def bot():
name = input('Enter your name here : ')
greeting(name)
introduction()
input_data()
print('Do you want to know about something else..',
'1.YES',
'2.NO',
sep='\n')
choice = int(input('Enter your choice : '))
while choice == 1:
input_data()
print('Do you want to know about something else..',
'1.YES',
'2.NO',
sep='\n')
choice = int(input('Enter your choice : '))
if choice == 2:
print('Hope this is useful',
'Thankyou for coming here ..🤗🤗',
sep='\n')
def __init__(self, args): super(Model_Run, self).__init__() for k, v in vars(args).items(): setattr(self, k, v) self.args = args self.data_generator = input_data(args) p_content = self.data_generator.p_content word_embed = self.data_generator.word_embed self.model = SHNE_Encoder(args, p_content, word_embed) # run with GPU if self.args.cuda: self.model.cuda() # setting optimizer self.parameters = filter(lambda p: p.requires_grad, self.model.parameters()) self.optim = optim.Adam(self.parameters, lr=self.lr, weight_decay=0.0)
def __initData(self, file_number):
inp = data.input_data()
#Read files
with open(inp[self.file_number] + ".kp") as level_file:
rows = level_file.read().split('\n')
print(rows[0])
number_of_items = (int)(rows[1])
self.items = []
self.file_name = inp[self.file_number - 1]
for i in range(4, number_of_items + 5 - 1):
x = rows[i].split(" ")[0]
y = rows[i].split(" ")[1]
self.items.append(((int)(x), (int)(y)))
self.maxCapacity = (int)(rows[2])
parser.add_argument(
'--data-valid',
default="/home/hemant/net/easy_net/data/val/",
help=
'enter the directory where the validation data of different classes is saved'
)
parser.add_argument(
'--data-train',
default="/home/hemant/net/easy_net/data/train/",
help=
'enter the directory where the train data of different classes is saved')
parser.add_argument("--pll", action='store_true', help='use multi-gpu or no')
parser.add_argument("--weights", action='store_true', help='use weights or no')
args = parser.parse_args()
input_train = input_data(root_dir=args.data_train, type="train")
train_dl = DataLoader(input_train,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
input_valid = input_data(root_dir=args.data_valid, type="valid")
valid_dl = DataLoader(input_valid,
batch_size=args.batch_size * 2,
shuffle=False,
num_workers=4)
if __name__ == '__main__':
model = nn.Sequential(feature_b(), feature_r(),
decision(out_classes=input_train[0][3]))
sess = tf.Session(config=config)
#Placeholder
x1 = tf.placeholder(tf.float32, [batch_size, Height, Width, Channel])
x2 = tf.placeholder(tf.float32, [batch_size, Height, Width, Channel])
x3 = tf.placeholder(tf.float32, [batch_size, Height, Width, Channel])
## MC-subnet
x1to2 = flow.warp_img(batch_size, x2, x1, False)
x3to2 = flow.warp_img(batch_size, x2, x3, True)
## QE-subnet
x2_enhanced = net.network(x1to2, x2, x3to2)
##Import data
PQF_Frame_93_Y, PQF_Frame_93_U, PQF_Frame_93_V = data.input_data(
Height, Width, 'Frame_93')
non_PQF_Frame_96_Y, non_PQF_Frame_96_U, non_PQF_Frame_96_V = data.input_data(
Height, Width, 'Frame_96')
PQF_Frame_97_Y, PQF_Frame_97_U, PQF_Frame_97_V = data.input_data(
Height, Width, 'Frame_97')
##Load model
saver = tf.train.Saver()
saver.restore(sess, './HEVC_QP37_model/model.ckpt')
##Run test
Enhanced_Y = sess.run(x2_enhanced,
feed_dict={
x1: PQF_Frame_93_Y[0:1, 0:Height, 0:Width, 0:1],
x2: non_PQF_Frame_96_Y[0:1, 0:Height, 0:Width, 0:1],
x3: PQF_Frame_97_Y[0:1, 0:Height, 0:Width, 0:1]
def main():
#from 1 to 12
# print('Input folder to run (1->13): ', end = '')
# folder_to_run = int(input())
inp = data.input_data()
check_point = 28
# for name in range((folder_to_run - 1)*8, (folder_to_run - 1)*8 + 8):
for name in range(check_point, len(inp)):
#Declare time
start = time.time()
elapsed = 0
print('File name: ' + inp[name])
print('Package number: ' + str(name))
with open(inp[name] + ".kp") as level_file:
rows = level_file.read().split('\n')
number_items = (int)(rows[1])
capacities = [(int)(rows[2])]
values = []
weights = [[]]
for i in range(4, number_items + 5 - 1):
x = rows[i].split(" ")[0]
y = rows[i].split(" ")[1]
values.append((int)(x))
weights[0].append((int)(y))
solver = pywrapknapsack_solver.KnapsackSolver(
pywrapknapsack_solver.KnapsackSolver.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER, 'KnapsackExample')
#Set time limit - seconds
#3 minutes -> 180 seconds
solver.set_time_limit(180)
solver.Init(values, weights, capacities)
computed_value = solver.Solve()
packed_items = []
packed_weights = []
total_weight = 0
for i in range(len(values)):
if solver.BestSolutionContains(i):
packed_items.append(i)
packed_weights.append(weights[0][i])
total_weight += weights[0][i]
elapsed = time.time() - start
# print("Capacity = {}\nTotal weight = {} \nTotal value = {} \nNumber of items: {} \n " \
# .format(capacities[0], total_weight, computed_value, len(packed_items)))
with open("output/Google-OR-Tools/" + "test " + str(name) + ".txt", 'w+') as solver_file:
solver_file.write('File name: {}.kp\nExecution time: {} sec\nCapacity = {} \nTotal weight = {} \nTotal value = {} \nNumber of items: {} \n' \
.format(inp[name], elapsed, capacities[0], total_weight, computed_value, len(packed_items)))
# solver_file.write('Execution time: {} sec\n'.format(elapsed))
# solver_file.write('Capacity = {} \n'.format(str(capacities[0])))
# solver_file.write('Total weight = {} \n'.format(str(total_weight)))
# solver_file.write('Total value = {} \n'.format(computed_value))
# solver_file.write('Number of items: {} \n'.format(str(len(packed_items))))
# solver_file.write('Packed items: {} \n'.format(packed_items))
# solver_file.write('Packed weights: {}'.format(len(packed_items)))
print('Execution time: ' + str(elapsed))
# print('Capacity = ' + str(capacities[0]))
# print('Total weight = ' + str(total_weight))
# print('Number of items: ' + str(len(packed_items)))
# print('Packed items:', packed_items)
# print('Packed_weights:', packed_weights)
print()