from squid.data import RandomCropPhoto2PhotoData from squid.model import SuperviseModel import torch import torch.nn as nn from squid.loss import VGGLoss from squid.net import AOD_Deep1_Residual_Net target_net = AOD_Deep1_Residual_Net() target_net = nn.DataParallel(target_net).cuda() model = SuperviseModel({ 'net': target_net, 'optimizer': torch.optim.Adam([{'name':'net_params', 'params':target_net.parameters(), 'base_lr':1e-5}], betas=(0.9, 0.999), weight_decay=0.0005), 'lr_step_ratio': 0.5, 'lr_step_size': 8, 'supervise':{ 'out': {'MSE_loss': {'obj': nn.MSELoss(size_average=True), 'factor':1.0, 'weight':1.0}}, }, 'metrics': {} }) train_dataset = Photo2PhotoData({ 'data_root': DATASET_DIR, 'desc_file_path': os.path.join(DATASET_TXT_DIR, DATASET_ID, 'train.txt'), }) valid_dataset = Photo2PhotoData({ 'data_root': DATASET_DIR, 'desc_file_path': os.path.join(DATASET_TXT_DIR, DATASET_ID, 'val.txt'), })
model = SuperviseModel({ 'net': target_net, 'optimizer': torch.optim.Adam([{ 'name': 'net_params', 'params': target_net.parameters(), 'base_lr': 1e-4 }], betas=(0.9, 0.999), weight_decay=0.0005), 'lr_step_ratio': 0.5, 'lr_step_size': 2, 'supervise': { 'out': { 'MSE_loss': { 'obj': nn.MSELoss(size_average=True), 'factor': 1.0, 'weight': 1.0 }, 'VGG_loss': { 'obj': VGGLoss1(vgg19_feature_model_path= '/root/group-competition/pretrain_model/vgg_tf.pth', out_feature_level=18), 'factor': 1e-7, 'weight': 1.0 } }, }, 'metrics': {} })
model = SuperviseModel({ 'net': target_net, # 'optimizer': torch.optim.Adam([{'name':'net_params', 'params':target_net.parameters(), 'base_lr':1e-4,'warm_epoch':None,'total_epoch':epochs}], betas=(0.9, 0.999), weight_decay=0.0005), 'optimizer': torch.optim.SGD([{ 'name': 'net_params', 'params': target_net.parameters(), 'base_lr': 1e-8, 'warm_epoch': None, 'total_epoch': epochs }], lr=1e-8, momentum=0.9, weight_decay=0.0005), # 'lr_step_ratio': 10, # 'lr_step_size': 1, 'supervise': { 'out': { 'L2_loss': { 'obj': nn.MSELoss(size_average=True), 'factor': 1.0, 'weight': 1.0 } } }, 'metrics': { 'out': { 'psnr': { 'obj': PSNR() } } }, 'not_show_gradient': True })
target_net = DarkNet() model = SuperviseModel({ 'net': target_net, 'optimizer': torch.optim.Adam([{ 'name': 'net_params', 'params': target_net.parameters(), 'base_lr': 1e-3, 'warm_epoch': 2, 'total_epoch': epochs }], betas=(0.9, 0.999), weight_decay=0.0005), 'not_show_gradient': True, 'supervise': { 'out': { 'L1_loss': { 'obj': nn.L1Loss(size_average=True), 'factor': 1.0, 'weight': 1.0 } }, }, 'metrics': {} }) # =================== dataset ========================================================================================== from data import DarkPipeline
from squid.model import SuperviseModel from squid.metric import IouScore from squid.metric import AccScore from squid.data import Photo2MaskData target_net = ICNet(nclass=15) model = SuperviseModel({ 'net': target_net, 'optimizer': torch.optim.Adam([{'name':'net_params', 'params':target_net.parameters(), 'base_lr':1e-3}], betas=(0.9, 0.999), weight_decay=0.0005), 'lr_step_ratio': 0.5, 'lr_step_size': 500, 'supervise':{ 'out1': {'cross_entrypy': {'obj': nn.CrossEntropyLoss(size_average=True), 'factor':1.0, 'weight': 1.0}}, 'out2': {'cross_entrypy': {'obj': nn.CrossEntropyLoss(size_average=True), 'factor':1.0, 'weight': 1.0}}, 'out3': {'cross_entrypy': {'obj': nn.CrossEntropyLoss(size_average=True), 'factor':1.0, 'weight': 1.0}}, 'out4': {'cross_entrypy': {'obj': nn.CrossEntropyLoss(size_average=True), 'factor':1.0, 'weight': 1.0}}, }, 'metrics':{ 'mask_out': {'iou': {'obj': IouScore(nclass=15)}, 'acc': {'obj': AccScore()} }, }, }) # =================== dataset ===================================================================== train_dataset = Photo2MaskData({ 'desc_file_path':'/3T/xcb/pytorch-srgan/examples/face_parse/txt/train_for_dev.txt', }) valid_dataset = Photo2MaskData({