mexicanamerican · sweep-ai-deprecated · Feb 1, 2024 · Feb 1, 2024 · Feb 1, 2024 · Feb 7, 2024
diff --git a/modules/control/proc/leres/pix2pix/models/base_model.py b/modules/control/proc/leres/pix2pix/models/base_model.py
@@ -38,7 +38,7 @@ def __init__(self, opt):
         self.isTrain = opt.isTrain
         self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')  # get device name: CPU or GPU
         self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)  # save all the checkpoints to save_dir
-        if opt.preprocess != 'scale_width':  # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark.
+        if opt.preprocess == 'scale_width':  # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark.
             torch.backends.cudnn.benchmark = True
         self.loss_names = []
         self.model_names = []
@@ -185,55 +185,20 @@ def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
         else:
             self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
 
-    def load_networks(self, epoch):
-        """Load all the networks from the disk.
-
-        Parameters:
-            epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
-        """
-        for name in self.model_names:
-            if isinstance(name, str):
-                load_filename = '%s_net_%s.pth' % (epoch, name)
-                load_path = os.path.join(self.save_dir, load_filename)
-                net = getattr(self, 'net' + name)
-                if isinstance(net, torch.nn.DataParallel):
-                    net = net.module
-                # print('Loading depth boost model from %s' % load_path)
-                # if you are using PyTorch newer than 0.4 (e.g., built from
-                # GitHub source), you can remove str() on self.device
-                state_dict = torch.load(load_path, map_location=str(self.device))
-                if hasattr(state_dict, '_metadata'):
-                    del state_dict._metadata
-
-                # patch InstanceNorm checkpoints prior to 0.4
-                for key in list(state_dict.keys()):  # need to copy keys here because we mutate in loop
-                    self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))
-                net.load_state_dict(state_dict)
-
-    def print_networks(self, verbose):
-        """Print the total number of parameters in the network and (if verbose) network architecture
-
-        Parameters:
-            verbose (bool) -- if verbose: print the network architecture
-        """
-        print('---------- Networks initialized -------------')
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, 'net' + name)
-                num_params = 0
-                for param in net.parameters():
-                    num_params += param.numel()
-                if verbose:
-                    print(net)
-                print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
-        print('-----------------------------------------------')
-
-    def set_requires_grad(self, nets, requires_grad=False):
+    load_filename = '%s_net_%s.pth' % (epoch, name)
+    load_path = os.path.join(self.save_dir, load_filename)
+    net = getattr(self, 'net' + name)
+    if isinstance(net, torch.nn.DataParallel):
+        net = net.module
+    state_dict = torch.load(load_path, map_location=str(self.device))
+    if hasattr(state_dict, '_metadata'):
+        del state_dict._metadata
+    net.load_state_dict(state_dict)
         """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
-        Parameters:
-            nets (network list)   -- a list of networks
-            requires_grad (bool)  -- whether the networks require gradients or not
-        """
+            Parameters:
+                nets (network list)   -- a list of networks
+                requires_grad (bool)  -- whether the networks require gradients or not
+            """
         if not isinstance(nets, list):
             nets = [nets]
         for net in nets: