fix atol/rtol cnf & turn off dkl

stevenygd/PointFlow#23

regressionFlow/models/cnf.py

@@ -52,6 +52,7 @@ def __init__(self, odefunc, conditional=True, T=1.0, train_T=False, regularizati
         self.solver_options = {}
         self.conditional = conditional
 
+
     def forward(self, x, context=None, logpx=None, integration_times=None, reverse=False):
         if logpx is None:
             _logpx = torch.zeros(*x.shape[:-1], 1).to(x)
@@ -64,9 +65,9 @@ def forward(self, x, context=None, logpx=None, integration_times=None, reverse=F
             assert context is not None
             states = (x, _logpx, context.to(x))
             # atol = [self.atol] * 3
-            # rtol = [self.rtol] * 3
-            atol = [self.atol] * 3
-            rtol = [self.rtol] * 3
+            # rtol = [self.rtol]  3
+            atol = self.atol
+            rtol = self.rtol
         else:
             states = (x, _logpx)
             atol = [self.atol] * 2

regressionFlow/models/networks_regression_SDD_conditional.py

@@ -148,26 +148,28 @@ def forward(self, x: torch.tensor):    # x.shape = 5,65 = 5,64 + bias
         # 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.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
-        y2, delta_log_py = self.point_cnf(target_networks_weights, z,
-                                         torch.zeros(batch_size, y.size(1), 1).to(y))
-
-        log_py = standard_normal_logprob(y2).view(batch_size, -1).sum(1, keepdim=True)
-        delta_log_py = delta_log_py.view(batch_size, y2.size(1), 1).sum(1)
-        log_px = log_py - delta_log_py
-        # policzyc gestosci flowa log p_0(F^{-1}_\theta(w_i) + J
-        loss = log_px.mean()
-        # policzyc gestosci priora log N(w_i | (0,I)) ale teraz inaczej niz ostatnio. Liczymy kazdy z pieciu wierszy osobno
-        # na rozkladzie normalnym wymiaru 65 (64 + bias poki co)
-        multivariate_normal_distrib = torch.distributions.MultivariateNormal(
-            torch.zeros(tn_num_values).to(loss), torch.eye(tn_num_values).to(loss))
-        # todo sprawdzic czy to zadziala, tzn czy cala macierz mozna tak wrzucic,
-        #  mamy odwzorowanie R^65 -> R na macierz R^5 X R^65
-        # tutaj biore srednia po 5 zestawach N_65
-        loss_density = multivariate_normal_distrib.log_prob(target_networks_weights).mean()
-        loss = loss - loss_density
-
+        # ------- LOSS ----------
+        # # 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
+        # y2, delta_log_py = self.point_cnf(target_networks_weights, z,
+        #                                  torch.zeros(batch_size, y.size(1), 1).to(y))
+        #
+        # log_py = standard_normal_logprob(y2).view(batch_size, -1).sum(1, keepdim=True)
+        # delta_log_py = delta_log_py.view(batch_size, y2.size(1), 1).sum(1)
+        # log_px = log_py - delta_log_py
+        # # policzyc gestosci flowa log p_0(F^{-1}_\theta(w_i) + J
+        # loss = log_px.mean()
+        # # policzyc gestosci priora log N(w_i | (0,I)) ale teraz inaczej niz ostatnio. Liczymy kazdy z pieciu wierszy osobno
+        # # na rozkladzie normalnym wymiaru 65 (64 + bias poki co)
+        # multivariate_normal_distrib = torch.distributions.MultivariateNormal(
+        #     torch.zeros(tn_num_values).to(loss), torch.eye(tn_num_values).to(loss))
+        # # todo sprawdzic czy to zadziala, tzn czy cala macierz mozna tak wrzucic,
+        # #  mamy odwzorowanie R^65 -> R na macierz R^5 X R^65
+        # # tutaj biore srednia po 5 zestawach N_65
+        # loss_density = multivariate_normal_distrib.log_prob(target_networks_weights).mean()
+        # loss = loss - loss_density
+        loss = torch.tensor([0])
+        target_networks_weights = target_networks_weights.reshape(5,65)
         return target_networks_weights, loss