1 files changed, 67 insertions, 0 deletions
diff --git a/Code/test.py b/Code/test.py
new file mode 100644
index 0000000..3b2cd86
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+++ b/Code/test.py
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+import numpy as np
+import constants as c
+from utils import normalize_frames, get_test_batch
+from glob import glob
+from scipy.misc import imread, 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 = 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)]
+
+        # 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/', 8, num_rec_out=64)
+
+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()
+\ No newline at end of file