├── .mypy_cache └── 3.6 │ ├── @plugins_snapshot.json │ ├── abc.meta.json │ ├── ast.meta.json │ ├── _ast.meta.json │ ├── mmap.meta.json │ ├── _random.meta.json │ ├── argparse.meta.json │ ├── posix.meta.json │ ├── os │ ├── path.meta.json │ └── __init__.meta.json │ ├── time.meta.json │ ├── Dataloader_Flow.meta.json │ ├── codecs.meta.json │ ├── random.meta.json │ ├── typing.meta.json │ ├── builtins.meta.json │ ├── types.meta.json │ ├── io.meta.json │ ├── Prepare.meta.json │ ├── _importlib_modulespec.meta.json │ ├── collections │ ├── abc.meta.json │ ├── __init__.meta.json │ └── abc.data.json │ ├── Model_VGGFace.meta.json │ ├── sys.meta.json │ ├── importlib │ ├── abc.meta.json │ ├── util.meta.json │ ├── __init__.meta.json │ ├── machinery.meta.json │ ├── __init__.data.json │ ├── util.data.json │ └── abc.data.json │ ├── Prepare.data.json │ ├── _random.data.json │ ├── Dataloader_Flow.data.json │ ├── abc.data.json │ ├── _importlib_modulespec.data.json │ ├── ast.data.json │ └── mmap.data.json ├── README.md ├── Utils └── Dataloader_Flow.py ├── Prepare.py └── Train └── main.py /.mypy_cache/3.6/@plugins_snapshot.json: -------------------------------------------------------------------------------- 1 | {} -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Micro Expression Recognition for MEGC2019 2 | 3 | Code for MEGC2019 Micro Expression Recognition 4 | 5 | 1. Using EVM algorithm to magnify the motion in the Micro Expression. 6 | 2. Using Prepare.py to preproccess the data. 7 | 3. Running main.py in the Train folder to run the model and output the sample file. 8 | 9 | The output file will be written into Sample_File folder. 10 | 11 | ## Required Package: 12 | python 3.6.5 13 | dlib 19.16.0 14 | opencv2 4.0.0 15 | numpy 1.14.3 16 | pytorch 0.4.1 17 | sklearn 0.19.1 18 | 19 | ## Required Dataset: 20 | CASMEII 21 | SAMM 22 | SMIC 23 | -------------------------------------------------------------------------------- /.mypy_cache/3.6/abc.meta.json: -------------------------------------------------------------------------------- 1 | {"child_modules": [], "data_mtime": 1548652924, "dep_lines": [1, 1], "dep_prios": [5, 5], "dependencies": ["typing", "builtins"], "deps_mtime": null, "hash": "2584c2f5c4667a231d7afdd171e0b0a2", "id": "abc", "ignore_all": true, "interface_hash": "db99f7243efdb72ce900554864bb2b30", "mtime": 1547921673, "options": {"allow_untyped_globals": false, "always_false": [], "always_true": [], "bazel": false, "check_untyped_defs": false, 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/Utils/Dataloader_Flow.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from PIL import Image 4 | from torch import stack 5 | from torch.utils.data import Dataset 6 | 7 | class Flow_loader(Dataset): 8 | def __init__(self, data_dir, label_dir, domain_label_dir, samples, transform=None, label_transform=None): 9 | self.data_dir = data_dir 10 | self.label_dir = label_dir 11 | self.domain_label_dir = domain_label_dir 12 | 13 | with open(self.data_dir, 'r') as d: 14 | self.data = d.readlines() 15 | 16 | with open(self.label_dir, 'r') as l: 17 | self.label = l.readlines() 18 | 19 | with open(self.domain_label_dir, 'r') as dl: 20 | self.domain_label = dl.readlines() 21 | 22 | self.transform = transform 23 | self.label_transform = label_transform 24 | 25 | self.samples = samples 26 | self.size = len(self.samples) 27 | 28 | def __getitem__(self, index): 29 | sample = self.samples[index] 30 | flow_sample = self.samples[index] 31 | 32 | flow_path = '.' + self.data[flow_sample].strip('\n') 33 | 34 | flow = Image.open(flow_path).convert('RGB') 35 | 36 | file_name = flow_path.split("/")[-2:-1] 37 | 38 | label = int(float(self.label[sample].strip('\n'))) 39 | 40 | domain_label = 0 if self.domain_label[sample] == 'Macro\n' else 1 41 | 42 | if self.transform: 43 | flow = self.transform(flow) 44 | 45 | if self.label_transform: 46 | label = self.label_transform(label) 47 | 48 | return {"image": flow, "label": label, "domain_label": domain_label, "file_name": file_name} 49 | 50 | def __len__(self): 51 | return self.size -------------------------------------------------------------------------------- /.mypy_cache/3.6/collections/abc.data.json: -------------------------------------------------------------------------------- 1 | {".class": "MypyFile", "_fullname": "collections.abc", "is_partial_stub_package": false, "is_stub": true, "names": {".class": "SymbolTable", "AsyncGenerator": {".class": "SymbolTableNode", "cross_ref": 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"SymbolTableNode", "cross_ref": "sys", "kind": "ModuleRef", "module_hidden": true, "module_public": false}, "types": {".class": "SymbolTableNode", "cross_ref": "types", "kind": "ModuleRef", "module_hidden": true, "module_public": false}}, "path": "/home/duheming/anaconda3/lib/python3.6/site-packages/mypy/typeshed/stdlib/3/importlib/util.pyi"} -------------------------------------------------------------------------------- /Prepare.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pandas as pd 3 | import numpy as np 4 | import dlib 5 | import time 6 | from PIL import Image 7 | import cv2 8 | 9 | def mix_prepare(): 10 | Expression_version = 'Mix' 11 | Version = '22' 12 | 13 | DATA_FILE = './{}_data_{}.txt'.format(Expression_version, Version) 14 | LABEL_FILE = './{}_label_{}.txt'.format(Expression_version, Version) 15 | SUBJECT_FILE = './{}_subject_{}.txt'.format(Expression_version, Version) 16 | FLOW_FILE = './{}_flow_{}.txt'.format(Expression_version, Version) 17 | 18 | Video_Motion_path = './results/' 19 | 20 | csv_path = './combined_3class_gt.csv' 21 | data = pd.read_csv(csv_path, header=None) 22 | 23 | # # ========================================================================================= 24 | apex_path = './SAMM/SAMM_label.csv' 25 | SAMM_path = './SAMM/' 26 | save_path = './SAMM_landmarks_2pic/' 27 | flow_path = './SAMM_motion_flow/' 28 | apex = pd.read_csv(apex_path, header=None) 29 | samm_data = data[data[0] == 'samm'] 30 | for _, row in samm_data.iterrows(): 31 | sub_path = os.path.join(SAMM_path, str(row[1]).zfill(3)) 32 | save_sub_path = os.path.join(save_path, str(row[1]).zfill(3)) 33 | flow_sub_path = os.path.join(flow_path, str(row[1]).zfill(3)) 34 | 35 | test_path = os.path.join(sub_path, str(row[2])) 36 | save_test_path = os.path.join(save_sub_path, str(row[2])) 37 | flow_test_path = os.path.join(flow_sub_path, str(row[2])) 38 | 39 | pixel = apex[apex[1] == row[2]] 40 | 41 | apex_num = int(pixel[4]) - int(pixel[3]) 42 | 43 | pic_list = os.listdir(test_path) 44 | pic_list.sort() 45 | 46 | pic_apex_name = pic_list[apex_num] 47 | pic_apex_path = os.path.join(test_path, pic_apex_name) 48 | save_apex_pic_path = os.path.join(save_test_path, pic_apex_name) 49 | 50 | pic_onset_name = pic_list[0] 51 | pic_onset_path = os.path.join(test_path, pic_onset_name) 52 | save_onset_pic_path = os.path.join(save_test_path, pic_onset_name) 53 | 54 | Video_name = 'samm' + '_' + str(row[1]).zfill(3) + '_' + str(row[2]) + '.avi' 55 | Video_path = os.path.join(Video_Motion_path, Video_name) 56 | 57 | # Output_Video(test_path, Video_path) 58 | 59 | if not os.path.exists(flow_test_path): 60 | os.makedirs(flow_test_path) 61 | 62 | Flow_pic_path = os.path.join(flow_test_path, 'motion_flow.png') 63 | 64 | print(test_path) 65 | 66 | # Read_Video(Video_path, apex_num, save_onset_pic_path, save_apex_pic_path, Flow_pic_path) 67 | 68 | # Flow_pic_path = os.path.join(flow_test_path, 'Merge.png') 69 | 70 | # if not os.path.exists(flow_test_path): 71 | # os.makedirs(flow_test_path) 72 | 73 | with open (FLOW_FILE, 'a') as m: 74 | m.write(Flow_pic_path + '\n') 75 | 76 | with open (LABEL_FILE, 'a') as l: 77 | l.write(str(row[3]) + '\n') 78 | with open (SUBJECT_FILE, 'a') as s: 79 | s.write('samm ' + str(row[1]).zfill(3) + '\n') 80 | with open (DATA_FILE, 'a') as dl: 81 | dl.write(save_onset_pic_path + '\n') 82 | dl.write(save_apex_pic_path + '\n') 83 | 84 | # if not os.path.exists(save_test_path): 85 | # os.makedirs(save_test_path) 86 | 87 | # crop_pic(pic_onset_path, pic_apex_path, save_onset_pic_path, save_apex_pic_path, Flow_pic_path) 88 | 89 | # ================================================================================================= 90 | 91 | emotion_dict = {'ne': 'negative', 'po': 'positive', 'sur': 'surprise'} 92 | 93 | SMIC_path = './SMIC_all_raw/HS/' 94 | SMIC_save_path = './SMIC_landmarks/' 95 | SMIC_flow_path = './SMIC_motion_flow/' 96 | au_path = './smic_AU.csv' 97 | 98 | SMIC_data = data[data[0] == 'smic'] 99 | au_data = pd.read_csv(au_path) 100 | 101 | for index, (_, row) in enumerate(SMIC_data.iterrows()): 102 | 103 | subject = int(row[1][1:]) 104 | subject_name = 's' + str(subject) 105 | SMIC_subject = 'smic ' + str(row[1]) 106 | video_name = row[2].split('_') 107 | emotion = emotion_dict[video_name[1]] 108 | test_name = subject_name + '_' + video_name[1] + '_' + video_name[2] 109 | test_path = os.path.join(SMIC_path, subject_name, 'micro', emotion, test_name) 110 | save_test_path = os.path.join(SMIC_save_path, subject_name, emotion, test_name) 111 | flow_test_path = os.path.join(SMIC_flow_path, subject_name, emotion, test_name) 112 | 113 | pic_list = os.listdir(test_path) 114 | pic_list.sort() 115 | 116 | onset_pic = pic_list[0] 117 | apex_pic = pic_list[int(len(pic_list) / 2)] 118 | apex_num = int(len(pic_list) / 2) 119 | 120 | onset_path = os.path.join(test_path, onset_pic) 121 | apex_path = os.path.join(test_path, apex_pic) 122 | 123 | onset_save_path = os.path.join(save_test_path, onset_pic) 124 | apex_save_path = os.path.join(save_test_path, apex_pic) 125 | 126 | Video_name = 'smic' + '_' + subject_name + '_' + emotion + '_' + test_name + '.avi' 127 | Video_path = os.path.join(Video_Motion_path, Video_name) 128 | 129 | # Output_Video(test_path, Video_path) 130 | 131 | if not os.path.exists(flow_test_path): 132 | os.makedirs(flow_test_path) 133 | 134 | Flow_pic_path = os.path.join(flow_test_path, 'motion_flow.png') 135 | 136 | print(test_path) 137 | 138 | # Read_Video(Video_path, apex_num, onset_save_path, apex_save_path, Flow_pic_path) 139 | 140 | with open (DATA_FILE, 'a') as d: 141 | d.write(onset_save_path + '\n') 142 | d.write(apex_save_path + '\n') 143 | 144 | with open (LABEL_FILE, 'a') as l: 145 | l.write(str(row[3]) + '\n') 146 | 147 | with open (SUBJECT_FILE, 'a') as s: 148 | s.write(SMIC_subject + '\n') 149 | 150 | # Flow_pic_path = os.path.join(flow_test_path, 'Merge.png') 151 | 152 | # if not os.path.exists(flow_test_path): 153 | # os.makedirs(flow_test_path) 154 | 155 | with open (FLOW_FILE, 'a') as m: 156 | m.write(Flow_pic_path + '\n') 157 | 158 | # if not os.path.exists(save_test_path): 159 | # os.makedirs(save_test_path) 160 | 161 | # crop_pic(onset_path, apex_path, onset_save_path, apex_save_path, Flow_pic_path) 162 | 163 | # =================================================================================================== 164 | 165 | CASMEII_label_file = './CASME2-coding-20140508.xlsx' 166 | CASMEII_path = './CASME2_RAW_selected/' 167 | CASMEII_save_path = './CASMEII_2pic/' 168 | CASMEII_flow_path = './CASMEII_motion_flow/' 169 | 170 | CASMEII_pic_data = pd.read_excel(CASMEII_label_file, header=0) 171 | 172 | CASMEII_data = data[data[0] == 'casme2'] 173 | 174 | for index, row in CASMEII_data.iterrows(): 175 | subject, test, label = row[1], row[2], row[3] 176 | 177 | CASMEII_subject = 'casme2 ' + subject 178 | 179 | CASMEII_temp = CASMEII_pic_data[CASMEII_pic_data['Subject'] == int(subject[3:])] 180 | CASMEII_temp = CASMEII_temp[CASMEII_temp['Filename'] == test] 181 | CASMEII_onset = 'img' + str(CASMEII_temp.values[0][3]) + '.jpg' 182 | CASMEII_apex = 'img' + str(CASMEII_temp.values[0][4]) + '.jpg' 183 | apex_num = CASMEII_temp.values[0][4] - CASMEII_temp.values[0][3] 184 | 185 | CASMEII_test_path = os.path.join(CASMEII_path, subject, test) 186 | CASMEII_save_test_path = os.path.join(CASMEII_save_path, subject, test) 187 | CASMEII_flow_test_path = os.path.join(CASMEII_flow_path, subject, test) 188 | 189 | CASMEII_onset_path = os.path.join(CASMEII_test_path, CASMEII_onset) 190 | CASMEII_onset_save_path = os.path.join(CASMEII_save_test_path, CASMEII_onset) 191 | 192 | CASMEII_apex_path = os.path.join(CASMEII_test_path, CASMEII_apex) 193 | CASMEII_apex_save_path = os.path.join(CASMEII_save_test_path, CASMEII_apex) 194 | 195 | # if not os.path.exists(CASMEII_save_test_path): 196 | # os.makedirs(CASMEII_save_test_path) 197 | 198 | Video_name = 'casme2' + '_' + subject + '_' + test + '.avi' 199 | Video_path = os.path.join(Video_Motion_path, Video_name) 200 | 201 | # Output_Video(CASMEII_test_path, Video_path) 202 | 203 | if not os.path.exists(CASMEII_flow_test_path): 204 | os.makedirs(CASMEII_flow_test_path) 205 | 206 | Flow_pic_path = os.path.join(CASMEII_flow_test_path, 'motion_flow.png') 207 | 208 | print(CASMEII_test_path) 209 | 210 | # Read_Video(Video_path, apex_num, CASMEII_onset_save_path, CASMEII_apex_save_path, Flow_pic_path) 211 | 212 | with open (DATA_FILE, 'a') as d: 213 | d.write(CASMEII_onset_save_path + '\n') 214 | d.write(CASMEII_apex_save_path + '\n') 215 | 216 | with open (LABEL_FILE, 'a') as l: 217 | l.write(str(label) + '\n') 218 | 219 | with open (SUBJECT_FILE, 'a') as s: 220 | s.write(CASMEII_subject + '\n') 221 | 222 | # Flow_pic_path = os.path.join(CASMEII_flow_test_path, 'flow.png') 223 | 224 | # if not os.path.exists(CASMEII_flow_test_path): 225 | # os.makedirs(CASMEII_flow_test_path) 226 | 227 | # crop_pic(CASMEII_onset_path, CASMEII_apex_path, CASMEII_onset_save_path, CASMEII_apex_save_path, Flow_pic_path) 228 | 229 | with open (FLOW_FILE, 'a') as m: 230 | m.write(Flow_pic_path + '\n') 231 | 232 | # ======================================================================================================= 233 | 234 | image_path = './CK+/cohn-kanade-images/' 235 | label_path = './CK+/Emotion/' 236 | save_path = './CK_2pic/' 237 | au_path = './CK+/FACS/' 238 | flow_path = './CK_flow/' 239 | 240 | CK_dict = {0: 1, 1: 0, 2: 0, 3: 0, 4: 0, 5: 1, 6: 1, 7: 2} 241 | 242 | for subject in os.listdir(label_path): 243 | subject_label_path = os.path.join(label_path, subject) 244 | subject_image_path = os.path.join(image_path, subject) 245 | subject_save_path = os.path.join(save_path, subject) 246 | subject_flow_path = os.path.join(flow_path, subject) 247 | for test in os.listdir(subject_label_path): 248 | test_label_path = os.path.join(subject_label_path, test) 249 | test_image_path = os.path.join(subject_image_path, test) 250 | test_save_path = os.path.join(subject_save_path, test) 251 | test_flow_path = os.path.join(subject_flow_path, test) 252 | if os.listdir(test_label_path): 253 | 254 | with open (os.path.join(test_label_path, os.listdir(test_label_path)[-1]), 'r') as rl: 255 | label = rl.readline() 256 | label = int(float(label.strip('\n'))) 257 | 258 | if label != 0: 259 | with open (LABEL_FILE, 'a') as l: 260 | l.write(str(CK_dict[label]) + '\n') 261 | 262 | image_dir = os.listdir(test_image_path) 263 | image_dir = [item for item in image_dir if os.path.splitext(item)[1] == '.png'] 264 | image_dir = sorted(image_dir) 265 | 266 | onset_path = os.path.join(test_image_path, image_dir[0]) 267 | onset_save_path = os.path.join(test_save_path, image_dir[0]) 268 | 269 | apex_num = int(len(image_dir) / 2) 270 | 271 | apex_path = os.path.join(test_image_path, image_dir[apex_num]) 272 | apex_save_path = os.path.join(test_save_path, image_dir[apex_num]) 273 | 274 | # if not os.path.exists(test_save_path): 275 | # os.makedirs(test_save_path) 276 | 277 | # image_landmark(onset_path, onset_save_path) 278 | # image_landmark(apex_path, apex_save_path) 279 | 280 | with open (DATA_FILE, 'a') as d: 281 | d.write(onset_save_path + '\n') 282 | d.write(apex_save_path + '\n') 283 | 284 | with open (SUBJECT_FILE, 'a') as s: 285 | s.write('Macro\n') 286 | 287 | Flow_pic_path = os.path.join(test_flow_path, 'flow.png') 288 | 289 | # if not os.path.exists(test_flow_path): 290 | # os.makedirs(test_flow_path) 291 | 292 | print(test_label_path) 293 | 294 | crop_pic(onset_path, apex_path, onset_save_path, apex_save_path, Flow_pic_path) 295 | 296 | with open (FLOW_FILE, 'a') as m: 297 | m.write(Flow_pic_path + '\n') 298 | 299 | 300 | def crop_pic(onset_path, apex_path, onset_save_path, apex_save_path, flow_save_path): 301 | detector = dlib.get_frontal_face_detector() 302 | predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat') 303 | 304 | onset_image = cv2.imread(onset_path, 1) 305 | apex_image = cv2.imread(apex_path, 1) 306 | 307 | onset_det = detector(onset_image, 1)[0] 308 | apex_det = detector(apex_image, 1)[0] 309 | 310 | onset_faces = dlib.full_object_detections() 311 | apex_faces = dlib.full_object_detections() 312 | 313 | onset_faces.append(predictor(onset_image, onset_det)) 314 | apex_faces.append(predictor(apex_image, apex_det)) 315 | 316 | onset_crops = dlib.get_face_chips(onset_image, onset_faces, size=320) 317 | apex_crops = dlib.get_face_chips(apex_image, apex_faces, size=320) 318 | 319 | onset_crop = onset_crops[0][:280, 50:270] 320 | apex_crop = apex_crops[0][:280, 50:270] 321 | 322 | onset_cropped = Image.fromarray(cv2.cvtColor(onset_crop, cv2.COLOR_BGR2RGB)) 323 | apex_cropped = Image.fromarray(cv2.cvtColor(apex_crop, cv2.COLOR_BGR2RGB)) 324 | 325 | onset_g = cv2.cvtColor(onset_crops[0], cv2.COLOR_RGB2GRAY) 326 | apex_g = cv2.cvtColor(apex_crops[0], cv2.COLOR_RGB2GRAY) 327 | 328 | pic_size = onset_crops[0].shape 329 | hsv = np.zeros(pic_size) 330 | hsv[:,:,1] = cv2.cvtColor(apex_crops[0], cv2.COLOR_RGB2HSV)[:,:,1] 331 | 332 | flow = cv2.calcOpticalFlowFarneback(onset_g, apex_g, flow=None, 333 | pyr_scale=0.5, levels=1, winsize=15, 334 | iterations=2, 335 | poly_n=5, poly_sigma=1.1, flags=0) 336 | 337 | mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1]) 338 | 339 | hsv[:,:,0] = ang * (180/ np.pi / 2) 340 | hsv[:,:,2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX) 341 | hsv = np.asarray(hsv, dtype=np.float32) 342 | rgb_flow = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB) 343 | rgb_flow = Image.fromarray(rgb_flow.astype('uint8')) 344 | 345 | rgb_flow.save(flow_save_path) 346 | onset_cropped.save(onset_save_path) 347 | apex_cropped.save(apex_save_path) 348 | 349 | def Output_Video(file_path, save_path): 350 | fps = 30 351 | path = file_path 352 | fourcc = cv2.VideoWriter_fourcc(*'DIVX') # ('D','I','V','X') 353 | pic_list = os.listdir(path) 354 | image_pic = cv2.imread(os.path.join(path, pic_list[0])) 355 | shape = image_pic.shape 356 | pic_list = [item.strip('.jpg') for item in pic_list] 357 | pic_list = [item[3:] for item in pic_list] 358 | pic_list = [int(item) for item in pic_list] 359 | pic_list.sort() 360 | shape_video = (shape[1], shape[0]) 361 | videoWriter = cv2.VideoWriter(save_path, fourcc, fps, shape_video) 362 | for pic in pic_list: 363 | pic_name = 'img' + str(pic) + '.jpg' 364 | img12 = cv2.imread(os.path.join(path, pic_name)) 365 | videoWriter.write(img12) 366 | videoWriter.release() 367 | 368 | def Read_Video(video_path, apex_num, onset_save_path, apex_save_path, flow_save_path): 369 | vc = cv2.VideoCapture(video_path) 370 | rval, frame = vc.read() 371 | frames = np.expand_dims(frame, axis=0) 372 | while rval: 373 | rval, frame = vc.read() 374 | if rval: 375 | frame = np.expand_dims(frame, axis=0) 376 | frames = np.append(frames, frame, axis=0) 377 | cv2.waitKey(1) 378 | else: 379 | break 380 | vc.release() 381 | # return frames 382 | 383 | detector = dlib.get_frontal_face_detector() 384 | predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat') 385 | 386 | onset_image = frames[0] 387 | apex_image = frames[apex_num] 388 | 389 | onset_image = cv2.cvtColor(onset_image, cv2.COLOR_RGB2BGR) 390 | apex_image = cv2.cvtColor(apex_image, cv2.COLOR_RGB2BGR) 391 | 392 | onset_det = detector(onset_image, 1)[0] 393 | apex_det = detector(apex_image, 1)[0] 394 | 395 | onset_faces = dlib.full_object_detections() 396 | apex_faces = dlib.full_object_detections() 397 | 398 | onset_faces.append(predictor(onset_image, onset_det)) 399 | apex_faces.append(predictor(apex_image, apex_det)) 400 | 401 | onset_crops = dlib.get_face_chips(onset_image, onset_faces, size=320) 402 | apex_crops = dlib.get_face_chips(apex_image, apex_faces, size=320) 403 | 404 | onset_crop = onset_crops[0][:280, 50:270] 405 | apex_crop = apex_crops[0][:280, 50:270] 406 | 407 | onset_cropped = Image.fromarray(cv2.cvtColor(onset_crop, cv2.COLOR_BGR2RGB)) 408 | apex_cropped = Image.fromarray(cv2.cvtColor(apex_crop, cv2.COLOR_BGR2RGB)) 409 | 410 | onset_g = cv2.cvtColor(onset_crops[0], cv2.COLOR_RGB2GRAY) 411 | apex_g = cv2.cvtColor(apex_crops[0], cv2.COLOR_RGB2GRAY) 412 | 413 | pic_size = onset_crops[0].shape 414 | hsv = np.zeros(pic_size) 415 | hsv[:,:,1] = cv2.cvtColor(apex_crops[0], cv2.COLOR_RGB2HSV)[:,:,1] 416 | 417 | flow = cv2.calcOpticalFlowFarneback(onset_g, apex_g, flow=None, 418 | pyr_scale=0.5, levels=1, winsize=15, 419 | iterations=2, 420 | poly_n=5, poly_sigma=1.1, flags=0) 421 | 422 | mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1]) 423 | 424 | hsv[:,:,0] = ang * (180/ np.pi / 2) 425 | hsv[:,:,2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX) 426 | hsv = np.asarray(hsv, dtype=np.float32) 427 | rgb_flow = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB) 428 | rgb_flow = Image.fromarray(rgb_flow.astype('uint8')) 429 | 430 | rgb_flow.save(flow_save_path) 431 | onset_cropped.save(onset_save_path) 432 | apex_cropped.save(apex_save_path) 433 | 434 | if __name__ == '__main__': 435 | start = time.time() 436 | mix_prepare() 437 | print('cost: ', time.time() - start) 438 | -------------------------------------------------------------------------------- /Train/main.py: -------------------------------------------------------------------------------- 1 | import sys 2 | sys.path.append("../Model/") 3 | sys.path.append("../Utils/") 4 | 5 | import random 6 | from torch.optim.lr_scheduler import ReduceLROnPlateau 7 | from sklearn.metrics import accuracy_score, f1_score, recall_score, confusion_matrix 8 | from torch.autograd import Variable 9 | from sklearn.model_selection import KFold 10 | from torchvision import transforms 11 | from torch.autograd import Function 12 | from torch.utils.data import DataLoader, Dataset 13 | from torch import stack, cuda, nn, optim 14 | from PIL import Image 15 | from Focal_Loss import FocalLoss 16 | from Dataloader_Flow import Flow_loader 17 | from Model_VGGFace import resnet18_pt_mcn, Flow_Part_npic 18 | import argparse 19 | import time 20 | import torch 21 | import dlib 22 | import numpy as np 23 | 24 | 25 | 26 | 27 | random.seed(1) 28 | 29 | 30 | super_para = {"LEARNING_RATE": 0.001, "FOLD": 68, "BATCH_SIZE": 32, "EPOCH": 100, "WEIGHT_DECAY": 0.000001, 31 | 'Clip_Norm': 1, 'Micro': "SAMM, CASME, SMIC", 'Macro': "CK", 32 | 'Sample_File': '../Sample_File/VGG_2.txt', 'Num_Workers': 4} 33 | 34 | LEARNING_RATE = super_para["LEARNING_RATE"] 35 | BATCH_SIZE = super_para["BATCH_SIZE"] 36 | EPOCH = super_para["EPOCH"] 37 | Clip_Norm = super_para['Clip_Norm'] 38 | Sample_File = super_para['Sample_File'] 39 | FOLD = super_para['FOLD'] 40 | Num_Workers = super_para['Num_Workers'] 41 | 42 | WEIGHT_DECAY = super_para["WEIGHT_DECAY"] 43 | 44 | print(super_para) 45 | 46 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) 47 | 48 | 49 | parser = argparse.ArgumentParser() 50 | parser.add_argument('--Micro_image', default='../Mix_data_21.txt') 51 | parser.add_argument('--Micro_label', default='../Mix_label_21.txt') 52 | parser.add_argument('--Micro_subject', default='../Mix_subject_21.txt') 53 | parser.add_argument('--Micro_merge', default='../Mix_flow_21.txt') 54 | parser.add_argument('-E', '--Epoch', default=EPOCH, type=int) 55 | 56 | 57 | class ReverseLayerF(torch.autograd.Function): 58 | def __init__(self, high_value=1.0): 59 | self.iter_num = 0 60 | self.alpha = 10 61 | self.low = 0.0 62 | self.high = high_value 63 | self.max_iter = 10000.0 64 | 65 | def forward(self, input): 66 | self.iter_num += 1 67 | output = input * 1.0 68 | return output 69 | 70 | def backward(self, gradOutput): 71 | self.coeff = np.float(2.0 * (self.high - self.low) / (1.0 + np.exp(-self.alpha * 72 | self.iter_num / self.max_iter)) - (self.high - self.low) + self.low) 73 | return -self.coeff * gradOutput 74 | 75 | 76 | ReverseLayerF = ReverseLayerF() 77 | 78 | 79 | def weight_init(m): 80 | classname = m.__class__.__name__ 81 | if classname.find('Linear') != -1: 82 | nn.init.xavier_normal_(m.weight.data) 83 | nn.init.constant_(m.bias.data, 0.0) 84 | 85 | def Dataset_Split(train_subjects): 86 | 87 | samm = [] 88 | smic = [] 89 | casme2 = [] 90 | 91 | for subject in train_subjects: 92 | if 'samm' in subject: 93 | samm.append(subject) 94 | 95 | elif 'smic' in subject: 96 | smic.append(subject) 97 | 98 | elif 'casme2' in subject: 99 | casme2.append(subject) 100 | 101 | return samm, smic, casme2 102 | 103 | 104 | def train(train_dataloader, model, criterion, optimizer, epoch, print_freq=7): 105 | 106 | model['resnet'].train() 107 | model['fc_top'].train() 108 | model['fc_bottom'].train() 109 | model['classifier'].train() 110 | model['classifier_top'].train() 111 | model['classifier_bottom'].train() 112 | model['discriminator'].train() 113 | 114 | correct = 0 115 | 116 | for i, sample in enumerate(train_dataloader): 117 | input, label, domain_label = sample['image'], sample['label'], sample['domain_label'] 118 | input, label, domain_label = input.cuda(), label.cuda(), domain_label.cuda() 119 | 120 | _, output_resnet_top, output_resnet_bottom = model['resnet'](input) 121 | 122 | output_fc_top = model['fc_top'](output_resnet_top) 123 | output_fc_bottom = model['fc_bottom'](output_resnet_bottom) 124 | 125 | features = torch.cat((output_fc_top, output_fc_bottom), 1) 126 | 127 | reversed_features = ReverseLayerF(features) 128 | 129 | output_domain = model['discriminator'](reversed_features).squeeze() 130 | 131 | output = model['classifier'](features) 132 | output_top = model['classifier_top'](output_fc_top) 133 | output_bottom = model['classifier_bottom'](output_fc_bottom) 134 | 135 | loss_domain = criterion['domain'](output_domain, domain_label.float()) 136 | 137 | loss_label = criterion['label'](output, label) 138 | loss_top = criterion['label'](output_top, label) 139 | loss_bottom = criterion['label'](output_bottom, label) 140 | 141 | loss = loss_label + loss_top + loss_bottom + loss_domain 142 | 143 | _, preds = torch.max(output, dim=1) 144 | 145 | correct = float((label.int() == preds.int()).sum()) 146 | accuracy = correct / len(label) 147 | 148 | optimizer.zero_grad() 149 | loss.backward() 150 | 151 | nn.utils.clip_grad_norm_( 152 | model['resnet'].parameters(), Clip_Norm, norm_type=2) 153 | nn.utils.clip_grad_norm_( 154 | model['fc_top'].parameters(), Clip_Norm, norm_type=2) 155 | nn.utils.clip_grad_norm_( 156 | model['fc_bottom'].parameters(), Clip_Norm, norm_type=2) 157 | nn.utils.clip_grad_norm_( 158 | model['classifier'].parameters(), Clip_Norm, norm_type=2) 159 | nn.utils.clip_grad_norm_( 160 | model['classifier_top'].parameters(), Clip_Norm, norm_type=2) 161 | nn.utils.clip_grad_norm_( 162 | model['classifier_bottom'].parameters(), Clip_Norm, norm_type=2) 163 | 164 | optimizer.step() 165 | 166 | if i % print_freq == 0: 167 | print('Train:\t' 168 | 'Epoch:[{0}][{1}/{2}] \t' 169 | 'Acc: {acc:.3f}\t' 170 | 'Label_Loss: {l_loss:.4f}\t' 171 | 'Top_Loss: {t_loss:.4f}\t' 172 | 'Bottom_Loss: {b_loss:.4f}\t' 173 | 'Domain_Loss: {d_loss:.4f}\t' 174 | 'Loss: {loss:.4f}\t'.format( 175 | epoch, i + 1, len(train_dataloader), acc=accuracy, 176 | l_loss=loss_label, t_loss=loss_top, b_loss=loss_bottom, d_loss=loss_domain, loss=loss)) 177 | 178 | 179 | def validate(validate_dataloader, model, criterion, epoch): 180 | 181 | model['resnet'].eval() 182 | model['fc_top'].eval() 183 | model['fc_bottom'].eval() 184 | model['classifier'].eval() 185 | model['classifier_top'].eval() 186 | model['classifier_bottom'].eval() 187 | 188 | losses = 0 189 | correctes = 0 190 | preds_return = torch.LongTensor([]) 191 | target_return = torch.LongTensor([]) 192 | 193 | sample_file = {} 194 | 195 | with torch.no_grad(): 196 | for sample in validate_dataloader: 197 | input, target, file_name = sample['image'], sample['label'], sample['file_name'] 198 | input, target = input.cuda(), target.cuda() 199 | 200 | _, output_resnet_top, output_resnet_bottom = model['resnet'](input) 201 | output_fc_top = model['fc_top'](output_resnet_top) 202 | output_fc_bottom = model['fc_bottom'](output_resnet_bottom) 203 | 204 | output_model = torch.cat((output_fc_top, output_fc_bottom), 1) 205 | 206 | output = model['classifier'](output_model) 207 | 208 | loss = criterion['label'](output, target) 209 | 210 | _, preds = torch.max(output, dim=1) 211 | 212 | preds_return = torch.cat((preds_return, preds.cpu()), 0) 213 | target_return = torch.cat((target_return, target.cpu()), 0) 214 | 215 | losses += loss 216 | 217 | for f in range(len(file_name[0])): 218 | sample_file[file_name[0][f]] = [ 219 | int(preds[f].item()), int(target[f].item())] 220 | 221 | return preds_return, target_return, losses, sample_file 222 | 223 | 224 | def build_model_2pic(num_classes=3): 225 | 226 | model = Flow_Part_npic(num_pic=2, num_classes=num_classes) 227 | 228 | model_resnet = resnet18_pt_mcn(weights_path='../Model/resnet18_pt_mcn.pth') 229 | pretrained_dict = model_resnet.state_dict() 230 | model_dict = model['resnet'].state_dict() 231 | pretrained_dict = {k: v for k, 232 | v in pretrained_dict.items() if k in model_dict} 233 | model_dict.update(pretrained_dict) 234 | 235 | model['resnet'].load_state_dict(model_dict) 236 | 237 | return model 238 | 239 | 240 | def main(): 241 | args = parser.parse_args() 242 | Micro_data = args.Micro_image 243 | Micro_label = args.Micro_label 244 | Micro_subject = args.Micro_subject 245 | Micro_au = args.Micro_au 246 | Micro_merge = args.Micro_merge 247 | 248 | start_time = time.time() 249 | 250 | transform = transforms.Compose([ 251 | transforms.Resize((256, 256)), 252 | transforms.RandomRotation(10), 253 | transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2), 254 | transforms.RandomCrop((224, 224), pad_if_needed=True), 255 | transforms.ToTensor(), 256 | transforms.Normalize(mean=[0.5, 0.5, 0.5], 257 | std=[0.5, 0.5, 0.5]) 258 | ]) 259 | 260 | with open(Micro_subject, 'r') as s: 261 | subject_origin = s.readlines() 262 | 263 | subject_origin = [subject.strip('\n') for subject in subject_origin] 264 | index_length = len(subject_origin) 265 | subject = set(subject_origin) 266 | subject = list(subject) 267 | subject.remove('Macro') 268 | data_array = np.arange(index_length) 269 | 270 | Fold_accuracy = 0 271 | 272 | TN_Fold = {} 273 | TN_Fold['samm'] = np.zeros(3, dtype=int) 274 | TN_Fold['smic'] = np.zeros(3, dtype=int) 275 | TN_Fold['casme2'] = np.zeros(3, dtype=int) 276 | TN_Fold['total'] = np.zeros(3, dtype=int) 277 | 278 | TP_Fold = {} 279 | TP_Fold['samm'] = np.zeros(3, dtype=int) 280 | TP_Fold['smic'] = np.zeros(3, dtype=int) 281 | TP_Fold['casme2'] = np.zeros(3, dtype=int) 282 | TP_Fold['total'] = np.zeros(3, dtype=int) 283 | 284 | FP_Fold = {} 285 | FP_Fold['samm'] = np.zeros(3, dtype=int) 286 | FP_Fold['smic'] = np.zeros(3, dtype=int) 287 | FP_Fold['casme2'] = np.zeros(3, dtype=int) 288 | FP_Fold['total'] = np.zeros(3, dtype=int) 289 | 290 | FN_Fold = {} 291 | FN_Fold['samm'] = np.zeros(3, dtype=int) 292 | FN_Fold['smic'] = np.zeros(3, dtype=int) 293 | FN_Fold['casme2'] = np.zeros(3, dtype=int) 294 | FN_Fold['total'] = np.zeros(3, dtype=int) 295 | 296 | kfold = KFold(FOLD, shuffle=True, random_state=10) 297 | for i, (train_index, test_index) in enumerate(kfold.split(subject)): 298 | print('Fold: ', i) 299 | 300 | train_subjects = [subject[i] for i in train_index] 301 | train_subjects.append('Macro') 302 | test_subjects = [subject[i] for i in test_index] 303 | 304 | print(test_subjects) 305 | with open(Sample_File, 'a') as S: 306 | S.writelines(str(test_subjects[0]) + '\n') 307 | 308 | train_index = [data_array[index] for index in range( 309 | index_length) if subject_origin[index] in train_subjects] 310 | test_index = [data_array[index] for index in range( 311 | index_length) if subject_origin[index] in test_subjects] 312 | 313 | train_dataset = Flow_loader( 314 | Micro_merge, Micro_label, Micro_subject, train_index, transform=transform) 315 | test_dataset = Flow_loader( 316 | Micro_merge, Micro_label, Micro_subject, test_index, transform=transform) 317 | 318 | train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, 319 | shuffle=True, pin_memory=True, num_workers=Num_Workers) 320 | test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, 321 | shuffle=True, pin_memory=True, num_workers=Num_Workers) 322 | 323 | model = build_model_2pic() 324 | 325 | model['discriminator'] = nn.Sequential( 326 | nn.Linear(128, 64), 327 | nn.ReLU(True), 328 | nn.Dropout(), 329 | nn.Linear(64, 32), 330 | nn.ReLU(True), 331 | nn.Dropout(), 332 | nn.Linear(32, 1) 333 | ) 334 | 335 | model['resnet'] = model['resnet'].cuda() 336 | model['fc_top'] = model['fc_top'].cuda() 337 | model['fc_bottom'] = model['fc_bottom'].cuda() 338 | model['fc_top'] = model['fc_top'].apply(weight_init) 339 | model['fc_bottom'] = model['fc_bottom'].apply(weight_init) 340 | 341 | model['classifier'] = model['classifier'].cuda() 342 | model['classifier_top'] = model['classifier_top'].cuda() 343 | model['classifier_bottom'] = model['classifier_bottom'].cuda() 344 | model['discriminator'] = model['discriminator'].cuda() 345 | 346 | model['classifier'] = model['classifier'].apply(weight_init) 347 | model['classifier_top'] = model['classifier_top'].apply(weight_init) 348 | model['classifier_bottom'] = model['classifier_bottom'].apply( 349 | weight_init) 350 | model['discriminator'] = model['discriminator'].apply(weight_init) 351 | 352 | criterion = {} 353 | criterion['label'] = torch.nn.CrossEntropyLoss() 354 | criterion['domain'] = torch.nn.BCEWithLogitsLoss() 355 | 356 | optimizer = optim.Adam([ 357 | {'params': model['resnet'].parameters(), 'lr':0.00001}, 358 | {'params': model['fc_top'].parameters(), 'lr':LEARNING_RATE}, 359 | {'params': model['fc_bottom'].parameters(), 'lr':LEARNING_RATE}, 360 | {'params': model['classifier'].parameters(), 'lr':LEARNING_RATE}, 361 | {'params': model['classifier_top'].parameters(), 362 | 'lr':LEARNING_RATE}, 363 | {'params': model['classifier_bottom'].parameters(), 364 | 'lr':LEARNING_RATE}, 365 | {'params':model['discriminator'].parameters(), 'lr':LEARNING_RATE}, 366 | ], weight_decay=WEIGHT_DECAY) 367 | # 0.65, 0.9 368 | 369 | Epoch_accuracy = 0 370 | Epoch_F1_score = 0 371 | Epoch_Recall = 0 372 | 373 | 374 | for epoch in range(args.Epoch): 375 | if epoch % 10 == 0 and epoch != 0: 376 | if optimizer.param_groups[2]['lr'] > 0.00001: 377 | optimizer.param_groups[1]['lr'] = optimizer.param_groups[1]['lr'] * 0.5 378 | optimizer.param_groups[2]['lr'] = optimizer.param_groups[2]['lr'] * 0.5 379 | optimizer.param_groups[3]['lr'] = optimizer.param_groups[3]['lr'] * 0.5 380 | optimizer.param_groups[4]['lr'] = optimizer.param_groups[4]['lr'] * 0.5 381 | optimizer.param_groups[5]['lr'] = optimizer.param_groups[5]['lr'] * 0.5 382 | optimizer.param_groups[6]['lr'] = optimizer.param_groups[6]['lr'] * 0.5 383 | 384 | train(train_loader, model, criterion, optimizer, epoch) 385 | preds, target, loss, temp_file = validate(test_loader, model, criterion, epoch) 386 | 387 | accuracy = accuracy_score(target, preds) 388 | matrix = confusion_matrix(target, preds, labels=[0, 1, 2]) 389 | FP = matrix.sum(axis=0) - np.diag(matrix) 390 | FN = matrix.sum(axis=1) - np.diag(matrix) 391 | TP = np.diag(matrix) 392 | TN = matrix.sum() - (FP + FN + TP) 393 | 394 | f1_s = np.ones([3]) 395 | deno = (2 * TP + FP + FN) 396 | for f in range(3): 397 | if deno[f] != 0: 398 | f1_s[f] = (2 * TP[f]) / (2 * TP[f] + FP[f] + FN[f]) 399 | else: 400 | f1_s[f] = 1 401 | 402 | f1 = np.mean(f1_s) 403 | 404 | losses = loss / len(test_index) 405 | 406 | if f1 >= Epoch_F1_score: 407 | Epoch_F1_score = f1 408 | Epoch_accuracy = accuracy 409 | FP_Epoch = FP 410 | FN_Epoch = FN 411 | TP_Epoch = TP 412 | TN_Epoch = TN 413 | sample_file = temp_file 414 | 415 | print( 416 | 'Validation:\t' 417 | 'Acc: {acc:.3f}\t' 418 | 'F1_Score: {f1_s:.3f} \t' 419 | 'Loss: {loss:.3f}\t' 420 | 'Best F1: {best_f1:.3f}'.format( 421 | acc=accuracy, f1_s=f1, loss=losses, best_f1=Epoch_F1_score)) 422 | 423 | with open(Sample_File, 'a') as S: 424 | for key in sample_file: 425 | S.writelines(str( 426 | key) + ' \t' + str(sample_file[key][0]) + '\t' + str(sample_file[key][1]) + '\n') 427 | 428 | if 'samm' in test_subjects[0]: 429 | TP_Fold['samm'] += TP_Epoch 430 | TN_Fold['samm'] += TN_Epoch 431 | FN_Fold['samm'] += FN_Epoch 432 | FP_Fold['samm'] += FP_Epoch 433 | elif 'smic' in test_subjects[0]: 434 | TP_Fold['smic'] += TP_Epoch 435 | TN_Fold['smic'] += TN_Epoch 436 | FN_Fold['smic'] += FN_Epoch 437 | FP_Fold['smic'] += FP_Epoch 438 | elif 'casme2' in test_subjects[0]: 439 | TP_Fold['casme2'] += TP_Epoch 440 | TN_Fold['casme2'] += TN_Epoch 441 | FN_Fold['casme2'] += FN_Epoch 442 | FP_Fold['casme2'] += FP_Epoch 443 | 444 | TP_Fold['total'] += TP_Epoch 445 | TN_Fold['total'] += TN_Epoch 446 | FN_Fold['total'] += FN_Epoch 447 | FP_Fold['total'] += FP_Epoch 448 | 449 | Fold_accuracy += Epoch_accuracy 450 | 451 | f1 = np.ones([3]) 452 | deno = (2 * TP_Fold['total'] + FP_Fold['total'] + FN_Fold['total']) 453 | for f in range(3): 454 | if deno[f] != 0: 455 | f1[f] = (2 * TP_Fold['total'][f]) / deno[f] 456 | else: 457 | f1[f] = 1 458 | 459 | current_f1 = np.mean(f1) 460 | 461 | print( 462 | 'Runned Subject:\t' 463 | 'F1 Score: {cur_f1:.3f}\t'.format( 464 | cur_f1=current_f1 465 | ) 466 | ) 467 | 468 | F1_Score = {} 469 | F1_Score['samm'] = (2 * TP_Fold['samm']) / (2 * TP_Fold['samm'] + FP_Fold['samm'] + FN_Fold['samm']) 470 | F1_Score['smic'] = (2 * TP_Fold['smic']) / (2 * TP_Fold['smic'] + FP_Fold['smic'] + FN_Fold['smic']) 471 | F1_Score['casme2'] = (2 * TP_Fold['casme2']) / (2 * TP_Fold['casme2'] + FP_Fold['casme2'] + FN_Fold['casme2']) 472 | F1_Score['total'] = (2 * TP_Fold['total']) / (2 * TP_Fold['total'] + FP_Fold['total'] + FN_Fold['total']) 473 | 474 | Recall_Score = {} 475 | Recall_Score['samm'] = TP_Fold['samm'] / (TP_Fold['samm'] + FN_Fold['samm']) 476 | Recall_Score['smic'] = TP_Fold['smic'] / (TP_Fold['smic'] + FN_Fold['smic']) 477 | Recall_Score['casme2'] = TP_Fold['casme2'] / (TP_Fold['casme2'] + FN_Fold['casme2']) 478 | Recall_Score['total'] = TP_Fold['total'] / (TP_Fold['total'] + FN_Fold['total']) 479 | 480 | Total_accuracy = Fold_accuracy / FOLD 481 | 482 | Total_F1_Score = {} 483 | Total_F1_Score['samm'] = np.mean(F1_Score['samm']) 484 | Total_F1_Score['smic'] = np.mean(F1_Score['smic']) 485 | Total_F1_Score['casme2'] = np.mean(F1_Score['casme2']) 486 | Total_F1_Score['total'] = np.mean(F1_Score['total']) 487 | 488 | Total_Recall = {} 489 | Total_Recall['samm'] = np.mean(Recall_Score['samm']) 490 | Total_Recall['smic'] = np.mean(Recall_Score['smic']) 491 | Total_Recall['casme2'] = np.mean(Recall_Score['casme2']) 492 | Total_Recall['total'] = np.mean(Recall_Score['total']) 493 | 494 | print('Fold accuracy: ', Total_accuracy) 495 | 496 | print('Fold F1 score SAMM: ', Total_F1_Score['samm']) 497 | print('Fold F1 score SMIC: ', Total_F1_Score['smic']) 498 | print('Fold F1 score CASMEII: ', Total_F1_Score['casme2']) 499 | print('Fold F1 score: ', Total_F1_Score['total']) 500 | 501 | print('Fold Recall score SAMM: ', Total_Recall['samm']) 502 | print('Fold Recall score SMIC: ', Total_Recall['smic']) 503 | print('Fold Recall score CASMEII: ', Total_Recall['casme2']) 504 | print('Fold Recall score: ', Total_Recall['total']) 505 | 506 | end_time = time.time() 507 | print('Total cost time: ', end_time - start_time) 508 | 509 | 510 | if __name__ == '__main__': 511 | main() 512 | -------------------------------------------------------------------------------- /.mypy_cache/3.6/ast.data.json: -------------------------------------------------------------------------------- 1 | {".class": "MypyFile", "_fullname": "ast", "is_partial_stub_package": false, "is_stub": true, "names": 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