Kaggle competition: Bengali.AI Handwritten Grapheme Classification
<SOS> metric: WeightedCategorizationAccuracy Kaggle data source: bengali.ai-handwritten-grapheme-classification<predict_on_test>
!pip install.. /input/kaggle-efficientnet-repo/efficientnet-1.0.0-py3-none-any.whl
<predict_on_test><EOS> 
def normalize(image):
image -= tf.constant([0.485 * 255, 0.456 * 255, 0.406 * 255])
 image /= tf.constant([0.229 * 255, 0.224 * 255, 0.225 * 255])
 return image
def get_model(input_size, backbone='efficientnet-b0', weights='imagenet', tta=False):
print(f'Using backbone {backbone} and weights {weights}')
 x = L.Input(shape=input_size, name='imgs', dtype='float32')
 y = normalize(x)
 if backbone.startswith('efficientnet'):
model_fn = getattr(efn, f'EfficientNetB{backbone[-1]}')
 y = model_fn(input_shape=input_size, weights=weights, include_top=False )(y)
 y = L.GlobalAveragePooling2D()(y)
 y = L.Dropout(0.2 )(y)
 y = L.Dense(1292, activation='softmax' )(y)
 model = tf.keras.Model(x, y)
 if tta:
assert False, 'This does not make sense yet'
x_flip = tf.reverse(x, [2])
 y_tta = tf.add(model(x), model(x_flip)) / 2.0
tta_model = tf.keras.Model(x, y_tta)
 return model, tta_model
return model
def normalize_image(img, org_width, org_height, new_width, new_height):
img = 255 - img
img =(img *(255.0 / img.max())).astype(np.uint8)
 img = img.reshape(org_height, org_width)
 image_resized = cv2.resize(img,(new_width, new_height), interpolation=cv2.INTER_LINEAR)
 return image_resized
def dump_images(args, org_width, org_height, new_width, new_height):
labels = pd.read_csv(args.labels)
 iids = labels['image_id']
root = labels['grapheme_root']
vowel = labels['vowel_diacritic']
consonant = labels['consonant_diacritic']
labels = {a:(b, c, d)for a, b, c, d in zip(iids, root, vowel, consonant)}
tuples = sorted(set(labels.values()))
tuples_to_int = {v: k for k, v in enumerate(tuples)}
print(f'Got {len(tuples)} unique combinations')
 for i in tqdm(range(0, 4)) :
df = pd.read_parquet(args.data_template % i)
 image_ids = df['image_id'].values
df = df.drop(['image_id'], axis=1)
 for image_id, index in tqdm(zip(image_ids, range(df.shape[0])) , total=df.shape[0]):
normalized = normalize_image(df.loc[df.index[index]].values,
org_width, org_height, new_width, new_height)
 r, v, c = labels[image_id]
tuple_int = tuples_to_int[(r, v, c)]
out_fn = os.path.join(args.image_dir, f'{image_id}_rt_{r}_vl_{v}_ct_{c}_ti_{tuple_int}.png')
 cv2.imwrite(out_fn, normalized)
 def decode_predictions(y_pred, inv_tuple_map):
y_argmax = np.argmax(y_pred, -1)
 decoded = []
for yy in y_argmax:
decoded.append(inv_tuple_map[int(yy)])
 return decoded
def decode_predictions_v2(y_pred, inv_tuple_map):
rr = np.zeros(( len(y_pred), 168), dtype=np.float32)
 vv = np.zeros(( len(y_pred), 11), dtype=np.float32)
 cc = np.zeros(( len(y_pred), 7), dtype=np.float32)
 for ti in range(y_pred.shape[1]):
r_index, v_index, c_index = inv_tuple_map[ti]
y_pred_ti = y_pred[:, ti]
rr[:, r_index] += y_pred_ti
vv[:, v_index] += y_pred_ti
cc[:, c_index] += y_pred_ti
rr = rr.argmax(-1)
 vv = vv.argmax(-1)
 cc = cc.argmax(-1)
 decoded = []
for k in range(len(y_pred)) :
decoded.append(( int(rr[k]), int(vv[k]), int(cc[k])))
 return decoded
def process_batch(image_id_batch, img_batch, row_id, target, model, inv_tuple_map):
img_batch = np.float32(img_batch)
 if img_batch.ndim != 4:
img_batch = np.expand_dims(img_batch, 0)
 y_pred = model.predict(img_batch)
 decoded = decode_predictions_v2(y_pred, inv_tuple_map)
 for iid, dd in zip(image_id_batch, decoded):
row_id.append(iid + '_grapheme_root')
 target.append(dd[0])
 row_id.append(iid + '_vowel_diacritic')
 target.append(dd[1])
 row_id.append(iid + '_consonant_diacritic')
 target.append(dd[2])
 def main() :
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=123)
 parser.add_argument('--input_size', type=str, default='224,224')
 parser.add_argument('--batch_size', type=int, default=64)
 parser.add_argument('--backbone', type=str, default='efficientnet-b3')
 parser.add_argument('--weights', type=str, default='.. /input/bengali-cnn-1/model-0000.h5')
 args, _ = parser.parse_known_args()
org_height = 137
org_width = 236
tuple_map = {(15, 9, 5): 64,(72, 4, 0): 522,(18, 0, 0): 76,(62, 7, 0): 418,(18, 7, 4): 84,(136, 0, 2): 1064,(43, 0, 0): 261,(149, 7, 5): 1184,(165, 7, 0): 1279,(86, 1, 0): 653,(17, 2, 0): 73,(115, 2, 0): 892,(139, 3, 0): 1088,(142, 3, 0): 1117,(23, 10, 0): 149,(150, 1, 0): 1189,(13, 0, 4): 21,(96, 7, 5): 744,(130, 0, 0): 1016,(133, 2, 0): 1035,(60, 1, 0): 407,(81, 4, 4): 615,(64, 4, 0): 437,(107, 0, 0): 801,(115, 9, 2): 907,(125, 1, 0): 993,(25, 2, 0): 155,(153, 2, 5): 1219,(153, 1, 0): 1215,(149, 9, 0): 1185,(103, 1, 1): 776,(148, 9, 0): 1170,(37, 2, 0): 215,(139, 0, 2): 1081,(74, 2, 0): 546,(86, 0, 5): 652,(83, 9, 5): 636,(155, 3, 0): 1232,(133, 4, 4): 1043,(70, 0, 0): 480,(135, 7, 0): 1061,(38, 1, 0): 219,(28, 9, 0): 166,(150, 4, 0): 1195,(81, 5, 4): 617,(59, 2, 0): 397,(108, 2, 0): 835,(69, 3, 0): 478,(140, 7, 0): 1100,(129, 1, 0): 1011,(31, 1, 0): 189,(118, 0, 0): 922,(96, 4, 1): 737,(98, 7, 0): 757,(32, 3, 0): 196,(43, 0, 2): 262,(123, 1, 0): 966,(157, 9, 0): 1241,(72, 6, 2): 529,(89, 2, 5): 675,(8, 0, 0): 12,(94, 0, 0): 709,(113, 0, 4): 857,(72, 0, 4): 507,(163, 1, 0): 1272,(151, 0, 0): 1202,(139, 7, 2): 1092,(79, 8, 0): 598,(144, 7, 0): 1127,(160, 0, 0): 1264,(13, 2, 0): 29,(29, 1, 4): 174,(22, 7, 0): 111,(72, 4, 4): 524,(57, 0, 0): 379,(111, 1, 0): 846,(149, 1, 5): 1176,(127, 4, 0): 1001,(162, 1, 0): 1270,(42, 9, 1): 260,(83, 2, 0): 631,(66, 2, 0): 465,(147, 2, 1): 1142,(97, 1, 0): 752,(53, 1, 0): 327,(120, 0, 0): 939,(167, 7, 0): 1290,(81, 10, 0): 625,(70, 1, 0): 482,(91, 2, 0): 693,(23, 2, 2): 127,(81, 5, 0): 616,(110, 0, 0): 842,(13, 9, 0): 49,(50, 2, 0): 312,(113, 4, 4): 869,(140, 2, 5): 1097,(147, 1, 0): 1136,(64, 6, 2): 443,(66, 0, 0): 463,(123, 0, 0): 965,(159, 2, 2): 1252,(79, 2, 4): 588,(88, 3, 0): 667,(53, 0, 0): 323,(124, 1, 0): 981,(143, 7, 0): 1123,(86, 7, 5): 661,(29, 1, 1): 172,(134, 2, 0): 1057,(70, 0, 4): 481,(133, 1, 0): 1031,(71, 2, 5): 495,(156, 2, 0): 1237,(107, 1, 6): 811,(29, 9, 0): 184,(147, 8, 0): 1155,(117, 6, 0): 919,(115, 7, 0): 902,(64, 0, 5): 423,(71, 1, 2): 491,(147, 4, 4): 1147,(123, 5, 0): 972,(44, 1, 0): 285,(89, 0, 0): 669,(94, 9, 0): 714,(133, 3, 0): 1039,(81, 3, 0): 612,(128, 9, 0): 1009,(16, 9, 0): 70,(23, 0, 4): 118,(21, 0, 2): 92,(98, 0, 0): 754,(15, 0, 0): 58,(72, 3, 2): 520,(76, 8, 0): 569,(96, 2, 1): 731,(132, 9, 0): 1026,(146, 7, 0): 1131,(18, 7, 0): 83,(59, 0, 0): 391,(61, 0, 0): 409,(51, 2, 0): 316,(92, 3, 0): 702,(133, 3, 5): 1041,(165, 2, 0): 1276,(81, 0, 2): 605,(115, 5, 0): 899,(112, 1, 4): 851,(89, 7, 0): 679,(147, 6, 0): 1150,(113, 4, 0): 867,(103, 7, 0): 788,(107, 1, 5): 810,(43, 4, 2): 275,(107, 2, 2): 813,(64, 7, 1): 445,(10, 0, 0): 16,(42, 1, 4): 253,(72, 1, 2): 512,(115, 3, 2): 895,(107, 8, 0): 830,(76, 9, 0): 570,(129, 7, 0): 1014,(95, 7, 0): 719,(122, 9, 0): 963,(141, 0, 5): 1103,(92, 1, 0): 699,(107, 4, 2): 819,(20, 3, 0): 90,(113, 5, 4): 873,(72, 4, 2): 523,(86, 9, 0): 662,(74, 0, 0): 542,(122, 7, 0): 960,(120, 7, 0): 945,(96, 0, 5): 724,(147, 1, 1): 1137,(137, 0, 0): 1070,(151, 2, 0): 1206,(29, 2, 0): 176,(151, 7, 4): 1209,(165, 4, 0): 1278,(103, 1, 0): 775,(90, 7, 0): 687,(123, 1, 4): 968,(147, 0, 4): 1134,(167, 9, 0): 1291,(36, 9, 0): 213,(153, 2, 0): 1218,(67, 0, 0): 468,(23, 8, 0): 144,(25, 0, 0): 152,(157, 1, 0): 1240,(54, 2, 0): 349,(56, 0, 5): 364,(159, 5, 0): 1257,(86, 1, 5): 654,(107, 7, 1): 826,(43, 0, 5): 265,(81, 1, 4): 609,(123, 9, 0): 975,(64, 2, 2): 431,(79, 7, 2): 596,(142, 7, 0): 1119,(93, 0, 0): 705,(147, 1, 5): 1140,(133, 7, 5): 1050,(156, 6, 0): 1239,(32, 0, 0): 193,(23, 2, 0): 126,(132, 1, 0): 1021,(51, 0, 0): 315,(159, 1, 0): 1247,(62, 3, 0): 416,(133, 0, 2): 1028,(136, 7, 0): 1068,(148, 0, 0): 1160,(22, 0, 0): 97,(71, 0, 4): 489,(132, 3, 0): 1023,(150, 9, 5): 1201,(43, 8, 4): 281,(101, 9, 0): 770,(36, 7, 0): 212,(96, 4, 5): 738,(79, 4, 0): 590,(72, 0, 2): 506,(31, 0, 0): 188,(3, 0, 1): 5,(68, 3, 0): 473,(118, 3, 0): 925,(55, 9, 1): 361,(150, 3, 0): 1193,(43, 9, 4): 283,(103, 9, 0): 790,(93, 7, 0): 708,(137, 7, 0): 1073,(98, 2, 0): 756,(13, 5, 5): 41,(113, 2, 0): 864,(50, 1, 0): 311,(113, 5, 0): 871,(125, 7, 0): 996,(55, 0, 0): 351,(43, 2, 2): 270,(138, 0, 0): 1074,(71, 9, 0): 503,(96, 0, 2): 722,(23, 7, 2): 141,(154, 9, 0): 1228,(153, 1, 4): 1216,(29, 4, 0): 177,(9, 0, 0): 13,(91, 4, 0): 695,(64, 2, 0): 430,(40, 7, 0): 245,(28, 1, 0): 162,(38, 4, 4): 227,(141, 7, 4): 1111,(42, 1, 2): 252,(74, 3, 0): 547,(64, 1, 0): 425,(142, 0, 0): 1113,(167, 5, 0): 1289,(91, 7, 4): 697,(107, 7, 0): 825,(152, 1, 0): 1210,(75, 7, 2): 558,(85, 3, 0): 644,(143, 0, 0): 1120,(53, 1, 2): 328,(115, 5, 2): 900,(96, 1, 6): 729,(23, 9, 1): 146,(150, 7, 0): 1198,(124, 7, 0): 988,(96, 9, 5): 748,(81, 2, 0): 610,(107, 4, 5): 821,(76, 2, 0): 563,(29, 1, 0): 171,(96, 2, 0): 730,(117, 2, 0): 915,(147, 9, 4): 1157,(140, 6, 0): 1099,(113, 4, 2): 868,(139, 2, 2): 1087,(117, 4, 0): 917,(165, 1, 0): 1275,(72, 9, 5): 538,(79, 0, 4): 581,(30, 7, 0): 187,(147, 10, 0): 1159,(31, 4, 0): 191,(138, 2, 0): 1075,(13, 9, 2): 50,(83, 2, 5): 632,(59, 7, 4): 404,(115, 4, 4): 898,(22, 0, 4): 99,(71, 0, 0): 486,(107, 7, 4): 828,(121, 1, 0): 948,(113, 7, 4): 878,(133, 7, 4): 1049,(79, 4, 5): 592,(55, 1, 4): 355,(150, 7, 5): 1199,(165, 3, 0): 1277,(55, 3, 0): 357,(88, 0, 0): 664,(109, 0, 0): 836,(35, 0, 0): 204,(147, 0, 2): 1133,(60, 0, 0): 406,(96, 9, 0): 746,(48, 1, 1): 302,(71, 7, 4): 501,(56, 2, 2): 370,(110, 2, 0): 843,(13, 4, 1): 36,(150, 0, 0): 1187,(103, 1, 4): 777,(35, 1, 0): 205,(24, 0, 0): 150,(27, 0, 0): 160,(145, 9, 0): 1129,(42, 7, 0): 258,(79, 2, 0): 586,(68, 7, 0): 474,(115, 1, 0): 888,(133, 2, 4): 1037,(156, 3, 0): 1238,(60, 4, 0): 408,(25, 1, 4): 154,(53, 2, 0): 332,(22, 8, 0): 113,(29, 1, 5): 175,(133, 5, 0): 1045,(56, 7, 5): 376,(85, 2, 0): 643,(64, 7, 2): 446,(132, 4, 0): 1024,(88, 2, 0): 666,(113, 9, 0): 880,(85, 0, 0): 640,(147, 3, 2): 1145,(115, 0, 0): 884,(22, 9, 1): 115,(67, 1, 0): 469,(161, 0, 0): 1267,(59, 0, 4): 393,(139, 7, 4): 1093,(48, 0, 2): 300,(13, 4, 2): 37,(22, 6, 0): 110,(69, 2, 0): 477,(119, 1, 5): 932,(17, 9, 0): 75,(126, 4, 0): 997,(115, 7, 4): 904,(128, 7, 0): 1008,(72, 1, 0): 510,(72, 5, 5): 527,(89, 1, 5): 673,(64, 0, 2): 421,(149, 9, 5): 1186,(133, 3, 2): 1040,(118, 4, 0): 926,(142, 0, 4): 1114,(131, 2, 0): 1018,(128, 1, 0): 1006,(18, 3, 0): 81,(71, 2, 0): 493,(64, 0, 6): 424,(13, 7, 1): 44,(149, 3, 0): 1180,(72, 2, 2): 516,(79, 10, 0): 600,(13, 1, 1): 24,(103, 4, 5): 787,(147, 1, 4): 1139,(135, 9, 0): 1062,(13, 10, 0): 52,(71, 8, 0): 502,(81, 8, 0): 622,(166, 0, 0): 1281,(159, 0, 2): 1244,(52, 1, 0): 318,(72, 0, 0): 505,(81, 9, 0): 623,(79, 5, 0): 593,(124, 3, 0): 986,(150, 2, 0): 1191,(133, 7, 0): 1047,(80, 0, 2): 602,(140, 1, 0): 1096,(39, 1, 0): 238,(109, 7, 0): 841,(48, 9, 0): 307,(38, 7, 4): 233,(17, 7, 0): 74,(27, 2, 0): 161,(151, 7, 0): 1208,(58, 10, 0): 390,(113, 4, 5): 870,(92, 7, 0): 704,(127, 9, 0): 1003,(117, 1, 0): 914,(111, 0, 2): 845,(154, 5, 0): 1227,(106, 0, 0): 796,(123, 3, 0): 970,(64, 0, 4): 422,(48, 4, 0): 304,(74, 4, 0): 548,(23, 9, 0): 145,(71, 7, 0): 499,(13, 4, 5): 39,(72, 2, 0): 515,(115, 9, 0): 906,(72, 9, 0): 535,(22, 4, 1): 109,(86, 3, 5): 658,(139, 9, 0): 1094,(140, 0, 0): 1095,(76, 7, 0): 568,(53, 4, 5): 339,(24, 1, 0): 151,(145, 0, 0): 1128,(143, 1, 0): 1121,(132, 2, 0): 1022,(56, 1, 4): 367,(91, 3, 0): 694,(38, 7, 0): 230,(64, 4, 4): 439,(116, 1, 0): 910,(65, 0, 4): 456,(128, 0, 0): 1004,(122, 7, 2): 961,(75, 7, 0): 557,(38, 1, 2): 221,(113, 5, 2): 872,(23, 1, 6): 125,(133, 1, 4): 1033,(127, 2, 0): 1000,(72, 2, 4): 517,(136, 2, 0): 1066,(47, 2, 0): 298,(103, 9, 5): 792,(25, 1, 0): 153,(119, 3, 0): 934,(106, 1, 0): 797,(79, 1, 2): 584,(151, 5, 0): 1207,(119, 7, 0): 937,(65, 3, 0): 459,(75, 1, 0): 554,(122, 0, 2): 951,(67, 7, 0): 470,(94, 7, 0): 713,(89, 4, 0): 678,(43, 1, 0): 266,(72, 0, 5): 508,(159, 0, 4): 1245,(154, 3, 0): 1225,(161, 1, 0): 1268,(123, 1, 1): 967,(64, 8, 5): 450,(84, 7, 0): 639,(107, 1, 0): 806,(58, 0, 4): 384,(112, 7, 0): 854,(81, 2, 2): 611,(114, 1, 0): 883,(32, 4, 0): 197,(96, 0, 1): 721,(149, 4, 0): 1182,(53, 7, 4): 343,(120, 2, 0): 942,(55, 0, 4): 352,(152, 7, 0): 1212,(81, 7, 2): 620,(46, 1, 0): 293,(96, 6, 0): 740,(79, 1, 0): 582,(160, 2, 0): 1265,(159, 2, 0): 1251,(149, 0, 0): 1171,(107, 2, 4): 814,(152, 2, 0): 1211,(56, 0, 2): 363,(112, 0, 0): 849,(13, 7, 0): 43,(91, 1, 4): 692,(141, 1, 4): 1105,(133, 9, 0): 1052,(53, 1, 5): 330,(102, 0, 0): 771,(56, 3, 0): 372,(96, 3, 5): 735,(89, 0, 5): 671,(65, 1, 0): 457,(148, 1, 4): 1163,(88, 7, 0): 668,(147, 9, 0): 1156,(148, 1, 0): 1162,(59, 1, 2): 395,(118, 2, 0): 924,(153, 1, 5): 1217,(98, 1, 0): 755,(13, 0, 0): 19,(79, 3, 0): 589,(80, 7, 2): 603,(103, 4, 1): 786,(137, 2, 0): 1071,(54, 0, 0): 347,(119, 9, 0): 938,(162, 3, 0): 1271,(13, 0, 2): 20,(59, 7, 2): 403,(92, 2, 0): 701,(113, 10, 0): 882,(107, 2, 0): 812,(107, 4, 4): 820,(139, 0, 0): 1080,(76, 4, 0): 566,(38, 8, 0): 234,(147, 4, 0): 1146,(23, 7, 0): 139,(71, 3, 2): 497,(29, 5, 0): 179,(90, 3, 0): 686,(66, 3, 0): 466,(159, 6, 0): 1258,(113, 5, 5): 874,(117, 7, 0): 920,(128, 2, 0): 1007,(42, 0, 2): 249,(159, 8, 0): 1262,(123, 10, 0): 977,(94, 4, 0): 712,(55, 2, 0): 356,(85, 1, 0): 642,(64, 7, 0): 444,(43, 9, 0): 282,(72, 8, 0): 534,(23, 1, 1): 121,(38, 9, 0): 235,(103, 3, 0): 783,(150, 3, 5): 1194,(144, 0, 0): 1124,(1, 0, 0): 1,(72, 6, 0): 528,(61, 2, 0): 411,(96, 7, 2): 743,(42, 2, 0): 254,(83, 1, 5): 630,(64, 0, 0): 420,(71, 3, 0): 496,(44, 9, 0): 290,(147, 1, 2): 1138,(150, 5, 0): 1196,(116, 3, 0): 911,(13, 1, 4): 26,(25, 3, 0): 156,(153, 0, 0): 1214,(81, 0, 0): 604,(111, 4, 0): 848,(23, 2, 4): 128,(165, 0, 0): 1274,(89, 7, 5): 681,(32, 6, 0): 198,(122, 4, 0): 958,(107, 0, 2): 802,(48, 2, 0): 303,(23, 7, 5): 143,(96, 2, 5): 733,(79, 2, 2): 587,(81, 1, 2): 608,(22, 1, 1): 101,(22, 1, 2): 102,(91, 0, 0): 688,(86, 7, 0): 660,(42, 2, 1): 255,(96, 1, 1): 726,(77, 2, 0): 574,(71, 7, 2): 500,(44, 4, 0): 288,(149, 1, 0): 1174,(77, 5, 0): 576,(33, 2, 0): 201,(23, 1, 5): 124,(40, 0, 0): 241,(52, 7, 0): 322,(13, 2, 5): 31,(57, 1, 0): 380,(107, 6, 0): 823,(73, 1, 0): 541,(32, 2, 0): 195,(139, 7, 0): 1091,(55, 4, 0): 358,(124, 2, 0): 984,(23, 4, 0): 133,(113, 1, 1): 860,(148, 2, 0): 1164,(28, 1, 4): 163,(86, 3, 0): 657,(86, 0, 0): 649,(119, 5, 0): 936,(90, 0, 0): 684,(48, 4, 1): 305,(23, 1, 2): 122,(124, 0, 2): 979,(75, 0, 2): 553,(71, 9, 5): 504,(40, 2, 0): 243,(113, 1, 4): 862,(133, 4, 5): 1044,(23, 4, 2): 135,(81, 4, 0): 614,(124, 2, 2): 985,(13, 3, 0): 32,(76, 0, 2): 560,(101, 1, 4): 766,(107, 0, 5): 805,(9, 0, 1): 14,(77, 1, 0): 572,(142, 2, 0): 1116,(113, 7, 2): 877,(77, 4, 0): 575,(151, 1, 0): 1204,(117, 0, 5): 913,(159, 0, 0): 1243,(48, 7, 0): 306,(53, 7, 2): 342,(107, 3, 5): 817,(64, 3, 0): 434,(86, 0, 4): 651,(99, 0, 0): 758,(139, 1, 4): 1085,(65, 3, 2): 460,(23, 4, 5): 136,(85, 7, 0): 647,(129, 0, 0): 1010,(112, 4, 0): 853,(72, 1, 1): 511,(79, 1, 4): 585,(120, 4, 0): 944,(115, 8, 0): 905,(43, 4, 4): 276,(124, 9, 0): 990,(43, 4, 0): 274,(150, 6, 0): 1197,(81, 7, 0): 619,(83, 7, 0): 634,(16, 6, 0): 68,(159, 7, 0): 1259,(75, 0, 0): 552,(147, 2, 0): 1141,(62, 0, 0): 413,(90, 2, 0): 685,(22, 1, 4): 103,(29, 6, 0): 180,(62, 1, 0): 414,(133, 1, 2): 1032,(18, 2, 0): 80,(43, 7, 4): 279,(57, 3, 0): 382,(149, 1, 6): 1177,(79, 0, 0): 579,(49, 0, 0): 308,(43, 7, 2): 278,(2, 0, 0): 2,(76, 1, 0): 562,(14, 1, 0): 54,(72, 0, 6): 509,(64, 4, 2): 438,(124, 10, 0): 991,(167, 0, 0): 1283,(159, 4, 1): 1256,(124, 0, 4): 980,(144, 1, 0): 1125,(45, 0, 0): 291,(107, 5, 4): 822,(115, 4, 0): 896,(52, 3, 0): 320,(53, 0, 4): 325,(123, 2, 0): 969,(79, 9, 0): 599,(118, 7, 0): 927,(15, 1, 0): 60,(13, 2, 2): 30,(91, 1, 0): 691,(50, 7, 0): 314,(122, 1, 4): 955,(95, 2, 0): 717,(154, 4, 0): 1226,(76, 3, 0): 565,(152, 9, 0): 1213,(141, 4, 0): 1109,(56, 2, 0): 369,(34, 1, 0): 202,(159, 0, 5): 1246,(119, 2, 0): 933,(136, 1, 0): 1065,(89, 9, 0): 682,(59, 4, 0): 401,(159, 9, 0): 1263,(64, 2, 5): 433,(65, 0, 2): 455,(119, 4, 0): 935,(133, 2, 5): 1038,(42, 4, 1): 257,(53, 5, 0): 340,(77, 0, 0): 571,(113, 6, 0): 875,(13, 1, 6): 28,(64, 9, 5): 452,(91, 0, 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0): 319,(13, 9, 5): 51,(29, 7, 0): 181,(123, 7, 0): 973,(96, 10, 0): 749,(38, 1, 4): 222,(103, 10, 0): 793,(89, 1, 0): 672,(56, 7, 0): 374,(92, 0, 0): 698,(53, 2, 5): 334,(76, 0, 0): 559,(41, 0, 0): 246,(23, 2, 5): 129,(18, 1, 0): 78,(69, 7, 0): 479,(71, 2, 2): 494,(13, 6, 0): 42,(23, 1, 4): 123,(3, 0, 0): 4,(107, 9, 5): 833,(96, 9, 2): 747,(40, 4, 0): 244,(112, 1, 0): 850,(167, 3, 0): 1287,(32, 7, 0): 199,(141, 7, 0): 1110,(71, 1, 0): 490}
inv_tuple_map = {v: k for k, v in tuple_map.items() }
args.input_size = tuple(int(x)for x in args.input_size.split(','))
np.random.seed(args.seed)
 tf.random.set_seed(args.seed)
 model = get_model(input_size=args.input_size +(3,), backbone=args.backbone,
weights=None)
 print(f'Loading weights {args.weights}')
 model.load_weights(args.weights)
 print(model.summary())
 row_id, target = [], []
image_id_batch, img_batch = [], []
for i in tqdm(range(4)) :
parquet_fn = f'.. /input/bengaliai-cv19/test_image_data_{i}.parquet'
df = pd.read_parquet(parquet_fn)
 image_ids = df['image_id'].values
df = df.drop(['image_id'], axis=1)
 for k in range(len(image_ids)) :
image_id = image_ids[k]
img = df.iloc[k].values
img = normalize_image(img, org_width, org_height, args.input_size[1], args.input_size[0])
 img_batch.append(np.dstack([img] * 3))
image_id_batch.append(image_id)
 if len(img_batch)>= args.batch_size:
process_batch(image_id_batch, img_batch, row_id, target, model, inv_tuple_map)
 image_id_batch, img_batch = [], []
if len(img_batch)> 0:
process_batch(image_id_batch, img_batch, row_id, target, model, inv_tuple_map)
 image_id_batch, img_batch = [], []
sub_fn = 'submission.csv'
sub = pd.DataFrame({'row_id': row_id, 'target': target})
 sub.to_csv(sub_fn, index=False)
 print(f'Done wrote to {sub_fn}')
 main()