some args updated after debugging

piotr310100 · Nov 20, 2022 · 77308d9 · 77308d9
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commit 77308d9
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diff --git a/methods/hypernets/fhypermaml.py b/methods/hypernets/fhypermaml.py
@@ -94,7 +94,7 @@ def __init__(self, model_func, n_way, n_support, n_query, params=None, approx=Fa
         #                            num_sample_shapes=10, num_sample_points=2048, use_sphere_dist=False,
         #                            use_div_approx_train=False, use_div_approx_test=False)
 
-        self.flow_args = Namespace(model_type='PointNet', logprob_type='Normal', input_dim=1, dims='325',
+        self.flow_args = Namespace(model_type='PointNet', logprob_type='Normal', input_dim=325, dims='500',
                                    # dims mozna zwiekszyc, to sa po prostu wymiary struktury flowa po myslnikach
                                    # num_blocks dajemy 3
                                    # rozklad bierzemy normalny

diff --git a/regressionFlow/models/networks_regression_SDD_conditional.py b/regressionFlow/models/networks_regression_SDD_conditional.py
@@ -143,10 +143,10 @@ def forward(self, x: torch.tensor):    # x.shape = 5,65 = 5,64 + bias
         if self.logprob_type == "Laplace":
             y = self.sample_laplace((*x.shape, self.input_dim), self.gpu)
         if self.logprob_type == "Normal":
-            y = self.sample_gaussian((*x.shape, self.input_dim), None, self.gpu)
-
+            # y = self.sample_gaussian((*x.shape, self.input_dim), None, self.gpu)
+            y = self.sample_gaussian((5,65,1), None, self.gpu)
         # 3) przerzuc przez flow -> w_i := F_{\theta}(z_i) tu z: embedding rozmiaru 100
-        target_networks_weights = self.point_cnf(y.reshape(1,-1), z, reverse=True).view(*y.size())
+        target_networks_weights = self.point_cnf(y.reshape(1,-1), z.reshape(1,-1), reverse=True).view(*y.size())
 
         # Liczenie DKL. Pytanie- na spotkaniu co rozumielismy przez batche? W konktescie TN to batchem
         # oznaczymy chyba zbior wag na kanaly wyjscia, ktorych jest piec, kazdy z nich bierze 64 wartosci + bias