Kaggle competition: Cornell Birdcall Identification
<SOS> metric: MeanFScoreBeta Kaggle data source: cornell-birdcall-identification<load_from_csv>
pd.options.display.max_rows = 500
pd.options.display.max_columns = 500
train=pd.read_csv("/kaggle/input/lish-moa/train_features.csv")
 train<load_from_csv>
def set_seed(seed: int = 42):
random.seed(seed)
 np.random.seed(seed)
 os.environ["PYTHONHASHSEED"] = str(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 @contextmanager
def timer(name: str)-> None:

 t0 = time.time()
print("[{}] start".format(name))
yield
print("[{}] done in {:.0f} s".format(name, time.time() - t0))
train_target = pd.read_csv(".. /input/lish-moa/train_targets_scored.csv" )<drop_column>
set_seed(1213 )
ctlVehicle_idx = train["cp_type"] != "ctl_vehicle"
train = train.loc[ctlVehicle_idx].reset_index(drop=True)
 train = train.drop("cp_type", axis=1)
 train_target = train_target.loc[ctlVehicle_idx].reset_index(drop=True )<define_variables>
ROOT = Path.cwd().parent
INPUT_ROOT = ROOT / "input"
RAW_DATA = INPUT_ROOT / "birdsong-recognition"
TRAIN_AUDIO_DIR = RAW_DATA / "train_audio"
TEST_AUDIO_DIR = RAW_DATA / "test_audio"
gcols = [g for g in train.columns if "g-" in g]
ccols = [c for c in train.columns if "c-" in c]
cpcols = [cp for cp in train.columns if "cp_" in cp]<load_from_csv>
train = pd.read_csv(RAW_DATA / "train.csv" )
test = pd.read_csv("/kaggle/input/lish-moa/test_features.csv")
 ctlVehicle_test = test["cp_type"] == "ctl_vehicle"
test = test.drop("cp_type", axis=1 )<feature_engineering>
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" )
for col in train.iloc[:, 3:].columns:
percent = train[col].quantile([0.01, 0.99] ).values
train[col] = np.clip(train[col], percent[0], percent[1])
 for col in test.iloc[:, 3:].columns:
percent = test[col].quantile([0.01, 0.99] ).values
test[col] = np.clip(test[col], percent[0], percent[1] )<normalization>
sub = pd.read_csv(".. /input/birdsong-recognition/sample_submission.csv")
 sub.to_csv("submission.csv", index=False )
sys.path.append(".. /input/rank-gauss")
 scaler = GaussRankScaler()
train[gcols + ccols] = scaler.fit_transform(train[gcols + ccols] )<categorify>
TARGET_SR = 32000
model_config_0 = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/training-aug-0913-add-noise-fold0-ext-4/best_model.pth"
}
model_config_1 = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/training-aug-0903-add-noise-fold1/best_model.pth"
}
model_config_2 = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/training-aug-0903-add-noise-fold2/best_model.pth"
}
model_config_3 = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/training-aug-0904-add-noise-fold3/best_model.pth"
}
model_config_4 = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/training-aug-0904-add-noise-fold4/best_model.pth"
}
model_config_5 = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/training-aug-0912-add-noise-fold1-ext-4/best_model.pth"
}
model_config_6 = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/training-aug-0912-add-noise-fold2-ext-4/best_model.pth"
}
model_config_7 = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/0914extrafold34/best_model_fold3-6-ext.pth"
}
model_config_8 = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/0914extrafold34/best_model_fold4-7-ext.pth"
}
model_config_9 = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/ext0914/best_model_ext-4.pth"
}
melspectrogram_parameters = {
"n_mels": 128,
"fmin": 20,
"fmax": 16000
}
train["cp_time"]=train["cp_time"].map({24: 1, 48: 2, 72: 3})
 train["cp_dose"] = train["cp_dose"].map({"D1": 1, "D2": 2})
 test["cp_time"]= test["cp_time"].map({24: 1, 48: 2, 72: 3})
 test["cp_dose"]=test["cp_dose"].map({"D1": 1, "D2": 2})
 train<normalization>
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() }
scaler2 = GaussRankScaler()
test[gcols + ccols] = scaler2.fit_transform(test[gcols + ccols] )<train_model>
MEAN = np.array([0.485, 0.456, 0.406])
 STD = np.array([0.229, 0.224, 0.225])
 def mono_to_color(X, eps=1e-6):
X = np.stack([X, X, X], axis=-1)
 _min, _max = X.min() , X.max()
if(_max - _min)> eps:
V = np.clip(X, _min, _max)
 V = 255 *(V - _min)/(_max - _min)
 V = V.astype(np.uint8)
 else:
V = np.zeros_like(X, dtype=np.uint8)
 return V
def normalize(image, mean, std):
image =(image / 255.0 ).astype(np.float32)
 image =(image - mean)/ std
return image
class TestDataset(data.Dataset):
def __init__(self, df: pd.DataFrame, clip: np.ndarray,
img_size=224, melspectrogram_parameters={}):
self.df = df
self.clip = clip
self.img_size = img_size
self.melspectrogram_parameters = melspectrogram_parameters
def __len__(self):
return len(self.df)
 def __getitem__(self, idx: int):
SR = 32000
sample = self.df.loc[idx, :]
site = sample.site
row_id = sample.row_id
if site == "site_3":
y = self.clip.astype(np.float32)
 len_y = len(y)
 start = 0
end = SR * 5
images = []
while len_y > start:
y_batch = y[start:end].astype(np.float32)
 if len(y_batch)!=(SR * 5):
break
start = end
end = end + SR * 5
melspec = librosa.feature.melspectrogram(y_batch,
sr=SR,
**self.melspectrogram_parameters)
 melspec = librosa.power_to_db(melspec ).astype(np.float32)
 image = mono_to_color(melspec)
 image = normalize(image, mean=MEAN, std=STD)
 height, width, _ = image.shape
image = cv2.resize(image,(int(width * self.img_size / height), self.img_size))
image = np.moveaxis(image, 2, 0)
 image =(image / 255.0 ).astype(np.float32)
 images.append(image)
 images = np.asarray(images)
 return images, row_id, site
else:
end_seconds = int(sample.seconds)
 start_seconds = int(end_seconds - 5)
 start_index = SR * start_seconds
end_index = SR * end_seconds
y = self.clip[start_index:end_index].astype(np.float32)
 melspec = librosa.feature.melspectrogram(y, sr=SR, **self.melspectrogram_parameters)
 melspec = librosa.power_to_db(melspec ).astype(np.float32)
 image = mono_to_color(melspec)
 image = normalize(image, mean=MEAN, std=STD)
 height, width, _ = image.shape
image = cv2.resize(image,(int(width * self.img_size / height), self.img_size))
image = np.moveaxis(image, 2, 0)
 image =(image / 255.0 ).astype(np.float32)
 return image, row_id, site
g_pca = PCA(n_components=0.9)
 c_pca = PCA(n_components=0.9)
 train_test_g_concat = pd.concat([train[gcols], test[gcols]], axis=0)
 train_test_c_concat = pd.concat([train[ccols], test[ccols]], axis=0)
 g_pca.fit(train_test_g_concat)
 c_pca.fit(train_test_c_concat)
 train_gtrans = pd.DataFrame(g_pca.transform(train[gcols]), columns=["g_PCA" + str(i)for i in range(g_pca.n_components_)], index=train.index)
 test_gtrans = pd.DataFrame(g_pca.transform(test[gcols]), columns=["g_PCA" + str(i)for i in range(g_pca.n_components_)], index=test.index)
 train_ctrans = pd.DataFrame(c_pca.transform(train[ccols]), columns=["c_PCA" + str(i)for i in range(c_pca.n_components_)], index=train.index)
 test_ctrans = pd.DataFrame(c_pca.transform(test[ccols]), columns=["c_PCA" + str(i)for i in range(c_pca.n_components_)], index=test.index)
 g_pca.n_components_, c_pca.n_components_<concatenate>
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
train = pd.concat([train_gtrans, train_ctrans, train[cpcols]], axis=1)
 test = pd.concat([test_gtrans, test_ctrans, test[cpcols]], axis=1)
 train<drop_column>
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
train_target = train_target.drop("sig_id", axis=1)
 train_target<compute_test_metric>
def get_model(args: tp.Dict):
model = ResNet(
 Bottleneck, [3, 4, 6, 3],
radix=1, groups=1, bottleneck_width=64,
deep_stem=True, stem_width=32, avg_down=True,
avd=True, avd_first=True)
 del model.fc
model.fc = nn.Sequential(
 nn.Linear(2048, 1024), nn.ReLU() , nn.Dropout(p=0.2),
nn.Linear(1024, 1024), nn.ReLU() , nn.Dropout(p=0.2),
nn.Linear(1024, args["num_classes"]))
state_dict = torch.load(args["trained_weights"])
 model.load_state_dict(state_dict)
 device = torch.device("cuda")
 model.to(device)
 model.eval()
return model
<choose_model_class><EOS> 
def prediction(test_df: pd.DataFrame,
test_audio: Path,
model_list: list,
mel_params: dict,
target_sr: int,
threshold=0.4,
maxpreds=3):
models = []
for m in model_list:
models.append(get_model(m))
unique_audio_id = test_df.audio_id.unique()
warnings.filterwarnings("ignore")
 prediction_dfs = []
for audio_id in unique_audio_id:
with timer(f"Loading {audio_id}"):
clip, _ = librosa.load(test_audio /(audio_id + ".mp3"),
sr=target_sr,
mono=True,
res_type="kaiser_fast")
 test_df_for_audio_id = test_df.query(
 f"audio_id == '{audio_id}'" ).reset_index(drop=True)
 with timer(f"Prediction on {audio_id}"):
prediction_dict = prediction_for_clip(test_df_for_audio_id,
clip=clip,
model_list = models,
mel_params=mel_params,
threshold=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)
 return prediction_df