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Browse files- CustomPipe.py +54 -0
- config.json +18 -6
- model.safetensors +2 -2
CustomPipe.py
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from PIL import Image
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import torch
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from transformers import (
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AutoModelForImageClassification,
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AutoImageProcessor,
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Pipeline,
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)
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import numpy as np
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from typing import Union
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class SiglipTaggerPipe(Pipeline):
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def __init__(self,**kwargs):
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self.processor = AutoImageProcessor.from_pretrained("p1atdev/siglip-tagger-test-3")
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if "torch_dtype" not in kwargs :
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kwargs["torch_dtype"] = torch.bfloat16
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Pipeline.__init__(self,**kwargs)
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def _sanitize_parameters(self, **kwargs):
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postprocess_kwargs = {}
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if "threshold" in kwargs :
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# if threshold parameter is present
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# we pass it to the postprocess method
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postprocess_kwargs["threshold"] = kwargs["threshold"]
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return {},{},postprocess_kwargs
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def preprocess(self,inputs: Union[str,Image.Image,np.ndarray]):
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if isinstance(inputs,str) :
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img = Image.open(inputs)
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elif isinstance(inputs,Image.Image) :
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img = inputs
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else :
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# TODO: double check this implementation
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# consider adding try except
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# maybe add url checker too
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img = Image.fromarray(inputs)
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inputs = self.processor(img, return_tensors="pt").to(self.model.device, self.model.dtype)
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return inputs
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def _forward(self,inputs):
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logits = self.model(**inputs).logits.detach().cpu().float()[0]
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logits = np.clip(logits, 0.0, 1.0)
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return logits
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def postprocess(self,logits,threshold:float=0):
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results = {
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self.model.config.id2label[i]: logit for i, logit in enumerate(logits) if logit > 0
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}
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results = sorted(results.items(), key=lambda x: x[1], reverse=True)
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out = {}
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for tag, score in results:
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if score >= threshold :
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out[tag] = f"{score*100:.2f}"
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return out
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config.json
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{
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"_name_or_path": "
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"architectures": [
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"auto_map": {
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"AutoModelForImageClassification": "modeling_siglip.SiglipForImageClassification"
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},
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"attention_dropout": 0.0,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"id2label": {
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"zzz": 9515,
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"|_": 9516
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},
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"layer_norm_eps": 1e-
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"model_type": "siglip_vision_model",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 27,
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"patch_size": 14,
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"problem_type": "multi_label_classification",
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"torch_dtype": "
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"transformers_version": "4.37.2"
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}
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{
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"_name_or_path": "p1atdev/siglip-tagger-test-3",
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"architectures": [
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"SiglipForImageClassification"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoModelForImageClassification": "p1atdev/siglip-tagger-test-3--modeling_siglip.SiglipForImageClassification"
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},
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"custom_pipelines": {
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"image-classification": {
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"impl": "CustomPipe.SiglipTaggerPipe",
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"pt": [
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"AutoModelForImageClassification"
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],
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"tf": [],
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"type": "image"
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}
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},
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"id2label": {
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"zzz": 9515,
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"|_": 9516
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},
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"layer_norm_eps": 1e-06,
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"model_type": "siglip_vision_model",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 27,
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"patch_size": 14,
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"problem_type": "multi_label_classification",
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"torch_dtype": "float32",
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"transformers_version": "4.37.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:1c57dce403a3fbb0b10dd311cd84cc12ecbf884ae444f54aa6f941f5fb3e06f7
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size 1756853084
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