Clean configs and arguments

Author: yqzhishen

configs/base.yaml

@@ -28,7 +28,6 @@ units_dim: 80  # 768
 midi_num_bins: 128
 model_cls: null
 midi_extractor_args: {}
-use_BCEWithLogitsLoss: false
 
 # training
 use_midi_loss: true

configs/discrete.yaml

@@ -38,7 +38,6 @@ midi_extractor_args:
   attention_drop: 0.1
   attention_heads: 8
   attention_heads_dim: 64
-  sig: false
 
 # training
 task_cls: training.QuantizedMIDIExtractionTask

configs/midi_conformer.yaml

@@ -19,7 +19,6 @@ midi_shift_range: [-12, 12]
 rest_threshold: 0.1
 
 
-use_BCEWithLogitsLoss: true
 midi_extractor_args:
   lay: 8
   dim: 512
@@ -32,6 +31,5 @@ midi_extractor_args:
   attention_drop: 0.1
   attention_heads: 8
   attention_heads_dim: 64
-  sig: false
 
 pl_trainer_precision: 'bf16'

configs/quant_tow_head_model.yaml configs/quant_two_head_model.yamlconfigs/quant_tow_head_model.yaml renamed to configs/quant_two_head_model.yaml

@@ -18,7 +18,6 @@ midi_extractor_args:
   attention_drop: 0.1
   attention_heads: 8
   attention_heads_dim: 64
-  sig: false
 
 # training
 task_cls: training.QuantizedMIDIExtractionTask

configs/unet.yaml

@@ -19,7 +19,6 @@ midi_shift_proportion: 0.0
 midi_shift_range: [-12, 12]
 rest_threshold: 0.1
 
-use_BCEWithLogitsLoss: true
 midi_extractor_args:
   output_lay: 3
   dim: 512
@@ -34,6 +33,5 @@ midi_extractor_args:
   unet_down: [2, 2, 2,2,2]
   unet_dim: [512, 512, 768,768,1024]
   unet_latentdim: 1024
-  sig: false
 
 pl_trainer_precision: 'bf16'

modules/conform/Gconform.py

@@ -94,9 +94,8 @@ def __init__(self, lay: int, dim: int, indim: int, outdim: int, use_lay_skip: bo
                  conv_drop: float = 0.1,
                  ffn_latent_drop: float = 0.1,
                  ffn_out_drop: float = 0.1, attention_drop: float = 0.1, attention_heads: int = 4,
-                 attention_heads_dim: int = 64,sig:bool=True):
+                 attention_heads_dim: int = 64):
         super().__init__()
-        self.sig=sig
 
         self.inln = nn.Linear(indim, dim)
         self.inln1 = nn.Linear(indim, dim)
@@ -137,6 +136,5 @@ def forward(self, x, pitch, mask=None):
         midiout = self.outln(x)
         cutprp = torch.sigmoid(cutprp)
         cutprp = torch.squeeze(cutprp, -1)
-        # if  self.sig:
-        #     midiout = torch.sigmoid(midiout)
+
         return midiout, cutprp

modules/conform/unet_with_conform.py

@@ -575,9 +575,8 @@ def __init__(self, output_lay: int, dim: int, indim: int, outdim: int,  kernel_s
                  conv_drop: float = 0.1,
                  ffn_latent_drop: float = 0.1,
                  ffn_out_drop: float = 0.1, attention_drop: float = 0.1, attention_heads: int = 4,
-                 attention_heads_dim: int = 64,sig:bool=True,unet_type='cf_unet_full',unet_down=[2, 2, 2], unet_dim=[512, 768, 1024], unet_latentdim=1024,):
+                 attention_heads_dim: int = 64, unet_type='cf_unet_full',unet_down=[2, 2, 2], unet_dim=[512, 768, 1024], unet_latentdim=1024,):
         super().__init__()
-        self.sig=sig
 
         self.unet=unet_adp(unet_type=unet_type, unet_down=unet_down, unet_dim=unet_dim, unet_latentdim=unet_latentdim,
                  unet_indim=indim, unet_outdim=dim,
@@ -620,12 +619,9 @@ def forward(self, x, pitch=None, mask=None):
         midiout = self.outln(xo)
         cutprp = torch.sigmoid(cutprp)
         cutprp = torch.squeeze(cutprp, -1)
-        # if  self.sig:
-        #     midiout = torch.sigmoid(midiout)
         return midiout, cutprp
 
 
-
 if __name__ == '__main__':
     fff = unet_base_cf( dim=512,indim=128,outdim=256,output_lay=1,unet_down=[2, 2, 4], unet_dim=[128, 128, 128], unet_latentdim=128)
     aaa = fff(torch.randn(2, 255, 128))