fixed prior and learning prior code for bair and moving mnist.

.gitignore

@@ -1,101 +1,3 @@
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-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
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-*.egg-info/
-.installed.cfg
-*.egg
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
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-
-# Installer logs
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-
-# Unit test / coverage reports
-htmlcov/
-.tox/
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-# Translations
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+*.pyc

data/bair.py

@@ -0,0 +1,63 @@
+import os
+import io
+from scipy.misc import imresize 
+import numpy as np
+from PIL import Image
+from scipy.misc import imresize
+from scipy.misc import imread 
+
+
+ROOT_DIR = '/misc/vlgscratch4/FergusGroup/denton/data/bair_robot_push/processed_data/'
+class RobotPush(object):
+
+    """Data Handler that loads robot pushing data."""
+
+    def __init__(self, train=True, seq_len=20, image_size=64):
+        self.root_dir = ROOT_DIR
+        if train:
+            self.data_dir = '%s/train' % self.root_dir
+            self.ordered = False
+        else:
+            self.data_dir = '%s/test' % self.root_dir
+            self.ordered = True 
+            #self.data_dir = '/misc/vlgscratch4/FergusGroup/denton/data/push-dataset/processed_data/push_testseen'
+        self.dirs = []
+        for d1 in os.listdir(self.data_dir):
+            for d2 in os.listdir('%s/%s' % (self.data_dir, d1)):
+                self.dirs.append('%s/%s/%s' % (self.data_dir, d1, d2))
+        self.seq_len = seq_len
+        self.image_size = image_size 
+        self.seed_is_set = False # multi threaded loading
+        self.d = 0
+
+    def set_seed(self, seed):
+        if not self.seed_is_set:
+            self.seed_is_set = True
+            np.random.seed(seed)
+
+    def __len__(self):
+        return 10000
+
+    def get_seq(self):
+        if self.ordered:
+            d = self.dirs[self.d]
+            if self.d == len(self.dirs) - 1:
+                self.d = 0
+            else:
+                self.d+=1
+        else:
+            d = self.dirs[np.random.randint(len(self.dirs))]
+        image_seq = []
+        for i in range(self.seq_len):
+            fname = '%s/%d.png' % (d, i)
+            im = imread(fname).reshape(1, 64, 64, 3)
+            image_seq.append(im/255.)
+        image_seq = np.concatenate(image_seq, axis=0)
+        return image_seq
+
+
+    def __getitem__(self, index):
+        self.set_seed(index)
+        return self.get_seq()
+
+