#coding=utf-8
import torch
import pytorch_analyser
from MobileNet import MobileNet
if __name__ == '__main__':
# customized net :mobilenet v1.
input_size = 64
number_classes = 2
name = 'MobileNet{}x{}'.format(input_size, input_size)
# hardware setting
device = 'cuda' if torch.cuda.is_available() else 'cpu'
device = 'cpu' # now it works only under 'cpu' settings
net = MobileNet(number_classes, input_size)
# if(device == 'cuda'):
net = net.to(device)
# load a pre-trained pyTorch model
checkpoint = torch.load("./best_ckpt_64x64_20190829.pth")
net.load_state_dict(checkpoint['weight'])
input_ = torch.ones([1, 3, input_size, input_size])
input = input_.to(device)
blob_dict, tracked_layers = pytorch_analyser.analyse(net, input)
pytorch_analyser.save_csv(tracked_layers,
'./tmp/' + name + '_analysis.csv')
import pytorch_analyser
from option import args
from model import mgn
if __name__ == '__main__':
# ckpt = torch.load(self.dir + '/map_log.pt')
net = mgn.MGN(args)
# print(net)
# net.load('/home/wdd/Work',)
# net = mgn.MGN()
# state_dict = torch.load('/home/wdd/Work/Pytorch/pytorch-caffe-darknet-convert-master/torch_model/model_160_max.pt')
checkpoint = torch.load(
'/home/shining/Projects/github-projects/pytorch-project/nn_tools/model_100.pt'
)
net.load_state_dict(checkpoint)
# net = Model(num_classes=2220)
# # print ('person_ReID:', m)
# state_dict = torch.load('/home/wdd/Work/Pytorch/pytorch-caffe-darknet-convert-master/torch_model/Ep600_ckpt.pth')
# # print ("state_dict: ", state_dict)
# state_dict = state_dict["state_dicts"][0]
# net.load_state_dict(state_dict)
name = 'MGN'
# net = inception_v3(True, transform_input=False)
net.eval()
input_tensor = torch.ones(1, 3, 384, 128)
blob_dict, tracked_layers = pytorch_analyser.analyse(net, input_tensor)
pytorch_analyser.save_csv(tracked_layers, '/tmp/analysis.csv')
import torch
import torch.nn as nn
from torchvision.models import resnet
import pytorch_analyser
if __name__ == '__main__':
resnet50 = resnet.resnet50()
input_tensor = torch.ones([1, 3, 224, 224])
blob_dict, tracked_layers = pytorch_analyser.analyse(
resnet50, input_tensor)
pytorch_analyser.save_csv(tracked_layers, './tmp/resnet50_analysis.csv')
print(blob_dict)