parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
##############################
-# picoclvr options
+# maze options
-parser.add_argument("--world_height", type=int, default=23)
+parser.add_argument("--world_height", type=int, default=13)
-parser.add_argument("--world_width", type=int, default=31)
+parser.add_argument("--world_width", type=int, default=21)
parser.add_argument("--world_nb_walls", type=int, default=15)
args = parser.parse_args()
-assert args.prune_properties in {"none", "train+eval", "eval"}
-
try:
os.mkdir(args.result_dir)
except FileExistsError:
######################################################################
-def masked_inplace_autoregression(
- model, batch_size, input, ar_mask, forbidden_tokens=None, device=torch.device("cpu")
-):
+# ar_mask is a Boolean matrix of same shape as input, with 1s on the
+# tokens that should be generated
+
+
+def masked_inplace_autoregression(model, batch_size, input, ar_mask):
for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
i = (ar_mask.sum(0) > 0).nonzero()
for s in range(i.min(), i.max() + 1):
output = model(mygpt.BracketedSequence(input, s, 1)).x
logits = output[:, s]
- if forbidden_tokens is not None:
- logits = logits.masked_fill(forbidden_tokens, float("-inf"))
if args.deterministic_synthesis:
t_next = logits.argmax(1)
else:
######################################################################
-import picoclvr
-
-
-class TaskPicoCLVR(Task):
-
- # Make a tensor from a list of strings
- def tensorize(self, descr):
- token_descr = [s.strip().split(" ") for s in descr]
- l = max([len(s) for s in token_descr])
- token_descr = [s + ["<nul>"] * (l - len(s)) for s in token_descr]
- id_descr = [[self.token2id[u] for u in s] for s in token_descr]
- return torch.tensor(id_descr, device=self.device)
-
- # Make a list of strings from a tensor
- def detensorize(self, x):
- return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
-
- # trim all the tensors in the tuple z to remove as much token from
- # left and right in the first tensor. If z is a tuple, all its
- # elements are trimed according to the triming for the first
- def trim(self, z, token="<nul>"):
- n = self.token2id[token]
- if type(z) == tuple:
- x = z[0]
- i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return tuple([t[:, a:b] for t in z])
- else:
- i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return z[:, a:b]
-
- ######################
- # Not the cleanest part of the code
-
- # Extract the last image of each sequence, from the last <img>
- # included, and set to <nul> all the tokens from the beginning of
- # that image to the end
- def excise_last_image(self, input):
- t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
-
- input = input.clone()
- t = (input == t_img).long()
- tail_masks = (t.cumsum(dim=1) == t.sum(dim=1, keepdim=True)).long()
- i = (t * tail_masks).nonzero(as_tuple=True)
- j = (
- i[0][:, None],
- i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
- )
- images = self.trim(input[j])
- input[j] = t_nul
- loss_masks = 1 - tail_masks
- input, loss_masks = self.trim((input, loss_masks))
- return input, loss_masks, images
-
- def add_true_image(self, input, images, loss_masks):
- t_nul = self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
- input = F.pad(input, (0, nb_img_tokens), value=t_nul)
- loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
- t = (input == t_nul).long()
- i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
- j = (
- i[0][:, None],
- i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
- )
- input[j] = images
- loss_masks[j] = 1
- input, loss_masks = self.trim((input, loss_masks))
- return input, loss_masks
-
- def add_generated_image(self, input, loss_masks, model):
- t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
-
- input = F.pad(input, (0, nb_img_tokens), value=t_nul)
- loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
- t = (input == t_nul).long()
- i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
- input[i] = t_img
-
- j = (
- i[0][:, None],
- i[1][:, None]
- + 1
- + torch.arange(nb_img_tokens - 1, device=input.device)[None, :],
- )
- ar_masks = input.new_zeros(input.size(), dtype=torch.int64)
- ar_masks[j] = 1
- forbidden_tokens = (
- torch.arange(self.vocabulary_size(), device=input.device) == t_nul
- )
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
- masked_inplace_autoregression(
- model,
- self.batch_size,
- input,
- ar_masks,
- forbidden_tokens,
- device=self.device,
- )
- model.train(t)
-
- input, loss_masks = self.trim((input, loss_masks))
-
- return input, loss_masks
-
- ######################
-
- def __init__(
- self,
- batch_size,
- height,
- width,
- nb_colors=5,
- device=torch.device("cpu"),
- pruner_train=None,
- pruner_eval=None,
- ):
- def generate_descr(nb, cache_suffix, pruner):
- return picoclvr.generate(
- nb,
- height=self.height,
- width=self.width,
- nb_colors=nb_colors,
- pruner=pruner,
- )
+import maze
+
+
+class TaskMaze(Task):
+ def map2seq(self, *m):
+ return torch.cat([x.flatten(1) for x in m], 1)
+
+ def seq2map(self, s):
+ s = s.reshape(s.size(0), -1, self.height, self.width)
+ return (s[:, k] for k in range(s.size(1)))
+ def __init__(self, batch_size, height, width, nb_walls, device=torch.device("cpu")):
+ self.batch_size = batch_size
self.height = height
self.width = width
- self.batch_size = batch_size
self.device = device
+
nb = args.data_size if args.data_size > 0 else 250000
- self.pruner_train = pruner_train
- self.pruner_eval = pruner_eval
-
- param = {
- "nb": nb,
- "height": height,
- "width": width,
- "nb_colors": nb_colors,
- "batch_size": batch_size,
- "rng_state": list(torch.get_rng_state()),
- }
-
- log_string(f"generating {nb} samples (can take some time)")
- self.train_descr = generate_descr(
- (nb * 4) // 5, "train", pruner=self.pruner_train
+
+ mazes_train, paths_train = maze.create_maze_data(
+ (4 * nb) // 5,
+ height=height,
+ width=width,
+ nb_walls=nb_walls,
+ progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
+ )
+ mazes_train, paths_train = mazes_train.to(device), paths_train.to(device)
+ self.train_input = self.map2seq(mazes_train, paths_train)
+ self.nb_codes = self.train_input.max() + 1
+
+ mazes_test, paths_test = maze.create_maze_data(
+ nb // 5,
+ height=height,
+ width=width,
+ nb_walls=nb_walls,
+ progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
)
- self.test_descr = generate_descr((nb * 1) // 5, "test", pruner=None)
-
- # Build the tokenizer
- tokens = {"<nul>", "<img>"}
- for d in [self.train_descr, self.test_descr]:
- for s in d:
- for t in s.strip().split(" "):
- tokens.add(t)
- # make this set a sorted list to get the same tensors given
- # the same descr
- tokens = list(tokens)
- tokens.sort()
- self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
- self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
-
- # Tokenize the train and test sets
- self.train_input = self.tensorize(self.train_descr)
- self.test_input = self.tensorize(self.test_descr)
+ mazes_test, paths_test = mazes_test.to(device), paths_test.to(device)
+ self.test_input = self.map2seq(mazes_test, paths_test)
def batches(self, split="train"):
assert split in {"train", "test"}
for batch in tqdm.tqdm(
input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
):
- yield self.trim(batch)
+ yield batch
def vocabulary_size(self):
- return len(self.token2id)
+ return self.nb_codes
- def compute_missing_properties(self, n_epoch, model, pruner=None):
-
- acc_nb_requested_properties = []
- acc_nb_missing_properties = []
- acc_nb_results = 0
-
- for input in tqdm.tqdm(
- self.test_input.split(self.batch_size),
- dynamic_ncols=True,
- desc=f"test-properties",
- ):
- tape, loss_masks, _ = self.excise_last_image(input)
- tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
- result_descr = self.detensorize(tape)
- np = picoclvr.nb_properties(
- result_descr,
- height=self.height,
- width=self.width,
- pruner=pruner,
- )
- nb_requested_properties, _, nb_missing_properties = zip(*np)
- acc_nb_requested_properties += nb_requested_properties
- acc_nb_missing_properties += nb_missing_properties
- acc_nb_results += len(result_descr)
-
- nb_requested_properties = sum(acc_nb_requested_properties)
- nb_missing_properties = sum(acc_nb_missing_properties)
-
- prefix = "" if pruner is None else "pruned_"
- log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
- log_string(
- f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
- )
- log_string(
- f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
- )
+ def compute_error(self, model, split="train"):
+ nb_total, nb_correct = 0, 0
+ for input in task.batches(split):
+ result = input.clone()
+ ar_mask = result.new_zeros(result.size())
+ ar_mask[:, self.height * self.width :] = 1
+ masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
+ mazes, paths = self.seq2map(result)
+ nb_correct += maze.path_correctness(mazes, paths).long().sum()
+ nb_total += mazes.size(0)
- ######################################################################
+ return nb_total, nb_correct
def produce_results(self, n_epoch, model):
-
- self.compute_missing_properties(n_epoch, model)
-
- if self.pruner_eval is not None:
- self.compute_missing_properties(n_epoch, model, self.pruner_eval)
-
- nb_tokens_to_generate = self.height * self.width + 3
- result_descr = []
- nb_per_primer = 8
- primer = []
-
- for primer_descr in [
- "red above green <sep> green top <sep> blue right of red",
- "there is red <sep> there is yellow <sep> there is blue",
- "red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
- "green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
- ]:
- primer += [primer_descr] * nb_per_primer
-
- tape = self.tensorize(primer)
- loss_masks = 1 - (tape == self.token2id["<nul>"]).long()
- tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
- result_descr = self.detensorize(tape)
-
- np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
-
- acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
- acc_nb_results = len(result_descr)
-
- nb_requested_properties = sum(acc_nb_requested_properties)
- nb_missing_properties = sum(acc_nb_missing_properties)
-
- prefix = "demo_"
- log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
+ train_nb_total, train_nb_correct = self.compute_error(model, "train")
log_string(
- f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
+ f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
)
+
+ test_nb_total, test_nb_correct = self.compute_error(model, "test")
log_string(
- f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
+ f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
)
- img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
+ input = self.test_input[:32]
+ result = input.clone()
+ ar_mask = result.new_zeros(result.size())
- if img.dim() == 5:
- if img.size(1) == 1:
- img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
- else:
- img = torch.cat(
- [
- torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
- for x in img
- ],
- 0,
- )
-
- image_name = os.path.join(args.result_dir, f"result_{n_epoch:04d}.png")
- torchvision.utils.save_image(
- img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=1.0
- )
- log_string(f"wrote {image_name}")
+ ar_mask[:, self.height * self.width :] = 1
+ masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
+
+ mazes, paths = self.seq2map(input)
+ _, predicted_paths = self.seq2map(result)
+ maze.save_image(f"result_{n_epoch:04d}.png", mazes, paths, predicted_paths)
######################################################################
log_string(f"device {device}")
-def pruner_horizontal_green(p):
- return not ("green" in p and ("left" in p or "right" in p))
-
-
-task = TaskPicoCLVR(
+task = TaskMaze(
batch_size=args.batch_size,
- height=args.height,
- width=args.width,
- nb_colors=args.nb_colors,
+ height=args.world_height,
+ width=args.world_width,
+ nb_walls=args.world_nb_walls,
device=device,
- pruner_train=pruner_horizontal_green
- if args.prune_properties in {"train+eval"}
- else None,
- pruner_eval=(lambda p: not pruner_horizontal_green(p))
- if args.prune_properties in {"train+eval", "eval"}
- else None,
)
+
vocabulary_size = task.vocabulary_size()
log_string(f"vocabulary_size {vocabulary_size}")
assert n < nmax
-def valid_paths(mazes, paths):
+def path_correctness(mazes, paths):
still_ok = (mazes - (paths * (paths < 4))).view(mazes.size(0), -1).abs().sum(1) == 0
reached = still_ok.new_zeros(still_ok.size())
current, pred_current = paths.clone(), paths.new_zeros(paths.size())
goal = (mazes == v_goal).long()
while not pred_current.equal(current):
- # print(current)
- # print(f'{still_ok=} {reached=}')
pred_current.copy_(current)
u = (current == v_start).long()
possible_next = (
######################################################################
-def create_maze_data(nb, h=11, w=17, nb_walls=8, dist_min=-1):
- mazes = torch.empty(nb, h, w, dtype=torch.int64)
- paths = torch.empty(nb, h, w, dtype=torch.int64)
+def create_maze_data(
+ nb, height=11, width=17, nb_walls=8, dist_min=10, progress_bar=lambda x: x
+):
+ mazes = torch.empty(nb, height, width, dtype=torch.int64)
+ paths = torch.empty(nb, height, width, dtype=torch.int64)
- for n in range(nb):
- maze = create_maze(h, w, nb_walls)
+ for n in progress_bar(range(nb)):
+ maze = create_maze(height, width, nb_walls)
i = (1 - maze).nonzero()
while True:
start, goal = i[torch.randperm(i.size(0))[:2]]
######################################################################
-def save_image(name, mazes, paths):
- mazes, paths = mazes.cpu(), paths.cpu()
+def save_image(name, mazes, target_paths, predicted_paths=None):
+ mazes, target_paths = mazes.cpu(), target_paths.cpu()
colors = torch.tensor(
[
)
mazes = colors[mazes.reshape(-1)].reshape(mazes.size() + (-1,)).permute(0, 3, 1, 2)
- paths = colors[paths.reshape(-1)].reshape(paths.size() + (-1,)).permute(0, 3, 1, 2)
+ target_paths = (
+ colors[target_paths.reshape(-1)]
+ .reshape(target_paths.size() + (-1,))
+ .permute(0, 3, 1, 2)
+ )
+ img = torch.cat((mazes.unsqueeze(1), target_paths.unsqueeze(1)), 1)
+
+ if predicted_paths is not None:
+ predicted_paths = predicted_paths.cpu()
+ predicted_paths = (
+ colors[predicted_paths.reshape(-1)]
+ .reshape(predicted_paths.size() + (-1,))
+ .permute(0, 3, 1, 2)
+ )
+ img = torch.cat((img, predicted_paths.unsqueeze(1)), 1)
- img = torch.cat((mazes.unsqueeze(1), paths.unsqueeze(1)), 1)
img = img.reshape((-1,) + img.size()[2:]).float() / 255.0
- torchvision.utils.save_image(img, name, padding=1, pad_value=0.5, nrow=8)
+ torchvision.utils.save_image(img, name, padding=1, pad_value=0.85, nrow=6)
######################################################################
if __name__ == "__main__":
- mazes, paths = create_maze_data(32, dist_min=10)
- save_image("test.png", mazes, paths)
- print(valid_paths(mazes, paths))
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ mazes, paths = create_maze_data(8)
+ mazes, paths = mazes.to(device), paths.to(device)
+ save_image("test.png", mazes, paths, paths)
+ print(path_correctness(mazes, paths))
######################################################################
projects / beaver.git / commitdiff