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363 changes: 44 additions & 319 deletions README.md
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35 changes: 35 additions & 0 deletions configs/panet/e2e_panet_R-50-FPN_1x_det.yaml
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Original file line number Diff line number Diff line change
@@ -0,0 +1,35 @@
MODEL:
TYPE: generalized_rcnn
CONV_BODY: FPN.fpn_ResNet50_conv5_body_bup
FASTER_RCNN: True
NUM_GPUS: 8
RESNETS:
IMAGENET_PRETRAINED_WEIGHTS: 'data/pretrained_model/resnet50_caffe.pth'
SOLVER:
WEIGHT_DECAY: 0.0001
LR_POLICY: steps_with_decay
BASE_LR: 0.02
GAMMA: 0.1
MAX_ITER: 90000
STEPS: [0, 60000, 80000]
FPN:
FPN_ON: True
MULTILEVEL_ROIS: True
MULTILEVEL_RPN: True
USE_GN: True # Note: use GN on the FPN-specific layers
FAST_RCNN:
ROI_BOX_HEAD: fast_rcnn_heads.roi_Xconv1fc_gn_head_panet # Note: this is a Conv GN head
ROI_XFORM_METHOD: RoIAlign
ROI_XFORM_RESOLUTION: 7
ROI_XFORM_SAMPLING_RATIO: 2
TRAIN:
SCALES: (1200,1200,1000,800,600,400)
MAX_SIZE: 1400
BATCH_SIZE_PER_IM: 512
RPN_PRE_NMS_TOP_N: 2000 # Per FPN level
TEST:
SCALE: 1000
MAX_SIZE: 1400
NMS: 0.5
RPN_PRE_NMS_TOP_N: 1000 # Per FPN level
RPN_POST_NMS_TOP_N: 1000
35 changes: 35 additions & 0 deletions configs/panet/e2e_panet_R-50-FPN_1x_det_2fc.yaml
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MODEL:
TYPE: generalized_rcnn
CONV_BODY: FPN.fpn_ResNet50_conv5_body_bup
FASTER_RCNN: True
NUM_GPUS: 8
RESNETS:
IMAGENET_PRETRAINED_WEIGHTS: 'data/pretrained_model/resnet50_caffe.pth'
SOLVER:
WEIGHT_DECAY: 0.0001
LR_POLICY: steps_with_decay
BASE_LR: 0.02
GAMMA: 0.1
MAX_ITER: 90000
STEPS: [0, 60000, 80000]
FPN:
FPN_ON: True
MULTILEVEL_ROIS: True
MULTILEVEL_RPN: True
USE_GN: True # Note: use GN on the FPN-specific layers
FAST_RCNN:
ROI_BOX_HEAD: fast_rcnn_heads.roi_2mlp_head_gn_panet # Note: this is a Conv GN head
ROI_XFORM_METHOD: RoIAlign
ROI_XFORM_RESOLUTION: 7
ROI_XFORM_SAMPLING_RATIO: 2
TRAIN:
SCALES: (1200,1200,1000,800,600,400)
MAX_SIZE: 1400
BATCH_SIZE_PER_IM: 512
RPN_PRE_NMS_TOP_N: 2000 # Per FPN level
TEST:
SCALE: 1000
MAX_SIZE: 1400
NMS: 0.5
RPN_PRE_NMS_TOP_N: 1000 # Per FPN level
RPN_POST_NMS_TOP_N: 1000
45 changes: 45 additions & 0 deletions configs/panet/e2e_panet_R-50-FPN_2x_mask.yaml
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MODEL:
TYPE: generalized_rcnn
CONV_BODY: FPN.fpn_ResNet50_conv5_body_bup
FASTER_RCNN: True
MASK_ON: True
NUM_GPUS: 8
SOLVER:
WEIGHT_DECAY: 0.0001
LR_POLICY: steps_with_decay
BASE_LR: 0.02
GAMMA: 0.1
MAX_ITER: 180000
STEPS: [0, 120000, 160000]
FPN:
FPN_ON: True
MULTILEVEL_ROIS: True
MULTILEVEL_RPN: True
USE_GN: True # Note: use GN on the FPN-specific layers
RESNETS:
IMAGENET_PRETRAINED_WEIGHTS: 'data/pretrained_model/resnet50_caffe.pth'
FAST_RCNN:
ROI_BOX_HEAD: fast_rcnn_heads.roi_Xconv1fc_gn_head_panet # Note: this is a Conv GN head
ROI_XFORM_METHOD: RoIAlign
ROI_XFORM_RESOLUTION: 7
ROI_XFORM_SAMPLING_RATIO: 2
MRCNN:
ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs_gn_adp_ff # Note: this is a GN mask head
RESOLUTION: 28 # (output mask resolution) default 14
ROI_XFORM_METHOD: RoIAlign
ROI_XFORM_RESOLUTION: 14 # default 7
ROI_XFORM_SAMPLING_RATIO: 2 # default 0
DILATION: 1 # default 2
CONV_INIT: MSRAFill # default GaussianFill
TRAIN:
SCALES: (1200, 1200, 1000, 800, 600, 400)
MAX_SIZE: 1400
BATCH_SIZE_PER_IM: 512
RPN_PRE_NMS_TOP_N: 2000 # Per FPN level
TEST:
SCALE: 1000
MAX_SIZE: 1400
NMS: 0.5
RPN_PRE_NMS_TOP_N: 1000 # Per FPN level
RPN_POST_NMS_TOP_N: 1000

60 changes: 56 additions & 4 deletions lib/modeling/FPN.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,12 @@ def fpn_ResNet50_conv5_body():
ResNet.ResNet50_conv5_body, fpn_level_info_ResNet50_conv5()
)

def fpn_ResNet50_conv5_body_bup():
return fpn(
ResNet.ResNet50_conv5_body, fpn_level_info_ResNet50_conv5(),
panet_buttomup=True
)


def fpn_ResNet50_conv5_P2only_body():
return fpn(
Expand Down Expand Up @@ -77,10 +83,11 @@ class fpn(nn.Module):
similarly for fpn_level_info.dims: e.g [2048, 1024, 512, 256]
similarly for spatial_scale: e.g [1/32, 1/16, 1/8, 1/4]
"""
def __init__(self, conv_body_func, fpn_level_info, P2only=False):
def __init__(self, conv_body_func, fpn_level_info, P2only=False, panet_buttomup=False):
super().__init__()
self.fpn_level_info = fpn_level_info
self.P2only = P2only
self.panet_buttomup = panet_buttomup

self.dim_out = fpn_dim = cfg.FPN.DIM
min_level, max_level = get_min_max_levels()
Expand Down Expand Up @@ -125,6 +132,35 @@ def __init__(self, conv_body_func, fpn_level_info, P2only=False):

self.spatial_scale.append(fpn_level_info.spatial_scales[i])

# add for panet buttom-up path
if self.panet_buttomup:
self.panet_buttomup_conv1_modules = nn.ModuleList()
self.panet_buttomup_conv2_modules = nn.ModuleList()
for i in range(self.num_backbone_stages - 1):
if cfg.FPN.USE_GN:
self.panet_buttomup_conv1_modules.append(nn.Sequential(
nn.Conv2d(fpn_dim, fpn_dim, 3, 2, 1, bias=True),
nn.GroupNorm(net_utils.get_group_gn(fpn_dim), fpn_dim,
eps=cfg.GROUP_NORM.EPSILON),
nn.ReLU(inplace=True)
))
self.panet_buttomup_conv2_modules.append(nn.Sequential(
nn.Conv2d(fpn_dim, fpn_dim, 3, 1, 1, bias=True),
nn.GroupNorm(net_utils.get_group_gn(fpn_dim), fpn_dim,
eps=cfg.GROUP_NORM.EPSILON),
nn.ReLU(inplace=True)
))
else:
self.panet_buttomup_conv1_modules.append(
nn.Conv2d(fpn_dim, fpn_dim, 3, 2, 1)
)
self.panet_buttomup_conv2_modules.append(
nn.Conv2d(fpn_dim, fpn_dim, 3, 1, 1)
)

#self.spatial_scale.append(fpn_level_info.spatial_scales[i])


#
# Step 2: build up starting from the coarsest backbone level
#
Expand Down Expand Up @@ -160,6 +196,7 @@ def _init_weights(self):
def init_func(m):
if isinstance(m, nn.Conv2d):
mynn.init.XavierFill(m.weight)
#mynn.init.MSRAFill(m.weight)
if m.bias is not None:
init.constant_(m.bias, 0)

Expand Down Expand Up @@ -236,10 +273,25 @@ def forward(self, x):
self.topdown_lateral_modules[i](fpn_inner_blobs[-1], conv_body_blobs[-(i+2)])
)
fpn_output_blobs = []
if self.panet_buttomup:
fpn_middle_blobs = []
for i in range(self.num_backbone_stages):
fpn_output_blobs.append(
self.posthoc_modules[i](fpn_inner_blobs[i])
)
if not self.panet_buttomup:
fpn_output_blobs.append(
self.posthoc_modules[i](fpn_inner_blobs[i])
)
else:
fpn_middle_blobs.append(
self.posthoc_modules[i](fpn_inner_blobs[i])
)
if self.panet_buttomup:
fpn_output_blobs.append(fpn_middle_blobs[-1])
for i in range(2, self.num_backbone_stages + 1):
fpn_tmp = self.panet_buttomup_conv1_modules[i - 2](fpn_output_blobs[0])
#print(fpn_middle_blobs[self.num_backbone_stages - i].size())
fpn_tmp = fpn_tmp + fpn_middle_blobs[self.num_backbone_stages - i]
fpn_tmp = self.panet_buttomup_conv2_modules[i - 2](fpn_tmp)
fpn_output_blobs.insert(0, fpn_tmp)

if hasattr(self, 'maxpool_p6'):
fpn_output_blobs.insert(0, self.maxpool_p6(fpn_output_blobs[0]))
Expand Down
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