_id]['scale_factor']
+            bbox_res = self._get_bboxes_single(cls_score_list, bbox_pred_list,
+                                               coeff_pred_list, mlvl_anchors,
+                                               img_shape, scale_factor, cfg,
+                                               rescale)
+            det_bboxes.append(bbox_res[0])
+            det_labels.append(bbox_res[1])
+            det_coeffs.append(bbox_res[2])
+        return det_bboxes, det_labels, det_coeffs
+
+    def _get_bboxes_single(self,
+                           cls_score_list,
+                           bbox_pred_list,
+                           coeff_preds_list,
+                           mlvl_anchors,
+                           img_shape,
+                           scale_factor,
+                           cfg,
+                           rescale=False):
+        """"Similiar to func:``AnchorHead._get_bboxes_single``, but
+        additionally processes coeff_preds_list and uses fast NMS instead of
+        traditional NMS.
+
+        Args:
+            cls_score_list (list[Tensor]): Box scores for a single scale level
+                Has shape (num_anchors * num_classes, H, W).
+            bbox_pred_list (list[Tensor]): Box energies / deltas for a single
+                scale level with shape (num_anchors * 4, H, W).
+            coeff_preds_list (list[Tensor]): Mask coefficients for a single
+                scale level with shape (num_anchors * num_protos, H, W).
+            mlvl_anchors (list[Tensor]): Box reference for a single scale level
+                with shape (num_total_anchors, 4).
+            img_shape (tuple[int]): Shape of the input image,
+                (height, width, 3).
+            scale_factor (ndarray): Scale factor of the image arange as
+                (w_scale, h_scale, w_scale, h_scale).
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+
+        Returns:
+            tuple[Tensor, Tensor, Tensor]: The first item is an (n, 5) tensor,
+                where the first 4 columns are bounding box positions
+                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score between
+                0 and 1. The second item is an (n,) tensor where each item is
+                the predicted class label of the corresponding box. The third
+                item is an (n, num_protos) tensor where each item is the
+                predicted mask coefficients of instance inside the
+                corresponding box.
+        """
+        cfg = self.test_cfg if cfg is None else cfg
+        assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors)
+        mlvl_bboxes = []
+        mlvl_scores = []
+        mlvl_coeffs = []
+        for cls_score, bbox_pred, coeff_pred, anchors in \
+                zip(cls_score_list, bbox_pred_list,
+                    coeff_preds_list, mlvl_anchors):
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+            cls_score = cls_score.permute(1, 2,
+                                          0).reshape(-1, self.cls_out_channels)
+            if self.use_sigmoid_cls:
+                scores = cls_score.sigmoid()
+            else:
+                scores = cls_score.softmax(-1)
+            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
+            coeff_pred = coeff_pred.permute(1, 2,
+                                            0).reshape(-1, self.num_protos)
+            nms_pre = cfg.get('nms_pre', -1)
+            if nms_pre > 0 and scores.shape[0] > nms_pre:
+                # Get maximum scores for foreground classes.
+                if self.use_sigmoid_cls:
+                    max_scores, _ = scores.max(dim=1)
+                else:
+                    # remind that we set FG labels to [0, num_class-1]
+                    # since mmdet v2.0
+                    # BG cat_id: num_class
+                    max_scores, _ = scores[:, :-1].max(dim=1)
+                _, topk_inds = max_scores.topk(nms_pre)
+                anchors = anchors[topk_inds, :]
+                bbox_pred = bbox_pred[topk_inds, :]
+                scores = scores[topk_inds, :]
+                coeff_pred = coeff_pred[topk_inds, :]
+            bboxes = self.bbox_coder.decode(
+                anchors, bbox_pred, max_shape=img_shape)
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+            mlvl_coeffs.append(coeff_pred)
+        mlvl_bboxes = torch.cat(mlvl_bboxes)
+        if rescale:
+            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
+        mlvl_scores = torch.cat(mlvl_scores)
+        mlvl_coeffs = torch.cat(mlvl_coeffs)
+        if self.use_sigmoid_cls:
+            # Add a dummy background class to the backend when using sigmoid
+            # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
+            # BG cat_id: num_class
+            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
+            mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
+        det_bboxes, det_labels, det_coeffs = fast_nms(mlvl_bboxes, mlvl_scores,
+                                                      mlvl_coeffs,
+                                                      cfg.score_thr,
+                                                      cfg.iou_thr, cfg.top_k,
+                                                      cfg.max_per_img)
+        return det_bboxes, det_labels, det_coeffs
+
+
+@HEADS.register_module()
+class YOLACTSegmHead(nn.Module):
+    """YOLACT segmentation head used in https://arxiv.org/abs/1904.02689.
+
+    Apply a semantic segmentation loss on feature space using layers that are
+    only evaluated during training to increase performance with no speed
+    penalty.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+        num_classes (int): Number of categories excluding the background
+            category.
+        loss_segm (dict): Config of semantic segmentation loss.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels=256,
+                 loss_segm=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0)):
+        super(YOLACTSegmHead, self).__init__()
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+        self.loss_segm = build_loss(loss_segm)
+        self._init_layers()
+        self.fp16_enabled = False
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.segm_conv = nn.Conv2d(
+            self.in_channels, self.num_classes, kernel_size=1)
+
+    def init_weights(self):
+        """Initialize weights of the head."""
+        xavier_init(self.segm_conv, distribution='uniform')
+
+    def forward(self, x):
+        """Forward feature from the upstream network.
+
+        Args:
+            x (Tensor): Feature from the upstream network, which is
+                a 4D-tensor.
+
+        Returns:
+            Tensor: Predicted semantic segmentation map with shape
+                (N, num_classes, H, W).
+        """
+        return self.segm_conv(x)
+
+    @force_fp32(apply_to=('segm_pred', ))
+    def loss(self, segm_pred, gt_masks, gt_labels):
+        """Compute loss of the head.
+
+        Args:
+            segm_pred (list[Tensor]): Predicted semantic segmentation map
+                with shape (N, num_classes, H, W).
+            gt_masks (list[Tensor]): Ground truth masks for each image with
+                the same shape of the input image.
+            gt_labels (list[Tensor]): Class indices corresponding to each box.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        loss_segm = []
+        num_imgs, num_classes, mask_h, mask_w = segm_pred.size()
+        for idx in range(num_imgs):
+            cur_segm_pred = segm_pred[idx]
+            cur_gt_masks = gt_masks[idx].float()
+            cur_gt_labels = gt_labels[idx]
+            segm_targets = self.get_targets(cur_segm_pred, cur_gt_masks,
+                                            cur_gt_labels)
+            if segm_targets is None:
+                loss = self.loss_segm(cur_segm_pred,
+                                      torch.zeros_like(cur_segm_pred),
+                                      torch.zeros_like(cur_segm_pred))
+            else:
+                loss = self.loss_segm(
+                    cur_segm_pred,
+                    segm_targets,
+                    avg_factor=num_imgs * mask_h * mask_w)
+            loss_segm.append(loss)
+        return dict(loss_segm=loss_segm)
+
+    def get_targets(self, segm_pred, gt_masks, gt_labels):
+        """Compute semantic segmentation targets for each image.
+
+        Args:
+            segm_pred (Tensor): Predicted semantic segmentation map
+                with shape (num_classes, H, W).
+            gt_masks (Tensor): Ground truth masks for each image with
+                the same shape of the input image.
+            gt_labels (Tensor): Class indices corresponding to each box.
+
+        Returns:
+            Tensor: Semantic segmentation targets with shape
+                (num_classes, H, W).
+        """
+        if gt_masks.size(0) == 0:
+            return None
+        num_classes, mask_h, mask_w = segm_pred.size()
+        with torch.no_grad():
+            downsampled_masks = F.interpolate(
+                gt_masks.unsqueeze(0), (mask_h, mask_w),
+                mode='bilinear',
+                align_corners=False).squeeze(0)
+            downsampled_masks = downsampled_masks.gt(0.5).float()
+            segm_targets = torch.zeros_like(segm_pred, requires_grad=False)
+            for obj_idx in range(downsampled_masks.size(0)):
+                segm_targets[gt_labels[obj_idx] - 1] = torch.max(
+                    segm_targets[gt_labels[obj_idx] - 1],
+                    downsampled_masks[obj_idx])
+            return segm_targets
+
+
+@HEADS.register_module()
+class YOLACTProtonet(nn.Module):
+    """YOLACT mask head used in https://arxiv.org/abs/1904.02689.
+
+    This head outputs the mask prototypes for YOLACT.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+        proto_channels (tuple[int]): Output channels of protonet convs.
+        proto_kernel_sizes (tuple[int]): Kernel sizes of protonet convs.
+        include_last_relu (Bool): If keep the last relu of protonet.
+        num_protos (int): Number of prototypes.
+        num_classes (int): Number of categories excluding the background
+            category.
+        loss_mask_weight (float): Reweight the mask loss by this factor.
+        max_masks_to_train (int): Maximum number of masks to train for
+            each image.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels=256,
+                 proto_channels=(256, 256, 256, None, 256, 32),
+                 proto_kernel_sizes=(3, 3, 3, -2, 3, 1),
+                 include_last_relu=True,
+                 num_protos=32,
+                 loss_mask_weight=1.0,
+                 max_masks_to_train=100):
+        super(YOLACTProtonet, self).__init__()
+        self.in_channels = in_channels
+        self.proto_channels = proto_channels
+        self.proto_kernel_sizes = proto_kernel_sizes
+        self.include_last_relu = include_last_relu
+        self.protonet = self._init_layers()
+
+        self.loss_mask_weight = loss_mask_weight
+        self.num_protos = num_protos
+        self.num_classes = num_classes
+        self.max_masks_to_train = max_masks_to_train
+        self.fp16_enabled = False
+
+    def _init_layers(self):
+        """A helper function to take a config setting and turn it into a
+        network."""
+        # Possible patterns:
+        # ( 256, 3) -> conv
+        # ( 256,-2) -> deconv
+        # (None,-2) -> bilinear interpolate
+        in_channels = self.in_channels
+        protonets = nn.ModuleList()
+        for num_channels, kernel_size in zip(self.proto_channels,
+                                             self.proto_kernel_sizes):
+            if kernel_size > 0:
+                layer = nn.Conv2d(
+                    in_channels,
+                    num_channels,
+                    kernel_size,
+                    padding=kernel_size // 2)
+            else:
+                if num_channels is None:
+                    layer = InterpolateModule(
+                        scale_factor=-kernel_size,
+                        mode='bilinear',
+                        align_corners=False)
+                else:
+                    layer = nn.ConvTranspose2d(
+                        in_channels,
+                        num_channels,
+                        -kernel_size,
+                        padding=kernel_size // 2)
+            protonets.append(layer)
+            protonets.append(nn.ReLU(inplace=True))
+            in_channels = num_channels if num_channels is not None \
+                else in_channels
+        if not self.include_last_relu:
+            protonets = protonets[:-1]
+        return nn.Sequential(*protonets)
+
+    def init_weights(self):
+        """Initialize weights of the head."""
+        for m in self.protonet:
+            if isinstance(m, nn.Conv2d):
+                xavier_init(m, distribution='uniform')
+
+    def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=None):
+        """Forward feature from the upstream network to get prototypes and
+        linearly combine the prototypes, using masks coefficients, into
+        instance masks. Finally, crop the instance masks with given bboxes.
+
+        Args:
+            x (Tensor): Feature from the upstream network, which is
+                a 4D-tensor.
+            coeff_pred (list[Tensor]): Mask coefficients for each scale
+                level with shape (N, num_anchors * num_protos, H, W).
+            bboxes (list[Tensor]): Box used for cropping with shape
+                (N, num_anchors * 4, H, W). During training, they are
+                ground truth boxes. During testing, they are predicted
+                boxes.
+            img_meta (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            sampling_results (List[:obj:``SamplingResult``]): Sampler results
+                for each image.
+
+        Returns:
+            list[Tensor]: Predicted instance segmentation masks.
+        """
+        prototypes = self.protonet(x)
+        prototypes = prototypes.permute(0, 2, 3, 1).contiguous()
+
+        num_imgs = x.size(0)
+        # Training state
+        if self.training:
+            coeff_pred_list = []
+            for coeff_pred_per_level in coeff_pred:
+                coeff_pred_per_level = \
+                    coeff_pred_per_level.permute(0, 2, 3, 1)\
+                    .reshape(num_imgs, -1, self.num_protos)
+                coeff_pred_list.append(coeff_pred_per_level)
+            coeff_pred = torch.cat(coeff_pred_list, dim=1)
+
+        mask_pred_list = []
+        for idx in range(num_imgs):
+            cur_prototypes = prototypes[idx]
+            cur_coeff_pred = coeff_pred[idx]
+            cur_bboxes = bboxes[idx]
+            cur_img_meta = img_meta[idx]
+
+            # Testing state
+            if not self.training:
+                bboxes_for_cropping = cur_bboxes
+            else:
+                cur_sampling_results = sampling_results[idx]
+                pos_assigned_gt_inds = \
+                    cur_sampling_results.pos_assigned_gt_inds
+                bboxes_for_cropping = cur_bboxes[pos_assigned_gt_inds].clone()
+                pos_inds = cur_sampling_results.pos_inds
+                cur_coeff_pred = cur_coeff_pred[pos_inds]
+
+            # Linearly combine the prototypes with the mask coefficients
+            mask_pred = cur_prototypes @ cur_coeff_pred.t()
+            mask_pred = torch.sigmoid(mask_pred)
+
+            h, w = cur_img_meta['img_shape'][:2]
+            bboxes_for_cropping[:, 0] /= w
+            bboxes_for_cropping[:, 1] /= h
+            bboxes_for_cropping[:, 2] /= w
+            bboxes_for_cropping[:, 3] /= h
+
+            mask_pred = self.crop(mask_pred, bboxes_for_cropping)
+            mask_pred = mask_pred.permute(2, 0, 1).contiguous()
+            mask_pred_list.append(mask_pred)
+        return mask_pred_list
+
+    @force_fp32(apply_to=('mask_pred', ))
+    def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_results):
+        """Compute loss of the head.
+
+        Args:
+            mask_pred (list[Tensor]): Predicted prototypes with shape
+                (num_classes, H, W).
+            gt_masks (list[Tensor]): Ground truth masks for each image with
+                the same shape of the input image.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            img_meta (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            sampling_results (List[:obj:``SamplingResult``]): Sampler results
+                for each image.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        loss_mask = []
+        num_imgs = len(mask_pred)
+        total_pos = 0
+        for idx in range(num_imgs):
+            cur_mask_pred = mask_pred[idx]
+            cur_gt_masks = gt_masks[idx].float()
+            cur_gt_bboxes = gt_bboxes[idx]
+            cur_img_meta = img_meta[idx]
+            cur_sampling_results = sampling_results[idx]
+
+            pos_assigned_gt_inds = cur_sampling_results.pos_assigned_gt_inds
+            num_pos = pos_assigned_gt_inds.size(0)
+            # Since we're producing (near) full image masks,
+            # it'd take too much vram to backprop on every single mask.
+            # Thus we select only a subset.
+            if num_pos > self.max_masks_to_train:
+                perm = torch.randperm(num_pos)
+                select = perm[:self.max_masks_to_train]
+                cur_mask_pred = cur_mask_pred[select]
+                pos_assigned_gt_inds = pos_assigned_gt_inds[select]
+                num_pos = self.max_masks_to_train
+            total_pos += num_pos
+
+            gt_bboxes_for_reweight = cur_gt_bboxes[pos_assigned_gt_inds]
+
+            mask_targets = self.get_targets(cur_mask_pred, cur_gt_masks,
+                                            pos_assigned_gt_inds)
+            if num_pos == 0:
+                loss = cur_mask_pred.sum() * 0.
+            elif mask_targets is None:
+                loss = F.binary_cross_entropy(cur_mask_pred,
+                                              torch.zeros_like(cur_mask_pred),
+                                              torch.zeros_like(cur_mask_pred))
+            else:
+                cur_mask_pred = torch.clamp(cur_mask_pred, 0, 1)
+                loss = F.binary_cross_entropy(
+                    cur_mask_pred, mask_targets,
+                    reduction='none') * self.loss_mask_weight
+
+                h, w = cur_img_meta['img_shape'][:2]
+                gt_bboxes_width = (gt_bboxes_for_reweight[:, 2] -
+                                   gt_bboxes_for_reweight[:, 0]) / w
+                gt_bboxes_height = (gt_bboxes_for_reweight[:, 3] -
+                                    gt_bboxes_for_reweight[:, 1]) / h
+                loss = loss.mean(dim=(1,
+                                      2)) / gt_bboxes_width / gt_bboxes_height
+                loss = torch.sum(loss)
+            loss_mask.append(loss)
+
+        if total_pos == 0:
+            total_pos += 1  # avoid nan
+        loss_mask = [x / total_pos for x in loss_mask]
+
+        return dict(loss_mask=loss_mask)
+
+    def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds):
+        """Compute instance segmentation targets for each image.
+
+        Args:
+            mask_pred (Tensor): Predicted prototypes with shape
+                (num_classes, H, W).
+            gt_masks (Tensor): Ground truth masks for each image with
+                the same shape of the input image.
+            pos_assigned_gt_inds (Tensor): GT indices of the corresponding
+                positive samples.
+        Returns:
+            Tensor: Instance segmentation targets with shape
+                (num_instances, H, W).
+        """
+        if gt_masks.size(0) == 0:
+            return None
+        mask_h, mask_w = mask_pred.shape[-2:]
+        gt_masks = F.interpolate(
+            gt_masks.unsqueeze(0), (mask_h, mask_w),
+            mode='bilinear',
+            align_corners=False).squeeze(0)
+        gt_masks = gt_masks.gt(0.5).float()
+        mask_targets = gt_masks[pos_assigned_gt_inds]
+        return mask_targets
+
+    def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale):
+        """Resize, binarize, and format the instance mask predictions.
+
+        Args:
+            mask_pred (Tensor): shape (N, H, W).
+            label_pred (Tensor): shape (N, ).
+            img_meta (dict): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool): If rescale is False, then returned masks will
+                fit the scale of imgs[0].
+        Returns:
+            list[ndarray]: Mask predictions grouped by their predicted classes.
+        """
+        ori_shape = img_meta['ori_shape']
+        scale_factor = img_meta['scale_factor']
+        if rescale:
+            img_h, img_w = ori_shape[:2]
+        else:
+            img_h = np.round(ori_shape[0] * scale_factor[1]).astype(np.int32)
+            img_w = np.round(ori_shape[1] * scale_factor[0]).astype(np.int32)
+
+        cls_segms = [[] for _ in range(self.num_classes)]
+        if mask_pred.size(0) == 0:
+            return cls_segms
+
+        mask_pred = F.interpolate(
+            mask_pred.unsqueeze(0), (img_h, img_w),
+            mode='bilinear',
+            align_corners=False).squeeze(0) > 0.5
+        mask_pred = mask_pred.cpu().numpy().astype(np.uint8)
+
+        for m, l in zip(mask_pred, label_pred):
+            cls_segms[l].append(m)
+        return cls_segms
+
+    def crop(self, masks, boxes, padding=1):
+        """Crop predicted masks by zeroing out everything not in the predicted
+        bbox.
+
+        Args:
+            masks (Tensor): shape [H, W, N].
+            boxes (Tensor): bbox coords in relative point form with
+                shape [N, 4].
+
+        Return:
+            Tensor: The cropped masks.
+        """
+        h, w, n = masks.size()
+        x1, x2 = self.sanitize_coordinates(
+            boxes[:, 0], boxes[:, 2], w, padding, cast=False)
+        y1, y2 = self.sanitize_coordinates(
+            boxes[:, 1], boxes[:, 3], h, padding, cast=False)
+
+        rows = torch.arange(
+            w, device=masks.device, dtype=x1.dtype).view(1, -1,
+                                                         1).expand(h, w, n)
+        cols = torch.arange(
+            h, device=masks.device, dtype=x1.dtype).view(-1, 1,
+                                                         1).expand(h, w, n)
+
+        masks_left = rows >= x1.view(1, 1, -1)
+        masks_right = rows < x2.view(1, 1, -1)
+        masks_up = cols >= y1.view(1, 1, -1)
+        masks_down = cols < y2.view(1, 1, -1)
+
+        crop_mask = masks_left * masks_right * masks_up * masks_down
+
+        return masks * crop_mask.float()
+
+    def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True):
+        """Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0,
+        and x2 <= image_size. Also converts from relative to absolute
+        coordinates and casts the results to long tensors.
+
+        Warning: this does things in-place behind the scenes so
+        copy if necessary.
+
+        Args:
+            _x1 (Tensor): shape (N, ).
+            _x2 (Tensor): shape (N, ).
+            img_size (int): Size of the input image.
+            padding (int): x1 >= padding, x2 <= image_size-padding.
+            cast (bool): If cast is false, the result won't be cast to longs.
+
+        Returns:
+            tuple:
+                x1 (Tensor): Sanitized _x1.
+                x2 (Tensor): Sanitized _x2.
+        """
+        x1 = x1 * img_size
+        x2 = x2 * img_size
+        if cast:
+            x1 = x1.long()
+            x2 = x2.long()
+        x1 = torch.min(x1, x2)
+        x2 = torch.max(x1, x2)
+        x1 = torch.clamp(x1 - padding, min=0)
+        x2 = torch.clamp(x2 + padding, max=img_size)
+        return x1, x2
+
+
+class InterpolateModule(nn.Module):
+    """This is a module version of F.interpolate.
+
+    Any arguments you give it just get passed along for the ride.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+
+        self.args = args
+        self.kwargs = kwargs
+
+    def forward(self, x):
+        """Forward features from the upstream network."""
+        return F.interpolate(x, *self.args, **self.kwargs)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/dense_heads/yolo_head.py

+# Copyright (c) 2019 Western Digital Corporation or its affiliates.
+
+import warnings
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule, normal_init
+from mmcv.runner import force_fp32
+
+from mmdet.core import (build_anchor_generator, build_assigner,
+                        build_bbox_coder, build_sampler, images_to_levels,
+                        multi_apply, multiclass_nms)
+from ..builder import HEADS, build_loss
+from .base_dense_head import BaseDenseHead
+from .dense_test_mixins import BBoxTestMixin
+
+
+@HEADS.register_module()
+class YOLOV3Head(BaseDenseHead, BBoxTestMixin):
+    """YOLOV3Head Paper link: https://arxiv.org/abs/1804.02767.
+
+    Args:
+        num_classes (int): The number of object classes (w/o background)
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (List[int]): The number of output channels per scale
+            before the final 1x1 layer. Default: (1024, 512, 256).
+        anchor_generator (dict): Config dict for anchor generator
+        bbox_coder (dict): Config of bounding box coder.
+        featmap_strides (List[int]): The stride of each scale.
+            Should be in descending order. Default: (32, 16, 8).
+        one_hot_smoother (float): Set a non-zero value to enable label-smooth
+            Default: 0.
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+            Default: dict(type='BN', requires_grad=True)
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='LeakyReLU', negative_slope=0.1).
+        loss_cls (dict): Config of classification loss.
+        loss_conf (dict): Config of confidence loss.
+        loss_xy (dict): Config of xy coordinate loss.
+        loss_wh (dict): Config of wh coordinate loss.
+        train_cfg (dict): Training config of YOLOV3 head. Default: None.
+        test_cfg (dict): Testing config of YOLOV3 head. Default: None.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 out_channels=(1024, 512, 256),
+                 anchor_generator=dict(
+                     type='YOLOAnchorGenerator',
+                     base_sizes=[[(116, 90), (156, 198), (373, 326)],
+                                 [(30, 61), (62, 45), (59, 119)],
+                                 [(10, 13), (16, 30), (33, 23)]],
+                     strides=[32, 16, 8]),
+                 bbox_coder=dict(type='YOLOBBoxCoder'),
+                 featmap_strides=[32, 16, 8],
+                 one_hot_smoother=0.,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 loss_conf=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 loss_xy=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 loss_wh=dict(type='MSELoss', loss_weight=1.0),
+                 train_cfg=None,
+                 test_cfg=None):
+        super(YOLOV3Head, self).__init__()
+        # Check params
+        assert (len(in_channels) == len(out_channels) == len(featmap_strides))
+
+        self.num_classes = num_classes
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.featmap_strides = featmap_strides
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            if hasattr(self.train_cfg, 'sampler'):
+                sampler_cfg = self.train_cfg.sampler
+            else:
+                sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+
+        self.one_hot_smoother = one_hot_smoother
+
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+
+        self.bbox_coder = build_bbox_coder(bbox_coder)
+        self.anchor_generator = build_anchor_generator(anchor_generator)
+
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_conf = build_loss(loss_conf)
+        self.loss_xy = build_loss(loss_xy)
+        self.loss_wh = build_loss(loss_wh)
+        # usually the numbers of anchors for each level are the same
+        # except SSD detectors
+        self.num_anchors = self.anchor_generator.num_base_anchors[0]
+        assert len(
+            self.anchor_generator.num_base_anchors) == len(featmap_strides)
+        self._init_layers()
+
+    @property
+    def num_levels(self):
+        return len(self.featmap_strides)
+
+    @property
+    def num_attrib(self):
+        """int: number of attributes in pred_map, bboxes (4) +
+        objectness (1) + num_classes"""
+
+        return 5 + self.num_classes
+
+    def _init_layers(self):
+        self.convs_bridge = nn.ModuleList()
+        self.convs_pred = nn.ModuleList()
+        for i in range(self.num_levels):
+            conv_bridge = ConvModule(
+                self.in_channels[i],
+                self.out_channels[i],
+                3,
+                padding=1,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg,
+                act_cfg=self.act_cfg)
+            conv_pred = nn.Conv2d(self.out_channels[i],
+                                  self.num_anchors * self.num_attrib, 1)
+
+            self.convs_bridge.append(conv_bridge)
+            self.convs_pred.append(conv_pred)
+
+    def init_weights(self):
+        """Initialize weights of the head."""
+        for m in self.convs_pred:
+            normal_init(m, std=0.01)
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple[Tensor]: A tuple of multi-level predication map, each is a
+                4D-tensor of shape (batch_size, 5+num_classes, height, width).
+        """
+
+        assert len(feats) == self.num_levels
+        pred_maps = []
+        for i in range(self.num_levels):
+            x = feats[i]
+            x = self.convs_bridge[i](x)
+            pred_map = self.convs_pred[i](x)
+            pred_maps.append(pred_map)
+
+        return tuple(pred_maps),
+
+    @force_fp32(apply_to=('pred_maps', ))
+    def get_bboxes(self,
+                   pred_maps,
+                   img_metas,
+                   cfg=None,
+                   rescale=False,
+                   with_nms=True):
+        """Transform network output for a batch into bbox predictions.
+
+        Args:
+            pred_maps (list[Tensor]): Raw predictions for a batch of images.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            cfg (mmcv.Config | None): Test / postprocessing configuration,
+                if None, test_cfg would be used. Default: None.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is an (n, 5) tensor, where the first 4 columns
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
+                5-th column is a score between 0 and 1. The second item is a
+                (n,) tensor where each item is the predicted class label of the
+                corresponding box.
+        """
+        result_list = []
+        num_levels = len(pred_maps)
+        for img_id in range(len(img_metas)):
+            pred_maps_list = [
+                pred_maps[i][img_id].detach() for i in range(num_levels)
+            ]
+            scale_factor = img_metas[img_id]['scale_factor']
+            proposals = self._get_bboxes_single(pred_maps_list, scale_factor,
+                                                cfg, rescale, with_nms)
+            result_list.append(proposals)
+        return result_list
+
+    def _get_bboxes_single(self,
+                           pred_maps_list,
+                           scale_factor,
+                           cfg,
+                           rescale=False,
+                           with_nms=True):
+        """Transform outputs for a single batch item into bbox predictions.
+
+        Args:
+            pred_maps_list (list[Tensor]): Prediction maps for different scales
+                of each single image in the batch.
+            scale_factor (ndarray): Scale factor of the image arrange as
+                (w_scale, h_scale, w_scale, h_scale).
+            cfg (mmcv.Config | None): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            tuple(Tensor):
+                det_bboxes (Tensor): BBox predictions in shape (n, 5), where
+                    the first 4 columns are bounding box positions
+                    (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
+                    between 0 and 1.
+                det_labels (Tensor): A (n,) tensor where each item is the
+                    predicted class label of the corresponding box.
+        """
+        cfg = self.test_cfg if cfg is None else cfg
+        assert len(pred_maps_list) == self.num_levels
+        multi_lvl_bboxes = []
+        multi_lvl_cls_scores = []
+        multi_lvl_conf_scores = []
+        num_levels = len(pred_maps_list)
+        featmap_sizes = [
+            pred_maps_list[i].shape[-2:] for i in range(num_levels)
+        ]
+        multi_lvl_anchors = self.anchor_generator.grid_anchors(
+            featmap_sizes, pred_maps_list[0][0].device)
+        for i in range(self.num_levels):
+            # get some key info for current scale
+            pred_map = pred_maps_list[i]
+            stride = self.featmap_strides[i]
+
+            # (h, w, num_anchors*num_attrib) -> (h*w*num_anchors, num_attrib)
+            pred_map = pred_map.permute(1, 2, 0).reshape(-1, self.num_attrib)
+
+            pred_map[..., :2] = torch.sigmoid(pred_map[..., :2])
+            bbox_pred = self.bbox_coder.decode(multi_lvl_anchors[i],
+                                               pred_map[..., :4], stride)
+            # conf and cls
+            conf_pred = torch.sigmoid(pred_map[..., 4]).view(-1)
+            cls_pred = torch.sigmoid(pred_map[..., 5:]).view(
+                -1, self.num_classes)  # Cls pred one-hot.
+
+            # Filtering out all predictions with conf < conf_thr
+            conf_thr = cfg.get('conf_thr', -1)
+            if conf_thr > 0:
+                # add as_tuple=False for compatibility in Pytorch 1.6
+                # flatten would create a Reshape op with constant values,
+                # and raise RuntimeError when doing inference in ONNX Runtime
+                # with a different input image (#4221).
+                conf_inds = conf_pred.ge(conf_thr).nonzero(
+                    as_tuple=False).squeeze(1)
+                bbox_pred = bbox_pred[conf_inds, :]
+                cls_pred = cls_pred[conf_inds, :]
+                conf_pred = conf_pred[conf_inds]
+
+            # Get top-k prediction
+            nms_pre = cfg.get('nms_pre', -1)
+            if 0 < nms_pre < conf_pred.size(0) and (
+                    not torch.onnx.is_in_onnx_export()):
+                _, topk_inds = conf_pred.topk(nms_pre)
+                bbox_pred = bbox_pred[topk_inds, :]
+                cls_pred = cls_pred[topk_inds, :]
+                conf_pred = conf_pred[topk_inds]
+
+            # Save the result of current scale
+            multi_lvl_bboxes.append(bbox_pred)
+            multi_lvl_cls_scores.append(cls_pred)
+            multi_lvl_conf_scores.append(conf_pred)
+
+        # Merge the results of different scales together
+        multi_lvl_bboxes = torch.cat(multi_lvl_bboxes)
+        multi_lvl_cls_scores = torch.cat(multi_lvl_cls_scores)
+        multi_lvl_conf_scores = torch.cat(multi_lvl_conf_scores)
+
+        if with_nms and (multi_lvl_conf_scores.size(0) == 0):
+            return torch.zeros((0, 5)), torch.zeros((0, ))
+
+        if rescale:
+            multi_lvl_bboxes /= multi_lvl_bboxes.new_tensor(scale_factor)
+
+        # In mmdet 2.x, the class_id for background is num_classes.
+        # i.e., the last column.
+        padding = multi_lvl_cls_scores.new_zeros(multi_lvl_cls_scores.shape[0],
+                                                 1)
+        multi_lvl_cls_scores = torch.cat([multi_lvl_cls_scores, padding],
+                                         dim=1)
+
+        # Support exporting to onnx without nms
+        if with_nms and cfg.get('nms', None) is not None:
+            det_bboxes, det_labels = multiclass_nms(
+                multi_lvl_bboxes,
+                multi_lvl_cls_scores,
+                cfg.score_thr,
+                cfg.nms,
+                cfg.max_per_img,
+                score_factors=multi_lvl_conf_scores)
+            return det_bboxes, det_labels
+        else:
+            return (multi_lvl_bboxes, multi_lvl_cls_scores,
+                    multi_lvl_conf_scores)
+
+    @force_fp32(apply_to=('pred_maps', ))
+    def loss(self,
+             pred_maps,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute loss of the head.
+
+        Args:
+            pred_maps (list[Tensor]): Prediction map for each scale level,
+                shape (N, num_anchors * num_attrib, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        num_imgs = len(img_metas)
+        device = pred_maps[0][0].device
+
+        featmap_sizes = [
+            pred_maps[i].shape[-2:] for i in range(self.num_levels)
+        ]
+        multi_level_anchors = self.anchor_generator.grid_anchors(
+            featmap_sizes, device)
+        anchor_list = [multi_level_anchors for _ in range(num_imgs)]
+
+        responsible_flag_list = []
+        for img_id in range(len(img_metas)):
+            responsible_flag_list.append(
+                self.anchor_generator.responsible_flags(
+                    featmap_sizes, gt_bboxes[img_id], device))
+
+        target_maps_list, neg_maps_list = self.get_targets(
+            anchor_list, responsible_flag_list, gt_bboxes, gt_labels)
+
+        losses_cls, losses_conf, losses_xy, losses_wh = multi_apply(
+            self.loss_single, pred_maps, target_maps_list, neg_maps_list)
+
+        return dict(
+            loss_cls=losses_cls,
+            loss_conf=losses_conf,
+            loss_xy=losses_xy,
+            loss_wh=losses_wh)
+
+    def loss_single(self, pred_map, target_map, neg_map):
+        """Compute loss of a single image from a batch.
+
+        Args:
+            pred_map (Tensor): Raw predictions for a single level.
+            target_map (Tensor): The Ground-Truth target for a single level.
+            neg_map (Tensor): The negative masks for a single level.
+
+        Returns:
+            tuple:
+                loss_cls (Tensor): Classification loss.
+                loss_conf (Tensor): Confidence loss.
+                loss_xy (Tensor): Regression loss of x, y coordinate.
+                loss_wh (Tensor): Regression loss of w, h coordinate.
+        """
+
+        num_imgs = len(pred_map)
+        pred_map = pred_map.permute(0, 2, 3,
+                                    1).reshape(num_imgs, -1, self.num_attrib)
+        neg_mask = neg_map.float()
+        pos_mask = target_map[..., 4]
+        pos_and_neg_mask = neg_mask + pos_mask
+        pos_mask = pos_mask.unsqueeze(dim=-1)
+        if torch.max(pos_and_neg_mask) > 1.:
+            warnings.warn('There is overlap between pos and neg sample.')
+            pos_and_neg_mask = pos_and_neg_mask.clamp(min=0., max=1.)
+
+        pred_xy = pred_map[..., :2]
+        pred_wh = pred_map[..., 2:4]
+        pred_conf = pred_map[..., 4]
+        pred_label = pred_map[..., 5:]
+
+        target_xy = target_map[..., :2]
+        target_wh = target_map[..., 2:4]
+        target_conf = target_map[..., 4]
+        target_label = target_map[..., 5:]
+
+        loss_cls = self.loss_cls(pred_label, target_label, weight=pos_mask)
+        loss_conf = self.loss_conf(
+            pred_conf, target_conf, weight=pos_and_neg_mask)
+        loss_xy = self.loss_xy(pred_xy, target_xy, weight=pos_mask)
+        loss_wh = self.loss_wh(pred_wh, target_wh, weight=pos_mask)
+
+        return loss_cls, loss_conf, loss_xy, loss_wh
+
+    def get_targets(self, anchor_list, responsible_flag_list, gt_bboxes_list,
+                    gt_labels_list):
+        """Compute target maps for anchors in multiple images.
+
+        Args:
+            anchor_list (list[list[Tensor]]): Multi level anchors of each
+                image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_total_anchors, 4).
+            responsible_flag_list (list[list[Tensor]]): Multi level responsible
+                flags of each image. Each element is a tensor of shape
+                (num_total_anchors, )
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            gt_labels_list (list[Tensor]): Ground truth labels of each box.
+
+        Returns:
+            tuple: Usually returns a tuple containing learning targets.
+                - target_map_list (list[Tensor]): Target map of each level.
+                - neg_map_list (list[Tensor]): Negative map of each level.
+        """
+        num_imgs = len(anchor_list)
+
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+
+        results = multi_apply(self._get_targets_single, anchor_list,
+                              responsible_flag_list, gt_bboxes_list,
+                              gt_labels_list)
+
+        all_target_maps, all_neg_maps = results
+        assert num_imgs == len(all_target_maps) == len(all_neg_maps)
+        target_maps_list = images_to_levels(all_target_maps, num_level_anchors)
+        neg_maps_list = images_to_levels(all_neg_maps, num_level_anchors)
+
+        return target_maps_list, neg_maps_list
+
+    def _get_targets_single(self, anchors, responsible_flags, gt_bboxes,
+                            gt_labels):
+        """Generate matching bounding box prior and converted GT.
+
+        Args:
+            anchors (list[Tensor]): Multi-level anchors of the image.
+            responsible_flags (list[Tensor]): Multi-level responsible flags of
+                anchors
+            gt_bboxes (Tensor): Ground truth bboxes of single image.
+            gt_labels (Tensor): Ground truth labels of single image.
+
+        Returns:
+            tuple:
+                target_map (Tensor): Predication target map of each
+                    scale level, shape (num_total_anchors,
+                    5+num_classes)
+                neg_map (Tensor): Negative map of each scale level,
+                    shape (num_total_anchors,)
+        """
+
+        anchor_strides = []
+        for i in range(len(anchors)):
+            anchor_strides.append(
+                torch.tensor(self.featmap_strides[i],
+                             device=gt_bboxes.device).repeat(len(anchors[i])))
+        concat_anchors = torch.cat(anchors)
+        concat_responsible_flags = torch.cat(responsible_flags)
+
+        anchor_strides = torch.cat(anchor_strides)
+        assert len(anchor_strides) == len(concat_anchors) == \
+               len(concat_responsible_flags)
+        assign_result = self.assigner.assign(concat_anchors,
+                                             concat_responsible_flags,
+                                             gt_bboxes)
+        sampling_result = self.sampler.sample(assign_result, concat_anchors,
+                                              gt_bboxes)
+
+        target_map = concat_anchors.new_zeros(
+            concat_anchors.size(0), self.num_attrib)
+
+        target_map[sampling_result.pos_inds, :4] = self.bbox_coder.encode(
+            sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes,
+            anchor_strides[sampling_result.pos_inds])
+
+        target_map[sampling_result.pos_inds, 4] = 1
+
+        gt_labels_one_hot = F.one_hot(
+            gt_labels, num_classes=self.num_classes).float()
+        if self.one_hot_smoother != 0:  # label smooth
+            gt_labels_one_hot = gt_labels_one_hot * (
+                1 - self.one_hot_smoother
+            ) + self.one_hot_smoother / self.num_classes
+        target_map[sampling_result.pos_inds, 5:] = gt_labels_one_hot[
+            sampling_result.pos_assigned_gt_inds]
+
+        neg_map = concat_anchors.new_zeros(
+            concat_anchors.size(0), dtype=torch.uint8)
+        neg_map[sampling_result.neg_inds] = 1
+
+        return target_map, neg_map
+
+    def aug_test(self, feats, img_metas, rescale=False):
+        """Test function with test time augmentation.
+
+        Args:
+            feats (list[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains features for all images in the batch.
+            img_metas (list[list[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch. each dict has image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[ndarray]: bbox results of each class
+        """
+        return self.aug_test_bboxes(feats, img_metas, rescale=rescale)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/__init__.py

+from .atss import ATSS
+from .base import BaseDetector
+from .cascade_rcnn import CascadeRCNN
+from .cornernet import CornerNet
+from .detr import DETR
+from .fast_rcnn import FastRCNN
+from .faster_rcnn import FasterRCNN
+from .fcos import FCOS
+from .fovea import FOVEA
+from .fsaf import FSAF
+from .gfl import GFL
+from .grid_rcnn import GridRCNN
+from .htc import HybridTaskCascade
+from .mask_rcnn import MaskRCNN
+from .mask_scoring_rcnn import MaskScoringRCNN
+from .nasfcos import NASFCOS
+from .paa import PAA
+from .point_rend import PointRend
+from .reppoints_detector import RepPointsDetector
+from .retinanet import RetinaNet
+from .rpn import RPN
+from .single_stage import SingleStageDetector
+from .sparse_rcnn import SparseRCNN
+from .trident_faster_rcnn import TridentFasterRCNN
+from .two_stage import TwoStageDetector
+from .vfnet import VFNet
+from .yolact import YOLACT
+from .yolo import YOLOV3
+
+__all__ = [
+    'ATSS', 'BaseDetector', 'SingleStageDetector', 'TwoStageDetector', 'RPN',
+    'FastRCNN', 'FasterRCNN', 'MaskRCNN', 'CascadeRCNN', 'HybridTaskCascade',
+    'RetinaNet', 'FCOS', 'GridRCNN', 'MaskScoringRCNN', 'RepPointsDetector',
+    'FOVEA', 'FSAF', 'NASFCOS', 'PointRend', 'GFL', 'CornerNet', 'PAA',
+    'YOLOV3', 'YOLACT', 'VFNet', 'DETR', 'TridentFasterRCNN', 'SparseRCNN'
+]

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/atss.py

+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class ATSS(SingleStageDetector):
+    """Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`_."""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super(ATSS, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                   test_cfg, pretrained)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/base.py

+from abc import ABCMeta, abstractmethod
+from collections import OrderedDict
+
+import mmcv
+import numpy as np
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from mmcv.runner import auto_fp16
+from mmcv.utils import print_log
+
+from mmdet.core.visualization import imshow_det_bboxes
+from mmdet.utils import get_root_logger
+
+
+class BaseDetector(nn.Module, metaclass=ABCMeta):
+    """Base class for detectors."""
+
+    def __init__(self):
+        super(BaseDetector, self).__init__()
+        self.fp16_enabled = False
+
+    @property
+    def with_neck(self):
+        """bool: whether the detector has a neck"""
+        return hasattr(self, 'neck') and self.neck is not None
+
+    # TODO: these properties need to be carefully handled
+    # for both single stage & two stage detectors
+    @property
+    def with_shared_head(self):
+        """bool: whether the detector has a shared head in the RoI Head"""
+        return hasattr(self, 'roi_head') and self.roi_head.with_shared_head
+
+    @property
+    def with_bbox(self):
+        """bool: whether the detector has a bbox head"""
+        return ((hasattr(self, 'roi_head') and self.roi_head.with_bbox)
+                or (hasattr(self, 'bbox_head') and self.bbox_head is not None))
+
+    @property
+    def with_mask(self):
+        """bool: whether the detector has a mask head"""
+        return ((hasattr(self, 'roi_head') and self.roi_head.with_mask)
+                or (hasattr(self, 'mask_head') and self.mask_head is not None))
+
+    @abstractmethod
+    def extract_feat(self, imgs):
+        """Extract features from images."""
+        pass
+
+    def extract_feats(self, imgs):
+        """Extract features from multiple images.
+
+        Args:
+            imgs (list[torch.Tensor]): A list of images. The images are
+                augmented from the same image but in different ways.
+
+        Returns:
+            list[torch.Tensor]: Features of different images
+        """
+        assert isinstance(imgs, list)
+        return [self.extract_feat(img) for img in imgs]
+
+    def forward_train(self, imgs, img_metas, **kwargs):
+        """
+        Args:
+            img (list[Tensor]): List of tensors of shape (1, C, H, W).
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys, see
+                :class:`mmdet.datasets.pipelines.Collect`.
+            kwargs (keyword arguments): Specific to concrete implementation.
+        """
+        # NOTE the batched image size information may be useful, e.g.
+        # in DETR, this is needed for the construction of masks, which is
+        # then used for the transformer_head.
+        batch_input_shape = tuple(imgs[0].size()[-2:])
+        for img_meta in img_metas:
+            img_meta['batch_input_shape'] = batch_input_shape
+
+    async def async_simple_test(self, img, img_metas, **kwargs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def simple_test(self, img, img_metas, **kwargs):
+        pass
+
+    @abstractmethod
+    def aug_test(self, imgs, img_metas, **kwargs):
+        """Test function with test time augmentation."""
+        pass
+
+    def init_weights(self, pretrained=None):
+        """Initialize the weights in detector.
+
+        Args:
+            pretrained (str, optional): Path to pre-trained weights.
+                Defaults to None.
+        """
+        if pretrained is not None:
+            logger = get_root_logger()
+            print_log(f'load model from: {pretrained}', logger=logger)
+
+    async def aforward_test(self, *, img, img_metas, **kwargs):
+        for var, name in [(img, 'img'), (img_metas, 'img_metas')]:
+            if not isinstance(var, list):
+                raise TypeError(f'{name} must be a list, but got {type(var)}')
+
+        num_augs = len(img)
+        if num_augs != len(img_metas):
+            raise ValueError(f'num of augmentations ({len(img)}) '
+                             f'!= num of image metas ({len(img_metas)})')
+        # TODO: remove the restriction of samples_per_gpu == 1 when prepared
+        samples_per_gpu = img[0].size(0)
+        assert samples_per_gpu == 1
+
+        if num_augs == 1:
+            return await self.async_simple_test(img[0], img_metas[0], **kwargs)
+        else:
+            raise NotImplementedError
+
+    def forward_test(self, imgs, img_metas, **kwargs):
+        """
+        Args:
+            imgs (List[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains all images in the batch.
+            img_metas (List[List[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch.
+        """
+        for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
+            if not isinstance(var, list):
+                raise TypeError(f'{name} must be a list, but got {type(var)}')
+
+        num_augs = len(imgs)
+        if num_augs != len(img_metas):
+            raise ValueError(f'num of augmentations ({len(imgs)}) '
+                             f'!= num of image meta ({len(img_metas)})')
+
+        # NOTE the batched image size information may be useful, e.g.
+        # in DETR, this is needed for the construction of masks, which is
+        # then used for the transformer_head.
+        for img, img_meta in zip(imgs, img_metas):
+            batch_size = len(img_meta)
+            for img_id in range(batch_size):
+                img_meta[img_id]['batch_input_shape'] = tuple(img.size()[-2:])
+
+        if num_augs == 1:
+            # proposals (List[List[Tensor]]): the outer list indicates
+            # test-time augs (multiscale, flip, etc.) and the inner list
+            # indicates images in a batch.
+            # The Tensor should have a shape Px4, where P is the number of
+            # proposals.
+            if 'proposals' in kwargs:
+                kwargs['proposals'] = kwargs['proposals'][0]
+            return self.simple_test(imgs[0], img_metas[0], **kwargs)
+        else:
+            assert imgs[0].size(0) == 1, 'aug test does not support ' \
+                                         'inference with batch size ' \
+                                         f'{imgs[0].size(0)}'
+            # TODO: support test augmentation for predefined proposals
+            assert 'proposals' not in kwargs
+            return self.aug_test(imgs, img_metas, **kwargs)
+
+    @auto_fp16(apply_to=('img', ))
+    def forward(self, img, img_metas, return_loss=True, **kwargs):
+        """Calls either :func:`forward_train` or :func:`forward_test` depending
+        on whether ``return_loss`` is ``True``.
+
+        Note this setting will change the expected inputs. When
+        ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor
+        and List[dict]), and when ``resturn_loss=False``, img and img_meta
+        should be double nested (i.e.  List[Tensor], List[List[dict]]), with
+        the outer list indicating test time augmentations.
+        """
+        if return_loss:
+            return self.forward_train(img, img_metas, **kwargs)
+        else:
+            return self.forward_test(img, img_metas, **kwargs)
+
+    def _parse_losses(self, losses):
+        """Parse the raw outputs (losses) of the network.
+
+        Args:
+            losses (dict): Raw output of the network, which usually contain
+                losses and other necessary infomation.
+
+        Returns:
+            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor \
+                which may be a weighted sum of all losses, log_vars contains \
+                all the variables to be sent to the logger.
+        """
+        log_vars = OrderedDict()
+        for loss_name, loss_value in losses.items():
+            if isinstance(loss_value, torch.Tensor):
+                log_vars[loss_name] = loss_value.mean()
+            elif isinstance(loss_value, list):
+                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
+            else:
+                raise TypeError(
+                    f'{loss_name} is not a tensor or list of tensors')
+
+        loss = sum(_value for _key, _value in log_vars.items()
+                   if 'loss' in _key)
+
+        log_vars['loss'] = loss
+        for loss_name, loss_value in log_vars.items():
+            # reduce loss when distributed training
+            if dist.is_available() and dist.is_initialized():
+                loss_value = loss_value.data.clone()
+                dist.all_reduce(loss_value.div_(dist.get_world_size()))
+            log_vars[loss_name] = loss_value.item()
+
+        return loss, log_vars
+
+    def train_step(self, data, optimizer):
+        """The iteration step during training.
+
+        This method defines an iteration step during training, except for the
+        back propagation and optimizer updating, which are done in an optimizer
+        hook. Note that in some complicated cases or models, the whole process
+        including back propagation and optimizer updating is also defined in
+        this method, such as GAN.
+
+        Args:
+            data (dict): The output of dataloader.
+            optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
+                runner is passed to ``train_step()``. This argument is unused
+                and reserved.
+
+        Returns:
+            dict: It should contain at least 3 keys: ``loss``, ``log_vars``, \
+                ``num_samples``.
+
+                - ``loss`` is a tensor for back propagation, which can be a \
+                weighted sum of multiple losses.
+                - ``log_vars`` contains all the variables to be sent to the
+                logger.
+                - ``num_samples`` indicates the batch size (when the model is \
+                DDP, it means the batch size on each GPU), which is used for \
+                averaging the logs.
+        """
+        losses = self(**data)
+        loss, log_vars = self._parse_losses(losses)
+
+        outputs = dict(
+            loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
+
+        return outputs
+
+    def val_step(self, data, optimizer):
+        """The iteration step during validation.
+
+        This method shares the same signature as :func:`train_step`, but used
+        during val epochs. Note that the evaluation after training epochs is
+        not implemented with this method, but an evaluation hook.
+        """
+        losses = self(**data)
+        loss, log_vars = self._parse_losses(losses)
+
+        outputs = dict(
+            loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
+
+        return outputs
+
+    def show_result(self,
+                    img,
+                    result,
+                    score_thr=0.3,
+                    bbox_color=(72, 101, 241),
+                    text_color=(72, 101, 241),
+                    mask_color=None,
+                    thickness=2,
+                    font_scale=0.5,
+                    font_size=13,
+                    win_name='',
+                    fig_size=(15, 10),
+                    show=False,
+                    wait_time=0,
+                    out_file=None):
+        """Draw `result` over `img`.
+
+        Args:
+            img (str or Tensor): The image to be displayed.
+            result (Tensor or tuple): The results to draw over `img`
+                bbox_result or (bbox_result, segm_result).
+            score_thr (float, optional): Minimum score of bboxes to be shown.
+                Default: 0.3.
+            bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
+               The tuple of color should be in BGR order. Default: 'green'
+            text_color (str or tuple(int) or :obj:`Color`):Color of texts.
+               The tuple of color should be in BGR order. Default: 'green'
+            mask_color (None or str or tuple(int) or :obj:`Color`):
+               Color of masks. The tuple of color should be in BGR order.
+               Default: None
+            thickness (int): Thickness of lines. Default: 2
+            font_scale (float): Font scales of texts. Default: 0.5
+            font_size (int): Font size of texts. Default: 13
+            win_name (str): The window name. Default: ''
+            fig_size (tuple): Figure size of the pyplot figure.
+                Default: (15, 10)
+            wait_time (float): Value of waitKey param.
+                Default: 0.
+            show (bool): Whether to show the image.
+                Default: False.
+            out_file (str or None): The filename to write the image.
+                Default: None.
+
+        Returns:
+            img (Tensor): Only if not `show` or `out_file`
+        """
+        img = mmcv.imread(img)
+        img = img.copy()
+        if isinstance(result, tuple):
+            bbox_result, segm_result = result
+            if isinstance(segm_result, tuple):
+                segm_result = segm_result[0]  # ms rcnn
+        else:
+            bbox_result, segm_result = result, None
+        bboxes = np.vstack(bbox_result)
+        labels = [
+            np.full(bbox.shape[0], i, dtype=np.int32)
+            for i, bbox in enumerate(bbox_result)
+        ]
+        labels = np.concatenate(labels)
+        # draw segmentation masks
+        segms = None
+        if segm_result is not None and len(labels) > 0:  # non empty
+            segms = mmcv.concat_list(segm_result)
+            if isinstance(segms[0], torch.Tensor):
+                segms = torch.stack(segms, dim=0).detach().cpu().numpy()
+            else:
+                segms = np.stack(segms, axis=0)
+        # if out_file specified, do not show image in window
+        if out_file is not None:
+            show = False
+        # draw bounding boxes
+        imshow_det_bboxes(
+            img,
+            bboxes,
+            labels,
+            segms,
+            class_names=self.CLASSES,
+            score_thr=score_thr,
+            bbox_color=bbox_color,
+            text_color=text_color,
+            mask_color=mask_color,
+            thickness=thickness,
+            font_scale=font_scale,
+            font_size=font_size,
+            win_name=win_name,
+            fig_size=fig_size,
+            show=show,
+            wait_time=wait_time,
+            out_file=out_file)
+
+        if not (show or out_file):
+            return img

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/cascade_rcnn.py

+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class CascadeRCNN(TwoStageDetector):
+    r"""Implementation of `Cascade R-CNN: Delving into High Quality Object
+    Detection <https://arxiv.org/abs/1906.09756>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 rpn_head=None,
+                 roi_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super(CascadeRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained)
+
+    def show_result(self, data, result, **kwargs):
+        """Show prediction results of the detector."""
+        if self.with_mask:
+            ms_bbox_result, ms_segm_result = result
+            if isinstance(ms_bbox_result, dict):
+                result = (ms_bbox_result['ensemble'],
+                          ms_segm_result['ensemble'])
+        else:
+            if isinstance(result, dict):
+                result = result['ensemble']
+        return super(CascadeRCNN, self).show_result(data, result, **kwargs)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/cornernet.py

+import torch
+
+from mmdet.core import bbox2result, bbox_mapping_back
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class CornerNet(SingleStageDetector):
+    """CornerNet.
+
+    This detector is the implementation of the paper `CornerNet: Detecting
+    Objects as Paired Keypoints <https://arxiv.org/abs/1808.01244>`_ .
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super(CornerNet, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                        test_cfg, pretrained)
+
+    def merge_aug_results(self, aug_results, img_metas):
+        """Merge augmented detection bboxes and score.
+
+        Args:
+            aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each
+                image.
+            img_metas (list[list[dict]]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+
+        Returns:
+            tuple: (bboxes, labels)
+        """
+        recovered_bboxes, aug_labels = [], []
+        for bboxes_labels, img_info in zip(aug_results, img_metas):
+            img_shape = img_info[0]['img_shape']  # using shape before padding
+            scale_factor = img_info[0]['scale_factor']
+            flip = img_info[0]['flip']
+            bboxes, labels = bboxes_labels
+            bboxes, scores = bboxes[:, :4], bboxes[:, -1:]
+            bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
+            recovered_bboxes.append(torch.cat([bboxes, scores], dim=-1))
+            aug_labels.append(labels)
+
+        bboxes = torch.cat(recovered_bboxes, dim=0)
+        labels = torch.cat(aug_labels)
+
+        if bboxes.shape[0] > 0:
+            out_bboxes, out_labels = self.bbox_head._bboxes_nms(
+                bboxes, labels, self.bbox_head.test_cfg)
+        else:
+            out_bboxes, out_labels = bboxes, labels
+
+        return out_bboxes, out_labels
+
+    def aug_test(self, imgs, img_metas, rescale=False):
+        """Augment testing of CornerNet.
+
+        Args:
+            imgs (list[Tensor]): Augmented images.
+            img_metas (list[list[dict]]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+
+        Note:
+            ``imgs`` must including flipped image pairs.
+
+        Returns:
+            list[list[np.ndarray]]: BBox results of each image and classes.
+                The outer list corresponds to each image. The inner list
+                corresponds to each class.
+        """
+        img_inds = list(range(len(imgs)))
+
+        assert img_metas[0][0]['flip'] + img_metas[1][0]['flip'], (
+            'aug test must have flipped image pair')
+        aug_results = []
+        for ind, flip_ind in zip(img_inds[0::2], img_inds[1::2]):
+            img_pair = torch.cat([imgs[ind], imgs[flip_ind]])
+            x = self.extract_feat(img_pair)
+            outs = self.bbox_head(x)
+            bbox_list = self.bbox_head.get_bboxes(
+                *outs, [img_metas[ind], img_metas[flip_ind]], False, False)
+            aug_results.append(bbox_list[0])
+            aug_results.append(bbox_list[1])
+
+        bboxes, labels = self.merge_aug_results(aug_results, img_metas)
+        bbox_results = bbox2result(bboxes, labels, self.bbox_head.num_classes)
+
+        return [bbox_results]

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/detr.py

+from mmdet.core import bbox2result
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class DETR(SingleStageDetector):
+    r"""Implementation of `DETR: End-to-End Object Detection with
+    Transformers <https://arxiv.org/pdf/2005.12872>`_"""
+
+    def __init__(self,
+                 backbone,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super(DETR, self).__init__(backbone, None, bbox_head, train_cfg,
+                                   test_cfg, pretrained)
+
+    def simple_test(self, img, img_metas, rescale=False):
+        """Test function without test time augmentation.
+
+        Args:
+            imgs (list[torch.Tensor]): List of multiple images
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[list[np.ndarray]]: BBox results of each image and classes.
+                The outer list corresponds to each image. The inner list
+                corresponds to each class.
+        """
+        batch_size = len(img_metas)
+        assert batch_size == 1, 'Currently only batch_size 1 for inference ' \
+            f'mode is supported. Found batch_size {batch_size}.'
+        x = self.extract_feat(img)
+        outs = self.bbox_head(x, img_metas)
+        bbox_list = self.bbox_head.get_bboxes(
+            *outs, img_metas, rescale=rescale)
+
+        bbox_results = [
+            bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
+            for det_bboxes, det_labels in bbox_list
+        ]
+        return bbox_results

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/fast_rcnn.py

+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class FastRCNN(TwoStageDetector):
+    """Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_"""
+
+    def __init__(self,
+                 backbone,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None):
+        super(FastRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained)
+
+    def forward_test(self, imgs, img_metas, proposals, **kwargs):
+        """
+        Args:
+            imgs (List[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains all images in the batch.
+            img_metas (List[List[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch.
+            proposals (List[List[Tensor]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch. The Tensor should have a shape Px4, where
+                P is the number of proposals.
+        """
+        for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
+            if not isinstance(var, list):
+                raise TypeError(f'{name} must be a list, but got {type(var)}')
+
+        num_augs = len(imgs)
+        if num_augs != len(img_metas):
+            raise ValueError(f'num of augmentations ({len(imgs)}) '
+                             f'!= num of image meta ({len(img_metas)})')
+
+        if num_augs == 1:
+            return self.simple_test(imgs[0], img_metas[0], proposals[0],
+                                    **kwargs)
+        else:
+            # TODO: support test-time augmentation
+            assert NotImplementedError

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/faster_rcnn.py

+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class FasterRCNN(TwoStageDetector):
+    """Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_"""
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None):
+        super(FasterRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/fcos.py

+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class FCOS(SingleStageDetector):
+    """Implementation of `FCOS <https://arxiv.org/abs/1904.01355>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                   test_cfg, pretrained)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/fovea.py

+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class FOVEA(SingleStageDetector):
+    """Implementation of `FoveaBox <https://arxiv.org/abs/1904.03797>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super(FOVEA, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                    test_cfg, pretrained)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/fsaf.py

+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class FSAF(SingleStageDetector):
+    """Implementation of `FSAF <https://arxiv.org/abs/1903.00621>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super(FSAF, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                   test_cfg, pretrained)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/gfl.py

+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class GFL(SingleStageDetector):
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super(GFL, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                  test_cfg, pretrained)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/grid_rcnn.py

+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class GridRCNN(TwoStageDetector):
+    """Grid R-CNN.
+
+    This detector is the implementation of:
+    - Grid R-CNN (https://arxiv.org/abs/1811.12030)
+    - Grid R-CNN Plus: Faster and Better (https://arxiv.org/abs/1906.05688)
+    """
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None):
+        super(GridRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained)

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/htc.py

+from ..builder import DETECTORS
+from .cascade_rcnn import CascadeRCNN
+
+
+@DETECTORS.register_module()
+class HybridTaskCascade(CascadeRCNN):
+    """Implementation of `HTC <https://arxiv.org/abs/1901.07518>`_"""
+
+    def __init__(self, **kwargs):
+        super(HybridTaskCascade, self).__init__(**kwargs)
+
+    @property
+    def with_semantic(self):
+        """bool: whether the detector has a semantic head"""
+        return self.roi_head.with_semantic

PyTorch/NLP/Conformer-main/mmdetection/mmdet/models/detectors/mask_rcnn.py

+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class MaskRCNN(TwoStageDetector):
+    """Implementation of `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_"""
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None):
+        super(MaskRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained)