Docker Requirements for additional postprocessing of the Stylegans

Dockerfile

@@ -12,6 +12,24 @@ ENV PYTHONDONTWRITEBYTECODE 1
 ENV PYTHONUNBUFFERED 1
 
 RUN pip install imageio imageio-ffmpeg==0.4.4 pyspng==0.1.0
+RUN pip install ipywidgets
+
+#When X11 forwarding matplotlib
+#RUN pip install cairocffi
+
+
+RUN apt-get update -y
+ENV TZ=Europe/Amsterdam
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
+#RUN apt-get install -y libcairo2 python3-gi python3-gi-cairo gir1.2-gtk-3.0
+RUN apt-get install -y libgirepository1.0-dev gcc libcairo2-dev pkg-config python3-dev gir1.2-gtk-3.0
+RUN pip install pycairo
+RUN pip install PyGObject
+RUN apt-get install -y mesa-utils
+
+# ffmpeg for cv2 video creation
+RUN apt-get install -y ffmpeg
+
 
 WORKDIR /workspace
 

dataset_tool.py

@@ -26,6 +26,9 @@ import numpy as np
 import PIL.Image
 from tqdm import tqdm
 
+
+PIL.Image.init() # required to initialise PIL.Image.EXTENSION
+
 #----------------------------------------------------------------------------
 
 def error(msg):
@@ -216,8 +219,11 @@ def open_mnist(images_gz: str, *, max_images: Optional[int]):
 def make_transform(
     transform: Optional[str],
     output_width: Optional[int],
-    output_height: Optional[int]
+    output_height: Optional[int],
+    crop_width: Optional[int],
+    crop_height: Optional[int]
 ) -> Callable[[np.ndarray], Optional[np.ndarray]]:
+
     def scale(width, height, img):
         w = img.shape[1]
         h = img.shape[0]
@@ -249,17 +255,38 @@ def make_transform(
         canvas = np.zeros([width, width, 3], dtype=np.uint8)
         canvas[(width - height) // 2 : (width + height) // 2, :] = img
         return canvas
+    
+    def scale_center_crop(width, height, crop_w, crop_h, img):
+        return scale(width, height, img[(img.shape[0] - crop_w) // 2 : (img.shape[0] + crop_w) // 2, (img.shape[1] - crop_h) // 2 : (img.shape[1] + crop_h) // 2])
+    
+    def scale_center_crop_wide(width, height, crop_w, crop_h, img):
+        error('not implemented')
 
-    if transform is None:
-        return functools.partial(scale, output_width, output_height)
-    if transform == 'center-crop':
-        if (output_width is None) or (output_height is None):
-            error ('must specify --resolution=WxH when using ' + transform + 'transform')
-        return functools.partial(center_crop, output_width, output_height)
-    if transform == 'center-crop-wide':
-        if (output_width is None) or (output_height is None):
-            error ('must specify --resolution=WxH when using ' + transform + ' transform')
-        return functools.partial(center_crop_wide, output_width, output_height)
+    crop_width = output_width if crop_width is None else crop_width
+    crop_height = output_height if crop_height is None else crop_height
+
+    if crop_width != output_width or crop_height != output_height:
+        if transform is None:
+            error ('must specify transform method (center-crop or center-crop-wide)')
+        if transform == 'center-crop':
+            if (output_width is None) or (output_height is None):
+                error ('must specify --resolution=WxH when using ' + transform + 'transform')
+            return functools.partial(scale_center_crop, output_height, output_width, crop_width, crop_height)
+        if transform == 'center-crop-wide':
+            if (output_width is None) or (output_height is None):
+                error ('must specify --resolution=WxH when using ' + transform + ' transform')
+            return functools.partial(scale_center_crop_wide, output_height, output_width, crop_width, crop_height)
+    else:
+        if transform is None:
+            return functools.partial(scale, output_width, output_height)
+        if transform == 'center-crop':
+            if (output_width is None) or (output_height is None):
+                error ('must specify --resolution=WxH when using ' + transform + 'transform')
+            return functools.partial(center_crop, output_width, output_height)
+        if transform == 'center-crop-wide':
+            if (output_width is None) or (output_height is None):
+                error ('must specify --resolution=WxH when using ' + transform + ' transform')
+            return functools.partial(center_crop_wide, output_width, output_height)
     assert False, 'unknown transform'
 
 #----------------------------------------------------------------------------
@@ -323,13 +350,15 @@ def open_dest(dest: str) -> Tuple[str, Callable[[str, Union[bytes, str]], None],
 @click.option('--max-images', help='Output only up to `max-images` images', type=int, default=None)
 @click.option('--transform', help='Input crop/resize mode', type=click.Choice(['center-crop', 'center-crop-wide']))
 @click.option('--resolution', help='Output resolution (e.g., \'512x512\')', metavar='WxH', type=parse_tuple)
+@click.option('--crop-resolution', help='Resolution of crop (can be larger small than final resoltion) (e.g., \'600x600\')', metavar='WxH', type=parse_tuple)
 def convert_dataset(
     ctx: click.Context,
     source: str,
     dest: str,
     max_images: Optional[int],
     transform: Optional[str],
-    resolution: Optional[Tuple[int, int]]
+    resolution: Optional[Tuple[int, int]],
+    crop_resolution: Optional[Tuple[int, int]],
 ):
     """Convert an image dataset into a dataset archive usable with StyleGAN2 ADA PyTorch.
 
@@ -387,7 +416,7 @@ def convert_dataset(
 
     \b
     python dataset_tool.py --source LSUN/raw/cat_lmdb --dest /tmp/lsun_cat \\
-        --transform=center-crop-wide --resolution=512x384
+        --transform=center-crop-wide --resolution=512x384 --crop-resolution=600x600
     """
 
     PIL.Image.init() # type: ignore
@@ -399,7 +428,8 @@ def convert_dataset(
     archive_root_dir, save_bytes, close_dest = open_dest(dest)
 
     if resolution is None: resolution = (None, None)
-    transform_image = make_transform(transform, *resolution)
+    if crop_resolution is None: crop_resolution = (None, None)
+    transform_image = make_transform(transform, *resolution, *crop_resolution)
 
     dataset_attrs = None
 

training/dataset.py

@@ -49,6 +49,7 @@ class Dataset(torch.utils.data.Dataset):
         if xflip:
             self._raw_idx = np.tile(self._raw_idx, 2)
             self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)])
+        # TODO, perform a similar trick, but then with random crops etc.
 
     def _get_raw_labels(self):
         if self._raw_labels is None: