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# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for Llava.
"""
from typing import List, Optional, Union
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.tokenization_utils_base import (
PaddingStrategy,
PreTokenizedInput,
TextInput,
TruncationStrategy,
)
from transformers.utils import TensorType
import torch
from open_clip.transform import PreprocessCfg, image_transform_v2
from modeling_llava import LlavaForConditionalGeneration
from PIL import Image
import math
class OpenCLIPImageProcessor:
def __init__(self, config, crop_size=384, max_tokens=100):
cfg = PreprocessCfg(**config)
transform = image_transform_v2(cfg=cfg, is_train=False)
self.transform = transform
self.crop_size = crop_size
self.max_tokens = max_tokens
def __call__(self, image: Image.Image):
output = self.transform_func(image)
return {
"pixel_values": output,
}
def transform_func(self, image: Image.Image):
outputs = []
outputs.append(self.transform(image))
width, height = image.size
crop_size = self.crop_size
if width <= crop_size and height <= crop_size:
outputs = torch.stack(outputs, dim=0)
return outputs
total_tokens = math.inf
while total_tokens > self.max_tokens:
total_tokens = math.floor(
(2 * width - crop_size)
/ crop_size
* (2 * height - crop_size)
/ crop_size
)
if total_tokens > self.max_tokens:
crop_size += 10
stride = crop_size // 2
x_steps = int(round((2 * width - crop_size) / crop_size))
if x_steps < 1:
x_steps = 1
y_steps = int(round((2 * height - crop_size) / crop_size))
if y_steps < 1:
y_steps = 1
x_coords = []
y_coords = []
for i in range(x_steps):
x_coords.append([i * stride, i * stride + crop_size])
if x_coords[-1][1] != width:
x_coords[-1][1] = width
for i in range(y_steps):
y_coords.append([i * stride, i * stride + crop_size])
if y_coords[-1][1] != height:
y_coords[-1][1] = height
image_parts = []
for i in range(len(x_coords)):
for j in range(len(y_coords)):
image_parts.append(
image.crop(
(x_coords[i][0], y_coords[j][0], x_coords[i][1], y_coords[j][1])
)
)
for image_part in image_parts:
outputs.append(self.transform(image_part))
outputs = torch.stack(outputs, dim=0)
return outputs
@property
def model_input_names(self):
return ["pixel_values"]
class LlavaProcessor:
def __init__(self, image_processor: OpenCLIPImageProcessor, tokenizer):
self.image_processor = image_processor
self.tokenizer = tokenizer
def __call__(
self,
text: Union[
TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
] = None,
images: ImageInput = None,
model: LlavaForConditionalGeneration = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length=None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> BatchFeature:
if images is not None:
pixel_values = self.image_processor(images)[
"pixel_values"
]
pixel_values = pixel_values.to(model.device).to(model.dtype)
image_outputs = model.vision_model(pixel_values)
image_features = model.multi_modal_projector(image_outputs)
image_features = image_features.unsqueeze(0)
else:
image_features = None
text_inputs = self.tokenizer(
text,
return_tensors=return_tensors,
padding=padding,
truncation=truncation,
max_length=max_length,
)
return BatchFeature(data={**text_inputs, "image_features": image_features})
def batch_decode(self, *args, **kwargs):
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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