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from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor
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from .utils import load_torch_file, transformers_convert
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import os
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import torch
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class ClipVisionModel():
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def __init__(self, json_config):
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config = CLIPVisionConfig.from_json_file(json_config)
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self.model = CLIPVisionModelWithProjection(config)
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self.processor = CLIPImageProcessor(crop_size=224,
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do_center_crop=True,
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do_convert_rgb=True,
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do_normalize=True,
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do_resize=True,
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image_mean=[ 0.48145466,0.4578275,0.40821073],
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image_std=[0.26862954,0.26130258,0.27577711],
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resample=3, #bicubic
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size=224)
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def load_sd(self, sd):
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self.model.load_state_dict(sd, strict=False)
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def encode_image(self, image):
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img = torch.clip((255. * image[0]), 0, 255).round().int()
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inputs = self.processor(images=[img], return_tensors="pt")
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outputs = self.model(**inputs)
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return outputs
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def convert_to_transformers(sd):
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sd_k = sd.keys()
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if "embedder.model.visual.transformer.resblocks.0.attn.in_proj_weight" in sd_k:
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keys_to_replace = {
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"embedder.model.visual.class_embedding": "vision_model.embeddings.class_embedding",
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"embedder.model.visual.conv1.weight": "vision_model.embeddings.patch_embedding.weight",
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"embedder.model.visual.positional_embedding": "vision_model.embeddings.position_embedding.weight",
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"embedder.model.visual.ln_post.bias": "vision_model.post_layernorm.bias",
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"embedder.model.visual.ln_post.weight": "vision_model.post_layernorm.weight",
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"embedder.model.visual.ln_pre.bias": "vision_model.pre_layrnorm.bias",
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"embedder.model.visual.ln_pre.weight": "vision_model.pre_layrnorm.weight",
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}
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for x in keys_to_replace:
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if x in sd_k:
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sd[keys_to_replace[x]] = sd.pop(x)
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if "embedder.model.visual.proj" in sd_k:
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sd['visual_projection.weight'] = sd.pop("embedder.model.visual.proj").transpose(0, 1)
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sd = transformers_convert(sd, "embedder.model.visual", "vision_model", 32)
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return sd
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def load_clipvision_from_sd(sd):
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sd = convert_to_transformers(sd)
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if "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
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json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
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else:
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json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
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clip = ClipVisionModel(json_config)
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clip.load_sd(sd)
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return clip
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def load(ckpt_path):
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sd = load_torch_file(ckpt_path)
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return load_clipvision_from_sd(sd)
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