From b54804c04a42442b54fb90851d2c0908e77b0674 Mon Sep 17 00:00:00 2001
From: Caroline DE POURTALES <cdepourt@montana.irit.fr>
Date: Thu, 5 May 2022 14:25:37 +0200
Subject: [PATCH] starting train
---
SuperTagger/Linker/Linker.py | 37 +++++++++++++++++++++-----
SuperTagger/Linker/utils.py | 7 +++++
bash_GPU.sh | 13 ---------
weighting.py | 51 ------------------------------------
4 files changed, 38 insertions(+), 70 deletions(-)
delete mode 100644 bash_GPU.sh
delete mode 100644 weighting.py
diff --git a/SuperTagger/Linker/Linker.py b/SuperTagger/Linker/Linker.py
index 1f43920..208e8d4 100644
--- a/SuperTagger/Linker/Linker.py
+++ b/SuperTagger/Linker/Linker.py
@@ -9,7 +9,7 @@ from SuperTagger.Linker.AtomEmbedding import AtomEmbedding
from SuperTagger.Linker.AtomTokenizer import AtomTokenizer
from SuperTagger.Linker.atom_map import atom_map
from SuperTagger.Linker.Sinkhorn import sinkhorn_fn_no_exp as sinkhorn
-from SuperTagger.Linker.utils import find_pos_neg_idexes, get_atoms_batch
+from SuperTagger.Linker.utils import find_pos_neg_idexes, get_atoms_batch, mesure_accuracy
from SuperTagger.Linker.AttentionLayer import FFN, AttentionLayer
from SuperTagger.utils import pad_sequence
@@ -98,11 +98,36 @@ class Linker(Module):
return link_weights
- def predict_axiom_links(self):
+ def predict_axiom_links(self, b_sents_tokenized, b_sents_mask):
return None
- def eval_batch(self):
- return None
+ def eval_batch(self, batch, cross_entropy_loss):
+ 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_category = batch[2].to("cuda" if torch.cuda.is_available() else "cpu")
- def eval_epoch(self):
- return None
+ logits_axiom_links_pred = self.predict_axiom_links(b_sents_tokenized, b_sents_mask)
+ # Softmax and argmax
+ axiom_links_pred = torch.argmax(torch.nn.functional.softmax(logits_axiom_links_pred, dim=2), dim=2)
+
+ accuracy = mesure_accuracy(b_category, axiom_links_pred)
+ loss = float(cross_entropy_loss(axiom_links_pred, b_category))
+
+ return accuracy, loss
+
+ 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
+ """
+ accuracy_average = 0
+ loss_average = 0
+ compt = 0
+ for step, batch in enumerate(dataloader):
+ compt += 1
+ accuracy, loss = self.eval_batch(batch, cross_entropy_loss)
+ accuracy_average += accuracy
+ loss_average += loss
+
+ return accuracy_average / compt, loss_average / compt
diff --git a/SuperTagger/Linker/utils.py b/SuperTagger/Linker/utils.py
index f2e72e1..ce569de 100644
--- a/SuperTagger/Linker/utils.py
+++ b/SuperTagger/Linker/utils.py
@@ -92,3 +92,10 @@ def find_pos_neg_idexes(batch_symbols):
list_symbols.append(cut_category_in_symbols(category))
list_batch.append(list_symbols)
return list_batch
+
+
+def mesure_accuracy(b_category, axiom_links_pred):
+
+ # Convert b_category into
+
+ return 0
\ No newline at end of file
diff --git a/bash_GPU.sh b/bash_GPU.sh
deleted file mode 100644
index 665f769..0000000
--- a/bash_GPU.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/sh
-#SBATCH --job-name=N-tensorboard
-#SBATCH --partition=RTX6000Node
-#SBATCH --gres=gpu:1
-#SBATCH --mem=32000
-#SBATCH --gres-flags=enforce-binding
-#SBATCH --error="error_rtx1.err"
-#SBATCH --output="out_rtx1.out"
-
-module purge
-module load singularity/3.0.3
-
-srun singularity exec /logiciels/containerCollections/CUDA11/pytorch-NGC-21-03-py3.sif python "train.py"
\ No newline at end of file
diff --git a/weighting.py b/weighting.py
deleted file mode 100644
index 0e6ee36..0000000
--- a/weighting.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from Configuration import Configuration
-from SuperTagger.Symbol.SymbolTokenizer import SymbolTokenizer
-from SuperTagger.utils import read_csv_pgbar
-from SuperTagger.Symbol.symbol_map import symbol_map
-
-from collections import Counter
-import numpy as np
-
-import statistics
-
-file_path = 'Datasets/m2_dataset.csv'
-max_symbols_in_sentence = int(Configuration.modelDecoderConfig['max_symbols_in_sentence'])
-max_len_sentence = int(Configuration.modelDecoderConfig['max_len_sentence'])
-
-df = read_csv_pgbar(file_path)
-all_symbols = [item for sublist in list(df['sub_tree']) for item in sublist]
-
-counter = Counter(all_symbols)
-print(counter)
-
-number_total_symbols = len(all_symbols)
-print(number_total_symbols)
-
-most_common_symbol, max_number_in_one_symbol = counter.most_common(1)[0]
-print(most_common_symbol)
-print(max_number_in_one_symbol)
-
-middle_common_symbol, middle_number_in_one_symbol = counter.most_common(6)[5]
-print(middle_common_symbol)
-print(middle_number_in_one_symbol)
-
-mean = statistics.mean(counter.values())
-print(mean)
-
-
-def get_weight(count_symbol_x, count_symbol_threashold):
- x = count_symbol_threashold / count_symbol_x
- return 1 + np.log(x + 1)**2
-
-
-dic_symbols_weights = {}
-for (key, value) in counter.items():
- dic_symbols_weights[key] = np.round(get_weight(value, mean), 4)
-print(dic_symbols_weights)
-
-list_ordered = []
-for symbol in symbol_map.keys():
- if symbol != '[START]' and symbol != '[PAD]':
- list_ordered.append(dic_symbols_weights[symbol])
-
-print(list_ordered)
--
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