--- /dev/null
+#!/usr/bin/env python
+
+import math
+
+import torch, torchvision
+
+from torch import nn
+from torch.nn import functional as F
+
+def random_var(nb_variables=None, variables=None):
+ if variables is None:
+ return chr(ord('A') + torch.randint(nb_variables, (1,)).item())
+ else:
+ l = list(variables)
+ return l[torch.randint(len(l), (1,)).item()]
+
+def random_expr(variables, budget):
+ if budget <= 5:
+ op=torch.randint(2, (1,)).item()
+ if op == 0 and len(variables) > 0:
+ return random_var(variables=variables)
+ else:
+ return str(torch.randint(10, (1,)).item())
+ else:
+ op=torch.randint(4, (1,)).item()
+ if op == 0:
+ e=random_expr(variables,budget-2)
+ if ("+" in e or "-" in e or "*" in e) and (e[0]!="(" or e[-1]!=")"):
+ return "("+e+")"
+ else:
+ return e
+ else:
+ b = 2 + torch.randint(budget-5, (1,)).item()
+ e1=random_expr(variables,b)
+ e2=random_expr(variables,budget-b-1)
+ if op == 1:
+ return e1+"+"+e2
+ elif op == 2:
+ return e1+"+"+e2
+ elif op == 3:
+ return e1+"*"+e2
+
+def generate_program(nb_variables, length):
+ s = ""
+ variables = set()
+ while len(s) < length:
+ v = random_var(nb_variables=nb_variables)
+ s += v+"="+random_expr(variables,budget = min(20,length-3-len(s)))+";"
+ variables.add(v)
+ return s, variables
+
+def generate_sequences(nb, nb_variables = 5, length=20):
+ sequences=[]
+ for n in range(nb):
+ result = None
+ while result==None or max(result.values())>100:
+ p,v=generate_program(nb_variables, length)
+ v=", ".join([ "\""+v+"\": "+v for v in v ])
+ ldict={}
+ exec(p+"result={"+v+"}",globals(),ldict)
+ result=ldict["result"]
+
+ k=list(result.keys())
+ k.sort()
+ sequences.append(p+" "+";".join([v+":"+str(result[v]) for v in k]))
+
+ return sequences
+
+if __name__ == "__main__":
+ import time
+ start_time = time.perf_counter()
+ sequences=generate_sequences(1000)
+ end_time = time.perf_counter()
+ for s in sequences[:10]:
+ print(s)
+ print(f"{len(sequences) / (end_time - start_time):.02f} samples per second")
+
)
parser.add_argument(
- "--task", type=str, default="picoclvr", help="picoclvr, mnist, maze, snake, stack"
+ "--task", type=str, default="picoclvr", help="picoclvr, mnist, maze, snake, stack, expr"
)
parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
"nb_train_samples": 100000,
"nb_test_samples": 1000,
},
+ "expr": {
+ "nb_epochs": 5,
+ "batch_size": 25,
+ "nb_train_samples": 100000,
+ "nb_test_samples": 1000,
+ },
}
if args.task in default_args:
progress_bar_desc="autoregression",
device=torch.device("cpu"),
):
- # p = logits.softmax(1)
- # entropy[:,s]= p.xlogy(p).sum(1) / math.log(2)
+
batches = zip(input.split(batch_size), ar_mask.split(batch_size))
+
if progress_bar_desc is not None:
batches = tqdm.tqdm(
batches,
desc=progress_bar_desc,
total=input.size(0) // batch_size,
)
+
for input, ar_mask in batches:
i = (ar_mask.sum(0) > 0).nonzero()
if i.min() > 0:
######################################################################
+import expr
+
+
+class TaskExpr(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ device=torch.device("cpu"),
+ ):
+ self.batch_size = batch_size
+ self.device = device
+
+ train_sequences = expr.generate_sequences(nb_train_samples)
+ test_sequences = expr.generate_sequences(nb_test_samples)
+ self.char2id = dict([ (c,n) for n,c in enumerate(set("".join(train_sequences + test_sequences))) ])
+ self.id2char = dict([ (n,c) for n,c in self.char2id.items() ])
+ len_max = max([len(x) for x in train_sequences + test_sequences])
+ self.train_input = torch.cat([torch.tensor([char2id(c) for c in s + " "*(len_max-len(s))] for s in train_sequences)], 0)
+ self.test_input = torch.cat([torch.tensor([char2id(c) for c in s + " "*(len_max-len(s))] for s in test_sequences)], 0)
+ self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+
+ 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
+ if nb_to_use > 0:
+ input = input[:nb_to_use]
+ if desc is None:
+ desc = f"epoch-{split}"
+ for batch in tqdm.tqdm(
+ input.split(self.batch_size), dynamic_ncols=True, desc=desc
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def produce_results(self, n_epoch, model):
+ # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+
+ def compute_nb_correct(input):
+ result = input.clone()
+ stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
+ ar_mask = (result != input).long()
+ masked_inplace_autoregression(
+ model, self.batch_size, result, ar_mask, device=self.device
+ )
+
+ errors = ((result != input).long() * ar_mask).reshape(
+ -1, 1 + self.nb_digits
+ )
+ ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
+
+ nb_total = ar_mask.max(1).values.sum()
+ nb_correct = nb_total - errors.max(1).values.sum()
+
+ return nb_total, nb_correct
+
+ test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+
+ log_string(
+ f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
+ )
+
+ ##############################################################
+ # Log a few generated sequences
+ input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
+ result = input.clone()
+ stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
+ ar_mask = (result != input).long()
+ for n in range(result.size(0)):
+ log_string(
+ f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
+ )
+ masked_inplace_autoregression(
+ model, self.batch_size, result, ar_mask, device=self.device
+ )
+ for n in range(result.size(0)):
+ log_string(
+ f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
+ )
+ ##############################################################
+
+ model.train(t)
+
+
+######################################################################
+
+
def picoclvr_pruner_horizontal_green(p):
return not ("green" in p and ("left" in p or "right" in p))
device=device,
)
+elif args.task == "expr":
+ task = TaskExpr(
+ nb_train_samples=args.nb_train_samples,
+ nb_test_samples=args.nb_test_samples,
+ batch_size=args.batch_size,
+ device=device,
+ )
+
else:
raise ValueError(f"Unknown task {args.task}")
projects / picoclvr.git / commitdiff