X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=04b8f84577097a7ef3de515fa90f6a461429e7e5;hb=1406521aecaab783d2ea267b0f973ac17e091bf7;hp=affc8cdcfae81d694e46c1e7afdcb2cc2d0656b4;hpb=5aee50805cfad1dd49bbf30b30fe65b05e03de78;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index affc8cd..04b8f84 100755
--- a/tasks.py
+++ b/tasks.py
@@ -225,6 +225,10 @@ class PicoCLVR(Task):
             primer += [primer_descr + " <img>"] * nb_per_primer
 
         result = self.tensorize(primer)
+        fill = result.new_full(
+            result.size()[:-1] + (self.height * self.width + 1,), self.t_nul
+        )
+        result = torch.cat((result, fill), 1)
         ar_mask = (result == self.t_nul).long()
         masked_inplace_autoregression(
             model,
@@ -744,18 +748,21 @@ class Stack(Task):
             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)):
-                logger(
-                    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,
-                    deterministic_synthesis,
-                    device=self.device,
-                )
+
+            # for n in range(result.size(0)):
+            # logger(
+            # 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,
+                deterministic_synthesis,
+                device=self.device,
+            )
+
             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)}"
@@ -772,6 +779,20 @@ import expr
 
 
 class Expr(Task):
+    def tensorize(self, sequences):
+        len_max = max([len(x) for x in sequences])
+        return torch.cat(
+            [
+                torch.tensor(
+                    [
+                        [self.char2id[c] for c in s + "#" * (len_max - len(s))]
+                        for s in sequences
+                    ]
+                )
+            ],
+            0,
+        ).to(self.device)
+
     def __init__(
         self,
         nb_train_samples,
@@ -788,51 +809,24 @@ class Expr(Task):
             nb_train_samples,
             nb_variables=nb_variables,
             length=sequence_length,
-            # length=2 * sequence_length,
-            # randomize_length=True,
         )
+
         test_sequences = expr.generate_sequences(
             nb_test_samples,
             nb_variables=nb_variables,
             length=sequence_length,
         )
-        self.char2id = dict(
-            [
-                (c, n)
-                for n, c in enumerate(
-                    set("#" + "".join(train_sequences + test_sequences))
-                )
-            ]
-        )
+
+        symbols = list(set("#" + "".join(train_sequences + test_sequences)))
+        symbols.sort()
+
+        self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
         self.id2char = dict([(n, c) for c, n in self.char2id.items()])
 
         self.filler, self.space = self.char2id["#"], self.char2id[" "]
 
-        len_max = max([len(x) for x in train_sequences])
-        self.train_input = torch.cat(
-            [
-                torch.tensor(
-                    [
-                        [self.char2id[c] for c in s + "#" * (len_max - len(s))]
-                        for s in train_sequences
-                    ]
-                )
-            ],
-            0,
-        ).to(device)
-
-        len_max = max([len(x) for x in test_sequences])
-        self.test_input = torch.cat(
-            [
-                torch.tensor(
-                    [
-                        [self.char2id[c] for c in s + "#" * (len_max - len(s))]
-                        for s in test_sequences
-                    ]
-                )
-            ],
-            0,
-        ).to(device)
+        self.train_input = self.tensorize(train_sequences)
+        self.test_input = self.tensorize(test_sequences)
 
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
@@ -858,7 +852,13 @@ class Expr(Task):
         return "".join([self.id2char[k.item()] for k in s])
 
     def produce_results(
-        self, n_epoch, model, result_dir, logger, deterministic_synthesis
+        self,
+        n_epoch,
+        model,
+        result_dir,
+        logger,
+        deterministic_synthesis,
+        input_file=None,
     ):
         with torch.autograd.no_grad():
             t = model.training
@@ -910,7 +910,7 @@ class Expr(Task):
                 test_nb_correct,
                 test_nb_delta,
                 test_nb_missed,
-            ) = compute_nb_correct(self.test_input[:1000])
+            ) = compute_nb_correct(self.test_input[:10000])
 
             logger(
                 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}%"
@@ -927,25 +927,36 @@ class Expr(Task):
 
             ##############################################################
             # Log a few generated sequences
-            input = self.test_input[:10]
+            if input_file is None:
+                input = self.test_input[:10]
+            else:
+                with open(input_file, "r") as f:
+                    sequences = [e.strip() for e in f.readlines()]
+                    sequences = [s + " " + "#" * 50 for s in sequences]
+                    input = self.tensorize(sequences)
+
             result = input.clone()
-            ar_mask = (result == self.space).long().cumsum(dim=1).clamp(max=1)
+            s = (result == self.space).long()
+            ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
             result = (1 - ar_mask) * result + ar_mask * self.filler
+
             for n in range(result.size(0)):
                 logger(f"test_before {self.seq2str(result[n])}")
-                masked_inplace_autoregression(
-                    model,
-                    self.batch_size,
-                    result,
-                    ar_mask,
-                    deterministic_synthesis,
-                    device=self.device,
-                )
+
+            masked_inplace_autoregression(
+                model,
+                self.batch_size,
+                result,
+                ar_mask,
+                deterministic_synthesis,
+                device=self.device,
+            )
+
             correct = (1 - ar_mask) * self.space + ar_mask * input
             for n in range(result.size(0)):
                 comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
                 logger(f"test_after  {self.seq2str(result[n])} {comment}")
-                logger(f"correct     {self.seq2str(correct[n])}")
+                logger(f"truth       {self.seq2str(correct[n])}")
             ##############################################################
 
             model.train(t)