unetlstm.py

import tensorflow as tf
from tensorflow.keras import layers, models  # type: ignore

def encoder_block(inputs, filters):
    x = layers.Conv3D(filters=filters, kernel_size=(3, 3, 4), padding="same", activation="relu")(inputs)
    x = layers.BatchNormalization()(x)
    return x

def convlstm_block(inputs, filters):
    # Reshape to (timesteps, height, width, channels) for ConvLSTM
    x = layers.Reshape((inputs.shape[1], inputs.shape[2], inputs.shape[3], inputs.shape[4]))(inputs)
    x = layers.ConvLSTM2D(filters=filters, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
    x = layers.BatchNormalization()(x)
    # Reshape back to 3D conv format
    x = layers.Reshape((inputs.shape[1], inputs.shape[2], inputs.shape[3], filters))(x)
    return x

def decoder_block(inputs, skip_connection, filters):
    x = layers.Conv3DTranspose(filters=filters, kernel_size=(3, 3, 4), padding="same", activation="relu")(inputs)
    x = layers.BatchNormalization()(x)
    skip_resized = layers.Conv3D(filters, (1, 1, 1), padding="same")(skip_connection)
    x = layers.Concatenate()([x, skip_resized]) 
    x = layers.ConvLSTM2D(filters=filters, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
    return x

def build_unet_convlstm(input_shape=(8, 95, 95, 3)):
    input_tensor = layers.Input(shape=input_shape)

    # Encoder with ConvLSTM
    skip1 = encoder_block(input_tensor, filters=8)
    skip1 = convlstm_block(skip1, filters=8)  # Added ConvLSTM
    
    skip2 = encoder_block(skip1, filters=16)
    skip2 = convlstm_block(skip2, filters=16)  # Added ConvLSTM

    # Bottleneck with ConvLSTM
    x = layers.Conv3D(filters=32, kernel_size=(3, 3, 3), padding="same", activation="relu")(skip2)
    x = layers.BatchNormalization()(x)
    x = convlstm_block(x, filters=32)  # Bottleneck ConvLSTM

    # Decoder
    x = decoder_block(x, skip2, filters=16)
    x = decoder_block(x, skip1, filters=8)

    # Final Output Layer
    x = layers.Conv3D(filters=1, kernel_size=(1, 1, 1), activation="relu")(x)
    x = layers.GlobalAveragePooling3D()(x)

    model = models.Model(inputs=input_tensor, outputs=x)
    return model


import tensorflow as tf
from tensorflow.keras import layers, models # type: ignore

def RSTNet(input_shape):
    """
    Creates the subnet for extracting TC radial structure features using a five-branch CNN design with 2D convolutions.

    Parameters:
    - input_shape: tuple, shape of the input data (e.g., (95, 95, 3))

    Returns:
    - model: tf.keras.Model, the radial structure subnet model
    """
    
    input_tensor = layers.Input(shape=input_shape)

    # Divide input data into four quadrants (NW, NE, SW, SE)
    # Assuming the input shape is (batch_size, height, width, channels)
    
    # Quadrant extraction - using slicing to separate quadrants
    nw_quadrant = input_tensor[:, :input_shape[0]//2, :input_shape[1]//2, :]
    ne_quadrant = input_tensor[:, :input_shape[0]//2, input_shape[1]//2:, :]
    sw_quadrant = input_tensor[:, input_shape[0]//2:, :input_shape[1]//2, :]
    se_quadrant = input_tensor[:, input_shape[0]//2:, input_shape[1]//2:, :]


    target_height = max(input_shape[0]//2, input_shape[0] - input_shape[0]//2)  # 48
    target_width = max(input_shape[1]//2, input_shape[1] - input_shape[1]//2)  # 48
    
    # Padding the quadrants to match the target size (48, 48)
    nw_quadrant = layers.ZeroPadding2D(padding=((0, target_height - nw_quadrant.shape[1]), 
                                                (0, target_width - nw_quadrant.shape[2])))(nw_quadrant)
    ne_quadrant = layers.ZeroPadding2D(padding=((0, target_height - ne_quadrant.shape[1]), 
                                                (0, target_width - ne_quadrant.shape[2])))(ne_quadrant)
    sw_quadrant = layers.ZeroPadding2D(padding=((0, target_height - sw_quadrant.shape[1]), 
                                                (0, target_width - sw_quadrant.shape[2])))(sw_quadrant)
    se_quadrant = layers.ZeroPadding2D(padding=((0, target_height - se_quadrant.shape[1]), 
                                                (0, target_width - se_quadrant.shape[2])))(se_quadrant)

    print(nw_quadrant.shape)
    print(ne_quadrant.shape)
    print(sw_quadrant.shape)
    print(se_quadrant.shape)
    # Main branch (processing the entire structure)
    main_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(input_tensor)
    y=layers.MaxPool2D()(main_branch)

    y = layers.ZeroPadding2D(padding=((0, target_height - y.shape[1]), 
                                   (0, target_width - y.shape[2])))(y)
    # Side branches (processing the individual quadrants)
    nw_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(nw_quadrant)
    ne_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(ne_quadrant)
    sw_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(sw_quadrant)
    se_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(se_quadrant)
    
    # Apply padding to the side branches to match the dimensions of the main branch
    # nw_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(nw_branch)
    # ne_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(ne_branch)
    # sw_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(sw_branch)
    # se_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(se_branch)
    
    # Fusion operations (concatenate the outputs from the main branch and side branches)
    fusion = layers.concatenate([y, nw_branch, ne_branch, sw_branch, se_branch], axis=-1)
    
    # Additional convolution layer to combine the fused features
    # x = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(fusion)
    x=layers.Reshape((1, 48, 48, 40))(fusion)
    x = layers.ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
    x=layers.Reshape((48, 48, 16))(x)
    x=layers.MaxPool2D(pool_size=(2, 2))(x)
    # Final dense layer for further processing
    nw_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(nw_branch)
    
    ne_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(ne_branch)
    sw_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(sw_branch)
    se_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(se_branch)
    nw_branch = layers.MaxPool2D(pool_size=(2, 2))(nw_branch)
    ne_branch = layers.MaxPool2D(pool_size=(2, 2))(ne_branch)
    sw_branch = layers.MaxPool2D(pool_size=(2, 2))(sw_branch)
    se_branch = layers.MaxPool2D(pool_size=(2, 2))(se_branch)

    fusion = layers.concatenate([x, nw_branch, ne_branch, sw_branch, se_branch], axis=-1)
    # x = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(fusion)
    x=layers.Reshape((1, 24, 24, 80))(fusion)
    x = layers.ConvLSTM2D(filters=32, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
    x=layers.Reshape((24, 24, 32))(x)
    x=layers.MaxPool2D(pool_size=(2, 2))(x)
    
    nw_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(nw_branch)
    
    ne_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(ne_branch)
    sw_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(sw_branch)
    se_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(se_branch)
    nw_branch = layers.MaxPool2D(pool_size=(2, 2))(nw_branch)
    ne_branch = layers.MaxPool2D(pool_size=(2, 2))(ne_branch)
    sw_branch = layers.MaxPool2D(pool_size=(2, 2))(sw_branch)
    se_branch = layers.MaxPool2D(pool_size=(2, 2))(se_branch)

    fusion = layers.concatenate([x, nw_branch, ne_branch, sw_branch, se_branch], axis=-1)
    # x = layers.Conv2D(filters=32, kernel_size=(3, 3),  activation='relu')(fusion)
    x=layers.Reshape((1,12, 12, 160))(fusion)
    x = layers.ConvLSTM2D(filters=32, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
    x=layers.Reshape((12, 12, 32))(x)
    x=layers.Conv2D(filters=32, kernel_size=(3, 3), activation=None)(x)
    # Create and return the model
    x=layers.Flatten()(x)
    model = models.Model(inputs=input_tensor, outputs=x)
    return model

from tensorflow.keras import layers, models # type: ignore

def build_cnn_model(input_shape=(8, 8, 1)):
    # Define the input layer
    input_tensor = layers.Input(shape=input_shape)
    
    # Convolutional layer
    x = layers.Conv2D(64, (3, 3), padding='same')(input_tensor)
    x = layers.BatchNormalization()(x)
    x = layers.ReLU()(x)
    
    # Flatten layer
    x = layers.Flatten()(x)
    
    # Create the model
    model = models.Model(inputs=input_tensor, outputs=x)
    
    return model

from tensorflow.keras import layers, models, Input # type: ignore

def build_combined_model():
    # Define input shapes
    input_shape_3d = (8, 95, 95, 2)
    input_shape_radial = (95, 95, 8)
    input_shape_cnn = (8, 8, 1)
    
    input_shape_latitude = (8,)
    input_shape_longitude = (8,)
    input_shape_other = (9,)

    # Build individual models
    model_3d = build_unet_convlstm(input_shape=input_shape_3d)
    model_radial = RSTNet(input_shape=input_shape_radial)
    model_cnn = build_cnn_model(input_shape=input_shape_cnn)

    # Define new inputs
    input_latitude = Input(shape=input_shape_latitude ,name="latitude_input")
    input_longitude = Input(shape=input_shape_longitude, name="longitude_input")
    input_other = Input(shape=input_shape_other, name="other_input")

    # Flatten the additional inputs
    flat_latitude = layers.Dense(32,activation='relu')(input_latitude)
    flat_longitude = layers.Dense(32,activation='relu')(input_longitude)
    flat_other = layers.Dense(64,activation='relu')(input_other)

    # Combine all outputs
    combined = layers.concatenate([
        model_3d.output, 
        model_radial.output, 
        model_cnn.output,
        flat_latitude, 
        flat_longitude, 
        flat_other
    ])

    # Add dense layers for final processing
    x = layers.Dense(128, activation='relu')(combined)  
    x = layers.Dense(1, activation=None)(x)

    # Create the final model
    final_model = models.Model(
        inputs=[model_3d.input, model_radial.input, model_cnn.input,
                input_latitude, input_longitude, input_other ],
        outputs=x
    )

    return final_model

import h5py
with h5py.File(r"E:\1MAIN PROJECT\tf_env\final_model.h5", 'r') as f:
    print(f.attrs.get('keras_version'))
    print(f.attrs.get('backend'))
    print("Model layers:", list(f['model_weights'].keys()))

model = build_combined_model()  # Your original model building function
model.load_weights(r"E:\1MAIN PROJECT\tf_env\final_model.h5")


def predict_unetlstm(reduced_images_test,hov_m_test,test_vmax_3d,lat_test,lon_test,int_diff_test):
    y=model.predict([reduced_images_test,hov_m_test,test_vmax_3d,lat_test,lon_test,int_diff_test ])
    return y