pruebas hechas

configs/recognition/tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb.py

@@ -5,10 +5,10 @@
 
 # dataset settings
 dataset_type = 'VideoDataset'
-data_root = 'data/kinetics400/videos_train'
-data_root_val = 'data/kinetics400/videos_val'
-ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt'
-ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt'
+data_root = 'data/kinetics400_tiny/train'
+data_root_val = 'data/kinetics400_tiny/val'
+ann_file_train = 'data/kinetics400_tiny/kinetics_tiny_train_video.txt'
+ann_file_val = 'data/kinetics400_tiny/kinetics_tiny_val_video.txt'
 
 file_client_args = dict(io_backend='disk')
 
@@ -93,8 +93,26 @@
 val_evaluator = dict(type='AccMetric')
 test_evaluator = val_evaluator
 
-default_hooks = dict(checkpoint=dict(interval=3, max_keep_ckpts=3))
-
+# set training batch size to 4
+train_dataloader['batch_size'] = 4
+# Save checkpoints every epoch, and only keep the latest checkpoint
+default_hooks = dict(
+    checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=1))
+# Set the maximum number of epochs to 10, and validate the model every 1 epochs
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=10, val_interval=1)
+# adjust learning rate schedule according to 10 epochs
+param_scheduler = [
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=10,
+        by_epoch=True,
+        milestones=[4, 8],
+        gamma=0.1)
+]
+model = dict(
+    cls_head=dict(num_classes=2))
+load_from = 'https://download.openmmlab.com/mmaction/v1.0/recognition/tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb_20220906-cd10898e.pth'
 # Default setting for scaling LR automatically
 #   - `enable` means enable scaling LR automatically
 #       or not by default.

tsn_imagenet-pretrained-r50_8xb32-1x1x8-100e_kinetics400-rgb.py

@@ -0,0 +1,237 @@
+ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt'
+ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt'
+auto_scale_lr = dict(base_batch_size=256, enable=False)
+data_root = 'data/kinetics400/videos_train'
+data_root_val = 'data/kinetics400/videos_val'
+dataset_type = 'VideoDataset'
+default_hooks = dict(
+    checkpoint=dict(
+        interval=3, max_keep_ckpts=3, save_best='auto', type='CheckpointHook'),
+    logger=dict(ignore_last=False, interval=20, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    runtime_info=dict(type='RuntimeInfoHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    sync_buffers=dict(type='SyncBuffersHook'),
+    timer=dict(type='IterTimerHook'))
+default_scope = 'mmaction'
+env_cfg = dict(
+    cudnn_benchmark=False,
+    dist_cfg=dict(backend='nccl'),
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+file_client_args = dict(io_backend='disk')
+load_from = None
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=20)
+model = dict(
+    backbone=dict(
+        depth=50,
+        norm_eval=False,
+        pretrained='https://download.pytorch.org/models/resnet50-11ad3fa6.pth',
+        type='ResNet'),
+    cls_head=dict(
+        average_clips='prob',
+        consensus=dict(dim=1, type='AvgConsensus'),
+        dropout_ratio=0.4,
+        in_channels=2048,
+        init_std=0.01,
+        num_classes=400,
+        spatial_type='avg',
+        type='TSNHead'),
+    data_preprocessor=dict(
+        format_shape='NCHW',
+        mean=[
+            123.675,
+            116.28,
+            103.53,
+        ],
+        std=[
+            58.395,
+            57.12,
+            57.375,
+        ],
+        type='ActionDataPreprocessor'),
+    test_cfg=None,
+    train_cfg=None,
+    type='Recognizer2D')
+optim_wrapper = dict(
+    clip_grad=dict(max_norm=40, norm_type=2),
+    optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0001))
+param_scheduler = [
+    dict(
+        begin=0,
+        by_epoch=True,
+        end=100,
+        gamma=0.1,
+        milestones=[
+            40,
+            80,
+        ],
+        type='MultiStepLR'),
+]
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=1,
+    dataset=dict(
+        ann_file='data/kinetics400/kinetics400_val_list_videos.txt',
+        data_prefix=dict(video='data/kinetics400/videos_val'),
+        pipeline=[
+            dict(io_backend='disk', type='DecordInit'),
+            dict(
+                clip_len=1,
+                frame_interval=1,
+                num_clips=25,
+                test_mode=True,
+                type='SampleFrames'),
+            dict(type='DecordDecode'),
+            dict(scale=(
+                -1,
+                256,
+            ), type='Resize'),
+            dict(crop_size=224, type='TenCrop'),
+            dict(input_format='NCHW', type='FormatShape'),
+            dict(type='PackActionInputs'),
+        ],
+        test_mode=True,
+        type='VideoDataset'),
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(type='AccMetric')
+test_pipeline = [
+    dict(io_backend='disk', type='DecordInit'),
+    dict(
+        clip_len=1,
+        frame_interval=1,
+        num_clips=25,
+        test_mode=True,
+        type='SampleFrames'),
+    dict(type='DecordDecode'),
+    dict(scale=(
+        -1,
+        256,
+    ), type='Resize'),
+    dict(crop_size=224, type='TenCrop'),
+    dict(input_format='NCHW', type='FormatShape'),
+    dict(type='PackActionInputs'),
+]
+train_cfg = dict(
+    max_epochs=100, type='EpochBasedTrainLoop', val_begin=1, val_interval=1)
+train_dataloader = dict(
+    batch_size=32,
+    dataset=dict(
+        ann_file='data/kinetics400/kinetics400_train_list_videos.txt',
+        data_prefix=dict(video='data/kinetics400/videos_train'),
+        pipeline=[
+            dict(io_backend='disk', type='DecordInit'),
+            dict(
+                clip_len=1, frame_interval=1, num_clips=8,
+                type='SampleFrames'),
+            dict(type='DecordDecode'),
+            dict(scale=(
+                -1,
+                256,
+            ), type='Resize'),
+            dict(
+                input_size=224,
+                max_wh_scale_gap=1,
+                random_crop=False,
+                scales=(
+                    1,
+                    0.875,
+                    0.75,
+                    0.66,
+                ),
+                type='MultiScaleCrop'),
+            dict(keep_ratio=False, scale=(
+                224,
+                224,
+            ), type='Resize'),
+            dict(flip_ratio=0.5, type='Flip'),
+            dict(input_format='NCHW', type='FormatShape'),
+            dict(type='PackActionInputs'),
+        ],
+        type='VideoDataset'),
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(io_backend='disk', type='DecordInit'),
+    dict(clip_len=1, frame_interval=1, num_clips=8, type='SampleFrames'),
+    dict(type='DecordDecode'),
+    dict(scale=(
+        -1,
+        256,
+    ), type='Resize'),
+    dict(
+        input_size=224,
+        max_wh_scale_gap=1,
+        random_crop=False,
+        scales=(
+            1,
+            0.875,
+            0.75,
+            0.66,
+        ),
+        type='MultiScaleCrop'),
+    dict(keep_ratio=False, scale=(
+        224,
+        224,
+    ), type='Resize'),
+    dict(flip_ratio=0.5, type='Flip'),
+    dict(input_format='NCHW', type='FormatShape'),
+    dict(type='PackActionInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=32,
+    dataset=dict(
+        ann_file='data/kinetics400/kinetics400_val_list_videos.txt',
+        data_prefix=dict(video='data/kinetics400/videos_val'),
+        pipeline=[
+            dict(io_backend='disk', type='DecordInit'),
+            dict(
+                clip_len=1,
+                frame_interval=1,
+                num_clips=8,
+                test_mode=True,
+                type='SampleFrames'),
+            dict(type='DecordDecode'),
+            dict(scale=(
+                -1,
+                256,
+            ), type='Resize'),
+            dict(crop_size=224, type='CenterCrop'),
+            dict(input_format='NCHW', type='FormatShape'),
+            dict(type='PackActionInputs'),
+        ],
+        test_mode=True,
+        type='VideoDataset'),
+    num_workers=8,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(type='AccMetric')
+val_pipeline = [
+    dict(io_backend='disk', type='DecordInit'),
+    dict(
+        clip_len=1,
+        frame_interval=1,
+        num_clips=8,
+        test_mode=True,
+        type='SampleFrames'),
+    dict(type='DecordDecode'),
+    dict(scale=(
+        -1,
+        256,
+    ), type='Resize'),
+    dict(crop_size=224, type='CenterCrop'),
+    dict(input_format='NCHW', type='FormatShape'),
+    dict(type='PackActionInputs'),
+]
+vis_backends = [
+    dict(type='LocalVisBackend'),
+]
+visualizer = dict(
+    type='ActionVisualizer', vis_backends=[
+        dict(type='LocalVisBackend'),
+    ])