Add scripts for visualizing eg3d generator architecture.

eg3d/visualize_pkl.py

@@ -0,0 +1,37 @@
+import pickle
+import torch
+import io
+import legacy
+import dnnlib
+import json
+
+def named_params_and_buffers(module):
+    assert isinstance(module, torch.nn.Module)
+    return list(module.named_parameters()) + list(module.named_buffers())
+
+def copy_params_and_buffers(src_module, dst_module, require_all=False):
+    assert isinstance(src_module, torch.nn.Module)
+    assert isinstance(dst_module, torch.nn.Module)
+    src_tensors = dict(named_params_and_buffers(src_module))
+    for name, tensor in named_params_and_buffers(dst_module):
+        assert (name in src_tensors) or (not require_all)
+        if name in src_tensors:
+            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
+
+
+device = torch.device('cpu')
+with dnnlib.util.open_url("ffhqrebalanced512-128.pkl") as f:
+
+    data = legacy.load_network_pkl(f)
+    G = data['G'].to(device)# type: ignore
+
+    G_em = data['G_ema'].to(device) # type: ignore
+    D = data['D'].to(device)
+    # train_set = data['training_set_kwargs'].to(device) # type: ignore
+    # augment_pipe = data['augment_pipe'].to(device)
+
+    print(G)
+    print("--------------------------------------------")
+    print(G_em)
+    print("--------------------------------------------")
+    # print(D)