clean up

src/models/bilinear_interpolation_1x.py

@@ -1,7 +1,11 @@
+# The function implementation below is a modification version from Tensorflow
+# Original code link: https://github.com/ashesh6810/DDWP-DA/blob/master/layers.py
+
+
 from keras import backend as K
 from keras.engine.topology import Layer
 
-if K.backend() == 'tensorflow':
+if K.backend() == "tensorflow":
  import tensorflow as tf
 
  def K_meshgrid(x, y):
@@ -29,7 +33,7 @@ def __init__(self, output_size, **kwargs):
 
  def get_config(self):
  return {
- 'output_size': self.output_size,
+ "output_size": self.output_size,
  }
 
  def compute_output_shape(self, input_shapes):
@@ -43,21 +47,20 @@ def call(self, tensors, mask=None):
  return output
 
  def _interpolate(self, image, sampled_grids, output_size):
-
  batch_size = K.shape(image)[0]
  height = K.shape(image)[1]
  width = K.shape(image)[2]
  num_channels = K.shape(image)[3]
 
- x = K.cast(K.flatten(sampled_grids[:, 0:1, :]), dtype='float32')
- y = K.cast(K.flatten(sampled_grids[:, 1:2, :]), dtype='float32')
+ x = K.cast(K.flatten(sampled_grids[:, 0:1, :]), dtype="float32")
+ y = K.cast(K.flatten(sampled_grids[:, 1:2, :]), dtype="float32")
 
- x = .5 * (x + 1.0) * K.cast(width, dtype='float32')
- y = .5 * (y + 1.0) * K.cast(height, dtype='float32')
+ x = 0.5 * (x + 1.0) * K.cast(width, dtype="float32")
+ y = 0.5 * (y + 1.0) * K.cast(height, dtype="float32")
 
- x0 = K.cast(x, 'int32')
+ x0 = K.cast(x, "int32")
  x1 = x0 + 1
- y0 = K.cast(y, 'int32')
+ y0 = K.cast(y, "int32")
  y1 = y0 + 1
 
  max_x = int(K.int_shape(image)[2] - 1)
@@ -87,16 +90,16 @@ def _interpolate(self, image, sampled_grids, output_size):
  indices_d = base_y1 + x1
 
  flat_image = K.reshape(image, shape=(-1, num_channels))
- flat_image = K.cast(flat_image, dtype='float32')
+ flat_image = K.cast(flat_image, dtype="float32")
  pixel_values_a = K.gather(flat_image, indices_a)
  pixel_values_b = K.gather(flat_image, indices_b)
  pixel_values_c = K.gather(flat_image, indices_c)
  pixel_values_d = K.gather(flat_image, indices_d)
 
- x0 = K.cast(x0, 'float32')
- x1 = K.cast(x1, 'float32')
- y0 = K.cast(y0, 'float32')
- y1 = K.cast(y1, 'float32')
+ x0 = K.cast(x0, "float32")
+ x1 = K.cast(x1, "float32")
+ y0 = K.cast(y0, "float32")
+ y1 = K.cast(y1, "float32")
 
  area_a = K.expand_dims(((x1 - x) * (y1 - y)), 1)
  area_b = K.expand_dims(((x1 - x) * (y - y0)), 1)
@@ -111,8 +114,8 @@ def _interpolate(self, image, sampled_grids, output_size):
 
  def _make_regular_grids(self, batch_size, height, width):
  # making a single regular grid
- x_linspace = K_linspace(-1., 1., width)
- y_linspace = K_linspace(-1., 1., height)
+ x_linspace = K_linspace(-1.0, 1.0, width)
+ y_linspace = K_linspace(-1.0, 1.0, height)
  x_coordinates, y_coordinates = K_meshgrid(x_linspace, y_linspace)
  x_coordinates = K.flatten(x_coordinates)
  y_coordinates = K.flatten(y_coordinates)
@@ -126,12 +129,11 @@ def _make_regular_grids(self, batch_size, height, width):
 
  def _transform(self, X, affine_transformation, output_size):
  batch_size, num_channels = K.shape(X)[0], K.shape(X)[3]
- transformations = K.reshape(affine_transformation,
- shape=(batch_size, 2, 3))
+ transformations = K.reshape(affine_transformation, shape=(batch_size, 2, 3))
  # transformations = K.cast(affine_transformation[:, 0:2, :], 'float32')
  regular_grids = self._make_regular_grids(batch_size, *output_size)
  sampled_grids = K.batch_dot(transformations, regular_grids)
  interpolated_image = self._interpolate(X, sampled_grids, output_size)
  new_shape = (batch_size, output_size[0], output_size[1], num_channels)
  interpolated_image = K.reshape(interpolated_image, new_shape)
- return interpolated_image
+ return interpolated_image

src/models/model_unet_stn_1x.py

@@ -1,3 +1,6 @@
+# The function implementation below is a modification version from Tensorflow
+# Original code link: https://github.com/ashesh6810/DDWP-DA/blob/master/EnKF_DD_all_time.py
+
 import tensorflow
 import keras.backend as K
 

src/models/tf_model_1x_test.ipynb

@@ -1,5 +1,16 @@
 {
  "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "481a4d5d-dbe6-479c-b26b-343102d0617d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The function implementation below is a modification version from Tensorflow\n",
+ "# Original code link: https://github.com/ashesh6810/DDWP-DA/blob/master/Unet_STN_lead12.ipynb"
+ ]
+ },
  {
  "cell_type": "code",
  "execution_count": 1,
@@ -4074,92 +4085,6 @@
  "\n",
  " count=count+1"
  ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "id": "7a01b664",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-08-30T20:58:55.759994Z",
- "start_time": "2023-08-30T20:58:55.744559Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1.13.1\n",
- "GPU Available: False\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "print(tf.__version__)\n",
- "print(\"GPU Available:\", tf.test.is_gpu_available())\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "37c676e6",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-08-30T21:57:53.380598Z",
- "start_time": "2023-08-30T21:57:53.374600Z"
- }
- },
- "outputs": [],
- "source": [
- "import h5py\n",
- "\n",
- "def modify_h5_file(filepath):\n",
- " \"\"\"\n",
- " Check and modify the 'keras_version' attribute of the HDF5 file.\n",
- " \"\"\"\n",
- " with h5py.File(filepath, 'r+') as f:\n",
- " if isinstance(f.attrs['keras_version'], bytes):\n",
- " f.attrs['keras_version'] = f.attrs['keras_version'].decode('utf8')\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "08816071",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-08-30T21:58:48.284673Z",
- "start_time": "2023-08-30T21:58:48.269675Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'str'> 2.2.4\n"
- ]
- }
- ],
- "source": [
- "import h5py\n",
- "\n",
- "with h5py.File('best_weights_lead12.h5', 'r') as f:\n",
- " keras_version = f.attrs['keras_version']\n",
- " print(type(keras_version), keras_version)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7087d02d",
- "metadata": {},
- "outputs": [],
- "source": []
  }
  ],
  "metadata": {
@@ -4179,7 +4104,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.7.12"
+ "version": "3.9.17"
  },
  "toc": {
  "base_numbering": 1,