Spaces:
Sleeping
Sleeping
File size: 13,909 Bytes
1f516b6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 |
import os
import argparse
from typing import List
import PIL
import torch
from torch.profiler import profile, record_function, ProfilerActivity
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg
from .pix2seq import build_pix2seq_model
from .tokenizer import get_tokenizer
from .dataset import make_transforms
from .data import postprocess_reactions, postprocess_bboxes, postprocess_coref_results, ReactionImageData, ImageData, CorefImageData
from molscribe import MolScribe
from huggingface_hub import hf_hub_download
import easyocr
class RxnScribe:
def __init__(self, model_path, device=None):
"""
RxnScribe Interface
:param model_path: path of the model checkpoint.
:param device: torch device, defaults to be CPU.
"""
args = self._get_args()
args.format = 'reaction'
states = torch.load(model_path, map_location=torch.device('cpu'))
if device is None:
device = torch.device('cpu')
self.device = device
self.tokenizer = get_tokenizer(args)
self.model = self.get_model(args, self.tokenizer, self.device, states['state_dict'])
self.transform = make_transforms('test', augment=False, debug=False)
self.molscribe = self.get_molscribe()
self.ocr_model = self.get_ocr_model()
def _get_args(self):
parser = argparse.ArgumentParser()
# * Backbone
parser.add_argument('--backbone', default='resnet50', type=str,
help="Name of the convolutional backbone to use")
parser.add_argument('--dilation', action='store_true',
help="If true, we replace stride with dilation in the last convolutional block (DC5)")
parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),
help="Type of positional embedding to use on top of the image features")
# * Transformer
parser.add_argument('--enc_layers', default=6, type=int, help="Number of encoding layers in the transformer")
parser.add_argument('--dec_layers', default=6, type=int, help="Number of decoding layers in the transformer")
parser.add_argument('--dim_feedforward', default=1024, type=int,
help="Intermediate size of the feedforward layers in the transformer blocks")
parser.add_argument('--hidden_dim', default=256, type=int,
help="Size of the embeddings (dimension of the transformer)")
parser.add_argument('--dropout', default=0.1, type=float, help="Dropout applied in the transformer")
parser.add_argument('--nheads', default=8, type=int,
help="Number of attention heads inside the transformer's attentions")
parser.add_argument('--pre_norm', action='store_true')
# Data
parser.add_argument('--format', type=str, default='reaction')
parser.add_argument('--input_size', type=int, default=1333)
args = parser.parse_args([])
args.pix2seq = True
args.pix2seq_ckpt = None
args.pred_eos = True
args.is_coco = False
args.use_hf_transformer = False
return args
def get_model(self, args, tokenizer, device, model_states):
def remove_prefix(state_dict):
return {k.replace('model.', ''): v for k, v in state_dict.items()}
model = build_pix2seq_model(args, tokenizer[args.format])
model.load_state_dict(remove_prefix(model_states), strict=False)
model.to(device)
model.eval()
return model
def get_molscribe(self):
ckpt_path = hf_hub_download("yujieq/MolScribe", "swin_base_char_aux_1m.pth")
molscribe = MolScribe(ckpt_path, device=self.device)
return molscribe
def get_ocr_model(self):
reader = easyocr.Reader(['en'], gpu=(self.device.type == 'cuda'))
return reader
def predict_images(self, input_images: List, batch_size=16, molscribe=False, ocr=False):
# images: a list of PIL images
device = self.device
tokenizer = self.tokenizer['reaction']
predictions = []
for idx in range(0, len(input_images), batch_size):
batch_images = input_images[idx:idx+batch_size]
images, refs = zip(*[self.transform(image) for image in batch_images])
images = torch.stack(images, dim=0).to(device)
with torch.no_grad():
pred_seqs, pred_scores = self.model(images, max_len=tokenizer.max_len)
for i, (seqs, scores) in enumerate(zip(pred_seqs, pred_scores)):
reactions = tokenizer.sequence_to_data(seqs.tolist(), scores.tolist(), scale=refs[i]['scale'])
reactions = postprocess_reactions(
reactions,
image=input_images[i],
molscribe=self.molscribe if molscribe else None,
ocr=self.ocr_model if ocr else None
)
predictions.append(reactions)
return predictions
def predict_image(self, image, **kwargs):
predictions = self.predict_images([image], **kwargs)
return predictions[0]
def predict_image_files(self, image_files: List, **kwargs):
input_images = []
for path in image_files:
image = PIL.Image.open(path).convert("RGB")
input_images.append(image)
return self.predict_images(input_images, **kwargs)
def predict_image_file(self, image_file: str, **kwargs):
predictions = self.predict_image_files([image_file], **kwargs)
return predictions[0]
def draw_predictions(self, predictions, image=None, image_file=None):
results = []
assert image or image_file
data = ReactionImageData(predictions=predictions, image=image, image_file=image_file)
h, w = np.array([data.height, data.width]) * 10 / max(data.height, data.width)
for r in data.pred_reactions:
fig, ax = plt.subplots(figsize=(w, h))
fig.tight_layout()
canvas = FigureCanvasAgg(fig)
ax.imshow(data.image)
ax.axis('off')
r.draw(ax)
canvas.draw()
buf = canvas.buffer_rgba()
results.append(np.asarray(buf))
plt.close(fig)
return results
def draw_predictions_combined(self, predictions, image=None, image_file=None):
assert image or image_file
data = ReactionImageData(predictions=predictions, image=image, image_file=image_file)
h, w = np.array([data.height, data.width]) * 10 / max(data.height, data.width)
n = len(data.pred_reactions)
fig, axes = plt.subplots(n, 1, figsize=(w, h * n))
if n == 1:
axes = [axes]
fig.tight_layout(rect=(0.02, 0.02, 0.99, 0.99))
canvas = FigureCanvasAgg(fig)
for i, r in enumerate(data.pred_reactions):
ax = axes[i]
ax.imshow(data.image)
ax.set_xticks([])
ax.set_yticks([])
ax.set_title(f'reaction # {i}', fontdict={'fontweight': 'bold', 'fontsize': 14})
r.draw(ax)
canvas.draw()
buf = canvas.buffer_rgba()
result_image = np.asarray(buf)
plt.close(fig)
return result_image
class MolDetect:
def __init__(self, model_path, device = None, coref = False):
"""
MolDetect Interface
:param model_path: path of the model checkpoint.
:param device: torch device, defaults to be CPU.
"""
args = self._get_args()
if not coref: args.format = 'bbox'
else: args.format = 'coref'
states = torch.load(model_path, map_location = torch.device('cpu'))
if device is None:
device = torch.device('cpu')
self.device = device
self.tokenizer = get_tokenizer(args)
self.model = self.get_model(args, self.tokenizer, self.device, states['state_dict'])
self.transform = make_transforms('test', augment=False, debug=False)
self.ocr_model = self.get_ocr_model()
self.molscribe = self.get_molscribe()
def _get_args(self):
parser = argparse.ArgumentParser()
# * Backbone
parser.add_argument('--backbone', default='resnet50', type=str,
help="Name of the convolutional backbone to use")
parser.add_argument('--dilation', action='store_true',
help="If true, we replace stride with dilation in the last convolutional block (DC5)")
parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),
help="Type of positional embedding to use on top of the image features")
# * Transformer
parser.add_argument('--enc_layers', default=6, type=int, help="Number of encoding layers in the transformer")
parser.add_argument('--dec_layers', default=6, type=int, help="Number of decoding layers in the transformer")
parser.add_argument('--dim_feedforward', default=1024, type=int,
help="Intermediate size of the feedforward layers in the transformer blocks")
parser.add_argument('--hidden_dim', default=256, type=int,
help="Size of the embeddings (dimension of the transformer)")
parser.add_argument('--dropout', default=0.1, type=float, help="Dropout applied in the transformer")
parser.add_argument('--nheads', default=8, type=int,
help="Number of attention heads inside the transformer's attentions")
parser.add_argument('--pre_norm', action='store_true')
# Data
parser.add_argument('--format', type=str, default='reaction')
parser.add_argument('--input_size', type=int, default=1333)
args = parser.parse_args([])
args.pix2seq = True
args.pix2seq_ckpt = None
args.pred_eos = True
args.is_coco = False
args.use_hf_transformer = True
return args
def get_model(self, args, tokenizer, device, model_states):
def remove_prefix(state_dict):
return {k.replace('model.', ''): v for k, v in state_dict.items()}
model = build_pix2seq_model(args, tokenizer[args.format])
model.load_state_dict(remove_prefix(model_states), strict=False)
model.to(device)
model.eval()
return model
def get_molscribe(self):
ckpt_path = hf_hub_download("yujieq/MolScribe", "swin_base_char_aux_1m.pth")
molscribe = MolScribe(ckpt_path, device=self.device)
return molscribe
def get_ocr_model(self):
reader = easyocr.Reader(['en'], gpu = (self.device.type == 'cuda'))
return reader
def predict_images(self, input_images: List, batch_size = 16, molscribe = False, coref = False, ocr = False):
device = self.device
if not coref:
tokenizer = self.tokenizer['bbox']
else:
tokenizer = self.tokenizer['coref']
predictions = []
for idx in range(0, len(input_images), batch_size):
batch_images = input_images[idx:idx+batch_size]
images, refs = zip(*[self.transform(image) for image in batch_images])
images = torch.stack(images, dim=0).to(device)
with torch.no_grad():
pred_seqs, pred_scores = self.model(images, max_len=tokenizer.max_len)
for i, (seqs, scores) in enumerate(zip(pred_seqs, pred_scores)):
bboxes = tokenizer.sequence_to_data(seqs.tolist(), scores.tolist(), scale=refs[i]['scale'])
if coref:
bboxes = postprocess_coref_results(bboxes, image = input_images[i], molscribe = self.molscribe if molscribe else None, ocr = self.ocr_model if ocr else None)
if not coref:
bboxes = postprocess_bboxes(bboxes, image = input_images[i], molscribe = self.molscribe if molscribe else None)
predictions.append(bboxes)
return predictions
def predict_image(self, image, molscribe = False, coref = False, ocr = False):
predictions = self.predict_images([image], molscribe = molscribe, coref = coref, ocr = ocr)
return predictions[0]
def predict_image_files(self, image_files: List, batch_size = 16, molscribe = False, coref = False, ocr = False):
input_images = []
for path in image_files:
image = PIL.Image.open(path).convert("RGB")
input_images.append(image)
return self.predict_images(input_images, batch_size = batch_size, molscribe = molscribe, coref = coref, ocr = ocr)
def predict_image_file(self, image_file: str, molscribe = False, coref = False, ocr = False, **kwargs):
predictions = self.predict_image_files([image_file], molscribe = molscribe, coref = coref, ocr = ocr)
return predictions[0]
def draw_bboxes(self, predictions, image=None, image_file=None, coref = False):
results = []
assert image or image_file
if not coref: data = ImageData(predictions = predictions, image = image, image_file = image_file)
else: data = CorefImageData(predictions = predictions['bboxes'], image = image, image_file = image_file)
h, w = np.array([data.height, data.width]) * 10 / max(data.height, data.width)
fig, ax = plt.subplots(figsize = (w, h))
fig.tight_layout()
canvas = FigureCanvasAgg(fig)
ax.imshow(data.image)
ax.axis('off')
data.draw_prediction(ax, data.image)
canvas.draw()
buf = canvas.buffer_rgba()
results.append(np.asarray(buf))
plt.close(fig)
return results
|