import torch, torchvision
import torch.nn.functional as F
-name_shapes = ["A", "B", "C", "D", "E", "F"]
-
-name_colors = ["red", "yellow", "blue", "green", "white", "purple"]
-
######################################################################
class GridFactory:
def __init__(
self,
- size=4,
+ size=6,
max_nb_items=4,
max_nb_transformations=3,
nb_questions=4,
+ nb_shapes=6,
+ nb_colors=6,
+ nb_play_steps=3,
):
+ assert size % 2 == 0
self.size = size
self.max_nb_items = max_nb_items
self.max_nb_transformations = max_nb_transformations
self.nb_questions = nb_questions
+ self.nb_play_steps = nb_play_steps
+ self.name_shapes = ["A", "B", "C", "D", "E", "F"]
+ self.name_colors = ["red", "yellow", "blue", "green", "white", "purple"]
+ self.vname_shapes = ["vA", "vB", "vC", "vD", "vE", "vF"]
+ self.vname_colors = ["vred", "vyellow", "vblue", "vgreen", "vwhite", "vpurple"]
def generate_scene(self):
nb_items = torch.randint(self.max_nb_items - 1, (1,)).item() + 2
col = torch.full((self.size * self.size,), -1)
shp = torch.full((self.size * self.size,), -1)
- a = torch.randperm(len(name_colors) * len(name_shapes))[:nb_items]
- col[:nb_items] = a % len(name_colors)
- shp[:nb_items] = a // len(name_colors)
+ a = torch.randperm(len(self.name_colors) * len(self.name_shapes))[:nb_items]
+ col[:nb_items] = a % len(self.name_colors)
+ shp[:nb_items] = a // len(self.name_colors)
i = torch.randperm(self.size * self.size)
col = col[i]
shp = shp[i]
return col.reshape(self.size, self.size), shp.reshape(self.size, self.size)
- def random_transformations(self, scene):
+ def random_object_move(self, scene):
col, shp = scene
+ while True:
+ a = (col.flatten() >= 0).nonzero()
+ a = a[torch.randint(a.size(0), (1,)).item()]
+ i, j = a // self.size, a % self.size
+ assert col[i, j] >= 0
+ dst = [(i, j), (i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)]
+ dst = list(
+ filter(
+ lambda x: x[0] >= 0
+ and x[1] >= 0
+ and x[0] < self.size
+ and x[1] < self.size
+ and col[x[0], x[1]] < 0,
+ dst,
+ )
+ )
+ if len(dst) > 0:
+ ni, nj = dst[torch.randint(len(dst), (1,)).item()]
+ col[ni, nj] = col[i, j]
+ shp[ni, nj] = shp[i, j]
+ col[i, j] = -1
+ shp[i, j] = -1
+ break
+ return col, shp
+
+ def transformation(self, t, scene):
+ col, shp = scene
+ if t == 0:
+ col, shp = col.flip(0), shp.flip(0)
+ description = "<chg> vertical flip"
+ elif t == 1:
+ col, shp = col.flip(1), shp.flip(1)
+ description = "<chg> horizontal flip"
+ elif t == 2:
+ col, shp = col.flip(0).t(), shp.flip(0).t()
+ description = "<chg> rotate 90 degrees"
+ elif t == 3:
+ col, shp = col.flip(0).flip(1), shp.flip(0).flip(1)
+ description = "<chg> rotate 180 degrees"
+ elif t == 4:
+ col, shp = col.flip(1).t(), shp.flip(1).t()
+ description = "<chg> rotate 270 degrees"
+
+ return (col.contiguous(), shp.contiguous()), description
+
+ def random_transformations(self, scene):
descriptions = []
nb_transformations = torch.randint(self.max_nb_transformations + 1, (1,)).item()
transformations = torch.randint(5, (nb_transformations,))
for t in transformations:
- if t == 0:
- col, shp = col.flip(0), shp.flip(0)
- descriptions += ["<chg> vertical flip"]
- elif t == 1:
- col, shp = col.flip(1), shp.flip(1)
- descriptions += ["<chg> horizontal flip"]
- elif t == 2:
- col, shp = col.flip(0).t(), shp.flip(0).t()
- descriptions += ["<chg> rotate 90 degrees"]
- elif t == 3:
- col, shp = col.flip(0).flip(1), shp.flip(0).flip(1)
- descriptions += ["<chg> rotate 180 degrees"]
- elif t == 4:
- col, shp = col.flip(1).t(), shp.flip(1).t()
- descriptions += ["<chg> rotate 270 degrees"]
-
- col, shp = col.contiguous(), shp.contiguous()
-
- return (col, shp), descriptions
+ scene, description = self.transformation(t, scene)
+ descriptions += [description]
+
+ return scene, descriptions
+
+ def visual_scene2str(self, scene):
+ col, shp = scene
+ r = []
+ for i in range(self.size):
+ s = []
+ for j in range(self.size):
+ if col[i, j] >= 0:
+ s += [self.vname_colors[col[i, j]], self.vname_shapes[shp[i, j]]]
+ else:
+ s += ["v_", "v+"]
+ r += s # .append(" ".join(s))
+ return " ".join(r)
def print_scene(self, scene):
col, shp = scene
# for i in range(self.size):
# for j in range(self.size):
# if col[i,j] >= 0:
- # print(f"at ({i},{j}) {name_colors[col[i,j]]} {name_shapes[shp[i,j]]}")
+ # print(f"at ({i},{j}) {self.name_colors[col[i,j]]} {self.name_shapes[shp[i,j]]}")
for i in range(self.size):
for j in range(self.size):
if col[i, j] >= 0:
- print(f"{name_colors[col[i,j]][0]}{name_shapes[shp[i,j]]}", end="")
+ print(
+ f"{self.name_colors[col[i,j]][0]}{self.name_shapes[shp[i,j]]}",
+ end="",
+ )
elif j == 0:
print(" +", end="")
else:
for i in range(self.size):
for j in range(self.size):
if col[i, j] >= 0:
- n = f"{name_colors[col[i,j]]} {name_shapes[shp[i,j]]}"
+ n = f"{self.name_colors[col[i,j]]} {self.name_shapes[shp[i,j]]}"
properties += [f"a {n} at {i} {j}"]
return properties
for i1 in range(self.size):
for j1 in range(self.size):
if col[i1, j1] >= 0:
- n1 = f"{name_colors[col[i1,j1]]} {name_shapes[shp[i1,j1]]}"
+ n1 = (
+ f"{self.name_colors[col[i1,j1]]} {self.name_shapes[shp[i1,j1]]}"
+ )
properties += [f"there is a {n1}"]
if i1 < self.size // 2:
properties += [f"a {n1} is in the top half"]
for i2 in range(self.size):
for j2 in range(self.size):
if col[i2, j2] >= 0:
- n2 = f"{name_colors[col[i2,j2]]} {name_shapes[shp[i2,j2]]}"
+ n2 = f"{self.name_colors[col[i2,j2]]} {self.name_shapes[shp[i2,j2]]}"
if i1 > i2:
properties += [f"a {n1} is below a {n2}"]
if i1 < i2:
properties += [f"a {n1} is right of a {n2}"]
if j1 < j2:
properties += [f"a {n1} is left of a {n2}"]
+ if abs(i1 - i2) + abs(j1 - j2) == 1:
+ properties += [f"a {n1} is next to a {n2}"]
return properties
+ def generate_scene_and_play(self):
+ scene = self.generate_scene()
+ steps = [self.visual_scene2str(scene)]
+ for t in range(self.nb_play_steps - 1):
+ if torch.randint(4, (1,)).item() == 0:
+ scene, _ = self.transformation(torch.randint(5, (1,)), scene)
+ else:
+ scene = self.random_object_move(scene)
+ steps.append(self.visual_scene2str(scene))
+ return " | ".join(steps)
+
def generate_scene_and_questions(self):
while True:
+ # We generate scenes until we get one with enough
+ # properties
+
while True:
- scene = self.generate_scene()
+ start_scene = self.generate_scene()
+ scene, transformations = self.random_transformations(start_scene)
true = self.all_properties(scene)
if len(true) >= self.nb_questions:
break
- start = self.grid_positions(scene)
-
- scene, transformations = self.random_transformations(scene)
-
- # transformations=[]
+ # We generate a bunch of false properties by shuffling the
+ # scene and sometimes adding properties from totally
+ # different scenes. We try ten times to get enough false
+ # properties and go back to generating the scene if we do
+ # not succeed
for a in range(10):
col, shp = scene
col.view(self.size, self.size),
shp.view(self.size, self.size),
)
- # other_scene = self.generate_scene()
- false = list(set(self.all_properties(other_scene)) - set(true))
+
+ false = self.all_properties(other_scene)
+
+ # We sometime add properties from a totally different
+ # scene to have negative "there is a xxx xxx"
+ # properties
+
+ if torch.rand(1).item() < 0.2:
+ other_scene = self.generate_scene()
+ false += self.all_properties(other_scene)
+
+ false = list(set(false) - set(true))
if len(false) >= self.nb_questions:
break
true = [true[k] for k in torch.randperm(len(true))[: self.nb_questions]]
false = [false[k] for k in torch.randperm(len(false))[: self.nb_questions]]
- true = ["<prop> " + q + " <true>" for q in true]
- false = ["<prop> " + q + " <false>" for q in false]
+ true = ["<prop> " + q + " <ans> true" for q in true]
+ false = ["<prop> " + q + " <ans> false" for q in false]
union = true + false
questions = [union[k] for k in torch.randperm(len(union))[: self.nb_questions]]
result = " ".join(
- ["<obj> " + x for x in self.grid_positions(scene)]
+ ["<obj> " + x for x in self.grid_positions(start_scene)]
+ transformations
+ questions
)
- return scene, result
+ return start_scene, scene, result
- def generate_samples(self, nb, progress_bar=None):
+ def generate_samples(self, nb, fraction_play=0.0, progress_bar=None):
result = []
- r = range(nb)
+ play = torch.rand(nb) < fraction_play
if progress_bar is not None:
- r = progress_bar(r)
+ play = progress_bar(play)
- for _ in r:
- result.append(self.generate_scene_and_questions()[1])
+ for p in play:
+ if p:
+ result.append(self.generate_scene_and_play())
+ else:
+ result.append(self.generate_scene_and_questions()[2])
return result
grid_factory = GridFactory()
- start_time = time.perf_counter()
- samples = grid_factory.generate_samples(10000)
- end_time = time.perf_counter()
- print(f"{len(samples) / (end_time - start_time):.02f} samples per second")
-
- scene, questions = grid_factory.generate_scene_and_questions()
+ # start_time = time.perf_counter()
+ # samples = grid_factory.generate_samples(10000)
+ # end_time = time.perf_counter()
+ # print(f"{len(samples) / (end_time - start_time):.02f} samples per second")
+
+ start_scene, scene, questions = grid_factory.generate_scene_and_questions()
+ print()
+ print("-- Original scene -----------------------------")
+ print()
+ grid_factory.print_scene(start_scene)
+ print()
+ print("-- Transformed scene --------------------------")
+ print()
grid_factory.print_scene(scene)
+ print()
+ print("-- Sequence -----------------------------------")
+ print()
print(questions)
+ # print(grid_factory.visual_scene2str(scene))
+
+ # grid_factory.print_scene(scene)
+ # for t in range(5):
+ # scene = grid_factory.random_object_move(scene)
+ # print()
+ # grid_factory.print_scene(scene)
+
+ print(grid_factory.generate_scene_and_play())
+
######################################################################
projects / picoclvr.git / blobdiff