def main():
args = parse_args()
cuda = torch.cuda.is_available() and args.cuda
torch.set_default_tensor_type(
torch.cuda.FloatTensor if cuda else torch.FloatTensor)
device = torch.device('cuda' if cuda else 'cpu')
print("Using %s for training" % ('GPU' if cuda else 'CPU'))
print('Loading dataset...', end='', flush=True)
metadata, vocab, train_iter, val_iter, test_iter = dataset_factory(
args, device)
print('Done.')
print('Saving vocab and args...', end='')
save_vocab(vocab, args.save_path + os.path.sep + 'vocab')
save_object(args, args.save_path + os.path.sep + 'args')
print('Done')
model = train_model_factory(args, metadata)
if cuda and args.multi_gpu:
model = nn.DataParallel(
model,
dim=1) # if we were using batch_first we'd have to use dim=0
print(model) # print models summary
optimizer = optim.Adam(model.parameters(),
lr=args.learning_rate,
amsgrad=True)
try:
best_val_loss = None
for epoch in range(args.max_epochs):
start = datetime.now()
# calculate train and val loss
train_loss = train(model, optimizer, train_iter, metadata,
args.gradient_clip)
val_loss = evaluate(model, val_iter, metadata)
print("[Epoch=%d/%d] train_loss %f - val_loss %f time=%s " %
(epoch + 1, args.max_epochs, train_loss, val_loss,
datetime.now() - start),
end='')
# save models if models achieved best val loss (or save every epoch is selected)
if args.save_every_epoch or not best_val_loss or val_loss < best_val_loss:
print('(Saving model...', end='')
save_model(args.save_path, model, epoch + 1, train_loss,
val_loss)
print('Done)', end='')
best_val_loss = val_loss
print()
except (KeyboardInterrupt, BrokenPipeError):
print('[Ctrl-C] Training stopped.')
test_loss = evaluate(model, test_iter, metadata)
print("Test loss %f" % test_loss)
def get_model(numeric_dataset, model_filename=None):
feature = ChainOperator(Resize((128,128)), SpatialHistogram())
#feature = ChainOperator(Resize((128,128)), Fisherfaces())
classifier = NearestNeighbor(dist_metric=NormalizedCorrelation(), k=1)
print "kjasnanscal"
#classifier = NearestNeighbor(dist_metric=EuclideanDistance(), k=1)
inner_model = PredictableModel(feature=feature, classifier=classifier)
model = PredictableModelWrapper(inner_model)
model.set_data(numeric_dataset)
model.compute()
if not model_filename is None:
save_model(model_filename, model)
return model
def run_rec():
# This is where we write the images, if an output_dir is given
# in command line:
out_dir = None
# Now read in the image data. This must be a valid path!
[X, y] = read_images('images')
# Then set up a handler for logging:
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
# Add handler to facerec modules, so we see what's going on inside:
logger = logging.getLogger("facerec")
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
# Define the Fisherfaces as Feature Extraction method:
feature = Fisherfaces()
# Define a 1-NN classifier with Euclidean Distance:
classifier = NearestNeighbor(dist_metric=EuclideanDistance(), k=1)
# Define the model as the combination
my_model = PredictableModel(feature=feature, classifier=classifier)
# Compute the Fisherfaces on the given data (in X) and labels (in y):
my_model.compute(X, y)
# We then save the model, which uses Pythons pickle module:
save_model('model.pkl', my_model)
model = load_model('model.pkl')
# Then turn the first (at most) 16 eigenvectors into grayscale
# images (note: eigenvectors are stored by column!)
#E = []
#for i in xrange(min(model.feature.eigenvectors.shape[1], 16)):
# e = model.feature.eigenvectors[:,i].reshape(X[0].shape)
# E.append(minmax_normalize(e,0,255, dtype=np.uint8))
# Plot them and store the plot to "python_fisherfaces_fisherfaces.pdf"
#subplot(title="Fisherfaces", images=E, rows=4, cols=4, sptitle="Fisherface", colormap=cm.jet, filename="fisherfaces.png")
# Perform a 10-fold cross validation
cv = KFoldCrossValidation(model, k=10)
cv.validate(X, y)
# And print the result:
cv.print_results()
im = Image.open('search.png')
im = im.convert("L")
predicted_label = model.predict(im)[0]
print(predicted_label)
return predicted_label
def run_rec():
# This is where we write the images, if an output_dir is given
# in command line:
out_dir = None
# Now read in the image data. This must be a valid path!
[X,y] = read_images('images')
# Then set up a handler for logging:
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
# Add handler to facerec modules, so we see what's going on inside:
logger = logging.getLogger("facerec")
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
# Define the Fisherfaces as Feature Extraction method:
feature = Fisherfaces()
# Define a 1-NN classifier with Euclidean Distance:
classifier = NearestNeighbor(dist_metric=EuclideanDistance(), k=1)
# Define the model as the combination
my_model = PredictableModel(feature=feature, classifier=classifier)
# Compute the Fisherfaces on the given data (in X) and labels (in y):
my_model.compute(X, y)
# We then save the model, which uses Pythons pickle module:
save_model('model.pkl', my_model)
model = load_model('model.pkl')
# Then turn the first (at most) 16 eigenvectors into grayscale
# images (note: eigenvectors are stored by column!)
#E = []
#for i in xrange(min(model.feature.eigenvectors.shape[1], 16)):
# e = model.feature.eigenvectors[:,i].reshape(X[0].shape)
# E.append(minmax_normalize(e,0,255, dtype=np.uint8))
# Plot them and store the plot to "python_fisherfaces_fisherfaces.pdf"
#subplot(title="Fisherfaces", images=E, rows=4, cols=4, sptitle="Fisherface", colormap=cm.jet, filename="fisherfaces.png")
# Perform a 10-fold cross validation
cv = KFoldCrossValidation(model, k=10)
cv.validate(X, y)
# And print the result:
cv.print_results()
im = Image.open('search.png')
im = im.convert("L")
predicted_label = model.predict(im)[0]
print(predicted_label)
return predicted_label
# Add handler to facerec modules, so we see what's going on inside:
logger = logging.getLogger("facerec")
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
# Define the Fisherfaces as Feature Extraction method:
feature = SpatialHistogram()
#feature = Fisherfaces()
# Define a 1-NN classifier with Euclidean Distance:
classifier = NearestNeighbor(dist_metric=NormalizedCorrelation(), k=1)
#classifier = SVM()
# Define the model as the combination
my_model = PredictableModel(feature=feature, classifier=classifier)
# Compute the Fisherfaces on the given data (in X) and labels (in y):
my_model.compute(X, y)
# We then save the model, which uses Pythons pickle module:
save_model('modelTry.pkl', my_model)
model = load_model('modelTry.pkl')
# Then turn the first (at most) 16 eigenvectors into grayscale
# images (note: eigenvectors are stored by column!)
E = []
#for i in range(min(model.feature.eigenvectors.shape[1], 16)):
# e = model.feature.eigenvectors[:,i].reshape(X[0].shape)
# E.append(minmax_normalize(e,0,255, dtype=np.uint8))
# Plot them and store the plot to "python_fisherfaces_fisherfaces.pdf"
subplot(title="Fisherfaces", images=E, rows=4, cols=4, sptitle="Fisherface", colormap=cm.jet, filename="fisherfaces.png")
# Perform a 10-fold cross validation
cv = KFoldCrossValidation(model, k=10)
cv.validate(X, y)
# And print the result:
cv.print_results()
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
handler.setFormatter(formatter)
# Add handler to facerec modules, so we see what's going on inside:
logger = logging.getLogger("facerec")
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
# Define the Fisherfaces as Feature Extraction method:
feature = Fisherfaces()
# Define a 1-NN classifier with Euclidean Distance:
classifier = NearestNeighbor(dist_metric=EuclideanDistance(), k=1)
# Define the model as the combination
my_model = PredictableModel(feature=feature, classifier=classifier)
# Compute the Fisherfaces on the given data (in X) and labels (in y):
my_model.compute(X, y)
# We then save the model, which uses Pythons pickle module:
save_model("model.pkl", my_model)
model = load_model("model.pkl")
# Then turn the first (at most) 16 eigenvectors into grayscale
# images (note: eigenvectors are stored by column!)
E = []
for i in xrange(min(model.feature.eigenvectors.shape[1], 16)):
e = model.feature.eigenvectors[:, i].reshape(X[0].shape)
E.append(minmax_normalize(e, 0, 255, dtype=np.uint8))
# Plot them and store the plot to "python_fisherfaces_fisherfaces.pdf"
subplot(
title="Fisherfaces", images=E, rows=4, cols=4, sptitle="Fisherface", colormap=cm.jet, filename="fisherfaces.png"
)
# Perform a 10-fold cross validation
cv = KFoldCrossValidation(model, k=10)
cv.validate(X, y)
# And print the result:
