| import numpy as np |
|
|
|
|
| def get_array_shape_from_lmdb(env, array_name): |
| with env.begin() as txn: |
| image_shape = txn.get(f"{array_name}_shape".encode()).decode() |
| image_shape = tuple(map(int, image_shape.split())) |
| return image_shape |
|
|
|
|
| def store_arrays_to_lmdb(env, arrays_dict, start_index=0): |
| """ |
| Store rows of multiple numpy arrays in a single LMDB. |
| Each row is stored separately with a naming convention. |
| """ |
| with env.begin(write=True) as txn: |
| for array_name, array in arrays_dict.items(): |
| for i, row in enumerate(array): |
| |
| if isinstance(row, str): |
| row_bytes = row.encode() |
| else: |
| row_bytes = row.tobytes() |
|
|
| data_key = f'{array_name}_{start_index + i}_data'.encode() |
|
|
| txn.put(data_key, row_bytes) |
|
|
|
|
| def process_data_dict(data_dict, seen_prompts): |
| output_dict = {} |
|
|
| all_videos = [] |
| all_prompts = [] |
| for prompt, video in data_dict.items(): |
| if prompt in seen_prompts: |
| continue |
| else: |
| seen_prompts.add(prompt) |
|
|
| video = video.half().numpy() |
| all_videos.append(video) |
| all_prompts.append(prompt) |
|
|
| if len(all_videos) == 0: |
| return {"latents": np.array([]), "prompts": np.array([])} |
|
|
| all_videos = np.concatenate(all_videos, axis=0) |
|
|
| output_dict['latents'] = all_videos |
| output_dict['prompts'] = np.array(all_prompts) |
|
|
| return output_dict |
|
|
|
|
| def retrieve_row_from_lmdb(lmdb_env, array_name, dtype, row_index, shape=None): |
| """ |
| Retrieve a specific row from a specific array in the LMDB. |
| """ |
| data_key = f'{array_name}_{row_index}_data'.encode() |
|
|
| with lmdb_env.begin() as txn: |
| row_bytes = txn.get(data_key) |
|
|
| if dtype == str: |
| array = row_bytes.decode() |
| else: |
| array = np.frombuffer(row_bytes, dtype=dtype) |
|
|
| if shape is not None and len(shape) > 0: |
| array = array.reshape(shape) |
| return array |
|
|
