Test on the trained model

[aniketzz]

I have trained a model with 1 class in SipMask-benchmark. I want to run the inference script using my model. when I tried to execute demo/fcos_demo.py. I have changed the path to the weight file and changed the class values inside demo/fcos_demo.py and predictor.py.

I have renamed the model as FCOS_imprv_R_50_FPN_1x.pth

I got the below error

[JialeCao001]

@aniketzz Thanks for interest. I donot use fcos_demo.py to show the result. But from the information you provided, it seems that there is a parameter about the number of the classes that needs to be changed to 1.

[aniketzz]

@JialeCao001 I agree but I have made the changes in all the config files as well as the files that are been used. I do not understand where I am missing. Is there any way to test the model on a video or image?

[JialeCao001]

@aniketzz Are you sure that the output channel of training model is 1? it seems 80.

[aniketzz]

@JialeCao001 Yes I have.
Here is the config:

Versions of relevant libraries:
[pip3] numpy==1.19.2
[pip3] torch==1.4.0
[pip3] torchvision==0.5.0
[conda] blas 1.0 mkl
[conda] mkl 2020.2 256
[conda] mkl-service 2.3.0 py37he8ac12f_0
[conda] mkl_fft 1.3.0 py37h54f3939_0
[conda] mkl_random 1.1.1 py37h0573a6f_0
[conda] pytorch 1.4.0 py3.7_cuda10.0.130_cudnn7.6.3_0 pytorch
[conda] torchvision 0.5.0 py37_cu100 pytorch
Pillow (6.2.2)
2021-03-25 07:27:28,085 fcos_core INFO: Loaded configuration file configs/sipmask/sipmask_R_50_FPN_1x.yaml
2021-03-25 07:27:28,085 fcos_core INFO:

FCOS with improvements

MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
RPN_ONLY: True
SIPMASK_ON: True
BACKBONE:
CONV_BODY: "R-50-FPN-RETINANET"
RESNETS:
BACKBONE_OUT_CHANNELS: 256
RETINANET:
USE_C5: False # FCOS uses P5 instead of C5
SIPMASK:
# normalizing the regression targets with FPN strides
NORM_REG_TARGETS: True
# positioning centerness on the regress branch.
# Please refer to tianzhi0549/FCOS#89 (comment)
CENTERNESS_ON_REG: True
# using center sampling and GIoU.
# Please refer to https://github.com/yqyao/FCOS_PLUS
CENTER_SAMPLING_RADIUS: 1.5
IOU_LOSS_TYPE: "giou"
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"

2021-03-25 07:27:28,086 fcos_core INFO: Running with config:
DATALOADER:
ASPECT_RATIO_GROUPING: True
NUM_WORKERS: 2
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:
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: 2
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: 2
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: 2
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: 1000
FPN_POST_NMS_TOP_N_TRAIN: 1000
MIN_SIZE: 0
NMS_THRESH: 0.7
POSITIVE_FRACTION: 0.5
POST_NMS_TOP_N_TEST: 500
POST_NMS_TOP_N_TRAIN: 1000
PRE_NMS_TOP_N_TEST: 3000
PRE_NMS_TOP_N_TRAIN: 6000
RPN_HEAD: SingleConvRPNHead
STRADDLE_THRESH: 0
USE_FPN: False
RPN_ONLY: True
SIPMASK:
CENTERNESS_ON_REG: True
CENTER_SAMPLING_RADIUS: 1.5
FPN_STRIDES: [8, 16, 32, 64, 128]
INFERENCE_TH: 0.05
IOU_LOSS_TYPE: giou
LOSS_ALPHA: 0.25
LOSS_GAMMA: 2.0
NMS_TH: 0.6
NORM_REG_TARGETS: True
NUM_CLASSES: 2
NUM_CONVS: 4
PRE_NMS_TOP_N: 1000
PRIOR_PROB: 0.01
USE_DCN_IN_TOWER: False
SIPMASK_ON: True
USE_SYNCBN: False
WEIGHT: catalog://ImageNetPretrained/MSRA/R-50
OUTPUT_DIR: training_dir/sipmask_R_50_FPN_1x
PATHS_CATALOG: /home/ubuntu/SipMask/SipMask-benchmark/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: False
MAX_SIZE: 4000
SCALES: ()
SCALE_H_FLIP: False
DETECTIONS_PER_IMG: 100
EXPECTED_RESULTS: []
EXPECTED_RESULTS_SIGMA_TOL: 4
IMS_PER_BATCH: 8

[JialeCao001]

@aniketzz It seems no problem. I am not sure about the problem. Maybe you check the whole things again:)

[aniketzz]

@JialeCao001 Can You please help me with the inference code to test the trained model on custom data?

[JialeCao001]

@aniketzz Hi aniketzz. I am sorry that I am not sure about the problem. I suggest you go through the inference step by step and check the paramter about classes carefully.

[aniketzz]

@JialeCao001, recompiling the code fixed the issue.
I am able to test the custom model but I do not see any bounding box or mask while executing "demo/fcos_demo.py". I am not sure what mistake I am doing here.