def main():
calAndSaveFeatures()
starttime = time()
if len(sys.argv) <= 1:
print("Missing arguments")
else:
fileName = sys.argv[1]
arguments = {"show" : True, "save" : False, "saveInfo" : False}
for i in range(2, len(sys.argv)):
if (sys.argv[i].split("=")[0])[2:] in arguments.keys():
arguments[(sys.argv[i].split("=")[0])[2:]] = \
(sys.argv[i].split("=")[1] == str(True))
print("loading model...")
clf = loadModel()
detector = Detector(clf)
print("detecting...")
detector.detectFace(fileName, _show=arguments['show'], _save=arguments['save'],
_saveInfo=arguments['saveInfo'])
endtime = time()
print("cost: " + str(endtime - starttime))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--train', action='store_true')
parser.add_argument('--test', action='store_true')
parser.add_argument('--model')
args = parser.parse_args()
if args.train and args.model:
nn = model.generate_model()
training_input = data_utils.load_data(training_input_dir)
training_output = data_utils.load_data(training_output_dir)
nn.fit(training_input, training_output, batch_size=128, epochs=50)
model.saveModel(nn, args.model)
test = input("Do you want to test with the test images too? ")
if test == 'yes':
test_input = data_utils.load_data(test_input_dir)
test_output = nn.predict(test_input)
print(test_output.shape)
data_utils.save_images(test_output_dir, test_input_dir,
test_output)
elif args.test and args.model:
nn = model.loadModel(args.model)
test_input = data_utils.load_data(test_input_dir)
test_output = nn.predict(test_input)
print(test_output.shape)
data_utils.save_images(test_output_dir, test_input_dir, test_output)
def test_hmm():
m = model.loadModel("hmm.pkl")
sentence = wordsToIds("what is your name")[1:-1]
print(idsToWords(sentence))
output = m.getOutput(sentence)
print(idsToWords(output))
loader = loadDataset(set_name="test")
with open("data/testdata/result.txt", "a") as result_file:
c = 0
for sample in loader:
if c > 3767:
input_sentence = sample["input"][0][1:-1]
#target_sentence = sample["target"][0]
output = idsToWords(m.getOutput(input_sentence))
try:
last_index = output.index("<EOS>")
line = " ".join(output[:last_index]) + "\n"
except:
line = " ".join(output) + "\n"
result_file.write(line)
print(c, end="\r")
c += 1
def __init__(self, Parent=None):
'''
Constructor
'''
super().__init__(Parent)
self.__InitData() #先初始化数据，再初始化界面
self.__InitView()
self.svmModel = model.loadModel()
def performIndividualTest(inputSentence, weightsFilename):
tokenizer, wordsToIndex = loadWordMapping()
sequenceLength = 300
vecSpaceSize = 8
#Load the model and its weights
model = loadModel(len(wordsToIndex) + 1, sequenceLength, vecSpaceSize)
model.load_weights(weightsFilename)
X = tokenizer.texts_to_sequences([inputSentence])
X = pad_sequences(X, maxlen=sequenceLength, padding='post')
result = model.predict(X)
print("\tPredicted: {}".format(result))
def test(inputFileName, imgSize=64):
# load model
model = loadModel('model_{0}.hd5'.format(imgSize))
# load input data
x_test = loadTestData(inputFileName, imgSize=imgSize)
# predict
pred = model.predict(x_test)
print(pred)
# show result
print('Prediction : {0}'.format(pred[0]))
num = np.argmax(pred[0])
print('Prediction : {0}'.format(num))
def __init__(self):
super(Example, self).__init__()
self.model = model.loadModel()
#resize设置宽高，move设置位置
self.resize(280, 280)
self.move(100, 100)
self.setWindowTitle("test")
#setMouseTracking设置为False，否则不按下鼠标时也会跟踪鼠标事件
self.setMouseTracking(False)
'''
要想将按住鼠标后移动的轨迹保留在窗体上
需要一个列表来保存所有移动过的点
'''
self.pos_xy = []
def main():
parser = argparse.ArgumentParser(
description=
'This code will train and test with a new model or loaded one given the desired type of architecture'
)
parser.add_argument('--train',
action='store_true',
help='bool determining whether to train or not')
parser.add_argument('--test',
action='store_true',
help='bool determining whether to test or not')
parser.add_argument(
'--model',
help=
'model name, to be of the form \'srcnn_v2\' with model type and version'
)
parser.add_argument('--epochs',
help='number of epochs to train with',
type=int,
default=20)
parser.add_argument('--batch',
help='batch size to calculate loss',
type=int,
default=64)
parser.add_argument('--lr',
help='model learning rate',
type=float,
default=0.001)
parser.add_argument('--validation',
help='validation split for training data',
type=float,
default=0.1)
args = parser.parse_args()
checkValid(args.model)
checkCurrDirectory()
if args.train and args.model:
nn = train(args.model, args.epochs, args.batch, args.lr,
args.validation)
test_or_not = input("Do you want to test with the test images too? ")
if test_or_not == 'yes':
test(nn, args.model + '/')
elif args.test and args.model:
nn = model.loadModel(models_path + args.model + '.h5')
test(nn, args.model + '/')
def performBatchTest(weightsFilename):
tokenizer, wordsToIndex = loadWordMapping()
datasetDirectory = "aclImdb/test/"
sequenceLength = 300
vecSpaceSize = 8
print("Evaluating model performance...")
#Get the testing dataset
reviews, ratings = readDataset(datasetDirectory, sequenceLength,
vecSpaceSize)
#Load the model and its weights
model = loadModel(len(wordsToIndex) + 1, sequenceLength, vecSpaceSize)
model.load_weights(weightsFilename)
X = tokenizer.texts_to_sequences(reviews)
X = pad_sequences(X, maxlen=sequenceLength, padding='post')
loss, accuracy = model.evaluate(X, ratings)
print("\tLoss: {}".format(loss))
print("\tAccuracy: {}".format(accuracy))
def main(status):
if status == 'generate':
generateDatasets(dataset_name, n_train, n_valid, n_test)
main('train')
elif status == 'train':
model_name = md.train(dataset_name, batch_size=512, debug=False)
main('evaluate')
pass
elif status == 'evaluate':
model = md.loadModel(model_path)
dataset = ds.loadDataset(dataset_path + dataset_name + '/' + test_file)
for path, y in zip(dataset['path'], dataset['y']):
img = utils.loadImage(path)
p = model.predict(img[None, :, :, :], batch_size=1)
print(p, y)
p = p[0]
y_pred = [[p[0], p[1]], [p[2], p[3]], [p[4], p[5]], [p[6], p[7]]]
img_dbg = drawPoints(img, y_pred, colors)
utils.showImage(img_dbg)
pass
values = line.split("\t")
line_dict = line2base(targets, values)
baseline.append(line_dict)
predicted = baseline
tr_pred = baseline
baseline = np.array(baseline)
predicted = np.array(predicted)
tr_pred = np.array(tr_pred)
fh.close()
# Load model, predict targets and exit
if args.load_model != None:
sys.stderr.write("Loading model from: %s\n" % (args.load_model))
m = model.loadModel(args.load_model)
res = m.predict(data_X)
for r in res:
print r
sys.exit()
data_X = np.array(data_X)
data_Y = np.array(data_Y)
# Model cross validation
m = model.Model(model_type, args.model_params, f_select, args.feat_selector_params, sparse=sparse)
pred = data_Y
sys.stderr.write("Starting crossvalidation\n")
# cv = cross_validation.StratifiedKFold(data_X, n_folds=10, shuffle=True, random_state=seed)
# scores = cross_validation.cross_val_score(m, data_X, data_Y, cv=10)
line = line.rstrip("\n")
values = line.split("\t")
line_dict = line2base(targets, values)
baseline.append(line_dict)
predicted = baseline
baseline = np.array(baseline)
predicted = np.array(predicted)
probas = predicted
fh.close()
# Load model, predict targets and exit
if args.load_model != None:
sys.stderr.write("Loading model from: %s\n" % (args.load_model))
m = model.loadModel(args.load_model)
res = m.predict(data_X)
for r in res:
print r
sys.exit()
data_X = np.array(data_X)
data_Y = np.array(data_Y)
# Model cross validation
m = model.Model(model_type,
args.model_params,
f_select,
args.feat_selector_params,
sparse=sparse)
pred = data_Y
parser.add_argument('data_file', metavar='test_data', type=str)
parser.add_argument('verbose',
metavar='verbose',
nargs='?',
type=int,
default=0)
args = parser.parse_args()
verbose = args.verbose
features_ignore = ["old_node_id", "wrong_form_1", "wrong_form_2"]
fh = gzip.open(args.data_file, 'rb', "UTF-8")
line = fh.readline().rstrip("\n")
feature_names = line.split("\t")
m = model.loadModel(args.model_file)
targets = m.get_classes()[0].keys()
base_targets = [x.replace("new", "old") for x in targets]
# Read the data
test_X = []
test_Y = []
base_Y = []
while True:
line = fh.readline().rstrip("\n")
if not line:
break
feat_values = line.split("\t")
feat_row = dict()
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((TCP_HOST, TCP_PORT))
s.listen(True)
while True:
conn, addr = s.accept()
data = getImage.getImgData(s, conn)
# Show image for testing, time ~ 5 seconds
# _show(data)
status = processing(data, recipient, predictor)
if status == 0:
print(status)
else:
print('error')
if __name__ == "__main__":
ip = sys.argv[1] # IP address of Server
recipient = sys.argv[2] # Phone number of Recipient
# Load model
path = os.path.join('/home', getpass.getuser(),
'findFire/server/models/model_final.pth')
cuda = False
predictor = model.loadModel(path, cuda)
# Listen for incoming images
getData(ip, recipient, predictor)
(options, args) = parser.parse_args()
movie = options.movie
if movie == '' or movie == None:
print('Error. Usage: python index.py --movie "<movie>"')
sys.exit()
try:
numberOfTweets = 1
tweets = []
ratingValue = 0
if model.isModelExists():
print("Loading Classifier Model...")
vectorizer, classifier = model.loadModel()
else:
print("Building Classifier Model...")
# Start building models
vectorizer, classifier = model.buildModel()
print("Retrieving Tweets...")
tso = TwitterSearchOrder()
tso.set_keywords([movie])
tso.set_language('en')
tso.set_include_entities(False)
ts = TwitterSearch(
consumer_key='kbibzVdoRoKOwd3dlxZCobum5',
consumer_secret='qEz32mJANlQ5hbFGacxmMfO2Pmyexs3WgPFeGq4QzA88qAOKe8',
access_token='1348353366-ofrMAMNiFfz102VY9c3MXdTrsAD2c4Dq91QiWVD',
loadedVectors = np.load("embeddings.npy", mmap_mode="r")
with open("embeddings.vocab", "r", encoding="utf8") as fileRead:
for index, word in enumerate(fileRead):
wordToVecMap[word.strip()] = loadedVectors[index]
return wordToVecMap"""
datasetDirectory = "aclImdb/train/"
sequenceLength = 300
vecSpaceSize = 8
reviews, ratings = readDataset(datasetDirectory, sequenceLength, vecSpaceSize)
#Get embedded matrix representing vocabulary
wordsToIndex, tokenizer = generateWordMapping(reviews)
#Generate model and output summary
model = loadModel(len(wordsToIndex) + 1, sequenceLength, vecSpaceSize)
model.summary()
#Define weights checkpoint
filepath = "data/weights-{epoch:d}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, mode='min')
#Train the model
X = tokenizer.texts_to_sequences(reviews)
X = pad_sequences(X, maxlen=sequenceLength, padding='post')
#print(encodedReviews.shape)
model.fit(X,
ratings,
epochs=10,
batch_size=32,
shuffle=True,
def trainModel(n_iters=100000,
teacher_forcing_ratio=0.,
print_every=1000,
plot_every=100,
learning_rate=0.01,
max_length=MAX_LENGTH):
training_pairs, vocab_size, word2ix, ix2word = loadDataset()
encoder, decoder = loadModel(vocab_size)
print("Training the model ... ")
start = time.time()
plot_losses = []
print_loss_total = 0 # reset every print_every
plot_loss_total = 0 # reset every plot_every
encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)
criterion = nn.NLLLoss()
for iter in range(1, n_iters + 1):
training_pair = training_pairs[iter - 1]
input_variable = training_pair['input']
target_variable = training_pair['target']
input_variable = Variable(torch.LongTensor(input_variable).view(-1, 1))
target_variable = Variable(
torch.LongTensor(target_variable).view(-1, 1))
if USE_CUDA:
input_variable = input_variable.cuda()
target_variable = target_variable.cuda()
print(input_variable)
loss = trainIter(input_variable,
target_variable,
encoder,
decoder,
encoder_optimizer,
decoder_optimizer,
criterion,
max_length=max_length,
teacher_forcing_ratio=teacher_forcing_ratio)
print_loss_total += loss
plot_loss_total += loss
# Keeping track of average loss and printing results on screen
if iter % print_every == 0:
print_loss_avg = print_loss_total / print_every
print_loss_total = 0
print('%s (%d %d%%) %.4f' %
(utils.timeSince(start, iter / n_iters), iter,
iter / n_iters * 100, print_loss_avg))
# Keeping track of average loss and plotting in figure
if iter % plot_every == 0:
plot_loss_avg = plot_loss_total / plot_every
plot_losses.append(plot_loss_avg)
if min(plot_losses) == plot_loss_avg:
#we save this version of the model
torch.save(encoder.state_dict(), "encoder.ckpt")
torch.save(decoder.state_dict(), "decoder.ckpt")
plot_loss_total = 0
utils.showPlot(plot_losses)
def predictNN(X):
#------------------------------------------ Load RNN Model --------------------------------------------#
print("Loading the Neural Network parameters.")
BiRNNmodel = loadModel("BiRNN")
predictedBiRNN = BiRNNmodel.predict([X,X], batch_size=10)
return predictedBiRNN
# Parse command line arguments
parser = argparse.ArgumentParser(description="Test scikit-learn model accuracy.")
parser.add_argument('model_file', metavar='model_file', type=str)
parser.add_argument('data_file', metavar='test_data', type=str)
parser.add_argument('verbose', metavar='verbose', nargs='?', type=int, default=0)
args = parser.parse_args()
verbose = args.verbose
features_ignore = [ "old_node_id", "wrong_form_1", "wrong_form_2" ]
fh = gzip.open(args.data_file, 'rb', "UTF-8")
line = fh.readline().rstrip("\n")
feature_names = line.split("\t")
m = model.loadModel(args.model_file)
targets = m.get_classes()[0].keys()
base_targets = [x.replace("new", "old") for x in targets]
# Read the data
test_X = []
test_Y = []
base_Y = []
while True:
line = fh.readline().rstrip("\n")
if not line:
break
feat_values = line.split("\t")
feat_row = dict()
def clearLists():
data['links'].clear()
data['category'].clear()
data['date'].clear()
data['image'].clear()
data['headline'].clear()
data['article'].clear()
data['summary'].clear()
data['sentiment'].clear()
def scraperScheduler():
getLinks()
getArticleData()
storeArticles()
clearLists()
def startScheduler():
scheduler = BlockingScheduler()
scheduler.add_job(scraperScheduler, 'interval', hours=0.5)
scheduler.start()
if __name__ == '__main__':
loadModel()
scraperScheduler()
startScheduler()
def preparePredict(imgSize=64):
# load model
model = loadModel('model_{0}.hd5'.format(imgSize))
return model
### Rest of Predictions : Predicted_2 ###
for i in range(2,X.shape[0]-1):
input_data = np.vstack(([x3[i-2],y3[i-2]],[x3[i-1],y3[i-1]],[x3[i],y3[i]])).reshape(1,3,2)
predicted_output = predictNN(input_data)
x3.append(predicted_output[0][0])
y3.append(predicted_output[0][1])
### Plot Predicted Trajectory ###
plt.plot(x2, y2, linewidth=1.0, color='green', linestyle='--', marker='*', label='Predicted_1')
plt.plot(x3, y3, linewidth=2.0, color='red', linestyle='--', marker='v', label='Predicted_2')
plt.legend()
plt.savefig("./plots/Projectile_Plot_%s.png" % (y+1))
plt.clf()
#------------------------------------------ Load RNN Model --------------------------------------------#
print("Loading the Neural Network parameters.")
BiRNNmodel = loadModel("BiRNN")
#------------------------------------------Testing Model on Data------------------------------------------------#
#Import the raw data
print('Reading the data file...')
df = np.loadtxt("./data/projectiles.csv", delimiter=',')
projCount, idx = 0, 0
while idx<df.shape[0]:
motion = df[idx].reshape(1,3)
idx = idx+1
while df[idx][0] != 0:
motion = np.append(motion, df[idx].reshape(1,3), axis=0)
idx = idx+1
if idx==df.shape[0]:
break
global guild, guildID, channel, channelID
print('We have logged in as {0.user}'.format(client))
guild = client.get_guild(guildID)
talkchannels = [client.get_channel(channelID)]
# limit = int(re.findall(r'limit=(\d+)', " ".join(sys.argv))[0])
# channelIDs = re.findall(r' (\d+) ', " ".join(sys.argv))
@client.event
async def on_message(message):
if message.author == client.user:
return
if not message.channel in talkchannels:
return
response = getBotResponse(message.content)
await message.channel.send(response)
model.loadModel()
with open('token.txt', 'r') as tokentxt:
# asyncio.get_event_loop().create_task(pingean())
client.run(tokentxt.read())
def main(config):
if not (os.path.exists(rawAudioPath) and os.path.exists(metadataPathTrain) and os.path.exists(metadataPathTest)):
print("The audio data and csv metadata must be located in the following paths:\n"
f"1. {rawAudioPath}\n2. {metadataPathTrain}\n3. {metadataPathTest}")
sys.exit()
config = parseArgs(config)
setSeed(config.randomSeed)
logs = {"epoch": [], "iter": [], "saveStep": config.saveStep, "loggingStep": config.loggingStep}
loadOptimizer = False
if config.pathCheckpoint is not None:
cdata = getCheckpointData(config.pathCheckpoint)
if cdata is not None:
data, logs, locArgs = cdata
print(f"Checkpoint detected at {data}")
loadArgs(config, locArgs, forbiddenAttr={"nGPU", "pathCheckpoint", "maxChunksInMem", "chunkSize"})
config.load, loadOptimizer = [data], True
config.loadCriterion = True
print(f'CONFIG: \n{json.dumps(vars(config), indent=4, sort_keys=True)}')
print('-' * 50)
useGPU = torch.cuda.is_available()
metadata_dir = f'data/musicnet_metadata_train_transcription_{config.labelsBy}_trainsplit.csv' if \
config.transcriptionWindow is not None \
else f'data/musicnet_metadata_train_{config.labelsBy}_trainsplit.csv'
if not os.path.exists(metadata_dir):
# if config.transcriptionWindow is not None:
# musicNetMetadataTrain = pd.read_csv('data/musicnet_metadata_transcript_train_alldata.csv')
# else:
musicNetMetadataTrain = pd.read_csv('data/musicnet_metadata_train.csv', index_col = 'id')
try:
if config.transcriptionWindow is not None:
metadataTrain, metadataVal = train_test_split(musicNetMetadataTrain, test_size=0.1)
# stratify=musicNetMetadataTrain[config.labelsBy])
else:
metadataTrain, metadataVal = train_test_split(musicNetMetadataTrain, test_size=0.1,
stratify=musicNetMetadataTrain[config.labelsBy])
print(metadataTrain.shape, metadataVal.shape)
except ValueError:
for col, count in zip(musicNetMetadataTrain[config.labelsBy].value_counts().index,
musicNetMetadataTrain[config.labelsBy].value_counts().values):
if count == 1:
subDF = musicNetMetadataTrain.loc[musicNetMetadataTrain[config.labelsBy] == col]
musicNetMetadataTrain = musicNetMetadataTrain.append(subDF)
metadataTrain, metadataVal = train_test_split(musicNetMetadataTrain, test_size=0.1,
stratify=musicNetMetadataTrain[config.labelsBy])
if config.transcriptionWindow is not None:
musicNetMetadataTranscript = pd.read_csv('data/musicnet_metadata_transcript_train_alldata.csv')
metadataTrain = musicNetMetadataTranscript[musicNetMetadataTranscript['id'].isin(metadataTrain.index)]
metadataVal = musicNetMetadataTranscript[musicNetMetadataTranscript['id'].isin(metadataVal.index)]
metadataTrain.to_csv(f'data/musicnet_metadata_train_transcription_{config.labelsBy}_trainsplit.csv')
metadataVal.to_csv(f'data/musicnet_metadata_train_transcription_{config.labelsBy}_valsplit.csv')
else:
metadataTrain.to_csv(f'data/musicnet_metadata_train_{config.labelsBy}_trainsplit.csv')
metadataVal.to_csv(f'data/musicnet_metadata_train_{config.labelsBy}_valsplit.csv')
else:
if config.transcriptionWindow is not None:
metadataTrain = pd.read_csv(f'data/musicnet_metadata_train_transcription_{config.labelsBy}_trainsplit.csv')
metadataVal = pd.read_csv(f'data/musicnet_metadata_train_transcription_{config.labelsBy}_valsplit.csv')
else:
metadataTrain = pd.read_csv(f'data/musicnet_metadata_train_{config.labelsBy}_trainsplit.csv', index = 'id')
metadataVal = pd.read_csv(f'data/musicnet_metadata_train_{config.labelsBy}_valsplit.csv', index = 'id')
print("Loading the training dataset")
trainDataset = AudioBatchData(rawAudioPath=rawAudioPath,
metadata=metadataTrain,
sizeWindow=config.sizeWindow,
labelsBy=config.labelsBy,
outputPath='data/musicnet_lousy/train_data/train',
CHUNK_SIZE=config.chunkSize,
NUM_CHUNKS_INMEM=config.maxChunksInMem,
useGPU=useGPU,
transcript_window=config.transcriptionWindow)
print("Training dataset loaded")
print("")
print("Loading the validation dataset")
valDataset = AudioBatchData(rawAudioPath=rawAudioPath,
metadata=metadataVal,
sizeWindow=config.sizeWindow,
labelsBy=config.labelsBy,
outputPath='data/musicnet_lousy/train_data/val',
CHUNK_SIZE=config.chunkSize,
NUM_CHUNKS_INMEM=config.maxChunksInMem,
useGPU=False,
transcript_window=config.transcriptionWindow)
print("Validation dataset loaded")
print("")
if config.load is not None:
cpcModel, config.hiddenGar, config.hiddenEncoder = loadModel(config.load, config)
else:
# Encoder network
encoderNet = CPCEncoder(config.hiddenEncoder, 'layerNorm', sincNet=config.encoderType == 'sinc')
# AR Network
arNet = getAR(config)
cpcModel = CPCModel(encoderNet, arNet)
batchSize = config.batchSize
cpcModel.supervised = config.supervised
# Training criterion
if config.load is not None and config.loadCriterion:
cpcCriterion = loadCriterion(config.load[0], cpcModel.gEncoder.DOWNSAMPLING,
len(metadataTrain[config.labelsBy].unique()))
else:
cpcCriterion = getCriterion(config, cpcModel.gEncoder.DOWNSAMPLING,
len(metadataTrain[config.labelsBy].unique())) # change for transcription labels
if loadOptimizer:
stateDict = torch.load(config.load[0], 'cpu')
cpcCriterion.load_state_dict(stateDict["cpcCriterion"])
if useGPU:
cpcCriterion.cuda()
cpcModel.cuda()
# Optimizer
gParams = list(cpcCriterion.parameters()) + list(cpcModel.parameters())
lr = config.learningRate
optimizer = torch.optim.Adam(gParams, lr=lr, betas=(config.beta1, config.beta2), eps=config.epsilon)
if loadOptimizer:
print("Loading optimizer " + config.load[0])
state_dict = torch.load(config.load[0], 'cpu')
if "optimizer" in state_dict:
optimizer.load_state_dict(state_dict["optimizer"])
# Checkpoint
expDescription = f'{config.samplingType}_'
if config.samplingType == 'samecategory':
expDescription += f'{config.labelsBy}_'
pathCheckpoint = f'logs/{expDescription}{datetime.now().strftime("%d-%m_%H-%M-%S")}'
os.makedirs(pathCheckpoint, exist_ok=True)
pathCheckpoint = os.path.join(pathCheckpoint, "checkpoint")
scheduler = None
if config.schedulerStep > 0:
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
config.schedulerStep,
gamma=0.5)
if config.schedulerRamp is not None:
n_epoch = config.schedulerRamp
print(f"Ramp activated. n_e = {n_epoch}")
scheduler_ramp = torch.optim.lr_scheduler.LambdaLR(optimizer,
lr_lambda=lambda epoch: rampSchedulingFunction(n_epoch,
epoch),
last_epoch=-1)
if scheduler is None:
scheduler = scheduler_ramp
else:
scheduler = SchedulerCombiner([scheduler_ramp, scheduler], [0, config.schedulerRamp])
if scheduler is not None:
for i in range(len(logs["epoch"])):
scheduler.step()
experiment = None
if config.log2Board:
comet_ml.init(project_name="jtm", workspace="tiagocuervo")
if not os.path.exists('.comet.config'):
cometKey = input("Please enter your Comet.ml API key: ")
experiment = comet_ml.Experiment(cometKey)
cometConfigFile = open(".comet.config", "w")
cometConfigFile.write(f"[comet]\napi_key={cometKey}")
cometConfigFile.close()
else:
experiment = comet_ml.Experiment()
experiment.log_parameters(vars(config))
run(trainDataset, valDataset, batchSize, config.samplingType, cpcModel, cpcCriterion, config.nEpoch, optimizer,
scheduler, pathCheckpoint, logs, useGPU, log2Board=config.log2Board, experiment=experiment)