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| Original file line number | Diff line number | Diff line change |
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| import torch | ||
| import torchaudio | ||
| import torch.nn as nn | ||
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| class SimpleCNN(nn.Module): | ||
| def __init__(self): | ||
| super().__init__() | ||
| conv_layers = [] | ||
| self.name = 'SimpleCNN' | ||
| # First Convolution Block with Relu and Batch Norm. Use Kaiming Initialization | ||
| self.conv1 = nn.Conv2d(1, 8, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2)) | ||
| self.relu1 = nn.ReLU() | ||
| self.bn1 = nn.BatchNorm2d(8) | ||
| conv_layers += [self.conv1, self.relu1, self.bn1] | ||
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| # Second Convolution Block | ||
| self.conv2 = nn.Conv2d(8, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | ||
| self.relu2 = nn.ReLU() | ||
| self.bn2 = nn.BatchNorm2d(16) | ||
| conv_layers += [self.conv2, self.relu2, self.bn2] | ||
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| # Third Convolution Block | ||
| self.conv3 = nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | ||
| self.relu3 = nn.ReLU() | ||
| self.bn3 = nn.BatchNorm2d(32) | ||
| conv_layers += [self.conv3, self.relu3, self.bn3] | ||
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| # Fourth Convolution Block | ||
| self.conv4 = nn.Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) | ||
| self.relu4 = nn.ReLU() | ||
| self.bn4 = nn.BatchNorm2d(64) | ||
| conv_layers += [self.conv4, self.relu4, self.bn4] | ||
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| # Linear Classifier | ||
| self.ap = nn.AdaptiveAvgPool2d(output_size=1) | ||
| self.lin = nn.Linear(in_features=64, out_features=8) | ||
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| self.conv = nn.Sequential(*conv_layers) | ||
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| def forward(self, x): | ||
| x = self.conv1(x) | ||
| y_1 = x.copy() | ||
| x = self.ap(x) | ||
| x = x.view(x.shape[0], -1) | ||
| x = self.lin(x) | ||
| return x, y_1 # y_1 output of the 1st layer | ||
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| class Res2DBlock(nn.Module): | ||
| expansion = 1 #we don't use the block.expansion here | ||
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| def __init__(self, inplanes, planes, stride=1,padding = 1): | ||
| super().__init__() | ||
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size = 3, stride=stride, | ||
| padding=padding, bias=False) | ||
| self.bn1 = nn.BatchNorm2d(planes) | ||
| self.relu = nn.ReLU(inplace=True) | ||
| self.conv2 = nn.Conv2d(planes, planes, kernel_size = 3, stride=1, | ||
| padding=padding, bias=False) | ||
| self.bn2 = nn.BatchNorm2d(planes) | ||
| self.downsample = nn.Sequential( | ||
| nn.Conv2d(inplanes, planes, 1, stride, bias=False), | ||
| nn.BatchNorm2d(planes)) | ||
| self.stride = stride | ||
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| def forward(self, x): | ||
| identity = x | ||
| out = self.conv1(x) | ||
| out = self.relu(out) | ||
| out = self.conv2(out) | ||
| out = self.bn2(out) | ||
| identity = self.downsample(x) | ||
| out += identity | ||
| out = self.relu(out) | ||
| return out | ||
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| class ResNet(nn.Module): | ||
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| def __init__(self, FN=16, num_classes=8, p_dropout=None): | ||
| super().__init__() | ||
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| self.FN = FN | ||
| if FN == 128: | ||
| self.name = 'ResNet34-XL' | ||
| elif FN == 64: | ||
| self.name = 'ResNet34-L' | ||
| elif FN == 32: | ||
| self.name = 'ResNet34-M' | ||
| elif FN == 16: | ||
| self.name = 'ResNet34-S' | ||
| else: | ||
| self.name = 'ResNet34' | ||
| layers = [3, 4, 6, 3] | ||
| self.c1 = nn.Conv2d(1, FN, kernel_size=7, stride=2, padding=3, bias=False) | ||
| self.bn1 = nn.BatchNorm2d(FN) | ||
| self.relu = nn.ReLU(inplace=True) | ||
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | ||
| self.layer1 = self._make_layer(FN, FN, layers[0]) | ||
| self.layer2 = self._make_layer(FN, FN * 2, layers[1], stride=2) | ||
| self.avgpool = nn.AdaptiveAvgPool2d(7) | ||
| self.fc = nn.Linear(FN * 98, num_classes) | ||
| self.p_dropout = p_dropout | ||
| if p_dropout: | ||
| self.dropout = nn.Dropout(p=p_dropout) | ||
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| def _make_layer(self, inplanes, planes, blocks, stride=1): | ||
| layers = [] | ||
| layers.append(Res2DBlock(inplanes, planes, stride)) | ||
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| self.inplanes = planes | ||
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| for _ in range(1, blocks): | ||
| layers.append(Res2DBlock(self.inplanes, planes)) | ||
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| return nn.Sequential(*layers) | ||
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| def forward(self, x): | ||
| x = self.c1(x) | ||
| x = self.bn1(x) | ||
| x = self.relu(x) | ||
| x = self.maxpool(x) | ||
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| x = self.layer1(x) | ||
| x = self.layer2(x) | ||
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| x = self.avgpool(x) | ||
| x = torch.flatten(x, 1) | ||
| x = self.fc(x) | ||
| if self.p_dropout: | ||
| x = self.dropout(x) | ||
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| return x | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,50 @@ | ||
| import os | ||
| import torch | ||
| import torchaudio | ||
| from torch.utils.data import Dataset | ||
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| class FMA2D_spec(Dataset): | ||
| def __init__(self, data_dir, track_ids, labels_onehot, transforms=True, augment_prob=0.5, max_mask_pct=0.3, n_freq_masks=2, | ||
| n_time_masks=2): | ||
| self.data_dir = data_dir | ||
| self.filenames = os.listdir(data_dir) | ||
| self.track_ids = track_ids | ||
| self.labels_onehot = labels_onehot | ||
| self.transforms = transforms | ||
| self.augment_prob = augment_prob | ||
| self.max_mask_pct = max_mask_pct | ||
| self.n_freq_masks = n_freq_masks | ||
| self.n_time_masks = n_time_masks | ||
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| def __getitem__(self, index): | ||
| tid = self.track_ids[index] | ||
| # load the spectrogram data | ||
| spec_path = os.path.join('./data/spectrograms/' + "{:06d}".format(tid) + '.pt') | ||
| try: | ||
| spec = torch.load(spec_path) | ||
| except Exception as e: | ||
| return self.__getitem__(index + 1) | ||
| if self.transforms is True and torch.rand(1) < self.augment_prob: | ||
| spec = self.spectro_augment(spec) | ||
| # get label | ||
| label = torch.from_numpy(self.labels_onehot.loc[tid].values).float() | ||
| return spec, label | ||
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| def __len__(self): | ||
| return len(self.track_ids) | ||
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| def spectro_augment(self, spec): | ||
| _, n_mels, n_steps = spec.shape | ||
| mask_value = spec.mean() | ||
| aug_spec = spec | ||
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| freq_mask_param = self.max_mask_pct * n_mels | ||
| for _ in range(self.n_freq_masks): | ||
| aug_spec = torchaudio.transforms.FrequencyMasking(freq_mask_param)(aug_spec, mask_value) | ||
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| time_mask_param = self.max_mask_pct * n_steps | ||
| for _ in range(self.n_time_masks): | ||
| aug_spec = torchaudio.transforms.TimeMasking(time_mask_param)(aug_spec, mask_value) | ||
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| return aug_spec |
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