Delete model.h5

model.h5

@@ -1,331 +0,0 @@
-import os
-import cv2
-import random
-import numpy as np
-from glob import glob
-from PIL import Image, ImageOps
-import matplotlib.pyplot as plt
-
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import layers
-
-from google.colab import drive
-drive.mount('/content/gdrive')
-
-
-random.seed(10)
-
-IMAGE_SIZE = 128
-BATCH_SIZE = 4
-MAX_TRAIN_IMAGES = 300
-
-
-def read_image(image_path):
-    image = tf.io.read_file(image_path)
-    image = tf.image.decode_png(image, channels=3)
-    image.set_shape([None, None, 3])
-    image = tf.cast(image, dtype=tf.float32) / 255.0
-
-    return image
-
-
-def random_crop(low_image, enhanced_image):
-    low_image_shape = tf.shape(low_image)[:2]
-    low_w = tf.random.uniform(
-        shape=(), maxval=low_image_shape[1] - IMAGE_SIZE + 1, dtype=tf.int32
-    )
-    low_h = tf.random.uniform(
-        shape=(), maxval=low_image_shape[0] - IMAGE_SIZE + 1, dtype=tf.int32
-    )
-    enhanced_w = low_w
-    enhanced_h = low_h
-    low_image_cropped = low_image[
-        low_h : low_h + IMAGE_SIZE, low_w : low_w + IMAGE_SIZE
-    ]
-    enhanced_image_cropped = enhanced_image[
-        enhanced_h : enhanced_h + IMAGE_SIZE, enhanced_w : enhanced_w + IMAGE_SIZE
-    ]
-    return low_image_cropped, enhanced_image_cropped
-
-
-def load_data(low_light_image_path, enhanced_image_path):
-    low_light_image = read_image(low_light_image_path)
-    enhanced_image = read_image(enhanced_image_path)
-    low_light_image, enhanced_image = random_crop(low_light_image, enhanced_image)
-    return low_light_image, enhanced_image
-
-
-def get_dataset(low_light_images, enhanced_images):
-    dataset = tf.data.Dataset.from_tensor_slices((low_light_images, enhanced_images))
-
-    dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)
-
-    dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
-    return dataset
-
-
-train_low_light_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/our485/low/*"))[:MAX_TRAIN_IMAGES]
-train_enhanced_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/our485/high/*"))[:MAX_TRAIN_IMAGES]
-
-val_low_light_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/our485/low/*"))[MAX_TRAIN_IMAGES:]
-val_enhanced_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/our485/high/*"))[MAX_TRAIN_IMAGES:]
-
-test_low_light_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/eval15/low/*"))
-test_enhanced_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/eval15/high/*"))
-
-
-train_dataset = get_dataset(train_low_light_images, train_enhanced_images)
-val_dataset = get_dataset(val_low_light_images, val_enhanced_images)
-
-
-print("Train Dataset:", train_dataset)
-print("Val Dataset:", val_dataset)
-
-
-def selective_kernel_feature_fusion(
-    multi_scale_feature_1, multi_scale_feature_2, multi_scale_feature_3
-):
-    channels = list(multi_scale_feature_1.shape)[-1]
-    combined_feature = layers.Add()(
-        [multi_scale_feature_1, multi_scale_feature_2, multi_scale_feature_3]
-    )
-    gap = layers.GlobalAveragePooling2D()(combined_feature)
-    channel_wise_statistics = tf.reshape(gap, shape=(-1, 1, 1, channels))
-    compact_feature_representation = layers.Conv2D(
-        filters=channels // 8, kernel_size=(1, 1), activation="relu"
-    )(channel_wise_statistics)
-    feature_descriptor_1 = layers.Conv2D(
-        channels, kernel_size=(1, 1), activation="softmax"
-    )(compact_feature_representation)
-    feature_descriptor_2 = layers.Conv2D(
-        channels, kernel_size=(1, 1), activation="softmax"
-    )(compact_feature_representation)
-    feature_descriptor_3 = layers.Conv2D(
-        channels, kernel_size=(1, 1), activation="softmax"
-    )(compact_feature_representation)
-    feature_1 = multi_scale_feature_1 * feature_descriptor_1
-    feature_2 = multi_scale_feature_2 * feature_descriptor_2
-    feature_3 = multi_scale_feature_3 * feature_descriptor_3
-    aggregated_feature = layers.Add()([feature_1, feature_2, feature_3])
-    return aggregated_feature
-
-
-
-
-def spatial_attention_block(input_tensor):
-    average_pooling = tf.reduce_max(input_tensor, axis=-1)
-    average_pooling = tf.expand_dims(average_pooling, axis=-1)
-    max_pooling = tf.reduce_mean(input_tensor, axis=-1)
-    max_pooling = tf.expand_dims(max_pooling, axis=-1)
-    concatenated = layers.Concatenate(axis=-1)([average_pooling, max_pooling])
-    feature_map = layers.Conv2D(1, kernel_size=(1, 1))(concatenated)
-    feature_map = tf.nn.sigmoid(feature_map)
-    return input_tensor * feature_map
-
-
-def channel_attention_block(input_tensor):
-    channels = list(input_tensor.shape)[-1]
-    average_pooling = layers.GlobalAveragePooling2D()(input_tensor)
-    feature_descriptor = tf.reshape(average_pooling, shape=(-1, 1, 1, channels))
-    feature_activations = layers.Conv2D(
-        filters=channels // 8, kernel_size=(1, 1), activation="relu"
-    )(feature_descriptor)
-    feature_activations = layers.Conv2D(
-        filters=channels, kernel_size=(1, 1), activation="sigmoid"
-    )(feature_activations)
-    return input_tensor * feature_activations
-
-
-def dual_attention_unit_block(input_tensor):
-    channels = list(input_tensor.shape)[-1]
-    feature_map = layers.Conv2D(
-        channels, kernel_size=(3, 3), padding="same", activation="relu"
-    )(input_tensor)
-    feature_map = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(
-        feature_map
-    )
-    channel_attention = channel_attention_block(feature_map)
-    spatial_attention = spatial_attention_block(feature_map)
-    concatenation = layers.Concatenate(axis=-1)([channel_attention, spatial_attention])
-    concatenation = layers.Conv2D(channels, kernel_size=(1, 1))(concatenation)
-    return layers.Add()([input_tensor, concatenation])
-
-
-# Recursive Residual Modules
-
-
-def down_sampling_module(input_tensor):
-    channels = list(input_tensor.shape)[-1]
-    main_branch = layers.Conv2D(channels, kernel_size=(1, 1), activation="relu")(
-        input_tensor
-    )
-    main_branch = layers.Conv2D(
-        channels, kernel_size=(3, 3), padding="same", activation="relu"
-    )(main_branch)
-    main_branch = layers.MaxPooling2D()(main_branch)
-    main_branch = layers.Conv2D(channels * 2, kernel_size=(1, 1))(main_branch)
-    skip_branch = layers.MaxPooling2D()(input_tensor)
-    skip_branch = layers.Conv2D(channels * 2, kernel_size=(1, 1))(skip_branch)
-    return layers.Add()([skip_branch, main_branch])
-
-
-def up_sampling_module(input_tensor):
-    channels = list(input_tensor.shape)[-1]
-    main_branch = layers.Conv2D(channels, kernel_size=(1, 1), activation="relu")(
-        input_tensor
-    )
-    main_branch = layers.Conv2D(
-        channels, kernel_size=(3, 3), padding="same", activation="relu"
-    )(main_branch)
-    main_branch = layers.UpSampling2D()(main_branch)
-    main_branch = layers.Conv2D(channels // 2, kernel_size=(1, 1))(main_branch)
-    skip_branch = layers.UpSampling2D()(input_tensor)
-    skip_branch = layers.Conv2D(channels // 2, kernel_size=(1, 1))(skip_branch)
-    return layers.Add()([skip_branch, main_branch])
-
-
-# MRB Block
-def multi_scale_residual_block(input_tensor, channels):
-    # features
-    level1 = input_tensor
-    level2 = down_sampling_module(input_tensor)
-    level3 = down_sampling_module(level2)
-    # DAU
-    level1_dau = dual_attention_unit_block(level1)
-    level2_dau = dual_attention_unit_block(level2)
-    level3_dau = dual_attention_unit_block(level3)
-    # SKFF
-    level1_skff = selective_kernel_feature_fusion(
-        level1_dau,
-        up_sampling_module(level2_dau),
-        up_sampling_module(up_sampling_module(level3_dau)),
-    )
-    level2_skff = selective_kernel_feature_fusion(
-        down_sampling_module(level1_dau), level2_dau, up_sampling_module(level3_dau)
-    )
-    level3_skff = selective_kernel_feature_fusion(
-        down_sampling_module(down_sampling_module(level1_dau)),
-        down_sampling_module(level2_dau),
-        level3_dau,
-    )
-    # DAU 2
-    level1_dau_2 = dual_attention_unit_block(level1_skff)
-    level2_dau_2 = up_sampling_module((dual_attention_unit_block(level2_skff)))
-    level3_dau_2 = up_sampling_module(
-        up_sampling_module(dual_attention_unit_block(level3_skff))
-    )
-    # SKFF 2
-    skff_ = selective_kernel_feature_fusion(level1_dau_2, level2_dau_2, level3_dau_2)
-    conv = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(skff_)
-    return layers.Add()([input_tensor, conv])
-
-
-
-
-def recursive_residual_group(input_tensor, num_mrb, channels):
-    conv1 = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(input_tensor)
-    for _ in range(num_mrb):
-        conv1 = multi_scale_residual_block(conv1, channels)
-    conv2 = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(conv1)
-    return layers.Add()([conv2, input_tensor])
-
-
-def mirnet_model(num_rrg, num_mrb, channels):
-    input_tensor = keras.Input(shape=[None, None, 3])
-    x1 = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(input_tensor)
-    for _ in range(num_rrg):
-        x1 = recursive_residual_group(x1, num_mrb, channels)
-    conv = layers.Conv2D(3, kernel_size=(3, 3), padding="same")(x1)
-    output_tensor = layers.Add()([input_tensor, conv])
-    return keras.Model(input_tensor, output_tensor)
-
-
-model = mirnet_model(num_rrg=3, num_mrb=2, channels=64)
-
-
-def charbonnier_loss(y_true, y_pred):
-    return tf.reduce_mean(tf.sqrt(tf.square(y_true - y_pred) + tf.square(1e-3)))
-
-
-def peak_signal_noise_ratio(y_true, y_pred):
-    return tf.image.psnr(y_pred, y_true, max_val=255.0)
-
-
-optimizer = keras.optimizers.Adam(learning_rate=1e-4)
-model.compile(
-    optimizer=optimizer, loss=charbonnier_loss, metrics=[peak_signal_noise_ratio]
-)
-
-history = model.fit(
-    train_dataset,
-    validation_data=val_dataset,
-    #epochs traning cycles set krna k lia
-    epochs=1,
-    callbacks=[
-        keras.callbacks.ReduceLROnPlateau(
-            monitor="val_peak_signal_noise_ratio",
-            factor=0.5,
-            patience=5,
-            verbose=1,
-            min_delta=1e-7,
-            mode="max",
-        )
-    ],
-)
-
-plt.plot(history.history["loss"], label="train_loss")
-plt.plot(history.history["val_loss"], label="val_loss")
-plt.xlabel("Epochs")
-plt.ylabel("Loss")
-plt.title("Train and Validation Losses Over Epochs", fontsize=14)
-plt.legend()
-plt.grid()
-plt.show()
-
-
-plt.plot(history.history["peak_signal_noise_ratio"], label="train_psnr")
-plt.plot(history.history["val_peak_signal_noise_ratio"], label="val_psnr")
-plt.xlabel("Epochs")
-plt.ylabel("PSNR")
-plt.title("Train and Validation PSNR Over Epochs", fontsize=14)
-plt.legend()
-plt.grid()
-plt.show()
-
-
-
-
-def plot_results(images, titles, figure_size=(12, 12)):
-    fig = plt.figure(figsize=figure_size)
-    for i in range(len(images)):
-        fig.add_subplot(1, len(images), i + 1).set_title(titles[i])
-        _ = plt.imshow(images[i])
-        plt.axis("off")
-    plt.show()
-
-
-def infer(original_image):
-    image = keras.preprocessing.image.img_to_array(original_image)
-    image = image.astype("float16") / 255.0
-    image = np.expand_dims(image, axis=0)
-    output = model.predict(image)
-    output_image = output[0] * 255.0
-    output_image = output_image.clip(0, 255)
-    output_image = output_image.reshape(
-        (np.shape(output_image)[0], np.shape(output_image)[1], 3)
-    )
-    output_image = Image.fromarray(np.uint8(output_image))
-    original_image = Image.fromarray(np.uint8(original_image))
-    return output_image
-
-
-
-for low_light_image in random.sample(test_low_light_images, 2):
-    original_image = Image.open(low_light_image)
-    enhanced_image = infer(original_image)
-    plot_results(
-        [original_image, ImageOps.autocontrast(original_image), enhanced_image],
-        ["Original", "PIL Autocontrast", "MIRNet Enhanced"],
-        (20, 12),
-    )