Update calc_metrics.py

calc_metrics.py

@@ -105,6 +105,16 @@ def parse_comma_separated_list(s):
 # ----------------------------------------------------------------------------
 
 
+def make_coords(resolution: float, scale: float):
+    coords = torch.linspace(0, 1, int(resolution * scale))
+    coords = coords.reshape(1, -1, 1, 1)
+    coords = coords.repeat(1, 1, 2, 1)
+    return coords
+
+
+# ----------------------------------------------------------------------------
+
+
 @click.command()
 @click.pass_context
 @click.option(
@@ -114,6 +124,13 @@ def parse_comma_separated_list(s):
     metavar="PATH",
     required=True,
 )
+@click.option(
+    "--scale",
+    help="Scale of generated images",
+    type=float,
+    default=1,
+    show_default=True,
+)
 @click.option(
     "--metrics",
     help="Quality metrics",
@@ -149,7 +166,7 @@ def parse_comma_separated_list(s):
     metavar="BOOL",
     show_default=True,
 )
-def calc_metrics(ctx, network_pkl, metrics, data, mirror, gpus, verbose):
+def calc_metrics(ctx, network_pkl, scale, metrics, data, mirror, gpus, verbose):
     """Calculate quality metrics for previous training run or pretrained network pickle.
 
     Examples:
@@ -198,6 +215,10 @@ def calc_metrics(ctx, network_pkl, metrics, data, mirror, gpus, verbose):
     with dnnlib.util.open_url(network_pkl, verbose=args.verbose) as f:
         network_dict = legacy.load_network_pkl(f)
         args.G = network_dict["G_ema"]  # subclass of torch.nn.Module
+
+    # Construct an input coordinates and pass to the creps generator.
+    if hasattr(args.G.synthesis.b4, "input"):
+        args.G.synthesis.b4.input.coords = make_coords(args.G.img_resolution, scale)
 
     # Initialize dataset options.
     if data is not None:
@@ -208,7 +229,7 @@ def calc_metrics(ctx, network_pkl, metrics, data, mirror, gpus, verbose):
         ctx.fail("Could not look up dataset options; please specify --data")
 
     # Finalize dataset options.
-    args.dataset_kwargs.resolution = args.G.img_resolution
+    args.dataset_kwargs.resolution = int(args.G.img_resolution * scale)
     args.dataset_kwargs.use_labels = args.G.c_dim != 0
     if mirror is not None:
         args.dataset_kwargs.xflip = mirror