transition
This commit is contained in:
Si11ium
parent
68431b848e
commit
f1f327ef17
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from argparse import Namespace
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import warnings
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import torch
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from torch import nn
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from einops import rearrange, repeat
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from ml_lib.modules.blocks import TransformerModule
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from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x)
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from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
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BaseDataloadersMixin, BaseTestMixin, BaseLossMixin)
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MIN_NUM_PATCHES = 16
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class VisualTransformer(DatasetMixin,
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BaseLossMixin,
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BaseDataloadersMixin,
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BaseTrainMixin,
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BaseValMixin,
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BaseTestMixin,
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BaseOptimizerMixin,
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LightningBaseModule
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):
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def __init__(self, hparams):
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super(VisualTransformer, self).__init__(hparams)
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# Dataset
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# =============================================================================
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self.dataset = self.build_dataset()
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self.in_shape = self.dataset.train_dataset.sample_shape
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assert len(self.in_shape) == 3, 'There need to be three Dimensions'
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channels, height, width = self.in_shape
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# Model Paramters
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# =============================================================================
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# Additional parameters
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self.embed_dim = self.params.embedding_size
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# Automatic Image Shaping
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self.patch_size = self.params.patch_size
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image_size = (max(height, width) // self.patch_size) * self.patch_size
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self.image_size = image_size + self.patch_size if image_size < max(height, width) else image_size
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# This should be obsolete
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assert self.image_size % self.patch_size == 0, 'image dimensions must be divisible by the patch size'
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num_patches = (self.image_size // self.patch_size) ** 2
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patch_dim = channels * self.patch_size ** 2
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assert num_patches >= MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for ' + \
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f'attention. Try decreasing your patch size'
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# Correct the Embedding Dim
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if not self.embed_dim % self.params.heads == 0:
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self.embed_dim = (self.embed_dim // self.params.heads) * self.params.heads
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message = ('Embedding Dimension was fixed to be devideable by the number' +
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f' of attention heads, is now: {self.embed_dim}')
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for func in print, warnings.warn:
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func(message)
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# Utility Modules
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self.autopad = AutoPadToShape((self.image_size, self.image_size))
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# Modules with Parameters
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self.transformer = TransformerModule(in_shape=self.embed_dim, hidden_size=self.params.lat_dim,
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n_heads=self.params.heads, num_layers=self.params.attn_depth,
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dropout=self.params.dropout, use_norm=self.params.use_norm,
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activation=self.params.activation_as_string
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)
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, self.embed_dim))
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self.patch_to_embedding = nn.Linear(patch_dim, self.embed_dim) if self.params.embedding_size \
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else F_x(self.embed_dim)
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self.cls_token = nn.Parameter(torch.randn(1, 1, self.embed_dim))
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self.dropout = nn.Dropout(self.params.dropout)
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self.to_cls_token = nn.Identity()
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self.mlp_head = nn.Sequential(
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nn.LayerNorm(self.embed_dim),
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nn.Linear(self.embed_dim, self.params.lat_dim),
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nn.GELU(),
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nn.Dropout(self.params.dropout),
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nn.Linear(self.params.lat_dim, 10),
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nn.Softmax()
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)
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def forward(self, x, mask=None):
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"""
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:param x: the sequence to the encoder (required).
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:param mask: the mask for the src sequence (optional).
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:return:
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"""
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tensor = self.autopad(x)
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p = self.params.patch_size
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tensor = rearrange(tensor, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)
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tensor = self.patch_to_embedding(tensor)
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b, n, _ = tensor.shape
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cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
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tensor = torch.cat((cls_tokens, tensor), dim=1)
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tensor += self.pos_embedding[:, :(n + 1)]
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tensor = self.dropout(tensor)
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tensor = self.transformer(tensor, mask)
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tensor = self.to_cls_token(tensor[:, 0])
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tensor = self.mlp_head(tensor)
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return Namespace(main_out=tensor)
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from argparse import Namespace
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import warnings
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import torch
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from torch import nn
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from ml_lib.modules.blocks import TransformerModule
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from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x, SlidingWindow)
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from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
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BaseDataloadersMixin, BaseTestMixin, BaseLossMixin)
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MIN_NUM_PATCHES = 16
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class HorizontalVisualTransformer(DatasetMixin,
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BaseLossMixin,
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BaseDataloadersMixin,
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BaseTrainMixin,
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BaseValMixin,
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BaseTestMixin,
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BaseOptimizerMixin,
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LightningBaseModule
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):
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def __init__(self, hparams):
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super(HorizontalVisualTransformer, self).__init__(hparams)
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# Dataset
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# =============================================================================
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self.dataset = self.build_dataset()
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self.in_shape = self.dataset.train_dataset.sample_shape
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assert len(self.in_shape) == 3, 'There need to be three Dimensions'
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channels, height, width = self.in_shape
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# Model Paramters
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# =============================================================================
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# Additional parameters
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self.n_classes = self.dataset.train_dataset.n_classes
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self.embed_dim = self.params.embedding_size
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self.patch_size = self.params.patch_size
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self.height = height
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self.width = width
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self.channels = channels
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self.new_height = ((self.height - self.patch_size)//1) + 1
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num_patches = self.new_height - (self.patch_size // 2)
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patch_dim = channels * self.patch_size * self.width
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assert num_patches >= MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for ' + \
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f'attention. Try decreasing your patch size'
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# Correct the Embedding Dim
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if not self.embed_dim % self.params.heads == 0:
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self.embed_dim = (self.embed_dim // self.params.heads) * self.params.heads
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message = ('Embedding Dimension was fixed to be devideable by the number' +
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f' of attention heads, is now: {self.embed_dim}')
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for func in print, warnings.warn:
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func(message)
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# Utility Modules
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self.autopad = AutoPadToShape((self.new_height, self.width))
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self.dropout = nn.Dropout(self.params.dropout)
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self.slider = SlidingWindow((channels, *self.autopad.target_shape), (self.patch_size, self.width),
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keepdim=False)
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# Modules with Parameters
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self.transformer = TransformerModule(in_shape=self.embed_dim, hidden_size=self.params.lat_dim,
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n_heads=self.params.heads, num_layers=self.params.attn_depth,
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dropout=self.params.dropout, use_norm=self.params.use_norm,
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activation=self.params.activation_as_string
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)
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, self.embed_dim))
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self.patch_to_embedding = nn.Linear(patch_dim, self.embed_dim) if self.params.embedding_size \
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else F_x(self.embed_dim)
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self.cls_token = nn.Parameter(torch.randn(1, 1, self.embed_dim))
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self.to_cls_token = nn.Identity()
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self.mlp_head = nn.Sequential(
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nn.LayerNorm(self.embed_dim),
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nn.Linear(self.embed_dim, self.params.lat_dim),
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nn.GELU(),
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nn.Dropout(self.params.dropout),
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nn.Linear(self.params.lat_dim, 10),
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nn.Softmax()
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)
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def forward(self, x, mask=None):
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"""
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:param x: the sequence to the encoder (required).
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:param mask: the mask for the src sequence (optional).
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:return:
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"""
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tensor = self.autopad(x)
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tensor = self.slider(tensor)
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tensor = self.patch_to_embedding(tensor)
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b, n, _ = tensor.shape
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# cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
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cls_tokens = self.cls_token.repeat((b, 1, 1))
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tensor = torch.cat((cls_tokens, tensor), dim=1)
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tensor += self.pos_embedding[:, :(n + 1)]
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tensor = self.dropout(tensor)
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tensor = self.transformer(tensor, mask)
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tensor = self.to_cls_token(tensor[:, 0])
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tensor = self.mlp_head(tensor)
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return Namespace(main_out=tensor)
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from argparse import Namespace
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import warnings
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import torch
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from torch import nn
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from ml_lib.modules.blocks import TransformerModule
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from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x, SlidingWindow)
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from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
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BaseDataloadersMixin, BaseTestMixin, BaseLossMixin)
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MIN_NUM_PATCHES = 16
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class VerticalVisualTransformer(DatasetMixin,
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BaseLossMixin,
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BaseDataloadersMixin,
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BaseTrainMixin,
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BaseValMixin,
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BaseTestMixin,
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BaseOptimizerMixin,
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LightningBaseModule
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):
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def __init__(self, hparams):
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super(VerticalVisualTransformer, self).__init__(hparams)
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# Dataset
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# =============================================================================
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self.dataset = self.build_dataset()
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self.in_shape = self.dataset.train_dataset.sample_shape
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assert len(self.in_shape) == 3, 'There need to be three Dimensions'
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channels, height, width = self.in_shape
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# Model Paramters
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# =============================================================================
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# Additional parameters
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self.embed_dim = self.params.embedding_size
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self.patch_size = self.params.patch_size
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self.height = height
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self.width = width
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self.channels = channels
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self.new_width = ((self.width - self.patch_size)//1) + 1
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num_patches = self.new_width - (self.patch_size // 2)
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patch_dim = channels * self.patch_size * self.height
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assert num_patches >= MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for ' + \
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f'attention. Try decreasing your patch size'
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# Correct the Embedding Dim
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if not self.embed_dim % self.params.heads == 0:
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self.embed_dim = (self.embed_dim // self.params.heads) * self.params.heads
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message = ('Embedding Dimension was fixed to be devideable by the number' +
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f' of attention heads, is now: {self.embed_dim}')
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for func in print, warnings.warn:
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func(message)
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# Utility Modules
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self.autopad = AutoPadToShape((self.height, self.new_width))
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self.dropout = nn.Dropout(self.params.dropout)
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self.slider = SlidingWindow((channels, *self.autopad.target_shape), (self.height, self.patch_size), keepdim=False)
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# Modules with Parameters
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self.transformer = TransformerModule(in_shape=self.embed_dim, hidden_size=self.params.lat_dim,
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n_heads=self.params.heads, num_layers=self.params.attn_depth,
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dropout=self.params.dropout, use_norm=self.params.use_norm,
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activation=self.params.activation_as_string
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)
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, self.embed_dim))
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self.patch_to_embedding = nn.Linear(patch_dim, self.embed_dim) if self.params.embedding_size \
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else F_x(self.embed_dim)
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self.cls_token = nn.Parameter(torch.randn(1, 1, self.embed_dim))
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self.to_cls_token = nn.Identity()
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self.mlp_head = nn.Sequential(
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nn.LayerNorm(self.embed_dim),
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nn.Linear(self.embed_dim, self.params.lat_dim),
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nn.GELU(),
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nn.Dropout(self.params.dropout),
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nn.Linear(self.params.lat_dim, 10),
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nn.Softmax()
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)
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def forward(self, x, mask=None):
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"""
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:param x: the sequence to the encoder (required).
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:param mask: the mask for the src sequence (optional).
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:return:
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"""
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tensor = self.autopad(x)
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tensor = self.slider(tensor)
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tensor = self.patch_to_embedding(tensor)
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b, n, _ = tensor.shape
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# cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
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cls_tokens = self.cls_token.repeat((b, 1, 1))
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tensor = torch.cat((cls_tokens, tensor), dim=1)
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tensor += self.pos_embedding[:, :(n + 1)]
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tensor = self.dropout(tensor)
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tensor = self.transformer(tensor, mask)
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tensor = self.to_cls_token(tensor[:, 0])
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tensor = self.mlp_head(tensor)
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return Namespace(main_out=tensor)
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11013
output/BCC_3cb16686fdb05f9648dcaa197d86f0f0_test_out.csv
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output/CC.pdf
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11013
output/manual_test_out.csv
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output/metrics.csv
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output/metrics.csv
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@ -0,0 +1,16 @@
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worker,root,class_name,normalized,use_preprocessed,n_mels,sr,hop_length,n_fft,mixup,stretch,loudness_ratio,shift_ratio,noise_ratio,mask_ratio,speed_ratio,speed_factor,mean,max,median,std
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11,data,BinaryMasksDataset,True,False,64,16000,256,512,False,True,0,0,0,0,0,0,63.79,63.97,63.85,0.19
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11,data,BinaryMasksDataset,True,False,64,16000,256,512,False,True,0,0,0,0,0,0,64.2,64.9,64.02,0.5
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11,data,BinaryMasksDataset,True,False,64,16000,256,512,False,True,0,0,0,0,0,0,63.34,63.92,63.45,0.49
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11,data,BinaryMasksDataset,True,False,64,16000,256,512,False,True,0,0,0,0,0,0,63.92,64.34,63.91,0.25
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11,data,BinaryMasksDataset,True,False,64,16000,256,512,False,True,0,0,0,0,0,0,64.72,65.38,64.6,0.51
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11,data,BinaryMasksDataset,True,True,64,16000,256,512,False,False,0,0,0,0,0,0,60.36,64.16,63.41,5.31
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11,data,BinaryMasksDataset,True,True,64,16000,256,512,False,False,0,0,0,0,0,0,58.4,64.18,63.86,6.85
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11,data,BinaryMasksDataset,True,True,64,16000,256,512,False,False,0,0,0,0,0,0,60.09,63.61,63.26,5.19
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11,data,BinaryMasksDataset,True,True,64,16000,256,512,False,False,0,0,0,0,0,0,59.96,64.34,63.32,5.4
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11,data,BinaryMasksDataset,True,True,64,16000,256,512,False,False,0,0,0,0,0,0,60.94,64.59,64.11,5.56
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11,data,BinaryMasksDataset,True,True,64,16000,256,512,False,0,0,0.3,0,0.3,0.7,50.0,50.0,50.0,0.0
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11,data,BinaryMasksDataset,True,True,64,16000,256,512,False,0,0,0.3,0,0.3,0.7,62.1,65.51,64.9,5.48
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11,data,BinaryMasksDataset,True,True,64,16000,256,512,False,0,0,0.3,0,0.3,0.7,61.29,64.76,64.31,5.68
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11,data,BinaryMasksDataset,True,True,64,16000,256,512,False,0,0,0.3,0,0.3,0.7,63.65,64.97,63.96,0.92
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11,data,BinaryMasksDataset,True,True,64,16000,256,512,False,0,0,0.3,0,0.3,0.7,64.59,65.51,64.43,0.76
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71
output/secondrun/bcc_metrics.csv
Normal file
71
output/secondrun/bcc_metrics.csv
Normal file
@ -0,0 +1,71 @@
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||||
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|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,65.14,65.083,0.323077218,Mask
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,65.31,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,65.18,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,65.37,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,65.04,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,65.18,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,65.18,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.4,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,65.4,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.63,,,
|
||||
|
71
output/secondrun/bcmc_metrics.csv
Normal file
71
output/secondrun/bcmc_metrics.csv
Normal file
@ -0,0 +1,71 @@
|
||||
worker,root,class_name,use_preprocessed,n_mels,sr,hop_length,n_fft,stretch,loudness_ratio,shift_ratio,noise_ratio,mask_ratio,speed_amount,speed_min,speed_max,max,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,60.29,61.349,0.670496499,noise
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,61.95,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,62.12,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,61.33,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,61.73,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,60.74,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,61.09,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,60.82,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,62.36,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,61.06,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.21,63.542,0.90676225,speed
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,65.9,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.72,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.88,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.2,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,62.72,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.12,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,62.77,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.45,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.45,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.23,63.488,0.788117307,all
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,63.7,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,62.52,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,63.4,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,62.51,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.39,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,62.34,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,63.7,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,63.66,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.43,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,62.61,62.268,0.334524538,mask
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,62.57,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,62.74,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,61.75,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,62.22,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,62.12,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,62.49,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,62.15,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,61.8,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,62.23,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.35,63.541,0.255884088,none
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.28,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.78,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.47,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.16,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.57,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.86,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.9,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.67,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.37,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,66.65,66.309,0.693468737,shift
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,66.8,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,66.64,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,66.75,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.74,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,66.78,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,66.41,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.6,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,64.81,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,66.91,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,61.44,62.869,0.640979286,loudness
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.03,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.33,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,62.98,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,62.46,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,62.56,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.13,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.72,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.41,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,62.63,,,
|
||||
|
71
output/secondrun/cc_metrics.csv
Normal file
71
output/secondrun/cc_metrics.csv
Normal file
@ -0,0 +1,71 @@
|
||||
worker,root,class_name,use_preprocessed,n_mels,sr,hop_length,n_fft,stretch,loudness_ratio,shift_ratio,noise_ratio,mask_ratio,speed_amount,speed_min,speed_max,max,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.84,64.714,0.302294926,None
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.67,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.45,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.88,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.19,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,65.08,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.87,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.58,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.44,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,65.14,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.6,64.784,0.403517864,Speed
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.95,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.86,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.69,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.79,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,65.1,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.91,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,65.4,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.5,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,65.04,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,65.17,65.029,0.406760918,All
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.34,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,65.33,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,65.27,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,65.21,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,65.29,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,65.37,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,65.09,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.98,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.24,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.77,63.618,0.224192378,noise
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.65,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.57,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.37,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.98,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.19,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.61,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.77,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.52,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.75,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.77,64.305,0.435488232,loudness
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,65.03,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.83,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.97,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.72,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.43,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.11,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.45,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.84,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.9,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.18,64.268,0.335949732,mask
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.3,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.51,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,65,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.13,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,63.71,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.01,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.24,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.38,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.22,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,66.1,65.394,0.54946843,shift
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,66.07,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.32,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.86,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.21,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.24,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,64.23,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.54,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.28,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.09,,,
|
||||
|
71
output/secondrun/rcc_metrics.csv
Normal file
71
output/secondrun/rcc_metrics.csv
Normal file
@ -0,0 +1,71 @@
|
||||
worker,root,class_name,use_preprocessed,n_mels,sr,hop_length,n_fft,stretch,loudness_ratio,shift_ratio,noise_ratio,mask_ratio,speed_amount,speed_min,speed_max,max,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.3,63.38,0.406639343,noise
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.27,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.54,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,62.69,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,64.05,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.71,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,62.8,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.55,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.58,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0.4,0,0,0,0,63.31,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,63.6,64.377,0.775901626,shift
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.57,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.39,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,64.55,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,63.87,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,64.15,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,63.76,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,64.17,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,63.46,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0.4,0,0,0,0,0,65.25,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.62,64.069,0.766571733,speed
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.32,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,62.61,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,65.44,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.48,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.26,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.05,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.66,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,63.99,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0,0.4,0.7,1.7,64.26,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,63.75,64.304,0.449102809,mask
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.02,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.31,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.54,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.23,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.52,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.25,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,63.79,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,65.33,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0,0,0,0.2,0,0,0,64.3,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.64,64.53,0.53264539,none
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.19,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,65.2,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.4,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.88,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.67,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,63.85,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.25,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,65.5,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,FALSE,0,0,0,0,0,0,0,64.72,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.84,64.14,0.372618363,loudness
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.5,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.38,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.37,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.9,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.11,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.24,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,63.97,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0,0,0,0,0,0,64.09,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.32,64.118,0.417047826,all
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.58,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,63.55,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.54,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.04,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,63.64,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,63.48,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.41,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.24,,,
|
||||
11,data,BinaryMasksDataset,FALSE,64,16000,256,512,TRUE,0.4,0.4,0.4,0.2,0.4,0.7,1.7,64.38,,,
|
||||
|
BIN
output/shift.png
Normal file
BIN
output/shift.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
BIN
output/speed.png
Normal file
BIN
output/speed.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
45
repair_outputs.py
Normal file
45
repair_outputs.py
Normal file
@ -0,0 +1,45 @@
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
import shutil
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
for old_out_file in (Path() / 'output').rglob('*_test_out.csv'):
|
||||
old_out_file.unlink()
|
||||
|
||||
for new_out_file in (Path() / 'output').rglob('*_test_out_repair.csv'):
|
||||
shutil.move(str(new_out_file), str(new_out_file).replace('_test_out_repair', '_test_out'))
|
||||
exit()
|
||||
'''
|
||||
with old_out_file.open('r') as old_f:
|
||||
predictions = []
|
||||
file_names = []
|
||||
|
||||
idx = 1
|
||||
zeros = '00000'
|
||||
|
||||
_ = old_f.readline()
|
||||
|
||||
for row in old_f:
|
||||
split_row = row.split(',')
|
||||
file_names.append(f'test_{zeros[:-len(str(idx))]}{idx}.wav')
|
||||
predictions.append(split_row[-1].strip()
|
||||
.replace('"', '').replace('(', '').replace(')', '').replace("'", '')
|
||||
)
|
||||
idx += 1
|
||||
try:
|
||||
(old_out_file.parent / f'{old_out_file.name}_repair').unlink()
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
with (old_out_file.parent / f'{old_out_file.name[:-4]}_repair.csv').open('w') as new_f:
|
||||
headers = ['file_name', 'prediction']
|
||||
|
||||
writer = csv.DictWriter(new_f, delimiter=',', lineterminator='\n', fieldnames=headers)
|
||||
writer.writeheader() # write a header
|
||||
|
||||
writer.writerows([dict(file_name=file_name, prediction=prediction)
|
||||
for file_name, prediction in zip(file_names, predictions)]
|
||||
)
|
||||
'''
|
||||
pass
|
||||
@ -162,7 +162,7 @@ class DatasetMixin:
|
||||
hop_length=self.params.hop_length)
|
||||
|
||||
# Utility
|
||||
normalize = NormalizeLocal()
|
||||
utility_transforms = Compose([NormalizeLocal(), ToTensor()])
|
||||
|
||||
# Data Augmentations
|
||||
mel_augmentations = Compose([
|
||||
@ -172,7 +172,7 @@ class DatasetMixin:
|
||||
ShiftTime(0.4),
|
||||
MaskAug(0.2),
|
||||
], p=0.6),
|
||||
normalize])
|
||||
utility_transforms])
|
||||
|
||||
# Datasets
|
||||
Dataset = namedtuple('Datasets', 'train_dataset val_dataset test_dataset')
|
||||
@ -187,13 +187,13 @@ class DatasetMixin:
|
||||
fold=9,
|
||||
reset=self.params.reset,
|
||||
mel_kwargs=mel_kwargs,
|
||||
mel_augmentations=normalize),
|
||||
mel_augmentations=utility_transforms),
|
||||
test_dataset=self.dataset_class(data_root=self.params.root, # TEST DATASET
|
||||
setting=V.DATA_OPTION_test,
|
||||
fold=10,
|
||||
reset=self.params.reset,
|
||||
mel_kwargs=mel_kwargs,
|
||||
mel_augmentations=normalize),
|
||||
mel_augmentations=utility_transforms),
|
||||
)
|
||||
|
||||
if dataset.train_dataset.task_type == V.TASK_OPTION_binary:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user