Unable to Download Pretrained SSD Model #42
Description
When I decided to use the SSD model for this repo. I followed your instructions but the SSD Model is unreachable.
Here is my output:
2021-09-28 19:14:43,252 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.10 | packaged by conda-forge | (default, Sep 13 2021, 19:43:44) [GCC 9.4.0]
CUDA available: True
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 10.0, V10.0.130
GPU 0: Tesla T4
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.6.0
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2021.3-Product Build 20210617 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 10.2
- 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
- CuDNN 7.6.5
- Magma 2.5.2
- Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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-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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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, USE_STATIC_DISPATCH=OFF,
TorchVision: 0.7.0
OpenCV: 4.5.3
MMCV: 1.0.5
MMDetection: 2.3.0+cbed89d
MMDetection Compiler: GCC 7.3
MMDetection CUDA Compiler: 10.2
------------------------------------------------------------
2021-09-28 19:14:43,253 - mmdet - INFO - Distributed training: True
2021-09-28 19:14:44,676 - mmdet - INFO - Config:
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=300,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indices=(22, 34),
l2_norm_scale=20),
neck=None,
bbox_head=dict(
type='SSDHead',
in_channels=(512, 1024, 512, 256, 256, 256),
C=20,
anchor_generator=dict(
type='SSDAnchorGenerator',
scale_major=False,
input_size=300,
basesize_ratio_range=(0.2, 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, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2])))
cudnn_benchmark = True
train_cfg = dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.0,
ignore_iof_thr=-1,
gt_max_assign_all=False),
smoothl1_beta=1.0,
allowed_border=-1,
pos_weight=-1,
neg_pos_ratio=3,
debug=False,
param_lambda=0.5)
test_cfg = dict(
nms=dict(type='nms', iou_threshold=0.45),
min_bbox_size=0,
score_thr=0.02,
max_per_img=200)
theta_f_1 = [
'bbox_head.f_1_convs.0.weight', 'bbox_head.f_1_convs.0.bias',
'bbox_head.f_1_convs.1.weight', 'bbox_head.f_1_convs.1.bias',
'bbox_head.f_1_convs.2.weight', 'bbox_head.f_1_convs.2.bias',
'bbox_head.f_1_convs.3.weight', 'bbox_head.f_1_convs.3.bias',
'bbox_head.f_1_convs.4.weight', 'bbox_head.f_1_convs.4.bias',
'bbox_head.f_1_convs.5.weight', 'bbox_head.f_1_convs.5.bias'
]
theta_f_2 = [
'bbox_head.f_2_convs.0.weight', 'bbox_head.f_2_convs.0.bias',
'bbox_head.f_2_convs.1.weight', 'bbox_head.f_2_convs.1.bias',
'bbox_head.f_2_convs.2.weight', 'bbox_head.f_2_convs.2.bias',
'bbox_head.f_2_convs.3.weight', 'bbox_head.f_2_convs.3.bias',
'bbox_head.f_2_convs.4.weight', 'bbox_head.f_2_convs.4.bias',
'bbox_head.f_2_convs.5.weight', 'bbox_head.f_2_convs.5.bias'
]
data_root = '/home/ubuntu/bahadir/datasets/VOCdevkit/'
dataset_type = 'VOCDataset'
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=[123.675, 116.28, 103.53],
to_rgb=True,
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',
mean=[123.675, 116.28, 103.53],
std=[1, 1, 1],
to_rgb=True),
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',
mean=[123.675, 116.28, 103.53],
std=[1, 1, 1],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=3,
train=dict(
type='RepeatDataset',
times=1,
dataset=dict(
type='VOCDataset',
ann_file=[
'/home/ubuntu/bahadir/datasets/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',
'/home/ubuntu/bahadir/datasets/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'
],
img_prefix=[
'/home/ubuntu/bahadir/datasets/VOCdevkit/VOC2007/',
'/home/ubuntu/bahadir/datasets/VOCdevkit/VOC2012/'
],
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=[123.675, 116.28, 103.53],
to_rgb=True,
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',
mean=[123.675, 116.28, 103.53],
std=[1, 1, 1],
to_rgb=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
])),
val=dict(
type='VOCDataset',
ann_file=
'/home/ubuntu/bahadir/datasets/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
img_prefix='/home/ubuntu/bahadir/datasets/VOCdevkit/VOC2007/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(300, 300),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1, 1, 1],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='VOCDataset',
ann_file=[
'/home/ubuntu/bahadir/datasets/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',
'/home/ubuntu/bahadir/datasets/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'
],
img_prefix=[
'/home/ubuntu/bahadir/datasets/VOCdevkit/VOC2007/',
'/home/ubuntu/bahadir/datasets/VOCdevkit/VOC2012/'
],
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(300, 300),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[1, 1, 1],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
evaluation = dict(interval=5, metric='mAP')
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[1])
epoch_ratio = [5, 1]
epoch = 2
X_L_repeat = 16
X_U_repeat = 16
k = 10000
X_S_size = 1000
X_L_0_size = 1000
cycles = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
work_directory = './work_dirs/MI-AOD_SSD'
gpu_ids = range(0, 1)
2021-09-28 19:14:44,676 - mmdet - INFO - Set random seed to 666, deterministic: False
2021-09-28 19:14:44,716 - mmdet - INFO - Set random seed to 666, deterministic: False
2021-09-28 19:14:44,958 - mmdet - INFO - load model from: open-mmlab://vgg16_caffe
Downloading: "https://open-mmlab.s3.ap-northeast-2.amazonaws.com/pretrain/third_party/vgg16_caffe-292e1171.pth" to /home/ubuntu/.cache/torch/hub/checkpoints/vgg16_caffe-292e1171.pth
Traceback (most recent call last):
File "./tools/train.py", line 257, in <module>
main()
File "./tools/train.py", line 131, in main
model = build_detector(cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
File "/home/ubuntu/bahadir/MI-AOD-SSD/mmdet/models/builder.py", line 67, in build_detector
return build(cfg, DETECTORS, dict(train_cfg=train_cfg, test_cfg=test_cfg))
File "/home/ubuntu/bahadir/MI-AOD-SSD/mmdet/models/builder.py", line 32, in build
return build_from_cfg(cfg, registry, default_args)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/site-packages/mmcv/utils/registry.py", line 167, in build_from_cfg
return obj_cls(**args)
File "/home/ubuntu/bahadir/MI-AOD-SSD/mmdet/models/detectors/single_stage.py", line 28, in __init__
self.init_weights(pretrained=pretrained)
File "/home/ubuntu/bahadir/MI-AOD-SSD/mmdet/models/detectors/single_stage.py", line 38, in init_weights
self.backbone.init_weights(pretrained=pretrained)
File "/home/ubuntu/bahadir/MI-AOD-SSD/mmdet/models/backbones/ssd_vgg.py", line 84, in init_weights
load_checkpoint(self, pretrained, strict=False, logger=logger)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/site-packages/mmcv/runner/checkpoint.py", line 224, in load_checkpoint
checkpoint = _load_checkpoint(filename, map_location)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/site-packages/mmcv/runner/checkpoint.py", line 189, in _load_checkpoint
checkpoint = load_url_dist(model_url)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/site-packages/mmcv/runner/checkpoint.py", line 111, in load_url_dist
checkpoint = model_zoo.load_url(url, model_dir=model_dir)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/site-packages/torch/hub.py", line 481, in load_state_dict_from_url
download_url_to_file(url, cached_file, hash_prefix, progress=progress)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/site-packages/torch/hub.py", line 379, in download_url_to_file
u = urlopen(req)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/urllib/request.py", line 222, in urlopen
return opener.open(url, data, timeout)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/urllib/request.py", line 531, in open
response = meth(req, response)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/urllib/request.py", line 641, in http_response
'http', request, response, code, msg, hdrs)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/urllib/request.py", line 569, in error
return self._call_chain(*args)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/urllib/request.py", line 503, in _call_chain
result = func(*args)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/urllib/request.py", line 649, in http_error_default
raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 403: Forbidden
Traceback (most recent call last):
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/site-packages/torch/distributed/launch.py", line 261, in <module>
main()
File "/home/ubuntu/anaconda3/envs/miaod/lib/python3.7/site-packages/torch/distributed/launch.py", line 257, in main
cmd=cmd)
subprocess.CalledProcessError: Command '['/home/ubuntu/anaconda3/envs/miaod/bin/python', '-u', './tools/train.py', '--local_rank=0', 'configs/MIAOD.py', '--launcher', 'pytorch']' returned non-zero exit status 1.