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retinanet_r18_cfpt.log
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2024-08-16 23:22:51,336 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 3090
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.3, V11.3.109
GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
PyTorch: 1.10.0+cu113
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX512
- CUDA Runtime 11.3
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
- CuDNN 8.2
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,
TorchVision: 0.11.1+cu113
OpenCV: 4.10.0
MMCV: 1.6.0
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.3
MMDetection: 2.24.1+2abfd1a
------------------------------------------------------------
2024-08-16 23:22:52,514 - mmdet - INFO - Distributed training: False
2024-08-16 23:22:53,679 - mmdet - INFO - Config:
optimizer = dict(
type='SGD',
lr=0.0025,
momentum=0.9,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys=dict(
absolute_position_bias=dict(decay_mult=0.0),
relative_position_bias_table=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0))))
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
model = dict(
type='RetinaNet',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(
type='CFPT',
in_channels=[64, 128, 256, 512],
out_channels=256,
alpha=1,
beta=1,
num_heads=4,
mlp_ratio=2,
reduction=4),
bbox_head=dict(
type='RetinaHead',
num_classes=10,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1,
gpu_assign_thr=200),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=500))
dataset_type = 'UAVDataset'
data_root = '/root/autodl-tmp/datasets/VisDrone/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
albu_train_transforms = [
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.1,
rotate_limit=45,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.1, 0.3],
contrast_limit=[0.1, 0.3],
p=0.2),
dict(
type='OneOf',
transforms=[
dict(
type='RGBShift',
r_shift_limit=10,
g_shift_limit=10,
b_shift_limit=10,
p=1.0),
dict(
type='HueSaturationValue',
hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
p=1.0)
],
p=0.1),
dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2),
dict(type='ChannelShuffle', p=0.1),
dict(
type='OneOf',
transforms=[
dict(type='Blur', blur_limit=3, p=1.0),
dict(type='MedianBlur', blur_limit=3, p=1.0)
],
p=0.1)
]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=64),
dict(
type='Albu',
transforms=[
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.1,
rotate_limit=45,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.1, 0.3],
contrast_limit=[0.1, 0.3],
p=0.2),
dict(
type='OneOf',
transforms=[
dict(
type='RGBShift',
r_shift_limit=10,
g_shift_limit=10,
b_shift_limit=10,
p=1.0),
dict(
type='HueSaturationValue',
hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
p=1.0)
],
p=0.1),
dict(
type='JpegCompression',
quality_lower=85,
quality_upper=95,
p=0.2),
dict(type='ChannelShuffle', p=0.1),
dict(
type='OneOf',
transforms=[
dict(type='Blur', blur_limit=3, p=1.0),
dict(type='MedianBlur', blur_limit=3, p=1.0)
],
p=0.1)
],
bbox_params=dict(
type='BboxParams',
format='pascal_voc',
label_fields=['gt_labels'],
min_visibility=0.0,
filter_lost_elements=True),
keymap=dict(img='image', gt_bboxes='bboxes'),
update_pad_shape=False,
skip_img_without_anno=True),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=64),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=5,
dataset=dict(
type='UAVDataset',
ann_file=
'/root/autodl-tmp/datasets/VisDrone/annotations/instances_UAVtrain.json',
img_prefix=
'/root/autodl-tmp/datasets/VisDrone//images/instances_UAVtrain/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=64),
dict(
type='Albu',
transforms=[
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.1,
rotate_limit=45,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.1, 0.3],
contrast_limit=[0.1, 0.3],
p=0.2),
dict(
type='OneOf',
transforms=[
dict(
type='RGBShift',
r_shift_limit=10,
g_shift_limit=10,
b_shift_limit=10,
p=1.0),
dict(
type='HueSaturationValue',
hue_shift_limit=20,
sat_shift_limit=30,
val_shift_limit=20,
p=1.0)
],
p=0.1),
dict(
type='JpegCompression',
quality_lower=85,
quality_upper=95,
p=0.2),
dict(type='ChannelShuffle', p=0.1),
dict(
type='OneOf',
transforms=[
dict(type='Blur', blur_limit=3, p=1.0),
dict(type='MedianBlur', blur_limit=3, p=1.0)
],
p=0.1)
],
bbox_params=dict(
type='BboxParams',
format='pascal_voc',
label_fields=['gt_labels'],
min_visibility=0.0,
filter_lost_elements=True),
keymap=dict(img='image', gt_bboxes='bboxes'),
update_pad_shape=False,
skip_img_without_anno=True),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
])),
val=dict(
type='UAVDataset',
ann_file=
'/root/autodl-tmp/datasets/VisDrone/annotations/instances_UAVval.json',
img_prefix=
'/root/autodl-tmp/datasets/VisDrone//images/instances_UAVval/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=64),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='UAVDataset',
ann_file=
'/root/autodl-tmp/datasets/VisDrone/annotations/instances_UAVval.json',
img_prefix=
'/root/autodl-tmp/datasets/VisDrone//images/instances_UAVval/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=64),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
evaluation = dict(interval=3, metric='bbox')
fp16 = dict(loss_scale=512.0)
work_dir = './work_dirs/retinanet_r18_cfpt_1x_visdrone'
auto_resume = False
gpu_ids = [0]
2024-08-16 23:22:53,680 - mmdet - INFO - Set random seed to 794762687, deterministic: False
2024-08-16 23:22:53,904 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet18'}
2024-08-16 23:22:53,970 - mmdet - INFO - initialize RetinaHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'retina_cls', 'std': 0.01, 'bias_prob': 0.01}}
Name of parameter - Initialization information
backbone.conv1.weight - torch.Size([64, 3, 7, 7]):
PretrainedInit: load from torchvision://resnet18
backbone.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.bn2.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.0.bn2.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.bn2.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer1.1.bn2.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.downsample.1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.0.downsample.1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer2.1.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.downsample.1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.0.downsample.1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer3.1.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.bn1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.bn1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.bn2.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.bn2.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.downsample.1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.0.downsample.1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.bn1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.bn1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.bn2.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
backbone.layer4.1.bn2.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet18
neck.conv2.conv.weight - torch.Size([256, 128, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv2.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv2.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv3.conv.weight - torch.Size([256, 256, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv3.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv3.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv4.conv.weight - torch.Size([256, 512, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv4.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv4.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv5.conv.weight - torch.Size([256, 512, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv5.bn.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.conv5.bn.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.0.0.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.0.0.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.0.1.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.0.1.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.1.0.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.1.0.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.1.1.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.1.1.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.2.0.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.2.0.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.2.1.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.2.1.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.3.0.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.3.0.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.3.1.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.cpe.3.1.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm1.0.norm.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm1.0.norm.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm1.1.norm.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm1.1.norm.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm1.2.norm.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm1.2.norm.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm1.3.norm.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm1.3.norm.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm2.0.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm2.0.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm2.1.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm2.1.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm2.2.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm2.2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm2.3.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.norm2.3.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.qkv.0.weight - torch.Size([192, 256, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.qkv.0.bias - torch.Size([192]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.qkv.1.weight - torch.Size([192, 256, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.qkv.1.bias - torch.Size([192]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.qkv.2.weight - torch.Size([192, 256, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.qkv.2.bias - torch.Size([192]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.qkv.3.weight - torch.Size([192, 256, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.qkv.3.bias - torch.Size([192]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.0.fc1.weight - torch.Size([512, 256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.0.fc1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.0.fc2.weight - torch.Size([256, 512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.0.fc2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.1.fc1.weight - torch.Size([512, 256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.1.fc1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.1.fc2.weight - torch.Size([256, 512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.1.fc2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.2.fc1.weight - torch.Size([512, 256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.2.fc1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.2.fc2.weight - torch.Size([256, 512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.2.fc2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.3.fc1.weight - torch.Size([512, 256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.3.fc1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.3.fc2.weight - torch.Size([256, 512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.mlp.3.fc2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.proj.0.weight - torch.Size([256, 64, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.proj.0.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.proj.1.weight - torch.Size([256, 64, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.proj.1.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.proj.2.weight - torch.Size([256, 64, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.proj.2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.proj.3.weight - torch.Size([256, 64, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.proj.3.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.pos_embed.relative_position_bias_table - torch.Size([289, 4]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_spatial_attn.pos_embed.absolute_position_bias - torch.Size([4, 4, 1, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.0.0.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.0.0.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.0.1.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.0.1.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.1.0.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.1.0.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.1.1.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.1.1.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.2.0.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.2.0.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.2.1.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.2.1.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.3.0.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.3.0.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.3.1.proj.weight - torch.Size([256, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.cpe.3.1.proj.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm1.0.norm.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm1.0.norm.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm1.1.norm.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm1.1.norm.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm1.2.norm.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm1.2.norm.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm1.3.norm.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm1.3.norm.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm2.0.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm2.0.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm2.1.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm2.1.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm2.2.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm2.2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm2.3.weight - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.norm2.3.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.qkv.0.weight - torch.Size([192, 256, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.qkv.0.bias - torch.Size([192]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.qkv.1.weight - torch.Size([192, 256, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.qkv.1.bias - torch.Size([192]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.qkv.2.weight - torch.Size([192, 256, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.qkv.2.bias - torch.Size([192]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.qkv.3.weight - torch.Size([192, 256, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.qkv.3.bias - torch.Size([192]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.proj.0.weight - torch.Size([256, 64, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.proj.0.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.proj.1.weight - torch.Size([256, 64, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.proj.1.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.proj.2.weight - torch.Size([256, 64, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.proj.2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.proj.3.weight - torch.Size([256, 64, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.proj.3.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.0.fc1.weight - torch.Size([512, 256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.0.fc1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.0.fc2.weight - torch.Size([256, 512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.0.fc2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.1.fc1.weight - torch.Size([512, 256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.1.fc1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.1.fc2.weight - torch.Size([256, 512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.1.fc2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.2.fc1.weight - torch.Size([512, 256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.2.fc1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.2.fc2.weight - torch.Size([256, 512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.2.fc2.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.3.fc1.weight - torch.Size([512, 256]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.3.fc1.bias - torch.Size([512]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.cross_layer_channel_attn.mlp.3.fc2.weight - torch.Size([256, 512]):