From bc508cdb21c1db75d0218f703cd39a8a89adadb0 Mon Sep 17 00:00:00 2001
From: Franck Dary <franck.dary@lis-lab.fr>
Date: Sat, 24 Apr 2021 11:18:47 +0200
Subject: [PATCH] Code refactoring and cosmetic changes to print
---
Train.py | 69 ++++++++++++++++++++++++++------------------------------
Util.py | 12 ++++++++++
2 files changed, 44 insertions(+), 37 deletions(-)
diff --git a/Train.py b/Train.py
index 689dca8..b61ecd3 100644
--- a/Train.py
+++ b/Train.py
@@ -6,7 +6,7 @@ import copy
from Transition import Transition, getMissingLinks, applyTransition
import Features
from Dicts import Dicts
-from Util import timeStamp
+from Util import timeStamp, prettyInt, numParameters
from Rl import ReplayMemory, selectAction, optimizeModel
import Networks
import Decode
@@ -61,7 +61,28 @@ def extractExamples(ts, strat, config, dicts, debug=False) :
################################################################################
################################################################################
-def trainModelOracle(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, silent=False) :
+def evalModelAndSave(debug, model, dicts, modelDir, devFile, bestLoss, totalLoss, bestScore, epoch, nbIter) :
+ devScore = ""
+ saved = True if bestLoss is None else totalLoss < bestLoss
+ bestLoss = totalLoss if bestLoss is None else min(bestLoss, totalLoss)
+ if devFile is not None :
+ outFilename = modelDir+"/predicted_dev.conllu"
+ Decode.decodeMode(debug, devFile, "model", modelDir, model, dicts, open(outFilename, "w"))
+ res = evaluate(load_conllu(open(devFile, "r")), load_conllu(open(outFilename, "r")), [])
+ UAS = res["UAS"][0].f1
+ score = UAS
+ saved = True if bestScore is None else score > bestScore
+ bestScore = score if bestScore is None else max(bestScore, score)
+ devScore = ", Dev : UAS=%.2f"%(UAS)
+ if saved :
+ torch.save(model, modelDir+"/network.pt")
+ print("{} : Epoch {:{}}/{}, loss={:6.2f}{} {}".format(timeStamp(), epoch, len(str(nbIter)), nbIter, totalLoss, devScore, "SAVED" if saved else ""), file=sys.stderr)
+
+ return bestLoss, bestScore
+################################################################################
+
+################################################################################
+def trainModelOracle(debug, modelDir, filename, nbEpochs, batchSize, devFile, transitionSet, strategy, sentences, silent=False) :
examples = []
dicts = Dicts()
dicts.readConllu(filename, ["FORM", "UPOS"])
@@ -69,15 +90,16 @@ def trainModelOracle(debug, modelDir, filename, nbIter, batchSize, devFile, tran
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)
+ print("%s : Extracted %s examples"%(timeStamp(), prettyInt(len(examples), 3)), file=sys.stderr)
examples = torch.stack(examples)
network = Networks.BaseNet(dicts, examples[0].size(0)-1, len(transitionSet))
+ print("%s : Model has %s parameters"%(timeStamp(), prettyInt((numParameters(network)), 3)), file=sys.stderr)
optimizer = torch.optim.Adam(network.parameters(), lr=0.0001)
lossFct = torch.nn.CrossEntropyLoss()
bestLoss = None
bestScore = None
- for iter in range(1,nbIter+1) :
+ for epoch in range(1,nbEpochs+1) :
network.train()
examples = examples.index_select(0, torch.randperm(examples.size(0)))
totalLoss = 0.0
@@ -99,21 +121,8 @@ def trainModelOracle(debug, modelDir, filename, nbIter, batchSize, devFile, tran
loss.backward()
optimizer.step()
totalLoss += float(loss)
- devScore = ""
- saved = True if bestLoss is None else totalLoss < bestLoss
- bestLoss = totalLoss if bestLoss is None else min(bestLoss, totalLoss)
- if devFile is not None :
- outFilename = modelDir+"/predicted_dev.conllu"
- Decode.decodeMode(debug, devFile, "model", modelDir, network, dicts, open(outFilename, "w"))
- res = evaluate(load_conllu(open(devFile, "r")), load_conllu(open(outFilename, "r")), [])
- UAS = res["UAS"][0].f1
- score = UAS
- saved = True if bestScore is None else score > bestScore
- bestScore = score if bestScore is None else max(bestScore, score)
- devScore = ", Dev : UAS=%.2f"%(UAS)
- if saved :
- torch.save(network, modelDir+"/network.pt")
- print("%s : Epoch %d, loss=%.2f%s %s"%(timeStamp(), iter, totalLoss, devScore, "SAVED" if saved else ""), file=sys.stderr)
+
+ bestLoss, bestScore = evalModelAndSave(debug, network, dicts, modelDir, devFile, bestLoss, totalLoss, bestScore, epoch, nbEpochs)
################################################################################
################################################################################
@@ -130,11 +139,13 @@ def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transiti
target_net.eval()
policy_net.train()
+ print("%s : Model has %s parameters"%(timeStamp(), prettyInt((numParameters(policy_net)), 3)), file=sys.stderr)
+
optimizer = torch.optim.Adam(policy_net.parameters(), lr=0.0001)
bestLoss = None
bestScore = None
- for epoch in range(nbIter) :
+ for epoch in range(1,nbIter+1) :
i = 0
totalLoss = 0.0
sentences = copy.deepcopy(sentencesOriginal)
@@ -167,22 +178,6 @@ def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transiti
target_net.eval()
policy_net.train()
i += 1
- # Fin epoch, compute score and save model
- devScore = ""
- saved = True if bestLoss is None else totalLoss < bestLoss
- bestLoss = totalLoss if bestLoss is None else min(bestLoss, totalLoss)
- if devFile is not None :
- outFilename = modelDir+"/predicted_dev.conllu"
- Decode.decodeMode(debug, devFile, "model", modelDir, policy_net, dicts, open(outFilename, "w"))
- res = evaluate(load_conllu(open(devFile, "r")), load_conllu(open(outFilename, "r")), [])
- UAS = res["UAS"][0].f1
- score = UAS
- saved = True if bestScore is None else score > bestScore
- bestScore = score if bestScore is None else max(bestScore, score)
- devScore = ", Dev : UAS=%.2f"%(UAS)
- if saved :
- torch.save(policy_net, modelDir+"/network.pt")
- print("%s : Epoch %d, loss=%.2f%s %s"%(timeStamp(), epoch, totalLoss, devScore, "SAVED" if saved else ""), file=sys.stderr)
-
+ bestLoss, bestScore = evalModelAndSave(debug, policy_net, dicts, modelDir, devFile, bestLoss, totalLoss, bestScore, epoch, nbIter)
################################################################################
diff --git a/Util.py b/Util.py
index ca3b088..362bb6d 100644
--- a/Util.py
+++ b/Util.py
@@ -10,3 +10,15 @@ def isEmpty(value) :
return value == "_" or value == ""
################################################################################
+################################################################################
+def prettyInt(value, p) :
+ l = ['' for _ in range((p-len(str(value))%p)%p)] + list(str(value))
+ l = ["".join(l[i:i+p]) for i in range(0,len(l),p)]
+ return " ".join(l)
+################################################################################
+
+################################################################################
+def numParameters(model) :
+ return sum(p.numel() for p in model.parameters() if p.requires_grad)
+################################################################################
+
--
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