freeze reqs before I install vb2

models/modules/causalinception.py

@@ -126,7 +126,8 @@ class CausalInceptionTimePlus(nn.Sequential):
         dilations = np.array([max(1, d*dilation) for d in range(depth)])
         d=np.array([dilations**i for i in range(3)]).T
         rf = ((ks-1)*d).sum(0)
-        print(f"receptive field {rf}={ks-1}*{d}")
+        # print(f"receptive field {rf}={ks-1}*{d}")
+        print(f"receptive field {rf}")
 
     def create_head(self, nf, c_out, seq_len, flatten=False, concat_pool=False, fc_dropout=0., bn=False, y_range=None):
         if flatten:

models/modules/encoders.py

@@ -133,14 +133,14 @@ class TransformerEncoder2(nn.Module):
         super().__init__()
         # d_model (82) must be divisible by n_heads (4)
         layer_size = layer_size // n_heads * n_heads
-        d_model = layer_size // 2 
+        d_model = layer_size // 4 
         self.net = TSPerceiver(
             c_in=c_in,
             c_out=c_out,
             seq_len=seq_len,
             
             # cat_szs=0, n_cont=0, 
-            n_latents=layer_size, d_latent=layer_size//4, 
+            n_latents=layer_size, d_latent=d_model, 
             # d_context=None, 
             self_per_cross_attn=1,
                 #  share_weights=True, cross_n_heads=1, d_head=None, 
@@ -229,7 +229,7 @@ class LSTMEncoder2(nn.Module):
             depth=layers,
             lstm_dropout=conv_dropout,
             fc_dropout=dropout,
-            pre_norm=False, use_token=True, use_pe=True, 
+            pre_norm=False, use_token=False, use_pe=False, 
             use_bn=False, 
         )
 

models/modules/metareghead.py

@@ -63,17 +63,17 @@ class TransformerHead(nn.Module):
 
 
 class RegressionHead(nn.Module):
-    def __init__(self, base_learner='Ridge', d=512, enable_scale=True, dropout=0.1, num_heads=16):
+    def __init__(self, lrn='Ridge', d=512, enable_scale=True, dropout=0.1, num_heads=16):
         super().__init__()
-        if ('Ridge' in base_learner):
+        if ('Ridge' in lrn):
             # the regular DeepTime one
             self.head = RidgeRegressor()
-        elif ("None" in base_learner):
+        elif ("None" in lrn):
             self.head = SumHead(d=d, dropout=dropout)
-        elif ("Transformer" in base_learner):
+        elif ("Transformer" in lrn):
             self.head = TransformerHead(d=d, dropout=dropout, num_heads=num_heads)
         else:
-            raise NotImplementedError(base_learner)
+            raise NotImplementedError(lrn)
         
         # Add a learnable scale
         self.enable_scale = enable_scale