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Bump Version to OpenSTL V0.3.0
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‎README.md‎

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[📘Documentation](https://openstl.readthedocs.io/en/latest/) |
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[🛠️Installation](docs/en/install.md) |
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[🚀Model Zoo](docs/en/model_zoos/video_benchmarks.md) |
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[👀Visualization](docs/en/visualization/video_visualization.md) |
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[🆕News](docs/en/changelog.md)
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## Introduction
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## News and Updates
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[2023-05-28] `OpenSTL` v0.2.0 is released and enhanced. Some bugs in the training loop, datasets, and dataloaders are fixed.
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[2023-06-19] `OpenSTL` v0.3.0 is released and will be enhanced in [#25](https://github.com/chengtan9907/OpenSTL/issues/25).
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## Installation
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‎configs/weather/mv_4_s6_5_625/ConvLSTM.py‎

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method = 'ConvLSTM'
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# reverse scheduled sampling
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reverse_scheduled_sampling = 0
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r_sampling_step_1 = 25000
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r_sampling_step_2 = 50000
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r_exp_alpha = 5000
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# scheduled sampling
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scheduled_sampling = 1
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sampling_stop_iter = 50000
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sampling_start_value = 1.0
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sampling_changing_rate = 0.00002
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# model
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num_hidden = '128,128,128,128'
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filter_size = 5
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stride = 1
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patch_size = 2
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layer_norm = 0
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# training
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lr = 5e-4
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batch_size = 16
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sched = 'cosine'
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warmup_epoch = 0

‎configs/weather/mv_4_s6_5_625/MAU.py‎

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method = 'MAU'
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# scheduled sampling
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scheduled_sampling = 1
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sampling_stop_iter = 50000
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sampling_start_value = 1.0
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sampling_changing_rate = 0.00002
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# model
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num_hidden = '64,64,64,64'
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filter_size = 5
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stride = 1
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patch_size = 1
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layer_norm = 0
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sr_size = 4
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tau = 5
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cell_mode = 'normal'
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model_mode = 'normal'
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# training
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lr = 5e-4
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batch_size = 16
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sched = 'cosine'
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warmup_epoch = 0

‎configs/weather/mv_4_s6_5_625/MIM.py‎

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method = 'MIM'
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# reverse scheduled sampling
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reverse_scheduled_sampling = 0
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r_sampling_step_1 = 25000
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r_sampling_step_2 = 50000
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r_exp_alpha = 5000
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# scheduled sampling
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scheduled_sampling = 1
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sampling_stop_iter = 50000
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sampling_start_value = 1.0
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sampling_changing_rate = 0.00002
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# model
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num_hidden = '128,128,128,128'
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filter_size = 5
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stride = 1
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patch_size = 4
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layer_norm = 0
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# training
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lr = 1e-4
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batch_size = 16
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val_batch_size = 16
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sched = 'cosine'
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warmup_epoch = 0
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drop_last = True

‎configs/weather/mv_4_s6_5_625/PhyDNet.py‎

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method = 'PhyDNet'
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# model
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patch_size = 2
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# training
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lr = 5e-4
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batch_size = 16
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sched = 'cosine'
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warmup_epoch = 0

‎configs/weather/mv_4_s6_5_625/PredRNN.py‎

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method = 'PredRNN'
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# reverse scheduled sampling
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reverse_scheduled_sampling = 0
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r_sampling_step_1 = 25000
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r_sampling_step_2 = 50000
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r_exp_alpha = 5000
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# scheduled sampling
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scheduled_sampling = 1
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sampling_stop_iter = 50000
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sampling_start_value = 1.0
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sampling_changing_rate = 0.00002
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# model
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num_hidden = '128,128,128,128'
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filter_size = 5
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stride = 1
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patch_size = 2
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layer_norm = 0
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# training
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lr = 1e-4
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batch_size = 16
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sched = 'cosine'
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warmup_epoch = 0

‎configs/weather/mv_4_s6_5_625/PredRNNpp.py‎

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method = 'PredRNNpp'
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# reverse scheduled sampling
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reverse_scheduled_sampling = 0
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r_sampling_step_1 = 25000
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r_sampling_step_2 = 50000
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r_exp_alpha = 5000
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# scheduled sampling
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scheduled_sampling = 1
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sampling_stop_iter = 50000
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sampling_start_value = 1.0
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sampling_changing_rate = 0.00002
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# model
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num_hidden = '128,128,128,128'
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filter_size = 5
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stride = 1
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patch_size = 2
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layer_norm = 0
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# training
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lr = 1e-4
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batch_size = 16
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sched = 'cosine'
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warmup_epoch = 0

‎configs/weather/mv_4_s6_5_625/PredRNNv2.py‎

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method = 'PredRNNv2'
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# reverse scheduled sampling
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reverse_scheduled_sampling = 1
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r_sampling_step_1 = 25000
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r_sampling_step_2 = 50000
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r_exp_alpha = 5000
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# scheduled sampling
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scheduled_sampling = 1
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sampling_stop_iter = 50000
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sampling_start_value = 1.0
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sampling_changing_rate = 0.00002
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# model
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num_hidden = '128,128,128,128'
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filter_size = 5
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stride = 1
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patch_size = 2
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layer_norm = 0
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decouple_beta = 0.1
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# training
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lr = 5e-4
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batch_size = 16
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sched = 'cosine'
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warmup_epoch = 0

‎configs/weather/mv_4_s6_5_625/SimVP_HorNet.py‎

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N_S = 2
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# training
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# lr = 1e-3
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lr = 5e-3
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batch_size = 16
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drop_path = 0.1
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sched = 'cosine'

‎configs/weather/mv_4_s6_5_625/SimVP_IncepU.py‎

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N_S = 2
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# training
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# lr = 1e-3
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lr = 5e-3
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batch_size = 16
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drop_path = 0.1
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sched = 'cosine'

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