utils_train_test.array_stats(X_leaderboard) # ============================================================================= # get output for leaderboard data # ============================================================================= Y_leaderboard=utils_train_test.getOutputAllFolds(X_leaderboard,configs,binaryMask=False) Y_leaderboard=utils_train_test.unpadArray(Y_leaderboard,configs.padSize) #Y_leaderboard,_=utils_train_test.data_resize(Y_leaderboard,None,101,101) utils_train_test.array_stats(Y_leaderboard) # ============================================================================= # store predictions of Ensemble model for Leaderboard data # ============================================================================= #utils_train_test.storePredictions(configs,Y_leaderboard,"test") utils_train_test.storePredictionsWithIds(configs,Y_leaderboard,ids_leaderboard,"test") # ============================================================================= # convert outputs to Run Length Dict # ============================================================================= rlcDict=utils_train_test.converMasksToRunLengthDict(Y_leaderboard>=configs.maskThreshold,ids_leaderboard) # ============================================================================= # crate submission # ============================================================================= utils_train_test.createSubmission(rlcDict,configs)
# ============================================================================= X_leaderboard,_,ids_leaderboard,_=utils_train_test.load_data_classify_prob(configs,"test") utils_train_test.array_stats(X_leaderboard) # ============================================================================= # get predictions for leaderboard data # ============================================================================= #Y_leaderboard=utils_train_test.getOutputAllFolds_classify_prob(X_leaderboard,configs) Y_leaderboard,Y_leaderboard_soft,y_leaderboard=utils_train_test.getOutputAllFolds_classify_prob_ySoft(X_leaderboard,configs) # ============================================================================= # store predictions of Ensemble model for Leaderboard data # ============================================================================= utils_train_test.storePredictionsWithIds_classify(configs,Y_leaderboard_soft,y_leaderboard,ids_leaderboard,"test") # ============================================================================= # convert outputs to Run Length Dict # ============================================================================= rlcDict=utils_train_test.converMasksToRunLengthDict(Y_leaderboard,ids_leaderboard) # ============================================================================= # crate submission # ============================================================================= utils_train_test.createSubmission(rlcDict,configs)