diff --git a/mmdet/models/backbones/hrnet.py b/mmdet/models/backbones/hrnet.py
index 9c942d6ace8c628a7319303db01d33b95065647a..c73fed01d22cc0f174f349e5d097b1ceda9b2867 100644
--- a/mmdet/models/backbones/hrnet.py
+++ b/mmdet/models/backbones/hrnet.py
@@ -201,6 +201,7 @@ class HRNet(nn.Module):
Args:
extra (dict): detailed configuration for each stage of HRNet.
+ in_channels (int): Number of input image channels. Normally 3.
conv_cfg (dict): dictionary to construct and config conv layer.
norm_cfg (dict): dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
@@ -210,12 +211,52 @@ class HRNet(nn.Module):
memory while slowing down the training speed.
zero_init_residual (bool): whether to use zero init for last norm layer
in resblocks to let them behave as identity.
+
+ Example:
+ >>> from mmdet.models import HRNet
+ >>> import torch
+ >>> extra = dict(
+ >>> stage1=dict(
+ >>> num_modules=1,
+ >>> num_branches=1,
+ >>> block='BOTTLENECK',
+ >>> num_blocks=(4, ),
+ >>> num_channels=(64, )),
+ >>> stage2=dict(
+ >>> num_modules=1,
+ >>> num_branches=2,
+ >>> block='BASIC',
+ >>> num_blocks=(4, 4),
+ >>> num_channels=(32, 64)),
+ >>> stage3=dict(
+ >>> num_modules=4,
+ >>> num_branches=3,
+ >>> block='BASIC',
+ >>> num_blocks=(4, 4, 4),
+ >>> num_channels=(32, 64, 128)),
+ >>> stage4=dict(
+ >>> num_modules=3,
+ >>> num_branches=4,
+ >>> block='BASIC',
+ >>> num_blocks=(4, 4, 4, 4),
+ >>> num_channels=(32, 64, 128, 256)))
+ >>> self = HRNet(extra, in_channels=1)
+ >>> self.eval()
+ >>> inputs = torch.rand(1, 1, 32, 32)
+ >>> level_outputs = self.forward(inputs)
+ >>> for level_out in level_outputs:
+ ... print(tuple(level_out.shape))
+ (1, 32, 8, 8)
+ (1, 64, 4, 4)
+ (1, 128, 2, 2)
+ (1, 256, 1, 1)
"""
blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck}
def __init__(self,
extra,
+ in_channels=3,
conv_cfg=None,
norm_cfg=dict(type='BN'),
norm_eval=True,
@@ -235,7 +276,7 @@ class HRNet(nn.Module):
self.conv1 = build_conv_layer(
self.conv_cfg,
- 3,
+ in_channels,
64,
kernel_size=3,
stride=2,
diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py
index 2967dd95dd99c69799eef06f0d07eeefa53060d3..ac14bc2d2d39cf27ffd1d0893ae1dbe1831b0e0a 100644
--- a/mmdet/models/backbones/resnet.py
+++ b/mmdet/models/backbones/resnet.py
@@ -335,6 +335,7 @@ class ResNet(nn.Module):
Args:
depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
+ in_channels (int): Number of input image channels. Normally 3.
num_stages (int): Resnet stages, normally 4.
strides (Sequence[int]): Strides of the first block of each stage.
dilations (Sequence[int]): Dilation of each stage.
@@ -378,6 +379,7 @@ class ResNet(nn.Module):
def __init__(self,
depth,
+ in_channels=3,
num_stages=4,
strides=(1, 2, 2, 2),
dilations=(1, 1, 1, 1),
@@ -426,7 +428,7 @@ class ResNet(nn.Module):
self.stage_blocks = stage_blocks[:num_stages]
self.inplanes = 64
- self._make_stem_layer()
+ self._make_stem_layer(in_channels)
self.res_layers = []
for i, num_blocks in enumerate(self.stage_blocks):
@@ -464,10 +466,10 @@ class ResNet(nn.Module):
def norm1(self):
return getattr(self, self.norm1_name)
- def _make_stem_layer(self):
+ def _make_stem_layer(self, in_channels):
self.conv1 = build_conv_layer(
self.conv_cfg,
- 3,
+ in_channels,
64,
kernel_size=7,
stride=2,
diff --git a/mmdet/models/backbones/resnext.py b/mmdet/models/backbones/resnext.py
index be2897686bfb719bcaa6d5582db9c429264514bf..38afee28b6f99307e2497203b93b7dba317648b6 100644
--- a/mmdet/models/backbones/resnext.py
+++ b/mmdet/models/backbones/resnext.py
@@ -160,6 +160,7 @@ class ResNeXt(ResNet):
Args:
depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
+ in_channels (int): Number of input image channels. Normally 3.
num_stages (int): Resnet stages, normally 4.
groups (int): Group of resnext.
base_width (int): Base width of resnext.
diff --git a/mmdet/models/backbones/ssd_vgg.py b/mmdet/models/backbones/ssd_vgg.py
index ae65711332b738ba174bad5b1e92c4a762f19376..b199444b9c128935517f00d113f4e478e71bea77 100644
--- a/mmdet/models/backbones/ssd_vgg.py
+++ b/mmdet/models/backbones/ssd_vgg.py
@@ -11,6 +11,26 @@ from ..registry import BACKBONES
@BACKBONES.register_module
class SSDVGG(VGG):
+ """VGG Backbone network for single-shot-detection
+
+ Args:
+ input_size (int): width and height of input, from {300, 512}.
+ depth (int): Depth of vgg, from {11, 13, 16, 19}.
+ out_indices (Sequence[int]): Output from which stages.
+
+ Example:
+ >>> self = SSDVGG(input_size=300, depth=11)
+ >>> self.eval()
+ >>> inputs = torch.rand(1, 3, 300, 300)
+ >>> level_outputs = self.forward(inputs)
+ >>> for level_out in level_outputs:
+ ... print(tuple(level_out.shape))
+ (1, 1024, 19, 19)
+ (1, 512, 10, 10)
+ (1, 256, 5, 5)
+ (1, 256, 3, 3)
+ (1, 256, 1, 1)
+ """
extra_setting = {
300: (256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256),
512: (256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128),
@@ -24,6 +44,7 @@ class SSDVGG(VGG):
out_indices=(3, 4),
out_feature_indices=(22, 34),
l2_norm_scale=20.):
+ # TODO: in_channels for mmcv.VGG
super(SSDVGG, self).__init__(
depth,
with_last_pool=with_last_pool,