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utils_cmn.py
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utils_cmn.py
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import numpy as np
import pandas as pd
import pickle
from sklearn import model_selection, metrics
TEXT_EMBEDDINGS = "./IEMOCAP/data/text/IEMOCAP_text_embeddings.pickle"
VIDEO_EMBEDDINGS = "./IEMOCAP/data/video/IEMOCAP_video_features.pickle"
AUDIO_EMBEDDINGS = "./IEMOCAP/data/audio/IEMOCAP_audio_features.pickle"
trainID = pickle.load(open("./IEMOCAP/data/trainID.pkl",'rb'), encoding="latin1")
testID = pickle.load(open("./IEMOCAP/data/testID.pkl",'rb'), encoding="latin1")
valID,_ = model_selection.train_test_split(testID, test_size=.4, random_state=1227)
# valID = testID
transcripts, labels, own_historyID, other_historyID, own_historyID_rank, other_historyID_rank = pickle.load(open("./IEMOCAP/data/dataset.pkl",'rb'), encoding="latin1")
label_idx = {'hap':0, 'sad':1, 'neu':2, 'ang':3, 'exc':4, 'fru':5}
def oneHot(trainLabels, valLabels, testLabels):
# Calculate the total number of classes
numOfClasses = np.max(trainLabels)+1
trainLabelOneHot = np.zeros((len(trainLabels),numOfClasses), dtype=np.float32)
valLabelOneHot = np.zeros((len(valLabels),numOfClasses), dtype=np.float32)
testLabelOneHot = np.zeros((len(testLabels),numOfClasses), dtype=np.float32)
for idx, label in enumerate(trainLabels):
trainLabelOneHot[idx, int(label)]=1.0
for idx, label in enumerate(valLabels):
valLabelOneHot[idx, int(label)]=1.0
for idx, label in enumerate(testLabels):
testLabelOneHot[idx, int(label)]=1.0
return trainLabelOneHot, valLabelOneHot, testLabelOneHot
def updateDictText(text_transcripts_emb, text_own_history_emb, text_other_history_emb, text_emb):
for ID, value in text_transcripts_emb.items():
if ID in text_emb.keys():
text_transcripts_emb[ID] = text_emb[ID]
# updating the context faeturs
for ID, value in text_own_history_emb.items():
ids = own_historyID[ID]
for idx, iD in enumerate(ids):
if iD in text_emb.keys():
text_own_history_emb[ID][idx]= text_emb[iD]
# updating the context faeturs
for ID, value in text_other_history_emb.items():
ids = other_historyID[ID]
for idx, iD in enumerate(ids):
if iD in text_emb.keys():
text_other_history_emb[ID][idx]= text_emb[iD]
return text_transcripts_emb, text_own_history_emb, text_other_history_emb
def loadData(FLAGS):
## Load Labels
trainLabels = np.asarray([label_idx[labels[ID]] for ID in trainID])
valLabels = np.asarray([label_idx[labels[ID]] for ID in valID])
testLabels = np.asarray([label_idx[labels[ID]] for ID in testID])
trainLabels, valLabels, testLabels = oneHot(trainLabels, valLabels, testLabels)
## Loading Text features
text_transcripts_emb, text_own_history_emb, text_other_history_emb = pickle.load( open(TEXT_EMBEDDINGS, 'rb'), encoding="latin1")
if FLAGS.context:
print("loading contextual features")
text_emb = pickle.load(open("./IEMOCAP/data/text/IEMOCAP_text_context.pickle", 'rb'), encoding="latin1")
text_transcripts_emb, text_own_history_emb, text_other_history_emb = updateDictText(text_transcripts_emb, text_own_history_emb, text_other_history_emb, text_emb)
## Loading Audio features
audio_emb = pickle.load(open(AUDIO_EMBEDDINGS, 'rb'), encoding="latin1")
if FLAGS.context:
audio_emb_context = pickle.load(open("./IEMOCAP/data/audio/IEMOCAP_audio_context.pickle", 'rb'), encoding="latin1")
for ID in audio_emb.keys():
if ID in audio_emb_context.keys():
audio_emb[ID] = audio_emb_context[ID]
## Loading Video features
video_emb = pickle.load(open(VIDEO_EMBEDDINGS, 'rb'), encoding="latin1")
# video_emb_context = pickle.load(open("./IEMOCAP/data/video/IEMOCAP_video_context.pickle", 'rb'), encoding="latin1")
# for ID in video_emb.keys():
# if ID in video_emb_context.keys():
# video_emb[ID] = video_emb_context[ID]
## Text Embeddings for the queries
text_trainQueries = np.asarray([text_transcripts_emb[ID] for ID in trainID])
text_valQueries = np.asarray([text_transcripts_emb[ID] for ID in valID])
text_testQueries = np.asarray([text_transcripts_emb[ID] for ID in testID])
## Audio Embeddings for the queries
audio_trainQueries = np.asarray([audio_emb[ID] for ID in trainID])
audio_valQueries = np.asarray([audio_emb[ID] for ID in valID])
audio_testQueries = np.asarray([audio_emb[ID] for ID in testID])
## Video Embeddings for the queries
video_trainQueries = np.asarray([video_emb[ID] for ID in trainID])
video_valQueries = np.asarray([video_emb[ID] for ID in valID])
video_testQueries = np.asarray([video_emb[ID] for ID in testID])
if FLAGS.mode == "text":
trainQueries = text_trainQueries
valQueries = text_valQueries
testQueries = text_testQueries
if FLAGS.mode == "video":
trainQueries = video_trainQueries
valQueries = video_valQueries
testQueries = video_testQueries
if FLAGS.mode == "audio":
trainQueries = audio_trainQueries
valQueries = audio_valQueries
testQueries = audio_testQueries
if FLAGS.mode == "textvideo":
trainQueries = np.concatenate((text_trainQueries, video_trainQueries), axis=1)
valQueries = np.concatenate((text_valQueries, video_valQueries), axis=1)
testQueries = np.concatenate((text_testQueries, video_testQueries), axis=1)
if FLAGS.mode == "audiovideo":
trainQueries = np.concatenate((audio_trainQueries, video_trainQueries), axis=1)
valQueries = np.concatenate((audio_valQueries, video_valQueries), axis=1)
testQueries = np.concatenate((audio_testQueries, video_testQueries), axis=1)
if FLAGS.mode == "textaudio":
trainQueries = np.concatenate((text_trainQueries, audio_trainQueries), axis=1)
valQueries = np.concatenate((text_valQueries, audio_valQueries), axis=1)
testQueries = np.concatenate((text_testQueries, audio_testQueries), axis=1)
if FLAGS.mode == "all":
trainQueries = np.concatenate((text_trainQueries, audio_trainQueries, video_trainQueries), axis=1)
valQueries = np.concatenate((text_valQueries, audio_valQueries, video_valQueries), axis=1)
testQueries = np.concatenate((text_testQueries, audio_testQueries, video_testQueries), axis=1)
## Pad the histories upto maximum length
#Train queries' histories
#(older to newer)
trainOwnHistory = np.zeros((len(trainID), FLAGS.timesteps, trainQueries.shape[1]), dtype = np.float32)
trainOtherHistory = np.zeros((len(trainID), FLAGS.timesteps, trainQueries.shape[1]), dtype = np.float32)
trainOwnHistoryMask = np.zeros((len(trainID), FLAGS.timesteps), dtype = np.float32)
trainOtherHistoryMask = np.zeros((len(trainID), FLAGS.timesteps), dtype = np.float32)
for iddx, ID in enumerate(trainID):
combined_historyID_rank = own_historyID_rank[ID][:] + other_historyID_rank[ID][:]
if len(combined_historyID_rank) > 0:
maxRank = np.max(combined_historyID_rank)
own_history_rank = [maxRank - currRank for currRank in own_historyID_rank[ID]]
other_history_rank = [maxRank - currRank for currRank in other_historyID_rank[ID]]
textOwnHistoryEmb = np.asarray(text_own_history_emb[ID])
textOtherHistoryEmb = np.asarray(text_other_history_emb[ID])
audioOwnHistoryEmb = np.asarray( [audio_emb[own_historyID[ID][idx]] for idx in range(len(own_historyID[ID]))] )
audioOtherHistoryEmb = np.asarray( [audio_emb[other_historyID[ID][idx]] for idx in range(len(other_historyID[ID]))] )
videoOwnHistoryEmb = np.asarray( [video_emb[own_historyID[ID][idx]] for idx in range(len(own_historyID[ID]))] )
videoOtherHistoryEmb = np.asarray( [video_emb[other_historyID[ID][idx]] for idx in range(len(other_historyID[ID]))] )
for idx, rank in enumerate(own_history_rank):
if rank < FLAGS.timesteps:
if FLAGS.mode == "text":
trainOwnHistory[iddx,rank] = textOwnHistoryEmb[idx]
elif FLAGS.mode == "video":
trainOwnHistory[iddx,rank] = videoOwnHistoryEmb[idx]
elif FLAGS.mode == "audio":
trainOwnHistory[iddx,rank] = audioOwnHistoryEmb[idx]
elif FLAGS.mode == "textvideo":
trainOwnHistory[iddx,rank] = np.concatenate((textOwnHistoryEmb[idx], videoOwnHistoryEmb[idx]))
elif FLAGS.mode == "audiovideo":
trainOwnHistory[iddx,rank] = np.concatenate((audioOwnHistoryEmb[idx], videoOwnHistoryEmb[idx]))
elif FLAGS.mode == "textaudio":
trainOwnHistory[iddx,rank] = np.concatenate((textOwnHistoryEmb[idx], audioOwnHistoryEmb[idx]))
elif FLAGS.mode == "all":
trainOwnHistory[iddx,rank] = np.concatenate((textOwnHistoryEmb[idx], audioOwnHistoryEmb[idx], videoOwnHistoryEmb[idx]))
trainOwnHistoryMask[iddx,rank] = 1.0
trainOwnHistory[iddx] = trainOwnHistory[iddx,::-1,:]
trainOwnHistoryMask[iddx] = trainOwnHistoryMask[iddx,::-1]
for idx, rank in enumerate(other_history_rank):
if rank < FLAGS.timesteps:
if FLAGS.mode == "text":
trainOtherHistory[iddx,rank] = textOtherHistoryEmb[idx]
elif FLAGS.mode == "video":
trainOtherHistory[iddx,rank] = videoOtherHistoryEmb[idx]
elif FLAGS.mode == "audio":
trainOtherHistory[iddx,rank] = audioOtherHistoryEmb[idx]
elif FLAGS.mode == "textvideo":
trainOtherHistory[iddx,rank] = np.concatenate((textOtherHistoryEmb[idx], videoOtherHistoryEmb[idx]))
elif FLAGS.mode == "audiovideo":
trainOtherHistory[iddx,rank] = np.concatenate((audioOtherHistoryEmb[idx], videoOtherHistoryEmb[idx]))
elif FLAGS.mode == "textaudio":
trainOtherHistory[iddx,rank] = np.concatenate((textOtherHistoryEmb[idx], audioOtherHistoryEmb[idx]))
elif FLAGS.mode == "all":
trainOtherHistory[iddx,rank] = np.concatenate((textOtherHistoryEmb[idx], audioOtherHistoryEmb[idx], videoOtherHistoryEmb[idx]))
trainOtherHistoryMask[iddx,rank] = 1.0
trainOtherHistory[iddx] = trainOtherHistory[iddx,::-1,:]
trainOtherHistoryMask[iddx] = trainOtherHistoryMask[iddx,::-1]
valOwnHistory = np.zeros((len(valID), FLAGS.timesteps, valQueries.shape[1]), dtype = np.float32)
valOtherHistory = np.zeros((len(valID), FLAGS.timesteps, valQueries.shape[1]), dtype = np.float32)
valOwnHistoryMask = np.zeros((len(valID), FLAGS.timesteps), dtype = np.float32)
valOtherHistoryMask = np.zeros((len(valID), FLAGS.timesteps), dtype = np.float32)
for iddx, ID in enumerate(valID):
combined_historyID_rank = own_historyID_rank[ID][:] + other_historyID_rank[ID][:]
if len(combined_historyID_rank) > 0:
maxRank = np.max(combined_historyID_rank)
own_history_rank = [maxRank - currRank for currRank in own_historyID_rank[ID]]
other_history_rank = [maxRank - currRank for currRank in other_historyID_rank[ID]]
textOwnHistoryEmb = np.asarray(text_own_history_emb[ID])
textOtherHistoryEmb = np.asarray(text_other_history_emb[ID])
audioOwnHistoryEmb = np.asarray( [audio_emb[own_historyID[ID][idx]] for idx in range(len(own_historyID[ID]))] )
audioOtherHistoryEmb = np.asarray( [audio_emb[other_historyID[ID][idx]] for idx in range(len(other_historyID[ID]))] )
videoOwnHistoryEmb = np.asarray( [video_emb[own_historyID[ID][idx]] for idx in range(len(own_historyID[ID]))] )
videoOtherHistoryEmb = np.asarray( [video_emb[other_historyID[ID][idx]] for idx in range(len(other_historyID[ID]))] )
for idx, rank in enumerate(own_history_rank):
if rank < FLAGS.timesteps:
if FLAGS.mode == "text":
valOwnHistory[iddx,rank] = textOwnHistoryEmb[idx]
elif FLAGS.mode == "video":
valOwnHistory[iddx,rank] = videoOwnHistoryEmb[idx]
elif FLAGS.mode == "audio":
valOwnHistory[iddx,rank] = audioOwnHistoryEmb[idx]
elif FLAGS.mode == "textvideo":
valOwnHistory[iddx,rank] = np.concatenate((textOwnHistoryEmb[idx], videoOwnHistoryEmb[idx]))
elif FLAGS.mode == "audiovideo":
valOwnHistory[iddx,rank] = np.concatenate((audioOwnHistoryEmb[idx], videoOwnHistoryEmb[idx]))
elif FLAGS.mode == "textaudio":
valOwnHistory[iddx,rank] = np.concatenate((textOwnHistoryEmb[idx], audioOwnHistoryEmb[idx]))
elif FLAGS.mode == "all":
valOwnHistory[iddx,rank] = np.concatenate((textOwnHistoryEmb[idx], audioOwnHistoryEmb[idx], videoOwnHistoryEmb[idx]))
valOwnHistoryMask[iddx,rank] = 1.0
valOwnHistory[iddx] = valOwnHistory[iddx,::-1,:]
valOwnHistoryMask[iddx] = valOwnHistoryMask[iddx,::-1]
for idx, rank in enumerate(other_history_rank):
if rank < FLAGS.timesteps:
if FLAGS.mode == "text":
valOtherHistory[iddx,rank] = textOtherHistoryEmb[idx]
elif FLAGS.mode == "video":
valOtherHistory[iddx,rank] = videoOtherHistoryEmb[idx]
elif FLAGS.mode == "audio":
valOtherHistory[iddx,rank] = audioOtherHistoryEmb[idx]
elif FLAGS.mode == "textvideo":
valOtherHistory[iddx,rank] = np.concatenate((textOtherHistoryEmb[idx], videoOtherHistoryEmb[idx]))
elif FLAGS.mode == "audiovideo":
valOtherHistory[iddx,rank] = np.concatenate((audioOtherHistoryEmb[idx], videoOtherHistoryEmb[idx]))
elif FLAGS.mode == "textaudio":
valOtherHistory[iddx,rank] = np.concatenate((textOtherHistoryEmb[idx], audioOtherHistoryEmb[idx]))
elif FLAGS.mode == "all":
valOtherHistory[iddx,rank] = np.concatenate((textOtherHistoryEmb[idx], audioOtherHistoryEmb[idx], videoOtherHistoryEmb[idx]))
valOtherHistoryMask[iddx,rank] = 1.0
valOtherHistory[iddx] = valOtherHistory[iddx,::-1,:]
valOtherHistoryMask[iddx] = valOtherHistoryMask[iddx,::-1]
#Test queries' histories
testOwnHistory = np.zeros((len(testID), FLAGS.timesteps, testQueries.shape[1]), dtype = np.float32)
testOtherHistory = np.zeros((len(testID), FLAGS.timesteps, testQueries.shape[1]), dtype = np.float32)
testOwnHistoryMask = np.zeros((len(testID), FLAGS.timesteps), dtype = np.float32)
testOtherHistoryMask = np.zeros((len(testID), FLAGS.timesteps), dtype = np.float32)
for iddx, ID in enumerate(testID):
combined_historyID_rank = own_historyID_rank[ID][:] + other_historyID_rank[ID][:]
if len(combined_historyID_rank) > 0:
maxRank = np.max(combined_historyID_rank)
own_history_rank = [maxRank - currRank for currRank in own_historyID_rank[ID]]
other_history_rank = [maxRank - currRank for currRank in other_historyID_rank[ID]]
textOwnHistoryEmb = np.asarray(text_own_history_emb[ID])
textOtherHistoryEmb = np.asarray(text_other_history_emb[ID])
audioOwnHistoryEmb = np.asarray( [audio_emb[own_historyID[ID][idx]] for idx in range(len(own_historyID[ID]))] )
audioOtherHistoryEmb = np.asarray( [audio_emb[other_historyID[ID][idx]] for idx in range(len(other_historyID[ID]))] )
videoOwnHistoryEmb = np.asarray( [video_emb[own_historyID[ID][idx]] for idx in range(len(own_historyID[ID]))] )
videoOtherHistoryEmb = np.asarray( [video_emb[other_historyID[ID][idx]] for idx in range(len(other_historyID[ID]))] )
for idx, rank in enumerate(own_history_rank):
if rank < FLAGS.timesteps:
if FLAGS.mode == "text":
testOwnHistory[iddx,rank] = textOwnHistoryEmb[idx]
elif FLAGS.mode == "video":
testOwnHistory[iddx,rank] = videoOwnHistoryEmb[idx]
elif FLAGS.mode == "audio":
testOwnHistory[iddx,rank] = audioOwnHistoryEmb[idx]
elif FLAGS.mode == "textvideo":
testOwnHistory[iddx,rank] = np.concatenate((textOwnHistoryEmb[idx], videoOwnHistoryEmb[idx]))
elif FLAGS.mode == "audiovideo":
testOwnHistory[iddx,rank] = np.concatenate((audioOwnHistoryEmb[idx], videoOwnHistoryEmb[idx]))
elif FLAGS.mode == "textaudio":
testOwnHistory[iddx,rank] = np.concatenate((textOwnHistoryEmb[idx], audioOwnHistoryEmb[idx]))
elif FLAGS.mode == "all":
testOwnHistory[iddx,rank] = np.concatenate((textOwnHistoryEmb[idx], audioOwnHistoryEmb[idx], videoOwnHistoryEmb[idx]))
testOwnHistoryMask[iddx,rank] = 1.0
testOwnHistory[iddx] = testOwnHistory[iddx,::-1,:]
testOwnHistoryMask[iddx] = testOwnHistoryMask[iddx,::-1]
for idx, rank in enumerate(other_history_rank):
if rank < FLAGS.timesteps:
if FLAGS.mode == "text":
testOtherHistory[iddx,rank] = textOtherHistoryEmb[idx]
elif FLAGS.mode == "video":
testOtherHistory[iddx,rank] = videoOtherHistoryEmb[idx]
elif FLAGS.mode == "audio":
testOtherHistory[iddx,rank] = audioOtherHistoryEmb[idx]
elif FLAGS.mode == "textvideo":
testOtherHistory[iddx,rank] = np.concatenate((textOtherHistoryEmb[idx], videoOtherHistoryEmb[idx]))
elif FLAGS.mode == "audiovideo":
testOtherHistory[iddx,rank] = np.concatenate((audioOtherHistoryEmb[idx], videoOtherHistoryEmb[idx]))
elif FLAGS.mode == "textaudio":
testOtherHistory[iddx,rank] = np.concatenate((textOtherHistoryEmb[idx], audioOtherHistoryEmb[idx]))
elif FLAGS.mode == "all":
testOtherHistory[iddx,rank] = np.concatenate((textOtherHistoryEmb[idx], audioOtherHistoryEmb[idx], videoOtherHistoryEmb[idx]))
testOtherHistoryMask[iddx,rank] = 1.0
testOtherHistory[iddx] = testOtherHistory[iddx,::-1,:]
testOtherHistoryMask[iddx] = testOtherHistoryMask[iddx,::-1]
return trainQueries, trainOwnHistory, trainOtherHistory, trainOwnHistoryMask, trainOtherHistoryMask, trainLabels, \
valQueries, valOwnHistory, valOtherHistory, valOwnHistoryMask, valOtherHistoryMask, valLabels, \
testQueries, testOwnHistory, testOtherHistory, testOwnHistoryMask, testOtherHistoryMask, testLabels
if __name__ == "__main__":
loadData()