Image Audio Alingment

.gitattributes

@@ -137,3 +137,8 @@ Vaani/sampleJSON.json filter=lfs diff=lfs merge=lfs -text
 tools/__pycache__/pynvml.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
 Vaani/VaaniLDM/samplesH/x0_0.png filter=lfs diff=lfs merge=lfs -text
 Vaani/SDFT/astronaut_horse_mars.png filter=lfs diff=lfs merge=lfs -text
+Vaani/Img_Audio_Alignment/000000039769.jpg filter=lfs diff=lfs merge=lfs -text
+Vaani/audio_urls[[:space:]]copy.txt filter=lfs diff=lfs merge=lfs -text
+Vaani/imageBY.csv filter=lfs diff=lfs merge=lfs -text
+Vaani/imageBY2.csv filter=lfs diff=lfs merge=lfs -text
+Vaani/imageBY3.csv filter=lfs diff=lfs merge=lfs -text

Vaani/Img_Audio_Alignment/CLAP-Audio-Encoder.txt

@@ -0,0 +1,292 @@
+AudioEncoder(
+  (base): HTSATWrapper(
+    (htsat): HTSAT_Swin_Transformer(
+      (spectrogram_extractor): Spectrogram(
+        (stft): STFT(
+          (conv_real): Conv1d(1, 513, kernel_size=(1024,), stride=(320,), bias=False)
+          (conv_imag): Conv1d(1, 513, kernel_size=(1024,), stride=(320,), bias=False)
+        )
+      )
+      (logmel_extractor): LogmelFilterBank()
+      (spec_augmenter): SpecAugmentation(
+        (time_dropper): DropStripes()
+        (freq_dropper): DropStripes()
+      )
+      (bn0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (patch_embed): PatchEmbed(
+        (proj): Conv2d(1, 96, kernel_size=(4, 4), stride=(4, 4))
+        (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
+      )
+      (pos_drop): Dropout(p=0.0, inplace=False)
+      (layers): ModuleList(
+        (0): BasicLayer(
+          dim=96, input_resolution=(64, 64), depth=2
+          (blocks): ModuleList(
+            (0): SwinTransformerBlock(
+              dim=96, input_resolution=(64, 64), num_heads=4, window_size=8, shift_size=0, mlp_ratio=4.0
+              (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=96, window_size=(8, 8), num_heads=4
+                (qkv): Linear(in_features=96, out_features=288, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=96, out_features=96, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): Identity()
+              (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=96, out_features=384, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=384, out_features=96, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (1): SwinTransformerBlock(
+              dim=96, input_resolution=(64, 64), num_heads=4, window_size=8, shift_size=4, mlp_ratio=4.0
+              (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=96, window_size=(8, 8), num_heads=4
+                (qkv): Linear(in_features=96, out_features=288, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=96, out_features=96, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): DropPath()
+              (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=96, out_features=384, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=384, out_features=96, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+          )
+          (downsample): PatchMerging(
+            input_resolution=(64, 64), dim=96
+            (reduction): Linear(in_features=384, out_features=192, bias=False)
+            (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+          )
+        )
+        (1): BasicLayer(
+          dim=192, input_resolution=(32, 32), depth=2
+          (blocks): ModuleList(
+            (0): SwinTransformerBlock(
+              dim=192, input_resolution=(32, 32), num_heads=8, window_size=8, shift_size=0, mlp_ratio=4.0
+              (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=192, window_size=(8, 8), num_heads=8
+                (qkv): Linear(in_features=192, out_features=576, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=192, out_features=192, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): DropPath()
+              (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=192, out_features=768, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=768, out_features=192, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (1): SwinTransformerBlock(
+              dim=192, input_resolution=(32, 32), num_heads=8, window_size=8, shift_size=4, mlp_ratio=4.0
+              (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=192, window_size=(8, 8), num_heads=8
+                (qkv): Linear(in_features=192, out_features=576, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=192, out_features=192, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): DropPath()
+              (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=192, out_features=768, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=768, out_features=192, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+          )
+          (downsample): PatchMerging(
+            input_resolution=(32, 32), dim=192
+            (reduction): Linear(in_features=768, out_features=384, bias=False)
+            (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+          )
+        )
+        (2): BasicLayer(
+          dim=384, input_resolution=(16, 16), depth=6
+          (blocks): ModuleList(
+            (0): SwinTransformerBlock(
+              dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=0, mlp_ratio=4.0
+              (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=384, window_size=(8, 8), num_heads=16
+                (qkv): Linear(in_features=384, out_features=1152, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=384, out_features=384, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): DropPath()
+              (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=384, out_features=1536, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=1536, out_features=384, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (1): SwinTransformerBlock(
+              dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=4, mlp_ratio=4.0
+              (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=384, window_size=(8, 8), num_heads=16
+                (qkv): Linear(in_features=384, out_features=1152, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=384, out_features=384, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): DropPath()
+              (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=384, out_features=1536, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=1536, out_features=384, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (2): SwinTransformerBlock(
+              dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=0, mlp_ratio=4.0
+              (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=384, window_size=(8, 8), num_heads=16
+                (qkv): Linear(in_features=384, out_features=1152, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=384, out_features=384, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): DropPath()
+              (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=384, out_features=1536, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=1536, out_features=384, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (3): SwinTransformerBlock(
+              dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=4, mlp_ratio=4.0
+              (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=384, window_size=(8, 8), num_heads=16
+                (qkv): Linear(in_features=384, out_features=1152, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=384, out_features=384, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): DropPath()
+              (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=384, out_features=1536, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=1536, out_features=384, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (4): SwinTransformerBlock(
+              dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=0, mlp_ratio=4.0
+              (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=384, window_size=(8, 8), num_heads=16
+                (qkv): Linear(in_features=384, out_features=1152, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=384, out_features=384, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): DropPath()
+              (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=384, out_features=1536, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=1536, out_features=384, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+            (5): SwinTransformerBlock(
+              dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=4, mlp_ratio=4.0
+              (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=384, window_size=(8, 8), num_heads=16
+                (qkv): Linear(in_features=384, out_features=1152, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=384, out_features=384, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): DropPath()
+              (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=384, out_features=1536, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=1536, out_features=384, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+          )
+          (downsample): PatchMerging(
+            input_resolution=(16, 16), dim=384
+            (reduction): Linear(in_features=1536, out_features=768, bias=False)
+            (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)
+          )
+        )
+        (3): BasicLayer(
+          dim=768, input_resolution=(8, 8), depth=2
+          (blocks): ModuleList(
+            (0-1): 2 x SwinTransformerBlock(
+              dim=768, input_resolution=(8, 8), num_heads=32, window_size=8, shift_size=0, mlp_ratio=4.0
+              (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (attn): WindowAttention(
+                dim=768, window_size=(8, 8), num_heads=32
+                (qkv): Linear(in_features=768, out_features=2304, bias=True)
+                (attn_drop): Dropout(p=0.0, inplace=False)
+                (proj): Linear(in_features=768, out_features=768, bias=True)
+                (proj_drop): Dropout(p=0.0, inplace=False)
+                (softmax): Softmax(dim=-1)
+              )
+              (drop_path): DropPath()
+              (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              (mlp): Mlp(
+                (fc1): Linear(in_features=768, out_features=3072, bias=True)
+                (act): GELU(approximate='none')
+                (fc2): Linear(in_features=3072, out_features=768, bias=True)
+                (drop): Dropout(p=0.0, inplace=False)
+              )
+            )
+          )
+        )
+      )
+      (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+      (avgpool): AdaptiveAvgPool1d(output_size=1)
+      (maxpool): AdaptiveMaxPool1d(output_size=1)
+      (tscam_conv): Conv2d(768, 527, kernel_size=(2, 3), stride=(1, 1), padding=(0, 1))
+      (head): Linear(in_features=527, out_features=527, bias=True)
+    )
+  )
+  (projection): Projection(
+    (linear1): Linear(in_features=768, out_features=1024, bias=False)
+    (linear2): Linear(in_features=1024, out_features=1024, bias=False)
+    (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+    (drop): Dropout(p=0.5, inplace=False)
+  )
+)
+

Vaani/Img_Audio_Alignment/LoRA-CLAP-Audio-Encoder.txt

@@ -0,0 +1,954 @@
+PeftModelForFeatureExtraction(
+  (base_model): LoraModel(
+    (model): AudioEncoder(
+      (base): HTSATWrapper(
+        (htsat): HTSAT_Swin_Transformer(
+          (spectrogram_extractor): Spectrogram(
+            (stft): STFT(
+              (conv_real): Conv1d(1, 513, kernel_size=(1024,), stride=(320,), bias=False)
+              (conv_imag): Conv1d(1, 513, kernel_size=(1024,), stride=(320,), bias=False)
+            )
+          )
+          (logmel_extractor): LogmelFilterBank()
+          (spec_augmenter): SpecAugmentation(
+            (time_dropper): DropStripes()
+            (freq_dropper): DropStripes()
+          )
+          (bn0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          (patch_embed): PatchEmbed(
+            (proj): lora.Conv2d(
+              (base_layer): Conv2d(1, 96, kernel_size=(4, 4), stride=(4, 4))
+              (lora_dropout): ModuleDict(
+                (default): Dropout(p=0.05, inplace=False)
+              )
+              (lora_A): ModuleDict(
+                (default): Conv2d(1, 8, kernel_size=(4, 4), stride=(4, 4), bias=False)
+              )
+              (lora_B): ModuleDict(
+                (default): Conv2d(8, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
+              )
+              (lora_embedding_A): ParameterDict()
+              (lora_embedding_B): ParameterDict()
+              (lora_magnitude_vector): ModuleDict()
+            )
+            (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
+          )
+          (pos_drop): Dropout(p=0.0, inplace=False)
+          (layers): ModuleList(
+            (0): BasicLayer(
+              dim=96, input_resolution=(64, 64), depth=2
+              (blocks): ModuleList(
+                (0): SwinTransformerBlock(
+                  dim=96, input_resolution=(64, 64), num_heads=4, window_size=8, shift_size=0, mlp_ratio=4.0
+                  (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=96, window_size=(8, 8), num_heads=4
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=96, out_features=288, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=96, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=288, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=96, out_features=96, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=96, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=96, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): Identity()
+                  (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=96, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=96, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=96, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=96, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+                (1): SwinTransformerBlock(
+                  dim=96, input_resolution=(64, 64), num_heads=4, window_size=8, shift_size=4, mlp_ratio=4.0
+                  (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=96, window_size=(8, 8), num_heads=4
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=96, out_features=288, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=96, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=288, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=96, out_features=96, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=96, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=96, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): DropPath()
+                  (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=96, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=96, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=96, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=96, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+              )
+              (downsample): PatchMerging(
+                input_resolution=(64, 64), dim=96
+                (reduction): Linear(in_features=384, out_features=192, bias=False)
+                (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+              )
+            )
+            (1): BasicLayer(
+              dim=192, input_resolution=(32, 32), depth=2
+              (blocks): ModuleList(
+                (0): SwinTransformerBlock(
+                  dim=192, input_resolution=(32, 32), num_heads=8, window_size=8, shift_size=0, mlp_ratio=4.0
+                  (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=192, window_size=(8, 8), num_heads=8
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=192, out_features=576, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=192, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=576, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=192, out_features=192, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=192, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=192, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): DropPath()
+                  (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=192, out_features=768, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=192, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=768, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=768, out_features=192, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=768, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=192, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+                (1): SwinTransformerBlock(
+                  dim=192, input_resolution=(32, 32), num_heads=8, window_size=8, shift_size=4, mlp_ratio=4.0
+                  (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=192, window_size=(8, 8), num_heads=8
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=192, out_features=576, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=192, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=576, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=192, out_features=192, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=192, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=192, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): DropPath()
+                  (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=192, out_features=768, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=192, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=768, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=768, out_features=192, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=768, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=192, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+              )
+              (downsample): PatchMerging(
+                input_resolution=(32, 32), dim=192
+                (reduction): Linear(in_features=768, out_features=384, bias=False)
+                (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+              )
+            )
+            (2): BasicLayer(
+              dim=384, input_resolution=(16, 16), depth=6
+              (blocks): ModuleList(
+                (0): SwinTransformerBlock(
+                  dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=0, mlp_ratio=4.0
+                  (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=384, window_size=(8, 8), num_heads=16
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1152, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1152, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): DropPath()
+                  (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1536, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1536, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=1536, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=1536, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+                (1): SwinTransformerBlock(
+                  dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=4, mlp_ratio=4.0
+                  (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=384, window_size=(8, 8), num_heads=16
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1152, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1152, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): DropPath()
+                  (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1536, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1536, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=1536, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=1536, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+                (2): SwinTransformerBlock(
+                  dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=0, mlp_ratio=4.0
+                  (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=384, window_size=(8, 8), num_heads=16
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1152, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1152, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): DropPath()
+                  (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1536, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1536, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=1536, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=1536, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+                (3): SwinTransformerBlock(
+                  dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=4, mlp_ratio=4.0
+                  (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=384, window_size=(8, 8), num_heads=16
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1152, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1152, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): DropPath()
+                  (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1536, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1536, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=1536, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=1536, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+                (4): SwinTransformerBlock(
+                  dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=0, mlp_ratio=4.0
+                  (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=384, window_size=(8, 8), num_heads=16
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1152, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1152, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): DropPath()
+                  (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1536, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1536, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=1536, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=1536, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+                (5): SwinTransformerBlock(
+                  dim=384, input_resolution=(16, 16), num_heads=16, window_size=8, shift_size=4, mlp_ratio=4.0
+                  (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=384, window_size=(8, 8), num_heads=16
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1152, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1152, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): DropPath()
+                  (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=384, out_features=1536, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=384, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=1536, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=1536, out_features=384, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=1536, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=384, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+              )
+              (downsample): PatchMerging(
+                input_resolution=(16, 16), dim=384
+                (reduction): Linear(in_features=1536, out_features=768, bias=False)
+                (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)
+              )
+            )
+            (3): BasicLayer(
+              dim=768, input_resolution=(8, 8), depth=2
+              (blocks): ModuleList(
+                (0-1): 2 x SwinTransformerBlock(
+                  dim=768, input_resolution=(8, 8), num_heads=32, window_size=8, shift_size=0, mlp_ratio=4.0
+                  (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                  (attn): WindowAttention(
+                    dim=768, window_size=(8, 8), num_heads=32
+                    (qkv): lora.Linear(
+                      (base_layer): Linear(in_features=768, out_features=2304, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=768, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=2304, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (attn_drop): Dropout(p=0.0, inplace=False)
+                    (proj): lora.Linear(
+                      (base_layer): Linear(in_features=768, out_features=768, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=768, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=768, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (proj_drop): Dropout(p=0.0, inplace=False)
+                    (softmax): Softmax(dim=-1)
+                  )
+                  (drop_path): DropPath()
+                  (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+                  (mlp): Mlp(
+                    (fc1): lora.Linear(
+                      (base_layer): Linear(in_features=768, out_features=3072, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=768, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=3072, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (act): GELU(approximate='none')
+                    (fc2): lora.Linear(
+                      (base_layer): Linear(in_features=3072, out_features=768, bias=True)
+                      (lora_dropout): ModuleDict(
+                        (default): Dropout(p=0.05, inplace=False)
+                      )
+                      (lora_A): ModuleDict(
+                        (default): Linear(in_features=3072, out_features=8, bias=False)
+                      )
+                      (lora_B): ModuleDict(
+                        (default): Linear(in_features=8, out_features=768, bias=False)
+                      )
+                      (lora_embedding_A): ParameterDict()
+                      (lora_embedding_B): ParameterDict()
+                      (lora_magnitude_vector): ModuleDict()
+                    )
+                    (drop): Dropout(p=0.0, inplace=False)
+                  )
+                )
+              )
+            )
+          )
+          (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+          (avgpool): AdaptiveAvgPool1d(output_size=1)
+          (maxpool): AdaptiveMaxPool1d(output_size=1)
+          (tscam_conv): Conv2d(768, 527, kernel_size=(2, 3), stride=(1, 1), padding=(0, 1))
+          (head): Linear(in_features=527, out_features=527, bias=True)
+        )
+      )
+      (projection): Projection(
+        (linear1): lora.Linear(
+          (base_layer): Linear(in_features=768, out_features=1024, bias=False)
+          (lora_dropout): ModuleDict(
+            (default): Dropout(p=0.05, inplace=False)
+          )
+          (lora_A): ModuleDict(
+            (default): Linear(in_features=768, out_features=8, bias=False)
+          )
+          (lora_B): ModuleDict(
+            (default): Linear(in_features=8, out_features=1024, bias=False)
+          )
+          (lora_embedding_A): ParameterDict()
+          (lora_embedding_B): ParameterDict()
+          (lora_magnitude_vector): ModuleDict()
+        )
+        (linear2): lora.Linear(
+          (base_layer): Linear(in_features=1024, out_features=1024, bias=False)
+          (lora_dropout): ModuleDict(
+            (default): Dropout(p=0.05, inplace=False)
+          )
+          (lora_A): ModuleDict(
+            (default): Linear(in_features=1024, out_features=8, bias=False)
+          )
+          (lora_B): ModuleDict(
+            (default): Linear(in_features=8, out_features=1024, bias=False)
+          )
+          (lora_embedding_A): ParameterDict()
+          (lora_embedding_B): ParameterDict()
+          (lora_magnitude_vector): ModuleDict()
+        )
+        (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+        (drop): Dropout(p=0.5, inplace=False)
+      )
+    )
+  )
+)
+

Vaani/Img_Audio_Alignment/_2_Train.py

@@ -0,0 +1,1495 @@
+# ==================================================================
+#             L A T E N T   D I F F U S I O N   M O D E L
+# ==================================================================
+# Author    : Ashish Kumar Uchadiya
+# Created   : May 11, 2025
+# Description: This script implements the training of a VQ-VAE model for
+# image reconstruction, integrated with Latent Diffusion Models (LDMs) and
+# audio conditioning. The VQ-VAE maps images to a discrete latent space, 
+# which is then modeled by the LDM for learning a diffusion process over the 
+# compressed representation. Audio features are used as conditioning inputs 
+# to guide the generation process. The training minimizes a combination of 
+# LPIPS (Learned Perceptual Image Patch Similarity) loss for perceptual 
+# fidelity and PatchGAN loss to enforce local realism. This setup enables 
+# efficient and semantically-aware generation of high-quality images driven 
+# by audio cues.
+# ==================================================================
+#                         I M P O R T S
+# ==================================================================
+from __future__ import annotations
+import warnings
+warnings.filterwarnings("ignore")
+
+import os
+import sys
+import math
+import random
+import collections
+import collections.abc
+import re
+from itertools import repeat
+from pathlib import Path
+from typing import Optional, Tuple, Union, List, Dict
+
+import requests
+from PIL import Image
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn.init import _calculate_fan_in_and_fan_out
+import torch.utils.checkpoint as checkpoint
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+import torchaudio
+import torchaudio.transforms as T
+from torchlibrosa.stft import Spectrogram, LogmelFilterBank
+from torchlibrosa.augmentation import SpecAugmentation
+
+from transformers import AutoModel, AutoTokenizer, logging
+from huggingface_hub.file_download import hf_hub_download
+from huggingface_hub.file_download import hf_hub_download
+from peft import get_peft_config, get_peft_model
+from transformers import CLIPVisionModel, AutoProcessor
+
+
+
+# ==================================================================
+#                        H T S - A T
+# ==================================================================
+class HTSATConfig:
+    # Ke Chen
+    # [email protected]
+    # HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
+    # The configuration for training the model
+
+    exp_name = "exp_htsat_pretrain" # the saved ckpt prefix name of the model 
+    workspace = "/home/kechen/Research/HTSAT" # the folder of your code
+    dataset_path = "/home/Research/audioset" # the dataset path
+    desed_folder = "/home/Research/DESED" # the desed file
+
+    dataset_type = "audioset" # "audioset" "esc-50" "scv2"
+    index_type = "full_train" # only works for audioset
+    balanced_data = True # only works for audioset
+
+    loss_type = "clip_bce" # 
+    # AudioSet & SCV2: "clip_bce" |  ESC-50: "clip_ce" 
+
+    # trained from a checkpoint, or evaluate a single model 
+    resume_checkpoint = None 
+    # "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_1.ckpt"
+    
+    esc_fold = 0 # just for esc dataset, select the fold you need for evaluation and (+1) validation
+
+
+    debug = False
+
+    random_seed = 970131 # 19970318 970131 12412 127777 1009 34047
+    batch_size = 32 * 4 # batch size per GPU x GPU number , default is 32 x 4 = 128
+    learning_rate = 1e-3 # 1e-4 also workable 
+    max_epoch = 100
+    num_workers = 3
+
+    lr_scheduler_epoch = [10,20,30]
+    lr_rate = [0.02, 0.05, 0.1]
+
+    # these data preparation optimizations do not bring many improvements, so deprecated
+    enable_token_label = False # token label
+    class_map_path = "class_hier_map.npy"
+    class_filter = None 
+    retrieval_index = [15382, 9202, 130, 17618, 17157, 17516, 16356, 6165, 13992, 9238, 5550, 5733, 1914, 1600, 3450, 13735, 11108, 3762, 
+        9840, 11318, 8131, 4429, 16748, 4992, 16783, 12691, 4945, 8779, 2805, 9418, 2797, 14357, 5603, 212, 3852, 12666, 1338, 10269, 2388, 8260, 4293, 14454, 7677, 11253, 5060, 14938, 8840, 4542, 2627, 16336, 8992, 15496, 11140, 446, 6126, 10691, 8624, 10127, 9068, 16710, 10155, 14358, 7567, 5695, 2354, 8057, 17635, 133, 16183, 14535, 7248, 4560, 14429, 2463, 10773, 113, 2462, 9223, 4929, 14274, 4716, 17307, 4617, 2132, 11083, 1039, 1403, 9621, 13936, 2229, 2875, 17840, 9359, 13311, 9790, 13288, 4750, 17052, 8260, 14900]
+    token_label_range = [0.2,0.6]
+    enable_time_shift = False # shift time
+    enable_label_enhance = False # enhance hierarchical label
+    enable_repeat_mode = False # repeat the spectrogram / reshape the spectrogram
+
+
+
+    # for model's design
+    enable_tscam = True # enbale the token-semantic layer
+
+    # for signal processing
+    sample_rate = 32000 # 16000 for scv2, 32000 for audioset and esc-50
+    clip_samples = sample_rate * 10 # audio_set 10-sec clip
+    window_size = 1024
+    hop_size = 320 # 160 for scv2, 320 for audioset and esc-50
+    mel_bins = 64
+    fmin = 50
+    fmax = 14000
+    shift_max = int(clip_samples * 0.5)
+
+    # for data collection
+    classes_num = 527 # esc: 50 | audioset: 527 | scv2: 35
+    patch_size = (25, 4) # deprecated
+    crop_size = None # int(clip_samples * 0.5) deprecated
+
+    # for htsat hyperparamater
+    htsat_window_size = 8
+    htsat_spec_size =  256
+    htsat_patch_size = 4 
+    htsat_stride = (4, 4)
+    htsat_num_head = [4,8,16,32]
+    htsat_dim = 96 
+    htsat_depth = [2,2,6,2]
+
+    swin_pretrain_path = None
+    # "/home/Research/model_backup/pretrain/swin_tiny_c24_patch4_window8_256.pth"
+
+    # Some Deprecated Optimization in the model design, check the model code for details
+    htsat_attn_heatmap = False
+    htsat_hier_output = False 
+    htsat_use_max = False
+
+
+    # for ensemble test 
+
+    ensemble_checkpoints = []
+    ensemble_strides = []
+
+
+    # weight average folder
+    wa_folder = "/home/version_0/checkpoints/"
+    # weight average output filename
+    wa_model_path = "HTSAT_AudioSet_Saved_x.ckpt"
+
+    esm_model_pathes = [
+        "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_1.ckpt",
+        "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_2.ckpt",
+        "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_3.ckpt",
+        "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_4.ckpt",
+        "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_5.ckpt",
+        "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_6.ckpt"
+    ]
+
+    # for framewise localization
+    heatmap_dir = "/home/Research/heatmap_output"
+    test_file = "htsat-test-ensemble"
+    fl_local = False # indicate if we need to use this dataset for the framewise detection
+    fl_dataset = "/home/Research/desed/desed_eval.npy"  
+    fl_class_num = [
+        "Speech", "Frying", "Dishes", "Running_water",
+        "Blender", "Electric_shaver_toothbrush", "Alarm_bell_ringing",
+        "Cat", "Dog", "Vacuum_cleaner"
+    ]
+
+    # map 527 classes into 10 classes
+    fl_audioset_mapping = [
+        [0,1,2,3,4,5,6,7],
+        [366, 367, 368],
+        [364],
+        [288, 289, 290, 291, 292, 293, 294, 295, 296, 297],
+        [369],
+        [382],
+        [310, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402],
+        [81, 82, 83, 84, 85],
+        [74, 75, 76, 77, 78, 79],
+        [377]
+    ]
+
+
+
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+    return parse
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+to_3tuple = _ntuple(3)
+to_4tuple = _ntuple(4)
+to_ntuple = _ntuple
+
+def do_mixup(x, mixup_lambda):
+    """Mixup x of even indexes (0, 2, 4, ...) with x of odd indexes 
+    (1, 3, 5, ...).
+    Args:
+      x: (batch_size * 2, ...)
+      mixup_lambda: (batch_size * 2,)
+    Returns:
+      out: (batch_size, ...)
+    """
+    out = (x[0 :: 2].transpose(0, -1) * mixup_lambda[0 :: 2] + \
+        x[1 :: 2].transpose(0, -1) * mixup_lambda[1 :: 2]).transpose(0, -1)
+    return out
+
+def interpolate(x, ratio):
+    """Interpolate data in time domain. This is used to compensate the 
+    resolution reduction in downsampling of a CNN.
+    
+    Args:
+      x: (batch_size, time_steps, classes_num)
+      ratio: int, ratio to interpolate
+    Returns:
+      upsampled: (batch_size, time_steps * ratio, classes_num)
+    """
+    (batch_size, time_steps, classes_num) = x.shape
+    upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)
+    upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)
+    return upsampled
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+class PatchEmbed(nn.Module):
+    """ 2D Image to Patch Embedding
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, patch_stride = 16):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patch_stride = to_2tuple(patch_stride)
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patch_stride = patch_stride
+        self.grid_size = (img_size[0] // patch_stride[0], img_size[1] // patch_stride[1])
+        self.num_patches = self.grid_size[0] * self.grid_size[1]
+        self.flatten = flatten
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+        
+        padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2)
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
+        x = self.norm(x)
+        return x
+
+class Mlp(nn.Module):
+    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
+    """
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+                      "The distribution of values may be incorrect.",
+                      stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor, float, float, float, float) -> Tensor
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    if mode == 'fan_in':
+        denom = fan_in
+    elif mode == 'fan_out':
+        denom = fan_out
+    elif mode == 'fan_avg':
+        denom = (fan_in + fan_out) / 2
+
+    variance = scale / denom
+
+    if distribution == "truncated_normal":
+        # constant is stddev of standard normal truncated to (-2, 2)
+        trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
+    elif distribution == "normal":
+        tensor.normal_(std=math.sqrt(variance))
+    elif distribution == "uniform":
+        bound = math.sqrt(3 * variance)
+        tensor.uniform_(-bound, bound)
+    else:
+        raise ValueError(f"invalid distribution {distribution}")
+
+
+def lecun_normal_(tensor):
+    variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
+
+
+# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
+# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x, attn
+
+    def extra_repr(self):
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+
+# We use the model based on Swintransformer Block, therefore we can use the swin-transformer pretrained model
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_before_mlp='ln'):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        self.norm_before_mlp = norm_before_mlp
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        if self.norm_before_mlp == 'ln':
+            self.norm2 = nn.LayerNorm(dim)
+        elif self.norm_before_mlp == 'bn':
+            self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(1, 2)
+        else:
+            raise NotImplementedError
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            H, W = self.input_resolution
+            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+            h_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            w_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    img_mask[:, h, w, :] = cnt
+                    cnt += 1
+
+            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+
+    def forward(self, x):
+        # pdb.set_trace()
+        H, W = self.input_resolution
+        # print("H: ", H)
+        # print("W: ", W)
+        # pdb.set_trace()
+        B, L, C = x.shape
+        # assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows, attn = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x, attn
+
+    def extra_repr(self):
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+    def extra_repr(self):
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 norm_before_mlp='ln'):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer, norm_before_mlp=norm_before_mlp)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        attns = []
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x, attn = blk(x)
+                if not self.training:
+                    attns.append(attn.unsqueeze(0))
+        if self.downsample is not None:
+            x = self.downsample(x)
+        if not self.training:
+            attn = torch.cat(attns, dim = 0)
+            attn = torch.mean(attn, dim = 0)
+        return x, attn
+
+    def extra_repr(self):
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+
+# The Core of HTSAT
+class HTSAT_Swin_Transformer(nn.Module):
+    r"""HTSAT based on the Swin Transformer
+    Args:
+        spec_size (int | tuple(int)): Input Spectrogram size. Default 256
+        patch_size (int | tuple(int)): Patch size. Default: 4
+        path_stride (iot | tuple(int)): Patch Stride for Frequency and Time Axis. Default: 4
+        in_chans (int): Number of input image channels. Default: 1 (mono)
+        num_classes (int): Number of classes for classification head. Default: 527
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each HTSAT-Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 8
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        config (module): The configuration Module from config.py (HTSATConfig Class)
+    """
+
+    def __init__(self, spec_size=256, patch_size=4, patch_stride=(4,4), 
+                in_chans=1, num_classes=527,
+                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[4, 8, 16, 32],
+                 window_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, 
+                 ape=False, patch_norm=True,
+                 use_checkpoint=False, norm_before_mlp='ln', config = None, **kwargs):
+        super(HTSAT_Swin_Transformer, self).__init__()
+
+        self.config = config
+        self.spec_size = spec_size 
+        self.patch_stride = patch_stride
+        self.patch_size = patch_size
+        self.window_size = window_size
+        self.embed_dim = embed_dim
+        self.depths = depths
+        self.ape = ape
+        self.in_chans = in_chans
+        self.num_classes = num_classes
+        self.num_heads = num_heads
+        self.num_layers = len(self.depths)
+        self.num_features = int(self.embed_dim * 2 ** (self.num_layers - 1))
+        
+        self.drop_rate = drop_rate
+        self.attn_drop_rate = attn_drop_rate
+        self.drop_path_rate = drop_path_rate
+
+        self.qkv_bias = qkv_bias
+        self.qk_scale = None
+
+        self.patch_norm = patch_norm
+        self.norm_layer = norm_layer if self.patch_norm else None
+        self.norm_before_mlp = norm_before_mlp
+        self.mlp_ratio = mlp_ratio
+
+        self.use_checkpoint = use_checkpoint
+
+        #  process mel-spec ; used only once
+        self.freq_ratio = self.spec_size // self.config.mel_bins
+        window = 'hann'
+        center = True
+        pad_mode = 'reflect'
+        ref = 1.0
+        amin = 1e-10
+        top_db = None
+        self.interpolate_ratio = 32     # Downsampled ratio
+        # Spectrogram extractor
+        self.spectrogram_extractor = Spectrogram(n_fft=config.window_size, hop_length=config.hop_size, 
+            win_length=config.window_size, window=window, center=center, pad_mode=pad_mode, 
+            freeze_parameters=True)
+        # Logmel feature extractor
+        self.logmel_extractor = LogmelFilterBank(sr=config.sample_rate, n_fft=config.window_size, 
+            n_mels=config.mel_bins, fmin=config.fmin, fmax=config.fmax, ref=ref, amin=amin, top_db=top_db, 
+            freeze_parameters=True)
+        # Spec augmenter
+        self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, 
+            freq_drop_width=8, freq_stripes_num=2) # 2 2
+        self.bn0 = nn.BatchNorm2d(self.config.mel_bins)
+
+
+        # split spctrogram into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=self.spec_size, patch_size=self.patch_size, in_chans=self.in_chans, 
+            embed_dim=self.embed_dim, norm_layer=self.norm_layer, patch_stride = patch_stride)
+
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.grid_size
+        self.patches_resolution = patches_resolution
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, self.embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=self.drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, self.drop_path_rate, sum(self.depths))]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(dim=int(self.embed_dim * 2 ** i_layer),
+                input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                                    patches_resolution[1] // (2 ** i_layer)),
+                depth=self.depths[i_layer],
+                num_heads=self.num_heads[i_layer],
+                window_size=self.window_size,
+                mlp_ratio=self.mlp_ratio,
+                qkv_bias=self.qkv_bias, qk_scale=self.qk_scale,
+                drop=self.drop_rate, attn_drop=self.attn_drop_rate,
+                drop_path=dpr[sum(self.depths[:i_layer]):sum(self.depths[:i_layer + 1])],
+                norm_layer=self.norm_layer,
+                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                use_checkpoint=use_checkpoint,
+                norm_before_mlp=self.norm_before_mlp)
+            self.layers.append(layer)
+
+        self.norm = self.norm_layer(self.num_features)
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+        self.maxpool = nn.AdaptiveMaxPool1d(1)
+
+        if self.config.enable_tscam:
+            SF = self.spec_size // (2 ** (len(self.depths) - 1)) // self.patch_stride[0] // self.freq_ratio
+            self.tscam_conv = nn.Conv2d(
+                in_channels = self.num_features,
+                out_channels = self.num_classes,
+                kernel_size = (SF,3),
+                padding = (0,1)
+            )
+            self.head = nn.Linear(num_classes, num_classes)
+        else:
+            self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def forward_features(self, x):
+        frames_num = x.shape[2]        
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+        for i, layer in enumerate(self.layers):
+            x, attn = layer(x)
+
+        if self.config.enable_tscam:
+            # for x
+            x = self.norm(x)
+            B, N, C = x.shape
+            SF = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0]
+            ST = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1]
+            x = x.permute(0,2,1).contiguous().reshape(B, C, SF, ST)
+            B, C, F, T = x.shape
+            # group 2D CNN
+            c_freq_bin = F // self.freq_ratio
+            x = x.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
+            x = x.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1)
+
+            # get latent_output
+            latent_output = self.avgpool(torch.flatten(x,2))
+            latent_output = torch.flatten(latent_output, 1)
+
+            # display the attention map, if needed
+            if self.config.htsat_attn_heatmap:
+                # for attn
+                attn = torch.mean(attn, dim = 1)
+                attn = torch.mean(attn, dim = 1)
+                attn = attn.reshape(B, SF, ST)
+                c_freq_bin = SF // self.freq_ratio
+                attn = attn.reshape(B, SF // c_freq_bin, c_freq_bin, ST) 
+                attn = attn.permute(0,2,1,3).contiguous().reshape(B, c_freq_bin, -1)
+                attn = attn.mean(dim = 1)
+                attn_max = torch.max(attn, dim = 1, keepdim = True)[0]
+                attn_min = torch.min(attn, dim = 1, keepdim = True)[0]
+                attn = ((attn * 0.15) + (attn_max * 0.85 - attn_min)) / (attn_max - attn_min)
+                attn = attn.unsqueeze(dim = 2)
+
+            x = self.tscam_conv(x)
+            x = torch.flatten(x, 2) # B, C, T
+
+            if self.config.htsat_attn_heatmap:
+                fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous() * attn, 8 * self.patch_stride[1]) 
+            else: 
+                fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) 
+            
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+
+            if self.config.loss_type == "clip_ce":
+                output_dict = {
+                    'framewise_output': fpx, # already sigmoided
+                    'clipwise_output': x,
+                    'latent_output': latent_output
+                }
+            else:
+                output_dict = {
+                    'framewise_output': fpx, # already sigmoided
+                    'clipwise_output': torch.sigmoid(x),
+                    'latent_output': latent_output
+                }
+           
+        else:
+            x = self.norm(x)  # B N C
+            B, N, C = x.shape
+            
+            fpx = x.permute(0,2,1).contiguous().reshape(B, C, frames_num // (2 ** (len(self.depths) + 1)), frames_num // (2 ** (len(self.depths) + 1)) )
+            B, C, F, T = fpx.shape
+            c_freq_bin = F // self.freq_ratio
+            fpx = fpx.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
+            fpx = fpx.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1)
+            fpx = torch.sum(fpx, dim = 2)
+            fpx = interpolate(fpx.permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) 
+            x = self.avgpool(x.transpose(1, 2))  # B C 1
+            x = torch.flatten(x, 1)
+            if self.num_classes > 0:
+                x = self.head(x)
+                fpx = self.head(fpx)
+            output_dict = {'framewise_output': torch.sigmoid(fpx), 
+                'clipwise_output': torch.sigmoid(x)}
+        return output_dict
+
+    def crop_wav(self, x, crop_size, spe_pos = None):
+        time_steps = x.shape[2]
+        tx = torch.zeros(x.shape[0], x.shape[1], crop_size, x.shape[3]).to(x.device)
+        for i in range(len(x)):
+            if spe_pos is None:
+                crop_pos = random.randint(0, time_steps - crop_size - 1)
+            else:
+                crop_pos = spe_pos
+            tx[i][0] = x[i, 0, crop_pos:crop_pos + crop_size,:]
+        return tx
+
+    # Reshape the wavform to a img size, if you want to use the pretrained swin transformer model
+    def reshape_wav2img(self, x):
+        B, C, T, F = x.shape
+        target_T = int(self.spec_size * self.freq_ratio)
+        target_F = self.spec_size // self.freq_ratio
+        assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size"
+        # to avoid bicubic zero error
+        if T < target_T:
+            x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True)
+        if F < target_F:
+            x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True)
+        x = x.permute(0,1,3,2).contiguous()
+        x = x.reshape(x.shape[0], x.shape[1], x.shape[2], self.freq_ratio, x.shape[3] // self.freq_ratio)
+        # print(x.shape)
+        x = x.permute(0,1,3,2,4).contiguous()
+        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3], x.shape[4])
+        return x
+    
+    # Repeat the wavform to a img size, if you want to use the pretrained swin transformer model
+    def repeat_wat2img(self, x, cur_pos):
+        B, C, T, F = x.shape
+        target_T = int(self.spec_size * self.freq_ratio)
+        target_F = self.spec_size // self.freq_ratio
+        assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size"
+        # to avoid bicubic zero error
+        if T < target_T:
+            x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True)
+        if F < target_F:
+            x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True)  
+        x = x.permute(0,1,3,2).contiguous() # B C F T
+        x = x[:,:,:,cur_pos:cur_pos + self.spec_size]
+        x = x.repeat(repeats = (1,1,4,1))
+        return x
+
+    def forward(self, x: torch.Tensor, mixup_lambda = None, infer_mode = False):# out_feat_keys: List[str] = None):
+        x = self.spectrogram_extractor(x)   # (batch_size, 1, time_steps, freq_bins)
+        x = self.logmel_extractor(x)    # (batch_size, 1, time_steps, mel_bins)
+        
+        
+        x = x.transpose(1, 3)
+        x = self.bn0(x)
+        x = x.transpose(1, 3)
+        if self.training:
+            x = self.spec_augmenter(x)
+        if self.training and mixup_lambda is not None:
+            x = do_mixup(x, mixup_lambda)
+        
+        if infer_mode:
+            # in infer mode. we need to handle different length audio input
+            frame_num = x.shape[2]
+            target_T = int(self.spec_size * self.freq_ratio)
+            repeat_ratio = math.floor(target_T / frame_num)
+            x = x.repeat(repeats=(1,1,repeat_ratio,1))
+            x = self.reshape_wav2img(x)
+            output_dict = self.forward_features(x)
+        elif self.config.enable_repeat_mode:
+            if self.training:
+                cur_pos = random.randint(0, (self.freq_ratio - 1) * self.spec_size - 1)
+                x = self.repeat_wat2img(x, cur_pos)
+                output_dict = self.forward_features(x)
+            else:
+                output_dicts = []
+                for cur_pos in range(0, (self.freq_ratio - 1) * self.spec_size + 1, self.spec_size):
+                    tx = x.clone()
+                    tx = self.repeat_wat2img(tx, cur_pos)
+                    output_dicts.append(self.forward_features(tx))
+                clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device)
+                framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device)
+                for d in output_dicts:
+                    clipwise_output += d["clipwise_output"]
+                    framewise_output += d["framewise_output"]
+                clipwise_output  = clipwise_output / len(output_dicts)
+                framewise_output = framewise_output / len(output_dicts)
+
+                output_dict = {
+                    'framewise_output': framewise_output, 
+                    'clipwise_output': clipwise_output
+                }
+        else:
+            if x.shape[2] > self.freq_ratio * self.spec_size:
+                if self.training:
+                    x = self.crop_wav(x, crop_size=self.freq_ratio * self.spec_size)
+                    x = self.reshape_wav2img(x)
+                    output_dict = self.forward_features(x)
+                else:
+                    # Change: Hard code here
+                    overlap_size = 344 #(x.shape[2] - 1) // 4
+                    output_dicts = []
+                    crop_size = 689 #(x.shape[2] - 1) // 2
+                    for cur_pos in range(0, x.shape[2] - crop_size - 1, overlap_size):
+                        tx = self.crop_wav(x, crop_size = crop_size, spe_pos = cur_pos)
+                        tx = self.reshape_wav2img(tx)
+                        output_dicts.append(self.forward_features(tx))
+                    clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device)
+                    framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device)
+                    latent_output = torch.zeros_like(output_dicts[0]["latent_output"]).float().to(x.device)
+                    for d in output_dicts:
+                        clipwise_output += d["clipwise_output"]
+                        framewise_output += d["framewise_output"]
+                        latent_output += d["latent_output"]
+                    clipwise_output  = clipwise_output / len(output_dicts)
+                    framewise_output = framewise_output / len(output_dicts)
+                    latent_output = latent_output / len(output_dicts)
+                    output_dict = {
+                        'framewise_output': framewise_output, 
+                        'clipwise_output': clipwise_output,
+                        'latent_output': latent_output,
+                    }
+            else: # this part is typically used, and most easy one
+                x = self.reshape_wav2img(x)
+                output_dict = self.forward_features(x)
+        # x = self.head(x)
+        return output_dict
+
+class HTSATWrapper(nn.Module):
+    def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, 
+        fmax, classes_num, out_emb):
+        super().__init__()
+
+        # print("parameters are being overidden when using HTSAT")
+        # print("HTSAT only support loading a pretrained model on AudioSet")
+        # @TODO later look at what parameters are same and can be merged
+
+        self.htsat = HTSAT_Swin_Transformer(config=HTSATConfig())
+
+    def forward(self, x):
+        out_dict = self.htsat(x)
+        out_dict['embedding'] = out_dict['latent_output']
+        return out_dict
+
+
+def get_audio_encoder(name: str):
+    if name == "HTSAT":
+        return HTSATWrapper
+    else:
+        raise Exception('The audio encoder name {} is incorrect or not supported'.format(name))
+
+class Projection(nn.Module):
+    def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None:
+        super().__init__()
+        self.linear1 = nn.Linear(d_in, d_out, bias=False)
+        self.linear2 = nn.Linear(d_out, d_out, bias=False)
+        self.layer_norm = nn.LayerNorm(d_out)
+        self.drop = nn.Dropout(p)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        embed1 = self.linear1(x)
+        embed2 = self.drop(self.linear2(F.gelu(embed1)))
+        embeds = self.layer_norm(embed1 + embed2)
+        return embeds
+
+class AudioEncoder(nn.Module):
+    def __init__(self, audioenc_name:str, d_in: int, d_out: int, sample_rate: int, window_size: int,
+            hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int) -> None:
+        super().__init__()
+
+        audio_encoder = get_audio_encoder(audioenc_name)
+
+        self.base = audio_encoder(
+            sample_rate, window_size,
+            hop_size, mel_bins, fmin, fmax,
+            classes_num, d_in)
+
+        self.projection = Projection(d_in, d_out)
+
+    def forward(self, x):
+        out_dict = self.base(x)
+        audio_features, audio_classification_output = out_dict['embedding'], out_dict['clipwise_output']
+        projected_vec = self.projection(audio_features)
+        return projected_vec, audio_classification_output
+
+class TextEncoder(nn.Module):
+    def __init__(self, d_out: int, text_model: str, transformer_embed_dim: int) -> None:
+        super().__init__()
+        self.text_model = text_model
+        self.base = AutoModel.from_pretrained(text_model)
+
+        if 'clip' in text_model:
+            self.clip_text_projection = self.base.text_projection
+            self.base = self.base.text_model
+            if 'base' in text_model:
+                transformer_embed_dim = 512
+        
+        self.projection = Projection(transformer_embed_dim, d_out)
+
+    def forward(self, x):
+        if 'clip' in self.text_model:
+            pooled_output = self.base(**x)[1] # get pooled output
+            out = self.clip_text_projection(pooled_output)  # get CLS token output
+        elif 'gpt' in self.text_model:
+            batch_size = x['input_ids'].shape[0]
+            hidden_states = self.base(**x)[0] # (batch_size=4, seq_len, 768)
+
+            sequence_lengths = torch.ne(x['input_ids'], 0).sum(-1) - 1 # tensor([13, 14, 18, 17])
+            out = hidden_states[torch.arange(batch_size, device=hidden_states.device), sequence_lengths] # [batch_size, 768] = [4, 768]
+        else:
+            out = self.base(**x)[0]
+            out = out[:, 0, :]  # get CLS token output
+        
+        projected_vec = self.projection(out)
+
+        return projected_vec
+
+class CLAP(nn.Module):
+    def __init__(self,
+                # audio
+                audioenc_name: str,
+                sample_rate: int, 
+                window_size: int, 
+                hop_size: int, 
+                mel_bins: int, 
+                fmin: int, 
+                fmax: int, 
+                classes_num: int, 
+                out_emb: int,
+                # text
+                text_model: str,
+                transformer_embed_dim: int,
+                # common
+                d_proj: int,
+                ):
+        super().__init__()
+
+        
+        self.audio_encoder = AudioEncoder(
+            audioenc_name, out_emb, d_proj,
+            sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num)
+
+        self.caption_encoder = TextEncoder(
+            d_proj, text_model, transformer_embed_dim
+        )
+
+        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+
+    def forward(self, audio, text):
+        audio_embed, _ = self.audio_encoder(audio)
+        caption_embed = self.caption_encoder(text)
+
+        return caption_embed, audio_embed, self.logit_scale.exp()
+    
+    
+    
+# ==================================================================
+#             A U D I O - P R E - P R O C E S S I N G
+# ==================================================================
+def read_audio(audio_path, resample=True):
+    r"""Loads audio file or array and returns a torch tensor"""
+    # Randomly sample a segment of audio_duration from the clip or pad to match duration
+    audio_time_series, sample_rate = torchaudio.load(audio_path)
+
+    resample_rate = clapConfig.sample_rate
+    if resample and resample_rate != sample_rate:
+        resampler = T.Resample(sample_rate, resample_rate)
+        audio_time_series = resampler(audio_time_series)
+    return audio_time_series, resample_rate
+
+def load_audio_into_tensor(audio_path, audio_duration, resample=False):
+    r"""Loads audio file and returns raw audio."""
+    # Randomly sample a segment of audio_duration from the clip or pad to match duration
+    audio_time_series, sample_rate = read_audio(audio_path, resample)
+    audio_time_series = audio_time_series.reshape(-1)
+
+    # audio_time_series is shorter than predefined audio duration,
+    # so audio_time_series is extended
+    if audio_duration*sample_rate >= audio_time_series.shape[0]:
+        repeat_factor = int(np.ceil((audio_duration*sample_rate) /
+                                    audio_time_series.shape[0]))
+        # Repeat audio_time_series by repeat_factor to match audio_duration
+        audio_time_series = audio_time_series.repeat(repeat_factor)
+        # remove excess part of audio_time_series
+        audio_time_series = audio_time_series[0:audio_duration*sample_rate]
+    else:
+        # audio_time_series is longer than predefined audio duration,
+        # so audio_time_series is trimmed
+        start_index = random.randrange(
+            audio_time_series.shape[0] - audio_duration*sample_rate)
+        audio_time_series = audio_time_series[start_index:start_index +
+                                                audio_duration*sample_rate]
+    return torch.FloatTensor(audio_time_series)
+
+np_str_obj_array_pattern = re.compile(r'[SaUO]')
+default_collate_err_msg_format = (
+    "default_collate: batch must contain tensors, numpy arrays, numbers, "
+    "dicts or lists; found {}")
+
+def default_collate(batch):
+    r"""Puts each data field into a tensor with outer dimension batch size"""
+    elem = batch[0]
+    elem_type = type(elem)
+    if isinstance(elem, torch.Tensor):
+        out = None
+        if torch.utils.data.get_worker_info() is not None:
+            # If we're in a background process, concatenate directly into a
+            # shared memory tensor to avoid an extra copy
+            numel = sum([x.numel() for x in batch])
+            storage = elem.storage()._new_shared(numel)
+            out = elem.new(storage)
+        return torch.stack(batch, 0, out=out)
+    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
+            and elem_type.__name__ != 'string_':
+        if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap':
+            # array of string classes and object
+            if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
+                raise TypeError(
+                    default_collate_err_msg_format.format(elem.dtype))
+
+            return default_collate([torch.as_tensor(b) for b in batch])
+        elif elem.shape == ():  # scalars
+            return torch.as_tensor(batch)
+    elif isinstance(elem, float):
+        return torch.tensor(batch, dtype=torch.float64)
+    elif isinstance(elem, int):
+        return torch.tensor(batch)
+    elif isinstance(elem, str):
+        return batch
+    elif isinstance(elem, collections.abc.Mapping):
+        return {key: default_collate([d[key] for d in batch]) for key in elem}
+    elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
+        return elem_type(*(default_collate(samples) for samples in zip(*batch)))
+    elif isinstance(elem, collections.abc.Sequence):
+        # check to make sure that the elements in batch have consistent size
+        it = iter(batch)
+        elem_size = len(next(it))
+        if not all(len(elem) == elem_size for elem in it):
+            raise RuntimeError(
+                'each element in list of batch should be of equal size')
+        transposed = zip(*batch)
+        return [default_collate(samples) for samples in transposed]
+
+    raise TypeError(default_collate_err_msg_format.format(elem_type))
+
+def preprocess_audio(audio_files, resample):
+    r"""Load list of audio files and return raw audio"""
+    audio_tensors = []
+    for audio_file in audio_files:
+        audio_tensor = load_audio_into_tensor(
+            audio_file, clapConfig.duration, resample)
+        audio_tensor = audio_tensor.reshape(1, -1).to(device)
+        audio_tensors.append(audio_tensor)
+    return default_collate(audio_tensors)
+
+
+
+# ==================================================================
+#            A U D I O - E M B E D D I N G S - H E L P E R
+# ==================================================================
+def get_audio_embeddings(audio_files: List[str], audio_encoder, resample=True):
+    """Load list of audio files and return audio embeddings"""
+    preprocessed_audio = preprocess_audio(audio_files, resample)
+    with torch.no_grad():
+        preprocessed_audio = preprocessed_audio.reshape(
+            preprocessed_audio.shape[0], preprocessed_audio.shape[2])
+        return audio_encoder(preprocessed_audio)[0]
+
+
+# ==================================================================
+#                            C L A P
+# ==================================================================
+class ClapConfig:
+    # TEXT ENCODER CONFIG
+    text_model = 'gpt2'
+    text_len = 77
+    transformer_embed_dim = 768
+    freeze_text_encoder_weights = True
+
+    # AUDIO ENCODER CONFIG
+    audioenc_name = 'HTSAT'
+    out_emb = 768
+    sample_rate = 44100
+    duration = 7
+    fmin = 50
+    fmax = 8000  # 14000
+    n_fft = 1024  # 1028
+    hop_size = 320
+    mel_bins = 64
+    window_size = 1024
+
+    # PROJECTION SPACE CONFIG 
+    d_proj = 1024
+    temperature = 0.003
+
+    # TRAINING AND EVALUATION CONFIG
+    num_classes = 527
+    batch_size = 1024
+    demo = False
+    
+
+clapConfig = ClapConfig()
+clap = CLAP(
+            audioenc_name=clapConfig.audioenc_name,
+            sample_rate=clapConfig.sample_rate,
+            window_size=clapConfig.window_size,
+            hop_size=clapConfig.hop_size,
+            mel_bins=clapConfig.mel_bins,
+            fmin=clapConfig.fmin,
+            fmax=clapConfig.fmax,
+            classes_num=clapConfig.num_classes,
+            out_emb=clapConfig.out_emb,
+            text_model=clapConfig.text_model,
+            transformer_embed_dim=clapConfig.transformer_embed_dim,
+            d_proj=clapConfig.d_proj
+        )
+
+model_repo = "microsoft/msclap"
+model_name = {
+    '2022': 'CLAP_weights_2022.pth',
+    '2023': 'CLAP_weights_2023.pth',
+    'clapcap': 'clapcap_weights_2023.pth'
+}
+
+version = '2023'
+model_fp = hf_hub_download(model_repo, model_name[version])
+
+model_state_dict = torch.load(model_fp, map_location=torch.device('cpu'))['model']
+clap.load_state_dict(model_state_dict, strict=False)
+clap.to(device)
+clap.eval()
+
+clap_audio_encoder = clap.audio_encoder.eval()
+
+
+ENGLISH_AUDIO_DIR = r"/home/IITB/ai-at-ieor/23m1521/datasets/Vaani/Audios/English"
+audio_files = [os.path.join(ENGLISH_AUDIO_DIR, i) for i in os.listdir(ENGLISH_AUDIO_DIR) if i.endswith(".wav")]
+audio_embedding = get_audio_embeddings(audio_files, clap_audio_encoder)
+print("CLAP Audio Encoder Embeddings:", audio_embedding.shape) # [5, 1024]
+
+
+# ==================================================================
+#                 C L A P - L o R A -  M O D E L
+# ==================================================================
+LoRAconfig = {
+    "peft_type": "LORA",
+    "task_type": "FEATURE_EXTRACTION",
+    "inference_mode": False,
+    "r": 16,
+    "target_modules": ["qkv", "fc1", "fc2", "proj", "linear1", "linear2"],
+    "lora_alpha": 32,
+    "lora_dropout": 0.05,
+    "fan_in_fan_out": False,
+    "bias": "all",
+}
+peft_config = get_peft_config(LoRAconfig)
+
+model = clap_audio_encoder
+peft_model = get_peft_model(model, peft_config)
+
+peft_model.print_trainable_parameters()
+
+# peft_model.base_model
+# peft_model
+
+peft_clap_audio_encoder = peft_model.base_model
+audio_embedding = get_audio_embeddings(audio_files, peft_clap_audio_encoder)
+print("CLAP LoRA Audio Encoder Embeddings:", audio_embedding.shape) # [5, 1024]
+
+
+# ==================================================================
+#                      C L I P - M O D E L
+# ==================================================================
+from transformers import CLIPImageProcessorFast, CLIPImageProcessor
+clip_vision_model = CLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32")
+# clip_vision_processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
+# clip_vision_processor = CLIPImageProcessorFast.from_pretrained("openai/clip-vit-base-patch32")
+clip_vision_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+image = Image.open("/home/IITB/ai-at-ieor/23m1521/ashish/MTP/Vaani/Img_Audio_Alignment/000000039769.jpg")
+inputs = clip_vision_processor(images=image, return_tensors="pt")
+print("CLIP input image:", inputs['pixel_values'].shape)
+# input_data = {'pixel_values': inputs}
+
+IMAGE_SIZE = 224
+dummy_input = torch.randn(1, 3, IMAGE_SIZE, IMAGE_SIZE) # [1, 3, 224, 224]
+# dummy_input_data = {'pixel_values': dummy_input}
+
+# class VisionModelWrapper(nn.Module):
+#     def __init__(self, peft_model):
+#         super().__init__()
+#         self.model = peft_model.base_model
+
+#     def forward(self, x):
+#         return self.model(pixel_values=x).last_hidden_state
+
+# wrapped_model = VisionModelWrapper(peft_model)
+# output = wrapped_model(input_data)
+output = clip_vision_model(inputs['pixel_values'])
+print("CLIP Image Encoder Embeddings:", output.last_hidden_state.shape) # [1, 50, 768]
+print("CLIP Image Encoder Pooled Output:", output.pooler_output.shape) # [1, 768]

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