def __init__(self, __C):
self.__C = __C
print('Loading training set ........')
self.dataset = DataSet(__C) # img_feat, ques_ix, ans
self.dataset_eval = None
if __C.EVAL_EVERY_EPOCH: # EVAL_EVERY_EPOCH = TRUE
__C_eval = copy.deepcopy(__C)
setattr(__C_eval, 'RUN_MODE', 'val')
print('Loading validation set for per-epoch evaluation ........')
self.dataset_eval = DataSet(__C_eval)
def __init__(self, __C):
self.__C = __C
print('Loading dataset ........')
self.dataset = DataSet(__C)
# If trigger the evaluation after every epoch
# Will create a new cfgs with RUN_MODE = 'val'
self.dataset_eval = None
if __C.EVAL_EVERY_EPOCH:
__C_eval = copy.deepcopy(__C)
setattr(__C_eval, 'RUN_MODE', 'val')
print('Loading eval every epoch dataset ........')
self.dataset_eval = DataSet(__C_eval)
def __init__(self, __C):
self.__C = __C
print('Loading training set ........')
#1.1加载训练数据集
self.dataset = DataSet(__C) #1. 执行完load_data，返回此处
#2.1 评估数据集设为None
self.dataset_eval = None
#EVAL_EVERY_EPOCH:设置为true进行脱机评估
if __C.EVAL_EVERY_EPOCH:
__C_eval = copy.deepcopy(__C)
#设置RUN_MODE = train
setattr(__C_eval, 'RUN_MODE', 'train')
#为每个epoch评估加载验证集,因为内存有限，此处我将val改成了train
print('Loading validation set for per-epoch evaluation ........')
#2.2 加载评估数据集（验证集，在评估方法上调用验证集数据集）
self.dataset_eval = DataSet(__C_eval)
def __init__(self, __C):
self.__C = __C
print('Loading training set ........')
self.dataset = DataSet(__C)
self.dataset_eval = None
if __C.EVAL_EVERY_EPOCH:
__C_eval = copy.deepcopy(__C)
setattr(__C_eval, 'RUN_MODE', 'val')
print('Loading validation set for per-epoch evaluation ........')
self.dataset_eval = DataSet(__C_eval)
#self.h_classifier = HierarchicClassification(__C)
self.writer = SummaryWriter(
log_dir=f'./results/tensorboard/{self.__C.VERSION}')
def __init__(self, __C):
self.__C = __C
print('========== Loading training set ........')
if self.__C.RUN_MODE == 'show':
i = int(input('input the image id: '))
q = input('input the question: ')
# qid = int(input('input the question id: '))
qid = 1
self.dataset = DataSet4Show(__C, i, q, qid)
else:
self.dataset = DataSet(__C)
self.dataset_eval = None
if __C.EVAL_EVERY_EPOCH:
__C_eval = copy.deepcopy(__C)
setattr(__C_eval, 'RUN_MODE', 'val')
print(
'========== Loading validation set for per-epoch evaluation ........'
)
self.dataset_eval = DataSet(__C_eval)
def main():
"""Visual Qustion Answering
Using Machine Learning and Streamlit
"""
__C = Cfgs()
args = parse_args()
args_dict = __C.parse_to_dict(args)
cfg_file = "cfgs/{}_model.yml".format(args.MODEL)
with open(cfg_file, 'r') as f:
yaml_dict = yaml.load(f)
args_dict = {**yaml_dict, **args_dict}
__C.add_args(args_dict)
__C.proc()
print('Hyper Parameters:')
print(__C)
st.title("VQA tool using Streamlit")
html_temp = """
<div style="background-color:tomato;padding:10px">
<h2 style="color:white;text-align:center;">ViPyKube ML App </h2>
</div>
"""
st.markdown(html_temp, unsafe_allow_html=True)
images = []
st.sidebar.title("Image selection and Question")
button = st.sidebar.radio(
'Randomly generate images',
('With predefined questons', 'With custom question'))
#while True:
img_path_list = []
for x in range(0, 10):
name = random.choice(os.listdir('./datasets/coco_extract/images'))
print(str(name))
name = './datasets/coco_extract/images/' + name
image = Image.open(name)
img_path_list.append(name)
images.append(image)
image_iterator = paginator("Select a sunset page", images)
indices_on_page, images_on_page = map(list, zip(*image_iterator))
st.image(images_on_page, width=200, caption=indices_on_page)
pick_img = st.sidebar.selectbox("Which image?",
[x for x in range(1, len(images))])
imp_path = img_path_list[int(pick_img)]
print("chosen image ", imp_path)
image = Image.open(imp_path)
st.header("Selected Image")
st.image(image)
img_id = imp_path[44:-4]
for ix in range(len(img_id)):
if img_id[ix] != "0":
img_id = img_id[ix:]
print(img_id)
break
q_list = []
if button == "With predefined questons":
pass
elif button == "With custom question":
question = st.sidebar.text_input("What is your question?")
q_list = [{
"image_id": int(img_id),
"question": question,
"question_id": 1
}]
start_eval = st.sidebar.button('Get the answer!')
if start_eval:
print('Loading testing set ........')
dataset = DataSet(__C, q_list, imp_path[31:], img_id)
eval(__C, dataset, valid=True)