back

config.py

@@ -92,7 +92,7 @@
 # train parameters
 resize = 224
 lr = 1e-3
-epochs = 1#5
+epochs = 5
 
 sim_batch = 128
 gen_batch = 128

main.py

@@ -35,35 +35,35 @@ def my_train_clip_encoder(dt, memory, attr, lesson):
 	loss = 10
 	ct = 0
 	centroid_sim = torch.rand(1, latent_dim).to(device)
-	#while loss > 0.008:
-	##
-	ct += 1
-	#if ct > 5:
-	#	break
-	for i in range(1):#200):
-		# Get Inputs: sim_batch, (sim_batch, 4, 128, 128)
-		base_name_sim, images_sim = dt.get_better_similar(attr, lesson)
-		images_sim = images_sim.to(device)
-
-		# run similar model
-		z_sim = model(clip_model, images_sim)
-		centroid_sim = centroid_sim.detach()
-		centroid_sim, loss_sim = get_sim_loss(torch.vstack((z_sim, centroid_sim)))
-
-		# Run Difference
-		base_name_dif, images_dif = dt.get_better_similar_not(attr, lesson)
-		images_dif = images_dif.to(device)
-
-		# run difference model
-		z_dif = model(clip_model, images_dif)
-		loss_dif = get_sim_not_loss(centroid_sim, z_dif)
-
-		# compute loss
-		loss = (loss_sim)**2 + (loss_dif-1)**2
-		optimizer.zero_grad()
-		loss.backward()
-		optimizer.step()
+	while loss > 0.008:
 		##
+		ct += 1
+		if ct > 5:
+			break
+		for i in range(200):
+			# Get Inputs: sim_batch, (sim_batch, 4, 128, 128)
+			base_name_sim, images_sim = dt.get_better_similar(attr, lesson)
+			images_sim = images_sim.to(device)
+
+			# run similar model
+			z_sim = model(clip_model, images_sim)
+			centroid_sim = centroid_sim.detach()
+			centroid_sim, loss_sim = get_sim_loss(torch.vstack((z_sim, centroid_sim)))
+
+			# Run Difference
+			base_name_dif, images_dif = dt.get_better_similar_not(attr, lesson)
+			images_dif = images_dif.to(device)
+
+			# run difference model
+			z_dif = model(clip_model, images_dif)
+			loss_dif = get_sim_not_loss(centroid_sim, z_dif)
+
+			# compute loss
+			loss = (loss_sim)**2 + (loss_dif-1)**2
+			optimizer.zero_grad()
+			loss.backward()
+			optimizer.step()
+			##
 		print('[', ct, ']', loss.detach().item(), loss_sim.detach().item(),
 				loss_dif.detach().item())