def __init__(self):
self.rotation_speed = 1 # higher is faster, positive is right
self.cam_z = -2
self.values = []
for x in range(7):
for r in range(23):
if x == 6 and r > 21:
break
Label(text=r + 1 + (x * 23), relief=RIDGE,
width=5).grid(row=r, column=0 + (x * 2))
s = Scale(master,
from_=0.,
to=1.,
resolution=0.1,
orient=HORIZONTAL)
s.set(1)
s.grid(row=r, column=1 + (x * 2)) # length=10,
self.values.append(s)
Button(master, text='max', command=self.max).grid(row=16,
column=14,
columnspan=2)
Button(master, text='randomize',
command=self.randomize).grid(row=17, column=14, columnspan=2)
Button(master, text='rotate right',
command=self.right).grid(row=18, column=14, columnspan=2)
Button(master, text='rotate left',
command=self.left).grid(row=19, column=14, columnspan=2)
Button(master, text='cam up', command=self.cam_up).grid(row=20,
column=14,
columnspan=2)
Button(master, text='cam down',
command=self.cam_down).grid(row=21, column=14, columnspan=2)
Button(master, text='print config',
command=self.print_config).grid(row=22, column=14, columnspan=2)
self.b = Button(master, text="enter values", command=self.popup)
self.b.grid(row=17, column=14, columnspan=2)
width = 512
height = 512
self.renderer = Renderer(width, height)
self.image = Image.fromarray(np.zeros((width, height), dtype=np.uint8))
self.canvas = Canvas(master, height=height, width=width)
self.canvas.grid(row=0, column=14, rowspan=15)
# image = image.resize((basewidth, hsize), PIL.Image.ANTIALIAS)
self.photo = ImageTk.PhotoImage(self.image)
self.photo_holder = self.canvas.create_image(
width - (self.image.size[0] / 2),
height - (self.image.size[1] / 2),
image=self.photo)
self.rot = 0
self.render()
class CubeSphereComparisonGenerator(object):
def __init__(self, width, height):
self.cube = Renderer(width, height, "cube", False)
self.sphere = Renderer(width, height, "sphere", True)
self.shape = np.ones(160) * 0.9
def sample(self):
cam = np.random.uniform(-1, 1, 3)
return self.cube.render(np.zeros(160),
cam), self.sphere.render(self.shape, cam)
class CubeGenerator(object):
def __init__(self, width, height):
self.renderer = Renderer(width, height, "cube", False)
def sample(self):
return self.renderer.render(np.zeros(160), np.random.uniform(-1, 1, 3))
class RotatingCubeGenerator(object):
def __init__(self, width, height):
self.renderer = Renderer(width, height, "cube", False)
self.cam = None
def sample(self):
self.cam = np.random.uniform(-1, 1, 3)
return self.renderer.render(np.zeros(160), self.cam)
class RotatingConstantShapeGenerator(object):
def __init__(self, width, height, radius=.5):
self.renderer = Renderer(width, height, "sphere", True)
self.shape = np.ones(160) * radius
def sample(self):
self.cam = np.random.uniform(-1, 1, 3)
return self.renderer.render(self.shape, self.cam)
class RotatingRandomShapeGenerator(object):
def __init__(self, width, height, smin=.4, smax=.8):
self.renderer = Renderer(width, height, "sphere", True)
self.shape = np.random.uniform(smin, smax, 160)
self.cam = None
def sample(self):
self.cam = np.random.uniform(-1, 1, 3)
return self.renderer.render(self.shape, self.cam)
class RandomSingleViewGenerator(object):
def __init__(self, width, height, smin=0, smax=1):
self.renderer = Renderer(width, height, "sphere", True)
self.cam = np.random.uniform(-1, 1, 3)
self.min = smin
self.max = smax
def sample(self):
return self.renderer.render(
np.random.uniform(self.min, self.max, 160), self.cam)
class RotatingSingle3DIQTTGenerator(object):
def __init__(self, width, height, smin=.5, smax=1):
self.renderer = Renderer(width, height, "iqtt", True)
self.shape = np.random.uniform(smin, smax, 160)
def sample(self, cam=None):
if cam is None:
self.cam = np.random.uniform(-1, 1, 3)
else:
self.cam = cam
return self.renderer.render(self.shape, self.cam)
def __init__(self, width, height):
self.renderer = Renderer(width, height, "sphere", True)
self.cam = np.random.uniform(-1, 1, 3)
def __init__(self, width, height, smin=.4, smax=.8):
self.renderer = Renderer(width, height, "sphere", True)
self.shape = np.random.uniform(smin, smax, 160)
self.cam = None
def __init__(self, width, height, smin=0, smax=1):
self.renderer = Renderer(width, height, "sphere", True)
self.cam = np.random.uniform(-1, 1, 3)
self.min = smin
self.max = smax
from threedee_tools.datasets import CubeLoader from threedee_tools.renderer import Renderer import numpy as np import matplotlib.pyplot as plt env = Renderer(128, 128, shape="ijcv") gen = CubeLoader() imga = gen.sample() print(gen.cam) print(gen.light) env.base_light = -gen.light + 1 imgb = env.render(np.ones((160)), np.array([0, 0, 0]), cam_pos=gen.cam + .7) imgb = np.array(imgb, dtype=np.float32) / 255 imgab = np.zeros((128, 128 * 2, 3), dtype=np.float32) imgab[:, :128, :] = imga imgab[:, 128:, :] = imgb plt.imshow(imgab) plt.show()
x = self.relu(self.conv3(x))
# mu, logprob
return torch.sigmoid(self.linear1_mu(x.view(
-1, 128 * 16 * 16))), torch.sigmoid(
self.linear1_stddev(x.view(-1, 128 * 16 * 16)))
def decode(self, z):
x = self.relu(self.linear2(z))
return torch.sigmoid(self.linear3(x))
def forward(self, x):
raise NotImplementedError("shouldn't use this directly")
env = Renderer(params["WIDTH"], params["HEIGHT"])
# data_generator = CubeSingleViewGenerator(params["WIDTH"], params["HEIGHT"])
data_generator = RotatingRandomShapeGenerator(params["WIDTH"],
params["HEIGHT"])
torch.manual_seed(params["SEED"])
np.random.seed(params["SEED"])
policy = Policy(params["LATENT_SIZE"], 160).to(device)
optimizer = torch.optim.Adam(policy.parameters(), lr=params["LR"])
eps = np.finfo(np.float32).eps.item()
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
wandb.init(project="rezende-30", config=params)
# experiment = Experiment(api_key="ZfKpzyaedH6ajYSiKmvaSwyCs",
# project_name="rezende", workspace="fgolemo")
# experiment.log_parameters(params)
# experiment.set_name(exp_name)
# experiment.add_tag("v3")
def normal(x, mu, sigma_sq):
a = (-1 * (x - mu).pow(2) / (2 * sigma_sq)).exp()
b = 1 / (2 * sigma_sq * pi.expand_as(sigma_sq)).sqrt()
return a * b
env = Renderer(params["WIDTH"], params["HEIGHT"], shape="ijcv")
data_generator = CubeLoader()
torch.manual_seed(params["SEED"])
np.random.seed(params["SEED"])
policy = ReinforcePolicy(params["LATENT_SIZE"], 160).to(device)
if LOGGING:
wandb.watch(policy)
optimizer = torch.optim.Adam(policy.parameters(), lr=params["LR"])
eps = np.finfo(np.float32).eps.item()
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
def __init__(self, width, height, smin=.5, smax=1):
self.renderer = Renderer(width, height, "iqtt", True)
self.shape = np.random.uniform(smin, smax, 160)
return self.sigmoid(self.linear1_mu(x.view(
-1, 128 * 16 * 16))), self.sigmoid(
self.linear1_stddev(x.view(-1, 128 * 16 * 16)))
def decode(self, z):
x = self.relu(self.linear2(z))
return self.sigmoid(self.linear3(x))
def forward(self, x):
# mu, logvar = self.encode(x)
# z = self.reparameterize(mu, logvar)
# return self.decode(z), mu, logvar
raise NotImplementedError("shouldn't use this directly")
env = Renderer(WIDTH, HEIGHT)
data_generator = RandomSingleViewGenerator(WIDTH, HEIGHT, smin=.5)
torch.manual_seed(SEED)
np.random.seed(SEED)
policy = Policy(LATENT_SIZE, 160).to(device)
optimizer = torch.optim.Adam(policy.parameters())
eps = torch.from_numpy(npa([np.finfo(np.float32).eps.item()
])).float().to(device)
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
super(Policy, self).__init__()
self.linear1 = nn.Linear(num_inputs, hidden_size)
self.linear2 = nn.Linear(hidden_size, num_outputs)
self.linear2_ = nn.Linear(hidden_size, num_outputs)
def forward(self, inputs):
x = inputs
x = F.relu(self.linear1(x))
mu = torch.sigmoid(self.linear2(x))
sigma_sq = torch.tanh(self.linear2_(x))
return mu, sigma_sq
env = Renderer(WIDTH, HEIGHT)
cube_generator = CubeGenerator(WIDTH, HEIGHT)
torch.manual_seed(SEED)
np.random.seed(SEED)
agent = REINFORCE(HIDDEN_SIZE, WIDTH * HEIGHT * 3, 163, Policy)
dir = 'ckpt_3dreinforcev1'
if not os.path.exists(dir):
os.mkdir(dir)
for i_episode in range(NUM_EPISODES):
target = cube_generator.sample()
env_state = np.ones(160, dtype=np.float32) * .5
class ShapeConfig():
def __init__(self):
self.rotation_speed = 1 # higher is faster, positive is right
self.cam_z = -2
self.values = []
for x in range(7):
for r in range(23):
if x == 6 and r > 21:
break
Label(text=r + 1 + (x * 23), relief=RIDGE,
width=5).grid(row=r, column=0 + (x * 2))
s = Scale(master,
from_=0.,
to=1.,
resolution=0.1,
orient=HORIZONTAL)
s.set(1)
s.grid(row=r, column=1 + (x * 2)) # length=10,
self.values.append(s)
Button(master, text='max', command=self.max).grid(row=16,
column=14,
columnspan=2)
Button(master, text='randomize',
command=self.randomize).grid(row=17, column=14, columnspan=2)
Button(master, text='rotate right',
command=self.right).grid(row=18, column=14, columnspan=2)
Button(master, text='rotate left',
command=self.left).grid(row=19, column=14, columnspan=2)
Button(master, text='cam up', command=self.cam_up).grid(row=20,
column=14,
columnspan=2)
Button(master, text='cam down',
command=self.cam_down).grid(row=21, column=14, columnspan=2)
Button(master, text='print config',
command=self.print_config).grid(row=22, column=14, columnspan=2)
self.b = Button(master, text="enter values", command=self.popup)
self.b.grid(row=17, column=14, columnspan=2)
width = 512
height = 512
self.renderer = Renderer(width, height)
self.image = Image.fromarray(np.zeros((width, height), dtype=np.uint8))
self.canvas = Canvas(master, height=height, width=width)
self.canvas.grid(row=0, column=14, rowspan=15)
# image = image.resize((basewidth, hsize), PIL.Image.ANTIALIAS)
self.photo = ImageTk.PhotoImage(self.image)
self.photo_holder = self.canvas.create_image(
width - (self.image.size[0] / 2),
height - (self.image.size[1] / 2),
image=self.photo)
self.rot = 0
self.render()
def popup(self):
self.w = popupWindow(master)
self.b["state"] = "disabled"
master.wait_window(self.w.top)
self.b["state"] = "normal"
x = ast.literal_eval(self.entryValue())
self.set_values(x)
def entryValue(self):
return self.w.value
def set_values(self, vs):
if len(vs) != 160:
print("ERROR: length of inputs should be 160, found:", len(vs))
return
for i in range(160):
self.values[i].set(vs[i])
def max(self):
self.set_values([1.] * 160)
def randomize(self):
self.set_values(np.random.uniform(.5, 1, 160).tolist())
def right(self):
if self.rotation_speed < 5:
self.rotation_speed += 1
if self.rotation_speed == 1:
self.render()
def left(self):
if self.rotation_speed > -5:
self.rotation_speed -= 1
if self.rotation_speed == -1:
self.render()
print(self.rotation_speed)
def render(self):
self.image = self.renderer.render(self.get_values(),
np.array((0, self.rot, 0)))
self.photo = ImageTk.PhotoImage(self.image)
self.canvas.itemconfig(self.photo_holder, image=self.photo)
self.rot += 0.1 * np.sign(self.rotation_speed) / (2 * np.pi)
if self.rotation_speed != 0:
master.after(25 * (6 - abs(self.rotation_speed)), self.render)
def get_values(self):
v = [m.get() for m in self.values]
return v
def update_img(self, event):
pass
def print_config(self):
print(self.get_values())
def cam_up(self):
self.cam_z += 1
def cam_down(self):
self.cam_z -= 1
def step1(self):
self.image = Image.open("ball.gif")
self.photo = ImageTk.PhotoImage(self.image)
self.canvas.itemconfig(self.photo_holder, image=self.photo)
def __init__(self, width, height, radius=.5):
self.renderer = Renderer(width, height, "sphere", True)
self.shape = np.ones(160) * radius
def __init__(self, width, height):
self.renderer = Renderer(width, height, "cube", False)
self.cam = np.random.uniform(-1, 1, 3)
def __init__(self, width, height):
self.cube = Renderer(width, height, "cube", False)
self.sphere = Renderer(width, height, "sphere", True)
self.shape = np.ones(160) * 0.9
def __init__(self, width, height):
self.renderer = Renderer(width, height, "cube", False)
self.cam = None
self.conv1 = nn.Conv2d(3, 32, (3, 3), (1, 1), (1, 1))
self.conv2 = nn.Conv2d(32, 64, (3, 3), (2, 2), (1, 1))
self.linear3 = nn.Linear(64 * 32 * 32, num_outputs)
self.linear3_ = nn.Linear(64 * 32 * 32, num_outputs)
def forward(self, inputs):
x = self.relu(self.conv1(inputs))
x = self.relu(self.conv2(x))
x = x.view(-1, 64 * 32 * 32)
mu = torch.sigmoid(self.linear3(x))
sigma_sq = torch.tanh(self.linear3_(x))
return mu, sigma_sq
env = Renderer(WIDTH, HEIGHT)
data_generator = ConstantShapeGenerator(WIDTH, HEIGHT)
torch.manual_seed(SEED)
np.random.seed(SEED)
agent = REINFORCE(HIDDEN_SIZE, WIDTH * HEIGHT * 3, 160, Policy)
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
reward_avg = []
for i_episode in range(NUM_EPISODES):
target = data_generator.sample()
pi = torch.Tensor([np.pi]).float().to(device)
exp_name = "29-iqtt"
exp_dir = "experiments/" + exp_name + "-" + strftime("%Y%m%d%H%M%S")
if LOGGING:
wandb.init(project="rezende-30", config=params)
def normal(x, mu, sigma_sq):
a = (-1 * (x - mu).pow(2) / (2 * sigma_sq)).exp()
b = 1 / (2 * sigma_sq * pi.expand_as(sigma_sq)).sqrt()
return a * b
env = Renderer(params["WIDTH"], params["HEIGHT"], shape="iqtt")
data_generator = IQTTLoader(greyscale=True)
torch.manual_seed(params["SEED"])
np.random.seed(params["SEED"])
policy = ReinforcePolicy(params["LATENT_SIZE"], 160).to(device)
contrast_loss = ContrastiveLoss()
optimizer = torch.optim.Adam(policy.parameters(), lr=params["LR"])
eps = np.finfo(np.float32).eps.item()
if not os.path.exists(exp_dir):
os.mkdir(exp_dir)
