{'mIoU': 70.57,
 'mAcc': 80.07,
 'aAcc': 95.29,
 'input_channel': [16, 64],
 # [num_branches, [num_convs], [num_channels]]
 'network_setting': [[1, [3], [128]],
  [2, [3, 3], [32, 48]],
  [2, [3, 3], [32, 48]],
  [2, [3, 3], [32, 48]],
  [3, [2, 3, 2], [16, 32, 16]],
  [3, [2, 3, 2], [16, 32, 16]],
  [4, [2, 4, 1, 1], [96, 112, 48, 80]]],
 'last_channel': 112,
 # [num_branches, num_block1, num_convs1, num_channels1, ..., num_block4, num_convs4, num_channels4, last_channel]
 'embedding': [16, 64, 1, 3, 128, 3, 3, 3, 32, 48, 2, 2, 3, 2, 16, 32, 16, 1, 2, 4, 1, 1, 96, 112, 48, 80]
}

import pickle
exps = pickle.load(open('data/seg/trainval.pkl', 'rb'))
# Then process each item in exps

import torch
from thop import profile
from models.supernet import MultiResolutionNet

# Get model using input_channel & network_setting & last_channel
model = MultiResolutionNet(input_channel=[16, 64],
                           network_setting=[[1, [3], [128]],
                            [2, [3, 3], [32, 48]],
                            [2, [3, 3], [32, 48]],
                            [2, [3, 3], [32, 48]],
                            [3, [2, 3, 2], [16, 32, 16]],
                            [3, [2, 3, 2], [16, 32, 16]],
                            [4, [2, 4, 1, 1], [96, 112, 48, 80]]],
                          last_channel=112)

# Get Flops and Parameters
input = torch.randn(1, 3, 224, 224)
macs, params = profile(model, inputs=(input, ))  

params = '96_128-1_1_1_48-1_2_1_1_128_8-1_3_1_1_1_128_128_120-4_4_4_4_4_4_128_128_128_128-64'
embedding = [int(item) for item in params.replace('-', '_').split('_')]

embedding = [ 96, 128,   1,   1,  48,   1,   1,   1, 128,   8,   1,   1,
           1,   1, 128, 128, 120,   4,   4,   4,   4,   4, 128, 128,
         128, 128, 64]
input_channels = embedding[0:2]
block_1 = embedding[2:3] + [1] + embedding[3:5]
block_2 = embedding[5:6] + [2] + embedding[6:10]
block_3 = embedding[10:11] + [3] + embedding[11:17]
block_4 = embedding[17:18] + [4] + embedding[18:26]
last_channels = embedding[26:27]
network_setting = []
for item in [block_1, block_2, block_3, block_4]:
    for _ in range(item[0]):
        network_setting.append([item[1], item[2:-int(len(item) / 2 - 1)], item[-int(len(item) / 2 - 1):]])

# network_setting = [[1, [1], [48]], 
#  [2, [1, 1], [128, 8]],
#  [3, [1, 1, 1], [128, 128, 120]], 
#  [4, [4, 4, 4, 4], [128, 128, 128, 128]], 
#  [4, [4, 4, 4, 4], [128, 128, 128, 128]], 
#  [4, [4, 4, 4, 4], [128, 128, 128, 128]], 
#  [4, [4, 4, 4, 4], [128, 128, 128, 128]]]
# input_channels = [96, 128]
# last_channels = [64]

input_channels = [str(item) for item in input_channels]
block_1 = [str(item) for item in block_1]
block_2 = [str(item) for item in block_2]
block_3 = [str(item) for item in block_3]
block_4 = [str(item) for item in block_4]
last_channels = [str(item) for item in last_channels]

params = [input_channels, block_1, block_2, block_3, block_4, last_channels]
params = ['_'.join(item) for item in params]
params = '-'.join(params)
# params
# 96_128-1_1_1_48-1_2_1_1_128_8-1_3_1_1_1_128_128_120-4_4_4_4_4_4_128_128_128_128-64'