change ssdvgg backbone

configs/ssd/ssd_vgg16_caffe_300_10x_coco.py

@@ -0,0 +1,119 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+# model settings
+input_size = 300
+model = dict(
+    type='SingleStageDetector',
+    pretrained='open-mmlab://vgg16_caffe',
+    backbone=dict(
+        type='SSDVGG',
+        depth=16,
+        with_last_pool=False,
+        ceil_mode=True,
+        out_indices=(3, 4),
+        out_feature_indices=(22, 34)),
+    neck=dict(
+        type='SSDNeck',
+        in_channels=(512, 1024),
+        out_channels=(512, 1024, 512, 256, 256, 256),
+        level_strides=(2, 2, 1, 1),
+        level_paddings=(1, 1, 0, 0),
+        is_vgg_neck=False,
+        l2_norm_scale=20),
+    bbox_head=dict(
+        type='SSDHead',
+        in_channels=(512, 1024, 512, 256, 256, 256),
+        num_classes=80,
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            input_size=input_size,
+            basesize_ratio_range=(0.15, 0.9),
+            strides=[8, 16, 32, 64, 100, 300],
+            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2])),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        smoothl1_beta=1.,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        nms=dict(type='nms', iou_threshold=0.45),
+        min_bbox_size=0,
+        score_thr=0.02,
+        max_per_img=200))
+cudnn_benchmark = True
+
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=3,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(_delete_=True)

mmdet/models/backbones/ssd_vgg.py

@@ -1,10 +1,8 @@
 import warnings
 
-import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from mmcv.cnn import VGG
-from mmcv.runner import BaseModule, Sequential
+from mmcv.runner import BaseModule
 
 from ..builder import BACKBONES
 
@@ -40,13 +38,11 @@ class SSDVGG(VGG, BaseModule):
     }
 
     def __init__(self,
-                 input_size,
                  depth,
                  with_last_pool=False,
                  ceil_mode=True,
                  out_indices=(3, 4),
                  out_feature_indices=(22, 34),
-                 l2_norm_scale=20.,
                  pretrained=None,
                  init_cfg=None):
         # TODO: in_channels for mmcv.VGG
@@ -55,8 +51,6 @@ def __init__(self,
             with_last_pool=with_last_pool,
             ceil_mode=ceil_mode,
             out_indices=out_indices)
-        assert input_size in (300, 512)
-        self.input_size = input_size
 
         self.features.add_module(
             str(len(self.features)),
@@ -72,12 +66,6 @@ def __init__(self,
             str(len(self.features)), nn.ReLU(inplace=True))
         self.out_feature_indices = out_feature_indices
 
-        self.inplanes = 1024
-        self.extra = self._make_extra_layers(self.extra_setting[input_size])
-        self.l2_norm = L2Norm(
-            self.features[out_feature_indices[0] - 1].out_channels,
-            l2_norm_scale)
-
         assert not (init_cfg and pretrained), \
             'init_cfg and pretrained cannot be setting at the same time'
         if isinstance(pretrained, str):
@@ -94,18 +82,6 @@ def __init__(self,
         else:
             raise TypeError('pretrained must be a str or None')
 
-        if init_cfg is None:
-            self.init_cfg += [
-                dict(
-                    type='Xavier',
-                    distribution='uniform',
-                    override=dict(name='extra')),
-                dict(
-                    type='Constant',
-                    val=self.l2_norm.scale,
-                    override=dict(name='l2_norm'))
-            ]
-
     def init_weights(self, pretrained=None):
         super(VGG, self).init_weights()
 
@@ -116,64 +92,8 @@ def forward(self, x):
             x = layer(x)
             if i in self.out_feature_indices:
                 outs.append(x)
-        for i, layer in enumerate(self.extra):
-            x = F.relu(layer(x), inplace=True)
-            if i % 2 == 1:
-                outs.append(x)
-        outs[0] = self.l2_norm(outs[0])
+
         if len(outs) == 1:
             return outs[0]
         else:
             return tuple(outs)
-
-    def _make_extra_layers(self, outplanes):
-        layers = []
-        kernel_sizes = (1, 3)
-        num_layers = 0
-        outplane = None
-        for i in range(len(outplanes)):
-            if self.inplanes == 'S':
-                self.inplanes = outplane
-                continue
-            k = kernel_sizes[num_layers % 2]
-            if outplanes[i] == 'S':
-                outplane = outplanes[i + 1]
-                conv = nn.Conv2d(
-                    self.inplanes, outplane, k, stride=2, padding=1)
-            else:
-                outplane = outplanes[i]
-                conv = nn.Conv2d(
-                    self.inplanes, outplane, k, stride=1, padding=0)
-            layers.append(conv)
-            self.inplanes = outplanes[i]
-            num_layers += 1
-        if self.input_size == 512:
-            layers.append(nn.Conv2d(self.inplanes, 256, 4, padding=1))
-
-        return Sequential(*layers)
-
-
-class L2Norm(nn.Module):
-
-    def __init__(self, n_dims, scale=20., eps=1e-10):
-        """L2 normalization layer.
-
-        Args:
-            n_dims (int): Number of dimensions to be normalized
-            scale (float, optional): Defaults to 20..
-            eps (float, optional): Used to avoid division by zero.
-                Defaults to 1e-10.
-        """
-        super(L2Norm, self).__init__()
-        self.n_dims = n_dims
-        self.weight = nn.Parameter(torch.Tensor(self.n_dims))
-        self.eps = eps
-        self.scale = scale
-
-    def forward(self, x):
-        """Forward function."""
-        # normalization layer convert to FP32 in FP16 training
-        x_float = x.float()
-        norm = x_float.pow(2).sum(1, keepdim=True).sqrt() + self.eps
-        return (self.weight[None, :, None, None].float().expand_as(x_float) *
-                x_float / norm).type_as(x)