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import torch
from transformers import Pipeline
from PIL import Image
from typing import Union
from copy import deepcopy
import matplotlib.pyplot as plt
import io
class VLEForVQAPipeline(Pipeline):
def __init__(self, vle_processor, *args, **kwargs):
self.vle_processor = vle_processor
super().__init__(*args, **kwargs)
def _sanitize_parameters(self, top_k=None, **kwargs):
preprocess_params, forward_params, postprocess_params = {}, {}, {}
if top_k is not None:
postprocess_params["top_k"] = top_k
return preprocess_params, forward_params, postprocess_params
def __call__(self, image: Union["Image.Image", str], question: str = None, **kwargs):
if isinstance(image, (Image.Image, str)) and isinstance(question, str):
inputs = {"image": image, "question": question}
else:
"""
Supports the following format
- {"image": image, "question": question}
- [{"image": image, "question": question}]
- Generator and datasets
"""
inputs = image
results = super().__call__(inputs, **kwargs)
return results
def preprocess(self, inputs):
model_inputs = self.vle_processor(text=inputs['question'], images=inputs['image'], return_tensors="pt",padding=True)
return model_inputs
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs, top_k=1):
if top_k > self.model.num_vqa_labels:
top_k = self.model.num_vqa_labels
probs = torch.softmax(model_outputs['logits'], dim=-1)
probs, preds = torch.sort(probs, descending=True)
probs = probs[:,:top_k].tolist()[0]
preds = preds[:,:top_k].tolist()[0]
return [{"score": score, "answer": self.model.config.id2label[pred]} for score, pred in zip(probs, preds)]
class VLEForPBCPipeline(Pipeline):
def __init__(self, vle_processor, *args, **kwargs):
self.vle_processor = vle_processor
self.id2label = {0:"False",1:"True"}
super().__init__(*args, **kwargs)
def _sanitize_parameters(self, **kwargs):
preprocess_params, forward_params, postprocess_params = {}, {}, {}
return preprocess_params, forward_params, postprocess_params
def __call__(self, image: Union["Image.Image", str], text: str = None, **kwargs):
if isinstance(image, (Image.Image, str)) and isinstance(text, str):
inputs = {"image": image, "text": text}
else:
"""
Supports the following format
- {"image": image, "text": text}
- [{"image": image, "text": text}]
- Generator and datasets
"""
inputs = image
results = super().__call__(inputs, **kwargs)
return results
def preprocess(self, inputs):
model_inputs = self.vle_processor(text=inputs['text'], images=inputs['image'], return_tensors="pt",padding=True)
return model_inputs, inputs['image']
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs[0])
return model_outputs, model_inputs[1]
def postprocess(self, model_outputs):
probs = torch.softmax(model_outputs[0]['logits'], dim=-1)
probs = probs.tolist()[0]
new_image = self.paint_in_image(model_outputs[0]['logits'], model_outputs[1])
return {"score": probs, "image": new_image}
def paint_in_image(self, logits, raw_image):
image_back = deepcopy(raw_image)
raw_image_size = image_back.size
resized_image_size = self.model.config.vision_config.image_size
patch_size = self.model.config.vision_config.patch_size
probs = torch.softmax(logits.detach()[0,:,1].to('cpu'),dim=-1).numpy().reshape(-1, resized_image_size//patch_size)
plt.close('all')
plt.axis('off')
plt.imshow(probs, cmap='gray', interpolation='None', vmin=(probs.max()-probs.min())*2/5+probs.min(),alpha=0.7)
plt.xticks([])
plt.yticks([])
buf = io.BytesIO()
plt.savefig(buf, dpi=100, transparent=True, bbox_inches='tight', pad_inches=0)
image_front = Image.open(buf)
def filter_image_front(img: Image.Image):
width, height = img.width, img.height
for x in range(width):
for y in range(height):
r,g,b,a = img.getpixel((x,y))
a = int (a * (1-r/255))
img.putpixel((x,y), (r,g,b,a))
return img
image_front = filter_image_front(image_front).resize(raw_image_size)
image_back.paste(image_front, (0,0), image_front)
mixed_image = image_back.resize(raw_image_size)
buf.close()
return mixed_image
class VLEForITMPipeline(Pipeline):
def __init__(self, vle_processor, *args, **kwargs):
self.vle_processor = vle_processor
self.id2label = {0:"False",1:"True"}
super().__init__(*args, **kwargs)
def _sanitize_parameters(self, **kwargs):
preprocess_params, forward_params, postprocess_params = {}, {}, {}
return preprocess_params, forward_params, postprocess_params
def __call__(self, image: Union["Image.Image", str], text: str = None, **kwargs):
if isinstance(image, (Image.Image, str)) and isinstance(text, str):
inputs = {"image": image, "text": text}
else:
"""
Supports the following format
- {"image": image, "text": text}
- [{"image": image, "text": text}]
- Generator and datasets
"""
inputs = image
results = super().__call__(inputs, **kwargs)
return results
def preprocess(self, inputs):
model_inputs = self.vle_processor(text=inputs['text'], images=inputs['image'], return_tensors="pt",padding=True)
return model_inputs
def _forward(self, model_inputs):
model_outputs = self.model(**model_inputs)
return model_outputs
def postprocess(self, model_outputs):
probs = torch.softmax(model_outputs['logits'], dim=-1)
preds = torch.argmax(probs, dim=-1)
probs = probs.tolist()[0]
preds = self.id2label[preds.tolist()[0]]
return {"score": probs, "match": preds} |