Kaggle competition: Cornell Birdcall Identification
<SOS> metric: MeanFScoreBeta Kaggle data source: cornell-birdcall-identification<feature_engineering>
!pip uninstall torch --y
!pip uninstall torchaudio --y
for col in(GENES + CELLS):
transformer = QuantileTransformer(n_quantiles=100,random_state=0, output_distribution="normal")
 vec_len = len(train_features[col].values)
 vec_len_test = len(test_features[col].values)
 raw_vec = train_features[col].values.reshape(vec_len, 1)
 transformer.fit(raw_vec)
 train_features[col] = transformer.transform(raw_vec ).reshape(1, vec_len)[0]
test_features[col] = transformer.transform(test_features[col].values.reshape(vec_len_test, 1)).reshape(1, vec_len_test)[0]<set_options>
!pip install.. /input/pytorch-160-with-torchvision-070/torch-1.6.0cu101-cp37-cp37m-linux_x86_64.whl
!pip install.. /input/torchaudio/torchaudio-0.6.0-cp37-cp37m-manylinux1_x86_64.whl
pd.options.display.max_rows = 500
pd.options.display.max_columns = 500
def seed_everything(seed=42):
random.seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 np.random.seed(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 torch.backends.cudnn.deterministic = True
seed_everything(seed=42 )<create_dataframe>
import os
data = pd.concat([pd.DataFrame(train_features[GENES]), pd.DataFrame(test_features[GENES])])
 data2 =(PCA(n_components=n_comp_GENES, random_state=42 ).fit_transform(data[GENES]))
train2 = data2[:train_features.shape[0]]; test2 = data2[-test_features.shape[0]:]
train2 = pd.DataFrame(train2, columns=[f'pca_G-{i}' for i in range(n_comp_GENES)])
 test2 = pd.DataFrame(test2, columns=[f'pca_G-{i}' for i in range(n_comp_GENES)])
 train_features = pd.concat(( train_features, train2), axis=1)
 test_features = pd.concat(( test_features, test2), axis=1 )<concatenate>
args = {
'n_mels': 224,
'hop_length': 384,
'std_sr': 32000,
'threshold':.5,
'model': '.. /input/birdweights/c2__17.pt',
'model2': '.. /input/birdweights/c2_9_18.pt',
'model3': '.. /input/birdweights/c2_0_41.pt',
}
model = torch.jit.load(args['model'], map_location='cpu' ).cuda()
model.eval() ;
model2 = torch.jit.load(args['model2'], map_location='cpu' ).cuda()
model2.eval() ;
model3 = torch.jit.load(args['model3'], map_location='cpu' ).cuda()
model3.eval() ;
root = os.path.join('.. /input', "birdsong-recognition")
 test_dir = os.path.join(root, 'test_audio')
 print(test_dir)
 if os.path.isdir(test_dir):
print('Running actual submission!')
 test_df = pd.read_csv(os.path.join(root, 'test.csv'))
sub = pd.read_csv(".. /input/birdsong-recognition/sample_submission.csv")
 else:
print('Running debug submission')
 test_dir = os.path.join('.. /input', 'birdcall-check', 'test_audio')
 test_df = pd.read_csv(os.path.join('.. /input', 'birdcall-check', 'test.csv'))
sub = pd.read_csv('.. /input/inference-pytorch-birdcall-resnet-baseline/submission.csv')
 print('Naturally no test dir found')
 sub.to_csv("submission.csv", index=False )
data = pd.concat([pd.DataFrame(train_features[CELLS]), pd.DataFrame(test_features[CELLS])])
 data2 =(PCA(n_components=n_comp_CELLS, random_state=42 ).fit_transform(data[CELLS]))
train2 = data2[:train_features.shape[0]]; test2 = data2[-test_features.shape[0]:]
train2 = pd.DataFrame(train2, columns=[f'pca_C-{i}' for i in range(n_comp_CELLS)])
 test2 = pd.DataFrame(test2, columns=[f'pca_C-{i}' for i in range(n_comp_CELLS)])
 train_features = pd.concat(( train_features, train2), axis=1)
 test_features = pd.concat(( test_features, test2), axis=1 )<concatenate>
BIRD_CODE = {
'aldfly': 0, 'ameavo': 1, 'amebit': 2, 'amecro': 3, 'amegfi': 4,
'amekes': 5, 'amepip': 6, 'amered': 7, 'amerob': 8, 'amewig': 9,
'amewoo': 10, 'amtspa': 11, 'annhum': 12, 'astfly': 13, 'baisan': 14,
'baleag': 15, 'balori': 16, 'banswa': 17, 'barswa': 18, 'bawwar': 19,
'belkin1': 20, 'belspa2': 21, 'bewwre': 22, 'bkbcuc': 23, 'bkbmag1': 24,
'bkbwar': 25, 'bkcchi': 26, 'bkchum': 27, 'bkhgro': 28, 'bkpwar': 29,
'bktspa': 30, 'blkpho': 31, 'blugrb1': 32, 'blujay': 33, 'bnhcow': 34,
'boboli': 35, 'bongul': 36, 'brdowl': 37, 'brebla': 38, 'brespa': 39,
'brncre': 40, 'brnthr': 41, 'brthum': 42, 'brwhaw': 43, 'btbwar': 44,
'btnwar': 45, 'btywar': 46, 'buffle': 47, 'buggna': 48, 'buhvir': 49,
'bulori': 50, 'bushti': 51, 'buwtea': 52, 'buwwar': 53, 'cacwre': 54,
'calgul': 55, 'calqua': 56, 'camwar': 57, 'cangoo': 58, 'canwar': 59,
'canwre': 60, 'carwre': 61, 'casfin': 62, 'caster1': 63, 'casvir': 64,
'cedwax': 65, 'chispa': 66, 'chiswi': 67, 'chswar': 68, 'chukar': 69,
'clanut': 70, 'cliswa': 71, 'comgol': 72, 'comgra': 73, 'comloo': 74,
'commer': 75, 'comnig': 76, 'comrav': 77, 'comred': 78, 'comter': 79,
'comyel': 80, 'coohaw': 81, 'coshum': 82, 'cowscj1': 83, 'daejun': 84,
'doccor': 85, 'dowwoo': 86, 'dusfly': 87, 'eargre': 88, 'easblu': 89,
'easkin': 90, 'easmea': 91, 'easpho': 92, 'eastow': 93, 'eawpew': 94,
'eucdov': 95, 'eursta': 96, 'evegro': 97, 'fiespa': 98, 'fiscro': 99,
'foxspa': 100, 'gadwal': 101, 'gcrfin': 102, 'gnttow': 103, 'gnwtea': 104,
'gockin': 105, 'gocspa': 106, 'goleag': 107, 'grbher3': 108, 'grcfly': 109,
'greegr': 110, 'greroa': 111, 'greyel': 112, 'grhowl': 113, 'grnher': 114,
'grtgra': 115, 'grycat': 116, 'gryfly': 117, 'haiwoo': 118, 'hamfly': 119,
'hergul': 120, 'herthr': 121, 'hoomer': 122, 'hoowar': 123, 'horgre': 124,
'horlar': 125, 'houfin': 126, 'houspa': 127, 'houwre': 128, 'indbun': 129,
'juntit1': 130, 'killde': 131, 'labwoo': 132, 'larspa': 133, 'lazbun': 134,
'leabit': 135, 'leafly': 136, 'leasan': 137, 'lecthr': 138, 'lesgol': 139,
'lesnig': 140, 'lesyel': 141, 'lewwoo': 142, 'linspa': 143, 'lobcur': 144,
'lobdow': 145, 'logshr': 146, 'lotduc': 147, 'louwat': 148, 'macwar': 149,
'magwar': 150, 'mallar3': 151, 'marwre': 152, 'merlin': 153, 'moublu': 154,
'mouchi': 155, 'moudov': 156, 'norcar': 157, 'norfli': 158, 'norhar2': 159,
'normoc': 160, 'norpar': 161, 'norpin': 162, 'norsho': 163, 'norwat': 164,
'nrwswa': 165, 'nutwoo': 166, 'olsfly': 167, 'orcwar': 168, 'osprey': 169,
'ovenbi1': 170, 'palwar': 171, 'pasfly': 172, 'pecsan': 173, 'perfal': 174,
'phaino': 175, 'pibgre': 176, 'pilwoo': 177, 'pingro': 178, 'pinjay': 179,
'pinsis': 180, 'pinwar': 181, 'plsvir': 182, 'prawar': 183, 'purfin': 184,
'pygnut': 185, 'rebmer': 186, 'rebnut': 187, 'rebsap': 188, 'rebwoo': 189,
'redcro': 190, 'redhea': 191, 'reevir1': 192, 'renpha': 193, 'reshaw': 194,
'rethaw': 195, 'rewbla': 196, 'ribgul': 197, 'rinduc': 198, 'robgro': 199,
'rocpig': 200, 'rocwre': 201, 'rthhum': 202, 'ruckin': 203, 'rudduc': 204,
'rufgro': 205, 'rufhum': 206, 'rusbla': 207, 'sagspa1': 208, 'sagthr': 209,
'savspa': 210, 'saypho': 211, 'scatan': 212, 'scoori': 213, 'semplo': 214,
'semsan': 215, 'sheowl': 216, 'shshaw': 217, 'snobun': 218, 'snogoo': 219,
'solsan': 220, 'sonspa': 221, 'sora': 222, 'sposan': 223, 'spotow': 224,
'stejay': 225, 'swahaw': 226, 'swaspa': 227, 'swathr': 228, 'treswa': 229,
'truswa': 230, 'tuftit': 231, 'tunswa': 232, 'veery': 233, 'vesspa': 234,
'vigswa': 235, 'warvir': 236, 'wesblu': 237, 'wesgre': 238, 'weskin': 239,
'wesmea': 240, 'wessan': 241, 'westan': 242, 'wewpew': 243, 'whbnut': 244,
'whcspa': 245, 'whfibi': 246, 'whtspa': 247, 'whtswi': 248, 'wilfly': 249,
'wilsni1': 250, 'wiltur': 251, 'winwre3': 252, 'wlswar': 253, 'wooduc': 254,
'wooscj2': 255, 'woothr': 256, 'y00475': 257, 'yebfly': 258, 'yebsap': 259,
'yehbla': 260, 'yelwar': 261, 'yerwar': 262, 'yetvir': 263
}
INV_BIRD_CODE = {v: k for k, v in BIRD_CODE.items() }
data = train_features.append(test_features)
 data<create_dataframe>
fmin, fmax = 25, 11000
f1 = MelSpectrogram(sample_rate=args['std_sr'], n_fft=1024, hop_length=args['hop_length'], f_min=fmin, f_max=fmax, n_mels=args['n_mels'] ).cuda()
f2 = MelSpectrogram(sample_rate=args['std_sr'], n_fft=2048, hop_length=args['hop_length'], f_min=fmin, f_max=fmax, n_mels=args['n_mels'] ).cuda()
f3 = MelSpectrogram(sample_rate=args['std_sr'], n_fft=4096, hop_length=args['hop_length'], f_min=fmin, f_max=fmax, n_mels=args['n_mels'] ).cuda()
var_thresh = VarianceThreshold(VarianceThreshold_for_FS)
 data = train_features.append(test_features)
 data_transformed = var_thresh.fit_transform(data.iloc[:, 4:])
 train_features_transformed = data_transformed[ : train_features.shape[0]]
test_features_transformed = data_transformed[-test_features.shape[0] : ]
train_features = pd.DataFrame(train_features[['sig_id','cp_type','cp_time','cp_dose']].values.reshape(-1, 4),\
columns=['sig_id','cp_type','cp_time','cp_dose'])
 train_features = pd.concat([train_features, pd.DataFrame(train_features_transformed)], axis=1)
 test_features = pd.DataFrame(test_features[['sig_id','cp_type','cp_time','cp_dose']].values.reshape(-1, 4),\
columns=['sig_id','cp_type','cp_time','cp_dose'])
 test_features = pd.concat([test_features, pd.DataFrame(test_features_transformed)], axis=1)
 train_features.shape<merge>
class TestDataset(data.Dataset):
def __init__(self, df, spectrogram):
self.df = df
self.spectrogram = spectrogram
def __len__(self):
return len(self.df)
 def normalize(self, spect):
spect = spect.contiguous()
miu = spect.view(spect.size(0), -1 ).mean(-1 ).unsqueeze(-1 ).unsqueeze(-1)
 std = spect.view(spect.size(0), -1 ).std(-1 ).unsqueeze(-1 ).unsqueeze(-1)+ 1e-8
return(spect - miu)/ std / 2
def __getitem__(self, idx):
sample = self.df.loc[idx, :]
site = sample.site
row_id = sample.row_id
if site == "site_3":
y = self.spectrogram
len_y = y.shape[-1]
start = 0
end = args['std_sr'] * 5 // args['hop_length']
y_all = []
while len_y > start:
y_batch = self.normalize(y[:, :, start:end])
 if end >= len_y:
clip_length = args['std_sr'] * 5 // args['hop_length']
y_batch = self.normalize(y[:, :, -clip_length:])
 y_all.append(y_batch)
 break
start = end
end = end + args['std_sr'] * 5 // args['hop_length']
y_all.append(y_batch)
 y_all = torch.stack(y_all)
 return y_all, row_id, site
else:
end_seconds = int(sample.seconds)
 start_seconds = int(end_seconds - 5)
 start_index = args['std_sr'] * start_seconds // args['hop_length']
end_index = args['std_sr'] * end_seconds // args['hop_length']
y = self.spectrogram[:, :, start_index:end_index]
y = self.normalize(y)
 return y, row_id, site
def prediction_for_clip(dataset, threshold=0.5):
loader = data.DataLoader(dataset, batch_size=1, shuffle=False)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.eval()
model2.eval()
model3.eval()
prediction_dict = {}
for image, row_id, site in loader:
site = site[0]
row_id = row_id[0]
print('imageshape',image.shape)
 image = image.to(device ).float()
if site in {"site_1", "site_2"}:
image = image.to(device ).float()
with torch.no_grad() :
prediction = model(image)
 print('prediction of model',prediction.shape)
 assert 1==2
proba = torch.square(prediction.view(-1 ).sigmoid() ).cpu().numpy()
prediction = model2(image)
 proba += torch.square(prediction.view(-1 ).sigmoid() ).cpu().numpy()
prediction = model3(image)
 proba += torch.square(prediction.view(-1 ).sigmoid() ).cpu().numpy()
proba = proba / 3
proba = np.sqrt(proba)
 events = proba >= threshold
labels = np.argwhere(events ).reshape(-1 ).tolist()
else:
image = image.squeeze(0)
 batch_size = 16
whole_size = image.size(0)
 if whole_size % batch_size == 0:
n_iter = whole_size // batch_size
else:
n_iter = whole_size // batch_size + 1
all_events = set()
for batch_i in range(n_iter):
batch = image[batch_i * batch_size:(batch_i + 1)* batch_size]
if batch.ndim == 3:
batch = batch.unsqueeze(0)
 batch = batch.to(device)
 with torch.no_grad() :
prediction = model(batch)
 proba = torch.square(prediction.view(batch.size(0), -1 ).sigmoid() ).cpu().numpy()
prediction = model2(batch)
 proba += torch.square(prediction.view(batch.size(0), -1 ).sigmoid() ).cpu().numpy()
prediction = model3(batch)
 proba += torch.square(prediction.view(batch.size(0), -1 ).sigmoid() ).cpu().numpy()
proba = proba / 3
proba = np.sqrt(proba)
 events = proba >= threshold
for i in range(len(events)) :
event = events[i, :]
labels = np.argwhere(event ).reshape(-1 ).tolist()
for label in labels:
all_events.add(label)
 labels = list(all_events)
 if len(labels)== 0:
prediction_dict[row_id] = "nocall"
else:
labels_str_list = list(map(lambda x: INV_BIRD_CODE[x], labels))
label_string = " ".join(labels_str_list)
 prediction_dict[row_id] = label_string
return prediction_dict
unique_audio_id = test_df.audio_id.unique()
warnings.filterwarnings("ignore")
 prediction_dfs = []
for audio_id in progress_bar(unique_audio_id):
signal, sample_rate = torchaudio.load(os.path.join(test_dir, audio_id+'.mp3'))
signal = signal.mean(0 ).cuda()
signal = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=args['std_sr'] )(signal)
 print('signal shape',signal.shape)
 spectrogram = torch.log(( torch.stack([f1(signal), f2(signal), f3(signal)], axis=0)) + 1e-8 ).half().float().cpu()
print('spectrogram after log and torchstack',spectrogram.shape)
 test_df_for_audio_id = test_df.query(
 f"audio_id == '{audio_id}'" ).reset_index(drop=True)
 dataset = TestDataset(test_df_for_audio_id, spectrogram)
 prediction_dict = prediction_for_clip(dataset=dataset, threshold=args['threshold'])
 row_id = list(prediction_dict.keys())
 birds = list(prediction_dict.values())
 prediction_df = pd.DataFrame({
"row_id": row_id,
"birds": birds
})
 prediction_dfs.append(prediction_df)
 prediction_df = pd.concat(prediction_dfs, axis=0, sort=False ).reset_index(drop=True)
 prediction_df['birds'].value_counts()
<drop_column><EOS> 
prediction_df.to_csv('submission.csv', index=False)
 prediction_df