78 lines
2.2 KiB
Python
78 lines
2.2 KiB
Python
import importlib
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import os
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import random
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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from transformers import PretrainedConfig
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def seed_everything(seed):
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os.environ["PL_GLOBAL_SEED"] = str(seed)
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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def is_torch2_available():
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return hasattr(F, "scaled_dot_product_attention")
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def instantiate_from_config(config):
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if "target" not in config:
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if config == '__is_first_stage__' or config == "__is_unconditional__":
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return None
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raise KeyError("Expected key `target` to instantiate.")
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return get_obj_from_str(config["target"])(**config.get("params", {}))
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def get_obj_from_str(string, reload=False):
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module, cls = string.rsplit(".", 1)
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if reload:
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module_imp = importlib.import_module(module)
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importlib.reload(module_imp)
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return getattr(importlib.import_module(module, package=None), cls)
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def drop_seq_token(seq, drop_rate=0.5):
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idx = torch.randperm(seq.size(1))
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num_keep_tokens = int(len(idx) * (1 - drop_rate))
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idx = idx[:num_keep_tokens]
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seq = seq[:, idx]
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return seq
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def import_model_class_from_model_name_or_path(
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pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
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):
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text_encoder_config = PretrainedConfig.from_pretrained(
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pretrained_model_name_or_path, subfolder=subfolder, revision=revision
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)
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model_class = text_encoder_config.architectures[0]
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if model_class == "CLIPTextModel":
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from transformers import CLIPTextModel
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return CLIPTextModel
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elif model_class == "CLIPTextModelWithProjection": # noqa RET505
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from transformers import CLIPTextModelWithProjection
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return CLIPTextModelWithProjection
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else:
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raise ValueError(f"{model_class} is not supported.")
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def resize_numpy_image_long(image, resize_long_edge=768):
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image=np.array(image)
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h, w = image.shape[:2]
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if max(h, w) <= resize_long_edge:
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return image
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k = resize_long_edge / max(h, w)
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h = int(h * k)
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w = int(w * k)
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image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
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return image
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