Adding Files

Dreamer/dmc2gym/natural_imgsource.py

@@ -1,36 +1,19 @@
+# This code provides the class that is used to generate backgrounds for the natural background setting
+# the class is used inside an environment wrapper and will be called each time the env generates an observation
+# the code is largely based on https://github.com/facebookresearch/deep_bisim4control
 
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import cv2
-import skvideo.io
 import random
-import tqdm
 
-class BackgroundMatting(object):
+import cv2
-    """
+import numpy as np
-    Produce a mask by masking the given color. This is a simple strategy
+import skvideo.io
-    but effective for many games.
-    """
-    def __init__(self, color):
-        """
-        Args:
-            color: a (r, g, b) tuple or single value for grayscale
-        """
-        self._color = color
-
-    def get_mask(self, img):
-        return img == self._color
 
 
 class ImageSource(object):
     """
     Source of natural images to be added to a simulated environment.
     """
+
     def get_image(self):
         """
         Returns:
@@ -43,141 +26,57 @@ class ImageSource(object):
         pass
 
 
-class FixedColorSource(ImageSource):
-    def __init__(self, shape, color):
-        """
-        Args:
-            shape: [h, w]
-            color: a 3-tuple
-        """
-        self.arr = np.zeros((shape[0], shape[1], 3))
-        self.arr[:, :] = color
-
-    def get_image(self):
-        return self.arr
-
-
-class RandomColorSource(ImageSource):
-    def __init__(self, shape):
-        """
-        Args:
-            shape: [h, w]
-        """
-        self.shape = shape
-        self.arr = None
-        self.reset()
-
-    def reset(self):
-        self._color = np.random.randint(0, 256, size=(3,))
-        self.arr = np.zeros((self.shape[0], self.shape[1], 3))
-        self.arr[:, :] = self._color
-
-    def get_image(self):
-        return self.arr
-
-
-class NoiseSource(ImageSource):
-    def __init__(self, shape, strength=255):
-        """
-        Args:
-            shape: [h, w]
-            strength (int): the strength of noise, in range [0, 255]
-        """
-        self.shape = shape
-        self.strength = strength
-
-    def get_image(self):
-        return np.random.randn(self.shape[0], self.shape[1], 3) * self.strength
-
-
-class RandomImageSource(ImageSource):
-    def __init__(self, shape, filelist, total_frames=None, grayscale=False):
-        """
-        Args:
-            shape: [h, w]
-            filelist: a list of image files
-        """
-        self.grayscale = grayscale
-        self.total_frames = total_frames
-        self.shape = shape
-        self.filelist = filelist
-        self.build_arr()
-        self.current_idx = 0
-        self.reset()
-
-    def build_arr(self):
-        self.total_frames = self.total_frames if self.total_frames else len(self.filelist)
-        self.arr = np.zeros((self.total_frames, self.shape[0], self.shape[1]) + ((3,) if not self.grayscale else (1,)))
-        for i in range(self.total_frames):
-            # if i % len(self.filelist) == 0: random.shuffle(self.filelist)
-            fname = self.filelist[i % len(self.filelist)]
-            if self.grayscale: im = cv2.imread(fname, cv2.IMREAD_GRAYSCALE)[..., None]
-            else:              im = cv2.imread(fname, cv2.IMREAD_COLOR)
-            self.arr[i] = cv2.resize(im, (self.shape[1], self.shape[0])) ## THIS IS NOT A BUG! cv2 uses (width, height)
-
-    def reset(self):
-        self._loc = np.random.randint(0, self.total_frames)
-
-    def get_image(self):
-        return self.arr[self._loc]
-
-
 class RandomVideoSource(ImageSource):
-    def __init__(self, shape, filelist, total_frames=None, grayscale=False):
+    def __init__(self, shape, filelist, random_bg=False, max_videos=100, grayscale=False):
         """
         Args:
             shape: [h, w]
             filelist: a list of video files
         """
         self.grayscale = grayscale
-        self.total_frames = total_frames
         self.shape = shape
         self.filelist = filelist
-        self.build_arr()
+        random.shuffle(self.filelist)
+        self.filelist = self.filelist[:max_videos]
+        self.max_videos = max_videos
+        self.random_bg = random_bg
         self.current_idx = 0
+        self._current_vid = None
         self.reset()
 
-    def build_arr(self):
+    def load_video(self, vid_id):
-        if not self.total_frames:
+        fname = self.filelist[vid_id]
-            self.total_frames = 0
-            self.arr = None
-            random.shuffle(self.filelist)
-            for fname in tqdm.tqdm(self.filelist, desc="Loading videos for natural", position=0):
-                if self.grayscale: frames = skvideo.io.vread(fname, outputdict={"-pix_fmt": "gray"})
-                else:              frames = skvideo.io.vread(fname)
-                local_arr = np.zeros((frames.shape[0], self.shape[0], self.shape[1]) + ((3,) if not self.grayscale else (1,)))
-                for i in tqdm.tqdm(range(frames.shape[0]), desc="video frames", position=1):
-                    local_arr[i] = cv2.resize(frames[i], (self.shape[1], self.shape[0])) ## THIS IS NOT A BUG! cv2 uses (width, height)
-                if self.arr is None:
-                    self.arr = local_arr
-                else:
-                    self.arr = np.concatenate([self.arr, local_arr], 0)
-                self.total_frames += local_arr.shape[0]
-        else:
-            self.arr = np.zeros((self.total_frames, self.shape[0], self.shape[1]) + ((3,) if not self.grayscale else (1,)))
-            total_frame_i = 0
-            file_i = 0
-            with tqdm.tqdm(total=self.total_frames, desc="Loading videos for natural") as pbar:
-                while total_frame_i < self.total_frames:
-                    if file_i % len(self.filelist) == 0: random.shuffle(self.filelist)
-                    file_i += 1
-                    fname = self.filelist[file_i % len(self.filelist)]
-                    if self.grayscale: frames = skvideo.io.vread(fname, outputdict={"-pix_fmt": "gray"})
-                    else:              frames = skvideo.io.vread(fname)
-                    for frame_i in range(frames.shape[0]):
-                        if total_frame_i >= self.total_frames: break
-                        if self.grayscale:
-                            self.arr[total_frame_i] = cv2.resize(frames[frame_i], (self.shape[1], self.shape[0]))[..., None] ## THIS IS NOT A BUG! cv2 uses (width, height)
-                        else:
-                            self.arr[total_frame_i] = cv2.resize(frames[frame_i], (self.shape[1], self.shape[0])) 
-                        pbar.update(1)
-                        total_frame_i += 1
 
+        if self.grayscale:
+            frames = skvideo.io.vread(fname, outputdict={"-pix_fmt": "gray"})
+        else:
+            frames = skvideo.io.vread(fname, num_frames=1000)
+        
+        img_arr = np.zeros((frames.shape[0], self.shape[0], self.shape[1]) + ((3,) if not self.grayscale else (1,)))
+        for i in range(frames.shape[0]):
+            if self.grayscale:
+                img_arr[i] = cv2.resize(frames[i], (self.shape[1], self.shape[0]))[..., None]  # THIS IS NOT A BUG! cv2 uses (width, height)
+            else:
+                img_arr[i] = cv2.resize(frames[i], (self.shape[1], self.shape[0]))
+        return img_arr
 
     def reset(self):
-        self._loc = np.random.randint(0, self.total_frames)
+        del self._current_vid
+        self._video_id = np.random.randint(0, len(self.filelist))
+        self._current_vid = self.load_video(self._video_id)
+        while True:
+            try:
+                self._video_id = np.random.randint(0, len(self.filelist))
+                self._current_vid = self.load_video(self._video_id)
+                break
+            except Exception:
+                continue
+        self._loc = np.random.randint(0, len(self._current_vid))
 
     def get_image(self):
-        img = self.arr[self._loc % self.total_frames]
+        if self.random_bg:
-        self._loc += 1
+            self._loc = np.random.randint(0, len(self._current_vid))
-        return img
+        else:
+            self._loc += 1
+        img = self._current_vid[self._loc % len(self._current_vid)]
+        return img

Dreamer/dmc2gym/wrappers.py

@@ -8,6 +8,11 @@ import skimage.io
 
 from dmc2gym import natural_imgsource
 
+high_noise = False
+
+def set_global_var(set_high_noise):
+    global high_noise
+    high_noise = set_high_noise
 
 def _spec_to_box(spec):
     def extract_min_max(s):
@@ -63,7 +68,6 @@ class DMCWrapper(core.Env):
         self._camera_id = camera_id
         self._frame_skip = frame_skip
         self._img_source = img_source
-        self._resource_files = resource_files
 
         # create task
         self._env = suite.load(
@@ -109,13 +113,16 @@ class DMCWrapper(core.Env):
                 self._bg_source = natural_imgsource.NoiseSource(shape2d)
             else:
                 files = glob.glob(os.path.expanduser(resource_files))
+                self.files = files
+                self.total_frames = total_frames
+                self.shape2d = shape2d
                 assert len(files), "Pattern {} does not match any files".format(
                     resource_files
                 )
                 if img_source == "images":
-                    self._bg_source = natural_imgsource.RandomImageSource(shape2d, files, grayscale=True, total_frames=total_frames)
+                    self._bg_source = natural_imgsource.RandomImageSource(shape2d, files, grayscale=False,  max_videos=100, random_bg=False)
                 elif img_source == "video":
-                    self._bg_source = natural_imgsource.RandomVideoSource(shape2d, files, grayscale=True, total_frames=total_frames)
+                    self._bg_source = natural_imgsource.RandomVideoSource(shape2d, files, grayscale=False,max_videos=100, random_bg=False)
                 else:
                     raise Exception("img_source %s not defined." % img_source)
 
@@ -136,9 +143,7 @@ class DMCWrapper(core.Env):
                 mask = np.logical_and((obs[:, :, 2] > obs[:, :, 1]), (obs[:, :, 2] > obs[:, :, 0]))  # hardcoded for dmc
                 bg = self._bg_source.get_image()
                 obs[mask] = bg[mask]
-            # obs = obs.transpose(2, 0, 1).copy()
+            obs = obs.transpose(2, 0, 1).copy()
-            # CHW to HWC for tensorflow
-            obs = obs.copy()
         else:
             obs = _flatten_obs(time_step.observation)
         return obs
@@ -188,6 +193,8 @@ class DMCWrapper(core.Env):
 
     def reset(self):
         time_step = self._env.reset()
+        self._bg_source.reset()
+        #self._bg_source = natural_imgsource.RandomVideoSource(self.shape2d, self.files, grayscale=True, total_frames=self.total_frames, high_noise=high_noise)
         obs = self._get_obs(time_step)
         return obs
 
@@ -198,4 +205,4 @@ class DMCWrapper(core.Env):
         camera_id = camera_id or self._camera_id
         return self._env.physics.render(
             height=height, width=width, camera_id=camera_id
-        )
+        )

Dreamer/dreamers.py

@@ -96,11 +96,12 @@ class Dreamer(tools.Module):
 
     @tf.function()
     def train(self, data, log_images=False):
-        self._strategy.experimental_run_v2(
+        self._strategy.run(
             self._train, args=(data, log_images))
 
     def _train(self, data, log_images):
         with tf.GradientTape() as model_tape:
+            data["image"] = tf.transpose(data["image"], perm=[0, 1, 3, 4, 2])
             embed = self._encode(data)
             post, prior = self._dynamics.observe(embed, data['action'])
             feat = self._dynamics.get_feat(post)

Dreamer/local_dm_control_suite/cheetah.xml

@@ -21,7 +21,7 @@
   <option timestep="0.01"/>
 
   <worldbody>
-    <geom name="ground" type="plane" conaffinity="1" pos="98 0 0" size="100 .8 .5" material="grid"/>
+    <geom name="ground" type="plane" conaffinity="1" pos="98 0 0" size="100 .8 .5" rgba="0.8 0.9 0.8 0" material="grid"/>
     <body name="torso" pos="0 0 .7" childclass="cheetah">
       <light name="light" pos="0 0 2" mode="trackcom"/>
       <camera name="side" pos="0 -3 0" quat="0.707 0.707 0 0" mode="trackcom"/>

Dreamer/run.py

@@ -62,10 +62,10 @@ def main(method, config):
         str(config.logdir), max_queue=1000, flush_millis=20000)
     writer.set_as_default()
     train_envs = [wrappers.Async(lambda: make_env(
-        config, writer, 'train', datadir, config.video_dir, store=True), config.parallel)
+        config, writer, 'train', datadir, config.video_dir_train, store=True), config.parallel)
         for _ in range(config.envs)]
     test_envs = [wrappers.Async(lambda: make_env(
-        config, writer, 'test', datadir, config.video_dir, store=False), config.parallel)
+        config, writer, 'test', datadir, config.video_dir_test, store=False), config.parallel)
         for _ in range(config.envs)]
     actspace = train_envs[0].action_space
 

Dreamer/tools.py

@@ -86,7 +86,11 @@ def video_summary(name, video, step=None, fps=20):
 
 def encode_gif(frames, fps):
     from subprocess import Popen, PIPE
+    print(frames[0].shape)
+    if frames[0].shape[-1] != 3:
+        frames =  np.transpose(frames, [0, 2, 3, 1])
     h, w, c = frames[0].shape
+    print(h,w,c)
     pxfmt = {1: 'gray', 3: 'rgb24'}[c]
     cmd = ' '.join([
         f'ffmpeg -y -f rawvideo -vcodec rawvideo',
@@ -123,6 +127,7 @@ def simulate(agent, envs, steps=0, episodes=0, state=None):
         # Step agents.
         # if use augmentation, need to modify dreamer.policy or here. 
         obs = {k: np.stack([o[k] for o in obs]) for k in obs[0]}
+        obs['image'] = tf.transpose(obs['image'], [0, 3, 2, 1])
         action, agent_state = agent(obs, done, agent_state)
         action = np.array(action)
         assert len(action) == len(envs)

Dreamer/train_configs/dreamer.yaml

@@ -1,7 +1,8 @@
 dmc: 
 
-  logdir: ./
+  logdir: /home/vedant/tia/Dreamer/logdir
-  video_dir: ./
+  video_dir_train: /media/vedant/cpsDataStorageWK/Vedant/natural_video_setting/train/
+  video_dir_test: /media/vedant/cpsDataStorageWK/Vedant/natural_video_setting/test/
   debug: False
   seed: 0
   steps: 1000000.0

Dreamer/train_configs/tia.yaml

@@ -1,7 +1,8 @@
 dmc:
 
-  logdir: ./
+  logdir: /home/vedant/tia/Dreamer/logdir
-  video_dir: ./
+  video_dir_train: /media/vedant/cpsDataStorageWK/Vedant/natural_video_setting/train/
+  video_dir_test: /media/vedant/cpsDataStorageWK/Vedant/natural_video_setting/test/
   debug: False
   seed: 0
   steps: 1000000.0