#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)