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diff --git a/SuperTagger/EncoderDecoder.py b/SuperTagger/EncoderDecoder.py
deleted file mode 100644
index 36311d552b3b54fb3c4cf0135ccb077989ef79a8..0000000000000000000000000000000000000000
--- a/SuperTagger/EncoderDecoder.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import torch
-from torch.nn import Dropout
-from torch.nn import Module
-
-from Configuration import Configuration
-from SuperTagger.Decoder.RNNDecoderLayer import RNNDecoderLayer
-from SuperTagger.Encoder.EncoderLayer import EncoderLayer
-from SuperTagger.eval import measure_supertagging_accuracy
-
-
-class EncoderDecoder(Module):
- """
- A standard Encoder-Decoder architecture. Base for this and many
- other models.
-
- decoder : instance of Decoder
- """
-
- def __init__(self, BASE_TOKENIZER, BASE_MODEL, symbols_map):
- super(EncoderDecoder, self).__init__()
-
- self.max_len_sentence = int(Configuration.datasetConfig['max_len_sentence'])
- self.max_symbols_in_sentence = int(Configuration.datasetConfig['max_symbols_in_sentence'])
- self.dim_decoder = int(Configuration.modelDecoderConfig['dim_decoder'])
-
- self.symbols_map = symbols_map
- self.sents_padding_id = BASE_TOKENIZER.pad_token_id
- self.sents_space_id = BASE_TOKENIZER.bos_token_id
-
- self.encoder = EncoderLayer(BASE_MODEL)
- self.decoder = RNNDecoderLayer(self.symbols_map)
-
- self.dropout = Dropout(0.1)
-
- def forward(self, sents_tokenized_batch, sents_mask_batch, symbols_tokenized_batch):
- r"""Training the translation from sentence to symbols
-
- Args:
- sents_tokenized_batch: [batch_size, max_len_sentence] the tokenized sentences
- sents_mask_batch : mask output from the encoder tokenizer
- symbols_tokenized_batch: [batch_size, max_symbols_in_sentence] the true symbols for each sentence.
- """
- last_hidden_state, pooler_output = self.encoder([sents_tokenized_batch, sents_mask_batch])
- last_hidden_state = self.dropout(last_hidden_state)
- return self.decoder(symbols_tokenized_batch, last_hidden_state, pooler_output)
-
- def decode_greedy_rnn(self, sents_tokenized_batch, sents_mask_batch):
- r"""Predicts the symbols for each sentence in sents_tokenized_batch.
-
- Args:
- sents_tokenized_batch: [batch_size, max_len_sentence] the tokenized sentences
- sents_mask_batch : mask output from the encoder tokenizer
- """
- last_hidden_state, pooler_output = self.encoder([sents_tokenized_batch, sents_mask_batch])
- last_hidden_state = self.dropout(last_hidden_state)
-
- predictions = self.decoder.predict_rnn(last_hidden_state, pooler_output)
-
- return predictions
-
- def eval_batch(self, batch, cross_entropy_loss):
- r"""Calls the evaluating methods after predicting the symbols from the sentence contained in batch
-
- Args:
- batch: contains the tokenized sentences, their masks and the true symbols.
- """
- b_sents_tokenized = batch[0].to("cuda" if torch.cuda.is_available() else "cpu")
- b_sents_mask = batch[1].to("cuda" if torch.cuda.is_available() else "cpu")
- b_symbols_tokenized = batch[2].to("cuda" if torch.cuda.is_available() else "cpu")
-
- type_predictions = self.decode_greedy_rnn(b_sents_tokenized, b_sents_mask)
-
- pred = torch.argmax(type_predictions, dim=2)
-
- predict_trad = [{v: k for k, v in self.symbols_map.items()}[int(i)] for i in pred[0]]
- true_trad = [{v: k for k, v in self.symbols_map.items()}[int(i)] for i in b_symbols_tokenized[0]]
- l = len([i for i in true_trad if i != '[PAD]'])
- print("\nsub true (", l, ") : ",
- [token for token in true_trad if token != '[PAD]'])
- print("\nsub predict (", len([i for i in predict_trad if i != '[PAD]']), ") : ",
- [token for token in predict_trad if token != '[PAD]'])
-
- return measure_supertagging_accuracy(pred, b_symbols_tokenized,
- ignore_idx=self.symbols_map["[PAD]"]), float(
- cross_entropy_loss(type_predictions, b_symbols_tokenized))
-
- def eval_epoch(self, dataloader, cross_entropy_loss):
- r"""Average the evaluation of all the batch.
-
- Args:
- dataloader: contains all the batch which contain the tokenized sentences, their masks and the true symbols
- """
- s_total, s_correct, w_total, w_correct = (0.1,) * 4
-
- for step, batch in enumerate(dataloader):
- batch = batch
- batch_output, loss = self.eval_batch(batch, cross_entropy_loss)
- ((bs_correct, bs_total), (bw_correct, bw_total)) = batch_output
- s_total += bs_total
- s_correct += bs_correct
- w_total += bw_total
- w_correct += bw_correct
-
- return s_correct / s_total, w_correct / w_total, loss
diff --git a/SuperTagger/Linker/Linker.py b/SuperTagger/Linker/Linker.py
index 6a39ae7658faf456301399cb811d634c8d9f0d38..1f439200a3ea7b520d6b4c26de098772fdaf6bdc 100644
--- a/SuperTagger/Linker/Linker.py
+++ b/SuperTagger/Linker/Linker.py
@@ -44,7 +44,7 @@ class Linker(Module):
LayerNorm(self.dim_polarity_transfo, eps=1e-12)
)
- def make_decoder_mask(self, atoms_batch) :
+ def make_decoder_mask(self, atoms_batch):
decoder_attn_mask = torch.ones_like(atoms_batch, dtype=torch.float64)
decoder_attn_mask[atoms_batch.eq(self.padding_id)] = 0.0
return decoder_attn_mask.unsqueeze(1).repeat(1, atoms_batch.shape[1], 1)
@@ -83,10 +83,12 @@ class Linker(Module):
# to do select with list of list
pos_encoding = pad_sequence(
[atoms_encoding.select(0, index=i).index_select(0, index=torch.as_tensor(sentence))
- for i, sentence in enumerate(pos_idx_per_atom_type)], max_len=self.max_atoms_in_sentence, padding_value=0)
+ for i, sentence in enumerate(pos_idx_per_atom_type)], max_len=self.max_atoms_in_sentence,
+ padding_value=0)
neg_encoding = pad_sequence(
[atoms_encoding.select(0, index=i).index_select(0, index=torch.as_tensor(sentence))
- for i, sentence in enumerate(neg_idx_per_atom_type)], max_len=self.max_atoms_in_sentence, padding_value=0)
+ for i, sentence in enumerate(neg_idx_per_atom_type)], max_len=self.max_atoms_in_sentence,
+ padding_value=0)
# pos_encoding = self.pos_transformation(pos_encoding)
# neg_encoding = self.neg_transformation(neg_encoding)
@@ -95,3 +97,12 @@ class Linker(Module):
link_weights.append(sinkhorn(weights, iters=3))
return link_weights
+
+ def predict_axiom_links(self):
+ return None
+
+ def eval_batch(self):
+ return None
+
+ def eval_epoch(self):
+ return None
diff --git a/SuperTagger/__pycache__/EncoderDecoder.cpython-38.pyc b/SuperTagger/__pycache__/EncoderDecoder.cpython-38.pyc
deleted file mode 100644
index 7745cbbcf4e476115694b33969f29399433509b5..0000000000000000000000000000000000000000
Binary files a/SuperTagger/__pycache__/EncoderDecoder.cpython-38.pyc and /dev/null differ
diff --git a/SuperTagger/eval.py b/SuperTagger/eval.py
index 372e68cab3a4cf4af8da655a7073f372de39cb5e..7a14ac5e20c2e15d723c978c02382dc7ee5ad72c 100644
--- a/SuperTagger/eval.py
+++ b/SuperTagger/eval.py
@@ -3,45 +3,6 @@ from torch import Tensor
from torch.nn import Module
from torch.nn.functional import nll_loss, cross_entropy
-# Another from Kokos function to calculate the accuracy of our predictions vs labels
-def measure_supertagging_accuracy(pred, truth, ignore_idx=0):
- r"""Evaluation of the decoder's output and the true symbols without considering the padding in the true targets.
-
- Args:
- pred: [batch_size, max_symbols_in_sentence] prediction of symbols for each symbols.
- truth: [batch_size, max_symbols_in_sentence] true values of symbols
- """
- correct_symbols = torch.ones(pred.size())
- correct_symbols[pred != truth] = 0
- correct_symbols[truth == ignore_idx] = 1
- num_correct_symbols = correct_symbols.sum().item()
- num_masked_symbols = len(truth[truth == ignore_idx])
-
- correct_sentences = correct_symbols.prod(dim=1)
- num_correct_sentences = correct_sentences.sum().item()
-
- return (num_correct_sentences, pred.shape[0]), \
- (num_correct_symbols - num_masked_symbols, pred.shape[0] * pred.shape[1] - num_masked_symbols)
-
-
-def count_sep(xs, sep_id, dim=-1):
- return xs.eq(sep_id).sum(dim=dim)
-
-
-class NormCrossEntropy(Module):
- r"""Loss based on the cross entropy, it considers the number of words and ignore the padding.
- """
-
- def __init__(self, ignore_index, sep_id, weights=None):
- super(NormCrossEntropy, self).__init__()
- self.ignore_index = ignore_index
- self.sep_id = sep_id
- self.weights = weights
-
- def forward(self, predictions, truths):
- return cross_entropy(predictions.flatten(0, -2), truths.flatten(), weight=self.weights,
- reduction='sum', ignore_index=self.ignore_index) / count_sep(truths.flatten(), self.sep_id)
-
class SinkhornLoss(Module):
def __init__(self):
@@ -49,4 +10,4 @@ class SinkhornLoss(Module):
def forward(self, predictions, truths):
return sum(nll_loss(link.flatten(0, 1), perm.flatten(), reduction='mean')
- for link, perm in zip(predictions, truths))
\ No newline at end of file
+ for link, perm in zip(predictions, truths))
diff --git a/SuperTagger/utils.py b/SuperTagger/utils.py
index cfacf2503ba924d1fd3ba07b340e27a2b13a2002..fc1511ee9cf9df9ec70f908f2b5542e61aea4971 100644
--- a/SuperTagger/utils.py
+++ b/SuperTagger/utils.py
@@ -55,32 +55,4 @@ def format_time(elapsed):
elapsed_rounded = int(round(elapsed))
# Format as hh:mm:ss
- return str(datetime.timedelta(seconds=elapsed_rounded))
-
-
-def checkpoint_save(model, opt, epoch, dir, loss):
- torch.save({
- 'epoch': epoch,
- 'model_state_dict': model.state_dict(),
- 'optimizer_state_dict': opt.state_dict(),
- 'loss': loss,
- }, dir + '/model_check.pt')
-
-
-def checkpoint_load(model, opt, path):
- epoch = 0
- loss = 0
- print("#" * 15)
-
- try:
- checkpoint = torch.load(path)
- model.load_state_dict(checkpoint['model_state_dict'])
- opt.load_state_dict(checkpoint['optimizer_state_dict'])
- epoch = checkpoint['epoch']
- loss = checkpoint['loss']
- print("\n The loading checkpoint was successful ! \n")
- print("#" * 10)
- except Exception as e:
- print("\nCan't load checkpoint model because : " + str(e) + "\n\nUse default model \n")
- print("#" * 15)
- return model, opt, epoch, loss
+ return str(datetime.timedelta(seconds=elapsed_rounded))
\ No newline at end of file
diff --git a/train.py b/train.py
index 25154db1b58abb38c2e7a6ffb94f89fcfb4d4b0d..9287436a5c86f2cd4c2c1fc3548b4a0c46d304b3 100644
--- a/train.py
+++ b/train.py
@@ -1,7 +1,5 @@
-import os
import pickle
import time
-from datetime import datetime
import numpy as np
import torch
@@ -13,8 +11,8 @@ from transformers import get_cosine_schedule_with_warmup
from Configuration import Configuration
from SuperTagger.Linker.Linker import Linker
from SuperTagger.Linker.atom_map import atom_map
-from SuperTagger.eval import NormCrossEntropy, SinkhornLoss
-from SuperTagger.utils import format_time, read_csv_pgbar, checkpoint_save, checkpoint_load
+from SuperTagger.eval import SinkhornLoss
+from SuperTagger.utils import format_time, read_csv_pgbar
from torch.utils.tensorboard import SummaryWriter