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modelADT.py
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modelADT.py
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#!/usr/bin/python
'''
Defines a generic wrapper class for all the network models
Utilizes params.py to create, initiate, load and train the network.
'''
import torch
from collections import OrderedDict
import scipy.io
from torch.autograd import Variable
import os
from Generators import *
from utils import *
class ModelADT():
def name(self):
return 'Pix2PixModel'
def initialize(self, opt):
self.opt = opt
# self.gpu_ids = opt.gpu_ids
gpu_ids = []
for i in range(torch.cuda.device_count()):
gpu_ids.append(str(i))
print(gpu_ids)
self.gpu_ids = gpu_ids
self.isTrain = opt.isTrain
self.loss_weight = opt.lambda_L
self.reg_loss_weight = opt.lambda_reg
self.cross_loss_weight = opt.lambda_cross
self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) # if self.gpu_ids else torch.device('cpu')
print(self.device)
self.model_name = self.opt.name
self.save_dir = os.path.join(self.opt.checkpoints_save_dir, self.model_name)
self.load_dir = os.path.join(self.opt.checkpoints_load_dir, self.model_name)
self.results_save_dir = opt.results_dir
self.loss_names = []
self.visual_names = []
self.image_paths = []
self.loss_names = ['loss_criterion']
self.visual_names = ['output']
self.net = get_model_funct(self.opt.net)(self.opt, self.gpu_ids)
self.net = self.net.to(self.device)
if self.isTrain:
if self.opt.loss_type == "L2":
self.loss_criterion = torch.nn.MSELoss()
elif self.opt.loss_type == "L1":
self.loss_criterion = torch.nn.L1Loss()
elif self.opt.loss_type == "L1_sumL2":
self.loss_criterion = torch.nn.L1Loss()
elif self.opt.loss_type == "L2_sumL2":
self.loss_criterion = torch.nn.MSELoss()
elif self.opt.loss_type == "L2_sumL1":
self.loss_criterion = torch.nn.MSELoss()
elif self.opt.loss_type == "L2_offset_loss":
self.loss_criterion = torch.nn.MSELoss()
elif self.opt.loss_type == "L1_offset_loss":
self.loss_criterion = torch.nn.L1Loss()
elif self.opt.loss_type == "L1_sumL2_cross":
self.loss_criterion = torch.nn.MSELoss()
elif self.opt.loss_type == "L2_sumL2_cross":
self.loss_criterion = torch.nn.MSELoss()
self.cross_loss_criterion = torch.nn.NLLLoss()
# initialize optimizers
self.optimizers = []
self.optimizer = torch.optim.Adam(self.net.parameters(),lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=opt.weight_decay)
self.optimizers.append(self.optimizer)
# load and print networks; create schedulers
def setup(self, opt, parser=None):
if self.isTrain:
self.schedulers = [get_scheduler(optimizer, opt) for optimizer in self.optimizers]
if not self.isTrain or opt.continue_train:
self.load_networks(opt.starting_epoch_count)
self.print_networks(opt.verbose)
# make models eval mode during test time
def eval(self):
self.net.eval()
# used in test time, wrapping `forward` in no_grad() so we don't save
# intermediate steps for backprop
def test(self):
with torch.no_grad():
self.forward()
def save_outputs(self):
if not os.path.exists(self.results_save_dir):
os.makedirs(self.results_save_dir, exist_ok=True)
to_save_dict = {}
for tensor in self.visual_names:
tensor_val = getattr(self, tensor).data.cpu().numpy()
to_save_dict[tensor] = tensor_val
scipy.io.savemat(self.results_save_dir+".mat", mdict=to_save_dict)
# update learning rate (called once every epoch)
def update_learning_rate(self):
for scheduler in self.schedulers:
scheduler.step()
lr = self.optimizers[0].param_groups[0]['lr']
print('learning rate = %.7f' % lr)
# return visualization images. train.py will display these images, and save the images to a html
def get_current_visuals(self):
visual_ret = OrderedDict()
for name in self.visual_names:
if isinstance(name, str):
visual_ret[name] = getattr(self, name)
return visual_ret
# return traning losses/errors. train.py will print out these errors as debugging information
def get_current_losses(self):
errors_ret = OrderedDict()
for name in self.loss_names:
if isinstance(name, str):
# float(...) works for both scalar tensor and float number
errors_ret[name] = float(getattr(self, 'loss_' + name))
return errors_ret
# save models to the disk
def save_networks(self, epoch):
name = self.model_name
save_filename = '%s_net_%s.pth' % (epoch, name)
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
save_path = os.path.join(self.save_dir, save_filename)
net = self.net
print(save_path)
print('net'+name)
if len(self.gpu_ids) > 0 and torch.cuda.is_available() and hasattr(net, 'module'):
torch.save(net.module.cpu().state_dict(), save_path)
else:
torch.save(net.cpu().state_dict(), save_path)
net.to(self.device)
def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
key = keys[i]
if i + 1 == len(keys): # at the end, pointing to a parameter/buffer
if module.__class__.__name__.startswith('InstanceNorm') and (key == 'running_mean' or key == 'running_var'):
if getattr(module, key) is None:
state_dict.pop('.'.join(keys))
if module.__class__.__name__.startswith('InstanceNorm') and (key == 'num_batches_tracked'):
state_dict.pop('.'.join(keys))
else:
self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
# load models from the disk
def load_networks(self, epoch, load_dir=""):
"""
epoch (int/str): epoch index / "best" / "latest"
"""
assert isinstance(epoch,int) or epoch=="best" or epoch=="latest"
load_filename = f'{epoch}_net_{self.model_name}.pth'
if load_dir:
# use given load dir
load_path = os.path.join(load_dir, self.model_name, load_filename)
else:
# use default load dir
load_path = os.path.join(self.load_dir, load_filename)
net = self.net
if isinstance(net, torch.nn.DataParallel):
net = net.module
print('loading the model from %s' % load_path)
# if you are using PyTorch newer than 0.4 (e.g., built from
# GitHub source), you can remove str() on self.device
state_dict = torch.load(load_path)
if hasattr(state_dict, '_metadata'):
del state_dict._metadata
# patch InstanceNorm checkpoints prior to 0.4
print(state_dict.keys())
for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop
self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))
net.load_state_dict(state_dict)
net = net.to(self.device)
# print network information
def print_networks(self, verbose):
print('---------- Networks initialized -------------')
net = self.net
name = self.model_name
num_params = 0
for param in net.parameters():
num_params += param.numel()
if verbose:
print(net)
print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
print('-----------------------------------------------')
# set requies_grad=Fasle to avoid computation
def set_requires_grad(self, nets, requires_grad=False):
if not isinstance(nets, list):
nets = [nets]
for net in nets:
if net is not None:
for param in net.parameters():
param.requires_grad = requires_grad
# Set the input and target data for the network to train on/evaluate against
def set_data(self, input, target, convert=False, shuffle_channel=False):
shape_in = input.shape
if shuffle_channel:
self.input = input[:,torch.randperm(shape_in[1]),:,:]
else:
self.input = input
self.target = target
if convert:
self.input, self.target = Variable(self.input), Variable(self.target)
self.input = self.input.to(self.device)
self.target = self.target.to(self.device)
# Define the forward pass to compute loss
def forward(self):
self.output = self.net(self.input)
if self.opt.loss_type != "NoLoss":
self.loss = self.loss_weight*self.loss_criterion(self.output, self.target)
if self.opt.loss_type == "L1_sumL2" or self.opt.loss_type == "L2_sumL2":
self.loss += self.reg_loss_weight*torch.norm(self.output).div(self.output.numel())
self.reg_loss = self.reg_loss_weight*torch.norm(self.output).div(self.output.numel())
if self.opt.loss_type == "L2_sumL1":
self.loss += self.reg_loss_weight*torch.norm(self.output,p=1).div(self.output.numel())
self.reg_loss = self.reg_loss_weight*torch.norm(self.output,p=1).div(self.output.numel())
if self.opt.loss_type == "L1_sumL2_cross" or self.opt.loss_type == "L2_sumL2_cross":
self.loss += self.reg_loss_weight*torch.norm(self.output).div(self.output.numel())
self.loss += self.cross_loss_weight*self.cross_loss_criterion(self.output.flatten(start_dim=1),self.target.flatten(start_dim=1))
if self.opt.loss_type == "L1_offset_loss" or self.opt.loss_type == "L2_offset_loss":
self.loss += self.reg_loss_weight*torch.norm(self.output).div(self.output.numel())
def backward(self):
self.loss.backward(retain_graph=True)
def optimize_parameters(self):
self.forward()
self.backward()
self.optimizer.step()
self.optimizer.zero_grad()