recovered original training routine

jshtok · Jan 26, 2020 · bed8691 · bed8691
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diff --git a/experiments/cfgs/resnet_v1_101_voc0712_trainval_fpn_dcn_oneshot_end2end_ohem_8_orig.yaml b/experiments/cfgs/resnet_v1_101_voc0712_trainval_fpn_dcn_oneshot_end2end_ohem_8_orig.yaml
@@ -0,0 +1,182 @@
+---
+MXNET_VERSION: "mxnet"
+output_path: "./output/fpn/voc_imagenet"
+symbol: resnet_v1_101_fpn_dcn_rcnn_oneshot_v3
+gpus: '0,1,2,3'
+CLASS_AGNOSTIC: true
+SCALES:
+- 800
+- 1280
+TEST_SCALES: [[800, 1280]] # single scale testing
+#TEST_SCALES: [[480, 800], [576, 900], [688, 1100], [800, 1200], [1200, 1600], [1400, 2000]] # multi-scale testing
+default:
+  frequent: 10
+  kvstore: device
+network:
+  pretrained: "./data/fpn_dcn_coco" 
+  pretrained_epoch: 0
+  pretrained_weights_are_priority: true
+  PIXEL_MEANS:
+  - 103.06
+  - 115.90
+  - 123.15
+  IMAGE_STRIDE: 32
+  RCNN_FEAT_STRIDE: 16
+  RPN_FEAT_STRIDE:
+  - 4
+  - 8
+  - 16
+  - 32
+  - 64
+  FIXED_PARAMS:
+  - conv1
+  - bn_conv1
+  - res2
+  - bn2
+  - gamma
+  - beta
+  FIXED_PARAMS_SHARED:
+  - conv1
+  - bn_conv1
+  - res2
+  - bn2
+  - res3
+  - bn3
+  - res4
+  - bn4
+  - gamma
+  - beta
+  ANCHOR_RATIOS:
+  - 0.5
+  - 1
+  - 2
+  ANCHOR_SCALES:
+  - 8
+  NUM_ANCHORS: 3
+  EMBEDDING_DIM: 256
+  REPS_PER_CLASS: 5
+  SIGMA: 0.5
+  EMBED_LOSS_ENABLED: True
+  EMBED_LOSS_MARGIN: 0.5
+  SOFTMAX_ENABLED: True
+  REP_L2_NORM: True
+  EMBED_L2_NORM: True
+  SEPARABLE_REPS: False
+  REPS_CLS_LOSS: False
+  SEPARABLE_REPS_INIT: True
+  ADDITIONAL_LINEAR_CLS_LOSS: True
+dataset:
+  NUM_CLASSES: 122
+  balance_classes: true
+  num_ex_per_class: 200
+  max_num_extra_classes: 122
+  dataset: PascalVOC;ImageNet
+  dataset_path: "/dccstor/leonidka1/data/VOCdevkit;/dccstor/leonidka1/data/imagenet/ILSVRC"
+  cls_filter_files: './data/Imagenet_LOC/Pascal_inloc_cls2id_map.pkl:./data/Imagenet_LOC/Pascal_inloc_first101_categories.txt'
+  image_set: 2007_trainval+2012_trainval;train_loc
+  per_category_epoch_max: 0;10
+  root_path: "./data"
+  test_image_set: ;val_partial
+  proposal: rpn
+TRAIN:
+  UPDATE_REPS_VIA_CLUSTERING: true
+  NUMEX_FOR_CLUSTERING: 200
+  REPS_LR_MULT: 1 #0.01
+  lr: 0.001
+  warmup_lr: 0.001
+  warmup_step: 250
+  warmup: true
+  lr_step: '4,20,30'
+  wd: 0.0001
+  begin_epoch: 15
+  end_epoch: 25
+  model_prefix: 'fpn_pascal_imagenet'
+  # whether resume training
+  RESUME: true
+  # whether flip image
+  FLIP: true
+  # whether shuffle image
+  SHUFFLE: true
+  # whether use OHEM
+  ENABLE_OHEM: true
+  # size of images for each device, 2 for rcnn, 1 for rpn and e2e
+  BATCH_IMAGES: 1
+  # e2e changes behavior of anchor loader and metric
+  END2END: true
+  # group images with similar aspect ratio
+  ASPECT_GROUPING: true
+  # R-CNN
+  # rcnn rois batch size
+  BATCH_ROIS: -1
+  BATCH_ROIS_OHEM: 512 #128
+  # rcnn rois sampling params
+  FG_FRACTION: 0.25
+  FG_THRESH: 0.5
+  BG_THRESH_HI: 0.5
+  BG_THRESH_LO: 0.0
+  # rcnn bounding box regression params
+  BBOX_REGRESSION_THRESH: 0.5
+  BBOX_WEIGHTS:
+  - 1.0
+  - 1.0
+  - 1.0
+  - 1.0
+  # RPN anchor loader
+  # rpn anchors batch size
+  RPN_BATCH_SIZE: 256
+  # rpn anchors sampling params
+  RPN_FG_FRACTION: 0.5
+  RPN_POSITIVE_OVERLAP: 0.7
+  RPN_NEGATIVE_OVERLAP: 0.3
+  RPN_CLOBBER_POSITIVES: false
+  # rpn bounding box regression params
+  RPN_BBOX_WEIGHTS:
+  - 1.0
+  - 1.0
+  - 1.0
+  - 1.0
+  RPN_POSITIVE_WEIGHT: -1.0
+  # used for end2end training
+  # RPN proposal
+  CXX_PROPOSAL: false
+  RPN_NMS_THRESH: 0.7
+  RPN_PRE_NMS_TOP_N: 12000
+  RPN_POST_NMS_TOP_N: 2000
+  RPN_MIN_SIZE: 0
+  # approximate bounding box regression
+  BBOX_NORMALIZATION_PRECOMPUTED: true
+  BBOX_MEANS:
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  BBOX_STDS:
+  - 0.1
+  - 0.1
+  - 0.2
+  - 0.2
+TEST:
+  # use rpn to generate proposal
+  HAS_RPN: true
+  # size of images for each device
+  BATCH_IMAGES: 1
+  # RPN proposal
+  CXX_PROPOSAL: false
+  RPN_NMS_THRESH: 0.7
+  RPN_PRE_NMS_TOP_N: 12000
+  RPN_POST_NMS_TOP_N: 2000
+  RPN_MIN_SIZE: 0
+  # RPN generate proposal
+  PROPOSAL_NMS_THRESH: 0.7
+  PROPOSAL_PRE_NMS_TOP_N: 20000
+  PROPOSAL_POST_NMS_TOP_N: 2000
+  PROPOSAL_MIN_SIZE: 0
+  # RCNN nms
+  NMS: 0.3
+  USE_SOFTNMS: true
+  SOFTNMS_THRESH: 0.6
+  test_epoch: 15
+  max_per_image: 100
+  # soft nms
+  USE_SOFTNMS: true
+  SOFTNMS_THRESH: 0.6
diff --git a/experiments/cfgs/resnet_v1_101_voc0712_trainval_fpn_dcn_oneshot_end2end_ohem_8_orig2.yaml b/experiments/cfgs/resnet_v1_101_voc0712_trainval_fpn_dcn_oneshot_end2end_ohem_8_orig2.yaml
@@ -0,0 +1,182 @@
+---
+MXNET_VERSION: "mxnet"
+output_path: "./output/fpn/voc_imagenet"
+symbol: resnet_v1_101_fpn_dcn_rcnn_oneshot_v3
+gpus: '0,1'
+CLASS_AGNOSTIC: true
+SCALES:
+- 800
+- 1280
+TEST_SCALES: [[800, 1280]] # single scale testing
+#TEST_SCALES: [[480, 800], [576, 900], [688, 1100], [800, 1200], [1200, 1600], [1400, 2000]] # multi-scale testing
+default:
+  frequent: 10
+  kvstore: device
+network:
+  pretrained: "./data/fpn_dcn_coco" 
+  pretrained_epoch: 0
+  pretrained_weights_are_priority: true
+  PIXEL_MEANS:
+  - 103.06
+  - 115.90
+  - 123.15
+  IMAGE_STRIDE: 32
+  RCNN_FEAT_STRIDE: 16
+  RPN_FEAT_STRIDE:
+  - 4
+  - 8
+  - 16
+  - 32
+  - 64
+  FIXED_PARAMS:
+  - conv1
+  - bn_conv1
+  - res2
+  - bn2
+  - gamma
+  - beta
+  FIXED_PARAMS_SHARED:
+  - conv1
+  - bn_conv1
+  - res2
+  - bn2
+  - res3
+  - bn3
+  - res4
+  - bn4
+  - gamma
+  - beta
+  ANCHOR_RATIOS:
+  - 0.5
+  - 1
+  - 2
+  ANCHOR_SCALES:
+  - 8
+  NUM_ANCHORS: 3
+  EMBEDDING_DIM: 256
+  REPS_PER_CLASS: 5
+  SIGMA: 0.5
+  EMBED_LOSS_ENABLED: True
+  EMBED_LOSS_MARGIN: 0.5
+  SOFTMAX_ENABLED: True
+  REP_L2_NORM: True
+  EMBED_L2_NORM: True
+  SEPARABLE_REPS: False
+  REPS_CLS_LOSS: False
+  SEPARABLE_REPS_INIT: True
+  ADDITIONAL_LINEAR_CLS_LOSS: True
+dataset:
+  NUM_CLASSES: 122
+  balance_classes: true
+  num_ex_per_class: 200
+  max_num_extra_classes: 122
+  dataset: PascalVOC;ImageNet
+  dataset_path: "/dccstor/leonidka1/data/VOCdevkit;/dccstor/leonidka1/data/imagenet/ILSVRC"
+  cls_filter_files: './data/Imagenet_LOC/Pascal_inloc_cls2id_map.pkl:./data/Imagenet_LOC/Pascal_inloc_first101_categories.txt'
+  image_set: 2007_trainval+2012_trainval;train_loc
+  per_category_epoch_max: 0;10
+  root_path: "./data"
+  test_image_set: ;val_partial
+  proposal: rpn
+TRAIN:
+  UPDATE_REPS_VIA_CLUSTERING: true
+  NUMEX_FOR_CLUSTERING: 200
+  REPS_LR_MULT: 1 #0.01
+  lr: 0.001
+  warmup_lr: 0.001
+  warmup_step: 250
+  warmup: true
+  lr_step: '4,6,20,30'
+  wd: 0.0001
+  begin_epoch: 0
+  end_epoch: 25
+  model_prefix: 'fpn_pascal_imagenet'
+  # whether resume training
+  RESUME: false
+  # whether flip image
+  FLIP: true
+  # whether shuffle image
+  SHUFFLE: true
+  # whether use OHEM
+  ENABLE_OHEM: true
+  # size of images for each device, 2 for rcnn, 1 for rpn and e2e
+  BATCH_IMAGES: 1
+  # e2e changes behavior of anchor loader and metric
+  END2END: true
+  # group images with similar aspect ratio
+  ASPECT_GROUPING: true
+  # R-CNN
+  # rcnn rois batch size
+  BATCH_ROIS: -1
+  BATCH_ROIS_OHEM: 512 #128
+  # rcnn rois sampling params
+  FG_FRACTION: 0.25
+  FG_THRESH: 0.5
+  BG_THRESH_HI: 0.5
+  BG_THRESH_LO: 0.0
+  # rcnn bounding box regression params
+  BBOX_REGRESSION_THRESH: 0.5
+  BBOX_WEIGHTS:
+  - 1.0
+  - 1.0
+  - 1.0
+  - 1.0
+  # RPN anchor loader
+  # rpn anchors batch size
+  RPN_BATCH_SIZE: 256
+  # rpn anchors sampling params
+  RPN_FG_FRACTION: 0.5
+  RPN_POSITIVE_OVERLAP: 0.7
+  RPN_NEGATIVE_OVERLAP: 0.3
+  RPN_CLOBBER_POSITIVES: false
+  # rpn bounding box regression params
+  RPN_BBOX_WEIGHTS:
+  - 1.0
+  - 1.0
+  - 1.0
+  - 1.0
+  RPN_POSITIVE_WEIGHT: -1.0
+  # used for end2end training
+  # RPN proposal
+  CXX_PROPOSAL: false
+  RPN_NMS_THRESH: 0.7
+  RPN_PRE_NMS_TOP_N: 12000
+  RPN_POST_NMS_TOP_N: 2000
+  RPN_MIN_SIZE: 0
+  # approximate bounding box regression
+  BBOX_NORMALIZATION_PRECOMPUTED: true
+  BBOX_MEANS:
+  - 0.0
+  - 0.0
+  - 0.0
+  - 0.0
+  BBOX_STDS:
+  - 0.1
+  - 0.1
+  - 0.2
+  - 0.2
+TEST:
+  # use rpn to generate proposal
+  HAS_RPN: true
+  # size of images for each device
+  BATCH_IMAGES: 1
+  # RPN proposal
+  CXX_PROPOSAL: false
+  RPN_NMS_THRESH: 0.7
+  RPN_PRE_NMS_TOP_N: 12000
+  RPN_POST_NMS_TOP_N: 2000
+  RPN_MIN_SIZE: 0
+  # RPN generate proposal
+  PROPOSAL_NMS_THRESH: 0.7
+  PROPOSAL_PRE_NMS_TOP_N: 20000
+  PROPOSAL_POST_NMS_TOP_N: 2000
+  PROPOSAL_MIN_SIZE: 0
+  # RCNN nms
+  NMS: 0.3
+  USE_SOFTNMS: true
+  SOFTNMS_THRESH: 0.6
+  test_epoch: 15
+  max_per_image: 100
+  # soft nms
+  USE_SOFTNMS: true
+  SOFTNMS_THRESH: 0.6
diff --git a/fpn/few_shot_benchmark.py b/fpn/few_shot_benchmark.py
@@ -493,7 +493,12 @@ def get_workpoint():
         model_case = args.test_name
         test_model_name = args.test_name
 
-
+    if args.test_name == 'RepMet_inloc_re':  # RepMet detector
+        cfg_fname = root+'/experiments/cfgs/resnet_v1_101_voc0712_trainval_fpn_dcn_oneshot_end2end_ohem_8_orig.yaml'
+        test_classes_fname =root+'/data/Imagenet_LOC/in_domain_categories.txt'  # 214 categories
+        roidb_fname = root+'/data/Imagenet_LOC/voc_inloc_roidb.pkl'
+        model_case = args.test_name
+        test_model_name = args.test_name
     if args.test_name == 'Vanilla_inloc':  # 'nb19_214_train_hist_11':
         cfg_fname = root+'/experiments/cfgs/resnet_v1_101_voc0712_trainval_fpn_dcn_oneshot_end2end_ohem_19_noemb.yaml'
         test_classes_fname = root+'/data/Imagenet_LOC/in_domain_categories.txt'  # 214 categories
@@ -694,6 +699,10 @@ def load_a_model(config,model_case=0):
         def_model = root+'/data/Imagenet_LOC/fpn_pascal_imagenet'
         epoch = 15
         arg_params, aux_params = load_param(def_model, epoch, process=True)
+    if model_case == 'RepMet_inloc_re':
+        def_model = root+'/output/fpn/voc_imagenet/resnet_v1_101_voc0712_trainval_fpn_dcn_oneshot_end2end_ohem_8_orig/2007_trainval+2012_trainval_train_loc/fpn_pascal_imagenet'
+        epoch = 22
+        arg_params, aux_params = load_param(def_model, epoch, process=True)
 
     if model_case == 'Vanilla_inloc':
         vanilla_model = root + '/data/Imagenet_LOC/fpn_pascal_imagenet_base'  # v

diff --git a/notebooks/logo_usecase_routines.py b/notebooks/logo_usecase_routines.py
@@ -56,7 +56,7 @@ def disp_dets2(img,dets,save_file_path):
     plt.imshow(img_d)
     #plt.figure(figsize=(28, 28))
     fig.savefig(save_file_path)
-    
+
 def test_on_query_image(fs_serv,test_img_fname,score_thresh=0.1,det_engines=1,figure_factor=2.2,FontScale=2.3):
     img = cv2.imread(test_img_fname, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)