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up_sampling_module(dual_attention_unit_block(level3_skff)) |
) |
# SKFF 2 |
skff_ = selective_kernel_feature_fusion(level1_dau_2, level3_dau_2, level3_dau_2) |
conv = layers.Conv2D(channels, kernel_size=(3, 3), padding=\"same\")(skff_) |
return layers.Add()([input_tensor, conv]) |
MIRNet Model |
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) |
Training |
We train MIRNet using Charbonnier Loss as the loss function and Adam Optimizer with a learning rate of 1e-4. |
We use Peak Signal Noise Ratio or PSNR as a metric which is an expression for the ratio between the maximum possible value (power) of a signal and the power of distorting noise that affects the quality of its representation. |
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=50, |
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() |
Epoch 1/50 |
75/75 [==============================] - 109s 731ms/step - loss: 0.2125 - peak_signal_noise_ratio: 62.0458 - val_loss: 0.1592 - val_peak_signal_noise_ratio: 64.1833 |
Epoch 2/50 |
75/75 [==============================] - 49s 651ms/step - loss: 0.1764 - peak_signal_noise_ratio: 63.1356 - val_loss: 0.1257 - val_peak_signal_noise_ratio: 65.6498 |
Epoch 3/50 |
75/75 [==============================] - 49s 652ms/step - loss: 0.1724 - peak_signal_noise_ratio: 63.3172 - val_loss: 0.1245 - val_peak_signal_noise_ratio: 65.6902 |
Epoch 4/50 |
75/75 [==============================] - 49s 653ms/step - loss: 0.1670 - peak_signal_noise_ratio: 63.4917 - val_loss: 0.1206 - val_peak_signal_noise_ratio: 65.8893 |
Epoch 5/50 |
75/75 [==============================] - 49s 653ms/step - loss: 0.1651 - peak_signal_noise_ratio: 63.6555 - val_loss: 0.1333 - val_peak_signal_noise_ratio: 65.6338 |
Epoch 6/50 |
75/75 [==============================] - 49s 654ms/step - loss: 0.1572 - peak_signal_noise_ratio: 64.1984 - val_loss: 0.1142 - val_peak_signal_noise_ratio: 66.7711 |
Epoch 7/50 |
75/75 [==============================] - 49s 654ms/step - loss: 0.1592 - peak_signal_noise_ratio: 64.0062 - val_loss: 0.1205 - val_peak_signal_noise_ratio: 66.1075 |
Epoch 8/50 |
75/75 [==============================] - 49s 654ms/step - loss: 0.1493 - peak_signal_noise_ratio: 64.4675 - val_loss: 0.1170 - val_peak_signal_noise_ratio: 66.1355 |
Epoch 9/50 |
75/75 [==============================] - 49s 654ms/step - loss: 0.1446 - peak_signal_noise_ratio: 64.7416 - val_loss: 0.1301 - val_peak_signal_noise_ratio: 66.0207 |
Epoch 10/50 |
75/75 [==============================] - 49s 655ms/step - loss: 0.1539 - peak_signal_noise_ratio: 64.3999 - val_loss: 0.1220 - val_peak_signal_noise_ratio: 66.7203 |
Epoch 11/50 |
75/75 [==============================] - 49s 654ms/step - loss: 0.1451 - peak_signal_noise_ratio: 64.7352 - val_loss: 0.1219 - val_peak_signal_noise_ratio: 66.3140 |
Epoch 00011: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05. |
Epoch 12/50 |
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