Kaggle competition: Natural Language Processing with Disaster Tweets
<SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<import_modules>
! pip install nlplot
import os
import gc
import math
import random
from pathlib import Path
import cv2
import librosa
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv2d, Module, Linear, BatchNorm2d, ReLU
from torch.nn.modules.utils import _pair
import torch.utils.data as data<import_modules>
tqdm.pandas()
stop=set(stopwords.words('english'))
%matplotlib inline
pd.set_option('display.max_columns', 300)
 pd.set_option('display.max_rows', 300)
 pd.set_option('display.max_colwidth', 300)
 pd.options.display.float_format = '{:.3f}'.format
import onnxruntime<define_variables>
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
 test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
 submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv' )
ROOT = Path.cwd().parent
INPUT_ROOT = ROOT / "input"
RAW_DATA = INPUT_ROOT / "birdsong-recognition"
TEST_AUDIO_DIR = RAW_DATA / "test_audio"<load_from_csv>
stopwords = npt.get_stopword(top_n=30, min_freq=0)
 stopwords
if not TEST_AUDIO_DIR.exists() :
TEST_AUDIO_DIR = INPUT_ROOT / "birdcall-check" / "test_audio"
test = pd.read_csv(INPUT_ROOT / "birdcall-check" / "test.csv")
 else:
test = pd.read_csv(RAW_DATA / "test.csv" )<load_from_csv>
df = pd.concat([train,test])
 df.shape
sub = pd.read_csv(".. /input/birdsong-recognition/sample_submission.csv")
<init_hyperparams>
def remove_URL(text):
url = re.compile(r'https?://\S+|www\.\S+')
 return url.sub(r'',text)
 def remove_html(text):
html = re.compile(r'<.*?>')
 return html.sub(r'',text)
 def remove_emoji(text):
emoji_pattern = re.compile("["
u"\U0001F600-\U0001F64F"
u"\U0001F300-\U0001F5FF"
u"\U0001F680-\U0001F6FF"
u"\U0001F1E0-\U0001F1FF"
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
"]+", flags=re.UNICODE)
 return emoji_pattern.sub(r'', text)
 def remove_punct(text):
table = str.maketrans('','',string.punctuation)
 return text.translate(table)
 def remove_number(text):
num = re.compile(r'\w*\d\w*')
 return num.sub(r'',text)
 def acronyms(text):
text = re.sub(r"MH370", "Malaysia Airlines Flight 370", text)
 text = re.sub(r"mÌ¼sica", "music", text)
 text = re.sub(r"okwx", "Oklahoma City Weather", text)
 text = re.sub(r"arwx", "Arkansas Weather", text)
 text = re.sub(r"gawx", "Georgia Weather", text)
 text = re.sub(r"scwx", "South Carolina Weather", text)
 text = re.sub(r"cawx", "California Weather", text)
 text = re.sub(r"tnwx", "Tennessee Weather", text)
 text = re.sub(r"azwx", "Arizona Weather", text)
 text = re.sub(r"alwx", "Alabama Weather", text)
 text = re.sub(r"wordpressdotcom", "wordpress", text)
 text = re.sub(r"usNWSgov", "United States National Weather Service", text)
 text = re.sub(r"Suruc", "Sanliurfa", text)
 text = re.sub(r"Bestnaijamade", "bestnaijamade", text)
 text = re.sub(r"SOUDELOR", "Soudelor", text)
 return text
def clean_text(text):

 text = acronyms(text)
 text = text.lower()
text = remove_URL(text)
 text = remove_html(text)
 text = remove_emoji(text)
 text = remove_punct(text)
 text = remove_number(text)
 return text
TARGET_SR = 32000
model_config = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/training-birdsong-baseline-resnest50-fast/best_model.pth"
}
melspectrogram_parameters = {
"n_mels": 128,
"fmin": 20,
"fmax": 16000
}<define_variables>
df['text'] = df['text'].progress_apply(lambda x: clean_text(x))
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() }<init_hyperparams>
def create_corpus(df):
corpus=[]
for tweet in tqdm(df['text']):
words=[word.lower() for word in word_tokenize(tweet)if(( word.isalpha() ==1)&(word not in stop)) ]
corpus.append(words)
 return corpus
settings = {}
settings['dataset'] = {}
settings['dataset']['params'] = {}
settings['dataset']['params']['img_size'] = 300
settings['dataset']['params']['melspectrogram_parameters'] = {}
settings['dataset']['params']['melspectrogram_parameters']['n_mels'] = 300
settings['dataset']['params']['melspectrogram_parameters']['fmin'] = 650
settings['dataset']['params']['melspectrogram_parameters']['fmax'] = 16000
settings['dataset']['params']['melspectrogram_parameters']['n_fft'] = 2400
settings['dataset']['params']['melspectrogram_parameters']['hop_length'] = 533<init_hyperparams>
corpus = create_corpus(df )
settings_effnetb1 = {}
settings_effnetb1['dataset'] = {}
settings_effnetb1['dataset']['params'] = {}
settings_effnetb1['dataset']['params']['img_size'] = 240
settings_effnetb1['dataset']['params']['melspectrogram_parameters'] = {}
settings_effnetb1['dataset']['params']['melspectrogram_parameters']['n_mels'] = 240
settings_effnetb1['dataset']['params']['melspectrogram_parameters']['fmin'] = 1000
settings_effnetb1['dataset']['params']['melspectrogram_parameters']['fmax'] = 12500
settings_effnetb1['dataset']['params']['melspectrogram_parameters']['n_fft'] = 1920
settings_effnetb1['dataset']['params']['melspectrogram_parameters']['hop_length'] = 666<init_hyperparams>
embedding_dict={}
with open('.. /input/glove-global-vectors-for-word-representation/glove.6B.100d.txt','r')as f:
for line in f:
values=line.split()
word=values[0]
vectors=np.asarray(values[1:],'float32')
 embedding_dict[word]=vectors
f.close()
settings_thefirst = {}
settings_thefirst['dataset'] = {}
settings_thefirst['dataset']['params'] = {}
settings_thefirst['dataset']['params']['img_size'] = 260
settings_thefirst['dataset']['params']['melspectrogram_parameters'] = {}
settings_thefirst['dataset']['params']['melspectrogram_parameters']['n_mels'] = 260
settings_thefirst['dataset']['params']['melspectrogram_parameters']['fmin'] = 20
settings_thefirst['dataset']['params']['melspectrogram_parameters']['fmax'] = 15000
settings_thefirst['dataset']['params']['melspectrogram_parameters']['n_fft'] = 1500
settings_thefirst['dataset']['params']['melspectrogram_parameters']['hop_length'] = 800<init_hyperparams>
MAX_LEN = 100
tokenizer_obj = Tokenizer()
tokenizer_obj.fit_on_texts(corpus)
 sequences = tokenizer_obj.texts_to_sequences(corpus)
 tweet_pad = pad_sequences(sequences,maxlen=MAX_LEN,truncating='post',padding='post' )
settings_thesecond = {}
settings_thesecond['dataset'] = {}
settings_thesecond['dataset']['params'] = {}
settings_thesecond['dataset']['params']['img_size'] = 260
settings_thesecond['dataset']['params']['melspectrogram_parameters'] = {}
settings_thesecond['dataset']['params']['melspectrogram_parameters']['n_mels'] = 260
settings_thesecond['dataset']['params']['melspectrogram_parameters']['fmin'] = 260
settings_thesecond['dataset']['params']['melspectrogram_parameters']['fmax'] = 16000
settings_thesecond['dataset']['params']['melspectrogram_parameters']['n_fft'] = 2080
settings_thesecond['dataset']['params']['melspectrogram_parameters']['hop_length'] = 615<init_hyperparams>
word_index=tokenizer_obj.word_index
print('Number of unique words:',len(word_index))
settings_inception = {}
settings_inception['dataset'] = {}
settings_inception['dataset']['params'] = {}
settings_inception['dataset']['params']['img_size'] = 299
settings_inception['dataset']['params']['melspectrogram_parameters'] = {}
settings_inception['dataset']['params']['melspectrogram_parameters']['n_mels'] = 299
settings_inception['dataset']['params']['melspectrogram_parameters']['fmin'] = 299
settings_inception['dataset']['params']['melspectrogram_parameters']['fmax'] = 16000
settings_inception['dataset']['params']['melspectrogram_parameters']['n_fft'] = 2392
settings_inception['dataset']['params']['melspectrogram_parameters']['hop_length'] = 535<init_hyperparams>
num_words=len(word_index)+1
embedding_matrix=np.zeros(( num_words,100))
for word,i in tqdm(word_index.items()):
if i > num_words:
continue
emb_vec=embedding_dict.get(word)
 if emb_vec is not None:
embedding_matrix[i]=emb_vec
settings_se = {}
settings_se['dataset'] = {}
settings_se['dataset']['params'] = {}
settings_se['dataset']['params']['img_size'] = 224
settings_se['dataset']['params']['melspectrogram_parameters'] = {}
settings_se['dataset']['params']['melspectrogram_parameters']['n_mels'] = 224
settings_se['dataset']['params']['melspectrogram_parameters']['fmin'] = 224
settings_se['dataset']['params']['melspectrogram_parameters']['fmax'] = 16000
settings_se['dataset']['params']['melspectrogram_parameters']['n_fft'] = 1792
settings_se['dataset']['params']['melspectrogram_parameters']['hop_length'] = 714<init_hyperparams>
train_tweet = tweet_pad[:train.shape[0]]
test_tweet = tweet_pad[train.shape[0]:]
settings_mixup = {}
settings_mixup['dataset'] = {}
settings_mixup['dataset']['params'] = {}
settings_mixup['dataset']['params']['img_size'] = 260
settings_mixup['dataset']['params']['melspectrogram_parameters'] = {}
settings_mixup['dataset']['params']['melspectrogram_parameters']['n_mels'] = 208
settings_mixup['dataset']['params']['melspectrogram_parameters']['fmin'] = 2000
settings_mixup['dataset']['params']['melspectrogram_parameters']['fmax'] = 15000
settings_mixup['dataset']['params']['melspectrogram_parameters']['n_fft'] = 1664
settings_mixup['dataset']['params']['melspectrogram_parameters']['hop_length'] = 769<init_hyperparams>
X_train,X_test,y_train,y_test=train_test_split(train_tweet, train['target'].values,test_size=0.1)
 print('Shape of train',X_train.shape)
 print("Shape of Validation ",X_test.shape )
settings_ls = {}
settings_ls['dataset'] = {}
settings_ls['dataset']['params'] = {}
settings_ls['dataset']['params']['img_size'] = 260
settings_ls['dataset']['params']['melspectrogram_parameters'] = {}
settings_ls['dataset']['params']['melspectrogram_parameters']['n_mels'] = 168
settings_ls['dataset']['params']['melspectrogram_parameters']['fmin'] = 800
settings_ls['dataset']['params']['melspectrogram_parameters']['fmax'] = 14000
settings_ls['dataset']['params']['melspectrogram_parameters']['n_fft'] = 1344
settings_ls['dataset']['params']['melspectrogram_parameters']['hop_length'] = 1024<define_search_model>
def build_BLSTM() :
model = Sequential()
model.add(Embedding(input_dim=embedding_matrix.shape[0],
output_dim=embedding_matrix.shape[1],
weights = [embedding_matrix],
input_length=MAX_LEN))
model.add(SpatialDropout1D(0.3))
model.add(Bidirectional(LSTM(MAX_LEN, return_sequences = True, recurrent_dropout=0.2)))
 model.add(GlobalMaxPool1D())
 model.add(BatchNormalization())
 model.add(Dropout(0.5))
model.add(Dense(MAX_LEN, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(MAX_LEN, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(1, activation = 'sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
 return model
class SplAtConv2d(Module):

 def __init__(self, in_channels, channels, kernel_size, stride=(1, 1), padding=(0, 0),
dilation=(1, 1), groups=1, bias=True,
radix=2, reduction_factor=4,
rectify=False, rectify_avg=False, norm_layer=None,
dropblock_prob=0.0, **kwargs):
super(SplAtConv2d, self ).__init__()
padding = _pair(padding)
 self.rectify = rectify and(padding[0] > 0 or padding[1] > 0)
 self.rectify_avg = rectify_avg
inter_channels = max(in_channels*radix//reduction_factor, 32)
 self.radix = radix
self.cardinality = groups
self.channels = channels
self.dropblock_prob = dropblock_prob
if self.rectify:
self.conv = RFConv2d(in_channels, channels*radix, kernel_size, stride, padding, dilation,
groups=groups*radix, bias=bias, average_mode=rectify_avg, **kwargs)
 else:
self.conv = Conv2d(in_channels, channels*radix, kernel_size, stride, padding, dilation,
groups=groups*radix, bias=bias, **kwargs)
 self.use_bn = norm_layer is not None
if self.use_bn:
self.bn0 = norm_layer(channels*radix)
 self.relu = ReLU(inplace=True)
 self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
 if self.use_bn:
self.bn1 = norm_layer(inter_channels)
 self.fc2 = Conv2d(inter_channels, channels*radix, 1, groups=self.cardinality)
 if dropblock_prob > 0.0:
self.dropblock = DropBlock2D(dropblock_prob, 3)
 self.rsoftmax = rSoftMax(radix, groups)
 def forward(self, x):
x = self.conv(x)
 if self.use_bn:
x = self.bn0(x)
 if self.dropblock_prob > 0.0:
x = self.dropblock(x)
 x = self.relu(x)
 batch, rchannel = x.shape[:2]
if self.radix > 1:
if torch.__version__ < '1.5':
splited = torch.split(x, int(rchannel//self.radix), dim=1)
 else:
splited = torch.split(x, rchannel//self.radix, dim=1)
 gap = sum(splited)
 else:
gap = x
gap = F.adaptive_avg_pool2d(gap, 1)
 gap = self.fc1(gap)
 if self.use_bn:
gap = self.bn1(gap)
 gap = self.relu(gap)
 atten = self.fc2(gap)
 atten = self.rsoftmax(atten ).view(batch, -1, 1, 1)
 if self.radix > 1:
if torch.__version__ < '1.5':
attens = torch.split(atten, int(rchannel//self.radix), dim=1)
 else:
attens = torch.split(atten, rchannel//self.radix, dim=1)
 out = sum([att*split for(att, split)in zip(attens, splited)])
 else:
out = atten * x
return out.contiguous()
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
 if self.radix > 1:
x = x.view(batch, self.cardinality, self.radix, -1 ).transpose(1, 2)
 x = F.softmax(x, dim=1)
 x = x.reshape(batch, -1)
 else:
x = torch.sigmoid(x)
 return x<import_modules>
checkpoint = ModelCheckpoint(
 'model.h5',
monitor = 'val_loss',
verbose = 1,
save_best_only = True 
)
 reduce_lr = ReduceLROnPlateau(
 monitor = 'val_loss',
factor = 0.2,
verbose = 1,
patience = 5,
min_lr = 0.001
)
class DropBlock2D(object):
def __init__(self, *args, **kwargs):
raise NotImplementedError
class GlobalAvgPool2d(nn.Module):
def __init__(self):

 super(GlobalAvgPool2d, self ).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool2d(inputs, 1 ).view(inputs.size(0), -1)
 class Bottleneck(nn.Module):

 expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None,
radix=1, cardinality=1, bottleneck_width=64,
avd=False, avd_first=False, dilation=1, is_first=False,
rectified_conv=False, rectify_avg=False,
norm_layer=None, dropblock_prob=0.0, last_gamma=False):
super(Bottleneck, self ).__init__()
group_width = int(planes *(bottleneck_width / 64.))* cardinality
self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
 self.bn1 = norm_layer(group_width)
 self.dropblock_prob = dropblock_prob
self.radix = radix
self.avd = avd and(stride > 1 or is_first)
 self.avd_first = avd_first
if self.avd:
self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
 stride = 1
if dropblock_prob > 0.0:
self.dropblock1 = DropBlock2D(dropblock_prob, 3)
 if radix == 1:
self.dropblock2 = DropBlock2D(dropblock_prob, 3)
 self.dropblock3 = DropBlock2D(dropblock_prob, 3)
 if radix >= 1:
self.conv2 = SplAtConv2d(
 group_width, group_width, kernel_size=3,
stride=stride, padding=dilation,
dilation=dilation, groups=cardinality, bias=False,
radix=radix, rectify=rectified_conv,
rectify_avg=rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 elif rectified_conv:
self.conv2 = RFConv2d(
 group_width, group_width, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation,
groups=cardinality, bias=False,
average_mode=rectify_avg)
 self.bn2 = norm_layer(group_width)
 else:
self.conv2 = nn.Conv2d(
 group_width, group_width, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation,
groups=cardinality, bias=False)
 self.bn2 = norm_layer(group_width)
 self.conv3 = nn.Conv2d(
 group_width, planes * 4, kernel_size=1, bias=False)
 self.bn3 = norm_layer(planes*4)
 if last_gamma:
zeros_(self.bn3.weight)
 self.relu = nn.ReLU(inplace=True)
 self.downsample = downsample
self.dilation = dilation
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
 out = self.bn1(out)
 if self.dropblock_prob > 0.0:
out = self.dropblock1(out)
 out = self.relu(out)
 if self.avd and self.avd_first:
out = self.avd_layer(out)
 out = self.conv2(out)
 if self.radix == 0:
out = self.bn2(out)
 if self.dropblock_prob > 0.0:
out = self.dropblock2(out)
 out = self.relu(out)
 if self.avd and not self.avd_first:
out = self.avd_layer(out)
 out = self.conv3(out)
 out = self.bn3(out)
 if self.dropblock_prob > 0.0:
out = self.dropblock3(out)
 if self.downsample is not None:
residual = self.downsample(x)
 out += residual
out = self.relu(out)
 return out
class ResNet(nn.Module):

 def __init__(self, block, layers, radix=1, groups=1, bottleneck_width=64,
num_classes=1000, dilated=False, dilation=1,
deep_stem=False, stem_width=64, avg_down=False,
rectified_conv=False, rectify_avg=False,
avd=False, avd_first=False,
final_drop=0.0, dropblock_prob=0,
last_gamma=False, norm_layer=nn.BatchNorm2d):
self.cardinality = groups
self.bottleneck_width = bottleneck_width
self.inplanes = stem_width*2 if deep_stem else 64
self.avg_down = avg_down
self.last_gamma = last_gamma
self.radix = radix
self.avd = avd
self.avd_first = avd_first
super(ResNet, self ).__init__()
self.rectified_conv = rectified_conv
self.rectify_avg = rectify_avg
if rectified_conv:
conv_layer = RFConv2d
else:
conv_layer = nn.Conv2d
conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}
if deep_stem:
self.conv1 = nn.Sequential(
 conv_layer(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False, **conv_kwargs),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(stem_width, stem_width*2, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs), 
)
 else:
self.conv1 = conv_layer(3, 64, kernel_size=7, stride=2, padding=3,
bias=False, **conv_kwargs)
 self.bn1 = norm_layer(self.inplanes)
 self.relu = nn.ReLU(inplace=True)
 self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
 self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer, is_first=False)
 self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
 if dilated or dilation == 4:
self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
dilation=2, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
dilation=4, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 elif dilation==2:
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilation=1, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
dilation=2, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 else:
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 self.avgpool = GlobalAvgPool2d()
self.drop = nn.Dropout(final_drop)if final_drop > 0.0 else None
self.fc = nn.Linear(512 * block.expansion, num_classes)
 for m in self.modules() :
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2./ n))
elif isinstance(m, norm_layer):
m.weight.data.fill_(1)
 m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=None,
dropblock_prob=0.0, is_first=True):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
down_layers = []
if self.avg_down:
if dilation == 1:
down_layers.append(nn.AvgPool2d(kernel_size=stride, stride=stride,
ceil_mode=True, count_include_pad=False))
else:
down_layers.append(nn.AvgPool2d(kernel_size=1, stride=1,
ceil_mode=True, count_include_pad=False))
down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=1, bias=False))
else:
down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False))
down_layers.append(norm_layer(planes * block.expansion))
downsample = nn.Sequential(*down_layers)
 layers = []
if dilation == 1 or dilation == 2:
layers.append(block(self.inplanes, planes, stride, downsample=downsample,
radix=self.radix, cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, avd_first=self.avd_first,
dilation=1, is_first=is_first, rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer, dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
elif dilation == 4:
layers.append(block(self.inplanes, planes, stride, downsample=downsample,
radix=self.radix, cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, avd_first=self.avd_first,
dilation=2, is_first=is_first, rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer, dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
else:
raise RuntimeError("=> unknown dilation size: {}".format(dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes,
radix=self.radix, cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, avd_first=self.avd_first,
dilation=dilation, rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer, dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
return nn.Sequential(*layers)
 def forward(self, x):
x = self.conv1(x)
 x = self.bn1(x)
 x = self.relu(x)
 x = self.maxpool(x)
 x = self.layer1(x)
 x = self.layer2(x)
 x = self.layer3(x)
 x = self.layer4(x)
 x = self.avgpool(x)
 x = torch.flatten(x, 1)
 if self.drop:
x = self.drop(x)
 x = self.fc(x)
 return x<set_options>
history = model.fit(
 X_train,
y_train,
epochs = 15,
batch_size = 64,
validation_data = [X_test, y_test],
verbose = 1,
callbacks = [reduce_lr, checkpoint]
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu" )<categorify>
loss, accuracy = model.evaluate(X_test, y_test)
 print('Loss:', loss)
 print('Accuracy:', accuracy )
def to_numpy(tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()<create_dataframe>
preds = model.predict_classes(X_test)
 metrics(preds, y_test )
def prediction_for_clip(test_df: pd.DataFrame,
clip: np.ndarray,
model,
model4,
model13,
model16,
model19,
model_inception,
model_inception2,
model_se,
model_mixup,
model_ls,
model_resnest,
mel_params: dict,
mel_params_resnest: dict,
mel_params_b1: dict,
mel_params_thefirst: dict,
mel_params_thesecond: dict,
mel_params_inception: dict,
mel_params_se: dict,
mel_params_mixup: dict,
mel_params_ls: dict,
threshold=0.5,
maxpreds=3, 
):

 dataset = TestDataset(df=test_df,
clip=clip,
img_size=300,
melspectrogram_parameters=mel_params,
imgsize_resnest=224,
resnest_melspec_params=mel_params_resnest,
img_size_b1=240, melspec_b1= mel_params_b1,
img_size_thefirst=260,melspec_thefirst=mel_params_thefirst,
img_size_thesecond=260,melspec_thesecond=mel_params_thesecond,
img_size_inception=299, melspec_inception=mel_params_inception,
img_size_se=224, melspec_se=mel_params_se,
img_size_mixup=260, melspec_mixup = mel_params_mixup,
img_size_ls=260, melspec_ls = mel_params_ls 
)
 loader = data.DataLoader(dataset, batch_size=1, shuffle=False)
 prediction_dict = {}
for image, row_id, site, image2, image3, image4, image5, image6, image7, image8, image9 in loader:
site = site[0]
row_id = row_id[0]
if site in {"site_1", "site_2"}:
with torch.no_grad() :
inps1 = {model.get_inputs() [0].name: to_numpy(image)}
inps2 = {model4.get_inputs() [0].name: to_numpy(image)}
inps3 = {model13.get_inputs() [0].name: to_numpy(image3)}
inps4 = {model16.get_inputs() [0].name: to_numpy(image4)}
inps5 = {model19.get_inputs() [0].name: to_numpy(image5)}
inps6 = {model_resnest.get_inputs() [0].name: to_numpy(image2)}
inps7 = {model_inception.get_inputs() [0].name: to_numpy(image6)}
inps8 = {model_inception2.get_inputs() [0].name: to_numpy(image6)}
inps9 = {model_se.get_inputs() [0].name: to_numpy(image7)}
inps10 = {model_mixup.get_inputs() [0].name: to_numpy(image8)}
inps11 = {model_ls.get_inputs() [0].name: to_numpy(image9)}
outs1 = model.run(None, inps1)
 outs2 = model4.run(None, inps2)
 outs3 = model13.run(None, inps3)
 outs4 = model16.run(None, inps4)
 outs5 = model19.run(None, inps5)
 outs6 = model_resnest.run(None, inps6)
 outs7 = model_inception.run(None, inps7)
 outs8 = model_inception2.run(None, inps8)
 outs9 = model_se.run(None, inps9)
 outs10 = model_mixup.run(None, inps10)
 outs11 = model_ls.run(None, inps11)
 outs1 = outs1[0][0]
outs2 = outs2[0][0]
outs3 = outs3[0][0]
outs4 = outs4[0][0]
outs5 = outs5[0][0]
outs6 = outs6[0][0]
outs7 = outs7[0][0]
outs8 = outs8[0][0]
outs9 = outs9[0][0]
outs10 = outs10[0][0]
outs11 = outs11[0][0]
outs1 = 1/(1 + np.exp(-outs1))
outs2 = 1/(1 + np.exp(-outs2))
outs3 = 1/(1 + np.exp(-outs3))
outs4 = 1/(1 + np.exp(-outs4))
outs5 = 1/(1 + np.exp(-outs5))
outs6 = 1/(1 + np.exp(-outs6))
outs7 = 1/(1 + np.exp(-outs7))
outs8 = 1/(1 + np.exp(-outs8))
outs9 = 1/(1 + np.exp(-outs9))
outs10 = 1/(1 + np.exp(-outs10))
outs11 = 1/(1 + np.exp(-outs11))
outs1 = outs1**2
outs2 = outs2**2
outs3 = outs3**2
outs4 = outs4**2
outs5 = outs5**2
outs6 = outs6**2
outs7 = outs7**2
outs8 = outs8**2
outs9 = outs9**2
outs10 = outs10**2
outs11 = outs11**2
proba = np.sqrt(( outs1+outs2+outs3+outs4+outs5+outs6+outs7+outs8+outs9+outs10+outs11)/11)
 events = proba >= threshold
labels = np.argsort(-proba)[:events.sum() ].tolist()
else:
image = image.squeeze(0)
 image2 = image2.squeeze(0)
 image3 = image3.squeeze(0)
 image4 = image4.squeeze(0)
 image5 = image5.squeeze(0)
 image6 = image6.squeeze(0)
 image7 = image7.squeeze(0)
 image8 = image8.squeeze(0)
 image9 = image9.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()
probas = []
for batch_i in range(n_iter):
batch = image[batch_i * batch_size:(batch_i + 1)* batch_size]
batch2 = image2[batch_i * batch_size:(batch_i + 1)* batch_size]
batch3 = image3[batch_i * batch_size:(batch_i + 1)* batch_size]
batch4 = image4[batch_i * batch_size:(batch_i + 1)* batch_size]
batch5 = image5[batch_i * batch_size:(batch_i + 1)* batch_size]
batch6 = image6[batch_i * batch_size:(batch_i + 1)* batch_size]
batch7 = image7[batch_i * batch_size:(batch_i + 1)* batch_size]
batch8 = image8[batch_i * batch_size:(batch_i + 1)* batch_size]
batch9 = image9[batch_i * batch_size:(batch_i + 1)* batch_size]
if batch.ndim == 3:
batch = batch.unsqueeze(0)
 if batch2.ndim == 3:
batch2 = batch2.unsqueeze(0)
 if batch3.ndim == 3:
batch3 = batch3.unsqueeze(0)
 if batch4.ndim == 3:
batch4 = batch4.unsqueeze(0)
 if batch5.ndim == 3:
batch5 = batch5.unsqueeze(0)
 if batch6.ndim == 3:
batch6 = batch6.unsqueeze(0)
 if batch7.ndim == 3:
batch7 = batch7.unsqueeze(0)
 if batch8.ndim == 3:
batch8 = batch8.unsqueeze(0)
 if batch9.ndim == 3:
batch9 = batch9.unsqueeze(0)
 with torch.no_grad() :
inps1 = {model.get_inputs() [0].name: to_numpy(batch)}
inps2 = {model4.get_inputs() [0].name: to_numpy(batch)}
inps3 = {model13.get_inputs() [0].name: to_numpy(batch3)}
inps4 = {model16.get_inputs() [0].name: to_numpy(batch4)}
inps5 = {model19.get_inputs() [0].name: to_numpy(batch5)}
inps6 = {model_resnest.get_inputs() [0].name: to_numpy(batch2)}
inps7 = {model_inception.get_inputs() [0].name: to_numpy(batch6)}
inps8 = {model_inception2.get_inputs() [0].name: to_numpy(batch6)}
inps9 = {model_se.get_inputs() [0].name: to_numpy(batch7)}
inps10 = {model_mixup.get_inputs() [0].name: to_numpy(batch8)}
inps11 = {model_ls.get_inputs() [0].name: to_numpy(batch9)}
outs1 = model.run(None, inps1)
 outs2 = model4.run(None, inps2)
 outs3 = model13.run(None, inps3)
 outs4 = model16.run(None, inps4)
 outs5 = model19.run(None, inps5)
 outs6 = model_resnest.run(None, inps6)
 outs7 = model_inception.run(None, inps7)
 outs8 = model_inception2.run(None, inps8)
 outs9 = model_se.run(None, inps9)
 outs10 = model_mixup.run(None, inps10)
 outs11 = model_ls.run(None, inps11)
 del inps1,inps2,inps3,inps4,inps5,inps6,inps8,inps9,inps10,inps11
gc.collect()
outs1 = outs1[0]
outs2 = outs2[0]
outs3 = outs3[0]
outs4 = outs4[0]
outs5 = outs5[0]
outs6 = outs6[0]
outs7 = outs7[0]
outs8 = outs8[0]
outs9 = outs9[0]
outs10 = outs10[0]
outs11 = outs11[0]
outs1 = 1/(1 + np.exp(-outs1))
outs2 = 1/(1 + np.exp(-outs2))
outs3 = 1/(1 + np.exp(-outs3))
outs4 = 1/(1 + np.exp(-outs4))
outs5 = 1/(1 + np.exp(-outs5))
outs6 = 1/(1 + np.exp(-outs6))
outs7 = 1/(1 + np.exp(-outs7))
outs8 = 1/(1 + np.exp(-outs8))
outs9 = 1/(1 + np.exp(-outs9))
outs10 = 1/(1 + np.exp(-outs10))
outs11 = 1/(1 + np.exp(-outs11))
outs1 = outs1**2
outs2 = outs2**2
outs3 = outs3**2
outs4 = outs4**2
outs5 = outs5**2
outs6 = outs6**2
outs7 = outs7**2
outs8 = outs8**2
outs9 = outs9**2
outs10 = outs10**2
outs11 = outs11**2
probas.append(np.sqrt(( outs1+outs2+outs3+outs4+outs5+outs6+outs7+outs8+outs9+outs10+outs11)/11))
del outs1,outs2,outs3,outs4,outs5,outs8,outs9,outs10,outs11
gc.collect()
probas = np.vstack(probas)
 probas = probas.max(0)
 events =(probas>=threshold)
 labels = np.argsort(-probas)[:events.sum() ].tolist()
if len(labels)== 0:
prediction_dict[row_id] = "nocall"
else:
labels_str_list = list(map(lambda x: INV_BIRD_CODE[x], labels))
if site in('site_1','site_2'):
label_string = " ".join(labels_str_list[:maxpreds])
 else:
label_string = " ".join(labels_str_list)
 prediction_dict[row_id] = label_string
return prediction_dict<predict_on_test>
y_pred = model.predict(test_tweet)
 y_pred = np.round(y_pred ).astype(int ).reshape(3263)
 sub = pd.DataFrame({'id':submission['id'].values.tolist() ,'target':y_pred} )
def prediction(test_df: pd.DataFrame,
test_audio: Path,
model_config: dict,
mel_params: dict,
resnest_melparams: dict,
b1_melparams: dict,
thefirst_melparams: dict,
thesecond_melparams: dict,
inception_melparams: dict,
se_melparams: dict,
mixup_melparams: dict,
ls_melparams: dict,
target_sr: int,
threshold=0.5,
maxpreds = 3, 
):
model = onnxruntime.InferenceSession(".. /input/save-out-onnx-models/model.onnx")
 model5 = onnxruntime.InferenceSession(".. /input/save-out-onnx-models/model4.onnx")
 model14 = onnxruntime.InferenceSession(".. /input/save-out-onnx-models/model13.onnx")
 model17 = onnxruntime.InferenceSession(".. /input/save-out-onnx-models/model16.onnx")
 model20 = onnxruntime.InferenceSession(".. /input/save-out-onnx-models/model19.onnx")
 model_inception = onnxruntime.InferenceSession('.. /input/save-out-onnx-models/model_inception.onnx')
 model_inception2 = onnxruntime.InferenceSession('.. /input/save-out-onnx-models/model_inception2.onnx')
 model_se = onnxruntime.InferenceSession('.. /input/save-out-onnx-models/model_se.onnx')
 model_mixup = onnxruntime.InferenceSession('.. /input/save-out-onnx-models/model_mixup.onnx')
 model_ls = onnxruntime.InferenceSession('.. /input/save-out-onnx-models/model_ls.onnx')
 model4 = onnxruntime.InferenceSession(".. /input/save-out-onnx-models/model_resnest.onnx")
 unique_audio_id = test_df.audio_id.unique()
prediction_dfs = []
for audio_id in unique_audio_id:
clip, sr_native = librosa.core.audio.__audioread_load(test_audio /(audio_id + '.mp3'),
offset=0,
duration=None,
dtype=np.float32)
 clip = librosa.to_mono(clip)
 cpmp_sr = 32000
if sr_native > 0:
clip = librosa.resample(clip, sr_native, cpmp_sr, res_type='kaiser_fast')
 test_df_for_audio_id = test_df.query(
 f"audio_id == '{audio_id}'" ).reset_index(drop=True)
 prediction_dict = prediction_for_clip(test_df_for_audio_id,
clip=clip,
model=model,
model4=model5,
model13=model14,
model16=model17,
model19=model20,
model_inception=model_inception,
model_inception2=model_inception2,
model_se=model_se,
model_mixup=model_mixup,
model_ls=model_ls,
model_resnest=model4,
mel_params=mel_params,
mel_params_resnest=resnest_melparams,
mel_params_b1 = b1_melparams,
mel_params_thefirst = thefirst_melparams,
mel_params_thesecond= thesecond_melparams,
mel_params_inception=inception_melparams,
mel_params_se=se_melparams,
mel_params_mixup=mixup_melparams,
mel_params_ls=ls_melparams,
threshold=threshold,
maxpreds = maxpreds, 
)
 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)
 return prediction_df<predict_on_test>
sub.to_csv('submission.csv',index=False )