hiwonjoon · December 24, 2019 17:19 · hiwonjoon · May 18, 2019 · hiwonjoon · Dec 24, 2019
diff --git a/README.md b/README.md
diff --git a/dm_control_gpu_headless_rendering_test.py b/dm_control_gpu_headless_rendering_test.py
 from dm_control import mujoco
 # Load a model from an MJCF XML string.
 xml_string = """
 <mujoco>
  <worldbody>
    <light name="top" pos="0 0 1.5"/>
    <geom name="floor" type="plane" size="1 1 .1"/>
    <body name="box" pos="0 0 .3">
      <joint name="up_down" type="slide" axis="0 0 1"/>
      <geom name="box" type="box" size=".2 .2 .2" rgba="1 0 0 1"/>
      <geom name="sphere" pos=".2 .2 .2" size=".1" rgba="0 1 0 1"/>
    </body>
  </worldbody>
 </mujoco>
 """
 physics = mujoco.Physics.from_xml_string(xml_string)
 # Render the default camera view as a numpy array of pixels.

 while True:
    pixels = physics.render()
diff --git a/mujoco_py_render_test.py b/mujoco_py_render_test.py
 import mujoco_py

 import os
 mj_path, _ = mujoco_py.utils.discover_mujoco()
 xml_path = os.path.join(mj_path, 'model', 'humanoid.xml')
 model = mujoco_py.load_model_from_path(xml_path)
 sim = mujoco_py.MjSim(model)

 viewer = mujoco_py.MjRenderContextOffscreen(sim, -1)

 while True:
    viewer.render(300, 300, camera_id=-1)
    data = viewer.read_pixels(300, 300, depth=False)
    print(data.shape)
diff --git a/mujoco_py_render_test_with_dm_control.py b/mujoco_py_render_test_with_dm_control.py
 from dm_control import mujoco
 # Load a model from an MJCF XML string.
 xml_string = """
 <mujoco>
  <worldbody>
    <light name="top" pos="0 0 1.5"/>
    <geom name="floor" type="plane" size="1 1 .1"/>
    <body name="box" pos="0 0 .3">
      <joint name="up_down" type="slide" axis="0 0 1"/>
      <geom name="box" type="box" size=".2 .2 .2" rgba="1 0 0 1"/>
      <geom name="sphere" pos=".2 .2 .2" size=".1" rgba="0 1 0 1"/>
    </body>
  </worldbody>
 </mujoco>
 """
 physics = mujoco.Physics.from_xml_string(xml_string)
 # Render the default camera view as a numpy array of pixels.

 pixels = physics.render()

 import mujoco_py

 import os
 mj_path, _ = mujoco_py.utils.discover_mujoco()
 xml_path = os.path.join(mj_path, 'model', 'humanoid.xml')
 model = mujoco_py.load_model_from_path(xml_path)
 sim = mujoco_py.MjSim(model)

 viewer = mujoco_py.MjRenderContextOffscreen(sim, -1)

 while True:
    viewer.render(300, 300, camera_id=-1)
    data = viewer.read_pixels(300, 300, depth=False)
    print(data.shape)


diff --git a/note.md b/note.md
diff --git a/tf-test.py b/tf-test.py
 from __future__ import absolute_import, division, print_function, unicode_literals

 import tensorflow as tf

 from tensorflow.keras.layers import Dense, Flatten, Conv2D
 from tensorflow.keras import Model

 mnist = tf.keras.datasets.mnist

 (x_train, y_train), (x_test, y_test) = mnist.load_data()
 x_train, x_test = x_train / 255.0, x_test / 255.0

 # 채널 차원을 추가합니다.
 x_train = x_train[..., tf.newaxis]
 x_test = x_test[..., tf.newaxis]

 train_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).shuffle(10000).batch(32)
 test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

 class MyModel(Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = Conv2D(32, 3, activation='relu')
    self.flatten = Flatten()
    self.d1 = Dense(128, activation='relu')
    self.d2 = Dense(10, activation='softmax')

  def call(self, x):
    x = self.conv1(x)
    x = self.flatten(x)
    x = self.d1(x)
    return self.d2(x)

 model = MyModel()

 loss_object = tf.keras.losses.SparseCategoricalCrossentropy()

 optimizer = tf.keras.optimizers.Adam()

 train_loss = tf.keras.metrics.Mean(name='train_loss')
 train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

 test_loss = tf.keras.metrics.Mean(name='test_loss')
 test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

 @tf.function
 def train_step(images, labels):
  with tf.GradientTape() as tape:
    predictions = model(images)
    loss = loss_object(labels, predictions)
  gradients = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))

  train_loss(loss)
  train_accuracy(labels, predictions)

 @tf.function
 def test_step(images, labels):
  predictions = model(images)
  t_loss = loss_object(labels, predictions)

  test_loss(t_loss)
  test_accuracy(labels, predictions)

 EPOCHS = 5

 for epoch in range(EPOCHS):
  for images, labels in train_ds:
    train_step(images, labels)

  for test_images, test_labels in test_ds:
    test_step(test_images, test_labels)

  template = 'epoch: {}, loss: {}, acc: {},  test_loss: {},  testacc: {}'
  print (template.format(epoch+1,
                         train_loss.result(),
                         train_accuracy.result()*100,
                         test_loss.result(),
                         test_accuracy.result()*100))
	from dm_control import mujoco
	# Load a model from an MJCF XML string.
	xml_string = """
	<mujoco>
	<worldbody>
	<light name="top" pos="0 0 1.5"/>
	<geom name="floor" type="plane" size="1 1 .1"/>
	<body name="box" pos="0 0 .3">
	<joint name="up_down" type="slide" axis="0 0 1"/>
	<geom name="box" type="box" size=".2 .2 .2" rgba="1 0 0 1"/>
	<geom name="sphere" pos=".2 .2 .2" size=".1" rgba="0 1 0 1"/>
	</body>
	</worldbody>
	</mujoco>
	"""
	physics = mujoco.Physics.from_xml_string(xml_string)
	# Render the default camera view as a numpy array of pixels.

	while True:
	pixels = physics.render()
	import mujoco_py

	import os
	mj_path, _ = mujoco_py.utils.discover_mujoco()
	xml_path = os.path.join(mj_path, 'model', 'humanoid.xml')
	model = mujoco_py.load_model_from_path(xml_path)
	sim = mujoco_py.MjSim(model)

	viewer = mujoco_py.MjRenderContextOffscreen(sim, -1)

	while True:
	viewer.render(300, 300, camera_id=-1)
	data = viewer.read_pixels(300, 300, depth=False)
	print(data.shape)
	from __future__ import absolute_import, division, print_function, unicode_literals

	import tensorflow as tf

	from tensorflow.keras.layers import Dense, Flatten, Conv2D
	from tensorflow.keras import Model

	mnist = tf.keras.datasets.mnist

	(x_train, y_train), (x_test, y_test) = mnist.load_data()
	x_train, x_test = x_train / 255.0, x_test / 255.0

	# 채널 차원을 추가합니다.
	x_train = x_train[..., tf.newaxis]
	x_test = x_test[..., tf.newaxis]

	train_ds = tf.data.Dataset.from_tensor_slices(
	(x_train, y_train)).shuffle(10000).batch(32)
	test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

	class MyModel(Model):
	def __init__(self):
	super(MyModel, self).__init__()
	self.conv1 = Conv2D(32, 3, activation='relu')
	self.flatten = Flatten()
	self.d1 = Dense(128, activation='relu')
	self.d2 = Dense(10, activation='softmax')

	def call(self, x):
	x = self.conv1(x)
	x = self.flatten(x)
	x = self.d1(x)
	return self.d2(x)

	model = MyModel()

	loss_object = tf.keras.losses.SparseCategoricalCrossentropy()

	optimizer = tf.keras.optimizers.Adam()

	train_loss = tf.keras.metrics.Mean(name='train_loss')
	train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

	test_loss = tf.keras.metrics.Mean(name='test_loss')
	test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

	@tf.function
	def train_step(images, labels):
	with tf.GradientTape() as tape:
	predictions = model(images)
	loss = loss_object(labels, predictions)
	gradients = tape.gradient(loss, model.trainable_variables)
	optimizer.apply_gradients(zip(gradients, model.trainable_variables))

	train_loss(loss)
	train_accuracy(labels, predictions)

	@tf.function
	def test_step(images, labels):
	predictions = model(images)
	t_loss = loss_object(labels, predictions)

	test_loss(t_loss)
	test_accuracy(labels, predictions)

	EPOCHS = 5

	for epoch in range(EPOCHS):
	for images, labels in train_ds:
	train_step(images, labels)

	for test_images, test_labels in test_ds:
	test_step(test_images, test_labels)

	template = 'epoch: {}, loss: {}, acc: {}, test_loss: {}, testacc: {}'
	print (template.format(epoch+1,
	train_loss.result(),
	train_accuracy.result()*100,
	test_loss.result(),
	test_accuracy.result()*100))