big update

2020-04-06 14:46:26 +02:00
parent 0f325676e5
commit 482f45df87
17 changed files with 1027 additions and 32 deletions
@@ -0,0 +1,134 @@
+from cfg import ALL_DATASET_PATHS
+from pathlib import Path
+import pandas as pd
+import numpy as np
+from sklearn.mixture import GaussianMixture, BayesianGaussianMixture
+from sklearn.ensemble import IsolationForest
+from sklearn.svm import OneClassSVM
+from sklearn.neighbors import KernelDensity
+from sklearn.metrics import roc_auc_score
+from sklearn.preprocessing import StandardScaler, MinMaxScaler
+from sklearn.decomposition import PCA
+from sklearn.pipeline import Pipeline
+from transfer_learning.extractors import AudioTransferLearningImageDataset, FeatureExtractor
+from torch.utils.data import DataLoader
+import torch.nn as nn
+from torch.optim import Adam
+from tqdm import tqdm
+from transfer_learning.my_model import MyModel
+import random
+np.random.seed(1337)
+random.seed(1337)
+
+# Switch
+OURS = False
+# Parameters
+leave_n_out = 150
+model = 'fan'
+model_id = 0
+
+# get all wav files
+wavs_normal = Path(f'/home/robert/coding/audio_anomaly_detection/data/mimii/-6_dB_{model}/id_0{model_id}/normal').glob('*.wav')
+wavs_abnormal = Path(f'/home/robert/coding/audio_anomaly_detection/data/mimii/-6_dB_{model}/id_0{model_id}/abnormal').glob('*.wav')
+
+# as list + shuffle
+normal_files = list(wavs_normal)
+abnormal_files = list(wavs_abnormal)
+random.shuffle(normal_files)
+random.shuffle(abnormal_files)
+
+normal_train_files = normal_files[leave_n_out:]
+normal_test_files = normal_files[:leave_n_out]
+
+abnormal_test_files = abnormal_files[:leave_n_out]
+
+print(len(normal_train_files), len(normal_test_files), len(normal_files))
+print(len(abnormal_test_files))
+
+if not OURS:
+    normal_df = pd.read_csv(Path(f'/home/robert/coding/audio_anomaly_detection/data/mimii/-6_dB_{model}/id_0{model_id}/normal/resnet18_features.csv'))
+    abnormal_df = pd.read_csv(Path(f'/home/robert/coding/audio_anomaly_detection/data/mimii/-6_dB_{model}/id_0{model_id}/abnormal/resnet18_features.csv'))
+
+    print(normal_df.shape, abnormal_df.shape)
+
+    normal_trainset = normal_df[normal_df.name.isin([int(x.stem.split('_')[0]) for x in normal_train_files])]
+    normal_testset = normal_df[normal_df.name.isin([int(x.stem.split('_')[0]) for x in normal_test_files])]
+    abnormal_testset = abnormal_df[abnormal_df.name.isin([int(x.stem.split('_')[0]) for x in abnormal_test_files])]
+
+    print(f'normal train: {normal_trainset.shape}')
+    print(f'normal test: {normal_testset.shape}')
+    print(f'abnormal test: {abnormal_testset.shape}')
+
+    only_features = lambda x: x[:, 2:]
+
+    print(f'#Normal files:{len(normal_files)}\t#Abnormal files: {len(abnormal_files)}')
+    print(f'#Normal test snippets normal: {len(normal_testset)}, #Abnormal test snippets: {len(abnormal_testset)}')
+
+    #model = BayesianGaussianMixture(n_components=64, max_iter=150, covariance_type='diag')
+    #model = GaussianMixture(n_components=64, covariance_type='diag')
+    #model = OneClassSVM(nu=1e-2)
+    #model = IsolationForest(n_estimators=128, contamination='auto')
+    model = KernelDensity(kernel='gaussian', bandwidth=0.1)
+    scaler = Pipeline(
+        [('Scaler', StandardScaler())]
+    )
+    X = only_features(normal_trainset.values)
+
+    scaler.fit(X)
+    model.fit(scaler.transform(X))
+
+    scores_normal_test = model.score_samples(
+        scaler.transform(only_features(normal_testset.values))
+    )
+
+    scores_abnormal_test = model.score_samples(
+        scaler.transform(only_features(abnormal_testset.values))
+    )
+
+    normal_with_scores = normal_testset[['name', 'seq_id']].copy()
+    normal_with_scores['score'] = -scores_normal_test
+    normal_with_scores['label'] = 0
+    normal_grouped = normal_with_scores.groupby(by=['name']).mean()
+
+    abnormal_with_scores = abnormal_testset[['name', 'seq_id']].copy()
+    abnormal_with_scores['score'] = -scores_abnormal_test
+    abnormal_with_scores['label'] = 0
+    abnormal_grouped = abnormal_with_scores.groupby(by=['name']).mean()
+
+    scores_normal_grouped = normal_grouped.score.values.tolist()
+    scores_abnormal_grouped = abnormal_grouped.score.values.tolist()
+
+    labels_normal = [0] * len(scores_normal_grouped)
+    labels_abnormal = [1] * len(scores_abnormal_grouped)
+    labels = labels_normal + labels_abnormal
+
+    scores = scores_normal_grouped + scores_abnormal_grouped
+
+    auc = roc_auc_score(y_score=scores, y_true=labels)
+    print(auc)
+
+else:
+    dataset = AudioTransferLearningImageDataset(root_or_files=normal_train_files)
+    dataloader = DataLoader(dataset, batch_size=80, shuffle=True)
+    F = FeatureExtractor(version='resnet18').to('cuda')
+    feature_size = F.feature_size
+    criterion = nn.MSELoss()
+    my_model = MyModel(F).to('cuda')
+
+    for e in range(0):
+        losses = []
+        for batch in tqdm(dataloader):
+            imgs, names, seq_ids = batch
+            imgs = imgs.to('cuda')
+            prediction, target = my_model(imgs)
+            loss = criterion(prediction, target)
+            my_model.optimizer.zero_grad()
+            loss.backward()
+            my_model.optimizer.step()
+            losses.append(loss.item())
+        print(sum(losses)/len(losses))
+
+    # test
+    dataset = AudioTransferLearningImageDataset(root_or_files=normal_test_files)
+    dataloader = DataLoader(dataset, batch_size=80, shuffle=False)
+    my_model.scores_from_dataloader(dataloader)