tianzhi0549 / FCOS

FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)
https://arxiv.org/abs/1904.01355
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Negative results -1 everywhere #350

Open Mahmood-Hussain opened 3 years ago

Mahmood-Hussain commented 3 years ago

I tried to train for 10000 iterations but results are -1

2021-07-02 11:05:55,223 fcos_core INFO: Using 2 GPUs 2021-07-02 11:05:55,223 fcos_core INFO: Namespace(config_file='configs/fcos/fcos_R_50_FPN_1x.yaml', distributed=True, local_rank=0, opts=['DATALOADER.NUM_WORKERS', '2', 'OUTPUT_DIR', 'training_dir/fcos_R__50_FPN_1x_logs'], skip_test=False) 2021-07-02 11:05:55,223 fcos_core INFO: Collecting env info (might take some time) 2021-07-02 11:05:56,617 fcos_core INFO: PyTorch version: 1.3.1 Is debug build: No CUDA used to build PyTorch: 10.1.243

OS: Ubuntu 18.04.5 LTS GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 CMake version: version 3.20.0

Python version: 3.7 Is CUDA available: Yes CUDA runtime version: 10.2.89 GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1070 GPU 1: NVIDIA GeForce GTX 1070

Nvidia driver version: 465.19.01 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.1

Versions of relevant libraries: [pip3] numpy==1.20.3 [pip3] torch==1.3.1 [pip3] torchvision==0.4.2 [conda] torch 1.3.1 pypi_0 pypi [conda] torchvision 0.4.2 pypi_0 pypi Pillow (8.2.0) 2021-07-02 11:05:56,617 fcos_core INFO: Loaded configuration file configs/fcos/fcos_R_50_FPN_1x.yaml 2021-07-02 11:05:56,617 fcos_core INFO: MODEL: META_ARCHITECTURE: "GeneralizedRCNN" WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50" RPN_ONLY: True FCOS_ON: True BACKBONE: CONV_BODY: "R-50-FPN-RETINANET" RESNETS: BACKBONE_OUT_CHANNELS: 256 RETINANET: USE_C5: False # FCOS uses P5 instead of C5 DATASETS: TRAIN: ("coco_2017_train", "coco_2017_val") TEST: ("coco_2017_test_dev",) 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: 16 WARMUP_METHOD: "constant" TEST: BBOX_AUG: ENABLED: False H_FLIP: True SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200) MAX_SIZE: 2000 SCALE_H_FLIP: True 2021-07-02 11:05:56,618 fcos_core INFO: Running with config: DATALOADER: ASPECT_RATIO_GROUPING: True NUM_WORKERS: 2 SIZE_DIVISIBILITY: 32 DATASETS: TEST: ('coco_2017_test_dev',) TRAIN: ('coco_2017_train', 'coco_2017_val') 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: 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: True 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: catalog://ImageNetPretrained/MSRA/R-50 OUTPUT_DIR: training_dir/fcos_R__50_FPN_1x_logs PATHS_CATALOG: /home/mahmood/FCOS/fcos_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: 16 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: True MAX_SIZE: 2000 SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200) SCALE_H_FLIP: True DETECTIONS_PER_IMG: 100 EXPECTED_RESULTS: [] EXPECTED_RESULTS_SIGMA_TOL: 4 IMS_PER_BATCH: 8 2021-07-02 11:05:56,968 fcos_core.utils.checkpoint INFO: Loading checkpoint from catalog://ImageNetPretrained/MSRA/R-50 2021-07-02 11:05:56,968 fcos_core.utils.checkpoint INFO: catalog://ImageNetPretrained/MSRA/R-50 points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl 2021-07-02 11:05:56,997 fcos_core.utils.checkpoint INFO: url https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl cached in /home/mahmood/.torch/models/R-50.pkl 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: Remapping C2 weights 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: conv1_b mapped name: conv1.bias 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: conv1_w mapped name: conv1.weight 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: fc1000_b mapped name: fc1000.bias 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: fc1000_w mapped name: fc1000.weight 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_b mapped name: layer1.0.downsample.0.bias 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_b mapped name: layer1.0.downsample.1.bias 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_s mapped name: layer1.0.downsample.1.weight 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_w mapped name: layer1.0.downsample.0.weight 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_b mapped name: layer1.0.conv1.bias 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_bn_b mapped name: layer1.0.bn1.bias 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_bn_s mapped name: layer1.0.bn1.weight 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2a_w mapped name: layer1.0.conv1.weight 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_b mapped name: layer1.0.conv2.bias 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_bn_b mapped name: layer1.0.bn2.bias 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_bn_s mapped name: layer1.0.bn2.weight 2021-07-02 11:05:57,048 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch2b_w mapped 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loaded from layer1.0.conv1.weight of shape (64, 64, 1, 1) 2021-07-02 11:05:57,067 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.0.conv2.weight loaded from layer1.0.conv2.weight of shape (64, 64, 3, 3) 2021-07-02 11:05:57,067 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.0.conv3.weight loaded from layer1.0.conv3.weight of shape (256, 64, 1, 1) 2021-07-02 11:05:57,067 fcos_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) 2021-07-02 11:05:57,067 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.0.downsample.1.bias loaded from layer1.0.downsample.1.bias of shape (256,) 2021-07-02 11:05:57,067 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.0.downsample.1.weight loaded from layer1.0.downsample.1.weight of shape (256,) 2021-07-02 11:05:57,067 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.1.bn1.bias loaded from layer1.1.bn1.bias of shape (64,) 2021-07-02 11:05:57,067 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.1.bn1.weight loaded from layer1.1.bn1.weight of shape (64,) 2021-07-02 11:05:57,067 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.1.bn2.bias loaded from layer1.1.bn2.bias of shape (64,) 2021-07-02 11:05:57,067 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.1.bn2.weight loaded from layer1.1.bn2.weight of shape (64,) 2021-07-02 11:05:57,067 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.1.bn3.bias loaded from layer1.1.bn3.bias of shape (256,) 2021-07-02 11:05:57,067 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.1.bn3.weight loaded from layer1.1.bn3.weight of shape (256,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.1.conv1.weight loaded from layer1.1.conv1.weight of shape (64, 256, 1, 1) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.1.conv2.weight loaded from layer1.1.conv2.weight of shape (64, 64, 3, 3) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.1.conv3.weight loaded from layer1.1.conv3.weight of shape (256, 64, 1, 1) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.2.bn1.bias loaded from layer1.2.bn1.bias of shape (64,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.2.bn1.weight loaded from layer1.2.bn1.weight of shape (64,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.2.bn2.bias loaded from layer1.2.bn2.bias of shape (64,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.2.bn2.weight loaded from layer1.2.bn2.weight of shape (64,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.2.bn3.bias loaded from layer1.2.bn3.bias of shape (256,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.2.bn3.weight loaded from layer1.2.bn3.weight of shape (256,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.2.conv1.weight loaded from layer1.2.conv1.weight of shape (64, 256, 1, 1) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.2.conv2.weight loaded from layer1.2.conv2.weight of shape (64, 64, 3, 3) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer1.2.conv3.weight loaded from layer1.2.conv3.weight of shape (256, 64, 1, 1) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.bn1.bias loaded from layer2.0.bn1.bias of shape (128,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.bn1.weight loaded from layer2.0.bn1.weight of shape (128,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.bn2.bias loaded from layer2.0.bn2.bias of shape (128,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.bn2.weight loaded from layer2.0.bn2.weight of shape (128,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.bn3.bias loaded from layer2.0.bn3.bias of shape (512,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.bn3.weight loaded from layer2.0.bn3.weight of shape (512,) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.conv1.weight loaded from layer2.0.conv1.weight of shape (128, 256, 1, 1) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.conv2.weight loaded from layer2.0.conv2.weight of shape (128, 128, 3, 3) 2021-07-02 11:05:57,068 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.conv3.weight loaded from layer2.0.conv3.weight of shape (512, 128, 1, 1) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.downsample.0.weight loaded from layer2.0.downsample.0.weight of shape (512, 256, 1, 1) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.downsample.1.bias loaded from layer2.0.downsample.1.bias of shape (512,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.0.downsample.1.weight loaded from layer2.0.downsample.1.weight of shape (512,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.1.bn1.bias loaded from layer2.1.bn1.bias of shape (128,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.1.bn1.weight loaded from layer2.1.bn1.weight of shape (128,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.1.bn2.bias loaded from layer2.1.bn2.bias of shape (128,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.1.bn2.weight loaded from layer2.1.bn2.weight of shape (128,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.1.bn3.bias loaded from layer2.1.bn3.bias of shape (512,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.1.bn3.weight loaded from layer2.1.bn3.weight of shape (512,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.1.conv1.weight loaded from layer2.1.conv1.weight of shape (128, 512, 1, 1) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.1.conv2.weight loaded from layer2.1.conv2.weight of shape (128, 128, 3, 3) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.1.conv3.weight loaded from layer2.1.conv3.weight of shape (512, 128, 1, 1) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.2.bn1.bias loaded from layer2.2.bn1.bias of shape (128,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.2.bn1.weight loaded from layer2.2.bn1.weight of shape (128,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.2.bn2.bias loaded from layer2.2.bn2.bias of shape (128,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.2.bn2.weight loaded from layer2.2.bn2.weight of shape (128,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.2.bn3.bias loaded from layer2.2.bn3.bias of shape (512,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.2.bn3.weight loaded from layer2.2.bn3.weight of shape (512,) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.2.conv1.weight loaded from layer2.2.conv1.weight of shape (128, 512, 1, 1) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.2.conv2.weight loaded from layer2.2.conv2.weight of shape (128, 128, 3, 3) 2021-07-02 11:05:57,069 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.2.conv3.weight loaded from layer2.2.conv3.weight of shape (512, 128, 1, 1) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.3.bn1.bias loaded from layer2.3.bn1.bias of shape (128,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.3.bn1.weight loaded from layer2.3.bn1.weight of shape (128,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.3.bn2.bias loaded from layer2.3.bn2.bias of shape (128,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.3.bn2.weight loaded from layer2.3.bn2.weight of shape (128,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.3.bn3.bias loaded from layer2.3.bn3.bias of shape (512,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.3.bn3.weight loaded from layer2.3.bn3.weight of shape (512,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.3.conv1.weight loaded from layer2.3.conv1.weight of shape (128, 512, 1, 1) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.3.conv2.weight loaded from layer2.3.conv2.weight of shape (128, 128, 3, 3) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer2.3.conv3.weight loaded from layer2.3.conv3.weight of shape (512, 128, 1, 1) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn1.bias loaded from layer3.0.bn1.bias of shape (256,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn1.weight loaded from layer3.0.bn1.weight of shape (256,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn2.bias loaded from layer3.0.bn2.bias of shape (256,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn2.weight loaded from layer3.0.bn2.weight of shape (256,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn3.bias loaded from layer3.0.bn3.bias of shape (1024,) 2021-07-02 11:05:57,070 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn3.weight loaded from layer3.0.bn3.weight of shape (1024,) 2021-07-02 11:05:57,070 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shape (1024,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.1.bn1.bias loaded from layer3.1.bn1.bias of shape (256,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.1.bn1.weight loaded from layer3.1.bn1.weight of shape (256,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.1.bn2.bias loaded from layer3.1.bn2.bias of shape (256,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.1.bn2.weight loaded from layer3.1.bn2.weight of shape (256,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.1.bn3.bias loaded from layer3.1.bn3.bias of shape (1024,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.1.bn3.weight loaded from layer3.1.bn3.weight of shape (1024,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.1.conv1.weight loaded from layer3.1.conv1.weight of shape (256, 1024, 1, 1) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.1.conv2.weight loaded from layer3.1.conv2.weight of shape (256, 256, 3, 3) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.1.conv3.weight loaded from layer3.1.conv3.weight of shape (1024, 256, 1, 1) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.2.bn1.bias loaded from layer3.2.bn1.bias of shape (256,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.2.bn1.weight loaded from layer3.2.bn1.weight of shape (256,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.2.bn2.bias loaded from layer3.2.bn2.bias of shape (256,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.2.bn2.weight loaded from layer3.2.bn2.weight of shape (256,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.2.bn3.bias loaded from layer3.2.bn3.bias of shape (1024,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.2.bn3.weight loaded from layer3.2.bn3.weight of shape (1024,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.2.conv1.weight loaded from layer3.2.conv1.weight of shape (256, 1024, 1, 1) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.2.conv2.weight loaded from layer3.2.conv2.weight of shape (256, 256, 3, 3) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.2.conv3.weight loaded from layer3.2.conv3.weight of shape (1024, 256, 1, 1) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.3.bn1.bias loaded from layer3.3.bn1.bias of shape (256,) 2021-07-02 11:05:57,071 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.3.bn1.weight loaded from layer3.3.bn1.weight of shape (256,) 2021-07-02 11:05:57,072 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.3.bn2.bias loaded from layer3.3.bn2.bias of shape (256,) 2021-07-02 11:05:57,072 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.3.bn2.weight loaded from layer3.3.bn2.weight of shape (256,) 2021-07-02 11:05:57,072 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.3.bn3.bias loaded from layer3.3.bn3.bias of shape (1024,) 2021-07-02 11:05:57,072 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.3.bn3.weight loaded from layer3.3.bn3.weight of shape (1024,) 2021-07-02 11:05:57,072 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.3.conv1.weight loaded from layer3.3.conv1.weight of 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layer4.2.bn1.weight of shape (512,) 2021-07-02 11:05:57,074 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.bias loaded from layer4.2.bn2.bias of shape (512,) 2021-07-02 11:05:57,074 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.weight loaded from layer4.2.bn2.weight of shape (512,) 2021-07-02 11:05:57,074 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.bias loaded from layer4.2.bn3.bias of shape (2048,) 2021-07-02 11:05:57,074 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.weight loaded from layer4.2.bn3.weight of shape (2048,) 2021-07-02 11:05:57,074 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.conv1.weight loaded from layer4.2.conv1.weight of shape (512, 2048, 1, 1) 2021-07-02 11:05:57,074 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.conv2.weight loaded from layer4.2.conv2.weight of shape (512, 512, 3, 3) 2021-07-02 11:05:57,074 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.conv3.weight loaded from layer4.2.conv3.weight of shape (2048, 512, 1, 1) 2021-07-02 11:05:57,074 fcos_core.utils.model_serialization INFO: module.backbone.body.stem.bn1.bias loaded from bn1.bias of shape (64,) 2021-07-02 11:05:57,074 fcos_core.utils.model_serialization INFO: module.backbone.body.stem.bn1.weight loaded from bn1.weight of shape (64,) 2021-07-02 11:05:57,074 fcos_core.utils.model_serialization INFO: module.backbone.body.stem.conv1.weight loaded from conv1.weight of shape (64, 3, 7, 7) 2021-07-02 11:05:57,104 fcos_core.data.build WARNING: When using more than one image per GPU you may encounter an out-of-memory (OOM) error if your GPU does not have sufficient memory. If this happens, you can reduce SOLVER.IMS_PER_BATCH (for training) or TEST.IMS_PER_BATCH (for inference). For training, you must also adjust the learning rate and schedule length according to the linear scaling rule. See for example: https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14 2021-07-02 11:06:12,581 fcos_core.trainer INFO: Start training 2021-07-02 11:06:44,558 fcos_core INFO: Using 2 GPUs 2021-07-02 11:06:44,558 fcos_core INFO: Namespace(config_file='configs/fcos/fcos_R_50_FPN_1x.yaml', distributed=True, local_rank=0, opts=['DATALOADER.NUM_WORKERS', '2', 'OUTPUT_DIR', 'training_dir/fcos_R__50_FPN_1x_logs'], skip_test=False) 2021-07-02 11:06:44,558 fcos_core INFO: Collecting env info (might take some time) 2021-07-02 11:06:46,084 fcos_core INFO: PyTorch version: 1.3.1 Is debug build: No CUDA used to build PyTorch: 10.1.243

OS: Ubuntu 18.04.5 LTS GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 CMake version: version 3.20.0

Python version: 3.7 Is CUDA available: Yes CUDA runtime version: 10.2.89 GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1070 GPU 1: NVIDIA GeForce GTX 1070

Nvidia driver version: 465.19.01 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.1 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.1

Versions of relevant libraries: [pip3] numpy==1.20.3 [pip3] torch==1.3.1 [pip3] torchvision==0.4.2 [conda] torch 1.3.1 pypi_0 pypi [conda] torchvision 0.4.2 pypi_0 pypi Pillow (8.2.0) 2021-07-02 11:06:46,084 fcos_core INFO: Loaded configuration file configs/fcos/fcos_R_50_FPN_1x.yaml 2021-07-02 11:06:46,085 fcos_core INFO: MODEL: META_ARCHITECTURE: "GeneralizedRCNN" WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50" RPN_ONLY: True FCOS_ON: True BACKBONE: CONV_BODY: "R-50-FPN-RETINANET" RESNETS: BACKBONE_OUT_CHANNELS: 256 RETINANET: USE_C5: False # FCOS uses P5 instead of C5 DATASETS: TRAIN: ("coco_2017_train", "coco_2017_val") TEST: ("coco_2017_test_dev",) 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" TEST: BBOX_AUG: ENABLED: False H_FLIP: True SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200) MAX_SIZE: 2000 SCALE_H_FLIP: True 2021-07-02 11:06:46,085 fcos_core INFO: Running with config: DATALOADER: ASPECT_RATIO_GROUPING: True NUM_WORKERS: 2 SIZE_DIVISIBILITY: 32 DATASETS: TEST: ('coco_2017_test_dev',) TRAIN: ('coco_2017_train', 'coco_2017_val') 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: 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: True 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: catalog://ImageNetPretrained/MSRA/R-50 OUTPUT_DIR: training_dir/fcos_R__50_FPN_1x_logs PATHS_CATALOG: /home/mahmood/FCOS/fcos_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: True MAX_SIZE: 2000 SCALES: (400, 500, 600, 700, 900, 1000, 1100, 1200) SCALE_H_FLIP: True DETECTIONS_PER_IMG: 100 EXPECTED_RESULTS: [] EXPECTED_RESULTS_SIGMA_TOL: 4 IMS_PER_BATCH: 8 2021-07-02 11:06:46,435 fcos_core.utils.checkpoint INFO: Loading checkpoint from catalog://ImageNetPretrained/MSRA/R-50 2021-07-02 11:06:46,436 fcos_core.utils.checkpoint INFO: catalog://ImageNetPretrained/MSRA/R-50 points to https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl 2021-07-02 11:06:46,463 fcos_core.utils.checkpoint INFO: url https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl cached in /home/mahmood/.torch/models/R-50.pkl 2021-07-02 11:06:46,512 fcos_core.utils.c2_model_loading INFO: Remapping C2 weights 2021-07-02 11:06:46,512 fcos_core.utils.c2_model_loading INFO: C2 name: conv1_b mapped name: conv1.bias 2021-07-02 11:06:46,512 fcos_core.utils.c2_model_loading INFO: C2 name: conv1_w mapped name: conv1.weight 2021-07-02 11:06:46,512 fcos_core.utils.c2_model_loading INFO: C2 name: fc1000_b mapped name: fc1000.bias 2021-07-02 11:06:46,512 fcos_core.utils.c2_model_loading INFO: C2 name: fc1000_w mapped name: fc1000.weight 2021-07-02 11:06:46,512 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_b mapped name: layer1.0.downsample.0.bias 2021-07-02 11:06:46,512 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_b mapped name: layer1.0.downsample.1.bias 2021-07-02 11:06:46,512 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_bn_s mapped name: layer1.0.downsample.1.weight 2021-07-02 11:06:46,513 fcos_core.utils.c2_model_loading INFO: C2 name: res2_0_branch1_w mapped name: 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from layer2.3.conv3.weight of shape (512, 128, 1, 1) 2021-07-02 11:06:46,531 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn1.bias loaded from layer3.0.bn1.bias of shape (256,) 2021-07-02 11:06:46,531 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn1.weight loaded from layer3.0.bn1.weight of shape (256,) 2021-07-02 11:06:46,531 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn2.bias loaded from layer3.0.bn2.bias of shape (256,) 2021-07-02 11:06:46,531 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn2.weight loaded from layer3.0.bn2.weight of shape (256,) 2021-07-02 11:06:46,531 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn3.bias loaded from layer3.0.bn3.bias of shape (1024,) 2021-07-02 11:06:46,531 fcos_core.utils.model_serialization INFO: module.backbone.body.layer3.0.bn3.weight loaded from layer3.0.bn3.weight of shape (1024,) 2021-07-02 11:06:46,531 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loaded from layer4.1.bn3.bias of shape (2048,) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.1.bn3.weight loaded from layer4.1.bn3.weight of shape (2048,) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.1.conv1.weight loaded from layer4.1.conv1.weight of shape (512, 2048, 1, 1) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.1.conv2.weight loaded from layer4.1.conv2.weight of shape (512, 512, 3, 3) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.1.conv3.weight loaded from layer4.1.conv3.weight of shape (2048, 512, 1, 1) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.bias loaded from layer4.2.bn1.bias of shape (512,) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn1.weight loaded from layer4.2.bn1.weight of shape (512,) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.bias loaded from layer4.2.bn2.bias of shape (512,) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn2.weight loaded from layer4.2.bn2.weight of shape (512,) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.bias loaded from layer4.2.bn3.bias of shape (2048,) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.bn3.weight loaded from layer4.2.bn3.weight of shape (2048,) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.conv1.weight loaded from layer4.2.conv1.weight of shape (512, 2048, 1, 1) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.conv2.weight loaded from layer4.2.conv2.weight of shape (512, 512, 3, 3) 2021-07-02 11:06:46,534 fcos_core.utils.model_serialization INFO: module.backbone.body.layer4.2.conv3.weight loaded from layer4.2.conv3.weight of shape (2048, 512, 1, 1) 2021-07-02 11:06:46,535 fcos_core.utils.model_serialization INFO: module.backbone.body.stem.bn1.bias loaded from bn1.bias of shape (64,) 2021-07-02 11:06:46,535 fcos_core.utils.model_serialization INFO: module.backbone.body.stem.bn1.weight loaded from bn1.weight of shape (64,) 2021-07-02 11:06:46,535 fcos_core.utils.model_serialization INFO: module.backbone.body.stem.conv1.weight loaded from conv1.weight of shape (64, 3, 7, 7) 2021-07-02 11:06:46,565 fcos_core.data.build WARNING: When using more than one image per GPU you may encounter an out-of-memory (OOM) error if your GPU does not have sufficient memory. If this happens, you can reduce SOLVER.IMS_PER_BATCH (for training) or TEST.IMS_PER_BATCH (for inference). For training, you must also adjust the learning rate and schedule length according to the linear scaling rule. See for example: https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14 2021-07-02 11:07:00,898 fcos_core.trainer INFO: Start training 2021-07-02 11:07:19,970 fcos_core.trainer INFO: eta: 23:50:00 iter: 20 loss: 4.0449 (4.8851) loss_centerness: 0.6671 (0.6769) loss_cls: 0.9865 (0.9798) loss_reg: 2.4229 (3.2285) time: 0.9332 (0.9536) data: 0.0112 (0.0335) lr: 0.003333 max mem: 5437 2021-07-02 11:07:39,217 fcos_core.trainer INFO: eta: 23:56:17 iter: 40 loss: 3.3816 (4.1503) loss_centerness: 0.6636 (0.6706) loss_cls: 0.9572 (0.9806) loss_reg: 1.7510 (2.4990) time: 0.9664 (0.9580) data: 0.0118 (0.0233) lr: 0.003333 max mem: 5437 2021-07-02 11:07:58,210 fcos_core.trainer INFO: eta: 23:51:49 iter: 60 loss: 3.4253 (3.9234) loss_centerness: 0.6615 (0.6676) loss_cls: 1.0673 (1.0380) loss_reg: 1.6240 (2.2177) time: 0.9402 (0.9552) data: 0.0113 (0.0193) lr: 0.003333 max mem: 5437 2021-07-02 11:08:17,308 fcos_core.trainer INFO: eta: 23:51:23 iter: 80 loss: 3.6165 (3.8745) loss_centerness: 0.6639 (0.6666) loss_cls: 1.4411 (1.1632) loss_reg: 1.5266 (2.0448) time: 0.9433 (0.9551) data: 0.0111 (0.0174) lr: 0.003333 max mem: 5437 2021-07-02 11:08:36,307 fcos_core.trainer INFO: eta: 23:49:32 iter: 100 loss: 5.0180 (4.1425) loss_centerness: 0.6658 (0.6664) loss_cls: 2.9491 (1.5407) loss_reg: 1.4968 (1.9354) time: 0.9497 (0.9541) data: 0.0105 (0.0162) lr: 0.003333 max mem: 5437 2021-07-02 11:08:55,458 fcos_core.trainer INFO: eta: 23:50:04 iter: 120 loss: 3.2846 (4.0038) loss_centerness: 0.6675 (0.6667) loss_cls: 1.0838 (1.4726) loss_reg: 1.5048 (1.8645) time: 0.9432 (0.9547) data: 0.0105 (0.0154) lr: 0.003333 max mem: 5437 2021-07-02 11:09:14,594 fcos_core.trainer INFO: eta: 23:50:13 iter: 140 loss: 2.8861 (3.8541) loss_centerness: 0.6643 (0.6664) loss_cls: 1.0570 (1.4200) loss_reg: 1.1126 (1.7677) time: 0.9477 (0.9550) data: 0.0111 (0.0150) lr: 0.003333 max mem: 5437 2021-07-02 11:09:34,099 fcos_core.trainer INFO: eta: 23:53:41 iter: 160 loss: 2.5796 (3.6977) loss_centerness: 0.6635 (0.6662) loss_cls: 0.9172 (1.3586) loss_reg: 1.0047 (1.6729) time: 0.9647 (0.9575) data: 0.0138 (0.0148) lr: 0.003333 max mem: 5437 2021-07-02 11:09:53,244 fcos_core.trainer INFO: eta: 23:53:20 iter: 180 loss: 2.6461 (3.5870) loss_centerness: 0.6627 (0.6660) loss_cls: 1.0130 (1.3270) loss_reg: 0.9641 (1.5940) time: 0.9486 (0.9575) data: 0.0107 (0.0144) lr: 0.003333 max mem: 5437 2021-07-02 11:10:12,768 fcos_core.trainer INFO: eta: 23:55:49 iter: 200 loss: 2.4959 (3.4789) loss_centerness: 0.6641 (0.6658) loss_cls: 0.9066 (1.2846) loss_reg: 0.9283 (1.5285) time: 0.9850 (0.9593) data: 0.0118 (0.0142) lr: 0.003333 max mem: 5437 2021-07-02 11:10:32,213 fcos_core.trainer INFO: eta: 23:57:15 iter: 220 loss: 2.4282 (3.3838) loss_centerness: 0.6626 (0.6656) loss_cls: 0.8428 (1.2455) loss_reg: 0.9155 (1.4728) time: 0.9679 (0.9605) data: 0.0111 (0.0140) lr: 0.003333 max mem: 5437 2021-07-02 11:10:51,506 fcos_core.trainer INFO: eta: 23:57:27 iter: 240 loss: 2.4058 (3.3022) loss_centerness: 0.6606 (0.6653) loss_cls: 0.8208 (1.2116) loss_reg: 0.8987 (1.4254) time: 0.9608 (0.9609) data: 0.0114 (0.0138) lr: 0.003333 max mem: 5437 ... ... 2021-07-02 14:06:16,802 fcos_core.trainer INFO: eta: 0:01:17 iter: 9920 loss: 1.3860 (1.6328) loss_centerness: 0.6087 (0.6173) loss_cls: 0.3495 (0.4635) loss_reg: 0.4339 (0.5521) time: 0.9434 (0.9705) data: 0.0103 (0.0125) lr: 0.010000 max mem: 5464 2021-07-02 14:06:36,118 fcos_core.trainer INFO: eta: 0:00:58 iter: 9940 loss: 1.4088 (1.6323) loss_centerness: 0.6106 (0.6172) loss_cls: 0.3538 (0.4632) loss_reg: 0.4595 (0.5518) time: 0.9484 (0.9705) data: 0.0124 (0.0125) lr: 0.010000 max mem: 5464 2021-07-02 14:06:55,563 fcos_core.trainer INFO: eta: 0:00:38 iter: 9960 loss: 1.4098 (1.6319) loss_centerness: 0.6073 (0.6172) loss_cls: 0.3573 (0.4631) loss_reg: 0.4199 (0.5516) time: 0.9693 (0.9705) data: 0.0114 (0.0125) lr: 0.010000 max mem: 5464 2021-07-02 14:07:14,770 fcos_core.trainer INFO: eta: 0:00:19 iter: 9980 loss: 1.3834 (1.6314) loss_centerness: 0.6083 (0.6172) loss_cls: 0.3522 (0.4628) loss_reg: 0.4167 (0.5513) time: 0.9404 (0.9704) data: 0.0120 (0.0125) lr: 0.010000 max mem: 5464 2021-07-02 14:07:34,379 fcos_core.trainer INFO: eta: 0:00:00 iter: 10000 loss: 1.4098 (1.6310) loss_centerness: 0.6093 (0.6172) loss_cls: 0.3580 (0.4626) loss_reg: 0.4474 (0.5511) time: 0.9797 (0.9705) data: 0.0123 (0.0125) lr: 0.010000 max mem: 5464 2021-07-02 14:07:34,380 fcos_core.utils.checkpoint INFO: Saving checkpoint to training_dir/fcos_R__50_FPN_1x_logs/model_0010000.pth 2021-07-02 14:07:34,873 fcos_core.utils.checkpoint INFO: Saving checkpoint to training_dir/fcos_R__50_FPN_1x_logs/model_final.pth 2021-07-02 14:07:35,156 fcos_core.trainer INFO: Total training time: 2:41:45.398653 (0.9705 s / it) 2021-07-02 14:07:37,302 fcos_core.data.build WARNING: When using more than one image per GPU you may encounter an out-of-memory (OOM) error if your GPU does not have sufficient memory. If this happens, you can reduce SOLVER.IMS_PER_BATCH (for training) or TEST.IMS_PER_BATCH (for inference). For training, you must also adjust the learning rate and schedule length according to the linear scaling rule. See for example: https://github.com/facebookresearch/Detectron/blob/master/configs/getting_started/tutorial_1gpu_e2e_faster_rcnn_R-50-FPN.yaml#L14 2021-07-02 14:07:37,356 fcos_core.inference INFO: Start evaluation on coco_2017_test_dev dataset(20288 images). 2021-07-02 14:25:17,312 fcos_core.inference INFO: Total run time: 0:17:39.956622 (0.10449099188159316 s / img per device, on 2 devices) 2021-07-02 14:25:17,313 fcos_core.inference INFO: Model inference time: 0:17:05.123736 (0.1010571506450229 s / img per device, on 2 devices) 2021-07-02 14:25:21,474 fcos_core.inference INFO: Preparing results for COCO format 2021-07-02 14:25:21,475 fcos_core.inference INFO: Preparing bbox results 2021-07-02 14:25:27,225 fcos_core.inference INFO: Evaluating predictions 2021-07-02 14:27:46,346 fcos_core.inference INFO: OrderedDict([('bbox', OrderedDict([('AP', -1.0), ('AP50', -1.0), ('AP75', -1.0), ('APs', -1.0), ('APm', -1.0), ('APl', -1.0)]))])

image

CrapbagMo commented 2 years ago

hello, I have encountered with the exact same problem. Have you solved yours?

Mahmood-Hussain commented 2 years ago

@CrapbagMo I could not find a solution to this. But I think there should be something wrong with the class names of the custom datasets (there is mismatch between class names of FCOS and custom dataset)

ls1rius commented 2 years ago

hello, i have noticed that you are using the config: TRAIN: ("coco_2017_train", "coco_2017_val") TEST: ("coco_2017_test_dev",) but the coco_2017_test_dev (image_info_test-dev2017.json) doesn't have the annotations data, so your predictions mismatch the bbox data and got the negative results -1

you can change your config like these: TRAIN: ("coco_2017_train", ) # TRAIN: ("coco_2017_train", "coco_2017_val") TEST: ("coco_2017_val", ) # TEST: ("coco_2017_test_dev",)