More or less working annotate_text. Should split on spaces...

Author: PonteIneptique

annotate_text.py

@@ -0,0 +1,33 @@
+input_text = """Segneur ,%% sachies que Mil et .C. .iiijxx. et .xvii. ans apries l'incarnation ihesucrist ,%.%. Au tans Innocent ,%% l'apostole de Rome ,%,%. et Phelippon ,%% roi de france ,%%
+et Richart ,%% roi d'engleterre ,%,%. ot vn saint home en france ,%,%. qui ot nom Fouques de Nuelly (%.%. Cil Nuellys siet entre Nuelly sour Marne %,%. et paris )%.%. Et il estoit priessres et tenoit la perroche de la uille .%.%. Et ichil Fouques dont ie vos di
+commencha a parle de diu %,%. par france %,%.
+et par les autres pais entour ;%.%.
+Et sachies que nostre sires fist maintes bieles miracles pour lui .%,%. et tant que la renommee de cel saint home ala ,%% tant qu'ele vint a l'apostole de Rome Innocent ;%.%. Et l'apostoles manda en france au saint home %,%. que il preechast des crois par s'auctorite ;%.%. Et apres i enuoia .i. sien cardonnal ,%% Maistre Pieron de Capes ,%% croisie ,%,%. et manda par lui le pardon tel con ie vous dirai :%.%.
+Tout chil qui se croiseroient %,%. et feroient le sieruice diu .i. an en l'ost %,%[punctelev]
+seroient quite de toz lor pechies quil auoient fais ,%% dont il seroient confies .%.%.
+Pour che que chius pardons fu si grans ,%,%. si s'en esmurent moult li cuer des gens ,%,%. et moult s'en croisierent
+pour chou que li pardons estoit si grans .§%.§%.
+
+
+EN l'autre an apries que chil preudom Fouques parla de diu ,%,%[punctelev] ot .i. tournoi en champaigne ,%%
+a .i. castiel qui a non Aicri .%,%. et par la grace de diu si auint ke Thiebaus ,%% quens de champaigne
+et de Brie ,%% prist la crois ,%,%. et li cuens Looys de Bloys %,%. et de chartaing .%.%.
+Et che fu a l'entree des Auens .%,%. et chil cuens thiebaus estoit iouenes hom et n'auoit pas plus de .xxij. ans ,%.%. Ne li cuens Looys n'auoit pas plus de .xxvij. ans .%,%. Chil doi conte ierent neueu le roi de france %,%. et cousin germain et neueu le roi d'engleterre %.%. De l'autre part .%% auoec ces .ij. contes se croisierent doi moult haut baron de france ,%.%. Symons de Montfort %,%. et Renaus de Mommirail .%.%. Moult fu grans la renommee par les terres .%,%[punctelev] quant cil doi se croisierent .§%.§%.
+
+
+EN la terre le conte de champaigne se croisa Gerniers li euesques de Troies ,%,%. et li cuens Gautiers de Braine ,%.%. Joffrois de Joinuile ,%,%.
+qui estoit senescaus de la tiere ,%.%.
+Robiers ses freres ,%.%. Gautiers de voignori ,%.%. Gautiers de Mombelyart ,%.%.
+Eustasces d'escouflans ,%.%. Guis dou plaissie %,%. et ses freres ,%% Henris D'ardillieres ,%.%. Ogiers de saint chienon ,%.%.""".replace(
+    "%", "").replace("\n", " ").replace(" ", "")
+
+print(input_text)
+
+from boudams.tagger import Seq2SeqTokenizer
+import logging
+
+logger = logging.getLogger()
+logger.setLevel(logging.DEBUG)
+
+tokenizer = Seq2SeqTokenizer.load("/home/thibault/dev/boudams/models/linear-conv2019-05-24--14:08:58-0.0001.tar", device="cpu")
+print("".join(tokenizer.annotate_text(input_text)))

boudams/model/linear.py

@@ -103,7 +103,7 @@ def predict(self, src, src_len, label_encoder: "LabelEncoder") -> torch.Tensor:
         """
         out = self(src, src_len, None, teacher_forcing_ratio=0)
         logits = torch.argmax(out, 2)
-        return label_encoder.reverse_batch(logits, masked=src)
+        return label_encoder.reverse_batch(logits, masked=src, ignore=(self.pad_idx, self.eos_idx, self.sos_idx))
 
     def gradient(
         self,

boudams/tagger.py

@@ -231,26 +231,31 @@ def load(cls, fpath="./model.tar", device=DEVICE):
 
         return obj
 
-    def annotate(self, texts: List[str]):
+    def annotate(self, texts: List[str], batch_size=32):
         self.model.eval()
-        for sentence in texts:
-
-            # it would be good at some point to keep and use order to batchify this
-            tensor, sentence_length, _ = self.vocabulary.pad_and_tensorize(
-                [self.vocabulary.inp_to_numerical(self.vocabulary.prepare(sentence))[0]],
-                device=self.device,
-                padding=self.out_max_sentence_length-len(sentence)
-            )
-
-            from .model.base import pprint_2d
-            #pprint_2d(tensor.t())
-            #print(sentence_length)
-
-            logging.debug("Input Tensor {}".format(tensor.shape))
-            logging.debug("Input Positions tensor {}".format(sentence_length.shape))
+        for n in range(0, len(texts), batch_size):
+            batch = texts[n:n+batch_size]
+            xs = [
+                self.vocabulary.inp_to_numerical(self.vocabulary.prepare(s))
+                for s in batch
+            ]
+            logging.info("Dealing with batch %s " % (int(n/batch_size)+1))
+            tensor, sentence_length, order = self.vocabulary.pad_and_tensorize(
+                    [x for x, _ in xs],
+                    device=self.device,
+                    padding=max(list(map(lambda x: x[1], xs)))
+                )
 
-            translation = self.model.predict(
+            translations = self.model.predict(
                 tensor, sentence_length, label_encoder=self.vocabulary
             )
 
-            yield "".join(translation[0])
+            for index in range(len(batch)):
+                yield "".join(translations[order.index(index)])
+
+    def annotate_text(self, string, batch_size=32):
+        strings = [
+            string[n:n+self.out_max_sentence_length-10]
+            for n in range(0, len(string), self.out_max_sentence_length - 10)
+        ]
+        yield from self.annotate(strings, batch_size=batch_size)

linear_run.py

@@ -8,7 +8,7 @@
 logger = logging.getLogger()
 logger.setLevel(logging.DEBUG)
 
-EPOCHS = 10
+EPOCHS = 100
 TEST = "seints"
 RANDOM = True
 DEVICE = "cuda"

load.py

@@ -21,7 +21,7 @@
     for line in Examples.split("\n")
 ]
 
-for model in glob.glob("/home/thibault/dev/boudams/models/lstm2019-05-22--09:23:38-0.0001.tar"):
+for model in glob.glob("/home/thibault/dev/boudams/models/linear-conv2019-05-24--14:08:58-0.0001.tar"):
     tokenizer = Seq2SeqTokenizer.load(model, device="cpu")
     print(tokenizer.model)
     treated = tokenizer.annotate([x[0] for x in Examples])

voc-2.json

@@ -1 +1 @@
-{"itos": {"0": "<SOS>", "1": "<EOS>", "2": "<PAD>", "5": "l", "4": "r", "6": "u", "7": "g", "8": "x", "9": "c", "10": "]", "11": "z", "12": " ", "13": "j", "14": ";", "15": "e", "16": "f", "17": "a", "18": "[", "19": "k", "20": "h", "21": "?", "22": "y", "23": "!", "24": "w", "25": "v", "26": "-", "27": ")", "28": "m", "29": "s", "30": "q", "31": "d", "32": "i", "33": "t", "34": "'", "35": ",", "36": ".", "37": "(", "38": "n", "39": "b", "40": "p", "41": ":", "42": "o"}, "stoi": {"<SOS>": 0, "<EOS>": 1, "<PAD>": 2, "<UNK>": 5, "r": 4, "l": 5, "u": 6, "g": 7, "x": 8, "c": 9, "]": 10, "z": 11, " ": 12, "j": 13, ";": 14, "e": 15, "f": 16, "a": 17, "[": 18, "k": 19, "h": 20, "?": 21, "y": 22, "!": 23, "w": 24, "v": 25, "-": 26, ")": 27, "m": 28, "s": 29, "q": 30, "d": 31, "i": 32, "t": 33, "'": 34, ",": 35, ".": 36, "(": 37, "n": 38, "b": 39, "p": 40, ":": 41, "o": 42}, "params": {"init_token": "<SOS>", "eos_token": "<EOS>", "pad_token": "<PAD>", "unk_token": "<UNK>", "mask_token": "x", "remove_diacriticals": true, "lower": true, "masked": true}}
+{"itos": {"0": "<SOS>", "1": "<EOS>", "2": "<PAD>", "5": "r", "4": "!", "6": " ", "7": "q", "8": "t", "9": "p", "10": "v", "11": "x", "12": ";", "13": "l", "14": "n", "15": ".", "16": "s", "17": "g", "18": ")", "19": "o", "20": "z", "21": "e", "22": "k", "23": "(", "24": "d", "25": ":", "26": "a", "27": "?", "28": "u", "29": "b", "30": "j", "31": "i", "32": "'", "33": "y", "34": "f", "35": "m", "36": "w", "37": "c", "38": "]", "39": "[", "40": "h", "41": ",", "42": "-"}, "stoi": {"<SOS>": 0, "<EOS>": 1, "<PAD>": 2, "<UNK>": 5, "!": 4, "r": 5, " ": 6, "q": 7, "t": 8, "p": 9, "v": 10, "x": 11, ";": 12, "l": 13, "n": 14, ".": 15, "s": 16, "g": 17, ")": 18, "o": 19, "z": 20, "e": 21, "k": 22, "(": 23, "d": 24, ":": 25, "a": 26, "?": 27, "u": 28, "b": 29, "j": 30, "i": 31, "'": 32, "y": 33, "f": 34, "m": 35, "w": 36, "c": 37, "]": 38, "[": 39, "h": 40, ",": 41, "-": 42}, "params": {"init_token": "<SOS>", "eos_token": "<EOS>", "pad_token": "<PAD>", "unk_token": "<UNK>", "mask_token": "x", "remove_diacriticals": true, "lower": true, "masked": true}}