add a new version code of PicoDet (#5170)

* add a new version code of PicoDet

configs/picodet/_base_/picodet_esnetv2.yml

+architecture: PicoDet
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x1_0_pretrained.pdparams
+export_post_process: False    # Whether post-processing is included in the network when export model.
+
+PicoDet:
+  backbone: ESNet
+  neck: ESPAN
+  head: PicoHeadV2
+
+ESNet:
+  scale: 1.0
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 1.0, 0.625, 0.5, 0.75, 0.625, 0.625, 0.5, 0.625, 1.0, 0.625, 0.75]
+
+ESPAN:
+  out_channels: 128
+  use_depthwise: True
+  num_features: 4
+
+PicoHeadV2:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 128
+    feat_out: 128
+    num_convs: 4
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+    use_se: True
+  fpn_stride: [8, 16, 32, 64]
+  feat_in_chan: 128
+  prior_prob: 0.01
+  reg_max: 7
+  cell_offset: 0.5
+  grid_cell_scale: 5.0
+  static_assigner_epoch: 100
+  use_align_head: True
+  static_assigner:
+    name: ATSSAssigner
+    topk: 9
+    force_gt_matching: False
+  assigner:
+    name: TaskAlignedAssigner
+    topk: 13
+    alpha: 1.0
+    beta: 6.0
+  loss_class:
+    name: VarifocalLoss
+    use_sigmoid: False
+    iou_weighted: True
+    loss_weight: 1.0
+  loss_dfl:
+    name: DistributionFocalLoss
+    loss_weight: 0.5
+  loss_bbox:
+    name: GIoULoss
+    loss_weight: 2.5
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.025
+    nms_threshold: 0.6

configs/picodet/_base_/picodet_v2_416_reader.yml

+worker_num: 6
+TrainReader:
+  sample_transforms:
+  - Decode: {}
+  - RandomCrop: {}
+  - RandomFlip: {prob: 0.5}
+  - RandomDistort: {}
+  batch_transforms:
+  - BatchRandomResize: {target_size: [352, 384, 416, 448, 480], random_size: True, random_interp: True, keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  - PadGT: {}
+  batch_size: 80
+  shuffle: true
+  drop_last: true
+
+
+EvalReader:
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: [416, 416], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 8
+  shuffle: false
+
+
+TestReader:
+  inputs_def:
+    image_shape: [1, 3, 416, 416]
+  sample_transforms:
+  - Decode: {}
+  - Resize: {interp: 2, target_size: [416, 416], keep_ratio: False}
+  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
+  - Permute: {}
+  batch_transforms:
+  - PadBatch: {pad_to_stride: 32}
+  batch_size: 1
+  shuffle: false

configs/picodet/more_config/picodet_v2_lcnet_1_5x_416_coco.yml

+_BASE_: [
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
+  '../_base_/picodet_esnetv2.yml',
+  '../_base_/optimizer_300e.yml',
+  '../_base_/picodet_v2_416_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/LCNet_x1_5_pretrained.pdparams
+weights: output/picodet_lcnet_1_5x_416_coco/model_final
+find_unused_parameters: True
+use_ema: true
+snapshot_epoch: 10
+
+PicoDet:
+  backbone: LCNet
+  neck: ESPAN
+  head: PicoHeadV2
+
+LCNet:
+  scale: 1.5
+  feature_maps: [3, 4, 5]
+
+TrainReader:
+  batch_size: 32
+
+LearningRate:
+  base_lr: 0.2
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300

configs/picodet/picodet_v2_s_416_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '_base_/picodet_esnetv2.yml',
+  '_base_/optimizer_300e.yml',
+  '_base_/picodet_v2_416_reader.yml',
+]
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x0_75_pretrained.pdparams
+weights: output/picodet_s_416_coco/model_final
+find_unused_parameters: True
+use_ema: true
+snapshot_epoch: 10
+
+ESNet:
+  scale: 0.75
+  feature_maps: [4, 11, 14]
+  act: hard_swish
+  channel_ratio: [0.875, 0.5, 0.5, 0.5, 0.625, 0.5, 0.625, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+
+ESPAN:
+  out_channels: 96
+
+PicoHeadV2:
+  conv_feat:
+    name: PicoFeat
+    feat_in: 96
+    feat_out: 96
+    num_convs: 2
+    num_fpn_stride: 4
+    norm_type: bn
+    share_cls_reg: True
+    use_se: True
+  feat_in_chan: 96
+
+TrainReader:
+  batch_size: 56
+
+LearningRate:
+  base_lr: 0.3
+  schedulers:
+  - !CosineDecay
+    max_epochs: 300
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 300

ppdet/engine/export_utils.py

@@ -167,12 +167,13 @@ def _dump_infer_config(config, path, image_shape, model):
     if infer_arch == 'PicoDet':
         if config.get('export_post_process', False):
             infer_cfg['arch'] = 'GFL'
-        infer_cfg['NMS'] = config['PicoHead']['nms']
+        head_name = 'PicoHeadV2' if config['PicoHeadV2'] else 'PicoHead'
+        infer_cfg['NMS'] = config[head_name]['nms']
         # In order to speed up the prediction, the threshold of nms 
         # is adjusted here, which can be changed in infer_cfg.yml
-        config['PicoHead']['nms']["score_threshold"] = 0.3
-        config['PicoHead']['nms']["nms_threshold"] = 0.5
-        infer_cfg['fpn_stride'] = config['PicoHead']['fpn_stride']
+        config[head_name]['nms']["score_threshold"] = 0.3
+        config[head_name]['nms']["nms_threshold"] = 0.5
+        infer_cfg['fpn_stride'] = config[head_name]['fpn_stride']
 
     yaml.dump(infer_cfg, open(path, 'w'))
     logger.info("Export inference config file to {}".format(os.path.join(path)))

ppdet/modeling/heads/pico_head.py

@@ -23,11 +23,38 @@ import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle import ParamAttr
 from paddle.nn.initializer import Normal, Constant
+from paddle.fluid.dygraph import parallel_helper
 
+from ppdet.modeling.ops import get_static_shape
+from ..initializer import normal_
+from ..assigners.utils import generate_anchors_for_grid_cell
+from ..bbox_utils import bbox_center, batch_distance2bbox, bbox2distance
 from ppdet.core.workspace import register
 from ppdet.modeling.layers import ConvNormLayer
-from ppdet.modeling.bbox_utils import batch_distance2bbox
 from .simota_head import OTAVFLHead
+from .gfl_head import Integral, GFLHead
+from ppdet.modeling.necks.csp_pan import DPModule
+
+eps = 1e-9
+
+__all__ = ['PicoHead', 'PicoHeadV2', 'PicoFeat']
+
+
+class PicoSE(nn.Layer):
+    def __init__(self, feat_channels):
+        super(PicoSE, self).__init__()
+        self.fc = nn.Conv2D(feat_channels, feat_channels, 1)
+        self.conv = ConvNormLayer(feat_channels, feat_channels, 1, 1)
+
+        self._init_weights()
+
+    def _init_weights(self):
+        normal_(self.fc.weight, std=0.001)
+
+    def forward(self, feat, avg_feat):
+        weight = F.sigmoid(self.fc(avg_feat))
+        out = self.conv(feat * weight)
+        return out
 
 
 @register
@@ -40,6 +67,9 @@ class PicoFeat(nn.Layer):
         feat_out (int): The channel number of output Tensor.
         num_convs (int): The convolution number of the LiteGFLFeat.
         norm_type (str): Normalization type, 'bn'/'sync_bn'/'gn'.
+        share_cls_reg (bool): Whether to share the cls and reg output.
+        act (str): The act of per layers.
+        use_se (bool): Whether to use se module.
     """
 
     def __init__(self,
@@ -49,14 +79,20 @@ class PicoFeat(nn.Layer):
                  num_convs=2,
                  norm_type='bn',
                  share_cls_reg=False,
-                 act='hard_swish'):
+                 act='hard_swish',
+                 use_se=False):
         super(PicoFeat, self).__init__()
         self.num_convs = num_convs
         self.norm_type = norm_type
         self.share_cls_reg = share_cls_reg
         self.act = act
+        self.use_se = use_se
         self.cls_convs = []
         self.reg_convs = []
+        if use_se:
+            assert share_cls_reg == True, \
+                'In the case of using se, share_cls_reg is not supported'
+            self.se = nn.LayerList()
         for stage_idx in range(num_fpn_stride):
             cls_subnet_convs = []
             reg_subnet_convs = []
@@ -112,6 +148,8 @@ class PicoFeat(nn.Layer):
                     reg_subnet_convs.append(reg_conv_pw)
             self.cls_convs.append(cls_subnet_convs)
             self.reg_convs.append(reg_subnet_convs)
+            if use_se:
+                self.se.append(PicoSE(feat_out))
 
     def act_func(self, x):
         if self.act == "leaky_relu":
@@ -126,8 +164,13 @@ class PicoFeat(nn.Layer):
         reg_feat = fpn_feat
         for i in range(len(self.cls_convs[stage_idx])):
             cls_feat = self.act_func(self.cls_convs[stage_idx][i](cls_feat))
+            reg_feat = cls_feat
             if not self.share_cls_reg:
                 reg_feat = self.act_func(self.reg_convs[stage_idx][i](reg_feat))
+        if self.use_se:
+            avg_feat = F.adaptive_avg_pool2d(cls_feat, (1, 1))
+            se_feat = self.act_func(self.se[stage_idx](cls_feat, avg_feat))
+            return cls_feat, se_feat
         return cls_feat, reg_feat
 
 
@@ -291,3 +334,286 @@ class PicoHead(OTAVFLHead):
             bboxes_reg_list.append(bbox_pred)
 
         return (cls_logits_list, bboxes_reg_list)
+
+
+@register
+class PicoHeadV2(GFLHead):
+    """
+    PicoHeadV2
+    Args:
+        conv_feat (object): Instance of 'PicoFeat'
+        num_classes (int): Number of classes
+        fpn_stride (list): The stride of each FPN Layer
+        prior_prob (float): Used to set the bias init for the class prediction layer
+        loss_class (object): Instance of VariFocalLoss.
+        loss_dfl (object): Instance of DistributionFocalLoss.
+        loss_bbox (object): Instance of bbox loss.
+        assigner (object): Instance of label assigner.
+        reg_max: Max value of integral set :math: `{0, ..., reg_max}`
+                n QFL setting. Default: 7.
+    """
+    __inject__ = [
+        'conv_feat', 'dgqp_module', 'loss_class', 'loss_dfl', 'loss_bbox',
+        'static_assigner', 'assigner', 'nms'
+    ]
+    __shared__ = ['num_classes']
+
+    def __init__(
+            self,
+            conv_feat='PicoFeatV2',
+            dgqp_module=None,
+            num_classes=80,
+            fpn_stride=[8, 16, 32],
+            prior_prob=0.01,
+            use_align_head=True,
+            loss_class='VariFocalLoss',
+            loss_dfl='DistributionFocalLoss',
+            loss_bbox='GIoULoss',
+            static_assigner_epoch=60,
+            static_assigner='ATSSAssigner',
+            assigner='TaskAlignedAssigner',
+            reg_max=16,
+            feat_in_chan=96,
+            nms=None,
+            nms_pre=1000,
+            cell_offset=0,
+            act='hard_swish',
+            grid_cell_scale=5.0, ):
+        super(PicoHeadV2, self).__init__(
+            conv_feat=conv_feat,
+            dgqp_module=dgqp_module,
+            num_classes=num_classes,
+            fpn_stride=fpn_stride,
+            prior_prob=prior_prob,
+            loss_class=loss_class,
+            loss_dfl=loss_dfl,
+            loss_bbox=loss_bbox,
+            reg_max=reg_max,
+            feat_in_chan=feat_in_chan,
+            nms=nms,
+            nms_pre=nms_pre,
+            cell_offset=cell_offset, )
+        self.conv_feat = conv_feat
+        self.num_classes = num_classes
+        self.fpn_stride = fpn_stride
+        self.prior_prob = prior_prob
+        self.loss_vfl = loss_class
+        self.loss_dfl = loss_dfl
+        self.loss_bbox = loss_bbox
+
+        self.static_assigner_epoch = static_assigner_epoch
+        self.static_assigner = static_assigner
+        self.assigner = assigner
+
+        self.reg_max = reg_max
+        self.feat_in_chan = feat_in_chan
+        self.nms = nms
+        self.nms_pre = nms_pre
+        self.cell_offset = cell_offset
+        self.act = act
+        self.grid_cell_scale = grid_cell_scale
+        self.use_align_head = use_align_head
+        self.cls_out_channels = self.num_classes
+
+        bias_init_value = -math.log((1 - self.prior_prob) / self.prior_prob)
+        # Clear the super class initialization
+        self.gfl_head_cls = None
+        self.gfl_head_reg = None
+        self.scales_regs = None
+
+        self.head_cls_list = []
+        self.head_reg_list = []
+        self.cls_align = nn.LayerList()
+
+        for i in range(len(fpn_stride)):
+            head_cls = self.add_sublayer(
+                "head_cls" + str(i),
+                nn.Conv2D(
+                    in_channels=self.feat_in_chan,
+                    out_channels=self.cls_out_channels,
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                    weight_attr=ParamAttr(initializer=Normal(
+                        mean=0., std=0.01)),
+                    bias_attr=ParamAttr(
+                        initializer=Constant(value=bias_init_value))))
+            self.head_cls_list.append(head_cls)
+            head_reg = self.add_sublayer(
+                "head_reg" + str(i),
+                nn.Conv2D(
+                    in_channels=self.feat_in_chan,
+                    out_channels=4 * (self.reg_max + 1),
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                    weight_attr=ParamAttr(initializer=Normal(
+                        mean=0., std=0.01)),
+                    bias_attr=ParamAttr(initializer=Constant(value=0))))
+            self.head_reg_list.append(head_reg)
+            if self.use_align_head:
+                self.cls_align.append(
+                    DPModule(
+                        self.feat_in_chan,
+                        1,
+                        5,
+                        act=self.act,
+                        use_act_in_out=False))
+
+    def forward(self, fpn_feats, deploy=False):
+        assert len(fpn_feats) == len(
+            self.fpn_stride
+        ), "The size of fpn_feats is not equal to size of fpn_stride"
+        anchors, num_anchors_list, stride_tensor_list = generate_anchors_for_grid_cell(
+            fpn_feats, self.fpn_stride, self.grid_cell_scale, self.cell_offset)
+
+        cls_score_list, reg_list, box_list = [], [], []
+        for i, fpn_feat, anchor, stride, align_cls in zip(
+                range(len(self.fpn_stride)), fpn_feats, anchors,
+                self.fpn_stride, self.cls_align):
+            b, _, h, w = get_static_shape(fpn_feat)
+            # task decomposition
+            conv_cls_feat, se_feat = self.conv_feat(fpn_feat, i)
+            cls_logit = self.head_cls_list[i](se_feat)
+            reg_pred = self.head_reg_list[i](se_feat)
+
+            # cls prediction and alignment
+            if self.use_align_head:
+                cls_prob = F.sigmoid(align_cls(conv_cls_feat))
+                cls_score = (F.sigmoid(cls_logit) * cls_prob + eps).sqrt()
+            else:
+                cls_score = F.sigmoid(cls_logit)
+
+            anchor_centers = bbox_center(anchor).unsqueeze(0) / stride
+            anchor_centers = anchor_centers.reshape([1, h, w, 2])
+
+            pred_distances = self.distribution_project(
+                reg_pred.transpose([0, 2, 3, 1])).reshape([b, h, w, 4])
+            reg_bbox = batch_distance2bbox(
+                anchor_centers, pred_distances, max_shapes=None)
+            if not self.training:
+                cls_score_list.append(
+                    cls_score.transpose([0, 2, 3, 1]).reshape(
+                        [b, -1, self.cls_out_channels]))
+                box_list.append(reg_bbox.reshape([b, -1, 4]) * stride)
+            else:
+                cls_score_list.append(cls_score.flatten(2).transpose([0, 2, 1]))
+                reg_list.append(reg_pred.flatten(2).transpose([0, 2, 1]))
+                box_list.append(reg_bbox.reshape([b, -1, 4]))
+
+        if not self.training:
+            return cls_score_list, box_list
+        else:
+            cls_score_list = paddle.concat(cls_score_list, axis=1)
+            box_list = paddle.concat(box_list, axis=1)
+            reg_list = paddle.concat(reg_list, axis=1)
+            anchors = paddle.concat(anchors)
+            anchors.stop_gradient = True
+            stride_tensor_list = paddle.concat(stride_tensor_list)
+            stride_tensor_list.stop_gradient = True
+            return cls_score_list, reg_list, box_list, anchors, num_anchors_list, stride_tensor_list
+
+    def get_loss(self, head_outs, gt_meta):
+        pred_scores, pred_regs, pred_bboxes, anchors, num_anchors_list, stride_tensor_list = head_outs
+        gt_labels = gt_meta['gt_class']
+        gt_bboxes = gt_meta['gt_bbox']
+        gt_scores = gt_meta['gt_score'] if 'gt_score' in gt_meta else None
+        num_imgs = gt_meta['im_id'].shape[0]
+        pad_gt_mask = gt_meta['pad_gt_mask']
+
+        centers = bbox_center(anchors)
+
+        # label assignment
+        if gt_meta['epoch_id'] < self.static_assigner_epoch:
+            assigned_labels, assigned_bboxes, assigned_scores = self.static_assigner(
+                anchors,
+                num_anchors_list,
+                gt_labels,
+                gt_bboxes,
+                pad_gt_mask,
+                bg_index=self.num_classes,
+                gt_scores=gt_scores,
+                pred_bboxes=pred_bboxes.detach() * stride_tensor_list)
+
+        else:
+            assigned_labels, assigned_bboxes, assigned_scores = self.assigner(
+                pred_scores.detach(),
+                pred_bboxes.detach() * stride_tensor_list,
+                centers,
+                num_anchors_list,
+                gt_labels,
+                gt_bboxes,
+                pad_gt_mask,
+                bg_index=self.num_classes,
+                gt_scores=gt_scores)
+
+        assigned_bboxes /= stride_tensor_list
+
+        centers_shape = centers.shape
+        flatten_centers = centers.expand(
+            [num_imgs, centers_shape[0], centers_shape[1]]).reshape([-1, 2])
+        flatten_strides = stride_tensor_list.expand(
+            [num_imgs, centers_shape[0], 1]).reshape([-1, 1])
+        flatten_cls_preds = pred_scores.reshape([-1, self.num_classes])
+        flatten_regs = pred_regs.reshape([-1, 4 * (self.reg_max + 1)])
+        flatten_bboxes = pred_bboxes.reshape([-1, 4])
+        flatten_bbox_targets = assigned_bboxes.reshape([-1, 4])
+        flatten_labels = assigned_labels.reshape([-1])
+        flatten_assigned_scores = assigned_scores.reshape(
+            [-1, self.num_classes])
+
+        pos_inds = paddle.nonzero(
+            paddle.logical_and((flatten_labels >= 0),
+                               (flatten_labels < self.num_classes)),
+            as_tuple=False).squeeze(1)
+
+        num_total_pos = len(pos_inds)
+
+        if num_total_pos > 0:
+            pos_bbox_targets = paddle.gather(
+                flatten_bbox_targets, pos_inds, axis=0)
+            pos_decode_bbox_pred = paddle.gather(
+                flatten_bboxes, pos_inds, axis=0)
+            pos_reg = paddle.gather(flatten_regs, pos_inds, axis=0)
+            pos_strides = paddle.gather(flatten_strides, pos_inds, axis=0)
+            pos_centers = paddle.gather(
+                flatten_centers, pos_inds, axis=0) / pos_strides
+
+            weight_targets = flatten_assigned_scores.detach()
+            weight_targets = paddle.gather(
+                weight_targets.max(axis=1, keepdim=True), pos_inds, axis=0)
+
+            pred_corners = pos_reg.reshape([-1, self.reg_max + 1])
+            target_corners = bbox2distance(pos_centers, pos_bbox_targets,
+                                           self.reg_max).reshape([-1])
+            # regression loss
+            loss_bbox = paddle.sum(
+                self.loss_bbox(pos_decode_bbox_pred,
+                               pos_bbox_targets) * weight_targets)
+
+            # dfl loss
+            loss_dfl = self.loss_dfl(
+                pred_corners,
+                target_corners,
+                weight=weight_targets.expand([-1, 4]).reshape([-1]),
+                avg_factor=4.0)
+        else:
+            loss_bbox = paddle.zeros([1])
+            loss_dfl = paddle.zeros([1])
+
+        avg_factor = flatten_assigned_scores.sum()
+        if paddle.fluid.core.is_compiled_with_dist(
+        ) and parallel_helper._is_parallel_ctx_initialized():
+            paddle.distributed.all_reduce(avg_factor)
+            avg_factor = paddle.clip(
+                avg_factor / paddle.distributed.get_world_size(), min=1)
+        loss_vfl = self.loss_vfl(
+            flatten_cls_preds, flatten_assigned_scores, avg_factor=avg_factor)
+
+        loss_bbox = loss_bbox / avg_factor
+        loss_dfl = loss_dfl / avg_factor
+
+        loss_states = dict(
+            loss_vfl=loss_vfl, loss_bbox=loss_bbox, loss_dfl=loss_dfl)
+
+        return loss_states

ppdet/modeling/necks/__init__.py

@@ -19,6 +19,7 @@ from . import ttf_fpn
 from . import centernet_fpn
 from . import bifpn
 from . import csp_pan
+from . import es_pan
 
 from .fpn import *
 from .yolo_fpn import *
@@ -28,3 +29,4 @@ from .centernet_fpn import *
 from .blazeface_fpn import *
 from .bifpn import *
 from .csp_pan import *
+from .es_pan import *

ppdet/modeling/necks/csp_pan.py

@@ -19,7 +19,6 @@ import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle import ParamAttr
-from paddle.regularizer import L2Decay
 from ppdet.core.workspace import register, serializable
 from ..shape_spec import ShapeSpec
 
@@ -36,8 +35,6 @@ class ConvBNLayer(nn.Layer):
                  act='leaky_relu'):
         super(ConvBNLayer, self).__init__()
         initializer = nn.initializer.KaimingUniform()
-        self.act = act
-        assert self.act in ['leaky_relu', "hard_swish"]
         self.conv = nn.Conv2D(
             in_channels=in_channel,
             out_channels=out_channel,
@@ -48,13 +45,14 @@ class ConvBNLayer(nn.Layer):
             weight_attr=ParamAttr(initializer=initializer),
             bias_attr=False)
         self.bn = nn.BatchNorm2D(out_channel)
+        if act == "hard_swish":
+            act = 'hardswish'
+        self.act = act
 
     def forward(self, x):
         x = self.bn(self.conv(x))
-        if self.act == "leaky_relu":
-            x = F.leaky_relu(x)
-        elif self.act == "hard_swish":
-            x = F.hardswish(x)
+        if self.act:
+            x = getattr(F, self.act)(x)
         return x
 
 
@@ -75,10 +73,11 @@ class DPModule(nn.Layer):
                  out_channel=96,
                  kernel_size=3,
                  stride=1,
-                 act='leaky_relu'):
+                 act='leaky_relu',
+                 use_act_in_out=True):
         super(DPModule, self).__init__()
         initializer = nn.initializer.KaimingUniform()
-        self.act = act
+        self.use_act_in_out = use_act_in_out
         self.dwconv = nn.Conv2D(
             in_channels=in_channel,
             out_channels=out_channel,
@@ -98,17 +97,17 @@ class DPModule(nn.Layer):
             weight_attr=ParamAttr(initializer=initializer),
             bias_attr=False)
         self.bn2 = nn.BatchNorm2D(out_channel)
-
-    def act_func(self, x):
-        if self.act == "leaky_relu":
-            x = F.leaky_relu(x)
-        elif self.act == "hard_swish":
-            x = F.hardswish(x)
-        return x
+        if act == "hard_swish":
+            act = 'hardswish'
+        self.act = act
 
     def forward(self, x):
-        x = self.act_func(self.bn1(self.dwconv(x)))
-        x = self.act_func(self.bn2(self.pwconv(x)))
+        x = self.bn1(self.dwconv(x))
+        if self.act:
+            x = getattr(F, self.act)(x)
+        x = self.bn2(self.pwconv(x))
+        if self.use_act_in_out and self.act:
+            x = getattr(F, self.act)(x)
         return x
 
 

ppdet/modeling/necks/es_pan.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+from paddle.regularizer import L2Decay
+from ppdet.core.workspace import register, serializable
+
+from ..shape_spec import ShapeSpec
+from ..backbones.esnet import SEModule
+from .csp_pan import ConvBNLayer, Channel_T, DPModule
+
+__all__ = ['ESPAN']
+
+
+class ES_Block(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 mid_channels,
+                 out_channels,
+                 kernel_size=5,
+                 stride=1,
+                 act='leaky_relu'):
+        super(ES_Block, self).__init__()
+        self._residual = ConvBNLayer(
+            in_channel=in_channels,
+            out_channel=out_channels,
+            kernel_size=1,
+            stride=1,
+            groups=1,
+            act=act)
+        self._conv_pw = ConvBNLayer(
+            in_channel=in_channels,
+            out_channel=mid_channels // 2,
+            kernel_size=1,
+            stride=1,
+            groups=1,
+            act=act)
+        self._conv_dw = ConvBNLayer(
+            in_channel=mid_channels // 2,
+            out_channel=mid_channels // 2,
+            kernel_size=kernel_size,
+            stride=stride,
+            groups=mid_channels // 2,
+            act=None)
+        self._se = SEModule(mid_channels)
+
+        self._conv_linear = ConvBNLayer(
+            in_channel=mid_channels,
+            out_channel=out_channels,
+            kernel_size=1,
+            stride=1,
+            groups=1,
+            act=act)
+
+        self._out_conv = ConvBNLayer(
+            in_channel=out_channels * 2,
+            out_channel=out_channels,
+            kernel_size=1,
+            stride=1,
+            groups=1,
+            act=act)
+
+    def forward(self, inputs):
+        x1 = self._residual(inputs)
+        x2 = self._conv_pw(inputs)
+        x3 = self._conv_dw(x2)
+        x3 = paddle.concat([x2, x3], axis=1)
+        x3 = self._se(x3)
+        x3 = self._conv_linear(x3)
+        out = paddle.concat([x1, x3], axis=1)
+        out = self._out_conv(out)
+        return out
+
+
+@register
+@serializable
+class ESPAN(nn.Layer):
+    """Path Aggregation Network with ES module.
+
+    Args:
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (int): Number of output channels (used at each scale)
+        kernel_size (int): The conv2d kernel size of this Module.
+        num_features (int): Number of output features of CSPPAN module.
+        num_csp_blocks (int): Number of bottlenecks in CSPLayer. Default: 1
+        use_depthwise (bool): Whether to depthwise separable convolution in
+            blocks. Default: True
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=5,
+                 num_features=3,
+                 use_depthwise=True,
+                 act='hard_swish',
+                 spatial_scales=[0.125, 0.0625, 0.03125]):
+        super(ESPAN, self).__init__()
+        self.conv_t = Channel_T(in_channels, out_channels, act=act)
+        in_channels = [out_channels] * len(spatial_scales)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.spatial_scales = spatial_scales
+        self.num_features = num_features
+        conv_func = DPModule if use_depthwise else ConvBNLayer
+
+        if self.num_features == 4:
+            self.first_top_conv = conv_func(
+                in_channels[0], in_channels[0], kernel_size, stride=2, act=act)
+            self.second_top_conv = conv_func(
+                in_channels[0], in_channels[0], kernel_size, stride=2, act=act)
+            self.spatial_scales.append(self.spatial_scales[-1] / 2)
+
+        # build top-down blocks
+        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
+        self.top_down_blocks = nn.LayerList()
+        for idx in range(len(in_channels) - 1, 0, -1):
+            self.top_down_blocks.append(
+                ES_Block(
+                    in_channels[idx - 1] * 2,
+                    in_channels[idx - 1],
+                    in_channels[idx - 1],
+                    kernel_size=kernel_size,
+                    stride=1,
+                    act=act))
+
+        # build bottom-up blocks
+        self.downsamples = nn.LayerList()
+        self.bottom_up_blocks = nn.LayerList()
+        for idx in range(len(in_channels) - 1):
+            self.downsamples.append(
+                conv_func(
+                    in_channels[idx],
+                    in_channels[idx],
+                    kernel_size=kernel_size,
+                    stride=2,
+                    act=act))
+            self.bottom_up_blocks.append(
+                ES_Block(
+                    in_channels[idx] * 2,
+                    in_channels[idx + 1],
+                    in_channels[idx + 1],
+                    kernel_size=kernel_size,
+                    stride=1,
+                    act=act))
+
+    def forward(self, inputs):
+        """
+        Args:
+            inputs (tuple[Tensor]): input features.
+
+        Returns:
+            tuple[Tensor]: CSPPAN features.
+        """
+        assert len(inputs) == len(self.in_channels)
+        inputs = self.conv_t(inputs)
+
+        # top-down path
+        inner_outs = [inputs[-1]]
+        for idx in range(len(self.in_channels) - 1, 0, -1):
+            feat_heigh = inner_outs[0]
+            feat_low = inputs[idx - 1]
+
+            upsample_feat = self.upsample(feat_heigh)
+
+            inner_out = self.top_down_blocks[len(self.in_channels) - 1 - idx](
+                paddle.concat([upsample_feat, feat_low], 1))
+            inner_outs.insert(0, inner_out)
+
+        # bottom-up path
+        outs = [inner_outs[0]]
+        for idx in range(len(self.in_channels) - 1):
+            feat_low = outs[-1]
+            feat_height = inner_outs[idx + 1]
+            downsample_feat = self.downsamples[idx](feat_low)
+            out = self.bottom_up_blocks[idx](paddle.concat(
+                [downsample_feat, feat_height], 1))
+            outs.append(out)
+
+        top_features = None
+        if self.num_features == 4:
+            top_features = self.first_top_conv(inputs[-1])
+            top_features = top_features + self.second_top_conv(outs[-1])
+            outs.append(top_features)
+
+        return tuple(outs)
+
+    @property
+    def out_shape(self):
+        return [
+            ShapeSpec(
+                channels=self.out_channels, stride=1. / s)
+            for s in self.spatial_scales
+        ]
+
+    @classmethod
+    def from_config(cls, cfg, input_shape):
+        return {'in_channels': [i.channels for i in input_shape], }