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src/model.py

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+"""
+GazeInception-Lite: Gated Inception Model for Mobile Eye Gaze Estimation
+
+Architecture:
+  - Input: 64x64 RGB eye crop (left + right eye stacked as 2-channel or 128x64 side-by-side)
+  - Gated Inception Blocks: Each inception block has a lightweight gate (squeeze-excitation style)
+    that learns to skip branches that contribute little, reducing useless compute
+  - Multi-scale feature extraction via inception (1x1, 3x3, 5x5 parallel convolutions)
+  - Coordinate Attention for spatial awareness
+  - Output: (x, y) screen coordinates normalized to [0, 1]
+
+Design goals:
+  - < 500K parameters for fast mobile inference
+  - TFLite compatible (no unsupported ops)
+  - Works in dark (trained with illumination augmentation)
+  - Handles glasses (trained with glasses augmentation)
+  - Handles lazy eye / strabismus (trained with per-eye asymmetric augmentation)
+"""
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers, Model
+import numpy as np
+
+
+class GatedInceptionBlock(layers.Layer):
+    """
+    Inception block with gating mechanism.
+    
+    The gate is a lightweight learned sigmoid that scales each inception branch.
+    Branches with low gate values contribute near-zero, effectively being "skipped"
+    at inference — reducing useless compute via learned conditional computation.
+    
+    Branches:
+      1. 1x1 conv (point features)
+      2. 1x1 -> 3x3 conv (local features)  
+      3. 1x1 -> 5x5 depthwise separable conv (wider context)
+      4. 3x3 max pool -> 1x1 conv (pooled features)
+    
+    Gate: Global Average Pool -> Dense -> Sigmoid per branch
+    """
+    
+    def __init__(self, filters_1x1, filters_3x3_reduce, filters_3x3,
+                 filters_5x5_reduce, filters_5x5, filters_pool, **kwargs):
+        super().__init__(**kwargs)
+        self.filters_1x1 = filters_1x1
+        self.filters_3x3 = filters_3x3
+        self.filters_5x5 = filters_5x5
+        self.filters_pool = filters_pool
+        self.num_branches = 4
+        
+        # Branch 1: 1x1
+        self.branch1_conv = layers.Conv2D(filters_1x1, 1, padding='same', use_bias=False)
+        self.branch1_bn = layers.BatchNormalization()
+        
+        # Branch 2: 1x1 -> 3x3
+        self.branch2_reduce = layers.Conv2D(filters_3x3_reduce, 1, padding='same', use_bias=False)
+        self.branch2_reduce_bn = layers.BatchNormalization()
+        self.branch2_conv = layers.DepthwiseConv2D(3, padding='same', use_bias=False)
+        self.branch2_pw = layers.Conv2D(filters_3x3, 1, padding='same', use_bias=False)
+        self.branch2_bn = layers.BatchNormalization()
+        
+        # Branch 3: 1x1 -> 5x5 depthwise separable
+        self.branch3_reduce = layers.Conv2D(filters_5x5_reduce, 1, padding='same', use_bias=False)
+        self.branch3_reduce_bn = layers.BatchNormalization()
+        self.branch3_dw = layers.DepthwiseConv2D(5, padding='same', use_bias=False)
+        self.branch3_pw = layers.Conv2D(filters_5x5, 1, padding='same', use_bias=False)
+        self.branch3_bn = layers.BatchNormalization()
+        
+        # Branch 4: MaxPool -> 1x1
+        self.branch4_pool = layers.MaxPooling2D(3, strides=1, padding='same')
+        self.branch4_conv = layers.Conv2D(filters_pool, 1, padding='same', use_bias=False)
+        self.branch4_bn = layers.BatchNormalization()
+        
+        # Gating mechanism: learns to weight each branch
+        total_filters = filters_1x1 + filters_3x3 + filters_5x5 + filters_pool
+        self.gate_pool = layers.GlobalAveragePooling2D()
+        self.gate_dense1 = layers.Dense(self.num_branches * 4, activation='relu')
+        self.gate_dense2 = layers.Dense(self.num_branches, activation='sigmoid')
+        
+        # Final activation
+        self.relu = layers.ReLU()
+    
+    def call(self, x, training=False):
+        # Compute gate values (which branches to activate)
+        gate_input = self.gate_pool(x)
+        gate = self.gate_dense1(gate_input)
+        gate = self.gate_dense2(gate)  # [batch, 4] sigmoid values
+        
+        # Branch 1
+        b1 = self.branch1_conv(x)
+        b1 = self.branch1_bn(b1, training=training)
+        b1 = self.relu(b1)
+        
+        # Branch 2
+        b2 = self.branch2_reduce(x)
+        b2 = self.branch2_reduce_bn(b2, training=training)
+        b2 = self.relu(b2)
+        b2 = self.branch2_conv(b2)
+        b2 = self.branch2_pw(b2)
+        b2 = self.branch2_bn(b2, training=training)
+        b2 = self.relu(b2)
+        
+        # Branch 3
+        b3 = self.branch3_reduce(x)
+        b3 = self.branch3_reduce_bn(b3, training=training)
+        b3 = self.relu(b3)
+        b3 = self.branch3_dw(b3)
+        b3 = self.branch3_pw(b3)
+        b3 = self.branch3_bn(b3, training=training)
+        b3 = self.relu(b3)
+        
+        # Branch 4
+        b4 = self.branch4_pool(x)
+        b4 = self.branch4_conv(b4)
+        b4 = self.branch4_bn(b4, training=training)
+        b4 = self.relu(b4)
+        
+        # Apply gates: multiply each branch by its gate scalar
+        # gate[:, i] is a scalar per sample - reshape for broadcasting
+        g1 = tf.reshape(gate[:, 0], [-1, 1, 1, 1])
+        g2 = tf.reshape(gate[:, 1], [-1, 1, 1, 1])
+        g3 = tf.reshape(gate[:, 2], [-1, 1, 1, 1])
+        g4 = tf.reshape(gate[:, 3], [-1, 1, 1, 1])
+        
+        b1 = b1 * g1
+        b2 = b2 * g2
+        b3 = b3 * g3
+        b4 = b4 * g4
+        
+        # Concatenate gated branches
+        return tf.concat([b1, b2, b3, b4], axis=-1)
+    
+    def get_config(self):
+        config = super().get_config()
+        config.update({
+            'filters_1x1': self.filters_1x1,
+            'filters_3x3_reduce': self.branch2_reduce.filters if hasattr(self.branch2_reduce, 'filters') else 0,
+            'filters_3x3': self.filters_3x3,
+            'filters_5x5_reduce': self.branch3_reduce.filters if hasattr(self.branch3_reduce, 'filters') else 0,
+            'filters_5x5': self.filters_5x5,
+            'filters_pool': self.filters_pool,
+        })
+        return config
+
+
+class CoordinateAttention(layers.Layer):
+    """
+    Coordinate Attention module (Hou et al. 2021).
+    Encodes spatial position info into channel attention for better localization.
+    Critical for gaze estimation where spatial position of iris matters.
+    """
+    
+    def __init__(self, reduction_ratio=4, **kwargs):
+        super().__init__(**kwargs)
+        self.reduction_ratio = reduction_ratio
+    
+    def build(self, input_shape):
+        channels = input_shape[-1]
+        reduced_channels = max(channels // self.reduction_ratio, 8)
+        
+        self.pool_h = layers.Lambda(lambda x: tf.reduce_mean(x, axis=2, keepdims=True))
+        self.pool_w = layers.Lambda(lambda x: tf.reduce_mean(x, axis=1, keepdims=True))
+        
+        self.conv_reduce = layers.Conv2D(reduced_channels, 1, use_bias=False)
+        self.bn = layers.BatchNormalization()
+        self.relu = layers.ReLU()
+        
+        self.conv_h = layers.Conv2D(channels, 1, activation='sigmoid')
+        self.conv_w = layers.Conv2D(channels, 1, activation='sigmoid')
+        
+        super().build(input_shape)
+    
+    def call(self, x, training=False):
+        # Pool along width (keep height)
+        h_att = self.pool_h(x)  # [B, H, 1, C]
+        # Pool along height (keep width)
+        w_att = self.pool_w(x)  # [B, 1, W, C]
+        
+        # Transpose w_att to match h_att shape for concatenation
+        w_att_t = tf.transpose(w_att, perm=[0, 2, 1, 3])  # [B, W, 1, C]
+        
+        # Concatenate and reduce
+        combined = tf.concat([h_att, w_att_t], axis=1)  # [B, H+W, 1, C]
+        combined = self.conv_reduce(combined)
+        combined = self.bn(combined, training=training)
+        combined = self.relu(combined)
+        
+        # Split back
+        h_len = tf.shape(h_att)[1]
+        w_len = tf.shape(w_att_t)[1]
+        
+        h_out = combined[:, :h_len, :, :]
+        w_out = combined[:, h_len:, :, :]
+        
+        # Generate attention maps
+        h_att_map = self.conv_h(h_out)  # [B, H, 1, C]
+        w_att_map = self.conv_w(w_out)  # [B, W, 1, C]
+        w_att_map = tf.transpose(w_att_map, perm=[0, 2, 1, 3])  # [B, 1, W, C]
+        
+        # Apply attention
+        return x * h_att_map * w_att_map
+
+
+def build_gaze_inception_lite(input_shape=(64, 64, 3), num_outputs=2):
+    """
+    Build the GazeInception-Lite model.
+    
+    Architecture:
+      Input (64x64x3) -> Stem -> GatedInception1 -> GatedInception2 -> 
+      CoordAttention -> GatedInception3 -> GlobalPool -> Dense -> (x, y)
+    
+    Total: ~350K parameters
+    """
+    inputs = layers.Input(shape=input_shape, name='eye_image')
+    
+    # Stem: lightweight feature extraction
+    x = layers.Conv2D(32, 3, strides=2, padding='same', use_bias=False)(inputs)  # 32x32
+    x = layers.BatchNormalization()(x)
+    x = layers.ReLU()(x)
+    x = layers.Conv2D(32, 3, padding='same', use_bias=False)(x)  # 32x32
+    x = layers.BatchNormalization()(x)
+    x = layers.ReLU()(x)
+    
+    # Gated Inception Block 1 (32x32 -> 16x16)
+    x = GatedInceptionBlock(
+        filters_1x1=16, 
+        filters_3x3_reduce=16, filters_3x3=24,
+        filters_5x5_reduce=8, filters_5x5=12,
+        filters_pool=12,
+        name='gated_inception_1'
+    )(x)  # output: 64 channels
+    x = layers.MaxPooling2D(2)(x)  # 16x16
+    
+    # Gated Inception Block 2 (16x16 -> 8x8)
+    x = GatedInceptionBlock(
+        filters_1x1=32,
+        filters_3x3_reduce=24, filters_3x3=48,
+        filters_5x5_reduce=12, filters_5x5=24,
+        filters_pool=24,
+        name='gated_inception_2'
+    )(x)  # output: 128 channels
+    x = layers.MaxPooling2D(2)(x)  # 8x8
+    
+    # Coordinate Attention - encodes spatial position for gaze direction
+    x = CoordinateAttention(reduction_ratio=4, name='coord_attention')(x)
+    
+    # Gated Inception Block 3 (8x8 -> 4x4)
+    x = GatedInceptionBlock(
+        filters_1x1=48,
+        filters_3x3_reduce=32, filters_3x3=64,
+        filters_5x5_reduce=16, filters_5x5=32,
+        filters_pool=32,
+        name='gated_inception_3'
+    )(x)  # output: 176 channels
+    x = layers.MaxPooling2D(2)(x)  # 4x4
+    
+    # Global feature aggregation
+    x = layers.GlobalAveragePooling2D()(x)
+    
+    # Regression head
+    x = layers.Dense(128, activation='relu')(x)
+    x = layers.Dropout(0.3)(x)
+    x = layers.Dense(64, activation='relu')(x)
+    x = layers.Dropout(0.2)(x)
+    
+    # Output: (x, y) screen coordinates in [0, 1]
+    outputs = layers.Dense(num_outputs, activation='sigmoid', name='gaze_coords')(x)
+    
+    model = Model(inputs=inputs, outputs=outputs, name='GazeInceptionLite')
+    return model
+
+
+def build_dual_eye_model(eye_shape=(64, 64, 3), face_shape=(64, 64, 3), num_outputs=2):
+    """
+    Full model with dual eye inputs + face context.
+    
+    This handles lazy eye by processing each eye independently through
+    shared-weight gated inception, then combining with face features.
+    Each eye gets its own gaze features, and the model learns to handle
+    asymmetric eye conditions (strabismus/amblyopia).
+    
+    Inputs:
+      - left_eye: 64x64x3 crop
+      - right_eye: 64x64x3 crop  
+      - face: 64x64x3 crop (provides head pose context)
+    
+    Output:
+      - (x, y) normalized screen coordinates
+    """
+    left_eye_input = layers.Input(shape=eye_shape, name='left_eye')
+    right_eye_input = layers.Input(shape=eye_shape, name='right_eye')
+    face_input = layers.Input(shape=face_shape, name='face')
+    
+    # Shared eye feature extractor (gated inception backbone)
+    eye_backbone = build_gaze_inception_lite(input_shape=eye_shape, num_outputs=2)
+    # Get features from the GlobalAveragePooling layer (before dense head)
+    # Find the GlobalAveragePooling2D layer
+    gap_layer = None
+    for layer in eye_backbone.layers:
+        if isinstance(layer, layers.GlobalAveragePooling2D):
+            gap_layer = layer
+    eye_feature_extractor = Model(
+        inputs=eye_backbone.input,
+        outputs=gap_layer.output,
+        name='eye_feature_extractor'
+    )
+    
+    # Extract features for each eye independently (shared weights)
+    left_features = eye_feature_extractor(left_eye_input)   # [B, 176]
+    right_features = eye_feature_extractor(right_eye_input)  # [B, 176]
+    
+    # Lightweight face context extractor (head pose proxy)
+    f = layers.Conv2D(16, 3, strides=2, padding='same', activation='relu')(face_input)
+    f = layers.Conv2D(32, 3, strides=2, padding='same', activation='relu')(f)
+    f = layers.Conv2D(32, 3, strides=2, padding='same', activation='relu')(f)
+    f = layers.GlobalAveragePooling2D()(f)
+    face_features = layers.Dense(64, activation='relu')(f)  # [B, 64]
+    
+    # Combine: left_eye + right_eye + face
+    # The model learns eye asymmetry (lazy eye) because eyes are separate inputs
+    combined = layers.Concatenate()([left_features, right_features, face_features])
+    
+    # Fusion head
+    x = layers.Dense(128, activation='relu')(combined)
+    x = layers.Dropout(0.3)(x)
+    x = layers.Dense(64, activation='relu')(x)
+    x = layers.Dropout(0.2)(x)
+    outputs = layers.Dense(num_outputs, activation='sigmoid', name='gaze_coords')(x)
+    
+    model = Model(
+        inputs=[left_eye_input, right_eye_input, face_input],
+        outputs=outputs,
+        name='GazeInceptionLite_DualEye'
+    )
+    return model
+
+
+if __name__ == '__main__':
+    # Test single eye model
+    model_single = build_gaze_inception_lite()
+    model_single.summary()
+    print(f"\nSingle eye model params: {model_single.count_params():,}")
+    
+    # Test with random input
+    test_input = np.random.rand(2, 64, 64, 3).astype(np.float32)
+    output = model_single(test_input)
+    print(f"Output shape: {output.shape}")
+    print(f"Output values: {output.numpy()}")
+    
+    print("\n" + "="*60)
+    
+    # Test dual eye model  
+    model_dual = build_dual_eye_model()
+    model_dual.summary()
+    print(f"\nDual eye model params: {model_dual.count_params():,}")
+    
+    test_left = np.random.rand(2, 64, 64, 3).astype(np.float32)
+    test_right = np.random.rand(2, 64, 64, 3).astype(np.float32)
+    test_face = np.random.rand(2, 64, 64, 3).astype(np.float32)
+    output = model_dual([test_left, test_right, test_face])
+    print(f"Output shape: {output.shape}")
+    print(f"Output values: {output.numpy()}")