X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=flatland.git;a=blobdiff_plain;f=test.py;fp=test.py;h=0000000000000000000000000000000000000000;hp=e058269e06d49ffb4543c88753d99ec7ff0275e9;hb=c1f1040936d977cd2b3a276c725e223198377d2a;hpb=90d1c5704c30e7f1d041e32eacbc2893741110e1

diff --git a/test.py b/test.py
deleted file mode 100755
index e058269..0000000
--- a/test.py
+++ /dev/null
@@ -1,95 +0,0 @@
-#!/usr/bin/env python-for-pytorch
-
-import torch
-import torchvision
-import argparse
-
-from _ext import flatland
-
-######################################################################
-
-parser = argparse.ArgumentParser(
-    description = 'Dummy test of the flatland sequence generation.',
-    formatter_class = argparse.ArgumentDefaultsHelpFormatter
-)
-
-parser.add_argument('--seed',
-                    type = int, default = 0,
-                    help = 'Random seed, < 0 is no seeding')
-
-parser.add_argument('--width',
-                    type = int, default = 80,
-                    help = 'Image width')
-
-parser.add_argument('--height',
-                    type = int, default = 80,
-                    help = 'Image height')
-
-parser.add_argument('--nb_shapes',
-                    type = int, default = 10,
-                    help = 'Image height')
-
-parser.add_argument('--nb_sequences',
-                    type = int, default = 1,
-                    help = 'How many sequences to generate')
-
-parser.add_argument('--nb_images_per_sequences',
-                    type = int, default = 3,
-                    help = 'How many images per sequence')
-
-parser.add_argument('--randomize_colors',
-                    action='store_true', default=False,
-                    help = 'Should the shapes be of different colors')
-
-parser.add_argument('--randomize_shape_size',
-                    action='store_true', default=False,
-                    help = 'Should the shapes be of different size')
-
-args = parser.parse_args()
-
-if args.seed >= 0:
-    torch.manual_seed(args.seed)
-
-######################################################################
-
-def sequences_to_image(x, gap = 1, gap_color = (0, 128, 255)):
-    from PIL import Image
-
-    nb_sequences = x.size(0)
-    nb_images_per_sequences = x.size(1)
-    nb_channels = 3
-
-    if x.size(2) != nb_channels:
-        print('Can only handle 3 channel tensors.')
-        exit(1)
-
-    height = x.size(3)
-    width = x.size(4)
-
-    result = torch.ByteTensor(nb_channels,
-                              gap + nb_sequences * (height + gap),
-                              gap + nb_images_per_sequences * (width + gap))
-
-    result.copy_(torch.Tensor(gap_color).view(-1, 1, 1).expand_as(result))
-
-    for s in range(0, nb_sequences):
-        for i in range(0, nb_images_per_sequences):
-            result.narrow(1, gap + s * (height + gap), height) \
-                  .narrow(2, gap + i * (width + gap), width) \
-                  .copy_(x[s][i])
-
-    result_numpy = result.cpu().byte().transpose(0, 2).transpose(0, 1).numpy()
-
-    return Image.fromarray(result_numpy, 'RGB')
-
-######################################################################
-
-x = flatland.generate_sequence(False,
-                               args.nb_sequences,
-                               args.nb_images_per_sequences,
-                               args.height, args.width,
-                               args.nb_shapes,
-                               args.randomize_colors,
-                               args.randomize_shape_size)
-
-sequences_to_image(x, gap = 1, gap_color = (0, 0, 0)).save('sequences.png')