default to bf16 for rcan, moesr

options/test/MoESR/MoESR2.yml

@@ -5,7 +5,7 @@
 name: 4x_MoESR2
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only.
 num_gpu: auto
 
 

options/test/RCAN/RCAN.yml

@@ -5,7 +5,7 @@
 name: 4x_RCAN
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only.
 num_gpu: auto
 
 

options/train/MoESR/MoESR2_OTF_bicubic_ms_ssim_l1_fromscratch.yml

@@ -6,7 +6,7 @@
 name: 4x_MoESR2_OTF_bicubic_ms_ssim_l1
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
 use_channels_last: false  # Enable channels last memory format while using AMP. Reduces VRAM and speeds up training for most architectures, but some architectures are slower with channels last.
 fast_matmul: false  # Trade precision for performance.
 num_gpu: auto

options/train/MoESR/MoESR2_OTF_finetune.yml

@@ -6,7 +6,7 @@
 name: 4x_MoESR2_OTF_finetune
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
 use_channels_last: false  # Enable channels last memory format while using AMP. Reduces VRAM and speeds up training for most architectures, but some architectures are slower with channels last.
 fast_matmul: false  # Trade precision for performance.
 num_gpu: auto

options/train/MoESR/MoESR2_OTF_fromscratch.yml

@@ -6,7 +6,7 @@
 name: 4x_MoESR2_OTF_fromscratch
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
 use_channels_last: false  # Enable channels last memory format while using AMP. Reduces VRAM and speeds up training for most architectures, but some architectures are slower with channels last.
 fast_matmul: false  # Trade precision for performance.
 num_gpu: auto

options/train/MoESR/MoESR2_finetune.yml

@@ -6,7 +6,7 @@
 name: 4x_MoESR2
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
 use_channels_last: false  # Enable channels last memory format while using AMP. Reduces VRAM and speeds up training for most architectures, but some architectures are slower with channels last.
 fast_matmul: false  # Trade precision for performance.
 num_gpu: auto

options/train/MoESR/MoESR2_fromscratch.yml

@@ -6,7 +6,7 @@
 name: 4x_MoESR2
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
 use_channels_last: false  # Enable channels last memory format while using AMP. Reduces VRAM and speeds up training for most architectures, but some architectures are slower with channels last.
 fast_matmul: false  # Trade precision for performance.
 num_gpu: auto

options/train/RCAN/RCAN_OTF_bicubic_ms_ssim_l1_fromscratch.yml

@@ -6,7 +6,7 @@
 name: 4x_RCAN_OTF_bicubic_ms_ssim_l1
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
 use_channels_last: true  # Enable channels last memory format while using AMP. Reduces VRAM and speeds up training for most architectures, but some architectures are slower with channels last.
 fast_matmul: false  # Trade precision for performance.
 num_gpu: auto

options/train/RCAN/RCAN_OTF_finetune.yml

@@ -6,7 +6,7 @@
 name: 4x_RCAN_OTF_finetune
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
 use_channels_last: true  # Enable channels last memory format while using AMP. Reduces VRAM and speeds up training for most architectures, but some architectures are slower with channels last.
 fast_matmul: false  # Trade precision for performance.
 num_gpu: auto

options/train/RCAN/RCAN_OTF_fromscratch.yml

@@ -6,7 +6,7 @@
 name: 4x_RCAN_OTF_fromscratch
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
 use_channels_last: true  # Enable channels last memory format while using AMP. Reduces VRAM and speeds up training for most architectures, but some architectures are slower with channels last.
 fast_matmul: false  # Trade precision for performance.
 num_gpu: auto

options/train/RCAN/RCAN_finetune.yml

@@ -6,7 +6,7 @@
 name: 4x_RCAN
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
 use_channels_last: true  # Enable channels last memory format while using AMP. Reduces VRAM and speeds up training for most architectures, but some architectures are slower with channels last.
 fast_matmul: false  # Trade precision for performance.
 num_gpu: auto

options/train/RCAN/RCAN_fromscratch.yml

@@ -6,7 +6,7 @@
 name: 4x_RCAN
 scale: 4  # 1, 2, 3, 4, 8
 use_amp: true  # Speed up training and reduce VRAM usage. NVIDIA only.
-amp_bf16: false  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
+amp_bf16: true  # Use bf16 instead of fp16 for AMP, RTX 3000 series or newer only. Only recommended if fp16 doesn't work.
 use_channels_last: true  # Enable channels last memory format while using AMP. Reduces VRAM and speeds up training for most architectures, but some architectures are slower with channels last.
 fast_matmul: false  # Trade precision for performance.
 num_gpu: auto

test_scripts/test_precision.py

@@ -52,12 +52,12 @@ def format_extra_params(extra_arch_params: dict[str, Any]) -> str:
 def compare_precision(
     net: nn.Module, input_tensor: Tensor, criterion: nn.Module
 ) -> tuple[float, float]:
-    with torch.no_grad():
+    with torch.inference_mode():
         fp32_output = net(input_tensor)
 
     fp16_loss = None
     try:
-        with autocast(dtype=torch.float16, device_type="cuda"):
+        with autocast(dtype=torch.float16, device_type="cuda"), torch.inference_mode():
             fp16_output = net(input_tensor)
         fp16_loss = criterion(fp16_output.float(), fp32_output).item()
     except Exception as e:
@@ -66,7 +66,7 @@ def compare_precision(
 
     bf16_loss = None
     try:
-        with autocast(dtype=torch.bfloat16, device_type="cuda"):
+        with autocast(dtype=torch.bfloat16, device_type="cuda"), torch.inference_mode():
             bf16_output = net(input_tensor)
         bf16_loss = criterion(bf16_output.float(), fp32_output).item()
     except Exception as e:
@@ -82,7 +82,15 @@ def compare_precision(
         label = f"{name} {format_extra_params(extra_arch_params)} {scale}x"
 
         try:
-            if "realplksr" not in name:
+            if name not in {
+                "rcan",
+                "esrgan",
+                "compact",
+                "span",
+                "dat_2",
+                "spanplus",
+                "realplksr",
+            }:
                 continue
 
             net: nn.Module = arch(scale=scale, **extra_arch_params).eval().to("cuda")

traiNNer/archs/arch_info.py

@@ -26,6 +26,8 @@
     "hit_sir",
     "hit_sng",
     "hit_srf",
+    "rcan",
+    "moesr2",
     "rgt_s",
     "rgt",
     "seemore_t",

traiNNer/archs/artcnn_arch.py

@@ -105,3 +105,22 @@ def artcnn_r8f64(
         kernel_size=kernel_size,
         act=act,
     )
+
+
+@ARCH_REGISTRY.register()
+def artcnn_r8f48(
+    in_ch: int = 3,
+    scale: int = 4,
+    filters: int = 48,
+    n_block: int = 8,
+    kernel_size: int = 3,
+    act: type[nn.Module] = nn.ReLU,
+) -> ArtCNN:
+    return ArtCNN(
+        scale=scale,
+        in_ch=in_ch,
+        n_block=n_block,
+        filters=filters,
+        kernel_size=kernel_size,
+        act=act,
+    )