From b87078aec53ead1e0a3ca44d4ac46c319bbcd63e Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Thu, 24 Aug 2023 22:34:32 +0200 Subject: [PATCH] Update. --- grid.py | 167 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ main.py | 12 ++-- 2 files changed, 173 insertions(+), 6 deletions(-) create mode 100755 grid.py diff --git a/grid.py b/grid.py new file mode 100755 index 0000000..08ddc23 --- /dev/null +++ b/grid.py @@ -0,0 +1,167 @@ +#!/usr/bin/env python + +# Any copyright is dedicated to the Public Domain. +# https://creativecommons.org/publicdomain/zero/1.0/ + +# Written by Francois Fleuret + +import math +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, + height=4, + width=4, + max_nb_items=4, + max_nb_transformations=4, + nb_questions=4, + ): + self.height = height + self.width = width + self.max_nb_items = max_nb_items + self.nb_questions = nb_questions + + def generate_scene(self): + nb_items = torch.randint(self.max_nb_items - 1, (1,)).item() + 2 + col = torch.full((self.height * self.width,), -1) + shp = torch.full((self.height * self.width,), -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) + i = torch.randperm(self.height * self.width) + col = col[i] + shp = shp[i] + return col.reshape(self.height, self.width), shp.reshape( + self.height, self.width + ) + + def random_transformations(self): + nb_transformations = torch.randint(self.max_nb_transformations + 1, (1,)).item() + + def print_scene(self, scene): + col, shp = scene + + # for i in range(self.height): + # for j in range(self.width): + # if col[i,j] >= 0: + # print(f"at ({i},{j}) {name_colors[col[i,j]]} {name_shapes[shp[i,j]]}") + + for i in range(self.height): + for j in range(self.width): + if col[i, j] >= 0: + print(f"{name_colors[col[i,j]][0]}{name_shapes[shp[i,j]]}", end="") + elif j == 0: + print(" +", end="") + else: + print("-+", end="") + if j < self.width - 1: + print("--", end="") + else: + print("") + if i < self.height - 1: + for j in range(self.width - 1): + print(" | ", end="") + print(" |") + + def grid_positions(self, scene): + col, shp = scene + + properties = [] + + for i in range(self.height): + for j in range(self.width): + if col[i, j] >= 0: + n = f"{name_colors[col[i,j]]} {name_shapes[shp[i,j]]}" + properties += [f"a {n} at {i} {j}"] + + return properties + + def all_properties(self, scene): + col, shp = scene + + properties = [] + + for i1 in range(self.height): + for j1 in range(self.width): + if col[i1, j1] >= 0: + n1 = f"{name_colors[col[i1,j1]]} {name_shapes[shp[i1,j1]]}" + properties += [f"there is a {n1}"] + if i1 < self.height // 2: + properties += [f"a {n1} is in the top half"] + if i1 >= self.height // 2: + properties += [f"a {n1} is in the bottom half"] + if j1 < self.width // 2: + properties += [f"a {n1} is in the left half"] + if j1 >= self.width // 2: + properties += [f"a {n1} is in the right half"] + for i2 in range(self.height): + for j2 in range(self.width): + if col[i2, j2] >= 0: + n2 = f"{name_colors[col[i2,j2]]} {name_shapes[shp[i2,j2]]}" + if i1 > i2: + properties += [f"a {n1} is below a {n2}"] + if i1 < i2: + properties += [f"a {n1} is above a {n2}"] + if j1 > j2: + properties += [f"a {n1} is right of a {n2}"] + if j1 < j2: + properties += [f"a {n1} is left of a {n2}"] + + return properties + + def generate_example(self): + while True: + while True: + scene = self.generate_scene() + true = self.all_properties(scene) + if len(true) >= self.nb_questions: + break + + start = self.grid_positions(scene) + + for a in range(10): + col, shp = scene + col, shp = col.view(-1), shp.view(-1) + p = torch.randperm(col.size(0)) + col, shp = col[p], shp[p] + other_scene = ( + col.view(self.height, self.width), + shp.view(self.height, self.width), + ) + # other_scene = self.generate_scene() + false = list(set(self.all_properties(other_scene)) - set(true)) + if len(false) >= self.nb_questions: + break + + if a < 10: + 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 = [(q, "yes") for q in true] + false = [(q, "no") for q in false] + + union = true + false + questions = [union[k] for k in torch.randperm(len(union))[: self.nb_questions]] + + return scene, questions + + +###################################################################### + +if __name__ == "__main__": + grid_factory = GridFactory() + scene, questions = grid_factory.generate_example() + grid_factory.print_scene(scene) + print(questions) + +###################################################################### diff --git a/main.py b/main.py index 8081850..ff831f4 100755 --- a/main.py +++ b/main.py @@ -89,13 +89,13 @@ parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth") ############################## # rpl options -parser.add_argument("--rpl_nb_starting_values", type=int, default=5) +parser.add_argument("--rpl_nb_starting_values", type=int, default=3) parser.add_argument("--rpl_max_input", type=int, default=9) -parser.add_argument("--rpl_prog_len", type=int, default=10) +parser.add_argument("--rpl_prog_len", type=int, default=8) -parser.add_argument("--rpl_nb_runs", type=int, default=8) +parser.add_argument("--rpl_nb_runs", type=int, default=5) parser.add_argument("--rpl_no_prog", action="store_true", default=False) @@ -249,10 +249,10 @@ default_task_args = { "nb_test_samples": 10000, }, "rpl": { - "model": "352M", + "model": "122M", "nb_epochs": 50, - "batch_size": 10, - "nb_train_samples": 2500000, + "batch_size": 5, + "nb_train_samples": 1000000, "nb_test_samples": 10000, }, "world": { -- 2.39.5