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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+weights.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,139 @@
+import gradio as gr
+import tensorflow as tf
+import keras 
+import numpy as np
+import sklearn
+from sklearnex import patch_sklearn, unpatch_sklearn
+patch_sklearn()
+import xgboost as xgb
+
+from keras import Model, Sequential
+from keras.layers import Layer
+import keras.layers as nn
+
+class ChannelAttn(Layer):
+    def __init__(self, c=512) -> None:
+        super(ChannelAttn,self).__init__()
+        self.gap = nn.AveragePooling2D(7)
+        self.attention = Sequential([
+            nn.Dense(32),
+            nn.BatchNormalization(),
+            nn.ReLU(),
+            nn.Dense(c,activation='sigmoid')]
+        )
+
+    def call(self, x):
+
+        x = self.gap(x)
+        x = nn.Flatten()(x)
+        y = self.attention(x)
+        return x * y
+
+
+class SpatialAttn(Layer):
+    def __init__(self, c=512):
+        super(SpatialAttn,self).__init__()
+        self.conv1x1 = Sequential([
+            nn.Conv2D(256, 1),
+            nn.BatchNormalization()]
+        )
+        self.conv_3x3 = Sequential([
+            nn.ZeroPadding2D(padding=(1, 1)),
+            nn.Conv2D(512, 3,1),
+            nn.BatchNormalization()]
+        )
+        self.conv_1x3 = Sequential([
+            nn.ZeroPadding2D(padding=(0, 1)),
+            nn.Conv2D(512, (1,3)),
+            nn.BatchNormalization()]
+        )
+        self.conv_3x1 = Sequential([
+            nn.ZeroPadding2D(padding=(1, 0)),
+            nn.Conv2D(512,(3,1)),
+            nn.BatchNormalization()]
+        )
+        self.norm = nn.ReLU()
+
+    def call(self, x) :
+        y = self.conv1x1(x)
+        y = self.norm(self.conv_3x3(y) + self.conv_1x3(y) + self.conv_3x1(y))
+        y = tf.math.reduce_sum(y,axis=1, keepdims=True)
+        return x*y
+
+
+class CrossAttnHead(Layer):
+    def __init__(self, c=512):
+        super(CrossAttnHead,self).__init__()
+        self.sa = SpatialAttn(c)
+        self.ca = ChannelAttn(c)
+
+    def call(self, x):
+        return self.ca(self.sa(x))
+
+
+@keras.saving.register_keras_serializable(package='custom')
+class DAN(Model):
+    def __init__(self, num_classes=8,trainable=True,dtype='float32'):
+        super(DAN,self).__init__()
+        self.mod = keras.applications.ResNet50(
+            include_top=False,
+            weights="imagenet",
+            input_shape=(224,224,3)
+        )
+        self.mod.trainable= False
+        self.num_head = 4
+        self.hd = CrossAttnHead()
+        self.hd=[]
+        for i in range(self.num_head):
+          self.hd.append(CrossAttnHead())
+        self.features = nn.Conv2D(512, 1,padding='same')
+        self.fc = nn.Dense(num_classes)
+        self.bn = nn.BatchNormalization()
+
+    def call(self, x) :
+        x = self.mod(x)
+        x=self.features(x)
+        heads = []
+        for h in self.hd:
+            heads.append(h(x))
+
+        heads = tf.transpose(tf.stack(heads),perm=(1,0,2))
+        heads = keras.ops.log_softmax(heads, axis=1)
+        return tf.math.reduce_sum(heads,axis=1)
+
+backbone = DAN()
+backbone.load_weights('weights.keras')
+
+xgb_params = {
+    'objective':                    'binary:logistic',
+    'predictor':                    'cpu_predictor',
+    'disable_default_eval_metric':  1,
+}
+
+model_xgb= xgb.XGBClassifier(**xgb_params)
+
+model_xgb.load_weights('xgb.json')
+emotions = ['anger', 'contempt', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+def fn(image):
+  if len(image.shape)==2:
+    img = np.stack([image,image,image],axis=2)
+    img = np.resize(img,(224,224,3))
+  elif len(image.shape)==3 and image.shape[2]==1:
+    img = np.stack([image[:,:,0],image[:,:,0],image[:,:,0]],axis=2)
+    img = np.resize(img,(224,224,3))
+  else:
+    img = np.resize(image,(224,224,3))
+  img = np.expand_dims(img,axis=0)
+  feats = backbone.predict(img)
+  pred = model_xgb.predict(feats)
+
+  return emotions[pred]
+    
+
+demo = gr.Interface(
+    fn,['image'],"text",
+    
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+numpy
+tensorflow
+scikit-learn-intelex
+keras
+xgboost

weights.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d0f8e5ebffc14265654c47cf7a433870bc6110b5d4dfd09a0c002e024c07e2b
+size 133529600