Monocular depth estimation - Keras 3 Migration (Only Tensorflow Backe…

…nd) (keras-team#1910)

* Monocular depth estimation - Keras 3 Migration (Only Tensorflow Backend)

* trim output

* Added PyDataset

examples/vision/depth_estimation.py

@@ -2,7 +2,7 @@
 Title: Monocular depth estimation
 Author: [Victor Basu](https://www.linkedin.com/in/victor-basu-520958147)
 Date created: 2021/08/30
-Last modified: 2021/08/30
+Last modified: 2024/08/13
 Description: Implement a depth estimation model with a convnet.
 Accelerator: GPU
 """
@@ -25,17 +25,21 @@
 """
 
 import os
+
+os.environ["KERAS_BACKEND"] = "tensorflow"
+
 import sys
 
 import tensorflow as tf
-from tensorflow.keras import layers
-
+import keras
+from keras import layers
+from keras import ops
 import pandas as pd
 import numpy as np
 import cv2
 import matplotlib.pyplot as plt
 
-tf.random.set_seed(123)
+keras.utils.set_random_seed(123)
 
 """
 ## Downloading the dataset
@@ -52,7 +56,7 @@
 
 annotation_folder = "/dataset/"
 if not os.path.exists(os.path.abspath(".") + annotation_folder):
-    annotation_zip = tf.keras.utils.get_file(
+    annotation_zip = keras.utils.get_file(
         "val.tar.gz",
         cache_subdir=os.path.abspath("."),
         origin="http://diode-dataset.s3.amazonaws.com/val.tar.gz",
@@ -89,7 +93,7 @@
 
 HEIGHT = 256
 WIDTH = 256
-LR = 0.0002
+LR = 0.00001
 EPOCHS = 30
 BATCH_SIZE = 32
 
@@ -105,8 +109,9 @@
 """
 
 
-class DataGenerator(tf.keras.utils.Sequence):
+class DataGenerator(keras.utils.PyDataset):
     def __init__(self, data, batch_size=6, dim=(768, 1024), n_channels=3, shuffle=True):
+        super().__init__()
         """
         Initialization
         """
@@ -178,7 +183,7 @@ def data_generation(self, batch):
                 self.data["depth"][batch_id],
                 self.data["mask"][batch_id],
             )
-
+        x, y = x.astype("float32"), y.astype("float32")
         return x, y
 
 
@@ -249,10 +254,10 @@ def __init__(
         super().__init__(**kwargs)
         self.convA = layers.Conv2D(filters, kernel_size, strides, padding)
         self.convB = layers.Conv2D(filters, kernel_size, strides, padding)
-        self.reluA = layers.LeakyReLU(alpha=0.2)
-        self.reluB = layers.LeakyReLU(alpha=0.2)
-        self.bn2a = tf.keras.layers.BatchNormalization()
-        self.bn2b = tf.keras.layers.BatchNormalization()
+        self.reluA = layers.LeakyReLU(negative_slope=0.2)
+        self.reluB = layers.LeakyReLU(negative_slope=0.2)
+        self.bn2a = layers.BatchNormalization()
+        self.bn2b = layers.BatchNormalization()
 
         self.pool = layers.MaxPool2D((2, 2), (2, 2))
 
@@ -278,10 +283,10 @@ def __init__(
         self.us = layers.UpSampling2D((2, 2))
         self.convA = layers.Conv2D(filters, kernel_size, strides, padding)
         self.convB = layers.Conv2D(filters, kernel_size, strides, padding)
-        self.reluA = layers.LeakyReLU(alpha=0.2)
-        self.reluB = layers.LeakyReLU(alpha=0.2)
-        self.bn2a = tf.keras.layers.BatchNormalization()
-        self.bn2b = tf.keras.layers.BatchNormalization()
+        self.reluA = layers.LeakyReLU(negative_slope=0.2)
+        self.reluB = layers.LeakyReLU(negative_slope=0.2)
+        self.bn2a = layers.BatchNormalization()
+        self.bn2b = layers.BatchNormalization()
         self.conc = layers.Concatenate()
 
     def call(self, x, skip):
@@ -305,8 +310,8 @@ def __init__(
         super().__init__(**kwargs)
         self.convA = layers.Conv2D(filters, kernel_size, strides, padding)
         self.convB = layers.Conv2D(filters, kernel_size, strides, padding)
-        self.reluA = layers.LeakyReLU(alpha=0.2)
-        self.reluB = layers.LeakyReLU(alpha=0.2)
+        self.reluA = layers.LeakyReLU(negative_slope=0.2)
+        self.reluB = layers.LeakyReLU(negative_slope=0.2)
 
     def call(self, x):
         x = self.convA(x)
@@ -328,13 +333,39 @@ def call(self, x):
 """
 
 
-class DepthEstimationModel(tf.keras.Model):
+def image_gradients(image):
+    if len(ops.shape(image)) != 4:
+        raise ValueError(
+            "image_gradients expects a 4D tensor "
+            "[batch_size, h, w, d], not {}.".format(ops.shape(image))
+        )
+
+    image_shape = ops.shape(image)
+    batch_size, height, width, depth = ops.unstack(image_shape)
+
+    dy = image[:, 1:, :, :] - image[:, :-1, :, :]
+    dx = image[:, :, 1:, :] - image[:, :, :-1, :]
+
+    # Return tensors with same size as original image by concatenating
+    # zeros. Place the gradient [I(x+1,y) - I(x,y)] on the base pixel (x, y).
+    shape = ops.stack([batch_size, 1, width, depth])
+    dy = ops.concatenate([dy, ops.zeros(shape, dtype=image.dtype)], axis=1)
+    dy = ops.reshape(dy, image_shape)
+
+    shape = ops.stack([batch_size, height, 1, depth])
+    dx = ops.concatenate([dx, ops.zeros(shape, dtype=image.dtype)], axis=2)
+    dx = ops.reshape(dx, image_shape)
+
+    return dy, dx
+
+
+class DepthEstimationModel(keras.Model):
     def __init__(self):
         super().__init__()
         self.ssim_loss_weight = 0.85
         self.l1_loss_weight = 0.1
         self.edge_loss_weight = 0.9
-        self.loss_metric = tf.keras.metrics.Mean(name="loss")
+        self.loss_metric = keras.metrics.Mean(name="loss")
         f = [16, 32, 64, 128, 256]
         self.downscale_blocks = [
             DownscaleBlock(f[0]),
@@ -353,28 +384,28 @@ def __init__(self):
 
     def calculate_loss(self, target, pred):
         # Edges
-        dy_true, dx_true = tf.image.image_gradients(target)
-        dy_pred, dx_pred = tf.image.image_gradients(pred)
-        weights_x = tf.exp(tf.reduce_mean(tf.abs(dx_true)))
-        weights_y = tf.exp(tf.reduce_mean(tf.abs(dy_true)))
+        dy_true, dx_true = image_gradients(target)
+        dy_pred, dx_pred = image_gradients(pred)
+        weights_x = ops.cast(ops.exp(ops.mean(ops.abs(dx_true))), "float32")
+        weights_y = ops.cast(ops.exp(ops.mean(ops.abs(dy_true))), "float32")
 
         # Depth smoothness
         smoothness_x = dx_pred * weights_x
         smoothness_y = dy_pred * weights_y
 
-        depth_smoothness_loss = tf.reduce_mean(abs(smoothness_x)) + tf.reduce_mean(
+        depth_smoothness_loss = ops.mean(abs(smoothness_x)) + ops.mean(
             abs(smoothness_y)
         )
 
         # Structural similarity (SSIM) index
-        ssim_loss = tf.reduce_mean(
+        ssim_loss = ops.mean(
             1
             - tf.image.ssim(
                 target, pred, max_val=WIDTH, filter_size=7, k1=0.01**2, k2=0.03**2
             )
         )
         # Point-wise depth
-        l1_loss = tf.reduce_mean(tf.abs(target - pred))
+        l1_loss = ops.mean(ops.abs(target - pred))
 
         loss = (
             (self.ssim_loss_weight * ssim_loss)
@@ -432,9 +463,9 @@ def call(self, x):
 ## Model training
 """
 
-optimizer = tf.keras.optimizers.Adam(
+optimizer = keras.optimizers.SGD(
     learning_rate=LR,
-    amsgrad=False,
+    nesterov=False,
 )
 model = DepthEstimationModel()
 # Compile the model
@@ -491,9 +522,9 @@ def call(self, x):
 ## References
 
 The following papers go deeper into possible approaches for depth estimation.
-1. [Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos](https://arxiv.org/pdf/1811.06152v1.pdf)
+1. [Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos](https://arxiv.org/abs/1811.06152v1)
 2. [Digging Into Self-Supervised Monocular Depth Estimation](https://openaccess.thecvf.com/content_ICCV_2019/papers/Godard_Digging_Into_Self-Supervised_Monocular_Depth_Estimation_ICCV_2019_paper.pdf)
-3. [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://arxiv.org/pdf/1606.00373v2.pdf)
+3. [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://arxiv.org/abs/1606.00373v2)
 
 You can also find helpful implementations in the papers with code depth estimation task.