def test_rouge():
data_paths = ("arxiv/inputs/", "arxiv/human-abstracts/", "arxiv/labels/")
glove_dir = "embeddings"
embedding_size = 300
model_path = sys.argv[1]
weight_matrix, word2idx = create_embeddings(f"{glove_dir}/glove.6B.{embedding_size}d.txt")
test_set = load_data(word2idx, data_paths, data_type="test")
test_docs = load_test_docs(data_paths, data_type="test")
print_rouge(model_path, test_set, test_docs)
def test_model():
# Perform a forward cycle with fictitious data
model = ExtSummModel.load("model/extsumm.bin")
data_paths = ("arxiv/inputs/", "arxiv/human-abstracts/", "arxiv/labels/")
# (doc, start_end, abstract, label)
test_set = load_data(data_paths, data_type="test")
# find the test accuracy of the model
accuracy = model.predict_and_eval(test_set)
print(f"Testing accuracy: {accuracy}")
def dtree_predict(params):
global boost_dt
global train_dtree
if train_dtree == False and not os.path.isfile('./models/dtree.pkl'):
boost_dt = dtree_train()
else:
boost_dt = load_data('./models/dtree.pkl')
print('DTree After:', boost_dt.coef_)
features = prepare_test_features(params)
pred = boost_dt.predict(features)
return pred[0]
def svm_predict(params):
global svm_clf
global train_svm
if train_svm == False and not os.path.isfile('./models/svm.pkl'):
svm_clf = svm_train()
else:
svm_clf = load_data('./models/svm.pkl')
print('SVM After:', svm_clf.coef_)
features = prepare_test_features(params)
pred = svm_clf.predict(features)
return pred[0]
def svm_test():
global svm_clf
global train_svm
if train_svm == False and not os.path.isfile('./models/svm.pkl'):
svm_train()
else:
svm_clf = load_data('./models/svm.pkl')
features, test_modified = prepare_test_dataset()
svm_test = svm_clf.predict(features)
svm_df = pd.DataFrame(test_modified['PassengerId']).join(pd.DataFrame(svm_test, columns=['Survived']))
display(svm_df.head())
svm_df.to_csv(path_or_buf='./csv/svm_predictions.csv', index=False)
def dtree_test():
global boost_dt
global train_dtree
if train_dtree == False and not os.path.isfile('./models/dtree.pkl'):
boost_dt = dtree_train()
else:
boost_dt = load_data('./models/dtree.pkl')
features, test_modified = prepare_test_dataset()
dt_test = boost_dt.predict(features)
dt_df = pd.DataFrame(test_modified['PassengerId']).join(pd.DataFrame(dt_test, columns=['Survived']))
display(dt_df.head())
dt_df.to_csv(path_or_buf='./csv/dt_predictions.csv', index=False)
def predict_model(image_directory, labels_path, season, asset_type, model,
crop):
labels = pd.read_csv(labels_path, index_col=0)
if model == 'InceptionV3':
model_name = model
img_width = 299
img_height = 299
model_class = InceptionV3
preprocess_func = iv3_preproc
freeze_depth = 172
elif model == 'VGG16':
model_name = model
img_width = 224
img_height = 224
model_class = VGG16
preprocess_func = lambda x: x
freeze_depth = 25
else:
raise Exception('{} is an unsupported model.'.format(model))
output_base = '{}_{}_{}_{}'.format(model_name, asset_type, season, crop)
model_path = os.path.join(PROJ_ROOT, 'models', output_base + '_keras.h5')
model = load_model(model_path)
images, targets, exisiting_image_ids = load_data(labels, img_width,
img_height, asset_type,
image_directory, season,
'{}_yield'.format(crop))
preds = model.predict(images)
print(preds.shape)
predicted_images = labels.loc[exisiting_image_ids, :].copy()
predicted_images['prediction'] = preds
preds_out_path = os.path.join(PROJ_ROOT, 'models',
output_base + '_preds.csv')
predicted_images.to_csv(preds_out_path)
geojson_preds_out = os.path.join(PROJ_ROOT, 'models',
output_base + '_preds.geojson')
write_geojson_predictions(image_directory,
"geojson_epsg4326_{}.geojson".format(crop), crop,
predicted_images, geojson_preds_out)
def main():
# Parse input data
args = parsing_inputs()
# Obtain the dataloaders and a dictionary class_to_idx we will use during prediction
trainloader, validloader, testloader, class_to_idx = load_data(args)
# Now we download the model based on the input and select the device we will train it on
possible_inputs = {'vgg16': 25088, 'alexnet': 9216}
model, device = build_model(possible_inputs, args)
# The next step is to define the criterion and train the model
criterion = nn.NLLLoss()
train(model, device, args, trainloader, validloader, criterion)
# We then perform a validation test on new unseen data
with torch.no_grad():
validation_test(model, testloader, device, criterion)
# Finally we save the checkpoint
save_check(args, model, class_to_idx, possible_inputs)
def main():
args = parse_args()
#args.dataset_size = 100000
print('-----------------------------------------------------------')
print('Dataset size: ', args.dataset_size)
args.batch_size = 1
gender = 'male' # female
print('Gender: ', gender)
device = torch.device("cuda:%d" %
args.gpu if torch.cuda.is_available() else "cpu")
torch.cuda.set_device(device)
parent_dic = "/home/yifu/workspace/data/synthetic/noise_free"
print('Data path: ', parent_dic)
dataloader = load_data(args.dataset_size, parent_dic, args)
model = myresnet50(device,
num_output=args.num_output,
use_pretrained=True,
num_views=args.num_views)
# save_name = 'out:%d_data:%d_par_w:%.1f.pth'%(args.num_output,args.dataset_size, args.par_loss_weight)
# folder: network weights
parent_dic = "/home/yifu/workspace/data/test/model_1"
save_name = 'data:%d.pth' % (100000)
save_path = os.path.join(parent_dic, save_name)
print('Load state dict from save path: ', save_path)
model.load_state_dict(torch.load(save_path))
print('-----------------------------------------------------------')
if raw_input('Confirm the above setting? (yes/no): ') != 'yes':
print('Terminated')
exit()
print('validation starts')
print('------------------------')
path = parent_dic
evaluate_model(model, dataloader, args.num_views, path, device, args)
def main():
filepath = './saved_models/model'
model = load_model(filepath)
# Get image arrays and labels for all image files
images, labels = load_data(sys.argv[1])
# Split data into training and testing sets
labels = tf.keras.utils.to_categorical(list(labels))
x_train, x_test, y_train, y_test = train_test_split(np.array(images),
np.array(labels),
test_size=TEST_SIZE)
for i in range(len(x_test[:3])):
plt.imsave(f"Image #{i}.jpg", x_test[i])
predictions = model.predict(x_test[:1])
print(x_test[:1].shape, x_test[0].shape)
classes = np.argmax(predictions, -1)
truth_table = {0: "Atom", 1: "Sanay", 2: "Aarav"}
for i in range(len(classes)):
print(f"Image #{i} is {truth_table[classes[i]]}")
def main():
args = parse_args()
args.dataset_size = 100000
args.batch_size = 1
args.num_output = 82
gender = 'male' # female
m = load_model('../../models/basicModel_%s_lbs_10_207_0_v1.0.0.pkl' %
gender[0])
parent_dic = "/home/yifu/workspace/data_smpl/A_pose_5/male/noise_free"
dataloader = load_data(args.dataset_size, parent_dic, args)
device = torch.device("cuda:%d" %
args.gpu if torch.cuda.is_available() else "cpu")
model = myresnet50(device,
num_output=args.num_output,
use_pretrained=True,
num_views=args.num_views)
#model = myresnet50(num_output=80)
save_name = 'trained_resnet_%d_%d.pth' % (args.num_output,
args.dataset_size)
path = os.path.join(parent_dic, save_name)
model.load_state_dict(torch.load(path))
path = parent_dic
evaluate_model(m, model, dataloader, args.num_views, path, device, args)
self.crop_to = crop_to
def _get_batches_of_transformed_samples(self, index_array):
batch_x, batch_y = super()._get_batches_of_transformed_samples(
index_array)
if self.crop_to > 0:
batch_x = self.image_data_generator.crop_data_bacth(batch_x)
return batch_x, batch_y
if __name__ == '__main__':
from train_model import load_data
data_dir = '/home/skliff13/work/PTD_Xray/datasets/tuberculosis/v2.2'
data_shape = (256, 256)
(x_train, y_train), (x_val, y_val) = load_data(data_dir, data_shape)
train_gen = ModifiedDataGenerator(rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
rescale=1.,
zoom_range=0.2,
fill_mode='nearest',
cval=0,
crop_to=224)
for q, v in train_gen.flow(x_train, y_train, batch_size=8):
print(q.shape)
exit(14)
print("Stacking started with run sequence:", run_sequence, "\n")
# if the stacking root path doesn't exist, create it
if not os.path.exists(STACK_PATH):
os.makedirs(STACK_PATH)
train_model.MODEL_PATH = STACK_PATH
predict.MODEL_PATH = STACK_PATH
for f, tr, te in zip([True, False], [TRAIN30_DATA_FILE, TRAIN8_DATA_FILE],
[TEST30_DATA_FILE, TEST8_DATA_FILE]):
#for f, tr, te in zip([False, True], [TRAIN8_DATA_FILE, TRAIN30_DATA_FILE], [TEST8_DATA_FILE, TEST30_DATA_FILE]):
# load the training and validation datasets
train, validation = train_model.load_data(
pickle_path=PICKLE_PATH,
train_file=tr,
validation_file=VALIDATION_DATA_FILE,
use_validation=USE_VALIDATION,
verbose=False)
# Load the test dataset (required for prediction calls)
test, ids, overlap = predict.load_data(pickle_path=PICKLE_PATH,
test_file=te,
id_file=TEST_IDS_FILE,
overlap_file=OVERLAP_FILE,
verbose=False)
# perform KFold stacking of N models
kfold_stack(train=train,
validation=validation,
kfold_splits=K_SPLITS,
model_names=MODEL_NAMES,
def deep_extraction():
# extract deep features
model = tf.keras.models.load_model('./models/extraction_model')
new_model = tf.keras.models.Model(inputs=model.input,
outputs=model.layers[6].output)
deep_features = new_model.predict_on_batch(windows)
return deep_features
if __name__ == "__main__":
filtered_path = './dataset/filtered'
extracted_path = './dataset/extracted'
# load each dataset and their labels
mu_windows, mu_labels = load_data(f'{filtered_path}/move_up.npy', 0)
md_windows, md_labels = load_data(f'{filtered_path}/move_down.npy', 1)
nm_windows, nm_labels = load_data(f'{filtered_path}/no_movement.npy', 2)
windows, labels = balance_datasets([mu_windows, md_windows, nm_windows],
[mu_labels, md_labels, nm_labels])
# concatenate data
windows = windows[0] + windows[1] + windows[2]
labels = np.asarray(labels[0] + labels[1] + labels[2])
# form np array
windows = np.array([window_df_to_array(window) for window in windows])
np.save(f'{extracted_path}/windows.npy', windows)
print(windows[1])
print(windows.shape)