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Update intern_image.py #283

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39 changes: 24 additions & 15 deletions detection/mmdet_custom/models/backbones/intern_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
from mmdet.models.builder import BACKBONES
import torch.nn.functional as F

from ops_dcnv3 import modules as dcnv3
from ops_dcnv3 import modules as opsm


class to_channels_first(nn.Module):
Expand Down Expand Up @@ -365,8 +365,7 @@ def __init__(self,
with_cp=False,
dw_kernel_size=None, # for InternImage-H/G
res_post_norm=False, # for InternImage-H/G
center_feature_scale=False,
use_dcn_v4_op=False): # for InternImage-H/G
center_feature_scale=False): # for InternImage-H/G
super().__init__()
self.channels = channels
self.groups = groups
Expand All @@ -386,8 +385,7 @@ def __init__(self,
act_layer=act_layer,
norm_layer=norm_layer,
dw_kernel_size=dw_kernel_size, # for InternImage-H/G
center_feature_scale=center_feature_scale,
use_dcn_v4_op=use_dcn_v4_op) # for InternImage-H/G
center_feature_scale=center_feature_scale) # for InternImage-H/G
self.drop_path = DropPath(drop_path) if drop_path > 0. \
else nn.Identity()
self.norm2 = build_norm_layer(channels, 'LN')
Expand Down Expand Up @@ -471,8 +469,7 @@ def __init__(self,
dw_kernel_size=None, # for InternImage-H/G
post_norm_block_ids=None, # for InternImage-H/G
res_post_norm=False, # for InternImage-H/G
center_feature_scale=False, # for InternImage-H/G
use_dcn_v4_op=False):
center_feature_scale=False): # for InternImage-H/G
super().__init__()
self.channels = channels
self.depth = depth
Expand All @@ -496,8 +493,7 @@ def __init__(self,
with_cp=with_cp,
dw_kernel_size=dw_kernel_size, # for InternImage-H/G
res_post_norm=res_post_norm, # for InternImage-H/G
center_feature_scale=center_feature_scale, # for InternImage-H/G
use_dcn_v4_op=use_dcn_v4_op
center_feature_scale=center_feature_scale # for InternImage-H/G
) for i in range(depth)
])
if not self.post_norm or center_feature_scale:
Expand Down Expand Up @@ -573,11 +569,12 @@ def __init__(self,
level2_post_norm_block_ids=None, # for InternImage-H/G
res_post_norm=False, # for InternImage-H/G
center_feature_scale=False, # for InternImage-H/G
use_dcn_v4_op=False,
out_indices=(0, 1, 2, 3),
init_cfg=None,
frozen_stages=-1, # you can freez level 1 -> num_levels(len(depths))
**kwargs):
super().__init__()
self.frozen_stages = frozen_stages
self.core_op = core_op
self.num_levels = len(depths)
self.depths = depths
Expand All @@ -596,7 +593,6 @@ def __init__(self,
logger.info(f"level2_post_norm: {level2_post_norm}")
logger.info(f"level2_post_norm_block_ids: {level2_post_norm_block_ids}")
logger.info(f"res_post_norm: {res_post_norm}")
logger.info(f"use_dcn_v4_op: {use_dcn_v4_op}")

in_chans = 3
self.patch_embed = StemLayer(in_chans=in_chans,
Expand All @@ -617,7 +613,7 @@ def __init__(self,
post_norm_block_ids = level2_post_norm_block_ids if level2_post_norm and (
i == 2) else None # for InternImage-H/G
level = InternImageBlock(
core_op=getattr(dcnv3, core_op),
core_op=getattr(opsm, core_op),
channels=int(channels * 2**i),
depth=depths[i],
groups=groups[i],
Expand All @@ -634,14 +630,27 @@ def __init__(self,
dw_kernel_size=dw_kernel_size, # for InternImage-H/G
post_norm_block_ids=post_norm_block_ids, # for InternImage-H/G
res_post_norm=res_post_norm, # for InternImage-H/G
center_feature_scale=center_feature_scale, # for InternImage-H/G
use_dcn_v4_op=use_dcn_v4_op,
center_feature_scale=center_feature_scale # for InternImage-H/G
)
self.levels.append(level)

self.num_layers = len(depths)
self.apply(self._init_weights)
self.apply(self._init_deform_weights)
self._freeze_stages()

def train(self, mode=True):
"""Convert the model into training mode while keep normalization layer
freezed."""
super(InternImage, self).train(mode)
self._freeze_stages()

def _freeze_stages(self):
if self.frozen_stages >= 0:
for level in self.levels[:self.frozen_stages]:
level.eval()
for param in level.parameters():
param.requires_grad = False

def init_weights(self):
logger = get_root_logger()
Expand Down Expand Up @@ -694,7 +703,7 @@ def _init_weights(self, m):
nn.init.constant_(m.weight, 1.0)

def _init_deform_weights(self, m):
if isinstance(m, getattr(dcnv3, self.core_op)):
if isinstance(m, getattr(opsm, self.core_op)):
m._reset_parameters()

def forward(self, x):
Expand Down