Kaggle competition: Cornell Birdcall Identification
<SOS> metric: MeanFScoreBeta Kaggle data source: cornell-birdcall-identification<choose_model_class>
pd.options.display.max_rows = 500
pd.options.display.max_columns = 500
class Model(nn.Module):
def __init__(self, num_features, num_targets):
super(Model, self ).__init__()
self.hidden_size = [1500, 1250, 1000, 750]
self.dropout_value = [0.5, 0.35, 0.3, 0.25]
self.batch_norm1 = nn.BatchNorm1d(num_features)
 self.dense1 = nn.Linear(num_features, self.hidden_size[0])
 self.batch_norm2 = nn.BatchNorm1d(self.hidden_size[0])
 self.dropout2 = nn.Dropout(self.dropout_value[0])
 self.dense2 = nn.Linear(self.hidden_size[0], self.hidden_size[1])
 self.batch_norm3 = nn.BatchNorm1d(self.hidden_size[1])
 self.dropout3 = nn.Dropout(self.dropout_value[1])
 self.dense3 = nn.Linear(self.hidden_size[1], self.hidden_size[2])
 self.batch_norm4 = nn.BatchNorm1d(self.hidden_size[2])
 self.dropout4 = nn.Dropout(self.dropout_value[2])
 self.dense4 = nn.Linear(self.hidden_size[2], self.hidden_size[3])
 self.batch_norm5 = nn.BatchNorm1d(self.hidden_size[3])
 self.dropout5 = nn.Dropout(self.dropout_value[3])
 self.dense5 = nn.utils.weight_norm(nn.Linear(self.hidden_size[3], num_targets))
def forward(self, x):
x = self.batch_norm1(x)
 x = F.leaky_relu(self.dense1(x))
x = self.batch_norm2(x)
 x = self.dropout2(x)
 x = F.leaky_relu(self.dense2(x))
x = self.batch_norm3(x)
 x = self.dropout3(x)
 x = F.leaky_relu(self.dense3(x))
x = self.batch_norm4(x)
 x = self.dropout4(x)
 x = F.leaky_relu(self.dense4(x))
x = self.batch_norm5(x)
 x = self.dropout5(x)
 x = self.dense5(x)
 return x
class LabelSmoothingLoss(nn.Module):
def __init__(self, classes, smoothing=0.0, dim=-1):
super(LabelSmoothingLoss, self ).__init__()
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
self.cls = classes
self.dim = dim
def forward(self, pred, target):
pred = pred.log_softmax(dim=self.dim)
 with torch.no_grad() :
true_dist = torch.zeros_like(pred)
 true_dist.fill_(self.smoothing /(self.cls - 1))
true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
 return torch.mean(torch.sum(-true_dist * pred, dim=self.dim))<choose_model_class>
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))
class FineTuneScheduler:
def __init__(self, epochs):
self.epochs = epochs
self.epochs_per_step = 0
self.frozen_layers = []
def copy_without_top(self, model, num_features, num_targets, num_targets_new):
self.frozen_layers = []
model_new = Model(num_features, num_targets)
 model_new.load_state_dict(model.state_dict())
 for name, param in model_new.named_parameters() :
layer_index = name.split('.')[0][-1]
if layer_index == 5:
continue
param.requires_grad = False
if layer_index not in self.frozen_layers:
self.frozen_layers.append(layer_index)
 self.epochs_per_step = self.epochs // len(self.frozen_layers)
 model_new.batch_norm5 = nn.BatchNorm1d(model_new.hidden_size[3])
 model_new.dropout5 = nn.Dropout(model_new.dropout_value[3])
 model_new.dense5 = nn.utils.weight_norm(nn.Linear(model_new.hidden_size[-1], num_targets_new))
model_new.to(DEVICE)
 return model_new
def step(self, epoch, model):
if len(self.frozen_layers)== 0:
return
if epoch % self.epochs_per_step == 0:
last_frozen_index = self.frozen_layers[-1]
for name, param in model.named_parameters() :
layer_index = name.split('.')[0][-1]
if layer_index == last_frozen_index:
param.requires_grad = True
del self.frozen_layers[-1]<categorify>
set_seed(1213 )
def process_data(data):
data = pd.get_dummies(data, columns=['cp_time','cp_dose'])
 return data<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"
feature_cols = [c for c in process_data(train ).columns if c not in all_target_cols]
feature_cols = [c for c in feature_cols if c not in ['kfold', 'sig_id', 'drug_id']]
num_features = len(feature_cols)
 num_features<define_variables>
train = pd.read_csv(RAW_DATA / "train.csv" )
DEVICE =('cuda' if torch.cuda.is_available() else 'cpu')
 EPOCHS = 24
BATCH_SIZE = 128
WEIGHT_DECAY = {'ALL_TARGETS': 1e-5, 'SCORED_ONLY': 3e-6}
MAX_LR = {'ALL_TARGETS': 1e-2, 'SCORED_ONLY': 3e-3}
DIV_FACTOR = {'ALL_TARGETS': 1e3, 'SCORED_ONLY': 1e2}
PCT_START = 0.1<choose_model_class>
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" )
model = Model(num_features, num_all_targets)
 model<define_search_model>
sub = pd.read_csv(".. /input/birdsong-recognition/sample_submission.csv")
 sub.to_csv("submission.csv", index=False )
def make_cv_folds(train, SEEDS, NFOLDS, DRUG_THRESH):
vc = train.drug_id.value_counts()
vc1 = vc.loc[vc <= DRUG_THRESH].index.sort_values()
vc2 = vc.loc[vc > DRUG_THRESH].index.sort_values()
for seed_id in range(SEEDS):
kfold_col = 'kfold_{}'.format(seed_id)
 dct1 = {}
dct2 = {}
skf = MultilabelStratifiedKFold(n_splits=NFOLDS, shuffle=True, random_state=seed_id)
 tmp = train.groupby('drug_id')[target_cols].mean().loc[vc1]
for fold,(idxT, idxV)in enumerate(skf.split(tmp, tmp[target_cols])) :
dd = {k: fold for k in tmp.index[idxV].values}
dct1.update(dd)
 skf = MultilabelStratifiedKFold(n_splits=NFOLDS, shuffle=True, random_state=seed_id)
 tmp = train.loc[train.drug_id.isin(vc2)].reset_index(drop=True)
 for fold,(idxT, idxV)in enumerate(skf.split(tmp, tmp[target_cols])) :
dd = {k: fold for k in tmp.sig_id[idxV].values}
dct2.update(dd)
 train[kfold_col] = train.drug_id.map(dct1)
 train.loc[train[kfold_col].isna() , kfold_col] = train.loc[train[kfold_col].isna() , 'sig_id'].map(dct2)
 train[kfold_col] = train[kfold_col].astype('int8')
 return train
SEEDS = 7
NFOLDS = 7
DRUG_THRESH = 18
train = make_cv_folds(train, SEEDS, NFOLDS, DRUG_THRESH)
 train.head()<train_model>
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
}
def run_training(fold_id, seed_id):
seed_everything(seed_id)
 train_ = process_data(train)
 test_ = process_data(test)
 kfold_col = f'kfold_{seed_id}'
trn_idx = train_[train_[kfold_col] != fold_id].index
val_idx = train_[train_[kfold_col] == fold_id].index
train_df = train_[train_[kfold_col] != fold_id].reset_index(drop=True)
 valid_df = train_[train_[kfold_col] == fold_id].reset_index(drop=True)
 def train_model(model, tag_name, target_cols_now, fine_tune_scheduler=None):
x_train, y_train = train_df[feature_cols].values, train_df[target_cols_now].values
x_valid, y_valid = valid_df[feature_cols].values, valid_df[target_cols_now].values
train_dataset = MoADataset(x_train, y_train)
 valid_dataset = MoADataset(x_valid, y_valid)
 trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
 validloader = torch.utils.data.DataLoader(valid_dataset, batch_size=BATCH_SIZE, shuffle=False)
 optimizer = torch.optim.Adam(model.parameters() , lr=1e-3, weight_decay=WEIGHT_DECAY[tag_name])
 scheduler = optim.lr_scheduler.OneCycleLR(optimizer=optimizer,
steps_per_epoch=len(trainloader),
pct_start=PCT_START,
div_factor=DIV_FACTOR[tag_name],
max_lr=MAX_LR[tag_name],
epochs=EPOCHS)
 loss_fn = nn.BCEWithLogitsLoss()
loss_tr = SmoothBCEwLogits(smoothing=0.001)
 oof = np.zeros(( len(train), len(target_cols_now)))
 best_loss = np.inf
for epoch in range(EPOCHS):
if fine_tune_scheduler is not None:
fine_tune_scheduler.step(epoch, model)
 train_loss = train_fn(model, optimizer, scheduler, loss_tr, trainloader, DEVICE)
 valid_loss, valid_preds = valid_fn(model, loss_fn, validloader, DEVICE)
 print(f"SEED: {seed_id}, FOLD: {fold_id}, {tag_name}, EPOCH: {epoch}, train_loss: {train_loss:.6f}, valid_loss: {valid_loss:.6f}")
 if np.isnan(valid_loss):
break
if valid_loss < best_loss:
best_loss = valid_loss
oof[val_idx] = valid_preds
torch.save(model.state_dict() , f"{tag_name}_FOLD{fold_id}_.pth")
 return oof
fine_tune_scheduler = FineTuneScheduler(EPOCHS)
 pretrained_model = Model(num_features, num_all_targets)
 pretrained_model.to(DEVICE)
 train_model(pretrained_model, 'ALL_TARGETS', all_target_cols)
 pretrained_model = Model(num_features, num_all_targets)
 pretrained_model.load_state_dict(torch.load(f"ALL_TARGETS_FOLD{fold_id}_.pth"))
pretrained_model.to(DEVICE)
 final_model = fine_tune_scheduler.copy_without_top(pretrained_model, num_features, num_all_targets, num_targets)
 oof = train_model(final_model, 'SCORED_ONLY', target_cols, fine_tune_scheduler)
 model = Model(num_features, num_targets)
 model.load_state_dict(torch.load(f"SCORED_ONLY_FOLD{fold_id}_.pth"))
model.to(DEVICE)
 x_test = test_[feature_cols].values
testdataset = TestDataset(x_test)
 testloader = torch.utils.data.DataLoader(testdataset, batch_size=BATCH_SIZE, shuffle=False)
 predictions = np.zeros(( len(test_), num_targets))
predictions = inference_fn(model, testloader, DEVICE)
 return oof, predictions<categorify>
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() }
def run_k_fold(NFOLDS, seed_id):
oof = np.zeros(( len(train), len(target_cols)))
 predictions = np.zeros(( len(test), len(target_cols)))
 for fold_id in range(NFOLDS):
oof_, pred_ = run_training(fold_id, seed_id)
 predictions += pred_ / NFOLDS
oof += oof_
return oof, predictions<categorify>
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
SEED = [0, 1, 2, 3, 4, 5, 6]
oof = np.zeros(( len(train), len(target_cols)))
 predictions = np.zeros(( len(test), len(target_cols)))
 time_begin = time()
for seed_id in SEED:
oof_, predictions_ = run_k_fold(NFOLDS, seed_id)
 oof += oof_ / len(SEED)
 predictions += predictions_ / len(SEED)
 time_diff = time() - time_begin
train[target_cols] = oof
test[target_cols] = predictions<feature_engineering>
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
str(timedelta(seconds=time_diff))<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"]))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model = model.to(device)
 state_dict = torch.load(args["trained_weights"], map_location=device)
 model.load_state_dict(state_dict)
 model.eval()
return model
valid_results = train_targets_scored.drop(columns=target_cols ).merge(train[['sig_id']+target_cols], on='sig_id', how='left' ).fillna(0)
 y_true = train_targets_scored[target_cols].values
y_pred = valid_results[target_cols].values
score = 0
for i in range(len(target_cols)) :
score += log_loss(y_true[:, i], y_pred[:, i])
 print("CV log_loss: ", score / y_pred.shape[1] )<save_to_csv>
def prediction_for_clip(test_df: pd.DataFrame,
clip: np.ndarray,
model: ResNet,
mel_params: dict,
threshold=0.5,
maxpreds=3, 
):

 dataset = TestDataset(df=test_df,
clip=clip,
img_size=224,
melspectrogram_parameters=mel_params)
 loader = data.DataLoader(dataset, batch_size=1, shuffle=False)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.eval()
prediction_dict = {}
for image, row_id, site in progress_bar(loader):
site = site[0]
row_id = row_id[0]
if site in {"site_1", "site_2"}:
image = image.to(device)
 with torch.no_grad() :
prediction = F.sigmoid(model(image))
proba = prediction.detach().cpu().numpy().reshape(-1)
 events = proba >= threshold
labels = np.argsort(-proba)[:events.sum() ].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()
probas = []
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 = F.sigmoid(model(batch))
proba = prediction.detach().cpu().numpy()
probas.append(proba)
 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))
label_string = " ".join(labels_str_list[:maxpreds])
 prediction_dict[row_id] = label_string
return prediction_dict
sub = sample_submission.drop(columns=target_cols ).merge(test[['sig_id']+target_cols], on='sig_id', how='left' ).fillna(0)
 sub.to_csv('submission.csv', index=False )<load_from_csv>
def prediction(test_df: pd.DataFrame,
test_audio: Path,
model_config: dict,
mel_params: dict,
target_sr: int,
threshold=0.5,
maxpreds = 3, 
):
model = get_model(model_config)
 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=model,
mel_params=mel_params,
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
<define_variables><EOS> 
submission = prediction(test_df=test,
test_audio=TEST_AUDIO_DIR,
model_config=model_config,
mel_params=melspectrogram_parameters,
target_sr=TARGET_SR,
threshold=0.56,
maxpreds=2, 
)
 submission.to_csv("submission.csv", index=False )