feat: cm export

util/export.py

@@ -1,11 +1,32 @@
 import torch
 
 
-from models import gan_networks
+from models import gan_networks, diffusion_networks
+
+
+class ConsistencyWrapper(torch.nn.Module):
+    """
+    Consistency model wrapper for onnx & jit trace
+    """
+
+    def __init__(self, model, sigmas):
+        super().__init__()
+        self.model = model
+        self.sigmas = sigmas
+
+    def forward(self, x, mask):
+        return self.model.restoration(x, None, self.sigmas, mask)
 
 
 def export(opt, cuda, model_in_file, model_out_file, opset_version, export_type):
-    model = gan_networks.define_G(**vars(opt))
+    if opt.model_type == "cm":
+        opt.alg_palette_sampling_method = ""
+        opt.alg_diffusion_cond_embed_dim = 256
+        model = diffusion_networks.define_G(**vars(opt))
+        model_input_nc = opt.model_input_nc + opt.model_output_nc
+    else:
+        model = gan_networks.define_G(**vars(opt))
+        model_input_nc = opt.model_input_nc
 
     model.eval()
     model.load_state_dict(torch.load(model_in_file))
@@ -18,21 +39,45 @@ def export(opt, cuda, model_in_file, model_out_file, opset_version, export_type)
         device = "cuda"
     else:
         device = "cpu"
+
     dummy_input = torch.randn(
-        1, opt.model_input_nc, opt.data_crop_size, opt.data_crop_size, device=device
+        1,
+        model_input_nc,
+        opt.data_crop_size,
+        opt.data_crop_size,
+        device=device,
     )
+    # print("dummy input size=", dummy_input.size())
+    dummy_inputs = [dummy_input]
+
+    if opt.model_type == "cm":
+        # at the moment, consistency models have two inputs: origin image and mask
+        # TODO allow to change number of sigmas
+        sigmas = [80.0, 24.4, 5.84, 0.9, 0.661]
+        model = ConsistencyWrapper(model, sigmas)
+        dummy_inputs += [
+            torch.randn(
+                1,
+                opt.model_input_nc,
+                opt.data_crop_size,
+                opt.data_crop_size,
+                device=device,
+            ),
+        ]
+
+    dummy_inputs = tuple(dummy_inputs)
 
     if export_type == "onnx":
         torch.onnx.export(
             model,
-            dummy_input,
+            dummy_inputs,
             model_out_file,
             verbose=False,
             opset_version=opset_version,
         )
 
     elif export_type == "jit":
-        jit_model = torch.jit.trace(model, dummy_input)
+        jit_model = torch.jit.trace(model, dummy_inputs)
         jit_model.save(model_out_file)
 
     else: