Refactor application

app.ipynb

@@ -0,0 +1,189 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "877fa66b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "OSError",
+     "evalue": "Unable to synchronously open file (file signature not found)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 95\u001b[0m\n\u001b[0;32m     92\u001b[0m swinunet_model\u001b[38;5;241m.\u001b[39meval()\n\u001b[0;32m     94\u001b[0m \u001b[38;5;66;03m# ----------- Load Classifier Model -----------\u001b[39;00m\n\u001b[1;32m---> 95\u001b[0m classifier_model \u001b[38;5;241m=\u001b[39m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkeras\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m./cnn-swinunet.keras\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     97\u001b[0m \u001b[38;5;66;03m# ----------- Transform -----------\u001b[39;00m\n\u001b[0;32m     98\u001b[0m transform \u001b[38;5;241m=\u001b[39m T\u001b[38;5;241m.\u001b[39mCompose([\n\u001b[0;32m     99\u001b[0m     T\u001b[38;5;241m.\u001b[39mResize((\u001b[38;5;241m224\u001b[39m, \u001b[38;5;241m224\u001b[39m)),\n\u001b[0;32m    100\u001b[0m     T\u001b[38;5;241m.\u001b[39mToTensor()\n\u001b[0;32m    101\u001b[0m ])\n",
+      "File \u001b[1;32mc:\\Users\\PC\\anaconda3\\envs\\main-gpu\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[1;32mc:\\Users\\PC\\anaconda3\\envs\\main-gpu\\lib\\site-packages\\h5py\\_hl\\files.py:564\u001b[0m, in \u001b[0;36mFile.__init__\u001b[1;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, fs_strategy, fs_persist, fs_threshold, fs_page_size, page_buf_size, min_meta_keep, min_raw_keep, locking, alignment_threshold, alignment_interval, meta_block_size, **kwds)\u001b[0m\n\u001b[0;32m    555\u001b[0m     fapl \u001b[38;5;241m=\u001b[39m make_fapl(driver, libver, rdcc_nslots, rdcc_nbytes, rdcc_w0,\n\u001b[0;32m    556\u001b[0m                      locking, page_buf_size, min_meta_keep, min_raw_keep,\n\u001b[0;32m    557\u001b[0m                      alignment_threshold\u001b[38;5;241m=\u001b[39malignment_threshold,\n\u001b[0;32m    558\u001b[0m                      alignment_interval\u001b[38;5;241m=\u001b[39malignment_interval,\n\u001b[0;32m    559\u001b[0m                      meta_block_size\u001b[38;5;241m=\u001b[39mmeta_block_size,\n\u001b[0;32m    560\u001b[0m                      \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    561\u001b[0m     fcpl \u001b[38;5;241m=\u001b[39m make_fcpl(track_order\u001b[38;5;241m=\u001b[39mtrack_order, fs_strategy\u001b[38;5;241m=\u001b[39mfs_strategy,\n\u001b[0;32m    562\u001b[0m                      fs_persist\u001b[38;5;241m=\u001b[39mfs_persist, fs_threshold\u001b[38;5;241m=\u001b[39mfs_threshold,\n\u001b[0;32m    563\u001b[0m                      fs_page_size\u001b[38;5;241m=\u001b[39mfs_page_size)\n\u001b[1;32m--> 564\u001b[0m     fid \u001b[38;5;241m=\u001b[39m \u001b[43mmake_fid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muserblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mswmr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mswmr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    566\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(libver, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m    567\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_libver \u001b[38;5;241m=\u001b[39m libver\n",
+      "File \u001b[1;32mc:\\Users\\PC\\anaconda3\\envs\\main-gpu\\lib\\site-packages\\h5py\\_hl\\files.py:238\u001b[0m, in \u001b[0;36mmake_fid\u001b[1;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[0;32m    236\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m swmr \u001b[38;5;129;01mand\u001b[39;00m swmr_support:\n\u001b[0;32m    237\u001b[0m         flags \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m h5f\u001b[38;5;241m.\u001b[39mACC_SWMR_READ\n\u001b[1;32m--> 238\u001b[0m     fid \u001b[38;5;241m=\u001b[39m \u001b[43mh5f\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfapl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    239\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m    240\u001b[0m     fid \u001b[38;5;241m=\u001b[39m h5f\u001b[38;5;241m.\u001b[39mopen(name, h5f\u001b[38;5;241m.\u001b[39mACC_RDWR, fapl\u001b[38;5;241m=\u001b[39mfapl)\n",
+      "File \u001b[1;32mh5py\\\\_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mh5py\\\\h5f.pyx:102\u001b[0m, in \u001b[0;36mh5py.h5f.open\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mOSError\u001b[0m: Unable to synchronously open file (file signature not found)"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import numpy as np\n",
+    "import timm\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision.transforms as T\n",
+    "import tensorflow as tf\n",
+    "from PIL import Image\n",
+    "from skimage.transform import resize\n",
+    "\n",
+    "# ----------- Constants -----------\n",
+    "CLASSES = [\"Glioma\", \"Meningioma\", \"No Tumor\", \"Pituitary\"]\n",
+    "IMG_SIZE = (224, 224)\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "# ----------- Segmentation Model Definition -----------\n",
+    "swin = timm.create_model('swin_base_patch4_window7_224', pretrained = False, features_only = True)\n",
+    "\n",
+    "class UNetDecoder(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        def conv_block(in_c, out_c):\n",
+    "            return nn.Sequential(\n",
+    "                nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),\n",
+    "                nn.ReLU(inplace=True),\n",
+    "                nn.Conv2d(out_c, out_c, kernel_size=3, padding=1),\n",
+    "                nn.ReLU(inplace=True)\n",
+    "            )\n",
+    "\n",
+    "        self.up3 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)\n",
+    "        self.dec3 = conv_block(768, 256)\n",
+    "\n",
+    "        self.up2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)\n",
+    "        self.dec2 = conv_block(384, 128)\n",
+    "\n",
+    "        self.up1 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)\n",
+    "        self.dec1 = conv_block(192, 64)\n",
+    "\n",
+    "        self.final = nn.Conv2d(64, 1, kernel_size=1)\n",
+    "\n",
+    "    def forward(self, features):\n",
+    "        e1, e2, e3, e4 = features  # e4 is reduced 512 channels\n",
+    "\n",
+    "        d3 = self.up3(e4)\n",
+    "        d3 = self.dec3(torch.cat([d3, e3], dim=1))  # concat 256 + 512 = 768\n",
+    "\n",
+    "        d2 = self.up2(d3)\n",
+    "        d2 = self.dec2(torch.cat([d2, e2], dim=1))  # concat 128 + 256 = 384\n",
+    "\n",
+    "        d1 = self.up1(d2)\n",
+    "        d1 = self.dec1(torch.cat([d1, e1], dim=1))  # concat 64 + 128 = 192\n",
+    "\n",
+    "        out = F.interpolate(d1, scale_factor=4, mode='bilinear', align_corners=False)\n",
+    "        return torch.sigmoid(self.final(out))\n",
+    "    \n",
+    "class SwinUNet(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.encoder = swin\n",
+    "        self.channel_reducer = nn.Conv2d(1024, 512, kernel_size=1)\n",
+    "        self.decoder = UNetDecoder()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        if x.shape[1] == 1:\n",
+    "            x = x.repeat(1, 3, 1, 1)\n",
+    "\n",
+    "        features = self.encoder(x)\n",
+    "        features = [self._to_channels_first(f) for f in features]\n",
+    "\n",
+    "        features[3] = self.channel_reducer(features[3])\n",
+    "\n",
+    "        output = self.decoder(features)\n",
+    "        return output\n",
+    "\n",
+    "    def _to_channels_first(self, feature):\n",
+    "        if feature.dim() == 4:\n",
+    "            return feature.permute(0, 3, 1, 2).contiguous()\n",
+    "        elif feature.dim() == 3:\n",
+    "            B, N, C = feature.shape\n",
+    "            H = W = int(N ** 0.5)\n",
+    "            feature = feature.permute(0, 2, 1).contiguous()\n",
+    "            return feature.view(B, C, H, W)\n",
+    "        else:\n",
+    "            raise ValueError(f\"Unexpected feature shape: {feature.shape}\")\n",
+    "\n",
+    "# ----------- Load Swin-UNet -----------\n",
+    "swinunet_model = SwinUNet()\n",
+    "swinunet_model.load_state_dict(torch.load(\"./swinunet.pth\", map_location = device))\n",
+    "swinunet_model = swinunet_model.to(device)\n",
+    "swinunet_model.eval()\n",
+    "\n",
+    "# ----------- Load Classifier Model -----------\n",
+    "classifier_model = tf.keras.models.load_model(\"./cnn-swinunet.keras\")\n",
+    "\n",
+    "# ----------- Transform -----------\n",
+    "transform = T.Compose([\n",
+    "    T.Resize((224, 224)),\n",
+    "    T.ToTensor()\n",
+    "])\n",
+    "\n",
+    "# ----------- Segmentation -----------\n",
+    "def segmentation(image: Image.Image) -> np.ndarray:\n",
+    "    # Convert to grayscale and tensor\n",
+    "    image = image.convert(\"L\")\n",
+    "    input_tensor = transform(image).unsqueeze(0).to(device)  # [1, 1, 224, 224]\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        mask_pred = swinunet_model(input_tensor)\n",
+    "        mask_pred = F.interpolate(mask_pred, size=(224, 224), mode=\"bilinear\", align_corners=False)\n",
+    "        mask_pred = (mask_pred > 0.5).float()\n",
+    "\n",
+    "    image_np = input_tensor.squeeze().cpu().numpy()  # [224, 224]\n",
+    "    mask_np = mask_pred.squeeze().cpu().numpy()      # [224, 224]\n",
+    "    \n",
+    "    combined = np.stack([image_np, mask_np], axis=-1)  # [224, 224, 2]\n",
+    "    return combined\n",
+    "\n",
+    "def predict(image: Image.Image):\n",
+    "    combined = segmentation(image)\n",
+    "    combined = np.expand_dims(combined, axis=0) # Shape: (1, 224, 224, 2)\n",
+    "\n",
+    "    probs = classifier_model.predict(combined)[0]\n",
+    "\n",
+    "    return CLASSES[int(np.argmax(probs, axis=1)[0])]\n",
+    "\n",
+    "demo = gr.Interface(\n",
+    "    fn = predict,\n",
+    "    inputs = gr.Image(type=\"pil\", label=\"Brain MRI\"),\n",
+    "    outputs = gr.Label(num_top_classes=4),\n",
+    "    title = \"Brain‑Tumor Classifier (.tflite)\",\n",
+    "    description = \"Returns: Glioma, Meningioma, No Tumor, Pituitary\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "demo.launch()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "main-gpu",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -1,35 +1,144 @@
 import gradio as gr
-import tensorflow as tf 
 import numpy as np
+import timm
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as T
+import keras
+import traceback
 from PIL import Image
+from skimage.transform import resize
 
-interpreter = tf.lite.Interpreter(model_path = "cnn.tflite")
-interpreter.allocate_tensors()
-input_details  = interpreter.get_input_details()
-output_details = interpreter.get_output_details()
-
+# ----------- Constants -----------
 CLASSES = ["Glioma", "Meningioma", "No Tumor", "Pituitary"]
 IMG_SIZE = (224, 224)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ----------- Segmentation Model Definition -----------
+swin = timm.create_model('swin_base_patch4_window7_224', pretrained = False, features_only = True)
+
+class UNetDecoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        def conv_block(in_c, out_c):
+            return nn.Sequential(
+                nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(out_c, out_c, kernel_size=3, padding=1),
+                nn.ReLU(inplace=True)
+            )
+
+        self.up3 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
+        self.dec3 = conv_block(768, 256)
+
+        self.up2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
+        self.dec2 = conv_block(384, 128)
+
+        self.up1 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
+        self.dec1 = conv_block(192, 64)
+
+        self.final = nn.Conv2d(64, 1, kernel_size=1)
+
+    def forward(self, features):
+        e1, e2, e3, e4 = features  # e4 is reduced 512 channels
+
+        d3 = self.up3(e4)
+        d3 = self.dec3(torch.cat([d3, e3], dim=1))  # concat 256 + 512 = 768
+
+        d2 = self.up2(d3)
+        d2 = self.dec2(torch.cat([d2, e2], dim=1))  # concat 128 + 256 = 384
+
+        d1 = self.up1(d2)
+        d1 = self.dec1(torch.cat([d1, e1], dim=1))  # concat 64 + 128 = 192
+
+        out = F.interpolate(d1, scale_factor=4, mode='bilinear', align_corners=False)
+        return torch.sigmoid(self.final(out))
+    
+class SwinUNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.encoder = swin
+        self.channel_reducer = nn.Conv2d(1024, 512, kernel_size=1)
+        self.decoder = UNetDecoder()
 
-def preprocess(img: Image.Image) -> np.ndarray:
-    img = img.resize(IMG_SIZE)
-    arr = np.asarray(img, dtype=np.float32) / 255.0
-    return np.expand_dims(arr, 0)      
+    def forward(self, x):
+        if x.shape[1] == 1:
+            x = x.repeat(1, 3, 1, 1)
 
-def predict(image):
-    x = preprocess(image)
-    interpreter.set_tensor(input_details[0]["index"], x)
-    interpreter.invoke()
-    probs = interpreter.get_tensor(output_details[0]["index"])
-    return CLASSES[int(np.argmax(probs, axis=1)[0])]
+        features = self.encoder(x)
+        features = [self._to_channels_first(f) for f in features]
+
+        features[3] = self.channel_reducer(features[3])
+
+        output = self.decoder(features)
+        return output
+
+    def _to_channels_first(self, feature):
+        if feature.dim() == 4:
+            return feature.permute(0, 3, 1, 2).contiguous()
+        elif feature.dim() == 3:
+            B, N, C = feature.shape
+            H = W = int(N ** 0.5)
+            feature = feature.permute(0, 2, 1).contiguous()
+            return feature.view(B, C, H, W)
+        else:
+            raise ValueError(f"Unexpected feature shape: {feature.shape}")
+
+# ----------- Load Swin-UNet -----------
+swinunet_model = SwinUNet()
+swinunet_model.load_state_dict(torch.load("swinunet.pth", map_location = device))
+swinunet_model = swinunet_model.to(device)
+swinunet_model.eval()
+
+# ----------- Load Classifier Model -----------
+classifier_model = keras.models.load_model("cnn-swinunet")
+
+# ----------- Transform -----------
+transform = T.Compose([
+    T.Resize((224, 224)),
+    T.ToTensor()
+])
+
+# ----------- Segmentation -----------
+def segmentation(image: Image.Image) -> np.ndarray:
+    # Convert to grayscale and tensor
+    image = image.convert("L")
+    input_tensor = transform(image).unsqueeze(0).to(device)  # [1, 1, 224, 224]
+
+    with torch.no_grad():
+        mask_pred = swinunet_model(input_tensor)
+        mask_pred = F.interpolate(mask_pred, size=(224, 224), mode="bilinear", align_corners=False)
+        mask_pred = (mask_pred > 0.5).float()
+
+    image_np = input_tensor.squeeze().cpu().numpy()  # [224, 224]
+    mask_np = mask_pred.squeeze().cpu().numpy()      # [224, 224]
+    
+    combined = np.stack([image_np, mask_np], axis=-1)  # [224, 224, 2]
+    return combined
+
+def predict(image: Image.Image):
+    try:
+      combined = segmentation(image)
+      combined = np.expand_dims(combined, axis=0) # Shape: (1, 224, 224, 2)
+
+      probs = classifier_model.predict(combined)[0]
+      return CLASSES[int(np.argmax(probs))]
+    except Exception as e:
+      traceback_str = traceback.format_exc()
+      print(traceback_str)      
+      return traceback_str
 
 demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Brain MRI"),
-    outputs=gr.Label(num_top_classes=4),
-    title="Brain‑Tumor Classifier (.tflite)",
-    description="Returns: Glioma, Meningioma, No Tumor, Pituitary"
+    fn = predict,
+    inputs = gr.Image(type = "pil", label = "Brain MRI"),
+    outputs = gr.Label(num_top_classes = 4),
+    title = "Brain‑Tumor Net)",
+    description = "Returns: Glioma, Meningioma, No Tumor, Pituitary"
 )
 
-if __name__ == "__main__":
+demo.launch()
+
+if __name__ == "main":
     demo.launch()

cnn-swinunet/config.json

@@ -0,0 +1 @@
+{"module": "keras", "class_name": "Sequential", "config": {"name": "sequential_2", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "layers": [{"module": "keras.layers", "class_name": "InputLayer", "config": {"batch_shape": [null, 224, 224, 2], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_layer_1"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null}, "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": {"module": "keras.regularizers", "class_name": "L2", "config": {"l2": 5e-05}, "registered_name": null}, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 224, 224, 2]}}, {"module": "keras.layers", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_2", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "axis": -1, "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "gamma_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "moving_mean_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "moving_variance_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null, "synchronized": false}, "registered_name": null, "build_config": {"input_shape": [null, 224, 224, 32]}}, {"module": "keras.layers", "class_name": "ReLU", "config": {"name": "re_lu_2", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "registered_name": null}, {"module": "keras.layers", "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": {"module": "keras.regularizers", "class_name": "L2", "config": {"l2": 5e-05}, "registered_name": null}, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 112, 112, 32]}}, {"module": "keras.layers", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_3", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "axis": -1, "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "gamma_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "moving_mean_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "moving_variance_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null, "synchronized": false}, "registered_name": null, "build_config": {"input_shape": [null, 112, 112, 64]}}, {"module": "keras.layers", "class_name": "ReLU", "config": {"name": "re_lu_3", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "registered_name": null}, {"module": "keras.layers", "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_3", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Conv2D", "config": {"name": "conv2d_4", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": {"module": "keras.regularizers", "class_name": "L2", "config": {"l2": 5e-05}, "registered_name": null}, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 56, 56, 64]}}, {"module": "keras.layers", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_4", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "axis": -1, "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "gamma_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "moving_mean_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "moving_variance_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null, "synchronized": false}, "registered_name": null, "build_config": {"input_shape": [null, 56, 56, 128]}}, {"module": "keras.layers", "class_name": "ReLU", "config": {"name": "re_lu_4", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "registered_name": null}, {"module": "keras.layers", "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_4", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Conv2D", "config": {"name": "conv2d_5", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": {"module": "keras.regularizers", "class_name": "L2", "config": {"l2": 5e-05}, "registered_name": null}, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 28, 28, 128]}}, {"module": "keras.layers", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "axis": -1, "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "gamma_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "moving_mean_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "moving_variance_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null, "synchronized": false}, "registered_name": null, "build_config": {"input_shape": [null, 28, 28, 128]}}, {"module": "keras.layers", "class_name": "ReLU", "config": {"name": "re_lu_5", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "registered_name": null}, {"module": "keras.layers", "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_5", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}, "registered_name": null}, {"module": "keras.layers", "class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "data_format": "channels_last"}, "registered_name": null, "build_config": {"input_shape": [null, 14, 14, 128]}}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "units": 128, "activation": "linear", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": {"module": "keras.regularizers", "class_name": "L2", "config": {"l2": 5e-05}, "registered_name": null}, "bias_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 25088]}}, {"module": "keras.layers", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_6", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "axis": -1, "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "gamma_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "moving_mean_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "moving_variance_initializer": {"module": "keras.initializers", "class_name": "Ones", "config": {}, "registered_name": null}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null, "synchronized": false}, "registered_name": null, "build_config": {"input_shape": [null, 128]}}, {"module": "keras.layers", "class_name": "ReLU", "config": {"name": "re_lu_6", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "registered_name": null}, {"module": "keras.layers", "class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "rate": 0.4, "seed": null, "noise_shape": null}, "registered_name": null}, {"module": "keras.layers", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": "float32"}, "registered_name": null, "shared_object_id": 2872295962608}, "units": 4, "activation": "softmax", "use_bias": true, "kernel_initializer": {"module": "keras.initializers", "class_name": "GlorotUniform", "config": {"seed": null}, "registered_name": null}, "bias_initializer": {"module": "keras.initializers", "class_name": "Zeros", "config": {}, "registered_name": null}, "kernel_regularizer": null, "bias_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "registered_name": null, "build_config": {"input_shape": [null, 128]}}], "build_input_shape": [null, 224, 224, 2]}, "registered_name": null, "build_config": {"input_shape": [null, 224, 224, 2]}, "compile_config": {"optimizer": {"module": "keras.optimizers", "class_name": "Adam", "config": {"name": "adam", "learning_rate": {"module": "keras.optimizers.schedules", "class_name": "ExponentialDecay", "config": {"initial_learning_rate": 0.0001, "decay_steps": 1000, "decay_rate": 0.95, "staircase": false, "name": "ExponentialDecay"}, "registered_name": null}, "weight_decay": null, "clipnorm": null, "global_clipnorm": null, "clipvalue": null, "use_ema": false, "ema_momentum": 0.99, "ema_overwrite_frequency": null, "loss_scale_factor": null, "gradient_accumulation_steps": null, "beta_1": 0.9, "beta_2": 0.999, "epsilon": 1e-07, "amsgrad": false}, "registered_name": null}, "loss": "categorical_crossentropy", "loss_weights": null, "metrics": [{"module": "keras.metrics", "class_name": "Recall", "config": {"name": "recall", "dtype": "float32", "thresholds": null, "top_k": null, "class_id": null}, "registered_name": null}], "weighted_metrics": null, "run_eagerly": false, "steps_per_execution": 1, "jit_compile": false}}

cnn-swinunet/metadata.json

@@ -0,0 +1 @@
+{"keras_version": "3.9.2", "date_saved": "2025-06-01@08:04:02"}

cnn.tflite → cnn-swinunet/model.weights.h5

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d621e23d2e705ed9a44696e02be7d93d312ba6ed3e21360e3795973cde31fb49
-size 51427444
+oid sha256:21a824cb97431d12f42cc1f154ef073566ad84b933b002b97bd0911a8d04f7d1
+size 41524048

requirements.txt

@@ -1,4 +1,8 @@
-tensorflow==2.15.0    
 gradio>=4.31,<5
-pillow
-numpy
+numpy==1.26.4
+timm==1.0.15
+torch==2.6.0
+torchvision==0.21.0
+keras==3.9.2
+pillow==10.4.0
+scikit-image==0.25.2

swinunet.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:043efac3101c1b1ad3d73b4d8f043bb2e8058f1ebe8e860009de418f2d8e07c9
+size 364350107