def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--model', help='model name to save/load checkpoints')
parser.add_argument('-c', '--checkpoint')
args = parser.parse_args()
model, misc = init(args.model, args.checkpoint)
voc, checkpoint, ckpt_mng, model_config = (
misc[k] for k in ['voc', 'checkpoint', 'ckpt_mng', 'model_config']
)
trainer_args = (model, ckpt_mng)
kargs = config_to_kargs(model_config)
if model_config.TRAINING_TASK == 'rate':
Trainer = RankerTrainer
kargs['rank_loss_type'] = model_config.RANK_LOSS_TYPE
kargs['loss_lambda'] = model_config.LOSS_LAMBDA
elif model_config.TRAINING_TASK == 'review':
Trainer = ReviewTrainer
kargs['voc'] = voc
elif model_config.TRAINING_TASK == 'nrt':
Trainer = NRTTaskTrainer
kargs['loss_lambda'] = model_config.LOSS_LAMBDA
else:
Trainer = MultiTaskTrainer
kargs['voc'] = voc
kargs['loss_lambda'] = model_config.LOSS_LAMBDA
kargs['rank_loss_type'] = model_config.RANK_LOSS_TYPE
if 'rank_loss_type' in kargs and kargs['rank_loss_type']:
kargs['grp_config'] = config.LOSS_TYPE_GRP_CONFIG[kargs['rank_loss_type']]
print(f'Training config:', {k: v for k, v in kargs.items() if k in KARGS_LOG_KEYS})
trainer = Trainer(
*trainer_args,
**kargs
)
if checkpoint:
trainer.resume(checkpoint)
train_dataset = load_dataset('train')
dev_dataset = load_dataset('dev')
# Ensure dropout layers are in train mode
model.train()
trainer.train(train_dataset, dev_dataset)
def best(experiment):
df = experiment.fetch_data().df
best_arm_name = df.arm_name[df['mean'] == df['mean'].min()].values[0]
best_arm = experiment.arms_by_name[best_arm_name]
print(best_arm)
dtype = torch.float
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = 100
train_dataloader, test_dataloader = load_dataset('data/aponc_sda.npz',
batch_size, device)
combined_train_test_set = torch.utils.data.ConcatDataset([
train_dataloader.dataset,
test_dataloader.dataset,
])
combined_train_test_loader = torch.utils.data.DataLoader(
combined_train_test_set,
batch_size=batch_size,
shuffle=True,
)
net = train(train_dataloader, best_arm.parameters, device)
test_mse_loss = test(test_dataloader, net)
print('MSE loss (test set): %f' % (test_mse_loss))
def train_test(parameterization):
dtype = torch.float
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = 100
train_dataloader, test_dataloader = load_dataset('data/aponc_sda.npz',
batch_size, device)
net = train(train_dataloader, parameterization, device)
return test(test_dataloader, net)
self.best_mse = float("inf")
self.population = []
self.init_population()
return self
def write(self):
pass
def visualize(self):
pass
if __name__ == "__main__":
import main
data = main.load_dataset("data/ripple_0.0_50_200")
# init ga
input_size = data.shape[1] - 1
hidden_layer_size = 5
output_size = 1
population_size = 10
selection_size = 4
learning_rate = 1e-3
epochs = 10
generations = 10
estimator = GeneticAlgorithm(
True,
input_size,
hidden_layer_size,
output_size,
population_size,
self.f = ""
self.learning_rate_decay_frequency = 99999999
self.batch_size = 32
self.num_epochs = 100
self.n_epochs_valudations = 5
self.loader_num_workers = 4
self.pretrained_weights = "runs/Sep23_05-38-54_ai-servers-3css_vn_vanilla_v2/latest_checkpoint.pth"
opt = opt_config()
if __name__ == "__main__":
opt = opt_config()
trainset, testset = load_dataset(opt)
texts = [t for t in trainset.get_all_texts()]
model, optimizer = create_model_and_optimizer(
opt, [t for t in trainset.get_all_texts()])
rec_model = TIRGReconstructionModel(model,
embed_dim=512,
img_shape=[3, 120, 180]).cuda()
pretrained_weights = torch.load("best_rec_model.pth")
rec_model.load_state_dict(pretrained_weights)
trainloader = trainset.get_loader(batch_size=opt.batch_size,
shuffle=True,
drop_last=True,
num_workers=opt.loader_num_workers)
if all_target_captions[i].split()[0] in [c.split()[0] for c in nns[:k]]:
r += 1
r /= len(nn_result)
out += [('recall_top' + str(k) + '_correct_adj', r)]
r = 0.0
for i, nns in enumerate(nn_result):
if all_target_captions[i].split()[1] in [c.split()[1] for c in nns[:k]]:
r += 1
r /= len(nn_result)
out += [('recall_top' + str(k) + '_correct_noun', r)]
return out
if __name__ == '__main__':
import main
import img_text_composition_models
opt = main.parse_opt()
trainset, testset = main.load_dataset(opt)
embed_dim = 512
path = 'checkpoints/checkpoint_fashion200k.pth'
device = torch.device('cpu')
model = img_text_composition_models.TIRG(
[t for t in trainset.get_all_texts()], embed_dim=embed_dim)
checkpoint = torch.load(path, map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
test(opt, model, testset)
def work(data, out_json_filename, out_dot_filename):
indexes = [i for i in range(len(data[0]) - 1)]
tree = build_decision_tree(headers, indexes, data)
# save tree to file
jsonstr = json.dumps(tree)
with open(out_json_filename, 'w') as f:
f.write(jsonstr)
dot.clear()
generate_graph(-1, '<', tree, 0)
dot.format = 'png'
dot.view(out_dot_filename, 'missing_value_output')
if __name__ == '__main__':
folder = 'data/'
output_folder = "missing_value_output"
train_data_filename = os.path.join(folder, 'gene_expression_with_missing_values.csv')
headers, origin_data = load_dataset(train_data_filename)
train_data_random = process_data_with_random(origin_data)
work(train_data_random, os.path.join(output_folder, 'random.json'), "random.dot")
train_data_average = process_data_with_average(origin_data)
work(train_data_average, os.path.join(output_folder, 'average.json'), "average.dot")
train_data_median = process_data_with_median(origin_data)
work(train_data_median, os.path.join(output_folder, 'median.json'), "median.dot")
import matplotlib.pyplot as plt
from scipy.stats import kendalltau
import seaborn as sns
if __name__ == '__main__':
parser = ArgumentParser(description='Analyze the underground dataset')
parser.add_argument('--dataset', required=True, type=str)
args = parser.parse_args()
location = args.dataset
if not (path.exists(location) and path.isfile(location)):
print("Please specify a valid path")
exit(1)
df = load_dataset(location)
df = Filter.apply_all_filters(df)
to_drop = [
'worldwide', ' worldwide', '', 'you', 'world.', 'web', 'internet',
'me on bmr', 'my email', 'torland', 'me', 'bmr', 'pm', 'world', 'bm',
'foron', 'tor', 'the matrix', '------ worldwide', 'here', 'bmr pm',
'my inbox', 'ww', 'email', 'my bmr pm', 'my computer', 'inbox', 'my',
'my pc', 'my pm', 'digital download', 'optiman', 'cyberspace',
'twilight zone', 'bettors paradise', 'international', 'bmr inbox',
'darknet', 'undeclared',
"atm's hack guides + biggest collection of ebooks/manuals",
'the united snakes of captivity', 'entire world', 'eu', 'europe',
'centre europe', 'midwest usa', 'korea, north', 'my bmr',
'north korea', 'earth', 'asia', 'north america',
'eu in sealed envelope', 'cherryflavor', 'www', 'browser',
weight_path = "logs/20200509-143023/ResNet_8_weights/checkpoint"
def get_confusion_matrix(weight_path, data):
(x_test, y_test) = data
x_test = x_test[:500]
y_test = y_test[:500]
model = models.ResNet(depth=8)
_ = model(x_test[0:2])
model.load_weights(weight_path)
predictions = model(x_test, training=False).numpy()
predictions = tf.argmax(predictions, axis=1)
labels = tf.argmax(y_test, axis=1)
confusion_matrix = tf.math.confusion_matrix(labels=labels, predictions=predictions, num_classes=10, dtype=tf.float32)
print(confusion_matrix)
confusion_matrix = tf.transpose(tf.divide(tf.transpose(confusion_matrix), tf.reduce_sum(y_test, axis=0)))
print(tf.reduce_sum(y_test, axis=0))
return confusion_matrix.numpy()
(_, _), data = main.load_dataset()
matrix = get_confusion_matrix(weight_path=weight_path, data=data)
df_cm = pd.DataFrame(matrix, range(10), range(10))
sn.set(font_scale=1.4) # for label size
sn.heatmap(df_cm, annot=True, annot_kws={"size": 14}) # font size
plt.show()
from os.path import dirname, join
from underthesea_flow.flow import Flow
from underthesea_flow.model import Model
from underthesea_flow.validation.validation import TrainTestSplitValidation
from main import load_dataset
from model.model_fasttext import FastTextClassifier
from models.fb_bank_2_act_fasttext.model_fasttext import FastTextPredictor
if __name__ == '__main__':
data_file = join(dirname(dirname(dirname(dirname(__file__)))), "data",
"fb_bank_act", "corpus", "data.xlsx")
X, y = load_dataset(data_file)
flow = Flow()
flow.log_folder = "log"
flow.data(X, y)
flow.add_model(Model(FastTextClassifier(), "FastText"))
# flow.set_learning_curve(0.7, 1, 0.3)
flow.set_validation(TrainTestSplitValidation(test_size=0.1))
# flow.set_validation(CrossValidation(cv=5))
# flow.validation()
model_name = "FastText"
model_filename = join("model", "fasttext.model")
def main():
opt = parse_opt()
trainset, testset = load_dataset(opt)
# input_img = input()
embed_dim = 512
# print(trainset[0])
texts = [t for t in trainset.get_all_texts()]
print(len(texts))
path = 'checkpoints/checkpoint_fashion200k.pth'
device = torch.device('cpu')
vocab = SimpleVocab()
for text in texts:
vocab.add_text_to_vocab(text)
vocab_size = vocab.get_size()
model = img_text_composition_models.TIRG(vocab_size, embed_dim=embed_dim)
checkpoint = torch.load(path, map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
texts = ['green linen-blend a-line dress']
# x = vocab.encode_text(texts)
in_dir = Path('./output/source')
in_dir.mkdir(parents=True, exist_ok=True)
input_img_path = Path(opt.dataset_path).joinpath(
'women/dresses/casual_and_day_dresses/51727804/51727804_0.jpeg')
with open(input_img_path, 'rb') as f:
img = PIL.Image.open(f)
img.save(str(in_dir.joinpath(input_img_path.parts[-1])))
img = img.convert('RGB')
print(input_img_path, str(in_dir.joinpath(input_img_path.parts[-1])))
transform = torchvision.transforms.Compose([
torchvision.transforms.Resize(224),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
img = transform(img).unsqueeze(0)
# print(img.shape)
# to tensor
# img = torch.rand(1, 3, 256, 256)
itexts = [vocab.encode_text(s) for s in texts]
lengths = [len(t) for t in itexts]
pitexts = torch.zeros((np.max(lengths), len(texts))).long() # [T, B]
for i in range(len(texts)):
pitexts[:lengths[i], i] = torch.tensor(itexts[i])
pitexts = pitexts.to(device)
all_queries = model.compose_img_text(img, pitexts,
lengths).data.cpu().numpy()
# compute all image features
if os.path.isfile('database.npy'):
with open('database.npy', 'rb') as f:
all_imgs = np.load(f)
else:
imgs = []
all_imgs = []
for i in tqdm(range(len(testset.imgs))):
imgs += [testset.get_img(i)]
if len(imgs) >= opt.batch_size or i == len(testset.imgs) - 1:
if 'torch' not in str(type(imgs[0])):
imgs = [torch.from_numpy(d).float() for d in imgs]
imgs = torch.stack(imgs).float()
imgs = imgs.to(device)
imgs = model.extract_img_feature(imgs).data.cpu().numpy()
all_imgs += [imgs]
imgs = []
all_imgs = np.concatenate(all_imgs)
with open('database.npy', 'wb') as f:
np.save(f, all_imgs)
# feature normalization
for i in range(all_queries.shape[0]):
all_queries[i, :] /= np.linalg.norm(all_queries[i, :])
for i in range(all_imgs.shape[0]):
all_imgs[i, :] /= np.linalg.norm(all_imgs[i, :])
# match test queries to target images, get nearest neighbors
top_n = 5
sims = all_queries.dot(all_imgs.T)
sims = np.squeeze(sims)
nn_result = np.argsort(-sims)[:top_n]
out_dir = Path('./output/target')
out_dir.mkdir(parents=True, exist_ok=True)
for n in nn_result:
file_path = Path(opt.dataset_path).joinpath(
testset.imgs[n]['file_path'])
print(file_path)
with open(file_path, 'rb') as f:
img = PIL.Image.open(f)
img.save(str(out_dir.joinpath(file_path.parts[-1])))
