Fix potential bug risks and fix anti-patterns

dnnlib/submission/submit.py

@@ -129,10 +129,9 @@ def get_path_from_template(path_template: str, path_type: PathType = PathType.AU
     # return correctly formatted path
     if path_type == PathType.WINDOWS:
         return str(pathlib.PureWindowsPath(path_template))
-    elif path_type == PathType.LINUX:
+    if path_type == PathType.LINUX:
         return str(pathlib.PurePosixPath(path_template))
-    else:
-        raise RuntimeError("Unknown platform")
+    raise RuntimeError("Unknown platform")
 
 
 def get_template_from_path(path: str) -> str:
@@ -158,9 +157,9 @@ def get_user_name():
     """Get the current user name."""
     if _user_name_override is not None:
         return _user_name_override
-    elif platform.system() == "Windows":
+    if platform.system() == "Windows":
         return os.getlogin()
-    elif platform.system() == "Linux":
+    if platform.system() == "Linux":
         try:
             import pwd
             return pwd.getpwuid(os.geteuid()).pw_name
@@ -283,15 +282,14 @@ def run_wrapper(submit_config: SubmitConfig) -> None:
     except:
         if is_local:
             raise
-        else:
-            traceback.print_exc()
+        traceback.print_exc()
 
-            log_src = os.path.join(submit_config.run_dir, "log.txt")
-            log_dst = os.path.join(get_path_from_template(submit_config.run_dir_root), "{0}-error.txt".format(submit_config.run_name))
-            shutil.copyfile(log_src, log_dst)
+        log_src = os.path.join(submit_config.run_dir, "log.txt")
+        log_dst = os.path.join(get_path_from_template(submit_config.run_dir_root), "{0}-error.txt".format(submit_config.run_name))
+        shutil.copyfile(log_src, log_dst)
 
-            # Defer sys.exit(1) to happen after we close the logs and create a _finished.txt
-            exit_with_errcode = True
+        # Defer sys.exit(1) to happen after we close the logs and create a _finished.txt
+        exit_with_errcode = True
     finally:
         open(os.path.join(submit_config.run_dir, "_finished.txt"), "w").close()
 

dnnlib/tflib/custom_ops.py

@@ -84,7 +84,7 @@ def _prepare_nvcc_cli(opts):
 #----------------------------------------------------------------------------
 # Main entry point.
 
-_plugin_cache = dict()
+_plugin_cache = {}
 
 def get_plugin(cuda_file):
     cuda_file_base = os.path.basename(cuda_file)

dnnlib/tflib/network.py

@@ -23,7 +23,7 @@
 from .tfutil import TfExpression, TfExpressionEx
 
 _import_handlers = []  # Custom import handlers for dealing with legacy data in pickle import.
-_import_module_src = dict()  # Source code for temporary modules created during pickle import.
+_import_module_src = {}  # Source code for temporary modules created during pickle import.
 
 
 def import_handler(handler_func):
@@ -120,7 +120,7 @@ def _init_fields(self) -> None:
         self._build_func = None  # User-supplied build function that constructs the network.
         self._build_func_name = None  # Name of the build function.
         self._build_module_src = None  # Full source code of the module containing the build function.
-        self._run_cache = dict()  # Cached graph data for Network.run().
+        self._run_cache = {}  # Cached graph data for Network.run().
 
     def _init_graph(self) -> None:
         # Collect inputs.
@@ -254,7 +254,7 @@ def set_var(self, var_or_local_name: Union[TfExpression, str], new_value: Union[
 
     def __getstate__(self) -> dict:
         """Pickle export."""
-        state = dict()
+        state = {}
         state["version"]            = 4
         state["name"]               = self.name
         state["static_kwargs"]      = dict(self.static_kwargs)

dnnlib/tflib/tfutil.py

@@ -83,7 +83,7 @@ def absolute_variable_scope(scope: str, **kwargs) -> tf.variable_scope:
 
 def _sanitize_tf_config(config_dict: dict = None) -> dict:
     # Defaults.
-    cfg = dict()
+    cfg = {}
     cfg["rnd.np_random_seed"]               = None      # Random seed for NumPy. None = keep as is.
     cfg["rnd.tf_random_seed"]               = "auto"    # Random seed for TensorFlow. 'auto' = derive from NumPy random state. None = keep as is.
     cfg["env.TF_CPP_MIN_LOG_LEVEL"]         = "1"       # 0 = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info.
@@ -227,20 +227,24 @@ def create_var_with_large_initial_value(initial_value: np.ndarray, *args, **kwar
     return var
 
 
-def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False):
+def convert_images_from_uint8(images, drange=None, nhwc_to_nchw=False):
     """Convert a minibatch of images from uint8 to float32 with configurable dynamic range.
     Can be used as an input transformation for Network.run().
     """
+    if drange is None:
+        drange = [-1,1]
     images = tf.cast(images, tf.float32)
     if nhwc_to_nchw:
         images = tf.transpose(images, [0, 3, 1, 2])
     return images * ((drange[1] - drange[0]) / 255) + drange[0]
 
 
-def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, shrink=1):
+def convert_images_to_uint8(images, drange=None, nchw_to_nhwc=False, shrink=1):
     """Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
     Can be used as an output transformation for Network.run().
     """
+    if drange is None:
+        drange = [-1,1]
     images = tf.cast(images, tf.float32)
     if shrink > 1:
         ksize = [1, 1, shrink, shrink]

module/cnf.py

@@ -25,10 +25,9 @@ def forward(self, x, context, logpx=None, reverse=False, inds=None, integration_
                 # print(x.shape)
                 x = self.chain[i](x, context, logpx, integration_times, reverse)
             return x
-        else:
-            for i in inds:
-                x, logpx = self.chain[i](x, context, logpx, integration_times, reverse)
-            return x, logpx
+        for i in inds:
+            x, logpx = self.chain[i](x, context, logpx, integration_times, reverse)
+        return x, logpx
 
 
 class CNF(nn.Module):
@@ -115,8 +114,7 @@ def forward(self, x, context=None, logpx=None, integration_times=None, reverse=F
 
         if logpx is not None:
             return z_t, logpz_t
-        else:
-            return z_t
+        return z_t
 
     def num_evals(self):
         return self.odefunc._num_evals.item()

module/normalization.py

@@ -40,8 +40,7 @@ def reset_parameters(self):
     def forward(self, x, c=None, logpx=None, reverse=False):
         if reverse:
             return self._reverse(x, logpx)
-        else:
-            return self._forward(x, logpx)
+        return self._forward(x, logpx)
 
     def _forward(self, x, logpx=None):
         num_channels = x.size(-1)
@@ -87,8 +86,7 @@ def _forward(self, x, logpx=None):
 
         if logpx is None:
             return y
-        else:
-            return y, logpx - self._logdetgrad(x, used_var).sum(-1, keepdim=True)
+        return y, logpx - self._logdetgrad(x, used_var).sum(-1, keepdim=True)
 
     def _reverse(self, y, logpy=None):
         used_mean = self.running_mean
@@ -105,8 +103,7 @@ def _reverse(self, y, logpy=None):
 
         if logpy is None:
             return x
-        else:
-            return x, logpy + self._logdetgrad(x, used_var).sum(-1, keepdim=True)
+        return x, logpy + self._logdetgrad(x, used_var).sum(-1, keepdim=True)
 
     def _logdetgrad(self, x, used_var):
         logdetgrad = -0.5 * torch.log(used_var + self.eps)

module/odefunc.py

@@ -134,9 +134,8 @@ def forward(self, t, states):
                 divergence = self.divergence_fn(dy, y, e=self._e).unsqueeze(-1)
 
                 return dy, -divergence, torch.zeros_like(c).requires_grad_(True)
-            elif len(states) == 2:  # unconditional CNF
+            if len(states) == 2:  # unconditional CNF
                 dy = self.diffeq(t, y)
                 divergence = self.divergence_fn(dy, y, e=self._e).view(-1, 1)
                 return dy, -divergence
-            else:
-                assert 0, "`len(states)` should be 2 or 3"
+            assert 0, "`len(states)` should be 2 or 3"

module/utils.py

@@ -106,7 +106,9 @@ def set_random_seed(seed):
 
 
 # Visualization
-def visualize_point_clouds(pts, gtr, idx, pert_order=[0, 1, 2]):
+def visualize_point_clouds(pts, gtr, idx, pert_order=None):
+    if pert_order is None:
+        pert_order = [0, 1, 2]
     pts = pts.cpu().detach().numpy()[:, pert_order]
     gtr = gtr.cpu().detach().numpy()[:, pert_order]
 

pretrained_networks.py

@@ -59,7 +59,7 @@ def get_path_or_url(path_or_gdrive_path):
 
 #----------------------------------------------------------------------------
 
-_cached_networks = dict()
+_cached_networks = {}
 
 def load_networks(path_or_gdrive_path):
     path_or_url = get_path_or_url(path_or_gdrive_path)

run_generator.py

@@ -52,7 +52,7 @@ def style_mixing_example(network_pkl, row_seeds, col_seeds, truncation_psi, col_
     all_z = np.stack([np.random.RandomState(seed).randn(*Gs.input_shape[1:]) for seed in all_seeds]) # [minibatch, component]
     all_w = Gs.components.mapping.run(all_z, None) # [minibatch, layer, component]
     all_w = w_avg + (all_w - w_avg) * truncation_psi # [minibatch, layer, component]
-    w_dict = {seed: w for seed, w in zip(all_seeds, list(all_w))} # [layer, component]
+    w_dict = dict(zip(all_seeds, list(all_w))) # [layer, component]
 
     print('Generating images...')
     all_images = Gs.components.synthesis.run(all_w, **Gs_syn_kwargs) # [minibatch, height, width, channel]

utils.py

@@ -10,7 +10,6 @@
 
 import numpy as np
 import PIL.Image
-import dnnlib
 import dnnlib.tflib as tflib
 import os
 import re
@@ -142,7 +141,9 @@ def get_style_loss(base_style, gram_target):
 
 #----------------------------------------------------------------------------
 
-def generate_im_official(network_pkl='gdrive:networks/stylegan2-ffhq-config-f.pkl', seeds=[22], truncation_psi=0.5):
+def generate_im_official(network_pkl='gdrive:networks/stylegan2-ffhq-config-f.pkl', seeds=None, truncation_psi=0.5):
+    if seeds is None:
+        seeds = [22]
     print('Loading networks from "%s"...' % network_pkl)
     _G, _D, Gs = pretrained_networks.load_networks(network_pkl)
     noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]