added frame interval 8 experiment

configs/experiments/vit-b_frame-int-8_gaussian-sampling-5s-clips-30-drop_priority-labeling_k400-hyperparams.py

@@ -0,0 +1,78 @@
+_base_ = [
+    "../models/vit-s-p16_videomaev2-vit-g-dist-k710-pre_16x4x1_kinetics-400_k400-hyperparams.py"
+]
+
+EXPERIMENT_NAME = "vit-b_frame-int-8_gaussian-sampling-5s-clips-30-drop_priority-labeling_k400-hyperparams"
+visualizer = dict(
+    vis_backends=dict(save_dir=f"experiments/tensorboard/{EXPERIMENT_NAME}/")
+)
+work_dir = f"experiments/{EXPERIMENT_NAME}"
+
+# Overrides
+default_hooks = dict(checkpoint=dict(interval=1))
+
+# 1487 samples in val -> 92 batches per node -> We want around 10 images
+custom_hooks = [dict(type="CustomVisualizationHook", enable=True, interval=300)]
+
+# Use ViT-B/16
+model = dict(
+    backbone=dict(embed_dims=768, depth=12, num_heads=12),
+    cls_head=dict(in_channels=768),
+)
+load_from = "weights/vit-base-p16_videomaev2-vit-g-dist-k710-pre_16x4x1_kinetics-400_20230510-3e7f93b2.pth"
+
+# Use frame_interval 8
+train_pipeline = [
+    dict(type="DecordInit"),
+    dict(type="ClipVideo"),
+    dict(
+        type="SampleFrames", clip_len=16, frame_interval=8, num_clips=1
+    ),  # This has changed
+    dict(type="DecordDecode"),
+    dict(type="Resize", scale=(-1, 224)),
+    dict(type="RandomCrop", size=224),
+    dict(type="Resize", scale=(224, 224), keep_ratio=False),
+    dict(type="Flip", flip_ratio=0.5),
+    dict(type="FormatShape", input_format="NCTHW"),
+    dict(type="PackActionInputs"),
+]
+
+
+# Use Gaussian sampling
+train_dataloader = dict(
+    dataset=dict(
+        sampling_strategy=dict(
+            type="GaussianSampling",
+            clip_len=5,
+            fallback_sampler=dict(
+                type="UniformSampling", clip_len=5, stride=5, overlap=False
+            ),
+        ),
+        drop_ratios=[0.0, 0.0, 0.30],
+        pipeline=train_pipeline,
+    )
+)
+# We are not changing the val/test dataloaders since gaussian sampling requires labels
+# and we cannot have a valid validation if we use labels in the preprocessing
+
+val_pipeline = [
+    dict(type="DecordInit"),
+    dict(type="ClipVideo"),
+    dict(
+        type="SampleFrames", clip_len=16, frame_interval=8, num_clips=1, test_mode=True
+    ),
+    dict(type="DecordDecode"),
+    dict(type="Resize", scale=(-1, 224)),
+    dict(type="CenterCrop", crop_size=224),  # From VideoMAEv2 repo
+    dict(type="FormatShape", input_format="NCTHW"),
+    dict(type="PackActionInputs"),
+]
+
+val_dataloader = dict(
+    dataset=dict(
+        sampling_strategy=dict(
+            type="UniformSampling", clip_len=5, stride=0, overlap=False
+        ),
+        pipeline=val_pipeline,
+    ),
+)

job_scripts/vit-b_frame-int-8_gaussian-sampling-5s-clips-30-drop_priority-labeling_k400-hyperparams.sh

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+#!/usr/bin/env bash
+#SBATCH -A NAISS2023-22-1160 -p alvis
+#SBATCH -N 1 --gpus-per-node=A40:1
+#SBATCH --time=48:00:00
+
+apptainer exec \
+    --env PYTHONPATH=$(pwd) \
+    containers/c3se_job_container.sif \
+    python mmaction2/tools/train.py \
+    configs/experiments/vit-b_frame-int-8_gaussian-sampling-5s-clips-30-drop_priority-labeling_k400-hyperparams.py