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import torch
import re
from functools import lru_cache
import layoutparser as lp
import pdf2image
from PIL import Image
from huggingface_hub import hf_hub_download, snapshot_download
from molscribe import MolScribe
from rxnscribe import RxnScribe, MolDetect
from .tableextractor import TableExtractor
from .utils import *
class ChemIEToolkit:
def __init__(self, device=None):
if device is None:
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
else:
self.device = torch.device(device)
self._molscribe = None
self._rxnscribe = None
self._pdfparser = None
self._moldet = None
self._coref = None
@property
def molscribe(self):
if self._molscribe is None:
self.init_molscribe()
return self._molscribe
@lru_cache(maxsize=None)
def init_molscribe(self, ckpt_path=None):
"""
Set model to custom checkpoint
Parameters:
ckpt_path: path to checkpoint to use, if None then will use default
"""
if ckpt_path is None:
ckpt_path = hf_hub_download("yujieq/MolScribe", "swin_base_char_aux_1m.pth")
self._molscribe = MolScribe(ckpt_path, device=self.device)
@property
def rxnscribe(self):
if self._rxnscribe is None:
self.init_rxnscribe()
return self._rxnscribe
@lru_cache(maxsize=None)
def init_rxnscribe(self, ckpt_path=None):
"""
Set model to custom checkpoint
Parameters:
ckpt_path: path to checkpoint to use, if None then will use default
"""
if ckpt_path is None:
ckpt_path = hf_hub_download("yujieq/RxnScribe", "pix2seq_reaction_full.ckpt")
self._rxnscribe = RxnScribe(ckpt_path, device=self.device)
@property
def pdfparser(self):
if self._pdfparser is None:
self.init_pdfparser()
return self._pdfparser
@lru_cache(maxsize=None)
def init_pdfparser(self, ckpt_path=None):
"""
Set model to custom checkpoint
Parameters:
ckpt_path: path to checkpoint to use, if None then will use default
"""
config_path = "lp://efficientdet/PubLayNet/tf_efficientdet_d1"
self._pdfparser = lp.AutoLayoutModel(config_path, model_path=ckpt_path, device=self.device.type)
@property
def moldet(self):
if self._moldet is None:
self.init_moldet()
return self._moldet
@lru_cache(maxsize=None)
def init_moldet(self, ckpt_path=None):
"""
Set model to custom checkpoint
Parameters:
ckpt_path: path to checkpoint to use, if None then will use default
"""
if ckpt_path is None:
ckpt_path = hf_hub_download("Ozymandias314/MolDetectCkpt", "best_hf.ckpt")
self._moldet = MolDetect(ckpt_path, device=self.device)
@property
def coref(self):
if self._coref is None:
self.init_coref()
return self._coref
@lru_cache(maxsize=None)
def init_coref(self, ckpt_path=None):
"""
Set model to custom checkpoint
Parameters:
ckpt_path: path to checkpoint to use, if None then will use default
"""
if ckpt_path is None:
ckpt_path = hf_hub_download("Ozymandias314/MolDetectCkpt", "coref_best_hf.ckpt")
self._coref = MolDetect(ckpt_path, device=self.device, coref=True)
@property
def tableextractor(self):
return TableExtractor()
def extract_figures_from_pdf(self, pdf, num_pages=None, output_bbox=False, output_image=True):
"""
Find and return all figures from a pdf page
Parameters:
pdf: path to pdf
num_pages: process only first `num_pages` pages, if `None` then process all
output_bbox: whether to output bounding boxes for each individual entry of a table
output_image: whether to include PIL image for figures. default is True
Returns:
list of content in the following format
[
{ # first figure
'title': str,
'figure': {
'image': PIL image or None,
'bbox': list in form [x1, y1, x2, y2],
}
'table': {
'bbox': list in form [x1, y1, x2, y2] or empty list,
'content': {
'columns': list of column headers,
'rows': list of list of row content,
} or None
}
'footnote': str or empty,
'page': int
}
# more figures
]
"""
pages = pdf2image.convert_from_path(pdf, last_page=num_pages)
table_ext = self.tableextractor
table_ext.set_pdf_file(pdf)
table_ext.set_output_image(output_image)
table_ext.set_output_bbox(output_bbox)
return table_ext.extract_all_tables_and_figures(pages, self.pdfparser, content='figures')
def extract_tables_from_pdf(self, pdf, num_pages=None, output_bbox=False, output_image=True):
"""
Find and return all tables from a pdf page
Parameters:
pdf: path to pdf
num_pages: process only first `num_pages` pages, if `None` then process all
output_bbox: whether to include bboxes for individual entries of the table
output_image: whether to include PIL image for figures. default is True
Returns:
list of content in the following format
[
{ # first table
'title': str,
'figure': {
'image': PIL image or None,
'bbox': list in form [x1, y1, x2, y2] or empty list,
}
'table': {
'bbox': list in form [x1, y1, x2, y2] or empty list,
'content': {
'columns': list of column headers,
'rows': list of list of row content,
}
}
'footnote': str or empty,
'page': int
}
# more tables
]
"""
pages = pdf2image.convert_from_path(pdf, last_page=num_pages)
table_ext = self.tableextractor
table_ext.set_pdf_file(pdf)
table_ext.set_output_image(output_image)
table_ext.set_output_bbox(output_bbox)
return table_ext.extract_all_tables_and_figures(pages, self.pdfparser, content='tables')
def extract_molecules_from_figures_in_pdf(self, pdf, batch_size=16, num_pages=None):
"""
Get all molecules and their information from a pdf
Parameters:
pdf: path to pdf, or byte file
batch_size: batch size for inference in all models
num_pages: process only first `num_pages` pages, if `None` then process all
Returns:
list of figures and corresponding molecule info in the following format
[
{ # first figure
'image': ndarray of the figure image,
'molecules': [
{ # first molecule
'bbox': tuple in the form (x1, y1, x2, y2),
'score': float,
'image': ndarray of cropped molecule image,
'smiles': str,
'molfile': str
},
# more molecules
],
'page': int
},
# more figures
]
"""
figures = self.extract_figures_from_pdf(pdf, num_pages=num_pages, output_bbox=True)
images = [figure['figure']['image'] for figure in figures]
results = self.extract_molecules_from_figures(images, batch_size=batch_size)
for figure, result in zip(figures, results):
result['page'] = figure['page']
return results
def extract_molecule_bboxes_from_figures(self, figures, batch_size=16):
"""
Return bounding boxes of molecules in images
Parameters:
figures: list of PIL or ndarray images
batch_size: batch size for inference
Returns:
list of results for each figure in the following format
[
[ # first figure
{ # first bounding box
'category': str,
'bbox': tuple in the form (x1, y1, x2, y2),
'category_id': int,
'score': float
},
# more bounding boxes
],
# more figures
]
"""
figures = [convert_to_pil(figure) for figure in figures]
return self.moldet.predict_images(figures, batch_size=batch_size)
def extract_molecules_from_figures(self, figures, batch_size=16):
"""
Get all molecules and their information from list of figures
Parameters:
figures: list of PIL or ndarray images
batch_size: batch size for inference
Returns:
list of results for each figure in the following format
[
{ # first figure
'image': ndarray of the figure image,
'molecules': [
{ # first molecule
'bbox': tuple in the form (x1, y1, x2, y2),
'score': float,
'image': ndarray of cropped molecule image,
'smiles': str,
'molfile': str
},
# more molecules
],
},
# more figures
]
"""
bboxes = self.extract_molecule_bboxes_from_figures(figures, batch_size=batch_size)
figures = [convert_to_cv2(figure) for figure in figures]
results, cropped_images, refs = clean_bbox_output(figures, bboxes)
mol_info = self.molscribe.predict_images(cropped_images, batch_size=batch_size)
for info, ref in zip(mol_info, refs):
ref.update(info)
return results
def extract_molecule_corefs_from_figures_in_pdf(self, pdf, batch_size=16, num_pages=None, molscribe = True, ocr = True):
"""
Get all molecule bboxes and corefs from figures in pdf
Parameters:
pdf: path to pdf, or byte file
batch_size: batch size for inference in all models
num_pages: process only first `num_pages` pages, if `None` then process all
Returns:
list of results for each figure in the following format:
[
{
'bboxes': [
{ # first bbox
'category': '[Sup]',
'bbox': (0.0050025012506253125, 0.38273870663142223, 0.9934967483741871, 0.9450094869920168),
'category_id': 4,
'score': -0.07593922317028046
},
# More bounding boxes
],
'corefs': [
[0, 1], # molecule bbox index, identifier bbox index
[3, 4],
# More coref pairs
],
'page': int
},
# More figures
]
"""
figures = self.extract_figures_from_pdf(pdf, num_pages=num_pages, output_bbox=True)
images = [figure['figure']['image'] for figure in figures]
results = self.extract_molecule_corefs_from_figures(images, batch_size=batch_size, molscribe=molscribe, ocr=ocr)
for figure, result in zip(figures, results):
result['page'] = figure['page']
return results
def extract_molecule_corefs_from_figures(self, figures, batch_size=16, molscribe=True, ocr=True):
"""
Get all molecule bboxes and corefs from list of figures
Parameters:
figures: list of PIL or ndarray images
batch_size: batch size for inference
Returns:
list of results for each figure in the following format:
[
{
'bboxes': [
{ # first bbox
'category': '[Sup]',
'bbox': (0.0050025012506253125, 0.38273870663142223, 0.9934967483741871, 0.9450094869920168),
'category_id': 4,
'score': -0.07593922317028046
},
# More bounding boxes
],
'corefs': [
[0, 1], # molecule bbox index, identifier bbox index
[3, 4],
# More coref pairs
],
},
# More figures
]
"""
figures = [convert_to_pil(figure) for figure in figures]
return self.coref.predict_images(figures, batch_size=batch_size, coref=True, molscribe = molscribe, ocr = ocr)
def extract_reactions_from_figures_in_pdf(self, pdf, batch_size=16, num_pages=None, molscribe=True, ocr=True):
"""
Get reaction information from figures in pdf
Parameters:
pdf: path to pdf, or byte file
batch_size: batch size for inference in all models
num_pages: process only first `num_pages` pages, if `None` then process all
molscribe: whether to predict and return smiles and molfile info
ocr: whether to predict and return text of conditions
Returns:
list of figures and corresponding molecule info in the following format
[
{
'figure': PIL image
'reactions': [
{
'reactants': [
{
'category': str,
'bbox': tuple (x1,x2,y1,y2),
'category_id': int,
'smiles': str,
'molfile': str,
},
# more reactants
],
'conditions': [
{
'category': str,
'bbox': tuple (x1,x2,y1,y2),
'category_id': int,
'text': list of str,
},
# more conditions
],
'products': [
# same structure as reactants
]
},
# more reactions
],
'page': int
},
# more figures
]
"""
figures = self.extract_figures_from_pdf(pdf, num_pages=num_pages, output_bbox=True)
images = [figure['figure']['image'] for figure in figures]
results = self.extract_reactions_from_figures(images, batch_size=batch_size, molscribe=molscribe, ocr=ocr)
for figure, result in zip(figures, results):
result['page'] = figure['page']
return results
def extract_reactions_from_figures(self, figures, batch_size=16, molscribe=True, ocr=True):
"""
Get reaction information from list of figures
Parameters:
figures: list of PIL or ndarray images
batch_size: batch size for inference in all models
molscribe: whether to predict and return smiles and molfile info
ocr: whether to predict and return text of conditions
Returns:
list of figures and corresponding molecule info in the following format
[
{
'figure': PIL image
'reactions': [
{
'reactants': [
{
'category': str,
'bbox': tuple (x1,x2,y1,y2),
'category_id': int,
'smiles': str,
'molfile': str,
},
# more reactants
],
'conditions': [
{
'category': str,
'bbox': tuple (x1,x2,y1,y2),
'category_id': int,
'text': list of str,
},
# more conditions
],
'products': [
# same structure as reactants
]
},
# more reactions
],
},
# more figures
]
"""
pil_figures = [convert_to_pil(figure) for figure in figures]
results = []
reactions = self.rxnscribe.predict_images(pil_figures, batch_size=batch_size, molscribe=molscribe, ocr=ocr)
for figure, rxn in zip(figures, reactions):
data = {
'figure': figure,
'reactions': rxn,
}
results.append(data)
return results
def extract_reactions_from_text_in_pdf_combined(self, pdf, num_pages=None):
"""
Get reaction information from text in pdf and combined with corefs from figures
Parameters:
pdf: path to pdf
num_pages: process only first `num_pages` pages, if `None` then process all
Returns:
list of pages and corresponding reaction info in the following format
[
{
'page': page number
'reactions': [
{
'tokens': list of words in relevant sentence,
'reactions' : [
{
# key, value pairs where key is the label and value is a tuple
# or list of tuples of the form (tokens, start index, end index)
# where indices are for the corresponding token list and start and end are inclusive
}
# more reactions
]
}
# more reactions in other sentences
]
},
# more pages
]
"""
results = self.extract_reactions_from_text_in_pdf(pdf, num_pages=num_pages)
results_coref = self.extract_molecule_corefs_from_figures_in_pdf(pdf, num_pages=num_pages)
return associate_corefs(results, results_coref)
def extract_reactions_from_figures_and_tables_in_pdf(self, pdf, num_pages=None, batch_size=16, molscribe=True, ocr=True):
"""
Get reaction information from figures and combine with table information in pdf
Parameters:
pdf: path to pdf, or byte file
batch_size: batch size for inference in all models
num_pages: process only first `num_pages` pages, if `None` then process all
molscribe: whether to predict and return smiles and molfile info
ocr: whether to predict and return text of conditions
Returns:
list of figures and corresponding molecule info in the following format
[
{
'figure': PIL image
'reactions': [
{
'reactants': [
{
'category': str,
'bbox': tuple (x1,x2,y1,y2),
'category_id': int,
'smiles': str,
'molfile': str,
},
# more reactants
],
'conditions': [
{
'category': str,
'text': list of str,
},
# more conditions
],
'products': [
# same structure as reactants
]
},
# more reactions
],
'page': int
},
# more figures
]
"""
figures = self.extract_figures_from_pdf(pdf, num_pages=num_pages, output_bbox=True)
images = [figure['figure']['image'] for figure in figures]
results = self.extract_reactions_from_figures(images, batch_size=batch_size, molscribe=molscribe, ocr=ocr)
results = process_tables(figures, results, self.molscribe, batch_size=batch_size)
results_coref = self.extract_molecule_corefs_from_figures_in_pdf(pdf, num_pages=num_pages)
results = replace_rgroups_in_figure(figures, results, results_coref, self.molscribe, batch_size=batch_size)
results = expand_reactions_with_backout(results, results_coref, self.molscribe)
return results
def extract_reactions_from_pdf(self, pdf, num_pages=None, batch_size=16):
"""
Returns:
dictionary of reactions from multimodal sources
{
'figures': [
{
'figure': PIL image
'reactions': [
{
'reactants': [
{
'category': str,
'bbox': tuple (x1,x2,y1,y2),
'category_id': int,
'smiles': str,
'molfile': str,
},
# more reactants
],
'conditions': [
{
'category': str,
'text': list of str,
},
# more conditions
],
'products': [
# same structure as reactants
]
},
# more reactions
],
'page': int
},
# more figures
]
'text': [
{
'page': page number
'reactions': [
{
'tokens': list of words in relevant sentence,
'reactions' : [
{
# key, value pairs where key is the label and value is a tuple
# or list of tuples of the form (tokens, start index, end index)
# where indices are for the corresponding token list and start and end are inclusive
}
# more reactions
]
}
# more reactions in other sentences
]
},
# more pages
]
}
"""
figures = self.extract_figures_from_pdf(pdf, num_pages=num_pages, output_bbox=True)
images = [figure['figure']['image'] for figure in figures]
results = self.extract_reactions_from_figures(images, batch_size=batch_size, molscribe=True, ocr=True)
table_expanded_results = process_tables(figures, results, self.molscribe, batch_size=batch_size)
results_coref = self.extract_molecule_corefs_from_figures_in_pdf(pdf, num_pages=num_pages)
figure_results = replace_rgroups_in_figure(figures, table_expanded_results, results_coref, self.molscribe, batch_size=batch_size)
table_expanded_results = expand_reactions_with_backout(figure_results, results_coref, self.molscribe)
return {
'figures': table_expanded_results,
}
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