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Naoki-Wake · Jun 8, 2022 · 2eb6ed4 · 2eb6ed4
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diff --git a/...cognition/arr_tsm/tsm_r50_1x1x8_50e_household_rgb_usepretrain_nonaddlayer_grasprelease.py b/...cognition/arr_tsm/tsm_r50_1x1x8_50e_household_rgb_usepretrain_nonaddlayer_grasprelease.py
@@ -15,12 +15,9 @@
 data_root_val = 'data/household/rawframes'
 ann_file_train = 'data/household/household_train_list_rawframes.txt'
 ann_file_val = 'data/household/household_val_list_rawframes.txt'
-ann_file_test = 'data/household/household_test_list_rawframes.txt'
-#ann_file_test = 'data/household/household_val_list_rawframes.txt'
-#ann_file_test = 'data/household/household_val_list_rawframes.txt'
-#ann_file_train = 'data/household/annotations_ignorelaterality/bac_configtexts/household_train_list_rawframes.txt'
-#ann_file_val = 'data/household/annotations_ignorelaterality/bac_configtexts/household_val_list_rawframes.txt'
-#ann_file_test = 'data/household/annotations_ignorelaterality/bac_configtexts/household_test_list_rawframes.txt'
+#ann_file_test = 'data/household/household_test_list_rawframes.txt'
+ann_file_test = 'data/household/household_val_list_rawframes.txt'
+
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
 train_pipeline = [

diff --git a/...0_1x1x8_50e_household_rgb_usepretrain_nonaddlayer_ignorelaterality_20210531_checkpoint.py b/...0_1x1x8_50e_household_rgb_usepretrain_nonaddlayer_ignorelaterality_20210531_checkpoint.py
@@ -0,0 +1,115 @@
+#_base_ = [
+#    '../../_base_/models/tsm_r50.py', '../../_base_/schedules/sgd_tsm_50e.py',
+#    '../../_base_/default_runtime.py'
+#]
+_base_ = [
+    '../../_base_/models/household_nonaddlayer_r50.py', '../../_base_/schedules/sgd_tsm_50e.py',
+    '../../_base_/default_runtime.py'
+]
+# model settings
+model = dict(cls_head=dict(num_classes=9))#7#11#174
+load_from = '/mmaction2/pretrained_models/tsm_r50_1x1x8_50e_sthv1_rgb_20210203-01dce462.pth'  # model path can be found in model zoo
+# dataset settings
+dataset_type = 'RawframeDataset'
+data_root = 'data/household/rawframes'
+data_root_val = 'data/household/rawframes'
+ann_file_train = 'data/household/household_train_list_rawframes.txt'
+ann_file_val = 'data/household/household_val_list_rawframes.txt'
+ann_file_test = 'data/household/household_test_list_rawframes.txt'
+#ann_file_test = 'data/household/household_val_list_rawframes.txt'
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
+train_pipeline = [
+    dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8),
+    dict(type='RawFrameDecode'),
+    #dict(type='MyDebug', note = 'input'),       # use a custom pipeline
+    dict(type='VideoAug', degrees = 0, prob = 0),       # use a custom pipeline
+    #dict(type='MyDebug', note = 'coloraug'),       # use a custom pipeline
+    dict(type='Resize', scale=(-1, 256)),
+    #dict(type='MyDebug', note = 'resize'),       # use a custom pipeline
+    dict(type='CenterCrop', crop_size=224),     # add center crop
+    #dict(type='MyDebug', note = 'centercrop'),       # use a custom pipeline
+    dict(
+        type='MultiScaleCrop',
+        input_size=224,
+        #scales=(1, 0.875, 0.75, 0.66),
+        #scales=(1,),
+        scales=(1, 9.5, 9.0, 0.875, 0.85),
+        random_crop=False,
+        max_wh_scale_gap=1,
+        num_fixed_crops=13),
+    #dict(type='MyDebug', note = 'scalecrop'),       # use a custom pipeline
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    #dict(type='MyDebug', note = 'resize224'),       # use a custom pipeline
+    dict(type='Normalize', **img_norm_cfg),
+    #dict(type='MyDebug', note = 'normalize'),       # use a custom pipeline
+    dict(type='FormatShape', input_format='NCHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs', 'label'])
+]
+val_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=1,
+        frame_interval=1,
+        num_clips=8,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+test_pipeline = [
+    dict(
+        type='SampleFrames',
+        clip_len=1,
+        frame_interval=1,
+        num_clips=8,
+        test_mode=True),
+    dict(type='RawFrameDecode'),
+    dict(type='Resize', scale=(-1, 256)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Resize', scale=(224, 224), keep_ratio=False),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='FormatShape', input_format='NCHW'),
+    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['imgs'])
+]
+data = dict(
+    videos_per_gpu=6,
+    workers_per_gpu=4,#4
+    train=dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=data_root_val,
+        pipeline=val_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=data_root_val,
+        pipeline=test_pipeline))
+evaluation = dict(
+    interval=2, metrics=['top_k_accuracy', 'mean_class_accuracy'])
+
+# optimizer
+optimizer = dict(
+    lr=0.0075,  # this lr is used for 8 gpus
+    #lr=0.00375,
+    weight_decay=0.0005)
+
+# runtime settings
+work_dir = './work_dirs/tmp/'
+#work_dir = './work_dirs/experiment20210422/debug/'
+#work_dir = './work_dirs/experiment20210428_with_handcrop/tsm_r50_1x1x8_50e_household_rgb_usepretrain_nonaddlayer_ignorelaterality/'
+#work_dir = './work_dirs/experiment20210421/tsm_r50_1x1x8_50e_household_rgb_usepretrain_nonaddlayer_ignorelaterality/'
+#work_dir = './work_dirs/experiment20210420/tsm_r50_1x1x8_50e_household_rgb_usepretrain_nonaddlayer_ignorelaterality/'
diff --git a/demo/arr_tsm/demo.py b/demo/arr_tsm/demo.py
@@ -50,7 +50,7 @@ def parse_args():
     parser.add_argument(
         '--split-time',
         default=None,
-        type=int,
+        type=float,
         help='split a video into windows, then apply the recognition, then concatnate the results. Split or chunk size in seconds, for example 10')
     parser.add_argument('--out-filename', default=None, help='output filename')
     args = parser.parse_args()