wip fp16 mat

lama_cleaner/model/mat.py

@@ -21,6 +21,7 @@ from lama_cleaner.model.utils import (
     MinibatchStdLayer,
     to_2tuple,
     normalize_2nd_moment,
+    set_seed,
 )
 from lama_cleaner.schema import Config
 
@@ -361,6 +362,7 @@ class MappingNet(torch.nn.Module):
         activation="lrelu",  # Activation function: 'relu', 'lrelu', etc.
         lr_multiplier=0.01,  # Learning rate multiplier for the mapping layers.
         w_avg_beta=0.995,  # Decay for tracking the moving average of W during training, None = do not track.
+        torch_dtype=torch.float32,
     ):
         super().__init__()
         self.z_dim = z_dim
@@ -369,6 +371,7 @@ class MappingNet(torch.nn.Module):
         self.num_ws = num_ws
         self.num_layers = num_layers
         self.w_avg_beta = w_avg_beta
+        self.torch_dtype = torch_dtype
 
         if embed_features is None:
             embed_features = w_dim
@@ -399,14 +402,16 @@ class MappingNet(torch.nn.Module):
     def forward(
         self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False
     ):
+        import ipdb
+
+        ipdb.set_trace()
         # Embed, normalize, and concat inputs.
         x = None
-        with torch.autograd.profiler.record_function("input"):
-            if self.z_dim > 0:
-                x = normalize_2nd_moment(z.to(torch.float32))
-            if self.c_dim > 0:
-                y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
-                x = torch.cat([x, y], dim=1) if x is not None else y
+        if self.z_dim > 0:
+            x = normalize_2nd_moment(z)
+        if self.c_dim > 0:
+            y = normalize_2nd_moment(self.embed(c))
+            x = torch.cat([x, y], dim=1) if x is not None else y
 
         # Main layers.
         for idx in range(self.num_layers):
@@ -415,26 +420,21 @@ class MappingNet(torch.nn.Module):
 
         # Update moving average of W.
         if self.w_avg_beta is not None and self.training and not skip_w_avg_update:
-            with torch.autograd.profiler.record_function("update_w_avg"):
-                self.w_avg.copy_(
-                    x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)
-                )
+            self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
 
         # Broadcast.
         if self.num_ws is not None:
-            with torch.autograd.profiler.record_function("broadcast"):
-                x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
+            x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
 
         # Apply truncation.
         if truncation_psi != 1:
-            with torch.autograd.profiler.record_function("truncate"):
-                assert self.w_avg_beta is not None
-                if self.num_ws is None or truncation_cutoff is None:
-                    x = self.w_avg.lerp(x, truncation_psi)
-                else:
-                    x[:, :truncation_cutoff] = self.w_avg.lerp(
-                        x[:, :truncation_cutoff], truncation_psi
-                    )
+            assert self.w_avg_beta is not None
+            if self.num_ws is None or truncation_cutoff is None:
+                x = self.w_avg.lerp(x, truncation_psi)
+            else:
+                x[:, :truncation_cutoff] = self.w_avg.lerp(
+                    x[:, :truncation_cutoff], truncation_psi
+                )
 
         return x
 
@@ -713,7 +713,6 @@ class WindowAttention(nn.Module):
         attn_drop=0.0,
         proj_drop=0.0,
     ):
-
         super().__init__()
         self.dim = dim
         self.window_size = window_size  # Wh, Ww
@@ -1058,7 +1057,6 @@ class BasicLayer(nn.Module):
         downsample=None,
         use_checkpoint=False,
     ):
-
         super().__init__()
         self.dim = dim
         self.input_resolution = input_resolution
@@ -1882,14 +1880,22 @@ class MAT(InpaintModel):
 
     def init_model(self, device, **kwargs):
         seed = 240  # pick up a random number
-        random.seed(seed)
-        np.random.seed(seed)
-        torch.manual_seed(seed)
+        set_seed(seed)
 
-        G = Generator(z_dim=512, c_dim=0, w_dim=512, img_resolution=512, img_channels=3)
-        self.model = load_model(G, MAT_MODEL_URL, device, MAT_MODEL_MD5)
-        self.z = torch.from_numpy(np.random.randn(1, G.z_dim)).to(device)  # [1., 512]
-        self.label = torch.zeros([1, self.model.c_dim], device=device)
+        self.torch_dtype = torch.float16
+        G = Generator(
+            z_dim=512,
+            c_dim=0,
+            w_dim=512,
+            img_resolution=512,
+            img_channels=3,
+            mapping_kwargs={"torch_dtype": self.torch_dtype},
+        )
+        # fmt: off
+        self.model = load_model(G, MAT_MODEL_URL, device, MAT_MODEL_MD5).to(self.torch_dtype)
+        self.z = torch.from_numpy(np.random.randn(1, G.z_dim)).to(self.torch_dtype).to(device)
+        self.label = torch.zeros([1, self.model.c_dim], device=device).to(self.torch_dtype)
+        # fmt: on
 
     @staticmethod
     def is_downloaded() -> bool:
@@ -1909,8 +1915,10 @@ class MAT(InpaintModel):
         mask = 255 - mask
         mask = norm_img(mask)
 
-        image = torch.from_numpy(image).unsqueeze(0).to(self.device)
-        mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
+        image = (
+            torch.from_numpy(image).unsqueeze(0).to(self.torch_dtype).to(self.device)
+        )
+        mask = torch.from_numpy(mask).unsqueeze(0).to(self.torch_dtype).to(self.device)
 
         output = self.model(
             image, mask, self.z, self.label, truncation_psi=1, noise_mode="none"