# Written by Francois Fleuret <francois@fleuret.org>
-import math, os, tqdm, warnings
+import math, os, tqdm, warnings, sys
import torch, torchvision
class Task:
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
pass
def vocabulary_size(self):
self.train_input = self.tensorize(self.train_descr)
self.test_input = self.tensorize(self.test_descr)
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
for batch in tqdm.tqdm(
######################################################################
+def generate_2d_fourier_basis(T):
+ # Create 1D vectors for time/space in both dimensions
+ t = torch.linspace(0, T - 1, T)
+
+ # Initialize an empty list to hold the basis vectors
+ basis = [torch.ones(T, T)] # The constant (DC) component
+
+ # Generate cosine and sine terms for both dimensions
+ for nx in range(1, T // 2 + 1):
+ for ny in range(1, T // 2 + 1):
+ # Cosine and sine components in x- and y-directions
+ cos_x = torch.cos(2 * math.pi * nx * t / T).unsqueeze(1)
+ sin_x = torch.sin(2 * math.pi * nx * t / T).unsqueeze(1)
+ cos_y = torch.cos(2 * math.pi * ny * t / T).unsqueeze(0)
+ sin_y = torch.sin(2 * math.pi * ny * t / T).unsqueeze(0)
+
+ # Basis functions in 2D as outer products
+ basis.append(torch.mm(cos_x, cos_y)) # cos(nx)cos(ny)
+ basis.append(torch.mm(sin_x, sin_y)) # sin(nx)sin(ny)
+ basis.append(torch.mm(cos_x, sin_y)) # cos(nx)sin(ny)
+ basis.append(torch.mm(sin_x, cos_y)) # sin(nx)cos(ny)
+
+ # Stack the basis into a 3D tensor (number_of_basis_vectors x T x T)
+ basis_matrix = torch.stack(basis[: T * T], dim=0)
+
+ return basis_matrix
+
+
class MNIST(Task):
+ def create_fourier_basis(self):
+ self.fourier_basis = generate_2d_fourier_basis(T=28).flatten(1)
+ self.fourier_basis_inverse = self.fourier_basis.inverse()
+ y = self.train_input.float() @ self.fourier_basis.t()
+ self.fourier_range = 4
+ self.fourier_mu = y.mean(dim=0, keepdim=True)
+ self.fourier_std = y.std(dim=0, keepdim=True)
+
+ def fourier_encode(self, x):
+ y = x.float() @ self.fourier_basis.t()
+ y = ((y - self.fourier_mu) / self.fourier_std).clamp(
+ min=-self.fourier_range, max=self.fourier_range
+ )
+ y = (((y + self.fourier_range) / (2 * self.fourier_range)) * 255).long()
+ return y
+
+ def fourier_decode(self, y):
+ y = (
+ (y / 255.0) * (2 * self.fourier_range) - self.fourier_range
+ ) * self.fourier_std.to(y.device) + self.fourier_mu.to(y.device)
+ return y.float() @ self.fourier_basis_inverse.to(y.device).t()
+
def __init__(
- self, nb_train_samples, nb_test_samples, batch_size, device=torch.device("cpu")
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ fourier_representation=True,
+ device=torch.device("cpu"),
):
super().__init__()
data_set = torchvision.datasets.MNIST(root="./data", train=False, download=True)
self.test_input = data_set.data[:nb_test_samples].view(-1, 28 * 28).long()
+ self.fourier_representation = fourier_representation
+
+ if fourier_representation:
+ self.create_fourier_basis()
+
+ self.train_input = self.fourier_encode(self.train_input)
+ self.test_input = self.fourier_encode(self.test_input)
+
def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
def produce_results(
self, n_epoch, model, result_dir, logger, deterministic_synthesis
):
+ if n_epoch == 0:
+ image_name = os.path.join(result_dir, "fourier.png")
+ torchvision.utils.save_image(
+ 0.5
+ - 0.5
+ * self.fourier_basis.reshape(-1, 1, 28, 28)
+ / self.fourier_basis.std(),
+ image_name,
+ nrow=28,
+ )
+
+ image_name = os.path.join(result_dir, "check-train.png")
+ torchvision.utils.save_image(
+ 1
+ - self.fourier_decode(self.train_input[:256]).reshape(-1, 1, 28, 28)
+ / 256,
+ image_name,
+ nrow=16,
+ )
+
results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
ar_mask = torch.full_like(results, 1)
masked_inplace_autoregression(
deterministic_synthesis,
device=self.device,
)
+
+ if self.fourier_representation:
+ results = self.fourier_decode(results)
+
image_name = os.path.join(result_dir, f"mnist_result_{n_epoch:04d}.png")
torchvision.utils.save_image(
1 - results.reshape(-1, 1, 28, 28) / 255.0,
def compute_error(
self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
):
+ model_device = next(model.parameters()).device
nb_total, nb_correct = 0, 0
count = torch.zeros(
self.width * self.height,
self.width * self.height,
- device=self.device,
+ device=model_device,
dtype=torch.int64,
)
for input in self.batches(split, nb_to_use):
+ input = input.to(model_device)
result = input.clone()
ar_mask = result.new_zeros(result.size())
ar_mask[:, self.height * self.width :] = 1
eol = " " if j < count.size(1) - 1 else "\n"
f.write(f"{count[i,j]}{eol}")
- input = self.test_input[:48]
+ input = self.test_input[:48].to(next(model.parameters()).device)
result = input.clone()
ar_mask = result.new_zeros(result.size())
ar_mask[:, self.height * self.width :] = 1
device=self.device,
)
+ #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+ for label, input in [
+ ("train", self.train_input[:32]),
+ ("test", self.test_input[:32]),
+ ]:
+ output = model(BracketedSequence(input)).x
+ output = output.log_softmax(dim=-1)
+ filename = os.path.join(
+ result_dir, f"stack_with_crossentropy_{n_epoch:04d}_{label}.txt"
+ )
+ with open(filename, "w") as f:
+ for n in range(input.size(0)):
+ s = stack.seq_to_str(
+ input[n], nb_stacks=self.nb_stacks, nb_digits=self.nb_digits
+ )
+ for t, k, w in zip(range(input[n].size(0)), input[n], s.split(" ")):
+ u = (
+ " " * (10 - len(w))
+ + w
+ + " "
+ + str(output[n][t][k].exp().item())
+ + "\n"
+ )
+ f.write(u)
+ f.write("\n")
+ logger(f"wrote {filename}")
+ #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
for n in range(result.size(0)):
logger(
f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
self.t_nul = self.token2id["#"]
self.t_true = self.token2id["true"]
self.t_false = self.token2id["false"]
- self.t_pipe = self.token2id["|"]
+ # self.t_pipe = self.token2id["|"]
# Tokenize the train and test sets
self.train_input = self.str2tensor(self.train_descr)
None if len(self.play_descr) == 0 else self.str2tensor(self.play_descr)
)
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
for batch in tqdm.tqdm(
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
for batch in tqdm.tqdm(
######################################################################
-import escape
+import greed
-class Escape(Task):
+class Greed(Task):
def __init__(
self,
nb_train_samples,
width,
T,
nb_walls,
+ nb_coins,
logger=None,
device=torch.device("cpu"),
):
self.batch_size = batch_size
self.device = device
- self.height = height
- self.width = width
- states, actions, rewards = escape.generate_episodes(
- nb_train_samples + nb_test_samples, height, width, T, nb_walls
+ self.world = greed.GreedWorld(height, width, T, nb_walls, nb_coins)
+
+ states, actions, rewards = self.world.generate_episodes(
+ nb_train_samples + nb_test_samples
)
- seq = escape.episodes2seq(states, actions, rewards)
- # seq = seq[:, seq.size(1) // 3 : 2 * seq.size(1) // 3]
+ seq = self.world.episodes2seq(states, actions, rewards)
self.train_input = seq[:nb_train_samples].to(self.device)
self.test_input = seq[nb_train_samples:].to(self.device)
- self.state_len = self.height * self.width
- self.index_lookahead_reward = 0
- self.index_states = 1
- self.index_action = self.state_len + 1
- self.index_reward = self.state_len + 2
- self.it_len = self.state_len + 3 # lookahead_reward / state / action / reward
+ def wipe_lookahead_rewards(self, batch):
+ t = torch.arange(batch.size(1), device=batch.device)[None, :]
+ u = torch.randint(batch.size(1), (batch.size(0), 1), device=batch.device)
+ lr_mask = (t <= u).long() * (
+ t % self.world.it_len == self.world.index_lookahead_reward
+ ).long()
+
+ return (
+ lr_mask * self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+ + (1 - lr_mask) * batch
+ )
def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
for batch in tqdm.tqdm(
input.split(self.batch_size), dynamic_ncols=True, desc=desc
):
- t = torch.arange(input.size(1), device=input.device)[None, :]
- u = torch.randint(input.size(1), (input.size(0), 1), device=input.device)
- lr_mask = (t <= u).long() * (
- t % self.it_len == self.index_lookahead_reward
- ).long()
-
- input = lr_mask * escape.lookahead_reward2code(2) + (1 - lr_mask) * input
- yield batch
+ yield self.wipe_lookahead_rewards(batch)
def vocabulary_size(self):
- return escape.nb_codes
+ return self.world.nb_codes
def thinking_autoregression(
self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
):
- result = self.test_input[:250].clone()
- t = torch.arange(result.size(1), device=result.device)[None, :]
-
- result[:, self.it_len :] = -1
-
snapshots = []
def ar(result, ar_mask, logit_biases=None):
progress_bar_desc=None,
)
warnings.warn("keeping thinking snapshots", RuntimeWarning)
- snapshots.append(result[:10].detach().clone())
+ snapshots.append(result[:100].detach().clone())
# Generate iteration after iteration
- optimistic_bias = result.new_zeros(escape.nb_codes, device=result.device)
- optimistic_bias[escape.lookahead_reward2code(-1)] = -math.log(1e1)
- optimistic_bias[escape.lookahead_reward2code(1)] = math.log(1e1)
+ result = self.test_input[:250].clone()
+ # Erase all the content but that of the first iteration
+ result[:, self.world.it_len :] = -1
+ # Set the lookahead_reward of the firs to UNKNOWN
+ result[:, self.world.index_lookahead_reward] = self.world.lookahead_reward2code(
+ greed.REWARD_UNKNOWN
+ )
+
+ t = torch.arange(result.size(1), device=result.device)[None, :]
for u in tqdm.tqdm(
- range(self.it_len, result.size(1) - self.it_len + 1, self.it_len),
+ range(0, result.size(1), self.world.it_len),
desc="thinking",
):
- # Generate the lookahead_reward and state
- ar_mask = (t % self.it_len == self.index_lookahead_reward).long() * (
- t <= u + self.index_lookahead_reward
+ # Generate the next state but keep the initial one, the
+ # lookahead_reward of previous iterations are set to
+ # UNKNOWN
+ if u > 0:
+ result[
+ :, u + self.world.index_lookahead_reward
+ ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+ ar_mask = (t >= u + self.world.index_states).long() * (
+ t < u + self.world.index_states + self.world.state_len
+ ).long()
+ ar(result, ar_mask)
+
+ # Generate the action and reward with lookahead_reward to +1
+ result[
+ :, u + self.world.index_lookahead_reward
+ ] = self.world.lookahead_reward2code(greed.REWARD_PLUS)
+ ar_mask = (t >= u + self.world.index_reward).long() * (
+ t <= u + self.world.index_action
).long()
ar(result, ar_mask)
- # Generate the lookahead_reward and state
- ar_mask = (t >= u + self.index_states).long() * (
- t < u + self.index_states + self.state_len
- ).long()
- ar(result, ar_mask)
-
- # Re-generate the lookahead_reward
- ar_mask = (t % self.it_len == self.index_lookahead_reward).long() * (
- t <= u + self.index_lookahead_reward
- ).long()
- ar(result, ar_mask, logit_biases=optimistic_bias)
-
- # Generate the action and reward
- ar_mask = (t >= u + self.index_action).long() * (
- t <= u + self.index_reward
- ).long()
- ar(result, ar_mask)
+ # Set the lookahead_reward to UNKNOWN for the next iterations
+ result[
+ :, u + self.world.index_lookahead_reward
+ ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
with open(filename, "w") as f:
- for n in range(10):
+ for n in range(snapshots[0].size(0)):
for s in snapshots:
- lr, s, a, r = escape.seq2episodes(
- s[n : n + 1], self.height, self.width
+ lr, s, a, r = self.world.seq2episodes(
+ s[n : n + 1],
)
- str = escape.episodes2str(
+ str = self.world.episodes2str(
lr, s, a, r, unicode=True, ansi_colors=True
)
f.write(str)
# Saving the generated sequences
- lr, s, a, r = escape.seq2episodes(result, self.height, self.width)
- str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+ lr, s, a, r = self.world.seq2episodes(result)
+ str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
filename = os.path.join(result_dir, f"test_thinking_seq_{n_epoch:04d}.txt")
with open(filename, "w") as f:
def produce_results(
self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
):
- result = self.test_input[:250].clone()
+ result = self.wipe_lookahead_rewards(self.test_input[:250].clone())
# Saving the ground truth
- lr, s, a, r = escape.seq2episodes(
+ lr, s, a, r = self.world.seq2episodes(
result,
- self.height,
- self.width,
)
- str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+ str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
filename = os.path.join(result_dir, f"test_true_seq_{n_epoch:04d}.txt")
with open(filename, "w") as f:
# Re-generating from the first frame
ar_mask = (
- torch.arange(result.size(1), device=result.device)
- >= self.height * self.width + 3
+ torch.arange(result.size(1), device=result.device) >= self.world.it_len
).long()[None, :]
ar_mask = ar_mask.expand_as(result)
result *= 1 - ar_mask # paraaaaanoiaaaaaaa
# Saving the generated sequences
- lr, s, a, r = escape.seq2episodes(
+ lr, s, a, r = self.world.seq2episodes(
result,
- self.height,
- self.width,
)
- str = escape.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+ str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
filename = os.path.join(result_dir, f"test_seq_{n_epoch:04d}.txt")
with open(filename, "w") as f:
projects / picoclvr.git / blobdiff