69 lines
1.9 KiB
YAML
69 lines
1.9 KiB
YAML
name: video_process
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suffix: ~ # add suffix to saved images
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model: extensibletrainer
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scale: 4
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gpu_ids: [0]
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fp16: true
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minivid_crf: 12 # Defines the 'crf' output video quality parameter fed to FFMPEG
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frames_per_mini_vid: 360 # How many frames to process before generating a small video segment. Used to reduce number of images you must store to convert an entire video.
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minivid_start_no: 360
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recurrent_mode: false
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dataset:
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n_workers: 1
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name: myvideo
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video_file: <your path> # <-- Path to your video file here. any format supported by ffmpeg works.
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frame_rate: 30 # Set to the frame rate of your video.
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start_at_seconds: 0 # Set this if you want to start somewhere other than the beginning of the video.
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end_at_seconds: 5000 # Set to the time you want to stop at.
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batch_size: 1 # Set to the number of frames to convert at once. Larger batches provide a modest performance increase.
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vertical_splits: 1 # Used for 3d binocular videos. Leave at 1.
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force_multiple: 1
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#### network structures
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networks:
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generator:
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type: generator
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which_model_G: RRDBNet
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in_nc: 3
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out_nc: 3
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initial_stride: 1
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nf: 64
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nb: 23
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scale: 4
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blocks_per_checkpoint: 3
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#### path
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path:
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pretrain_model_generator: <your path> # <-- Set your generator path here.
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steps:
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generator:
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training: generator
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generator: generator
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# Optimizer params. Not used, but currently required to initialize ExtensibleTrainer, even in eval mode.
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lr: !!float 5e-6
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weight_decay: 0
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beta1: 0.9
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beta2: 0.99
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injectors:
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gen_inj:
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type: generator
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generator: generator
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in: lq
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out: gen
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# Train section is required, even though we are just evaluating.
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train:
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niter: 500000
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warmup_iter: -1
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mega_batch_factor: 1
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val_freq: 500
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default_lr_scheme: MultiStepLR
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gen_lr_steps: [20000, 40000, 80000, 100000, 140000, 180000]
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lr_gamma: 0.5
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eval:
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output_state: gen |