add x3d-m for uavhuman

mmaction/models/backbones/x3d.py

@@ -241,6 +241,7 @@ def __init__(self,
         self.gamma_d = gamma_d
 
         self.pretrained = pretrained
+
         self.in_channels = in_channels
         # Hard coded, can be changed by gamma_w
         self.base_channels = 24

work_dir/uavhuman/model/x3d.py

@@ -0,0 +1,24 @@
+# model settings
+model = dict(
+    type='Recognizer3D',
+    backbone=dict(type='X3D', 
+                  gamma_w=1, 
+                  gamma_b=2.25, 
+                  gamma_d=2.2,
+                  ),
+    cls_head=dict(
+        type='X3DHead',
+        in_channels=432,
+        num_classes=155,
+        spatial_type='avg',
+        dropout_ratio=0.5,
+        fc1_bias=False,
+        average_clips='prob'),
+    data_preprocessor=dict(
+        type='ActionDataPreprocessor',
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5],
+        format_shape='NCTHW'),
+    # model training and testing settings
+    train_cfg=None,
+    test_cfg=None)

work_dir/uavhuman/x3d_m_16x5x1_facebook-kinetics500-rgb.py

@@ -0,0 +1,159 @@
+# -*- coding: utf-8 -*-
+# @Time : 2024/04/07 20:54
+# @Author : Liang Hao
+# @FileName : x3d_m_16x5x1_facebook-kinetics500-rgb.py
+# @Email : lianghao@whu.edu.cn
+
+_base_ = [
+    './model/x3d.py',
+    '../default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        pretrained="model_zoo/x3d_m_16x5x1_facebook-kinetics400-rgb_20201027-3f42382a.pth"
+    ))
+
+
+dataset_type = 'RawframeDataset'
+data_root = '/data/dataset/uavhuman/rawframes'
+data_root_val = '/data/dataset/uavhuman/rawframes'
+split = 2  # official train/test splits. valid numbers: 1, 2, 3
+ann_file_train = f'data/uavhuman/uavhuman_train_split_{split}_rawframes.txt'
+ann_file_val = f'data/uavhuman/uavhuman_val_split_{split}_rawframes.txt'
+ann_file_test = f'data/uavhuman/uavhuman_val_split_{split}_rawframes.txt'
+
+file_client_args_train = dict(
+    io_backend='disk',
+    nori_file = 'data/uavhuman/uavhuman_train_split_1_nid.json',
+    dtype = 'uint8',
+    retry = 60
+)
+
+file_client_args_eval = dict(
+    io_backend='disk',
+    nori_file = 'data/uavhuman/uavhuman_val_split_1_nid.json',
+    dtype = 'uint8',
+    retry = 60
+)
+
+train_pipeline = [
+    # dict(type='DecordInit', **file_client_args),
+    dict(type='SampleFrames', clip_len=8, frame_interval=1, num_clips=1),
+    # dict(type='DecordDecode'),
+    dict(type='RawFrameDecodeNoir2', **file_client_args_train),
+    dict(type='Resize', scale=(620, 620)),
+    dict(type='RandomResizedCrop'),
+    dict(type='Resize', scale=(540, 540), keep_ratio=False),
+    # dict(type='ThreeCrop', crop_size=540),
+    dict(type='Flip', flip_ratio=0.5),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='PackActionInputs')
+]
+
+val_pipeline = [
+    # dict(type='DecordInit', **file_client_args),
+    dict(
+        type='SampleFrames',
+        clip_len=8,
+        frame_interval=1,
+        num_clips=1,
+        test_mode=True),
+    # dict(type='DecordDecode'),
+    dict(type='RawFrameDecodeNoir2', **file_client_args_eval),
+    dict(type='Resize', scale=(540, 540), keep_ratio=False),
+    # dict(type='ThreeCrop', crop_size=540),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='PackActionInputs')
+]
+
+test_pipeline = [
+    # dict(type='DecordInit', **file_client_args),
+    dict(
+        type='SampleFrames',
+        clip_len=8,
+        frame_interval=1,
+        num_clips=1,
+        test_mode=True),
+    # dict(type='DecordDecode'),
+    dict(type='RawFrameDecodeNoir2', **file_client_args_eval),
+    dict(type='Resize', scale=(540, 540), keep_ratio=False),
+    # dict(type='ThreeCrop', crop_size=540),
+    dict(type='FormatShape', input_format='NCTHW'),
+    dict(type='PackActionInputs')
+]
+
+
+train_dataloader = dict(
+    batch_size=3,
+    num_workers=8,
+    # 数据加载完并不会关闭worker进程，而是保持现有的worker进程
+    # 继续进行下一个Epoch的数据加载，加快训练速度，要求num_workers ≥ 1
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset = dict(
+        type=dataset_type,
+        ann_file=ann_file_train,
+        data_prefix=dict(img=data_root),
+        pipeline=train_pipeline
+    )
+)
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        ann_file=ann_file_val,
+        data_prefix=dict(img=data_root_val),
+        pipeline=val_pipeline,
+        test_mode=True
+    )
+)
+
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        ann_file=ann_file_test,
+        data_prefix=dict(img=data_root_val),
+        pipeline=test_pipeline,
+        test_mode=True
+    )
+)
+
+
+val_evaluator = dict(type='AccMetric')
+test_evaluator = val_evaluator
+
+train_cfg = dict(
+    type='EpochBasedTrainLoop', max_epochs=400, val_begin=1, val_interval=5)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+optim_wrapper = dict(
+    optimizer=dict(
+        type='SGD', lr=2e-5, momentum=0.9, weight_decay=5e-5),
+    )
+
+param_scheduler = [
+    dict(
+        type='CosineAnnealingLR',
+        T_max=400,
+        eta_min=1e-7,
+        by_epoch=True)
+]
+
+default_hooks = dict(
+    checkpoint=dict(interval=5, max_keep_ckpts=5), logger=dict(interval=100))
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (8 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=24)