from loss_functions.multi_angular_loss import multi_angular_loss from torchvision import transforms import torch import torchvision import os import time from thop import profile dataset = ImageNet( train=False, transform=transforms.Compose([transforms.ToTensor()]), target_transform=transforms.Compose([transforms.ToTensor()]), ) testloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=8) model = ResNetMCC() model.to(device=DEVICE) macs, params = profile(model, inputs=(torch.randn(1, 3, 224, 224).to(DEVICE), )) print("Model's macs is %f, params is %f" % (macs, params)) def run(): statistical_angular_errors = StatisticalValue() sub_dir = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) print('Test start.') with torch.no_grad(): for idx, (images, labels, names) in enumerate(testloader): images, labels = images.to(DEVICE), labels.to(DEVICE)
from os.path import join import torch import torchvision import numpy as np import os trainloader = DataLoader(ImageNet( train=True, transform=transforms.Compose([transforms.ToTensor()]), target_transform=transforms.Compose([transforms.ToTensor()]), ), batch_size=10, shuffle=False, num_workers=8) model = ResNetMCC() model.to(device=DEVICE) criterion = torch.nn.MSELoss(reduction='sum') # criterion = multi_angular_loss optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY) # optimizer = torch.optim.SGD( # model.parameters(), # momentum=0.9, # lr=LEARNING_RATE, # weight_decay=WEIGHT_DECAY # ) scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1)
from constant import DEVICE, TMP_ROOT from utils.StatisticalValue import StatisticalValue from loss_functions.multi_angular_loss import multi_angular_loss from torchvision import transforms import torch import torchvision import os import time from thop import profile dataset = ImageNet( train=False, transform=transforms.Compose([ transforms.ToTensor() ]), target_transform=transforms.Compose([ transforms.ToTensor() ]), ) testloader = DataLoader( dataset, batch_size=1, shuffle=False, num_workers=8 ) model = ResNetMCC(resnet50=True) model.to(device=DEVICE) macs, params = profile(model, inputs=(torch.randn(1, 3, 224, 224).to(DEVICE), )) print("Model's macs is %f, params is %f" % (macs, params))
from loss_functions.multi_angular_loss import multi_angular_loss from torchvision import transforms import torch import torchvision import os import time from thop import profile dataset = ImageNet( train=False, transform=transforms.Compose([transforms.ToTensor()]), target_transform=transforms.Compose([transforms.ToTensor()]), ) testloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=8) model = ResNetMCC(layer_count=152) model.to(device=DEVICE) # macs, params = profile(model, inputs=(torch.randn(1, 3, 224, 224).to(DEVICE), )) # print("Model's macs is %f, params is %f" % (macs, params)) def run(): statistical_angular_errors = StatisticalValue() sub_dir = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) print('Test start.') with torch.no_grad(): for idx, (images, labels, names) in enumerate(testloader): images, labels = images.to(DEVICE), labels.to(DEVICE)
trainloader = DataLoader( ImageNet( train=True, transform=transforms.Compose([ transforms.ToTensor() ]), target_transform=transforms.Compose([ transforms.ToTensor() ]), ), batch_size=10, shuffle=False, num_workers=8 ) model = ResNetMCC(layer_count=152) model.to(device=DEVICE) criterion = torch.nn.MSELoss(reduction='sum') # criterion = multi_angular_loss optimizer = torch.optim.Adam( model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY ) # optimizer = torch.optim.SGD( # model.parameters(), # momentum=0.9, # lr=LEARNING_RATE, # weight_decay=WEIGHT_DECAY # )