From 67c67305c1d953907a5eedf1757db21524e9509c Mon Sep 17 00:00:00 2001
From: Franck Dary <franck.dary@lis-lab.fr>
Date: Thu, 15 Apr 2021 19:29:49 +0200
Subject: [PATCH] Cleaned main, put functions into Train.py and Decode.py
---
Decode.py | 27 +++++++++++++++
Train.py | 70 ++++++++++++++++++++++++++++++++++++-
Util.py | 7 ++++
main.py | 101 ++----------------------------------------------------
4 files changed, 106 insertions(+), 99 deletions(-)
create mode 100644 Util.py
diff --git a/Decode.py b/Decode.py
index 3982739..80b46ea 100644
--- a/Decode.py
+++ b/Decode.py
@@ -2,6 +2,7 @@ import random
import sys
from Transition import Transition, getMissingLinks, applyTransition
from Features import extractFeatures
+import Config
import torch
################################################################################
@@ -62,3 +63,29 @@ def decodeModel(ts, strat, config, network, dicts, debug) :
EOS.apply(config)
################################################################################
+
+################################################################################
+def decodeMode(debug, filename, type, network=None, dicts=None, output=sys.stdout) :
+ transitionSet = [Transition(elem) for elem in ["RIGHT", "LEFT", "SHIFT", "REDUCE"]]
+ strategy = {"RIGHT" : 1, "SHIFT" : 1, "LEFT" : 0, "REDUCE" : 0}
+
+ sentences = Config.readConllu(filename)
+
+ if type in ["random", "oracle"] :
+ decodeFunc = oracleDecode if type == "oracle" else randomDecode
+ for config in sentences :
+ decodeFunc(transitionSet, strategy, config, debug)
+ sentences[0].print(sys.stdout, header=True)
+ for config in sentences[1:] :
+ config.print(sys.stdout, header=False)
+ elif type == "model" :
+ for config in sentences :
+ decodeModel(transitionSet, strategy, config, network, dicts, debug)
+ sentences[0].print(output, header=True)
+ for config in sentences[1:] :
+ config.print(output, header=False)
+ else :
+ print("ERROR : unknown type '%s'"%type, file=sys.stderr)
+ exit(1)
+################################################################################
+
diff --git a/Train.py b/Train.py
index d8b018a..8bb6c93 100644
--- a/Train.py
+++ b/Train.py
@@ -1,9 +1,31 @@
import sys
import random
+import torch
+
from Transition import Transition, getMissingLinks, applyTransition
import Features
+from Dicts import Dicts
+from Util import timeStamp
+import Networks
+import Decode
+import Config
-import torch
+from conll18_ud_eval import load_conllu, evaluate
+
+################################################################################
+def trainMode(debug, filename, type, modelDir, nbIter, batchSize, devFile, silent=False) :
+ transitionSet = [Transition(elem) for elem in ["RIGHT", "LEFT", "SHIFT", "REDUCE"]]
+ strategy = {"RIGHT" : 1, "SHIFT" : 1, "LEFT" : 0, "REDUCE" : 0}
+
+ sentences = Config.readConllu(filename)
+
+ if type == "oracle" :
+ trainModelOracle(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, silent)
+ return
+
+ print("ERROR : unknown type '%s'"%type, file=sys.stderr)
+ exit(1)
+################################################################################
################################################################################
def extractExamples(ts, strat, config, dicts, debug=False) :
@@ -32,3 +54,49 @@ def extractExamples(ts, strat, config, dicts, debug=False) :
return examples
################################################################################
+################################################################################
+def trainModelOracle(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, silent=False) :
+ examples = []
+ dicts = Dicts()
+ dicts.readConllu(filename, ["FORM", "UPOS"])
+ dicts.save(modelDir+"/dicts.json")
+ print("%s : Starting to extract examples..."%(timeStamp()), file=sys.stderr)
+ for config in sentences :
+ examples += extractExamples(transitionSet, strategy, config, dicts, debug)
+ print("%s : Extracted %d examples"%(timeStamp(), len(examples)), file=sys.stderr)
+ examples = torch.stack(examples)
+
+ network = Networks.BaseNet(dicts, examples[0].size(0)-1, len(transitionSet))
+ network.train()
+ optimizer = torch.optim.Adam(network.parameters(), lr=0.0001)
+ lossFct = torch.nn.CrossEntropyLoss()
+ for iter in range(1,nbIter+1) :
+ examples = examples.index_select(0, torch.randperm(examples.size(0)))
+ totalLoss = 0.0
+ nbEx = 0
+ printInterval = 2000
+ advancement = 0
+ for batchIndex in range(0,examples.size(0)-batchSize,batchSize) :
+ batch = examples[batchIndex:batchIndex+batchSize]
+ targets = batch[:,:1].view(-1)
+ inputs = batch[:,1:]
+ nbEx += targets.size(0)
+ advancement += targets.size(0)
+ if not silent and advancement >= printInterval :
+ advancement = 0
+ print("Curent epoch %6.2f%%"%(100.0*nbEx/examples.size(0)), end="\r", file=sys.stderr)
+ outputs = network(inputs)
+ loss = lossFct(outputs, targets)
+ network.zero_grad()
+ loss.backward()
+ optimizer.step()
+ totalLoss += float(loss)
+ devScore = ""
+ if devFile is not None :
+ outFilename = modelDir+"/predicted_dev.conllu"
+ Decode.decodeMode(debug, devFile, "model", network, dicts, open(outFilename, "w"))
+ res = evaluate(load_conllu(open(devFile, "r")), load_conllu(open(outFilename, "r")), [])
+ devScore = ", Dev : UAS=%.2f"%(res["UAS"][0].f1)
+ print("%s : Epoch %d, loss=%.2f%s"%(timeStamp(), iter, totalLoss, devScore), file=sys.stderr)
+################################################################################
+
diff --git a/Util.py b/Util.py
new file mode 100644
index 0000000..b9e2094
--- /dev/null
+++ b/Util.py
@@ -0,0 +1,7 @@
+from datetime import datetime
+
+################################################################################
+def timeStamp() :
+ return "[%s]"%datetime.now().strftime("%H:%M:%S")
+################################################################################
+
diff --git a/main.py b/main.py
index 2abef05..132f058 100755
--- a/main.py
+++ b/main.py
@@ -3,104 +3,9 @@
import sys
import os
import argparse
-from datetime import datetime
-import Config
-import Decode
import Train
-from Transition import Transition
-import Networks
-from Dicts import Dicts
-
-from conll18_ud_eval import load_conllu, evaluate
-
-import torch
-
-################################################################################
-def timeStamp() :
- return "[%s]"%datetime.now().strftime("%H:%M:%S")
-################################################################################
-
-################################################################################
-def trainMode(debug, filename, type, modelDir, nbIter, batchSize, devFile, silent=False) :
- transitionSet = [Transition(elem) for elem in ["RIGHT", "LEFT", "SHIFT", "REDUCE"]]
- strategy = {"RIGHT" : 1, "SHIFT" : 1, "LEFT" : 0, "REDUCE" : 0}
-
- sentences = Config.readConllu(filename)
-
- if type == "oracle" :
- examples = []
- dicts = Dicts()
- dicts.readConllu(filename, ["FORM", "UPOS"])
- dicts.save(modelDir+"/dicts.json")
- print("%s : Starting to extract examples..."%(timeStamp()), file=sys.stderr)
- for config in sentences :
- examples += Train.extractExamples(transitionSet, strategy, config, dicts, args.debug)
- print("%s : Extracted %d examples"%(timeStamp(), len(examples)), file=sys.stderr)
- examples = torch.stack(examples)
-
- network = Networks.BaseNet(dicts, examples[0].size(0)-1, len(transitionSet))
- network.train()
- optimizer = torch.optim.Adam(network.parameters(), lr=0.0001)
- lossFct = torch.nn.CrossEntropyLoss()
- for iter in range(1,nbIter+1) :
- examples = examples.index_select(0, torch.randperm(examples.size(0)))
- totalLoss = 0.0
- nbEx = 0
- printInterval = 2000
- advancement = 0
- for batchIndex in range(0,examples.size(0)-batchSize,batchSize) :
- batch = examples[batchIndex:batchIndex+batchSize]
- targets = batch[:,:1].view(-1)
- inputs = batch[:,1:]
- nbEx += targets.size(0)
- advancement += targets.size(0)
- if not silent and advancement >= printInterval :
- advancement = 0
- print("Curent epoch %6.2f%%"%(100.0*nbEx/examples.size(0)), end="\r", file=sys.stderr)
- outputs = network(inputs)
- loss = lossFct(outputs, targets)
- network.zero_grad()
- loss.backward()
- optimizer.step()
- totalLoss += float(loss)
- devScore = ""
- if devFile is not None :
- outFilename = modelDir+"/predicted_dev.conllu"
- decodeMode(debug, devFile, "model", network, dicts, open(outFilename, "w"))
- res = evaluate(load_conllu(open(devFile, "r")), load_conllu(open(outFilename, "r")), [])
- devScore = ", Dev : UAS=%.2f"%(res["UAS"][0].f1)
- print("%s : Epoch %d, loss=%.2f%s"%(timeStamp(), iter, totalLoss, devScore), file=sys.stderr)
- return
-
- print("ERROR : unknown type '%s'"%type, file=sys.stderr)
- exit(1)
-################################################################################
-
-################################################################################
-def decodeMode(debug, filename, type, network=None, dicts=None, output=sys.stdout) :
- transitionSet = [Transition(elem) for elem in ["RIGHT", "LEFT", "SHIFT", "REDUCE"]]
- strategy = {"RIGHT" : 1, "SHIFT" : 1, "LEFT" : 0, "REDUCE" : 0}
-
- sentences = Config.readConllu(filename)
-
- if type in ["random", "oracle"] :
- decodeFunc = Decode.oracleDecode if type == "oracle" else Decode.randomDecode
- for config in sentences :
- decodeFunc(transitionSet, strategy, config, args.debug)
- sentences[0].print(sys.stdout, header=True)
- for config in sentences[1:] :
- config.print(sys.stdout, header=False)
- elif type == "model" :
- for config in sentences :
- Decode.decodeModel(transitionSet, strategy, config, network, dicts, args.debug)
- sentences[0].print(output, header=True)
- for config in sentences[1:] :
- config.print(output, header=False)
- else :
- print("ERROR : unknown type '%s'"%type, file=sys.stderr)
- exit(1)
-################################################################################
+import Decode
################################################################################
if __name__ == "__main__" :
@@ -128,9 +33,9 @@ if __name__ == "__main__" :
os.makedirs(args.model, exist_ok=True)
if args.mode == "train" :
- trainMode(args.debug, args.corpus, args.type, args.model, int(args.iter), int(args.batchSize), args.dev, args.silent)
+ Train.trainMode(args.debug, args.corpus, args.type, args.model, int(args.iter), int(args.batchSize), args.dev, args.silent)
elif args.mode == "decode" :
- decodeMode(args.debug, args.corpus, args.type)
+ Decode.decodeMode(args.debug, args.corpus, args.type)
else :
print("ERROR : unknown mode '%s'"%args.mode, file=sys.stderr)
exit(1)
--
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