100 lines
2.7 KiB
Python
100 lines
2.7 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from abc import ABC, abstractmethod
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from typing import Optional
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import torch
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from chronoedit._ext.imaginaire.utils.env_parsers.cred_env_parser import CRED_ENVS
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class VideoTokenizerInterface(ABC):
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def __init__(self, s3_credential_path: Optional[str] = None):
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assert s3_credential_path is None or isinstance(s3_credential_path, str)
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if s3_credential_path is None:
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self.backend_args = None
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elif os.path.exists(s3_credential_path) or CRED_ENVS.APP_ENV in ["prod", "dev", "stg"]:
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self.backend_args = {
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"backend": "s3",
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"path_mapping": None,
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"s3_credential_path": s3_credential_path,
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}
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else:
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raise FileNotFoundError(f"Invalid s3_credential_path: {s3_credential_path} and APP_ENV is not prod/dev/stg")
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@abstractmethod
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def reset_dtype(self):
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"""
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Reset the dtype of the model to the dtype its weights were trained with or quantized to.
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"""
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pass
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@abstractmethod
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def encode(self, state: torch.Tensor) -> torch.Tensor:
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pass
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@abstractmethod
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def decode(self, latent: torch.Tensor) -> torch.Tensor:
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pass
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@abstractmethod
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def get_latent_num_frames(self, num_pixel_frames: int) -> int:
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pass
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@abstractmethod
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def get_pixel_num_frames(self, num_latent_frames: int) -> int:
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pass
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@property
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@abstractmethod
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def spatial_compression_factor(self):
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pass
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@property
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@abstractmethod
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def temporal_compression_factor(self):
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pass
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@property
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@abstractmethod
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def spatial_resolution(self):
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pass
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@property
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@abstractmethod
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def pixel_chunk_duration(self):
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pass
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@property
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@abstractmethod
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def latent_chunk_duration(self):
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pass
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@property
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@abstractmethod
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def latent_ch(self) -> int:
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pass
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@property
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def is_chunk_overlap(self):
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return False
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@property
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def is_causal(self):
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return True
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