update
This commit is contained in:
XHChen0528
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#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: trainer_naiv512.py
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# Created Date: Sunday January 9th 2022
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Thursday, 17th March 2022 1:01:52 am
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# Modified By: Chen Xuanhong
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# Copyright (c) 2022 Shanghai Jiao Tong University
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#############################################################
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import os
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import time
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import random
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import shutil
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import tempfile
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torch_utils import misc
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from torch_utils import training_stats
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from torch_utils.ops import conv2d_gradfix
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from torch_utils.ops import grid_sample_gradfix
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from arcface_torch.backbones.iresnet import iresnet100
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from utilities.plot import plot_batch
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from losses.cos import cosin_metric
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from train_scripts.trainer_multigpu_base import TrainerBase
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class Trainer(TrainerBase):
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def __init__(self,
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config,
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reporter):
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super(Trainer, self).__init__(config, reporter)
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import inspect
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print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe()))
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def train(self):
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# Launch processes.
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num_gpus = len(self.config["gpus"])
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print('Launching processes...')
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torch.multiprocessing.set_start_method('spawn')
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with tempfile.TemporaryDirectory() as temp_dir:
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torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus)
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# TODO modify this function to build your models
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def init_framework(config, reporter, device, rank):
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'''
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This function is designed to define the framework,
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and print the framework information into the log file
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'''
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#===============build models================#
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print("build models...")
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# TODO [import models here]
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torch.cuda.set_device(rank)
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torch.cuda.empty_cache()
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model_config = config["model_configs"]
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if config["phase"] == "train":
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gscript_name = "components." + model_config["g_model"]["script"]
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file1 = os.path.join("components", model_config["g_model"]["script"]+".py")
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tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py")
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shutil.copyfile(file1,tgtfile1)
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dscript_name = "components." + model_config["d_model"]["script"]
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file1 = os.path.join("components", model_config["d_model"]["script"]+".py")
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tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py")
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shutil.copyfile(file1,tgtfile1)
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elif config["phase"] == "finetune":
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gscript_name = config["com_base"] + model_config["g_model"]["script"]
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dscript_name = config["com_base"] + model_config["d_model"]["script"]
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class_name = model_config["g_model"]["class_name"]
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package = __import__(gscript_name, fromlist=True)
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gen_class = getattr(package, class_name)
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gen = gen_class(**model_config["g_model"]["module_params"])
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# print and recorde model structure
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reporter.writeInfo("Generator structure:")
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reporter.writeModel(gen.__str__())
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class_name = model_config["d_model"]["class_name"]
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package = __import__(dscript_name, fromlist=True)
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dis_class = getattr(package, class_name)
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dis = dis_class(**model_config["d_model"]["module_params"])
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# print and recorde model structure
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reporter.writeInfo("Discriminator structure:")
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reporter.writeModel(dis.__str__())
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# arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu"))
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# arcface = arcface1['model'].module
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arcface = iresnet100(pretrained=False, fp16=False)
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arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu'))
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arcface.eval()
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# train in GPU
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# if in finetune phase, load the pretrained checkpoint
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if config["phase"] == "finetune":
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model_path = os.path.join(config["project_checkpoints"],
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"step%d_%s.pth"%(config["ckpt"],
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config["checkpoint_names"]["generator_name"]))
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gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu"))
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model_path = os.path.join(config["project_checkpoints"],
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"step%d_%s.pth"%(config["ckpt"],
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config["checkpoint_names"]["discriminator_name"]))
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dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu"))
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print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"]))
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gen = gen.to(device)
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dis = dis.to(device)
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arcface= arcface.to(device)
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arcface.requires_grad_(False)
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arcface.eval()
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return gen, dis, arcface
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# TODO modify this function to configurate the optimizer of your pipeline
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def setup_optimizers(config, reporter, gen, dis, rank):
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torch.cuda.set_device(rank)
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torch.cuda.empty_cache()
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g_train_opt = config['g_optim_config']
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d_train_opt = config['d_optim_config']
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g_optim_params = []
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d_optim_params = []
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for k, v in gen.named_parameters():
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if v.requires_grad:
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g_optim_params.append(v)
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else:
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reporter.writeInfo(f'Params {k} will not be optimized.')
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print(f'Params {k} will not be optimized.')
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for k, v in dis.named_parameters():
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if v.requires_grad:
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d_optim_params.append(v)
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else:
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reporter.writeInfo(f'Params {k} will not be optimized.')
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print(f'Params {k} will not be optimized.')
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optim_type = config['optim_type']
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if optim_type == 'Adam':
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g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt)
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d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt)
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else:
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raise NotImplementedError(
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f'optimizer {optim_type} is not supperted yet.')
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# self.optimizers.append(self.optimizer_g)
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if config["phase"] == "finetune":
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opt_path = os.path.join(config["project_checkpoints"],
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"step%d_optim_%s.pth"%(config["ckpt"],
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config["optimizer_names"]["generator_name"]))
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g_optimizer.load_state_dict(torch.load(opt_path))
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opt_path = os.path.join(config["project_checkpoints"],
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"step%d_optim_%s.pth"%(config["ckpt"],
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config["optimizer_names"]["discriminator_name"]))
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d_optimizer.load_state_dict(torch.load(opt_path))
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print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"]))
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return g_optimizer, d_optimizer
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def train_loop(
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rank,
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config,
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reporter,
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temp_dir
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):
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version = config["version"]
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ckpt_dir = config["project_checkpoints"]
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sample_dir = config["project_samples"]
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log_freq = config["log_step"]
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model_freq = config["model_save_step"]
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sample_freq = config["sample_step"]
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total_step = config["total_step"]
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random_seed = config["dataset_params"]["random_seed"]
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id_w = config["id_weight"]
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rec_w = config["reconstruct_weight"]
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feat_w = config["feature_match_weight"]
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num_gpus = len(config["gpus"])
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batch_gpu = config["batch_size"] // num_gpus
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init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
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if os.name == 'nt':
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init_method = 'file:///' + init_file.replace('\\', '/')
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torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus)
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else:
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init_method = f'file://{init_file}'
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torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus)
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# Init torch_utils.
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sync_device = torch.device('cuda', rank)
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training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
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if rank == 0:
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img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1)
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img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1)
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# Initialize.
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device = torch.device('cuda', rank)
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np.random.seed(random_seed * num_gpus + rank)
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torch.manual_seed(random_seed * num_gpus + rank)
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torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy.
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torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy.
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conv2d_gradfix.enabled = True # Improves training speed.
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grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe.
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# Create dataloader.
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if rank == 0:
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print('Loading training set...')
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dataset = config["dataset_paths"][config["dataset_name"]]
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#================================================#
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print("Prepare the train dataloader...")
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dlModulename = config["dataloader"]
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package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True)
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dataloaderClass = getattr(package, 'GetLoader')
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dataloader_class= dataloaderClass
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dataloader = dataloader_class(dataset,
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rank,
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num_gpus,
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batch_gpu,
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**config["dataset_params"])
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# Construct networks.
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if rank == 0:
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print('Constructing networks...')
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gen, dis, arcface = init_framework(config, reporter, device, rank)
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# Check for existing checkpoint
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# Print network summary tables.
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# if rank == 0:
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# attr = torch.empty([batch_gpu, 3, 512, 512], device=device)
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# id = torch.empty([batch_gpu, 3, 112, 112], device=device)
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# latent = misc.print_module_summary(arcface, [id])
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# img = misc.print_module_summary(gen, [attr, latent])
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# misc.print_module_summary(dis, [img, None])
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# del attr
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# del id
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# del latent
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# del img
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# torch.cuda.empty_cache()
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# Distribute across GPUs.
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if rank == 0:
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print(f'Distributing across {num_gpus} GPUs...')
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for module in [gen, dis, arcface]:
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if module is not None and num_gpus > 1:
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for param in misc.params_and_buffers(module):
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torch.distributed.broadcast(param, src=0)
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# Setup training phases.
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if rank == 0:
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print('Setting up training phases...')
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#===============build losses===================#
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# TODO replace below lines to build your losses
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# MSE_loss = torch.nn.MSELoss()
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l1_loss = torch.nn.L1Loss()
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cos_loss = torch.nn.CosineSimilarity()
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g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank)
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# Initialize logs.
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if rank == 0:
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print('Initializing logs...')
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#==============build tensorboard=================#
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if config["logger"] == "tensorboard":
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import torch.utils.tensorboard as tensorboard
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tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"])
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logger = tensorboard_writer
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elif config["logger"] == "wandb":
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import wandb
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wandb.init(project="Simswap_HQ", entity="xhchen", notes="512",
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tags=[config["tag"]], name=version)
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wandb.config = {
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"total_step": config["total_step"],
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"batch_size": config["batch_size"]
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}
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logger = wandb
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random.seed(random_seed)
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randindex = [i for i in range(batch_gpu)]
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# set the start point for training loop
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if config["phase"] == "finetune":
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start = config["ckpt"]
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else:
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start = 0
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if rank == 0:
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import datetime
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start_time = time.time()
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# Caculate the epoch number
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print("Total step = %d"%total_step)
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print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
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from utilities.logo_class import logo_class
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logo_class.print_start_training()
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dis.feature_network.requires_grad_(False)
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for step in range(start, total_step):
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gen.train()
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dis.train()
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for interval in range(2):
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random.shuffle(randindex)
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src_image1, src_image2 = dataloader.next()
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# if rank ==0:
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# elapsed = time.time() - start_time
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# elapsed = str(datetime.timedelta(seconds=elapsed))
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# print("dataloader:",elapsed)
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if step%2 == 0:
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img_id = src_image2
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else:
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img_id = src_image2[randindex]
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img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic')
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latent_id = arcface(img_id_112)
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latent_id = F.normalize(latent_id, p=2, dim=1)
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if interval == 0:
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img_fake = gen(src_image1, latent_id)
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gen_logits,_ = dis(img_fake.detach(), None)
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loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean()
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real_logits,_ = dis(src_image2,None)
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loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean()
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loss_D = loss_Dgen + loss_Dreal
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d_optimizer.zero_grad(set_to_none=True)
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loss_D.backward()
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with torch.autograd.profiler.record_function('discriminator_opt'):
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# params = [param for param in dis.parameters() if param.grad is not None]
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# if len(params) > 0:
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# flat = torch.cat([param.grad.flatten() for param in params])
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# if num_gpus > 1:
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# torch.distributed.all_reduce(flat)
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# flat /= num_gpus
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# misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat)
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# grads = flat.split([param.numel() for param in params])
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# for param, grad in zip(params, grads):
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# param.grad = grad.reshape(param.shape)
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params = [param for param in dis.parameters() if param.grad is not None]
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flat = torch.cat([param.grad.flatten() for param in params])
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torch.distributed.all_reduce(flat)
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flat /= num_gpus
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misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat)
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grads = flat.split([param.numel() for param in params])
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for param, grad in zip(params, grads):
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param.grad = grad.reshape(param.shape)
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d_optimizer.step()
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# if rank ==0:
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# elapsed = time.time() - start_time
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# elapsed = str(datetime.timedelta(seconds=elapsed))
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# print("Discriminator training:",elapsed)
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else:
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# model.netD.requires_grad_(True)
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img_fake = gen(src_image1, latent_id)
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# G loss
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gen_logits,feat = dis(img_fake, None)
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loss_Gmain = (-gen_logits).mean()
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img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic')
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latent_fake = arcface(img_fake_down)
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latent_fake = F.normalize(latent_fake, p=2, dim=1)
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loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean()
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real_feat = dis.get_feature(src_image1)
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feat_match_loss = l1_loss(feat["3"],real_feat["3"])
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loss_G = loss_Gmain + loss_G_ID * id_w + \
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feat_match_loss * feat_w
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if step%2 == 0:
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#G_Rec
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loss_G_Rec = l1_loss(img_fake, src_image1)
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loss_G += loss_G_Rec * rec_w
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g_optimizer.zero_grad(set_to_none=True)
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loss_G.backward()
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with torch.autograd.profiler.record_function('generator_opt'):
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params = [param for param in gen.parameters() if param.grad is not None]
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flat = torch.cat([param.grad.flatten() for param in params])
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torch.distributed.all_reduce(flat)
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flat /= num_gpus
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misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat)
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grads = flat.split([param.numel() for param in params])
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for param, grad in zip(params, grads):
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param.grad = grad.reshape(param.shape)
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g_optimizer.step()
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# if rank ==0:
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# elapsed = time.time() - start_time
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# elapsed = str(datetime.timedelta(seconds=elapsed))
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# print("Generator training:",elapsed)
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# Print out log info
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if rank == 0 and (step + 1) % log_freq == 0:
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elapsed = time.time() - start_time
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elapsed = str(datetime.timedelta(seconds=elapsed))
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# print("ready to report losses")
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# ID_Total= loss_G_ID
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# torch.distributed.all_reduce(ID_Total)
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|
||||
epochinformation="[{}], Elapsed [{}], Step [{}/{}], \
|
||||
G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \
|
||||
D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \
|
||||
format(version, elapsed, step, total_step, \
|
||||
loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \
|
||||
loss_D.item(), loss_Dgen.item(), loss_Dreal.item())
|
||||
print(epochinformation)
|
||||
reporter.writeInfo(epochinformation)
|
||||
|
||||
if config["logger"] == "tensorboard":
|
||||
logger.add_scalar('G/G_loss', loss_G.item(), step)
|
||||
logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step)
|
||||
logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step)
|
||||
logger.add_scalar('G/G_ID', loss_G_ID.item(), step)
|
||||
logger.add_scalar('D/D_loss', loss_D.item(), step)
|
||||
logger.add_scalar('D/D_fake', loss_Dgen.item(), step)
|
||||
logger.add_scalar('D/D_real', loss_Dreal.item(), step)
|
||||
elif config["logger"] == "wandb":
|
||||
logger.log({"G_Loss": loss_G.item()}, step = step)
|
||||
logger.log({"G_Rec": loss_G_Rec.item()}, step = step)
|
||||
logger.log({"G_feat_match": feat_match_loss.item()}, step = step)
|
||||
logger.log({"G_ID": loss_G_ID.item()}, step = step)
|
||||
logger.log({"D_loss": loss_D.item()}, step = step)
|
||||
logger.log({"D_fake": loss_Dgen.item()}, step = step)
|
||||
logger.log({"D_real": loss_Dreal.item()}, step = step)
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0):
|
||||
gen.eval()
|
||||
with torch.no_grad():
|
||||
imgs = []
|
||||
zero_img = (torch.zeros_like(src_image1[0,...]))
|
||||
imgs.append(zero_img.cpu().numpy())
|
||||
save_img = ((src_image1.cpu())* img_std + img_mean).numpy()
|
||||
for r in range(batch_gpu):
|
||||
imgs.append(save_img[r,...])
|
||||
arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic')
|
||||
id_vector_src1 = arcface(arcface_112)
|
||||
id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1)
|
||||
|
||||
for i in range(batch_gpu):
|
||||
|
||||
imgs.append(save_img[i,...])
|
||||
image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1)
|
||||
img_fake = gen(image_infer, id_vector_src1).cpu()
|
||||
|
||||
img_fake = img_fake * img_std
|
||||
img_fake = img_fake + img_mean
|
||||
img_fake = img_fake.numpy()
|
||||
for j in range(batch_gpu):
|
||||
imgs.append(img_fake[j,...])
|
||||
print("Save test data")
|
||||
imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1)
|
||||
plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg'))
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
|
||||
#===============adjust learning rate============#
|
||||
# if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]:
|
||||
# print("Learning rate decay")
|
||||
# for p in self.optimizer.param_groups:
|
||||
# p['lr'] *= self.config["lr_decay"]
|
||||
# print("Current learning rate is %f"%p['lr'])
|
||||
|
||||
#===============save checkpoints================#
|
||||
if rank == 0 and (step+1) % model_freq==0:
|
||||
|
||||
torch.save(gen.state_dict(),
|
||||
os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1,
|
||||
config["checkpoint_names"]["generator_name"])))
|
||||
torch.save(dis.state_dict(),
|
||||
os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1,
|
||||
config["checkpoint_names"]["discriminator_name"])))
|
||||
|
||||
torch.save(g_optimizer.state_dict(),
|
||||
os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1,
|
||||
config["checkpoint_names"]["generator_name"])))
|
||||
|
||||
torch.save(d_optimizer.state_dict(),
|
||||
os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1,
|
||||
config["checkpoint_names"]["discriminator_name"])))
|
||||
print("Save step %d model checkpoint!"%(step+1))
|
||||
torch.cuda.empty_cache()
|
||||
print("Rank %d process done!"%rank)
|
||||
torch.distributed.barrier()
|
||||
@@ -0,0 +1,534 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding:utf-8 -*-
|
||||
#############################################################
|
||||
# File: trainer_naiv512.py
|
||||
# Created Date: Sunday January 9th 2022
|
||||
# Author: Chen Xuanhong
|
||||
# Email: chenxuanhongzju@outlook.com
|
||||
# Last Modified: Thursday, 17th March 2022 1:01:52 am
|
||||
# Modified By: Chen Xuanhong
|
||||
# Copyright (c) 2022 Shanghai Jiao Tong University
|
||||
#############################################################
|
||||
|
||||
import os
|
||||
import time
|
||||
import random
|
||||
import shutil
|
||||
import tempfile
|
||||
|
||||
import numpy as np
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from torch_utils import misc
|
||||
from torch_utils import training_stats
|
||||
from torch_utils.ops import conv2d_gradfix
|
||||
from torch_utils.ops import grid_sample_gradfix
|
||||
|
||||
from arcface_torch.backbones.iresnet import iresnet100
|
||||
|
||||
from utilities.plot import plot_batch
|
||||
from losses.cos import cosin_metric
|
||||
from train_scripts.trainer_multigpu_base import TrainerBase
|
||||
|
||||
|
||||
class Trainer(TrainerBase):
|
||||
|
||||
def __init__(self,
|
||||
config,
|
||||
reporter):
|
||||
super(Trainer, self).__init__(config, reporter)
|
||||
|
||||
import inspect
|
||||
print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe()))
|
||||
|
||||
def train(self):
|
||||
# Launch processes.
|
||||
num_gpus = len(self.config["gpus"])
|
||||
print('Launching processes...')
|
||||
torch.multiprocessing.set_start_method('spawn')
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus)
|
||||
|
||||
# TODO modify this function to build your models
|
||||
def init_framework(config, reporter, device, rank):
|
||||
'''
|
||||
This function is designed to define the framework,
|
||||
and print the framework information into the log file
|
||||
'''
|
||||
#===============build models================#
|
||||
print("build models...")
|
||||
# TODO [import models here]
|
||||
torch.cuda.set_device(rank)
|
||||
torch.cuda.empty_cache()
|
||||
model_config = config["model_configs"]
|
||||
|
||||
if config["phase"] == "train":
|
||||
gscript_name = "components." + model_config["g_model"]["script"]
|
||||
file1 = os.path.join("components", model_config["g_model"]["script"]+".py")
|
||||
tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py")
|
||||
shutil.copyfile(file1,tgtfile1)
|
||||
dscript_name = "components." + model_config["d_model"]["script"]
|
||||
file1 = os.path.join("components", model_config["d_model"]["script"]+".py")
|
||||
tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py")
|
||||
shutil.copyfile(file1,tgtfile1)
|
||||
|
||||
elif config["phase"] == "finetune":
|
||||
gscript_name = config["com_base"] + model_config["g_model"]["script"]
|
||||
dscript_name = config["com_base"] + model_config["d_model"]["script"]
|
||||
|
||||
class_name = model_config["g_model"]["class_name"]
|
||||
package = __import__(gscript_name, fromlist=True)
|
||||
gen_class = getattr(package, class_name)
|
||||
gen = gen_class(**model_config["g_model"]["module_params"])
|
||||
|
||||
# print and recorde model structure
|
||||
reporter.writeInfo("Generator structure:")
|
||||
reporter.writeModel(gen.__str__())
|
||||
|
||||
class_name = model_config["d_model"]["class_name"]
|
||||
package = __import__(dscript_name, fromlist=True)
|
||||
dis_class = getattr(package, class_name)
|
||||
dis = dis_class(**model_config["d_model"]["module_params"])
|
||||
|
||||
|
||||
# print and recorde model structure
|
||||
reporter.writeInfo("Discriminator structure:")
|
||||
reporter.writeModel(dis.__str__())
|
||||
|
||||
# arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu"))
|
||||
# arcface = arcface1['model'].module
|
||||
|
||||
arcface = iresnet100(pretrained=False, fp16=False)
|
||||
arcface.load_state_dict(torch.load(config["arcface_ckpt"], map_location='cpu'))
|
||||
arcface.eval()
|
||||
|
||||
# train in GPU
|
||||
|
||||
# if in finetune phase, load the pretrained checkpoint
|
||||
if config["phase"] == "finetune":
|
||||
model_path = os.path.join(config["project_checkpoints"],
|
||||
"step%d_%s.pth"%(config["ckpt"],
|
||||
config["checkpoint_names"]["generator_name"]))
|
||||
gen.load_state_dict(torch.load(model_path), map_location=torch.device("cpu"))
|
||||
|
||||
model_path = os.path.join(config["project_checkpoints"],
|
||||
"step%d_%s.pth"%(config["ckpt"],
|
||||
config["checkpoint_names"]["discriminator_name"]))
|
||||
dis.load_state_dict(torch.load(model_path), map_location=torch.device("cpu"))
|
||||
|
||||
print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"]))
|
||||
|
||||
gen = gen.to(device)
|
||||
dis = dis.to(device)
|
||||
arcface= arcface.to(device)
|
||||
arcface.requires_grad_(False)
|
||||
arcface.eval()
|
||||
|
||||
|
||||
|
||||
return gen, dis, arcface
|
||||
|
||||
# TODO modify this function to configurate the optimizer of your pipeline
|
||||
def setup_optimizers(config, reporter, gen, dis, rank):
|
||||
|
||||
torch.cuda.set_device(rank)
|
||||
torch.cuda.empty_cache()
|
||||
g_train_opt = config['g_optim_config']
|
||||
d_train_opt = config['d_optim_config']
|
||||
|
||||
g_optim_params = []
|
||||
d_optim_params = []
|
||||
for k, v in gen.named_parameters():
|
||||
if v.requires_grad:
|
||||
g_optim_params.append(v)
|
||||
else:
|
||||
reporter.writeInfo(f'Params {k} will not be optimized.')
|
||||
print(f'Params {k} will not be optimized.')
|
||||
|
||||
for k, v in dis.named_parameters():
|
||||
if v.requires_grad:
|
||||
d_optim_params.append(v)
|
||||
else:
|
||||
reporter.writeInfo(f'Params {k} will not be optimized.')
|
||||
print(f'Params {k} will not be optimized.')
|
||||
|
||||
optim_type = config['optim_type']
|
||||
|
||||
if optim_type == 'Adam':
|
||||
g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt)
|
||||
d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt)
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f'optimizer {optim_type} is not supperted yet.')
|
||||
# self.optimizers.append(self.optimizer_g)
|
||||
if config["phase"] == "finetune":
|
||||
opt_path = os.path.join(config["project_checkpoints"],
|
||||
"step%d_optim_%s.pth"%(config["ckpt"],
|
||||
config["optimizer_names"]["generator_name"]))
|
||||
g_optimizer.load_state_dict(torch.load(opt_path))
|
||||
|
||||
opt_path = os.path.join(config["project_checkpoints"],
|
||||
"step%d_optim_%s.pth"%(config["ckpt"],
|
||||
config["optimizer_names"]["discriminator_name"]))
|
||||
d_optimizer.load_state_dict(torch.load(opt_path))
|
||||
|
||||
print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"]))
|
||||
return g_optimizer, d_optimizer
|
||||
|
||||
|
||||
def train_loop(
|
||||
rank,
|
||||
config,
|
||||
reporter,
|
||||
temp_dir
|
||||
):
|
||||
|
||||
version = config["version"]
|
||||
|
||||
ckpt_dir = config["project_checkpoints"]
|
||||
sample_dir = config["project_samples"]
|
||||
|
||||
log_freq = config["log_step"]
|
||||
model_freq = config["model_save_step"]
|
||||
sample_freq = config["sample_step"]
|
||||
total_step = config["total_step"]
|
||||
random_seed = config["dataset_params"]["random_seed"]
|
||||
|
||||
|
||||
id_w = config["id_weight"]
|
||||
rec_w = config["reconstruct_weight"]
|
||||
rec_fm_w = config["rec_feature_match_weight"]
|
||||
cycle_fm_w = config["cycle_feature_match_weight"]
|
||||
cycle_w = config["cycle_weight"]
|
||||
num_gpus = len(config["gpus"])
|
||||
batch_gpu = config["batch_size"] // num_gpus
|
||||
|
||||
init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
|
||||
if os.name == 'nt':
|
||||
init_method = 'file:///' + init_file.replace('\\', '/')
|
||||
torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus)
|
||||
else:
|
||||
init_method = f'file://{init_file}'
|
||||
torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus)
|
||||
|
||||
# Init torch_utils.
|
||||
sync_device = torch.device('cuda', rank)
|
||||
training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
|
||||
|
||||
|
||||
|
||||
if rank == 0:
|
||||
img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1)
|
||||
img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1)
|
||||
|
||||
|
||||
# Initialize.
|
||||
device = torch.device('cuda', rank)
|
||||
np.random.seed(random_seed * num_gpus + rank)
|
||||
torch.manual_seed(random_seed * num_gpus + rank)
|
||||
torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy.
|
||||
torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy.
|
||||
conv2d_gradfix.enabled = True # Improves training speed.
|
||||
grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe.
|
||||
|
||||
# Create dataloader.
|
||||
if rank == 0:
|
||||
print('Loading training set...')
|
||||
|
||||
dataset = config["dataset_paths"][config["dataset_name"]]
|
||||
#================================================#
|
||||
print("Prepare the train dataloader...")
|
||||
dlModulename = config["dataloader"]
|
||||
package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True)
|
||||
dataloaderClass = getattr(package, 'GetLoader')
|
||||
dataloader_class= dataloaderClass
|
||||
dataloader = dataloader_class(dataset,
|
||||
rank,
|
||||
num_gpus,
|
||||
batch_gpu,
|
||||
**config["dataset_params"])
|
||||
|
||||
# Construct networks.
|
||||
if rank == 0:
|
||||
print('Constructing networks...')
|
||||
gen, dis, arcface = init_framework(config, reporter, device, rank)
|
||||
|
||||
# Check for existing checkpoint
|
||||
|
||||
# Print network summary tables.
|
||||
# if rank == 0:
|
||||
# attr = torch.empty([batch_gpu, 3, 512, 512], device=device)
|
||||
# id = torch.empty([batch_gpu, 3, 112, 112], device=device)
|
||||
# latent = misc.print_module_summary(arcface, [id])
|
||||
# img = misc.print_module_summary(gen, [attr, latent])
|
||||
# misc.print_module_summary(dis, [img, None])
|
||||
# del attr
|
||||
# del id
|
||||
# del latent
|
||||
# del img
|
||||
# torch.cuda.empty_cache()
|
||||
|
||||
|
||||
# Distribute across GPUs.
|
||||
if rank == 0:
|
||||
print(f'Distributing across {num_gpus} GPUs...')
|
||||
for module in [gen, dis, arcface]:
|
||||
if module is not None and num_gpus > 1:
|
||||
for param in misc.params_and_buffers(module):
|
||||
torch.distributed.broadcast(param, src=0)
|
||||
|
||||
# Setup training phases.
|
||||
if rank == 0:
|
||||
print('Setting up training phases...')
|
||||
#===============build losses===================#
|
||||
# TODO replace below lines to build your losses
|
||||
# MSE_loss = torch.nn.MSELoss()
|
||||
l1_loss = torch.nn.L1Loss()
|
||||
cos_loss = torch.nn.CosineSimilarity()
|
||||
|
||||
g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank)
|
||||
|
||||
# Initialize logs.
|
||||
if rank == 0:
|
||||
print('Initializing logs...')
|
||||
#==============build tensorboard=================#
|
||||
if config["logger"] == "tensorboard":
|
||||
import torch.utils.tensorboard as tensorboard
|
||||
tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"])
|
||||
logger = tensorboard_writer
|
||||
|
||||
elif config["logger"] == "wandb":
|
||||
import wandb
|
||||
wandb.init(project="Simswap_HQ", entity="xhchen", notes="512",
|
||||
tags=[config["tag"]], name=version)
|
||||
|
||||
wandb.config = {
|
||||
"total_step": config["total_step"],
|
||||
"batch_size": config["batch_size"]
|
||||
}
|
||||
logger = wandb
|
||||
|
||||
|
||||
random.seed(random_seed)
|
||||
randindex = [i for i in range(batch_gpu)]
|
||||
|
||||
# set the start point for training loop
|
||||
if config["phase"] == "finetune":
|
||||
start = config["ckpt"]
|
||||
else:
|
||||
start = 0
|
||||
if rank == 0:
|
||||
import datetime
|
||||
start_time = time.time()
|
||||
|
||||
# Caculate the epoch number
|
||||
print("Total step = %d"%total_step)
|
||||
|
||||
print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
|
||||
|
||||
from utilities.logo_class import logo_class
|
||||
logo_class.print_start_training()
|
||||
|
||||
dis.feature_network.requires_grad_(False)
|
||||
|
||||
for step in range(start, total_step):
|
||||
gen.train()
|
||||
dis.train()
|
||||
for interval in range(2):
|
||||
random.shuffle(randindex)
|
||||
src_image1, src_image2 = dataloader.next()
|
||||
# if rank ==0:
|
||||
|
||||
# elapsed = time.time() - start_time
|
||||
# elapsed = str(datetime.timedelta(seconds=elapsed))
|
||||
# print("dataloader:",elapsed)
|
||||
|
||||
if step%2 == 0:
|
||||
img_id = src_image2
|
||||
else:
|
||||
img_id = src_image2[randindex]
|
||||
|
||||
img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic')
|
||||
latent_id = arcface(img_id_112)
|
||||
latent_id = F.normalize(latent_id, p=2, dim=1)
|
||||
|
||||
if interval == 0:
|
||||
img_fake = gen(src_image1, latent_id)
|
||||
gen_logits,_ = dis(img_fake.detach(), None)
|
||||
loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean()
|
||||
|
||||
real_logits,_ = dis(src_image2,None)
|
||||
loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean()
|
||||
|
||||
loss_D = loss_Dgen + loss_Dreal
|
||||
d_optimizer.zero_grad(set_to_none=True)
|
||||
loss_D.backward()
|
||||
with torch.autograd.profiler.record_function('discriminator_opt'):
|
||||
# params = [param for param in dis.parameters() if param.grad is not None]
|
||||
# if len(params) > 0:
|
||||
# flat = torch.cat([param.grad.flatten() for param in params])
|
||||
# if num_gpus > 1:
|
||||
# torch.distributed.all_reduce(flat)
|
||||
# flat /= num_gpus
|
||||
# misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat)
|
||||
# grads = flat.split([param.numel() for param in params])
|
||||
# for param, grad in zip(params, grads):
|
||||
# param.grad = grad.reshape(param.shape)
|
||||
params = [param for param in dis.parameters() if param.grad is not None]
|
||||
flat = torch.cat([param.grad.flatten() for param in params])
|
||||
torch.distributed.all_reduce(flat)
|
||||
flat /= num_gpus
|
||||
misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat)
|
||||
grads = flat.split([param.numel() for param in params])
|
||||
for param, grad in zip(params, grads):
|
||||
param.grad = grad.reshape(param.shape)
|
||||
d_optimizer.step()
|
||||
# if rank ==0:
|
||||
|
||||
# elapsed = time.time() - start_time
|
||||
# elapsed = str(datetime.timedelta(seconds=elapsed))
|
||||
# print("Discriminator training:",elapsed)
|
||||
else:
|
||||
|
||||
# model.netD.requires_grad_(True)
|
||||
img_fake = gen(src_image1, latent_id)
|
||||
# G loss
|
||||
gen_logits,feat = dis(img_fake, None)
|
||||
real_feat = dis.get_feature(src_image1)
|
||||
loss_Gmain = (-gen_logits).mean()
|
||||
img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic')
|
||||
latent_fake = arcface(img_fake_down)
|
||||
latent_fake = F.normalize(latent_fake, p=2, dim=1)
|
||||
loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean()
|
||||
|
||||
if step%2 == 0:
|
||||
#G_Rec
|
||||
rec_fm = l1_loss(feat["3"],real_feat["3"])
|
||||
loss_G_Rec = l1_loss(img_fake, src_image1)
|
||||
loss_G += loss_G_Rec * rec_w + rec_fm * rec_fm_w
|
||||
else:
|
||||
source1_down = F.interpolate(src_image1, size=(112,112), mode='bicubic')
|
||||
latent_source1 = arcface(source1_down)
|
||||
latent_source1 = F.normalize(latent_source1, p=2, dim=1)
|
||||
cycle_src = gen(img_fake, latent_source1)
|
||||
cycle_loss = l1_loss(src_image1,cycle_src)
|
||||
cycle_feat = dis.get_feature(cycle_src)
|
||||
cycle_fm = l1_loss(real_feat["3"],cycle_feat["3"])
|
||||
loss_G += cycle_loss * cycle_w + cycle_fm * cycle_fm_w
|
||||
|
||||
loss_G = loss_Gmain + loss_G_ID * id_w
|
||||
g_optimizer.zero_grad(set_to_none=True)
|
||||
loss_G.backward()
|
||||
with torch.autograd.profiler.record_function('generator_opt'):
|
||||
params = [param for param in gen.parameters() if param.grad is not None]
|
||||
flat = torch.cat([param.grad.flatten() for param in params])
|
||||
torch.distributed.all_reduce(flat)
|
||||
flat /= num_gpus
|
||||
misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat)
|
||||
grads = flat.split([param.numel() for param in params])
|
||||
for param, grad in zip(params, grads):
|
||||
param.grad = grad.reshape(param.shape)
|
||||
g_optimizer.step()
|
||||
# if rank ==0:
|
||||
|
||||
# elapsed = time.time() - start_time
|
||||
# elapsed = str(datetime.timedelta(seconds=elapsed))
|
||||
# print("Generator training:",elapsed)
|
||||
|
||||
|
||||
# Print out log info
|
||||
if rank == 0 and (step + 1) % log_freq == 0:
|
||||
elapsed = time.time() - start_time
|
||||
elapsed = str(datetime.timedelta(seconds=elapsed))
|
||||
# print("ready to report losses")
|
||||
# ID_Total= loss_G_ID
|
||||
# torch.distributed.all_reduce(ID_Total)
|
||||
|
||||
epochinformation="[{}], Elapsed [{}], Step [{}/{}], \
|
||||
G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \
|
||||
D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \
|
||||
format(version, elapsed, step, total_step, \
|
||||
loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \
|
||||
loss_D.item(), loss_Dgen.item(), loss_Dreal.item())
|
||||
print(epochinformation)
|
||||
reporter.writeInfo(epochinformation)
|
||||
|
||||
if config["logger"] == "tensorboard":
|
||||
logger.add_scalar('G/G_loss', loss_G.item(), step)
|
||||
logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step)
|
||||
logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step)
|
||||
logger.add_scalar('G/G_ID', loss_G_ID.item(), step)
|
||||
logger.add_scalar('D/D_loss', loss_D.item(), step)
|
||||
logger.add_scalar('D/D_fake', loss_Dgen.item(), step)
|
||||
logger.add_scalar('D/D_real', loss_Dreal.item(), step)
|
||||
elif config["logger"] == "wandb":
|
||||
logger.log({"G_Loss": loss_G.item()}, step = step)
|
||||
logger.log({"G_Rec": loss_G_Rec.item()}, step = step)
|
||||
logger.log({"G_feat_match": feat_match_loss.item()}, step = step)
|
||||
logger.log({"G_ID": loss_G_ID.item()}, step = step)
|
||||
logger.log({"D_loss": loss_D.item()}, step = step)
|
||||
logger.log({"D_fake": loss_Dgen.item()}, step = step)
|
||||
logger.log({"D_real": loss_Dreal.item()}, step = step)
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0):
|
||||
gen.eval()
|
||||
with torch.no_grad():
|
||||
imgs = []
|
||||
zero_img = (torch.zeros_like(src_image1[0,...]))
|
||||
imgs.append(zero_img.cpu().numpy())
|
||||
save_img = ((src_image1.cpu())* img_std + img_mean).numpy()
|
||||
for r in range(batch_gpu):
|
||||
imgs.append(save_img[r,...])
|
||||
arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic')
|
||||
id_vector_src1 = arcface(arcface_112)
|
||||
id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1)
|
||||
|
||||
for i in range(batch_gpu):
|
||||
|
||||
imgs.append(save_img[i,...])
|
||||
image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1)
|
||||
img_fake = gen(image_infer, id_vector_src1).cpu()
|
||||
|
||||
img_fake = img_fake * img_std
|
||||
img_fake = img_fake + img_mean
|
||||
img_fake = img_fake.numpy()
|
||||
for j in range(batch_gpu):
|
||||
imgs.append(img_fake[j,...])
|
||||
print("Save test data")
|
||||
imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1)
|
||||
plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg'))
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
|
||||
#===============adjust learning rate============#
|
||||
# if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]:
|
||||
# print("Learning rate decay")
|
||||
# for p in self.optimizer.param_groups:
|
||||
# p['lr'] *= self.config["lr_decay"]
|
||||
# print("Current learning rate is %f"%p['lr'])
|
||||
|
||||
#===============save checkpoints================#
|
||||
if rank == 0 and (step+1) % model_freq==0:
|
||||
|
||||
torch.save(gen.state_dict(),
|
||||
os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1,
|
||||
config["checkpoint_names"]["generator_name"])))
|
||||
torch.save(dis.state_dict(),
|
||||
os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1,
|
||||
config["checkpoint_names"]["discriminator_name"])))
|
||||
|
||||
torch.save(g_optimizer.state_dict(),
|
||||
os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1,
|
||||
config["checkpoint_names"]["generator_name"])))
|
||||
|
||||
torch.save(d_optimizer.state_dict(),
|
||||
os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1,
|
||||
config["checkpoint_names"]["discriminator_name"])))
|
||||
print("Save step %d model checkpoint!"%(step+1))
|
||||
torch.cuda.empty_cache()
|
||||
print("Rank %d process done!"%rank)
|
||||
torch.distributed.barrier()
|
||||
Reference in New Issue
Block a user