From 2b5ffe1c6571d67864b0d0f0c36046182387fe68 Mon Sep 17 00:00:00 2001
From: Gduret <guillaume.duret@ec-lyon.fr>
Date: Fri, 3 Mar 2023 00:27:10 +0100
Subject: [PATCH] add path Fruits
---
data.py | 32 ++++++++++++++++++++
models.py | 87 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
2 files changed, 119 insertions(+)
diff --git a/data.py b/data.py
index ff4a828..2406026 100644
--- a/data.py
+++ b/data.py
@@ -64,6 +64,15 @@ def getMasterList(basePath): # returns list with image, mask, and label filenam
return [[a, b, c] for a, b, c in zip(imageList, maskList, labelList)]
+def getMasterList_Fruits(basePath): # returns list with image, mask, and label filenames
+ imageList = sorted(os.listdir(basePath + '/RGB_resized/'))
+ maskList = sorted(os.listdir(basePath + '/Instance_Mask_resized/'))
+ labelList = sorted(os.listdir(basePath + '/FPS_resized/'))
+ if len(imageList) != len(maskList) or len(imageList) != len(labelList):
+ raise Exception("image, mask, and label list lengths do not match.")
+
+ return [[a, b, c] for a, b, c in zip(imageList, maskList, labelList)]
+
def classTrainingGenerator(model, batchSize, masterList=None, height=480, width=640, augmentation=True,
**unused): # take input image, resize and store as rgb, create mask training data
basePath = os.path.dirname(os.path.realpath(__file__)) + '/LINEMOD/' + model
@@ -284,6 +293,29 @@ def getDataSplit(genNew=False, split=.8, modelClass='cat'):
splitDict = pickle.load(f)
return splitDict["trainData"], splitDict["validData"]
+def getDataSplit_Fruits(genNew=False, split=.8, modelClass='cat'):
+ # access training data, get jpeg, mask, label filenames split into training / validation sets
+ if genNew: # create split
+ basePathTraining = f'/home/gduret/Documents/guimod/Generated_Worlds_Training/{modelClass}' # os.path.dirname(os.path.realpath(__file__)) + '/LINEMOD/' + modelClass
+ basePathEvaluating = f'/home/gduret/Documents/guimod/Generated_Worlds_Evaluating/{modelClass}'
+ masterList_Training = getMasterList_Fruits(basePathTraining)
+ masterList_Evalution = getMasterList_Fruits(basePathEvaluating)
+ random.shuffle(masterList_Training)
+ random.shuffle(masterList_Evalution)
+
+ #splitPoint = round(len(masterList) * split)
+
+ splitDict = {"trainData": masterList_Training, "validData": masterList_Evalution}
+
+ with open("{0}_trainSplit".format(modelClass), 'wb') as f:
+ pickle.dump(splitDict, f)
+
+ else: # load saved split
+ with open("{0}_trainSplit".format(modelClass), 'rb') as f:
+ splitDict = pickle.load(f)
+ return splitDict["trainData"], splitDict["validData"]
+
+
def genAltLabels(p3dOld, p3dNew, matrix=np.array([[572.4114, 0., 325.2611], [0., 573.57043, 242.04899], [0., 0., 1.]]),
method=cv2.SOLVEPNP_ITERATIVE, modelClass='cat', height=480, width=640,
diff --git a/models.py b/models.py
index 6c9e9bf..4934cbd 100644
--- a/models.py
+++ b/models.py
@@ -391,6 +391,93 @@ def trainModel(modelStruct, modelGen, modelClass='cat', batchSize=2, optimizer=t
return model
+
+def trainModel_Fruits(modelStruct, modelGen, modelClass='cat', batchSize=2, optimizer=tf.keras.optimizers.Adam,
+ learning_rate=0.01, losses=None, metrics=None, saveModel=True, modelName='stvNet_weights',
+ epochs=1, loss_weights=None, outVectors=False, outClasses=False, dataSplit=True, altLabels=True,
+ augmentation=True): # train and save model weights
+ if metrics is None:
+ metrics = ['accuracy']
+ if not (outVectors or outClasses):
+ print("At least one of outVectors or outClasses must be set to True.")
+ return
+ model = modelStruct(outVectors=outVectors, outClasses=outClasses, modelName=modelName)
+ model.summary()
+ model.compile(optimizer=optimizer(learning_rate=learning_rate), loss=losses, metrics=metrics,
+ loss_weights=loss_weights)
+
+ trainData, validData = None, None
+ if dataSplit: # if using datasplit, otherwise all available data is used
+ trainData, validData = data.getDataSplit_Fruits(modelClass=modelClass)
+
+ logger = tf.keras.callbacks.CSVLogger("models/history/" + modelName + "_" + modelClass + "_history.csv",
+ append=True)
+ # evalLogger = tf.keras.callbacks.CSVLogger("models/history/" + modelName + "_" + modelClass + "_eval_history.csv", append = True)
+
+ history, valHistory = [], []
+
+ if type(losses) is dict:
+ outKeys = list(losses.keys())
+ if len(outKeys) == 2: # combined output
+ for i in range(epochs):
+ print("Epoch {0} of {1}".format(i + 1, epochs))
+ hist = model.fit(modelGen(modelClass, batchSize, masterList=trainData, out0=outKeys[0], out1=outKeys[1],
+ altLabels=altLabels, augmentation=augmentation),
+ steps_per_epoch=math.ceil(len(trainData) / batchSize), max_queue_size=2,
+ callbacks=[logger])
+ history.append(hist.history)
+ if dataSplit:
+ print("Validation:")
+ valHist = model.evaluate(
+ modelGen(modelClass, batchSize, masterList=validData, out0=outKeys[0], out1=outKeys[1],
+ altLabels=altLabels, augmentation=False), steps=math.ceil(len(validData) / batchSize),
+ max_queue_size=2)
+ valHistory.append(valHist)
+ else:
+ raise Exception("Probably shouldn't be here ever..")
+ else:
+ for i in range(epochs):
+ print("Epoch {0} of {1}".format(i + 1, epochs))
+ hist = model.fit(
+ modelGen(modelClass, batchSize, masterList=trainData, altLabels=altLabels, augmentation=augmentation),
+ steps_per_epoch=math.ceil(len(trainData) / batchSize), max_queue_size=2, callbacks=[logger])
+ history.append(hist.history)
+ if dataSplit:
+ print("Validation:")
+ valHist = model.evaluate(
+ modelGen(modelClass, batchSize, masterList=validData, altLabels=altLabels, augmentation=False),
+ steps=math.ceil(len(validData) / batchSize), max_queue_size=2)
+ valHistory.append(valHist)
+
+ historyLog = {"struct": modelStruct.__name__,
+ "class": modelClass,
+ "optimizer": optimizer,
+ "lr": learning_rate,
+ "losses": losses,
+ "name": modelName,
+ "epochs": epochs,
+ "history": history,
+ "evalHistory": valHistory,
+ "timestamp": datetime.now().strftime("%d/%m/%Y %H:%M:%S"),
+ }
+
+ if saveModel:
+ model.save_weights(os.path.dirname(os.path.realpath(__file__)) + '/models/' + modelName + '_' + modelClass)
+ model.save(os.path.dirname(os.path.realpath(__file__)) + '/models/' + modelName + '_' + modelClass)
+ if not os.path.exists("models/history/" + modelName + '_trainHistory'):
+ with open("models/history/" + modelName + '_' + modelClass + '_trainHistory',
+ 'wb') as f: # create model history
+ pickle.dump([], f)
+ with open("models/history/" + modelName + '_' + modelClass + '_trainHistory', 'rb') as f: # loading old history
+ histories = pickle.load(f)
+ histories.append(historyLog)
+ with open("models/history/" + modelName + '_' + modelClass + '_trainHistory',
+ 'wb') as f: # saving the history of the model
+ pickle.dump(histories, f)
+
+ return model
+
+
def trainModels(modelSets, shutDown=False):
for modelSet in modelSets:
print("Training {0}".format(modelSet.name))
--
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