Merge pull request #61 from valentingol/dev

🆙 Upgrade to 2.5.1

README.md

@@ -75,12 +75,16 @@ DCGAN architecture).
 
 ### Installation
 
-Install the module and dependencies in a virtual environment with Python 3.7-3.10:
+Install the module and dependencies in a virtual environment with Python 3.7-3.10.
+Using Linux or WSL is recommended to use [JAX](https://github.com/google/jax)
+for faster metrics computation but it is not mandatory.
 
 ```bash
 pip install -e .
 pip install -r requirements.txt
-# for developers only:
+# dependencies without JAX (windows users):
+pip install -r requirements-nojax.txt
+# for developers:
 pip install -r requirements-dev.txt
 ```
 
@@ -90,9 +94,6 @@ A small dataset is available by default in this repository. It contains 2000
 synthesized images representing some channels and 3 kind of facies and was
 generated in the [GANSim project](https://github.com/SuihongSong/GeoModeling_GANSim-2D_Condition_to_Well_Facies_and_Global_Features)
 (under [MIT license](./assets/third_party_licenses/GANSim%20MIT%20LICENSE)).
-More synthesized data are available
-[here](https://zenodo.org/record/3993791#.X1FQuMhKhaR). **If you use this dataset
-in your work, please cite the original authors.**
 
 You can simply run a train on the default dataset with unconditional SAGAN
 model using the following command in `gan_facies` folder:
@@ -104,6 +105,13 @@ python gan_facies/apps/train.py
 You can see the progress of the training in the terminal and the resulted
 images and trained networks in the `res` folder.
 
+More data are available in this repository. Simply extract `datasets/datasets.zip`
+in the `datasets` folder. It contains a bigger version of the GANSim dataset as
+well as [Rongier et al. (2016)](https://hal.archives-ouvertes.fr/hal-01351694/) dataset
+and [Stanford VI-E dataset](https://github.com/SCRFpublic/Stanford-VI-E/tree/master/Facies).
+
+**If you use this dataset in your work, please cite the original authors.**
+
 ## Use your own dataset
 
 Of course, you can use your own dataset. Simply drop it in the `datasets` folder.
@@ -148,7 +156,7 @@ configuration, then set the generator learning rate to 0.001 and the generator
 random input dimension to 64:
 
 ```bash
-python gan_facies/apps/train.py --config gan_facies/configs/exp/my_config.yaml--training.g_lr=0.001\
+python gan_facies/apps/train.py --config gan_facies/configs/exp/my_config.yaml --training.g_lr=0.001\
  --model.z_dim=64
 ```
 

gan_facies/apps/post_process.ipynb

@@ -272,7 +272,7 @@
    "source": [
     "### Pipeline\n",
     "\n",
-    "Indicator cipplings are \"superposable\" with each other."
+    "Indicator clipping are \"superposable\" with each other."
    ]
   },
   {
@@ -422,7 +422,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.15"
+   "version": "3.8.16 (default, Dec  7 2022, 01:12:06) \n[GCC 11.3.0]"
   },
   "orig_nbformat": 4,
   "vscode": {

gan_facies/metrics/components.py

@@ -4,14 +4,21 @@
 """
 from typing import Dict, List, Tuple
 
-import jax
-import jax.numpy as jnp
 import numpy as np
 from skimage.measure import label, regionprops
 
-# Force Jax using CPU (using Jax with GPU will enter in conflict
-# with Pytorch that should use all VRAM available)
-jax.config.update('jax_platform_name', 'cpu')
+try:
+    import jax
+    import jax.numpy as jnp
+    JAX_AVAILABLE = True
+except ImportError:
+    jnp = np
+    JAX_AVAILABLE = False
+
+if JAX_AVAILABLE:
+    # Force Jax using CPU (using Jax with GPU will enter in conflict
+    # with Pytorch that should use all VRAM available)
+    jax.config.update('jax_platform_name', 'cpu')
 
 PropertiesType = Dict[str, np.ndarray]
 
@@ -149,9 +156,8 @@ def get_perimeter(components: np.ndarray, neighbors: np.ndarray,
                   class_id: int) -> np.ndarray:
     """Compute perimeter or surface area."""
 
-    @jax.jit
-    def perimeter_component_jit(components: jnp.ndarray, i: jnp.ndarray,
-                                mask_ext: jnp.ndarray) -> jnp.ndarray:
+    def perimeter_component(components: jnp.ndarray, i: jnp.ndarray,
+                            mask_ext: jnp.ndarray) -> jnp.ndarray:
         """Compute perimeter of component i and stock it in perimeters."""
         axis = tuple(range(1, neighbors.ndim))
         mask_compo_i = jnp.expand_dims(components == i, axis=-1)
@@ -161,6 +167,9 @@ def perimeter_component_jit(components: jnp.ndarray, i: jnp.ndarray,
         perimeter = jnp.sum(index, axis=axis, dtype=jnp.int32)
         return perimeter
 
+    if JAX_AVAILABLE:
+        perimeter_component = jax.jit(perimeter_component)
+
     # connect_1: neighbors with 1-connectivity
     # 2D: 4-neighbors, 3D: 6-neighbors
     connect_1 = (neighbors[..., :4]
@@ -176,9 +185,9 @@ def perimeter_component_jit(components: jnp.ndarray, i: jnp.ndarray,
     components = jnp.array(components, dtype=jnp.int32)
     mask_ext = jnp.array(mask_ext, dtype=jnp.uint8)
     for i in range(1, jnp.max(components) + 1):
-        perimeter = perimeter_component_jit(components,
-                                            jnp.array(i, dtype=jnp.uint8),
-                                            mask_ext)
+        perimeter = perimeter_component(components,
+                                        jnp.array(i, dtype=jnp.uint8),
+                                        mask_ext)
         perimeters.append(perimeter)
     perimeters_np = np.array(perimeters).T
     return perimeters_np  # shape (n_imgs, max_n_components), dtype=np.int32

requirements-nojax.txt

@@ -0,0 +1,13 @@
+# clearml==1.6.3  # if you want to use clearml
+einops==0.4.1
+matplotlib==3.5.2
+opencv-python==4.6.0.66
+pandas==1.4.3
+Pillow==9.2.0
+pytorch-ignite==0.4.9
+rich==12.5.1
+scikit_image==0.19.3
+thop
+torch==1.8.1
+# wandb==0.12.21  # if you want to use wandb
+yaecs==1.0.1

setup.py

@@ -1,18 +1,19 @@
 """Setup of gan-face-editing."""
 
+import os
 import pathlib
 
 from setuptools import setup
 
 HERE = pathlib.Path(__file__).parent
 
 # The text of the README file
-README = (HERE / "README.md").read_text()
+README = os.path.join(HERE, "README.md")
 
 # Installation
 config = {
     'name': 'gan-facies',
-    'version': '2.5.0',
+    'version': '2.5.1',
     'description': 'Facies modeling with GAN.',
     'long_description': README,
     'long_description_content_type': 'text/markdown',