def getfeatures(folder, target_size, model='Resnet50'):
model = ResNet50(weights="imagenet", include_top=False)
images = getFilesInDir(folder)
images_train, images_test = test_train_split(images, fraction)
tensors_train = []
tensors_test = []
for i, j in images_train.items():
for k in j:
tensors_train.append(img_to_tensor(k, target_size=(target_size)))
for i, j in images_test.items():
for k in j:
tensors_test.append(img_to_tensor(k, target_size=(target_size)))
# print()
preprocessed_tensors_train = [preprocess_input(i) for i in tensors_train]
preprocessed_tensors_test = [preprocess_input(i) for i in tensors_test]
print("Total Training Tensors created:" + str(len(tensors_train)))
print("Total Testing Tensors created:" + str(len(tensors_test)))
labels_train = create_labels(images_train, output_classes=21)
labels_test = create_labels(images_test, output_classes=21)
print("Total Training Lables created:" + str(len(labels_train)))
print("Total Testing Lables created:" + str(len(labels_test)))
features_list_train = []
features_list_test = []
features_list_train = [
model.predict(x) for x in preprocessed_tensors_train
]
features_list_test = [model.predict(x) for x in preprocessed_tensors_test]
return (features_list_train, labels_train, features_list_test, labels_test)
def find_best_acc_and_thresh(labels_csv: Path,
inference_folder: Path, classes: List[str]) -> \
Dict[str, float]:
"""
Find the best accuracy and threshold for the given images.
Args:
labels_csv: CSV file containing the ground truth labels.
inference_folder: Folder containing the predicted labels.
classes: Names of the classes in the dataset.
Returns:
A dictionary mapping class names to the best thresholds.
"""
gt_labels = create_labels(csv_path=labels_csv)
prediction_csv_paths = get_csv_paths(folder=inference_folder)
best_acc = 0
best_thresholds = None
best_csv = None
for prediction_csv_path in prediction_csv_paths:
prediction_labels = create_labels(csv_path=prediction_csv_path)
avg_class_acc, conf_matrix = get_scores(
gt_labels=gt_labels,
prediction_labels=prediction_labels,
classes=classes)
print(f"thresholds {parse_thresholds(csv_path=prediction_csv_path)} "
f"has average class accuracy {avg_class_acc:.5f}")
if best_acc < avg_class_acc:
best_acc = avg_class_acc
best_csv = prediction_csv_path
best_thresholds = parse_thresholds(csv_path=prediction_csv_path)
print(f"view these predictions in {best_csv}")
return best_thresholds
def print_final_test_results(labels_csv: Path, inference_folder: Path,
classes: List[str]) -> None:
"""
Print the final accuracy and confusion matrix.
Args:
labels_csv: CSV file containing the ground truth labels.
inference_folder: Folder containing the predicted labels.
classes: Names of the classes in the dataset.
"""
gt_labels = create_labels(csv_path=labels_csv)
prediction_csv_paths = get_csv_paths(folder=inference_folder)
for prediction_csv_path in prediction_csv_paths:
prediction_labels = create_labels(csv_path=prediction_csv_path)
avg_class_acc, conf_matrix = get_scores(
gt_labels=gt_labels,
prediction_labels=prediction_labels,
classes=classes)
print(f"test set has final avg class acc: {avg_class_acc:.5f}"
f"\n{conf_matrix}")
def inference():
# Inference Path #
make_dirs(config.inference_path)
# Prepare Data Loader #
test_loader = get_celeba_loader('test', config.batch_size,
config.selected_attrs)
# Prepare Generator #
G = Generator(num_classes=len(config.selected_attrs)).to(device)
G.load_state_dict(
torch.load(
os.path.join(
config.weights_path,
'StarGAN_Generator_Epoch_{}.pkl'.format(config.num_epochs))))
# Test #
print("StarGAN | Generating Aligned CelebA Images started...")
for i, (image, label) in enumerate(test_loader):
# Prepare Data #
image = image.to(device)
fixed_labels = create_labels(label,
selected_attrs=config.selected_attrs)
# Generate Fake Images #
x_fake_list = [image]
for c_fixed in fixed_labels:
x_fake_list.append(G(image, c_fixed))
x_concat = torch.cat(x_fake_list, dim=3)
# Save Images #
save_image(denorm(x_concat.data.cpu()),
os.path.join(
config.inference_path,
'StarGAN_Aligned_CelebA_Results_%04d.png' % (i + 1)),
nrow=1,
padding=0)
make_gifs_test("StarGAN", config.inference_path)
def flow(self):
i = 0
while True:
images_batch = []
labels_batch = []
for b in range(self.batch_size):
if i == len(self.images):
i = 0
image = cv2.imread(self.images[i])
image = resize(image, resize=self.resize)
image = crop(image, ROI=self.ROI)
label_img = cv2.imread(self.masks[i], cv2.IMREAD_GRAYSCALE)
label_img = resize(label_img, resize=self.resize)
label_img = crop(label_img, ROI=self.ROI)
images_batch.append(norm_data(image))
labels_batch.append(create_labels(label_img, self.classes))
i += 1
yield np.array(images_batch, dtype=np.float32), np.array(labels_batch, dtype=np.float32)
clip_list_temp.extend(all_selec)
# create variable buttons
num_vars_prev = num_vars
clip_list, num_vars = check_and_create(clip_list_temp,
clip_list_prev,
frame,
1,
num_vars,
'',
button_font)
# create function buttons
num_funcs_prev = num_funcs
func_clip_list, num_funcs = check_and_create(clip_list_func_temp,
clip_list_func_prev,
frame,
2,
num_funcs,
'Control',
button_font)
# create labels
create_labels([num_vars, num_funcs],
[num_vars_prev, num_funcs_prev],
frame,
button_font)
# update tk
root.update_idletasks()
root.update()
# print(ord(i))
for i in test_string:
if ord(i) < 32 or ord(i) > 122:
print(i, ord(i), chr(ord(i)), train_string.find(i))
# print(ord(i)
X_train = create_input_array(train_string)
print(X_train.shape)
X_test = create_input_array(test_string)
print(X_test.shape)
print(X_train[0])
Y_train = create_labels(train_string, hashmap)
print(Y_train.shape)
Y_test = create_labels(test_string, hashmap)
print(Y_test.shape)
# print(Y_train)
print(Y_train.shape)
# print(Y_test)
print(Y_test.shape)
encrypter = Sequential()
encrypter.add(layers.Dense(91, input_shape=(91, )))
encrypter.add(layers.LeakyReLU())
Later will become a null vector where indices would representing dimensions where value is one
- bag_of_words: similar to naive but unknown words are ignored.
Later will become a null vector where indicies would representing dimensions where value is one or more (in case a word is present X times, the value will be X)
- tfidf: similar to bag_of_word instead of having discrete value, compute importance of each word using TF-IDF (widely used in Information Extraction)
- word_embeddings: List of indices from word_to_index_we. Later, these indices will be mapped to the embeddings of index_we_to_emb
- sentence_embeddings: Vector of 600 dimensions representing the sentence embeddings.
'''
X_naive = transform_for_naive(data, word_to_index)
X_bow = transform_for_bag_of_words(data, word_to_index)
X_tfidf = transform_for_tfidf(data)
X_we = transform_for_word_embeddings(data, word_to_index_we,
index_we_to_emb)
X_se = transform_for_sentence_embeddings(data)
X_topics = transform_for_topics(data)
Y = create_labels(data)
'''
#Visualize using only one kind of features
visualize_tsne(X_naive, Y, 'naive')
visualize_tsne(X_bow, Y, 'bow')
visualize_tsne(X_tfidf, Y, 'tfidf')
visualize_tsne(X_we, Y, 'word_emb', index_we_to_emb)
visualize_tsne(X_se, Y, 'sent_emb')
visualize_tsne(X_topics, Y, 'topics')
#'''
X_bow_se = np.concatenate([X_bow, X_se], axis=1)
X_bow_topics = np.concatenate([X_bow, X_topics], axis=1)
X_tfidf_se = np.concatenate([X_tfidf, X_se], axis=1)
X_tfidf_topics = np.concatenate([X_tfidf, X_topics], axis=1)
X_se_topics = np.concatenate([X_se, X_topics], axis=1)
def train():
data = np.load("../dataset/dev.npy")
labels = np.load("../dataset/dev_transcripts.npy")
# temorary dataset
data = data[0:2]
labels = labels[0:2]
# temporary dataset
vocab = create_vocab(labels)
labels = create_labels(labels, vocab)
shuffle_index = np.arange(len(data))
shuffle(shuffle_index)
batch_size = cfg.BATCH_SIZE
learning_rate = cfg.LEARNING_RATE
# my_listener = Listener(40, 256, 0.0)
# my_speller = Speller(33, 512, 512, 256, 3)
if isfile("../weights/listener.pt"):
with open("../weights/listener.pt", 'rb') as fl:
my_listener = torch.load(fl)
with open("../weights/speller.pt", 'rb') as fs:
my_speller = torch.load(fs)
print("model loading completed.")
else:
my_listener = Listener(40, 256, 0.0)
my_speller = Speller(33, 512, 512, 256, 3)
loss_fn = torch.nn.CrossEntropyLoss(reduce=False)
my_optimizer = torch.optim.Adam([{
'params': my_speller.parameters()
}, {
'params': my_listener.parameters()
}],
lr=cfg.LEARNING_RATE)
start_index = 0
for epoch in range(cfg.EPOCH):
losses = 0.0
start_index = 0
while (start_index + batch_size <= len(data)):
batch_data = data[shuffle_index[start_index:start_index +
batch_size]]
batch_labels = labels[shuffle_index[start_index:start_index +
batch_size]]
batch_data, batch_labels, batch_lengths, batch_label_lengths = preprocess(
batch_data, batch_labels)
one_hot_batch_labels = OneHot(batch_labels, 33)
listener_output = my_listener(batch_data, batch_lengths)
speller_output = my_speller(batch_labels.size(1), listener_output,
one_hot_batch_labels)
batch_loss = loss_fn(
speller_output[0].contiguous().view(-1, 33),
torch.autograd.Variable(batch_labels).view(-1, ))
batch_loss = batch_loss.view(speller_output[0].size(0),
speller_output[0].size(1))
mask = torch.zeros(batch_loss.size())
for i in range(batch_label_lengths.size(0)):
mask[i, :batch_label_lengths[i]] = 1.0
batch_loss = torch.mul(batch_loss, torch.autograd.Variable(mask))
batch_loss = torch.sum(batch_loss) / torch.sum(mask)
print("epoch {} batch_loss == {:.5f}".format(
epoch, batch_loss.data[0]))
batch_loss.backward()
losses += batch_loss.data.cpu().numpy()
my_optimizer.step()
start_index += batch_size
# break
if (epoch % 3 == 0):
with open("../weights/listener.pt", 'wb') as fl:
torch.save(my_listener, fl)
with open("../weights/speller.pt", 'wb') as fs:
torch.save(my_speller, fs)
def cross_validation(k, X, y, params, regression):
"""
Performing regression using K-Cross Validation.
This function is used to generate a model, given data, a regression function
and a set of parameters.
Args:
k (int): k for cross validation
X (nd.array): training samples of form N x D
y (nd.array): training samples of form N
params (dict): dictionary of training samples
regression (function): regression function
Returns:
float: mean loss on validation datasets
float: mean accuracy on validation datasets
Raise:
ValueError: if the regression function raises an error
"""
# Cross-validation
k_indices = build_k_indices(y, k)
accuracies = []
losses = []
# print(f"(max_iters: {params['max_iters']}, gamma: {params['gamma']}, lambda: {params['lambda_']})")
# each iteration for each split of training and validation
for k_iteration in range(k):
# split the data accordingly into training and validation
X_train, Y_train, X_val, Y_val = cross_validation_iter(
y, X, k_indices, k_iteration)
# initial weights
W_init = np.random.rand(D, )
# initialize dictionary for the training regression model
args_train = {
"tx": X_train,
"y": Y_train,
"initial_w": W_init,
"max_iters": params["max_iters"],
"gamma": params["gamma"],
"lambda_": params["lambda_"]
}
# try to train the model, if this doesnt work, raise an error
try:
W, loss_tr = regression(**args_train)
except ValueError:
print("Regression diverged with these parameters.")
return None, None
if "Logistic" in f_name:
prediction_val_regression = sigmoid(X_val @ W)
else:
prediction_val_regression = X_val @ W
# calculate prediction for the validation dataset
prediction_val = create_labels(prediction_val_regression)
# calculate corresponding loss and accuracy
loss_val = calculate_mse_loss(Y_val, prediction_val)
acc_val = calculate_acc(Y_val, prediction_val)
losses.append(loss_val)
accuracies.append(acc_val)
# finally, generate the means
mean_loss_val = np.array(losses).mean()
mean_acc_val = np.array(accuracies).mean()
return mean_loss_val, mean_acc_val