Detector and denoiser models!

DenoiserModels/Denoiser.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d86133494ad8166a6d3441ed03e2c670317d84665936fe1b63ddb6dd444655dc
+size 32320151

DenoiserModels/Denoiser.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de89d73bcfb6ea0325d4f17f49cf5ecd10cb9c486d884fe8375250f81689411d
+size 8046884

DenoiserModels/model_summary.txt

@@ -0,0 +1,201 @@
+Model Summary
+=============
+Total parameters: 2,008,800 (2.009 M)
+Trainable parameters: 2,008,800 (2.009 M)
+
+Training Config:
+{
+  "dataset_path": "c:\\Users\\admin\\Desktop\\denoiser\\dat\\train",
+  "eval_path": "c:\\Users\\admin\\Desktop\\denoiser\\dat\\val",
+  "output_dir": "runs\\bias_free",
+  "patch_size": 256,
+  "paired": true,
+  "clean_subdir": "good",
+  "noisy_subdir": "bad",
+  "train_noise_levels": [
+    5.0,
+    15.0,
+    25.0,
+    50.0
+  ],
+  "eval_noise_levels": [
+    5.0,
+    15.0,
+    25.0,
+    50.0,
+    75.0
+  ],
+  "color_mode": "rgb",
+  "batch_size": 14,
+  "grad_accum": 1,
+  "num_epochs": 200,
+  "learning_rate": 0.0002,
+  "weight_decay": 1e-06,
+  "ema_decay": 0.999,
+  "warmup_steps": 200,
+  "ssim_weight": 0.15,
+  "gradient_weight": 0.05,
+  "spectral_weight": 0.1,
+  "residual_kernel_sizes": [
+    3,
+    5,
+    7
+  ],
+  "residual_aug_prob": 0.5,
+  "visualize_samples_per_epoch": 2,
+  "num_workers": 4,
+  "device": "cuda",
+  "precision": "amp",
+  "log_interval": 25,
+  "sample_interval": 1,
+  "sample_step_interval": 1000,
+  "checkpoint_interval": 5,
+  "max_train_images": null,
+  "pin_memory": true,
+  "seed": 17,
+  "resume_checkpoint": "c:\\Users\\admin\\Desktop\\gdsfd\\runs\\bias_free\\checkpoint_epoch_0015.pt",
+  "hard_mining": true,
+  "hard_buffer_size": 512,
+  "hard_mining_topk": 4,
+  "hard_mining_interval": 10
+}
+
+Architecture:
+BiasFreeResidualDenoiser(
+  (prior_extractor): ResidualPriorExtractor()
+  (entry): BiasFreeConv(13, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (encoder_stages): ModuleList(
+    (0): ResidualStage(
+      (blocks): ModuleList(
+        (0-1): 2 x GatedResidualBlock(
+          (norm): LayerNorm2d()
+          (local): DepthwiseSeparableConv(
+            (depth): BiasFreeConv(48, 48, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=48, bias=False)
+            (point): BiasFreeConv(48, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          )
+          (activation): GELU(approximate='none')
+          (proj): BiasFreeConv(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (gate): GlobalGating(
+            (proj): BiasFreeConv(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          )
+        )
+      )
+    )
+    (1): ResidualStage(
+      (blocks): ModuleList(
+        (0-1): 2 x GatedResidualBlock(
+          (norm): LayerNorm2d()
+          (local): DepthwiseSeparableConv(
+            (depth): BiasFreeConv(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)
+            (point): BiasFreeConv(96, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          )
+          (activation): GELU(approximate='none')
+          (proj): BiasFreeConv(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (gate): GlobalGating(
+            (proj): BiasFreeConv(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          )
+        )
+      )
+    )
+    (2): ResidualStage(
+      (blocks): ModuleList(
+        (0-1): 2 x GatedResidualBlock(
+          (norm): LayerNorm2d()
+          (local): DepthwiseSeparableConv(
+            (depth): BiasFreeConv(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)
+            (point): BiasFreeConv(192, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          )
+          (activation): GELU(approximate='none')
+          (proj): BiasFreeConv(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (gate): GlobalGating(
+            (proj): BiasFreeConv(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          )
+        )
+      )
+    )
+  )
+  (downs): ModuleList(
+    (0): BiasFreeConv(48, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+    (1): BiasFreeConv(96, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+  )
+  (bottleneck): ResidualStage(
+    (blocks): ModuleList(
+      (0-2): 3 x GatedResidualBlock(
+        (norm): LayerNorm2d()
+        (local): DepthwiseSeparableConv(
+          (depth): BiasFreeConv(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False)
+          (point): BiasFreeConv(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        )
+        (activation): GELU(approximate='none')
+        (proj): BiasFreeConv(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (gate): GlobalGating(
+          (proj): BiasFreeConv(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        )
+      )
+    )
+  )
+  (up_projs): ModuleList(
+    (0): Sequential(
+      (0): Upsample(scale_factor=2.0, mode='bilinear')
+      (1): BiasFreeConv(192, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+    )
+    (1): Sequential(
+      (0): Upsample(scale_factor=2.0, mode='bilinear')
+      (1): BiasFreeConv(96, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+    )
+  )
+  (merge_convs): ModuleList(
+    (0): BiasFreeConv(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
+    (1): BiasFreeConv(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
+  )
+  (decoder_stages): ModuleList(
+    (0): ResidualStage(
+      (blocks): ModuleList(
+        (0-1): 2 x GatedResidualBlock(
+          (norm): LayerNorm2d()
+          (local): DepthwiseSeparableConv(
+            (depth): BiasFreeConv(96, 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=96, bias=False)
+            (point): BiasFreeConv(96, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          )
+          (activation): GELU(approximate='none')
+          (proj): BiasFreeConv(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (gate): GlobalGating(
+            (proj): BiasFreeConv(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          )
+        )
+      )
+    )
+    (1): ResidualStage(
+      (blocks): ModuleList(
+        (0-1): 2 x GatedResidualBlock(
+          (norm): LayerNorm2d()
+          (local): DepthwiseSeparableConv(
+            (depth): BiasFreeConv(48, 48, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=48, bias=False)
+            (point): BiasFreeConv(48, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          )
+          (activation): GELU(approximate='none')
+          (proj): BiasFreeConv(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          (gate): GlobalGating(
+            (proj): BiasFreeConv(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
+          )
+        )
+      )
+    )
+  )
+  (exit): BiasFreeConv(48, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+  (conditioner): NoiseConditioner(
+    (encoder): Sequential(
+      (0): Linear(in_features=4, out_features=96, bias=True)
+      (1): GELU(approximate='none')
+      (2): Linear(in_features=96, out_features=96, bias=True)
+      (3): GELU(approximate='none')
+    )
+    (heads): ModuleList(
+      (0-1): 2 x Linear(in_features=96, out_features=96, bias=True)
+      (2): Linear(in_features=96, out_features=192, bias=True)
+      (3-4): 2 x Linear(in_features=96, out_features=384, bias=True)
+      (5): Linear(in_features=96, out_features=192, bias=True)
+      (6): Linear(in_features=96, out_features=96, bias=True)
+    )
+  )
+)

DetectorModels/grdnet_v2.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f7cccbe4f9f70ba57ee3dde88247896b2fd8d7baa26150364b992eb798c2a93
+size 528184663

DetectorModels/grdnet_v2.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eefecc38be536ee801d504c872c0c42e9c3ff6b3e72559aaff34f159e9060f04
+size 176070024