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shpe mismatch in loss.py #2

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qianwangn opened this issue Dec 6, 2019 · 12 comments
Closed

shpe mismatch in loss.py #2

qianwangn opened this issue Dec 6, 2019 · 12 comments

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@qianwangn
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Traceback (most recent call last): File "tools/train_net.py", line 192, in <module> main() File "tools/train_net.py", line 185, in main model = train(cfg, args.local_rank, args.distributed) File "tools/train_net.py", line 79, in train arguments, File "/home/wangqian/ATSS/atss_core/engine/trainer.py", line 69, in do_train loss_dict = model(images, targets) File "/home/wangqian/anaconda3/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in __call__ result = self.forward(*input, **kwargs) File "/home/wangqian/anaconda3/envs/torch1.3/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 442, in forward output = self.module(*inputs[0], **kwargs[0]) File "/home/wangqian/anaconda3/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in __call__ result = self.forward(*input, **kwargs) File "/home/wangqian/ATSS/atss_core/modeling/detector/generalized_rcnn.py", line 50, in forward proposals, proposal_losses = self.rpn(images, features, targets) File "/home/wangqian/anaconda3/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in __call__ result = self.forward(*input, **kwargs) File "/home/wangqian/ATSS/atss_core/modeling/rpn/atss/atss.py", line 210, in forward return self._forward_train(box_cls, box_regression, centerness, targets, anchors) File "/home/wangqian/ATSS/atss_core/modeling/rpn/atss/atss.py", line 216, in _forward_train box_cls, box_regression, centerness, targets, anchors File "/home/wangqian/ATSS/atss_core/modeling/rpn/atss/loss.py", line 239, in __call__ labels, reg_targets = self.prepare_targets(targets, anchors) File "/home/wangqian/ATSS/atss_core/modeling/rpn/atss/loss.py", line 178, in prepare_targets ious[candidate_idxs[is_pos[:, ng] == 1, ng], ng] RuntimeError: shape mismatch: value tensor of shape [6] cannot be broadcast to indexing result of shape [5]

@sfzhang15
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@johnlanbor
Did you modify anything?

@qianwangn
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nothing but except data path(absolute path, not soft link), pretrained model path.
when I set POSITIVE_TYPE to IoU, everything seems to perfect.

@sfzhang15
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@johnlanbor
Before I released the code, I downloaded it and ran it once. This error did not occur. I will try again to see if this error occurs. Thank you for your feedback.

@sfzhang15
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@johnlanbor
We have tried this code two times on two different devices, the above error did not occur. Can you provide more information about this error? What dataset did you use? Did this error appear at the beginning?

@qianwangn
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qianwangn commented Dec 8, 2019 •
edited

@sfzhang15 I tried run this code in two different devide too. and redownload it.
the same error occurs.
The dataset I use is coco.
Yes, that error appear in the first iter.

The full log is as below.
I also modified bs to 8 in this log.
`2019-12-08 15:36:42,385 atss_core INFO: Using 8 GPUs
2019-12-08 15:36:42,386 atss_core INFO: Namespace(config_file='configs/atss/atss_R_50_FPN_1x.yaml', distributed=True, epoch=None, in_path='/home/test/standard/COCO', local_rank=0, opts=['atss_retinanet_R_50_FPN', '--job_id', '2019-12-08-15-48-53_wangqian+**+atss_retinanet_R_50_FPN'], out_path='/node01/jobs/io/out/wangqian/atss_retinanet_R_50_FPN', phase=None, pretrained_path='/node01/jobs/io/pretrained/', skip_test=False)
2019-12-08 15:36:42,386 atss_core INFO: Collecting env info (might take some time)
2019-12-08 15:36:44,187 atss_core INFO:
PyTorch version: 1.3.0
Is debug build: No
CUDA used to build PyTorch: 10.0.130

OS: Ubuntu 16.04.4 LTS
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.10) 5.4.0 20160609
CMake version: Could not collect

Python version: 3.6
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration:
GPU 0: GeForce GTX 1080 Ti
GPU 1: GeForce GTX 1080 Ti
GPU 2: GeForce GTX 1080 Ti
GPU 3: GeForce GTX 1080 Ti
GPU 4: TITAN Xp
GPU 5: GeForce GTX 1080 Ti
GPU 6: GeForce GTX 1080 Ti
GPU 7: GeForce GTX 1080 Ti

Nvidia driver version: 418.39
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.5.0

Versions of relevant libraries:
[pip] Could not collect
[conda] Could not collect
Pillow (4.2.1)
2019-12-08 15:36:44,187 atss_core INFO: Loaded configuration file configs/atss/atss_R_50_FPN_1x.yaml
2019-12-08 15:36:44,187 atss_core INFO:
OUTPUT_DIR: /node01/jobs/io/out/wangqian/atss_retinanet_R_50_FPN
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "/node01/jobs/io/pretrained/torchvision/R-50.pkl"
RPN_ONLY: True
ATSS_ON: True
BACKBONE:
CONV_BODY: "R-50-FPN-RETINANET"
RESNETS:
BACKBONE_OUT_CHANNELS: 256
RETINANET:
USE_C5: False
ATSS:
ANCHOR_SIZES: (64, 128, 256, 512, 1024) # 8S
ASPECT_RATIOS: (1.0,)
SCALES_PER_OCTAVE: 1
USE_DCN_IN_TOWER: False
POSITIVE_TYPE: 'ATSS' # how to select positves: ATSS (Ours) , SSC (FCOS), IoU (RetinaNet)
TOPK: 9 # topk for selecting candidate positive samples from each level
REGRESSION_TYPE: 'BOX' # regressing from a 'BOX' or a 'POINT'
DATASETS:
TRAIN: ("coco_2017_train",)
TEST: ("coco_2017_val",)
INPUT:
MIN_SIZE_TRAIN: (800,)
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
DATALOADER:
SIZE_DIVISIBILITY: 32
SOLVER:
BASE_LR: 0.01
WEIGHT_DECAY: 0.0001
STEPS: (60000, 80000)
MAX_ITER: 90000
IMS_PER_BATCH: 8
WARMUP_METHOD: "constant"

2019-12-08 15:36:44,188 atss_core INFO: Running with config:
DATALOADER:
ASPECT_RATIO_GROUPING: True
NUM_WORKERS: 4
SIZE_DIVISIBILITY: 32
DATASETS:
TEST: ('coco_2017_val',)
TRAIN: ('coco_2017_train',)
INPUT:
MAX_SIZE_TEST: 1333
MAX_SIZE_TRAIN: 1333
MIN_SIZE_RANGE_TRAIN: (-1, -1)
MIN_SIZE_TEST: 800
MIN_SIZE_TRAIN: (800,)
PIXEL_MEAN: [102.9801, 115.9465, 122.7717]
PIXEL_STD: [1.0, 1.0, 1.0]
TO_BGR255: True
MODEL:
ATSS:
ANCHOR_SIZES: (64, 128, 256, 512, 1024)
ANCHOR_STRIDES: (8, 16, 32, 64, 128)
ASPECT_RATIOS: (1.0,)
BG_IOU_THRESHOLD: 0.4
FG_IOU_THRESHOLD: 0.5
INFERENCE_TH: 0.05
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
NMS_TH: 0.6
NUM_CLASSES: 81
NUM_CONVS: 4
OCTAVE: 2.0
POSITIVE_TYPE: ATSS
PRE_NMS_TOP_N: 1000
PRIOR_PROB: 0.01
REGRESSION_TYPE: BOX
REG_LOSS_WEIGHT: 2.0
SCALES_PER_OCTAVE: 1
STRADDLE_THRESH: 0
TOPK: 9
USE_DCN_IN_TOWER: False
ATSS_ON: True
BACKBONE:
CONV_BODY: R-50-FPN-RETINANET
FREEZE_CONV_BODY_AT: 2
USE_GN: False
CLS_AGNOSTIC_BBOX_REG: False
DEVICE: cuda
FBNET:
ARCH: default
ARCH_DEF:
BN_TYPE: bn
DET_HEAD_BLOCKS: []
DET_HEAD_LAST_SCALE: 1.0
DET_HEAD_STRIDE: 0
DW_CONV_SKIP_BN: True
DW_CONV_SKIP_RELU: True
KPTS_HEAD_BLOCKS: []
KPTS_HEAD_LAST_SCALE: 0.0
KPTS_HEAD_STRIDE: 0
MASK_HEAD_BLOCKS: []
MASK_HEAD_LAST_SCALE: 0.0
MASK_HEAD_STRIDE: 0
RPN_BN_TYPE:
RPN_HEAD_BLOCKS: 0
SCALE_FACTOR: 1.0
WIDTH_DIVISOR: 1
FCOS:
CENTERNESS_ON_REG: False
CENTER_SAMPLING_RADIUS: 0.0
FPN_STRIDES: [8, 16, 32, 64, 128]
INFERENCE_TH: 0.05
IOU_LOSS_TYPE: iou
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
NMS_TH: 0.6
NORM_REG_TARGETS: False
NUM_CLASSES: 81
NUM_CONVS: 4
PRE_NMS_TOP_N: 1000
PRIOR_PROB: 0.01
USE_DCN_IN_TOWER: False
FCOS_ON: False
FPN:
USE_GN: False
USE_RELU: False
GROUP_NORM:
DIM_PER_GP: -1
EPSILON: 1e-05
NUM_GROUPS: 32
KEYPOINT_ON: False
MASK_ON: False
META_ARCHITECTURE: GeneralizedRCNN
RESNETS:
BACKBONE_OUT_CHANNELS: 256
DEFORMABLE_GROUPS: 1
NUM_GROUPS: 1
RES2_OUT_CHANNELS: 256
RES5_DILATION: 1
STAGE_WITH_DCN: (False, False, False, False)
STEM_FUNC: StemWithFixedBatchNorm
STEM_OUT_CHANNELS: 64
STRIDE_IN_1X1: True
TRANS_FUNC: BottleneckWithFixedBatchNorm
WIDTH_PER_GROUP: 64
WITH_MODULATED_DCN: False
RETINANET:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDES: (8, 16, 32, 64, 128)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BBOX_REG_BETA: 0.11
BBOX_REG_WEIGHT: 4.0
BG_IOU_THRESHOLD: 0.4
FG_IOU_THRESHOLD: 0.5
INFERENCE_TH: 0.05
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
NMS_TH: 0.4
NUM_CLASSES: 81
NUM_CONVS: 4
OCTAVE: 2.0
PRE_NMS_TOP_N: 1000
PRIOR_PROB: 0.01
SCALES_PER_OCTAVE: 3
STRADDLE_THRESH: 0
USE_C5: False
RETINANET_ON: False
ROI_BOX_HEAD:
CONV_HEAD_DIM: 256
DILATION: 1
FEATURE_EXTRACTOR: ResNet50Conv5ROIFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 81
NUM_STACKED_CONVS: 4
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
PREDICTOR: FastRCNNPredictor
USE_GN: False
ROI_HEADS:
BATCH_SIZE_PER_IMAGE: 512
BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
BG_IOU_THRESHOLD: 0.5
DETECTIONS_PER_IMG: 100
FG_IOU_THRESHOLD: 0.5
NMS: 0.5
POSITIVE_FRACTION: 0.25
SCORE_THRESH: 0.05
USE_FPN: False
ROI_KEYPOINT_HEAD:
CONV_LAYERS: (512, 512, 512, 512, 512, 512, 512, 512)
FEATURE_EXTRACTOR: KeypointRCNNFeatureExtractor
MLP_HEAD_DIM: 1024
NUM_CLASSES: 17
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
PREDICTOR: KeypointRCNNPredictor
RESOLUTION: 14
SHARE_BOX_FEATURE_EXTRACTOR: True
ROI_MASK_HEAD:
CONV_LAYERS: (256, 256, 256, 256)
DILATION: 1
FEATURE_EXTRACTOR: ResNet50Conv5ROIFeatureExtractor
MLP_HEAD_DIM: 1024
POOLER_RESOLUTION: 14
POOLER_SAMPLING_RATIO: 0
POOLER_SCALES: (0.0625,)
POSTPROCESS_MASKS: False
POSTPROCESS_MASKS_THRESHOLD: 0.5
PREDICTOR: MaskRCNNC4Predictor
RESOLUTION: 14
SHARE_BOX_FEATURE_EXTRACTOR: True
USE_GN: False
RPN:
ANCHOR_SIZES: (32, 64, 128, 256, 512)
ANCHOR_STRIDE: (16,)
ASPECT_RATIOS: (0.5, 1.0, 2.0)
BATCH_SIZE_PER_IMAGE: 256
BG_IOU_THRESHOLD: 0.3
FG_IOU_THRESHOLD: 0.7
FPN_POST_NMS_TOP_N_TEST: 2000
FPN_POST_NMS_TOP_N_TRAIN: 2000
MIN_SIZE: 0
NMS_THRESH: 0.7
POSITIVE_FRACTION: 0.5
POST_NMS_TOP_N_TEST: 1000
POST_NMS_TOP_N_TRAIN: 2000
PRE_NMS_TOP_N_TEST: 6000
PRE_NMS_TOP_N_TRAIN: 12000
RPN_HEAD: SingleConvRPNHead
STRADDLE_THRESH: 0
USE_FPN: False
RPN_ONLY: True
USE_SYNCBN: False
WEIGHT: /node01/jobs/io/pretrained/torchvision/R-50.pkl
OUTPUT_DIR: /node01/jobs/io/out/wangqian/atss_retinanet_R_50_FPN
PATHS_CATALOG: /node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.01
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 2500
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH: 8
MAX_ITER: 90000
MOMENTUM: 0.9
STEPS: (60000, 80000)
WARMUP_FACTOR: 0.3333333333333333
WARMUP_ITERS: 500
WARMUP_METHOD: constant
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
TEST:
BBOX_AUG:
ENABLED: False
H_FLIP: False
MAX_SIZE: 4000
MERGE_TYPE: vote
SCALES: ()
SCALE_H_FLIP: False
SCALE_RANGES: ()
VOTE: False
VOTE_TH: 0.66
DETECTIONS_PER_IMG: 100
EXPECTED_RESULTS: []
EXPECTED_RESULTS_SIGMA_TOL: 4
IMS_PER_BATCH: 8
2019-12-08 15:36:44,711 atss_core.utils.checkpoint INFO: Loading checkpoint from /node01/jobs/io/pretrained/torchvision/R-50.pkl
2019-12-08 15:36:44,805 atss_core.utils.c2_model_loading INFO: Remapping C2 weights
2019-12-08 15:36:44,805 atss_core.utils.c2_model_loading INFO: C2 name: conv1_b mapped name: conv1.bias
2019-12-08 15:36:44,806 atss_core.utils.c2_model_loading INFO: C2 name: conv1_w mapped name: conv1.weight
2019-12-08 15:36:44,806 atss_core.utils.c2_model_loading INFO: C2 name: fc1000_b mapped name: fc1000.bias
2019-12-08 15:36:44,806 atss_core.utils.c2_model_loading INFO: C2 name: fc1000_w mapped name: fc1000.weight
2019-12-08 15:36:44,806 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_b mapped name: layer1.0.downsample.0.bias
2019-12-08 15:36:44,806 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_b mapped name: layer1.0.downsample.1.bias
2019-12-08 15:36:44,806 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_s mapped name: layer1.0.downsample.1.weight
2019-12-08 15:36:44,806 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_w mapped name: layer1.0.downsample.0.weight
2019-12-08 15:36:44,806 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_b mapped name: layer1.0.conv1.bias
2019-12-08 15:36:44,806 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_bn_b mapped name: layer1.0.bn1.bias
2019-12-08 15:36:44,807 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_bn_s mapped name: layer1.0.bn1.weight
2019-12-08 15:36:44,807 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_w mapped name: layer1.0.conv1.weight
2019-12-08 15:36:44,807 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_b mapped name: layer1.0.conv2.bias
2019-12-08 15:36:44,807 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_bn_b mapped name: layer1.0.bn2.bias
2019-12-08 15:36:44,807 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_bn_s mapped name: layer1.0.bn2.weight
2019-12-08 15:36:44,807 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_w mapped name: layer1.0.conv2.weight
2019-12-08 15:36:44,807 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_b mapped name: layer1.0.conv3.bias
2019-12-08 15:36:44,807 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_bn_b mapped name: layer1.0.bn3.bias
2019-12-08 15:36:44,807 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_bn_s mapped name: layer1.0.bn3.weight
2019-12-08 15:36:44,807 atss_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2c_w mapped name: layer1.0.conv3.weight
2019-12-08 15:36:44,808 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_b mapped name: layer1.1.conv1.bias
2019-12-08 15:36:44,808 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_bn_b mapped name: layer1.1.bn1.bias
2019-12-08 15:36:44,808 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_bn_s mapped name: layer1.1.bn1.weight
2019-12-08 15:36:44,808 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2a_w mapped name: layer1.1.conv1.weight
2019-12-08 15:36:44,808 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2b_b mapped name: layer1.1.conv2.bias
2019-12-08 15:36:44,808 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2b_bn_b mapped name: layer1.1.bn2.bias
2019-12-08 15:36:44,808 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2b_bn_s mapped name: layer1.1.bn2.weight
2019-12-08 15:36:44,808 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2b_w mapped name: layer1.1.conv2.weight
2019-12-08 15:36:44,808 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2c_b mapped name: layer1.1.conv3.bias
2019-12-08 15:36:44,808 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2c_bn_b mapped name: layer1.1.bn3.bias
2019-12-08 15:36:44,809 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2c_bn_s mapped name: layer1.1.bn3.weight
2019-12-08 15:36:44,809 atss_core.utils.c2_model_loading INFO: C2 name: res2_1_branch2c_w mapped name: layer1.1.conv3.weight
2019-12-08 15:36:44,809 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2a_b mapped name: layer1.2.conv1.bias
2019-12-08 15:36:44,809 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2a_bn_b mapped name: layer1.2.bn1.bias
2019-12-08 15:36:44,809 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2a_bn_s mapped name: layer1.2.bn1.weight
2019-12-08 15:36:44,809 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2a_w mapped name: layer1.2.conv1.weight
2019-12-08 15:36:44,809 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2b_b mapped name: layer1.2.conv2.bias
2019-12-08 15:36:44,809 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2b_bn_b mapped name: layer1.2.bn2.bias
2019-12-08 15:36:44,809 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2b_bn_s mapped name: layer1.2.bn2.weight
2019-12-08 15:36:44,810 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2b_w mapped name: layer1.2.conv2.weight
2019-12-08 15:36:44,810 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2c_b mapped name: layer1.2.conv3.bias
2019-12-08 15:36:44,810 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2c_bn_b mapped name: layer1.2.bn3.bias
2019-12-08 15:36:44,810 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2c_bn_s mapped name: layer1.2.bn3.weight
2019-12-08 15:36:44,810 atss_core.utils.c2_model_loading INFO: C2 name: res2_2_branch2c_w mapped name: layer1.2.conv3.weight
2019-12-08 15:36:44,810 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch1_b mapped name: layer2.0.downsample.0.bias
2019-12-08 15:36:44,810 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch1_bn_b mapped name: layer2.0.downsample.1.bias
2019-12-08 15:36:44,810 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch1_bn_s mapped name: layer2.0.downsample.1.weight
2019-12-08 15:36:44,810 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch1_w mapped name: layer2.0.downsample.0.weight
2019-12-08 15:36:44,810 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch2a_b mapped name: layer2.0.conv1.bias
2019-12-08 15:36:44,811 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch2a_bn_b mapped name: layer2.0.bn1.bias
2019-12-08 15:36:44,811 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch2a_bn_s mapped name: layer2.0.bn1.weight
2019-12-08 15:36:44,811 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch2a_w mapped name: layer2.0.conv1.weight
2019-12-08 15:36:44,811 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch2b_b mapped name: layer2.0.conv2.bias
2019-12-08 15:36:44,811 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch2b_bn_b mapped name: layer2.0.bn2.bias
2019-12-08 15:36:44,811 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch2b_bn_s mapped name: layer2.0.bn2.weight
2019-12-08 15:36:44,811 atss_core.utils.c2_model_loading INFO: C2 name: res3_0_branch2b_w mapped name: layer2.0.conv2.weight
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2019-12-08 15:36:44,825 atss_core.utils.c2_model_loading INFO: C2 name: res5_1_branch2c_w mapped name: layer4.1.conv3.weight
2019-12-08 15:36:44,825 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2a_b mapped name: layer4.2.conv1.bias
2019-12-08 15:36:44,825 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2a_bn_b mapped name: layer4.2.bn1.bias
2019-12-08 15:36:44,826 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2a_bn_s mapped name: layer4.2.bn1.weight
2019-12-08 15:36:44,826 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2a_w mapped name: layer4.2.conv1.weight
2019-12-08 15:36:44,826 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2b_b mapped name: layer4.2.conv2.bias
2019-12-08 15:36:44,826 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2b_bn_b mapped name: layer4.2.bn2.bias
2019-12-08 15:36:44,826 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2b_bn_s mapped name: layer4.2.bn2.weight
2019-12-08 15:36:44,826 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2b_w mapped name: layer4.2.conv2.weight
2019-12-08 15:36:44,826 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2c_b mapped name: layer4.2.conv3.bias
2019-12-08 15:36:44,826 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2c_bn_b mapped name: layer4.2.bn3.bias
2019-12-08 15:36:44,826 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2c_bn_s mapped name: layer4.2.bn3.weight
2019-12-08 15:36:44,826 atss_core.utils.c2_model_loading INFO: C2 name: res5_2_branch2c_w mapped name: layer4.2.conv3.weight
2019-12-08 15:36:44,827 atss_core.utils.c2_model_loading INFO: C2 name: res_conv1_bn_b mapped name: bn1.bias
2019-12-08 15:36:44,827 atss_core.utils.c2_model_loading INFO: C2 name: res_conv1_bn_s mapped name: bn1.weight
2019-12-08 15:36:44,827 atss_core.utils.c2_model_loading INFO: Remapping conv weights for deformable conv weights
2019-12-08 15:36:44,850 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.0.bn1.bias loaded from layer1.0.bn1.bias of shape (64,)
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2019-12-08 15:36:44,850 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.0.bn3.bias loaded from layer1.0.bn3.bias of shape (256,)
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2019-12-08 15:36:44,851 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.0.conv2.weight loaded from layer1.0.conv2.weight of shape (64, 64, 3, 3)
2019-12-08 15:36:44,851 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.0.conv3.weight loaded from layer1.0.conv3.weight of shape (256, 64, 1, 1)
2019-12-08 15:36:44,851 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.0.downsample.0.weight loaded from layer1.0.downsample.0.weight of shape (256, 64, 1, 1)
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2019-12-08 15:36:44,851 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.1.bn3.bias loaded from layer1.1.bn3.bias of shape (256,)
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2019-12-08 15:36:44,852 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.1.conv1.weight loaded from layer1.1.conv1.weight of shape (64, 256, 1, 1)
2019-12-08 15:36:44,852 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.1.conv2.weight loaded from layer1.1.conv2.weight of shape (64, 64, 3, 3)
2019-12-08 15:36:44,852 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.1.conv3.weight loaded from layer1.1.conv3.weight of shape (256, 64, 1, 1)
2019-12-08 15:36:44,852 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.2.bn1.bias loaded from layer1.2.bn1.bias of shape (64,)
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2019-12-08 15:36:44,852 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.2.bn3.bias loaded from layer1.2.bn3.bias of shape (256,)
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2019-12-08 15:36:44,852 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.2.conv2.weight loaded from layer1.2.conv2.weight of shape (64, 64, 3, 3)
2019-12-08 15:36:44,852 atss_core.utils.model_serialization INFO: module.backbone.body.layer1.2.conv3.weight loaded from layer1.2.conv3.weight of shape (256, 64, 1, 1)
2019-12-08 15:36:44,853 atss_core.utils.model_serialization INFO: module.backbone.body.layer2.0.bn1.bias loaded from layer2.0.bn1.bias of shape (128,)
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2019-12-08 15:36:44,854 atss_core.utils.model_serialization INFO: module.backbone.body.layer2.2.bn1.bias loaded from layer2.2.bn1.bias of shape (128,)
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2019-12-08 15:36:44,860 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.4.conv2.weight loaded from layer3.4.conv2.weight of shape (256, 256, 3, 3)
2019-12-08 15:36:44,860 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.4.conv3.weight loaded from layer3.4.conv3.weight of shape (1024, 256, 1, 1)
2019-12-08 15:36:44,860 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.5.bn1.bias loaded from layer3.5.bn1.bias of shape (256,)
2019-12-08 15:36:44,860 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.5.bn1.weight loaded from layer3.5.bn1.weight of shape (256,)
2019-12-08 15:36:44,860 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.5.bn2.bias loaded from layer3.5.bn2.bias of shape (256,)
2019-12-08 15:36:44,860 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.5.bn2.weight loaded from layer3.5.bn2.weight of shape (256,)
2019-12-08 15:36:44,860 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.5.bn3.bias loaded from layer3.5.bn3.bias of shape (1024,)
2019-12-08 15:36:44,860 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.5.bn3.weight loaded from layer3.5.bn3.weight of shape (1024,)
2019-12-08 15:36:44,860 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.5.conv1.weight loaded from layer3.5.conv1.weight of shape (256, 1024, 1, 1)
2019-12-08 15:36:44,860 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.5.conv2.weight loaded from layer3.5.conv2.weight of shape (256, 256, 3, 3)
2019-12-08 15:36:44,861 atss_core.utils.model_serialization INFO: module.backbone.body.layer3.5.conv3.weight loaded from layer3.5.conv3.weight of shape (1024, 256, 1, 1)
2019-12-08 15:36:44,861 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.bn1.bias loaded from layer4.0.bn1.bias of shape (512,)
2019-12-08 15:36:44,861 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.bn1.weight loaded from layer4.0.bn1.weight of shape (512,)
2019-12-08 15:36:44,861 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.bn2.bias loaded from layer4.0.bn2.bias of shape (512,)
2019-12-08 15:36:44,861 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.bn2.weight loaded from layer4.0.bn2.weight of shape (512,)
2019-12-08 15:36:44,861 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.bn3.bias loaded from layer4.0.bn3.bias of shape (2048,)
2019-12-08 15:36:44,861 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.bn3.weight loaded from layer4.0.bn3.weight of shape (2048,)
2019-12-08 15:36:44,861 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.conv1.weight loaded from layer4.0.conv1.weight of shape (512, 1024, 1, 1)
2019-12-08 15:36:44,861 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.conv2.weight loaded from layer4.0.conv2.weight of shape (512, 512, 3, 3)
2019-12-08 15:36:44,861 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.conv3.weight loaded from layer4.0.conv3.weight of shape (2048, 512, 1, 1)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.0.weight loaded from layer4.0.downsample.0.weight of shape (2048, 1024, 1, 1)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.bias loaded from layer4.0.downsample.1.bias of shape (2048,)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.0.downsample.1.weight loaded from layer4.0.downsample.1.weight of shape (2048,)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.bias loaded from layer4.1.bn1.bias of shape (512,)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.1.bn1.weight loaded from layer4.1.bn1.weight of shape (512,)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.bias loaded from layer4.1.bn2.bias of shape (512,)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.1.bn2.weight loaded from layer4.1.bn2.weight of shape (512,)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.1.bn3.bias loaded from layer4.1.bn3.bias of shape (2048,)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.1.bn3.weight loaded from layer4.1.bn3.weight of shape (2048,)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.1.conv1.weight loaded from layer4.1.conv1.weight of shape (512, 2048, 1, 1)
2019-12-08 15:36:44,863 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.1.conv2.weight loaded from layer4.1.conv2.weight of shape (512, 512, 3, 3)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.1.conv3.weight loaded from layer4.1.conv3.weight of shape (2048, 512, 1, 1)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.bias loaded from layer4.2.bn1.bias of shape (512,)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.weight loaded from layer4.2.bn1.weight of shape (512,)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.bias loaded from layer4.2.bn2.bias of shape (512,)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.weight loaded from layer4.2.bn2.weight of shape (512,)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.bias loaded from layer4.2.bn3.bias of shape (2048,)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.weight loaded from layer4.2.bn3.weight of shape (2048,)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.2.conv1.weight loaded from layer4.2.conv1.weight of shape (512, 2048, 1, 1)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.2.conv2.weight loaded from layer4.2.conv2.weight of shape (512, 512, 3, 3)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.layer4.2.conv3.weight loaded from layer4.2.conv3.weight of shape (2048, 512, 1, 1)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.stem.bn1.bias loaded from bn1.bias of shape (64,)
2019-12-08 15:36:44,864 atss_core.utils.model_serialization INFO: module.backbone.body.stem.bn1.weight loaded from bn1.weight of shape (64,)
2019-12-08 15:36:44,865 atss_core.utils.model_serialization INFO: module.backbone.body.stem.conv1.weight loaded from conv1.weight of shape (64, 3, 7, 7)
loading annotations into memory...
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Done (t=16.12s)
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2019-12-08 15:37:07,768 atss_core.trainer INFO: Start training
/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py:101: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead
warnings.warn("torch.distributed.reduce_op is deprecated, please use "
Traceback (most recent call last):
File "tools/train_net.py", line 192, in
main()
File "tools/train_net.py", line 185, in main
model = train(cfg, args.local_rank, args.distributed)
File "tools/train_net.py", line 80, in train
arguments,
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/engine/trainer.py", line 69, in do_train
loss_dict = model(images, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 442, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/detector/generalized_rcnn.py", line 50, in forward
proposals, proposal_losses = self.rpn(images, features, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 210, in forward
return self._forward_train(box_cls, box_regression, centerness, targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 216, in _forward_train
box_cls, box_regression, centerness, targets, anchors
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 239, in call
labels, reg_targets = self.prepare_targets(targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 178, in prepare_targets
ious[candidate_idxs[is_pos[:, ng] == 1, ng], ng]
RuntimeError: copy_if failed to synchronize: an illegal memory access was encountered
Traceback (most recent call last):
File "tools/train_net.py", line 192, in
main()
File "tools/train_net.py", line 185, in main
model = train(cfg, args.local_rank, args.distributed)
File "tools/train_net.py", line 80, in train
arguments,
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/engine/trainer.py", line 69, in do_train
loss_dict = model(images, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 442, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/detector/generalized_rcnn.py", line 50, in forward
proposals, proposal_losses = self.rpn(images, features, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 210, in forward
return self._forward_train(box_cls, box_regression, centerness, targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 216, in _forward_train
box_cls, box_regression, centerness, targets, anchors
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 239, in call
labels, reg_targets = self.prepare_targets(targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 178, in prepare_targets
ious[candidate_idxs[is_pos[:, ng] == 1, ng], ng]
RuntimeError: shape mismatch: value tensor of shape [2] cannot be broadcast to indexing result of shape [1]
/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py:101: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead
warnings.warn("torch.distributed.reduce_op is deprecated, please use "
Traceback (most recent call last):
File "tools/train_net.py", line 192, in
main()
File "tools/train_net.py", line 185, in main
model = train(cfg, args.local_rank, args.distributed)
File "tools/train_net.py", line 80, in train
arguments,
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/engine/trainer.py", line 69, in do_train
loss_dict = model(images, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 442, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/detector/generalized_rcnn.py", line 50, in forward
proposals, proposal_losses = self.rpn(images, features, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 210, in forward
return self._forward_train(box_cls, box_regression, centerness, targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 216, in _forward_train
box_cls, box_regression, centerness, targets, anchors
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 239, in call
labels, reg_targets = self.prepare_targets(targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 178, in prepare_targets
ious[candidate_idxs[is_pos[:, ng] == 1, ng], ng]
RuntimeError: shape mismatch: value tensor of shape [7] cannot be broadcast to indexing result of shape [3]
Traceback (most recent call last):
File "tools/train_net.py", line 192, in
main()
File "tools/train_net.py", line 185, in main
model = train(cfg, args.local_rank, args.distributed)
File "tools/train_net.py", line 80, in train
arguments,
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/engine/trainer.py", line 69, in do_train
loss_dict = model(images, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 442, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/detector/generalized_rcnn.py", line 50, in forward
proposals, proposal_losses = self.rpn(images, features, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 210, in forward
return self._forward_train(box_cls, box_regression, centerness, targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 216, in _forward_train
box_cls, box_regression, centerness, targets, anchors
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 239, in call
labels, reg_targets = self.prepare_targets(targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 178, in prepare_targets
ious[candidate_idxs[is_pos[:, ng] == 1, ng], ng]
RuntimeError: shape mismatch: value tensor of shape [5] cannot be broadcast to indexing result of shape [2]
Traceback (most recent call last):
File "tools/train_net.py", line 192, in
main()
File "tools/train_net.py", line 185, in main
model = train(cfg, args.local_rank, args.distributed)
File "tools/train_net.py", line 80, in train
arguments,
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/engine/trainer.py", line 69, in do_train
loss_dict = model(images, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 442, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/detector/generalized_rcnn.py", line 50, in forward
proposals, proposal_losses = self.rpn(images, features, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 210, in forward
return self._forward_train(box_cls, box_regression, centerness, targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 216, in _forward_train
box_cls, box_regression, centerness, targets, anchors
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 239, in call
labels, reg_targets = self.prepare_targets(targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 178, in prepare_targets
ious[candidate_idxs[is_pos[:, ng] == 1, ng], ng]
RuntimeError: shape mismatch: value tensor of shape [6] cannot be broadcast to indexing result of shape [3]
Traceback (most recent call last):
File "tools/train_net.py", line 192, in
main()
File "tools/train_net.py", line 185, in main
model = train(cfg, args.local_rank, args.distributed)
File "tools/train_net.py", line 80, in train
arguments,
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/engine/trainer.py", line 69, in do_train
loss_dict = model(images, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 442, in forward
output = self.module(*inputs[0], **kwargs[0])
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/detector/generalized_rcnn.py", line 50, in forward
proposals, proposal_losses = self.rpn(images, features, targets)
File "/node01/jobs/io/env/detection_env/anaconda3_atss/envs/torch1.3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 210, in forward
return self._forward_train(box_cls, box_regression, centerness, targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/atss.py", line 216, in _forward_train
box_cls, box_regression, centerness, targets, anchors
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 239, in call
labels, reg_targets = self.prepare_targets(targets, anchors)
File "/node01/jobs/codes_v2/wangqian/atss_retinanet_R_50_FPN/atss_core/modeling/rpn/atss/loss.py", line 178, in prepare_targets
ious[candidate_idxs[is_pos[:, ng] == 1, ng], ng]
RuntimeError: shape mismatch: value tensor of shape [5] cannot be broadcast to indexing result of shape [8]
terminate called after throwing an instance of 'std::runtime_error'
what(): NCCL error in: /opt/conda/conda-bld/pytorch_1570710853631/work/torch/lib/c10d/../c10d/NCCLUtils.hpp:84, unhandled cuda error`
Thanks a lot!

@qianwangn
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The bugs is caused by this line.
ious_inf[candidate_idxs[is_pos[:, ng] == 1, ng], ng] = ious[candidate_idxs[is_pos[:, ng] == 1, ng], ng]

so , I checked this two tensor shape. Its always the same size. but pytorch report shape mismatch assert. Thats so weird.

@sfzhang15
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@johnlanbor
It is weird. You can try to run with pytorch 1.1.0, or just use the first four GPUs (because the fifth GPU is not the same).

@qianwangn
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qianwangn commented Dec 9, 2019 •
edited

@i Solved this issue by change pytorch version ro 1.1.0.

Thanks a lot!

@anthony123
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I encontered the same problem, my pytorch version is 1.3.0+cu100, is there any way other than downgrad pytroch to 1.1 of solving the problem?

@sfzhang15
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@anthony123
We will debug in the PyTorch 1.3.0 environment. If we find a solution, we will release it.

@yqyao
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yqyao commented Dec 19, 2019

@anthony123 We have updated the code, and hope it works.

@anthony123
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@anthony123 We have updated the code, and hope it works.

It works for me, thanks a lot

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