removed test

1 files changed, 0 insertions, 70 deletions

diff --git a/Code/test.py b/Code/test.py
deleted file mode 100644
index 9072c2b..0000000
--- a/Code/test.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import numpy as np
-import constants as c
-from utils import normalize_frames, get_test_batch
-from glob import glob
-from scipy.ndimage import imread
-from scipy.misc import imsave
-import os
-
-def save_batch(batch, num_rec_out):
-    # TEST
-    for clip_num, clip in enumerate(batch):
-        for frame_num in xrange(c.HIST_LEN + num_rec_out):
-            imsave(c.get_dir('TEST/' + str(clip_num) + '/') + str(frame_num) + '.png',
-                   clip[:, :, frame_num * 3:(frame_num + 1) * 3])
-
-def get_full_clips(data_dir, num_clips, num_rec_out=1):
-    """
-    Loads a batch of random clips from the unprocessed train or test data.
-
-    @param data_dir: The directory of the data to read. Should be either c.TRAIN_DIR or c.TEST_DIR.
-    @param num_clips: The number of clips to read.
-    @param num_rec_out: The number of outputs to predict. Outputs > 1 are computed recursively,
-                        using the previously-generated frames as input. Default = 1.
-
-    @return: An array of shape
-             [num_clips, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + num_rec_out))].
-             A batch of frame sequences with values normalized in range [-1, 1].
-    """
-    clips = np.empty([num_clips,
-                      c.FULL_HEIGHT,
-                      c.FULL_WIDTH,
-                      (3 * (c.HIST_LEN + num_rec_out))])
-
-    # get num_clips random episodes
-    ep_dirs = np.random.choice(glob(data_dir + '*'), num_clips)
-    print ep_dirs
-
-    # get a random clip of length HIST_LEN + num_rec_out from each episode
-    for clip_num, ep_dir in enumerate(ep_dirs):
-        ep_frame_paths = sorted(glob(os.path.join(ep_dir, '*')))
-        start_index = np.random.choice(len(ep_frame_paths) - (c.HIST_LEN + num_rec_out - 1))
-        clip_frame_paths = ep_frame_paths[start_index:start_index + (c.HIST_LEN + num_rec_out)]
-        print clip_num
-        print clip_frame_paths
-
-        # read in frames
-        for frame_num, frame_path in enumerate(clip_frame_paths):
-            frame = imread(frame_path, mode='RGB')
-            norm_frame = normalize_frames(frame)
-
-            clips[clip_num, :, :, frame_num * 3:(frame_num + 1) * 3] = norm_frame
-
-    # TEST
-    save_batch(clips, num_rec_out)
-
-    return clips
-
-get_full_clips('../Data/Ms_Pacman/Test/', 1, num_rec_out=1)
-
-# def test():
-#     """
-#     Runs one test step on the generator network.
-#     """
-#     batch = get_test_batch(c.BATCH_SIZE, num_rec_out=2)
-#     save_batch(batch, 2)
-#
-#     # self.g_model.test_batch(
-#     #     batch, self.global_step, num_rec_out=2)
-#
-# test()