def __init__(self,model_path=None, normalize=True):
super(ResNet50Fc, self).__init__()
if model_path:
if os.path.exists(model_path):
self.model_resnet = models.resnet50(pretrained=False)
self.model_resnet.load_state_dict(torch.load(model_path))
else:
raise Exception('invalid model path!')
else:
self.model_resnet = models.resnet50(pretrained=True)
if model_path or normalize:
# pretrain model is used, use ImageNet normalization
self.normalize = True
self.register_buffer('mean', torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
self.register_buffer('std', torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
else:
self.normalize = False
model_resnet = self.model_resnet
self.conv1 = model_resnet.conv1
self.bn1 = model_resnet.bn1
self.relu = model_resnet.relu
self.maxpool = model_resnet.maxpool
self.layer1 = model_resnet.layer1
self.layer2 = model_resnet.layer2
self.layer3 = model_resnet.layer3
self.layer4 = model_resnet.layer4
self.avgpool = model_resnet.avgpool
self.__in_features = model_resnet.fc.in_features
def __init__(self, num_classes=1):
super(DinkNet50, self).__init__()
filters = [256, 512, 1024, 2048]
resnet = models.resnet50(pretrained=True)
self.firstconv = resnet.conv1
self.firstbn = resnet.bn1
self.firstrelu = resnet.relu
self.firstmaxpool = resnet.maxpool
self.encoder1 = resnet.layer1
self.encoder2 = resnet.layer2
self.encoder3 = resnet.layer3
self.encoder4 = resnet.layer4
self.dblock = Dblock_more_dilate(2048)
self.decoder4 = DecoderBlock(filters[3], filters[2])
self.decoder3 = DecoderBlock(filters[2], filters[1])
self.decoder2 = DecoderBlock(filters[1], filters[0])
self.decoder1 = DecoderBlock(filters[0], filters[0])
self.finaldeconv1 = nn.ConvTranspose2d(filters[0], 32, 4, 2, 1)
self.finalrelu1 = nonlinearity
self.finalconv2 = nn.Conv2d(32, 32, 3, padding=1)
self.finalrelu2 = nonlinearity
self.finalconv3 = nn.Conv2d(32, num_classes, 3, padding=1)
def __init__(self, class_num ):
super(ft_net_middle, self).__init__()
model_ft = models.resnet50(pretrained=True)
# avg pooling to global pooling
model_ft.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.model = model_ft
self.classifier = ClassBlock(2048+1024, class_num)
def __init__(self, num_classes):
super(IDE, self).__init__()
resnet = resnet50(pretrained=True)
self.backbone = nn.Sequential(
resnet.conv1,
resnet.bn1,
resnet.relu,
resnet.maxpool,
resnet.layer1,
resnet.layer2,
resnet.layer3,
resnet.layer4,
nn.AdaptiveAvgPool2d((1, 1)),
)
self.classifier = nn.Sequential(
nn.Linear(2048, 512),
nn.BatchNorm1d(512),
nn.LeakyReLU(0.1),
nn.Dropout(p=0.5),
nn.Linear(512, num_classes)
)
nn.init.kaiming_normal_(self.classifier[0].weight, mode='fan_out')
nn.init.constant_(self.classifier[0].bias, 0.)
nn.init.normal_(self.classifier[1].weight, mean=1., std=0.02)
nn.init.constant_(self.classifier[1].bias, 0.)
nn.init.normal_(self.classifier[4].weight, std=0.001)
nn.init.constant_(self.classifier[4].bias, 0.)
def resnet50(num_classes=1000, pretrained='imagenet'):
"""Constructs a ResNet-50 model.
"""
model = models.resnet50(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['resnet50'][pretrained]
model = load_pretrained(model, num_classes, settings)
return model
def __init__(self, descriptor_name):
super(Net, self).__init__()
# if descriptor_name == 'vgg16':
# self.select = ['30']
# self.vgg16 = models.vgg16(pretrained=True)
# self.sequence = []
# for name, layer in self.vgg16.features._modules.items():
# self.sequence += [layer]
# for name, layer in self.vgg16.classifier._modules.items():
# self.sequence += [layer]
# break
# self.model = nn.Sequential(*self.sequence)
if descriptor_name == 'vgg16':
self.select = ['30']
self.vgg16 = models.vgg16(pretrained=True)
self.sequence = []
for name, layer in self.vgg16.features._modules.items():
self.sequence += [layer]
for name, layer in self.vgg16.classifier._modules.items():
if name == '6':
break
self.sequence += [layer]
layer = nn.Linear(4096, 10)
# init.xavier_normal(layer.weight.data, gain = 1)
self.sequence += [layer]
self.model = nn.Sequential(*self.sequence)
elif descriptor_name == 'vgg19':
self.select = ['36']
self.vgg19 = models.vgg19(pretrained=True)
self.sequence = []
for name, layer in self.vgg19.features._modules.items():
self.sequence += [layer]
for name, layer in self.vgg19.classifier._modules.items():
self.sequence += [layer]
break
self.model = nn.Sequential(*self.sequence)
elif descriptor_name == 'resnet50':
self.select = ['avgpool']
self.model = models.resnet50(pretrained=True)
self.model.fc = nn.Linear(2048, 10)
elif descriptor_name == 'resnet101':
self.select = ['avgpool']
self.model = models.resnet101(pretrained=True)
elif descriptor_name == 'resnet152':
self.select = ['avgpool']
self.model = models.resnet152(pretrained=True)
self.model.fc = nn.Linear(2048, 10)
def __init__(self): super(ResNet50Fc, self).__init__() model_resnet50 = models.resnet50(pretrained=True) self.conv1 = model_resnet50.conv1 self.bn1 = model_resnet50.bn1 self.relu = model_resnet50.relu self.maxpool = model_resnet50.maxpool self.layer1 = model_resnet50.layer1 self.layer2 = model_resnet50.layer2 self.layer3 = model_resnet50.layer3 self.layer4 = model_resnet50.layer4 self.avgpool = model_resnet50.avgpool self.__in_features = model_resnet50.fc.in_features
def __init__(self, class_num ):
super(PCB, self).__init__()
self.part = 6 # We cut the pool5 to 6 parts
model_ft = models.resnet50(pretrained=True)
self.model = model_ft
self.avgpool = nn.AdaptiveAvgPool2d((self.part,1))
self.dropout = nn.Dropout(p=0.5)
# remove the final downsample
self.model.layer4[0].downsample[0].stride = (1,1)
self.model.layer4[0].conv2.stride = (1,1)
# define 6 classifiers
for i in range(self.part):
name = 'classifier'+str(i)
setattr(self, name, ClassBlock(2048, class_num, True, False, 256))
def get_model(num_classes, model_type='resnet50'):
if model_type == 'resnet50':
model = resnet50(pretrained=True).cuda()
model.fc = nn.Linear(model.fc.in_features, num_classes).cuda()
elif model_type == 'resnet101':
model = resnet101(pretrained=True).cuda()
model.fc = nn.Linear(model.fc.in_features, num_classes).cuda()
elif model_type == 'resnet152':
model = resnet152(pretrained=True).cuda()
model.fc = nn.Linear(model.fc.in_features, num_classes).cuda()
elif model_type == 'densenet121':
model = densenet121(pretrained=True).cuda()
model.classifier = nn.Linear(model.classifier.in_features, num_classes).cuda()
elif model_type == 'densenet161':
model = densenet161(pretrained=True).cuda()
model.classifier = nn.Linear(model.classifier.in_features, num_classes).cuda()
elif model_type == 'densenet201':
model = densenet201(pretrained=True).cuda()
model.classifier = nn.Linear(model.classifier.in_features, num_classes).cuda()
return model
def __init__(self, requires_grad=False, pretrained=True, num=18):
super(resnet, self).__init__()
if(num==18):
self.net = models.resnet18(pretrained=pretrained)
elif(num==34):
self.net = models.resnet34(pretrained=pretrained)
elif(num==50):
self.net = models.resnet50(pretrained=pretrained)
elif(num==101):
self.net = models.resnet101(pretrained=pretrained)
elif(num==152):
self.net = models.resnet152(pretrained=pretrained)
self.N_slices = 5
self.conv1 = self.net.conv1
self.bn1 = self.net.bn1
self.relu = self.net.relu
self.maxpool = self.net.maxpool
self.layer1 = self.net.layer1
self.layer2 = self.net.layer2
self.layer3 = self.net.layer3
self.layer4 = self.net.layer4
# Importing libraries
import torch
import matplotlib.pyplot as plt
import torchvision.transforms as t
import cv2 as cv
import torchvision.models as models
# Importing the module
from extractor import Extractor
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Loading the model
resnet = models.resnet50()
extractor = Extractor(resnet)
extractor.activate()
# Visualising the filters
plt.figure(figsize=(35, 35))
for index, filter in enumerate(extractor.CNN_weights[0]):
plt.subplot(8, 8, index + 1)
plt.imshow(filter[0, :, :].detach(), cmap='gray')
plt.axis('off')
plt.show()
# Filter Map
img = cv.cvtColor(cv.imread('Featuremaps&Filters/img.png'), cv.COLOR_BGR2RGB)
img = t.Compose([
t.ToPILImage(),
t.Resize((128, 128)),
# t.Grayscale(),
def main():
global opt, name, logger, model, criterion, SSIM_loss, start_time, mcs_num
opt = parser.parse_args()
print(opt)
# Tag_BatchSize
name = "%s_%d" % (opt.tag, opt.batchSize)
mcs_num = "%s" % (opt.num_mcs)
logger = SummaryWriter("runs/" + name)
cuda = opt.cuda
if cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run without --cuda")
seed = 1334
torch.manual_seed(seed)
if cuda:
torch.cuda.manual_seed(seed)
cudnn.benchmark = True
print("==========> Loading datasets")
train_dataset = DatasetFromFolder(opt.train, transform=Compose([
ToTensor()
]))
indoor_test_dataset = TestFromFolder(opt.test, transform=Compose([
ToTensor()
]))
training_data_loader = DataLoader(dataset=train_dataset, num_workers=opt.threads, batch_size=opt.batchSize,
pin_memory=True, shuffle=True)
testing_data_loader = DataLoader(dataset=indoor_test_dataset, num_workers=opt.threads, batch_size=1, pin_memory=True,
shuffle=True)
print("==========> Building model")
backbone = models.resnet50(pretrained=True)
model = ResNet50(backbone, num_classes=15)
# criterion = EMDLoss()
criterion =nn.L1Loss()
# optionally resume from a checkpoint
if opt.resume:
if os.path.isfile(opt.resume):
print("=> loading checkpoint '{}'".format(opt.resume))
checkpoint = torch.load(opt.resume)
opt.start_epoch = checkpoint["epoch"] + 1
model.load_state_dict(checkpoint["state_dict"])
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
# optionally copy weights from a checkpoint
if opt.pretrained:
if os.path.isfile(opt.pretrained):
print("=> loading model '{}'".format(opt.pretrained))
weights = torch.load(opt.pretrained)
model.load_state_dict(weights['state_dict'].state_dict())
else:
print("=> no model found at '{}'".format(opt.pretrained))
print("==========> Setting GPU")
if cuda:
model = model.cuda()
criterion = criterion.cuda()
else:
model = model.cpu()
criterion = criterion.cpu()
print("==========> Setting Optimizer")
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr)
old = -1
print("==========> Training")
for epoch in range(opt.start_epoch, opt.nEpochs + 1):
start_time = time.time()
val = test(testing_data_loader, epoch)
train(training_data_loader, optimizer, epoch)
if val >= old:
save_checkpoint_val_best(model, epoch, name, mcs_num)
old = val
from torch import optim
from torch import nn
from utils import data_loader
from torchvision import models
import torch
train_loader = data_loader(True)
cuda = torch.cuda.is_available()
if cuda:
resnet50 = models.resnet50(num_classes=61).cuda()
else:
resnet50 = models.resnet50(num_classes=61)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(params=resnet50.parameters(),
lr=0.01,
weight_decay=1e-4)
epochs = 60
average_loss_series = []
for epoch in range(epochs):
running_loss = 0.0
for i, data in enumerate(train_loader):
inputs, labels = data
inputs = inputs.cuda() if cuda else inputs
labels = labels.cuda() if cuda else labels
optimizer.zero_grad()
outputs = resnet50(inputs)
loss = criterion(outputs, labels)
loss.backward()
import torchvision.models as models
import torchvision.transforms as transforms
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
import torch as torch
import copy
from torch.autograd.gradcheck import zero_gradients
import sys, os
from skimage.io import imread, imsave
from PIL import Image
input_file = sys.argv[1]
ouput_file = sys.argv[2]
model = models.resnet50(pretrained=True)
model.eval()
is_cuda = torch.cuda.is_available()
if is_cuda:
print("Using GPU")
model = model.cuda()
else:
print("Using CPU")
pre_mean = [0.485, 0.456, 0.406]
pre_std = [0.229, 0.224, 0.225]
post_mean = np.array([-0.485, -0.456, -0.406])
post_std = np.array([1 / 0.229, 1 / 0.224, 1 / 0.225])
add_std = [0.229 / 255, 0.224 / 255, 0.225 / 255]
preprocess = transforms.Compose(
def get_resnet():
resnet50 = models.resnet50(num_classes=1000, pretrained='imagenet').cuda()
return resnet50
def __init__(self):
super(Extractor,self).__init__()
basenet = models.resnet50(pretrained=True)
self.extractor = nn.Sequential(*list(basenet.children())[:-1])
self.feat_dim = 2048
def main():
parser = argparse.ArgumentParser(
description='Compute TCGA features from SimCLR embedder')
parser.add_argument('--num_classes',
default=2,
type=int,
help='Number of output classes [2]')
parser.add_argument('--batch_size',
default=128,
type=int,
help='Batch size of dataloader [128]')
parser.add_argument('--num_workers',
default=4,
type=int,
help='Number of threads for datalodaer')
parser.add_argument('--gpu_index',
type=int,
nargs='+',
default=(0, ),
help='GPU ID(s) [0]')
parser.add_argument('--backbone',
default='resnet18',
type=str,
help='Embedder backbone [resnet18]')
parser.add_argument('--norm_layer',
default='instance',
type=str,
help='Normalization layer [instance]')
parser.add_argument(
'--magnification',
default='single',
type=str,
help=
'Magnification to compute features. Use `tree` for multiple magnifications. Use `high` if patches are cropped for multiple resolution and only process higher level, `low` for only processing lower level.'
)
parser.add_argument('--weights',
default=None,
type=str,
help='Folder of the pretrained weights, simclr/runs/*')
parser.add_argument(
'--weights_high',
default=None,
type=str,
help=
'Folder of the pretrained weights of high magnification, FOLDER < `simclr/runs/[FOLDER]`'
)
parser.add_argument(
'--weights_low',
default=None,
type=str,
help=
'Folder of the pretrained weights of low magnification, FOLDER <`simclr/runs/[FOLDER]`'
)
parser.add_argument('--dataset',
default='TCGA-lung-single',
type=str,
help='Dataset folder name [TCGA-lung-single]')
args = parser.parse_args()
gpu_ids = tuple(args.gpu_index)
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(x) for x in gpu_ids)
if args.norm_layer == 'instance':
norm = nn.InstanceNorm2d
pretrain = False
elif args.norm_layer == 'batch':
norm = nn.BatchNorm2d
if args.weights == 'ImageNet':
pretrain = True
else:
pretrain = False
if args.backbone == 'resnet18':
resnet = models.resnet18(pretrained=pretrain, norm_layer=norm)
num_feats = 512
if args.backbone == 'resnet34':
resnet = models.resnet34(pretrained=pretrain, norm_layer=norm)
num_feats = 512
if args.backbone == 'resnet50':
resnet = models.resnet50(pretrained=pretrain, norm_layer=norm)
num_feats = 2048
if args.backbone == 'resnet101':
resnet = models.resnet101(pretrained=pretrain, norm_layer=norm)
num_feats = 2048
for param in resnet.parameters():
param.requires_grad = False
resnet.fc = nn.Identity()
if args.magnification == 'tree' and args.weights_high != None and args.weights_low != None:
i_classifier_h = mil.IClassifier(resnet,
num_feats,
output_class=args.num_classes).cuda()
i_classifier_l = mil.IClassifier(copy.deepcopy(resnet),
num_feats,
output_class=args.num_classes).cuda()
if args.weights_high == 'ImageNet' or args.weights_low == 'ImageNet' or args.weights == 'ImageNet':
if args.norm_layer == 'batch':
print('Use ImageNet features.')
else:
raise ValueError(
'Please use batch normalization for ImageNet feature')
else:
weight_path = os.path.join('simclr', 'runs', args.weights_high,
'checkpoints', 'model.pth')
state_dict_weights = torch.load(weight_path)
for i in range(4):
state_dict_weights.popitem()
state_dict_init = i_classifier_h.state_dict()
new_state_dict = OrderedDict()
for (k, v), (k_0, v_0) in zip(state_dict_weights.items(),
state_dict_init.items()):
name = k_0
new_state_dict[name] = v
i_classifier_h.load_state_dict(new_state_dict, strict=False)
os.makedirs(os.path.join('embedder', args.dataset), exist_ok=True)
torch.save(
new_state_dict,
os.path.join('embedder', args.dataset, 'embedder-high.pth'))
weight_path = os.path.join('simclr', 'runs', args.weights_low,
'checkpoints', 'model.pth')
state_dict_weights = torch.load(weight_path)
for i in range(4):
state_dict_weights.popitem()
state_dict_init = i_classifier_l.state_dict()
new_state_dict = OrderedDict()
for (k, v), (k_0, v_0) in zip(state_dict_weights.items(),
state_dict_init.items()):
name = k_0
new_state_dict[name] = v
i_classifier_l.load_state_dict(new_state_dict, strict=False)
os.makedirs(os.path.join('embedder', args.dataset), exist_ok=True)
torch.save(
new_state_dict,
os.path.join('embedder', args.dataset, 'embedder-low.pth'))
print('Use pretrained features.')
elif args.magnification == 'single' or args.magnification == 'high' or args.magnification == 'low':
i_classifier = mil.IClassifier(resnet,
num_feats,
output_class=args.num_classes).cuda()
if args.weights == 'ImageNet':
if args.norm_layer == 'batch':
print('Use ImageNet features.')
else:
print('Please use batch normalization for ImageNet feature')
else:
if args.weights is not None:
weight_path = os.path.join('simclr', 'runs', args.weights,
'checkpoints', 'model.pth')
else:
weight_path = glob.glob('simclr/runs/*/checkpoints/*.pth')[-1]
state_dict_weights = torch.load(weight_path)
for i in range(4):
state_dict_weights.popitem()
state_dict_init = i_classifier.state_dict()
new_state_dict = OrderedDict()
for (k, v), (k_0, v_0) in zip(state_dict_weights.items(),
state_dict_init.items()):
name = k_0
new_state_dict[name] = v
i_classifier.load_state_dict(new_state_dict, strict=False)
os.makedirs(os.path.join('embedder', args.dataset), exist_ok=True)
torch.save(new_state_dict,
os.path.join('embedder', args.dataset, 'embedder.pth'))
print('Use pretrained features.')
if args.magnification == 'tree' or args.magnification == 'low' or args.magnification == 'high':
bags_path = os.path.join('WSI', args.dataset, 'pyramid', '*', '*')
else:
bags_path = os.path.join('WSI', args.dataset, 'single', '*', '*')
feats_path = os.path.join('datasets', args.dataset)
os.makedirs(feats_path, exist_ok=True)
bags_list = glob.glob(bags_path)
if args.magnification == 'tree':
compute_tree_feats(args, bags_list, i_classifier_l, i_classifier_h,
feats_path, 'fusion')
else:
compute_feats(args, bags_list, i_classifier, feats_path,
args.magnification)
n_classes = glob.glob(
os.path.join('datasets', args.dataset, '*' + os.path.sep))
n_classes = sorted(n_classes)
all_df = []
for i, item in enumerate(n_classes):
bag_csvs = glob.glob(os.path.join(item, '*.csv'))
bag_df = pd.DataFrame(bag_csvs)
bag_df['label'] = i
bag_df.to_csv(os.path.join('datasets', args.dataset,
item.split(os.path.sep)[2] + '.csv'),
index=False)
all_df.append(bag_df)
bags_path = pd.concat(all_df, axis=0, ignore_index=True)
bags_path = shuffle(bags_path)
bags_path.to_csv(os.path.join('datasets', args.dataset,
args.dataset + '.csv'),
index=False)
import torch
import torch.nn as nn
import torchvision
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import time
import os
from torchvision.models import resnet50
NUM_TARGET_CATEGORIES = 20 # Target categories number
NUM_OF_LAYERS = 116
NUM_OF_TRAINING_LAYER = 10
NUM_OF_FROZEN_LAYER = NUM_OF_LAYERS - NUM_OF_TRAINING_LAYER
basemodel = resnet50(pretrained=True, progress=True)
FC_input = basemodel.fc.in_features
basemodel.fc = nn.Linear(FC_input, NUM_TARGET_CATEGORIES)
def resnetRetrain(n_retrain_layer):
trainlayer = NUM_OF_LAYERS - n_retrain_layer
basemodel = resnet50(pretrained=True, progress=True)
FC_input = basemodel.fc.in_features
basemodel.fc = nn.Linear(FC_input, NUM_TARGET_CATEGORIES)
layer_counter = 0
for gchild in basemodel.children():
for child in gchild.children():
for child in child.children():
def __init__(self):
super(Res50_sentiment, self).__init__()
ResNet50 = models.resnet50(pretrained=True)
modules = list(ResNet50.children())[:-1]
self.backbone = nn.Sequential(*modules)
self.fc1 = nn.Linear(2048, 3)
def initialize_model(model_name, num_classes, resume_from=None):
# Initialize these variables which will be set in this if statement. Each of these
# variables is model specific.
# The model (nn.Module) to return
model_ft = None
# The input image is expected to be (input_size, input_size)
input_size = 0
# You may NOT use pretrained models!!
use_pretrained = False
# By default, all parameters will be trained (useful when you're starting from scratch)
# Within this function you can set .requires_grad = False for various parameters, if you
# don't want to learn them
class Flatten(torch.nn.Module):
def forward(self, x):
batch_size = x.shape[0]
return x.view(batch_size, -1)
if model_name == "resnet18":
""" Resnet18
"""
model_ft = models.resnet18(pretrained=use_pretrained)
num_ftrs = model_ft.fc.in_features
# dropout = nn.Dropout()
# flatten = Flatten()
# hist = AHist.HistPool(num_bins=10)
# layers = list(model_ft.children())
# layers.insert(-1, hist)
# del layers[-1]
# del layers[-2]
# model_ft = nn.Sequential(*layers)
model_ft.fc = nn.Linear(num_ftrs, num_classes)
print(model_ft.children())
input_size = 224
elif model_name == "resnet50":
""" Resnet50
"""
model_ft = models.resnet50(pretrained=use_pretrained)
num_ftrs = model_ft.fc.in_features
# dropout = nn.Dropout(p=.2)
# flatten = Flatten()
# layers = list(model_ft.children())
# layers.insert(-1, flatten)
# layers.insert(-1, dropout)
# del layers[-1]
# model_ft = nn.Sequential(*layers)
# print(list(model_ft.children()))
model_ft.fc = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "resnet152":
""" Resnet152
"""
model_ft = models.resnet152(pretrained=use_pretrained)
num_ftrs = model_ft.fc.in_features
dropout = nn.Dropout()
flatten = Flatten()
layers = list(model_ft.children())
layers.insert(-1, flatten)
layers.insert(-1, dropout)
del layers[-1]
model_ft = nn.Sequential(*layers)
# print(list(model_ft.children()))
model_ft.fc = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "alexnet":
""" Alexnet
"""
model_ft = models.alexnet(pretrained=use_pretrained)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "vgg":
""" VGG11_bn
"""
model_ft = models.vgg11_bn(pretrained=use_pretrained)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "squeezenet":
""" Squeezenet
"""
model_ft = models.squeezenet1_0(pretrained=use_pretrained)
model_ft.classifier[1] = nn.Conv2d(512,
num_classes,
kernel_size=(1, 1),
stride=(1, 1))
model_ft.num_classes = num_classes
input_size = 224
elif model_name == "densenet":
""" Densenet
"""
model_ft = models.densenet121(pretrained=use_pretrained)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, num_classes)
input_size = 224
else:
raise Exception("Invalid model name!")
if resume_from is not None:
print("Loading weights from %s" % resume_from)
x = torch.load(resume_from)
model_ft.load_state_dict(x)
return model_ft, input_size
# print(x.shape)
x4 = self.layer4(x3)
# print(x.shape)
return x1, x2, x3, x4
def resnet50(pre_trained=False, model_weight_path='./path/resnet50.pth'):
model = ResNet(Bottleneck, [3, 4, 6, 3])
if pre_trained:
model.load_state_dict(torch.load(model_weight_path), strict=False)
return model
if __name__ == '__main__':
net = resnet50(pre_trained=False)
for param in net.layer1.parameters():
param.requires_grad = False
for p in net.layer2.parameters(
): # 将fine-tuning 的参数的 requires_grad 设置为 True
p.requires_grad = False
for p in net.layer3.parameters(
): # 将fine-tuning 的参数的 requires_grad 设置为 True
p.requires_grad = False
for p in net.layer4.parameters(
): # 将fine-tuning 的参数的 requires_grad 设置为 True
p.requires_grad = False
net.cuda()
for name, param in net.named_parameters():
if param.requires_grad:
print(name)
def visualize_model(num_images=6):
plt.ion() #交互模式
data_transforms = {
'train':
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val':
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
data_dir = './hymenoptera_data'
#构建数据集：trainset valset, 返回值是图片路径以及类别的索引值
#在image_datasets中有两个set:trainset、valset. 每一个set中有class_to_idx{'ants': 0, 'bees': 1},有类名classes['ants', 'bees']
#有imgs[('E:/PytorchLearning/T...013035.jpg', 0), ('E:/PytorchLearning/T...c608f9.jpg', 0), ('E:/PytorchLearning/T...d8afde.jpg', 0), ('E:/PytorchLearning/T...9d3250.jpg', 0), ('E:/PytorchLearning/T...26745d.jpg', 0), ('E:/PytorchLearning/T...56588f.jpg', 0), ('E:/PytorchLearning/T...ada201.jpg', 0), ('E:/PytorchLearning/T...92cdab.jpg', 0), ('E:/PytorchLearning/T...e80de1.jpg', 0), ('E:/PytorchLearning/T...0adea2.jpg', 0), ('E:/PytorchLearning/T...8c5eea.jpg', 0), ('E:/PytorchLearning/T...e2fb6d.jpg', 0), ('E:/PytorchLearning/T...aacea6.jpg', 0), ('E:/PytorchLearning/T...84f5a4.jpg', 0), ...]
image_datasets = {
x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])
for x in ['train', 'val']
}
#数据加载器:加载数据集然后进行处理:batch、shuffle、sample等
dataloaders = {
x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=4,
shuffle=True)
for x in ['train', 'val']
}
#数据集大小以及类名
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
#创建一个跟训练时一样的模型,把训练好的模型参数加载进来
model = models.resnet50()
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 2)
model.to(device)
model.load_state_dict(torch.load('./model_ft.pth', map_location='cpu'))
#测试模型
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad(): #实现不需要求梯度
for data in dataloaders['val']:
inputs = data[0].to(device)
labels = data[1].to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
if images_so_far > num_images:
break
for j in range(inputs.size()[0]):
images_so_far += 1
if images_so_far > num_images:
break
ax = plt.subplot(num_images // 2, 2, images_so_far)
ax.axis('off')
ax.set_title('predicted: {}/label: {}'.format(
class_names[preds[j]], class_names[labels[j]]))
imshow(inputs.cpu().data[j])
#关闭交互模式
plt.ioff()
#显示结果图,不再关闭
plt.show()
def __init__(self):
self.num_classes = 1000
self.IMG_SIZE = 224
self.resnet_model = models.resnet50(pretrained=True).cuda()
random.seed(0)
#sample100 = random.sample(range(1000),100)
with open('imagenet_class_index.json') as f:
label_data = json.load(f)
labeltoclass = {}
for i in xrange(1000):
labeltoclass[label_data[str(i)][0]] = i
'''
training_images = []
training_labels = []
for subdir, dirs, files in os.walk('/local/yuchi/train/'):
for folder in dirs:
if folder == "ILSVRC2012_img_train":
continue
for folder_subdir, folder_dirs, folder_files in os.walk(os.path.join(subdir, folder)):
for file in folder_files:
#if labeltoclass[folder] in sample100:
training_images.append(os.path.join(folder_subdir, file))
training_labels.append(labeltoclass[folder])
self.training_images = training_images
self.training_labels = training_labels
'''
testing_images = []
testing_images_unsampled = []
testing_labels = []
testing_labels_unsampled = []
test_label_file = open("ILSVRC2012_validation_ground_truth.txt", "r")
labels = test_label_file.readlines()
mapping_file = open("ILSVRC2012_mapping.txt", "r")
mapping = mapping_file.readlines()
idtosynset = {}
for m in mapping:
temp = m.strip('\n').split(" ")
idtosynset[int(temp[0])] = temp[1]
for l in labels:
testing_labels_unsampled.append(labeltoclass[idtosynset[int(l)]])
for subdir, dirs, files in os.walk(
'/local/yuchi/dataset/ILSVRC2012/val/'):
print(len(files))
for file in sorted(files):
testing_images_unsampled.append(os.path.join(subdir, file))
#print(file)
for x, y in zip(testing_labels_unsampled, testing_images_unsampled):
#if x in sample100:
testing_labels.append(x)
testing_images.append(y)
self.testing_images = testing_images
self.testing_labels = testing_labels
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
self.train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
self.val_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])
# define loss function (criterion) and pptimizer
self.criterion = nn.CrossEntropyLoss().cuda()
self.optimizer = torch.optim.SGD(self.resnet_model.parameters(),
0.1,
momentum=0.9,
weight_decay=1e-4)
# In[169]:
## TODO: Specify data loaders
loaders_transfer = loaders_scratch.copy()
# ### (IMPLEMENTATION) Model Architecture
#
# Use transfer learning to create a CNN to classify dog breed. Use the code cell below, and save your initialized model as the variable `model_transfer`.
# In[170]:
import torchvision.models as models
import torch.nn as nn
## TODO: Specify model architecture
model_transfer = models.resnet50(pretrained=True)
for param in model_transfer.parameters():
param.requires_grad = False
model_transfer.fc = nn.Linear(2048, 133, bias=True)
fc_parameters = model_transfer.fc.parameters()
for param in fc_parameters:
param.requires_grad = True
if use_cuda:
model_transfer = model_transfer.cuda()
# In[171]:
from torchvision import datasets, models, transforms
import torch.nn as nn
from torch.nn import functional as F
import torch.optim as optim
torch.__version__
#!pip install -q torch==0.4.1 torchvision
import torchvision
torchvision.__version__ # should be 0.2.1
input_path = "/content/gdrive/My Drive/APS360_Project/data"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device
model = models.resnet50(pretrained=True).to(device)
for param in model.parameters():
param.requires_grad = False
model.fc = nn.Sequential(
nn.Linear(2048, 32), #3208
nn.ReLU(inplace=True),
nn.Linear(32, 2)).to(device)
#nn.Linear(32, 2))
#Generate a name for the model consisting of all the hyperparameter values
def get_model_name(size=320, batchSize=500, learningRate=0.0008, epoch=29):
return "/content/gdrive/My Drive/APS360_Project/RainNet/Models/size{0}_bs{1}_lr{2}_epoch{3}".format(
def __init__(self, name, config):
"""
Initializes the ``LeNet5`` model, creates the required layers.
:param name: Name of the model (taken from the configuration file).
:param config: Parameters read from configuration file.
:type config: ``ptp.configuration.ConfigInterface``
"""
super(GenericImageEncoder, self).__init__(name, GenericImageEncoder,
config)
# Get key mappings.
self.key_inputs = self.stream_keys["inputs"]
self.key_outputs = self.stream_keys["outputs"]
# Get operation modes.
self.return_feature_maps = self.config["return_feature_maps"]
pretrained = self.config["pretrained"]
# Get model type from configuration.
self.model_type = get_value_from_dictionary(
"model_type", self.config,
"vgg16 | densenet121 | resnet152 | resnet50".split(" | "))
if (self.model_type == 'vgg16'):
# Get VGG16
self.model = models.vgg16(pretrained=pretrained)
if self.return_feature_maps:
# Use only the "feature encoder".
self.model = self.model.features
# Height of the returned features tensor (SET)
self.feature_maps_height = 7
self.globals["feature_maps_height"] = self.feature_maps_height
# Width of the returned features tensor (SET)
self.feature_maps_width = 7
self.globals["feature_maps_width"] = self.feature_maps_width
# Depth of the returned features tensor (SET)
self.feature_maps_depth = 512
self.globals["feature_maps_depth"] = self.feature_maps_depth
else:
# Use the whole model, but cut/reshape only the last layer.
self.output_size = self.globals["output_size"]
# "Replace" the last layer.
self.model.classifier._modules['6'] = torch.nn.Linear(
4096, self.output_size)
elif (self.model_type == 'densenet121'):
# Get densenet121
self.model = models.densenet121(pretrained=pretrained)
if self.return_feature_maps:
raise ConfigurationError(
"'densenet121' doesn't support 'return_feature_maps' mode (yet)"
)
# Use the whole model, but cut/reshape only the last layer.
self.output_size = self.globals["output_size"]
self.model.classifier = torch.nn.Linear(1024, self.output_size)
elif (self.model_type == 'resnet152'):
# Get resnet152
self.model = models.resnet152(pretrained=pretrained)
if self.return_feature_maps:
# Get all modules exluding last (avgpool) and (fc)
modules = list(self.model.children())[:-2]
self.model = torch.nn.Sequential(*modules)
# Height of the returned features tensor (SET)
self.feature_maps_height = 7
self.globals["feature_maps_height"] = self.feature_maps_height
# Width of the returned features tensor (SET)
self.feature_maps_width = 7
self.globals["feature_maps_width"] = self.feature_maps_width
# Depth of the returned features tensor (SET)
self.feature_maps_depth = 2048
self.globals["feature_maps_depth"] = self.feature_maps_depth
else:
# Use the whole model, but cut/reshape only the last layer.
self.output_size = self.globals["output_size"]
self.model.fc = torch.nn.Linear(2048, self.output_size)
elif (self.model_type == 'resnet50'):
# Get resnet50
self.model = models.resnet50(pretrained=pretrained)
if self.return_feature_maps:
# Get all modules exluding last (avgpool) and (fc)
modules = list(self.model.children())[:-2]
self.model = torch.nn.Sequential(*modules)
# Height of the returned features tensor (SET)
self.feature_maps_height = 7
self.globals["feature_maps_height"] = self.feature_maps_height
# Width of the returned features tensor (SET)
self.feature_maps_width = 7
self.globals["feature_maps_width"] = self.feature_maps_width
# Depth of the returned features tensor (SET)
self.feature_maps_depth = 2048
self.globals["feature_maps_depth"] = self.feature_maps_depth
else:
# Use the whole model, but cut/reshape only the last layer.
self.output_size = self.globals["output_size"]
self.model.fc = torch.nn.Linear(2048, self.output_size)
score = softmax(pred)
pred_id = np.argmax(score) # 返回score数组中最大值的索引
stop = time.time()
print('cost time', stop - start)
print("预测结果：{:s}".format(labels[pred_id][0]))
return labels[pred_id][0]
if __name__ == "__main__":
test_list = 'test.txt'
# 预处理测试集（214个）
test_data = Garbage_Loader(test_list, train_flag=False)
test_loader = DataLoader(dataset=test_data, num_workers=2, pin_memory=True, batch_size=1)
# 定义网络
model = models.resnet50(pretrained=False)
fc_inputs = model.fc.in_features
model.fc = nn.Sequential(
nn.Dropout(0.2),
nn.Linear(fc_inputs, 214)
)
model = model.cuda()
# 加载训练好的最佳模型
checkpoint = torch.load('model_best_checkpoint_resnet50.pth.tar')
model.load_state_dict(checkpoint['state_dict'])
# ------------------------------------ step 1/2 : 计算测试集的准确率 ------------------------------------
computeTestSetAccuracy(test_loader, model)
# ------------------------------------ step 2/2 : 预测单个图像的结果 ------------------------------------
# predict(model, "垃圾图片库/有害垃圾_电池/img_电池_343.jpeg")
# 打印模型的 state_dict
def main():
global best_loss
data_dir = args.data_dir
img_dir_train = os.path.join(data_dir, "img/train")
img_dir_val = os.path.join(data_dir, "img/test") #use test data for validation
txt_file_train = os.path.join(data_dir, "annot_train.txt")
txt_file_val = os.path.join(data_dir, "annot_test.txt")
# load pretrained resnet50 and modify last fully connected layer
model = models.resnet50(pretrained = True)
model.fc = FinalLayer()
# model.fc = nn.Linear(2048, 12)
# we need three different criterion for training
criterion_protest = nn.BCELoss()
criterion_violence = nn.MSELoss()
criterion_visattr = nn.BCELoss()
criterions = [criterion_protest, criterion_violence, criterion_visattr]
if args.cuda and not torch.cuda.is_available():
raise Exception("No GPU Found")
if args.cuda:
model = model.cuda()
criterions = [criterion.cuda() for criterion in criterions]
optimizer = torch.optim.Adam(
model.parameters(), args.lr,
)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = ProtestDataset(
txt_file = txt_file_train,
img_dir = img_dir_train,
transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
val_dataset = ProtestDataset(
txt_file = txt_file_val,
img_dir = img_dir_val,
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]))
train_loader = DataLoader(
train_dataset,
num_workers = args.workers,
batch_size = args.batch_size,
shuffle = True
)
val_loader = DataLoader(
val_dataset,
num_workers = args.workers,
batch_size = args.batch_size)
for epoch in range(args.epochs):
# adjust_learning_rate(optimizer, epoch)
train(train_loader, model, criterions, optimizer, epoch)
loss = validate(val_loader, model, criterions, epoch)
is_best = loss < best_loss
if is_best:
print('best model!!')
best_loss = min(loss, best_loss)
save_checkpoint({
'epoch' : epoch + 1,
'state_dict' : model.state_dict(),
'best_loss' : best_loss,
'optimizer' : optimizer.state_dict(),
}, is_best)
os.makedirs(output_prefix.parent, exist_ok=True)
if not model_file.exists():
raise (RuntimeError("%s does not exist." % str(model_file)))
# Create dataset
dataset = ImageFolderDataset(image_folder,
file_names,
resize=256,
crop=224)
print('Image folder: %s' % args.image_folder)
loader = data.DataLoader(dataset, batch_size=args.batch_size)
print('Creating CNN instance.')
cnn = resnet50(pretrained=False)
resnet_dict = torch.hub.load_state_dict_from_url("",
file_name=str(model_file),
progress=True)
cnn.load_state_dict(resnet_dict)
# Remove final classifier layer
del cnn.fc
# Move to GPU and switch to evaluation mode
cnn.cuda()
cnn.train(False)
# Create memmaped files
res4f_feats = np.lib.format.open_memmap(str(output_prefix) +
"-resnet50-res4frelu.npy",
from torchvision.models import resnet50, resnet34
import torch.nn.functional as F
import torch.nn as nn
from utils import local, Config
if local:
torch_model = resnet50(pretrained=Config['finetune'])
else:
torch_model = resnet34(pretrained=Config['finetune'])
class Ruda_Model(nn.Module):
def __init__(self):
super(Ruda_Model, self).__init__()
self.conv1_1 = nn.Conv2d(6, 64, 3, bias=False)
self.conv1_2 = nn.Conv2d(64, 64, 3, bias=False)
self.bn1_1 = nn.BatchNorm2d(64)
self.bn1_2 = nn.BatchNorm2d(64)
self.pool1 = nn.MaxPool2d(kernel_size=2, dilation=2)
self.conv2_1 = nn.Conv2d(64, 128, 3, bias=False)
self.conv2_2 = nn.Conv2d(128, 128, 3, bias=False)
self.bn2_1 = nn.BatchNorm2d(128)
self.bn2_2 = nn.BatchNorm2d(128)
self.pool2 = nn.MaxPool2d(kernel_size=2, dilation=2)
self.conv3_1 = nn.Conv2d(128, 256, 3, bias=False)
self.conv3_2 = nn.Conv2d(256, 256, 3, bias=False)
self.bn3_1 = nn.BatchNorm2d(256)
self.bn3_2 = nn.BatchNorm2d(256)
def __init_with_imagenet(self, baseModel):
model = resnet50(pretrained=True)
del model.fc
baseModel.copy_weight(model.state_dict())
def __init__(self):
super(AppearanceEncoder, self).__init__()
self.resnet = models.resnet50()
self.resnet.load_state_dict(torch.load(resnet_checkpoint))
del self.resnet.fc
def __init__(
self,
pretrained: bool = True,
resnet: int = 18,
freeze: Union[str, int] = 6,
head_layers: Optional[List[int]] = None,
head_dropout: Optional[List[float]] = None,
head_batchnorm: Optional[bool] = False,
):
super(DeepImage, self).__init__()
self.head_layers = head_layers
if pretrained:
if resnet == 18:
vision_model = models.resnet18(pretrained=True)
elif resnet == 34:
vision_model = models.resnet34(pretrained=True)
elif resnet == 50:
vision_model = models.resnet50(pretrained=True)
backbone_layers = list(vision_model.children())[:-1]
if isinstance(freeze, str):
frozen_layers = []
for layer in backbone_layers:
for param in layer.parameters():
param.requires_grad = False
frozen_layers.append(layer)
self.backbone = nn.Sequential(*frozen_layers)
if isinstance(freeze, int):
assert (
freeze < 8
), "freeze' must be less than 8 when using resnet architectures"
frozen_layers = []
trainable_layers = backbone_layers[freeze:]
for layer in backbone_layers[:freeze]:
for param in layer.parameters():
param.requires_grad = False
frozen_layers.append(layer)
backbone_layers = frozen_layers + trainable_layers
self.backbone = nn.Sequential(*backbone_layers)
else:
self.backbone = nn.Sequential(
conv_layer(3, 64, 3),
conv_layer(64, 128, 1, maxpool=False),
conv_layer(128, 256, 1, maxpool=False),
conv_layer(256, 512, 1, maxpool=False, adaptiveavgpool=True),
)
# the output_dim attribute will be used as input_dim when "merging" the models
self.output_dim = 512
if self.head_layers is not None:
assert self.head_layers[0] == self.output_dim, (
"The output dimension from the backbone ({}) is not consistent with "
"the expected input dimension ({}) of the fc-head".format(
self.output_dim, self.head_layers[0]
)
)
if not head_dropout:
head_dropout = [0.0] * len(head_layers)
self.imagehead = nn.Sequential()
for i in range(1, len(head_layers)):
self.imagehead.add_module(
"dense_layer_{}".format(i - 1),
dense_layer(
head_layers[i - 1],
head_layers[i],
head_dropout[i - 1],
head_batchnorm,
),
)
self.output_dim = head_layers[-1]
self.feature_extractor = nn.Sequential(
*list(base_model.children())[:-2])
tmp = OrderedDict()
tmp['last_conv'] = nn.Conv2d(2048, nb_classes, 1, 1)
tmp['gap'] = nn.AvgPool2d(kernel_size=7, stride=1, padding=0)
self.classifier_layer = nn.Sequential(tmp)
def forward(self, inputs):
x = self.feature_extractor(inputs)
x = self.classifier_layer(x)
return x
base_model = vmodels.resnet50(pretrained=True)
net = Resnet_fc(base_model, 5)
net.to(device)
loss_function = nn.CrossEntropyLoss()
opt = optim.SGD(net.parameters(), lr=0.001, momentum=0.9, weight_decay=0.001)
def train_model(model, loss_function, optimizer, num_epochs=30):
since = time.time()
best_acc = 0.0
for epoch in range(num_epochs):
print("Epoch {}/{}".format(epoch, num_epochs - 1))
print("-" * 10)
def create_res50():
model_ft = models.resnet50(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 3)
model_ft = model_ft.cuda()
return model_ft
def __init__(self, num_landmarks):
super(ResNet50Int, self).__init__()
self.num_landmarks = num_landmarks
# Load pretrained resnet-50 model
pretrained = models.resnet50(pretrained=True)
# Remove last 2 layers
modules = list(pretrained.children())[:-2]
# load resnet with gcnet
# self.encoder = resnet.resnet50(pretrained=True)
# modules = nn.ModuleList([])
# Add 1st deconv block (stride = 16)
modules.append(
nn.Sequential(
nn.ConvTranspose2d(2048,
256,
kernel_size=4,
stride=2,
padding=1,
bias=False), nn.BatchNorm2d(256),
nn.ReLU(inplace=True)))
# Add 2nd deconv block (stride = 32)
modules.append(
nn.Sequential(
nn.ConvTranspose2d(256,
256,
kernel_size=4,
stride=2,
padding=1,
bias=False), nn.BatchNorm2d(256),
nn.ReLU(inplace=True)))
# Add 3rd deconv block (stride = 64)
modules.append(
nn.Sequential(
nn.ConvTranspose2d(256,
256,
kernel_size=4,
stride=2,
padding=1,
bias=False), nn.BatchNorm2d(256),
nn.ReLU(inplace=True)))
# Add regression layer
modules.append(nn.Conv2d(256, num_landmarks, 1))
self.module = nn.ModuleList(modules)
# For integration
self.relu = nn.ReLU(inplace=True)
self.register_buffer('wx', torch.arange(64.0) * 4.0 + 2.0)
self.wx = self.wx.reshape(1, 64).repeat(64, 1).reshape(64 * 64, 1)
self.register_buffer('wy', torch.arange(64.0) * 4.0 + 2.0)
self.wy = self.wy.reshape(64, 1).repeat(1, 64).reshape(64 * 64, 1)
self.fliptest = False
def parse_feed_forward(args, input_shape):
"""Parses a sequential feed-forward neural network from json config."""
# parse known networks
if isinstance(args, dict):
if args["net"] == "resnet-cifar10":
from modules.resnet_cifar import ResNet34
net = ResNet34(num_classes=10)
output_shape = infer_shape([net], input_shape)
print("output.shape:", output_shape)
return net, output_shape
if args["net"] == "resnet-cifar100":
from modules.resnet_cifar import ResNet34
net = ResNet34(num_classes=100)
output_shape = infer_shape([net], input_shape)
print("output.shape:", output_shape)
return net, output_shape
if args["net"] == "resnet-clothing1M":
from torchvision.models import resnet50
net = resnet50(num_classes=14)
output_shape = infer_shape([net], input_shape)
print("output.shape:", output_shape)
return net, output_shape
if args["net"] == "resnet34-clothing1M":
from torchvision.models import resnet34
net = resnet34(num_classes=14)
output_shape = infer_shape([net], input_shape)
print("output.shape:", output_shape)
return net, output_shape
if args["net"] == "double-descent-cifar10-resnet18":
from modules.resnet18_double_descent import make_resnet18k
net = make_resnet18k(k=args["k"], num_classes=10)
output_shape = infer_shape([net], input_shape)
print("output.shape:", output_shape)
return net, output_shape
if args["net"] == "double-descent-cifar100-resnet18":
from modules.resnet18_double_descent import make_resnet18k
net = make_resnet18k(k=args["k"], num_classes=100)
output_shape = infer_shape([net], input_shape)
print("output.shape:", output_shape)
return net, output_shape
if args["net"] == "resnet34-imagenet":
from torchvision.models import resnet34
net = resnet34(num_classes=1000)
output_shape = infer_shape([net], input_shape)
print("output.shape:", output_shape)
return net, output_shape
net = []
for cur_layer in args:
layer_type = cur_layer["type"]
prev_shape = infer_shape(net, input_shape)
print(prev_shape)
if layer_type == "fc":
dim = cur_layer["dim"]
assert len(prev_shape) == 2
net.append(nn.Linear(prev_shape[1], dim))
if cur_layer.get("batch_norm", False):
net.append(nn.BatchNorm1d(dim))
add_activation(net, cur_layer.get("activation", "linear"))
if "dropout" in cur_layer:
net.append(nn.Dropout(cur_layer["dropout"]))
if layer_type == "flatten":
net.append(Flatten())
if layer_type == "reshape":
net.append(Reshape(cur_layer["shape"]))
if layer_type == "conv":
assert len(prev_shape) == 4
net.append(
nn.Conv2d(
in_channels=prev_shape[1],
out_channels=cur_layer["filters"],
kernel_size=cur_layer["kernel_size"],
stride=cur_layer["stride"],
padding=cur_layer.get("padding", 0),
)
)
if cur_layer.get("batch_norm", False):
net.append(torch.nn.BatchNorm2d(num_features=cur_layer["filters"]))
add_activation(net, cur_layer.get("activation", "linear"))
if layer_type == "deconv":
assert len(prev_shape) == 4
net.append(
nn.ConvTranspose2d(
in_channels=prev_shape[1],
out_channels=cur_layer["filters"],
kernel_size=cur_layer["kernel_size"],
stride=cur_layer["stride"],
padding=cur_layer.get("padding", 0),
output_padding=cur_layer.get("output_padding", 0),
)
)
if cur_layer.get("batch_norm", False):
net.append(torch.nn.BatchNorm2d(num_features=cur_layer["filters"]))
add_activation(net, cur_layer.get("activation", "linear"))
if layer_type == "identity":
net.append(Identity())
if layer_type == "upsampling":
net.append(
torch.nn.UpsamplingNearest2d(scale_factor=cur_layer["scale_factor"])
)
if layer_type == "gaussian":
# this has to be the last layer
net = nn.Sequential(*net)
output_shape = infer_shape(net, input_shape)
mu = nn.Sequential(nn.Linear(output_shape[1], cur_layer["dim"]))
logvar = nn.Sequential(nn.Linear(output_shape[1], cur_layer["dim"]))
output_shape = [None, cur_layer["dim"]]
print("output.shape:", output_shape)
return ConditionalGaussian(net, mu, logvar), output_shape
if layer_type == "uniform":
# this has to be the last layer
net = nn.Sequential(*net)
output_shape = infer_shape(net, input_shape)
center = nn.Sequential(nn.Linear(output_shape[1], cur_layer["dim"]))
radius = nn.Sequential(nn.Linear(output_shape[1], cur_layer["dim"]))
output_shape = [None, cur_layer["dim"]]
print("output.shape:", output_shape)
return ConditionalUniform(net, center, radius), output_shape
if layer_type == "dirac":
# this has to be the last layer
net = nn.Sequential(*net)
output_shape = infer_shape(net, input_shape)
print("output.shape:", output_shape)
return ConditionalDiracDelta(net), output_shape
output_shape = infer_shape(net, input_shape)
print("output.shape:", output_shape)
return nn.Sequential(*net), output_shape
def __init__(self, num_classes, pretrained=False):
'''Creates an `PSPNet` instance for semantic segmentation.
Args:
num_classes: number of classes to predict.
pretrained: use a pre-trained `ResNet` backbone for convolutional feature extraction.
'''
super().__init__()
# Backbone network we use to harvest convolutional image features from
self.resnet = resnet50(pretrained=pretrained)
# https://github.com/pytorch/vision/blob/c84aa9989f5256480487cafe280b521e50ddd113/torchvision/models/resnet.py#L101-L105
self.block0 = nn.Sequential(self.resnet.conv1, self.resnet.bn1, self.resnet.relu, self.resnet.maxpool)
# https://github.com/pytorch/vision/blob/c84aa9989f5256480487cafe280b521e50ddd113/torchvision/models/resnet.py#L106-L109
self.block1 = self.resnet.layer1
self.block2 = self.resnet.layer2
self.block3 = self.resnet.layer3
self.block4 = self.resnet.layer4
# See https://arxiv.org/abs/1606.02147v1 section 4: Information-preserving dimensionality changes
#
# "When downsampling, the first 1x1 projection of the convolutional branch is performed with a stride of 2
# in both dimensions, which effectively discards 75% of the input. Increasing the filter size to 2x2 allows
# to take the full input into consideration, and thus improves the information flow and accuracy."
#
# We can not change the kernel_size on the fly but we can change the stride instead from (2, 2) to (1, 1).
assert self.block3[0].downsample[0].stride == (2, 2)
assert self.block4[0].downsample[0].stride == (2, 2)
self.block3[0].downsample[0].stride = (1, 1)
self.block4[0].downsample[0].stride = (1, 1)
# See https://arxiv.org/abs/1511.07122 and https://arxiv.org/abs/1706.05587 for dilated convolutions.
# ResNets reduce spatial dimension too much for segmentation => patch in dilated convolutions.
for name, module in self.block3.named_modules():
if 'conv2' in name:
module.dilation = (2, 2)
module.padding = (2, 2)
module.stride = (1, 1)
for name, module in self.block4.named_modules():
if 'conv2' in name:
module.dilation = (4, 4)
module.padding = (4, 4)
module.stride = (1, 1)
# PSPNet's pyramid: 2048 feature maps from conv net => pool into scales of {1, 2, 3, 6}
self.pyramid1 = PyramidBlock(1, 2048, 2048 // 4)
self.pyramid2 = PyramidBlock(2, 2048, 2048 // 4)
self.pyramid3 = PyramidBlock(3, 2048, 2048 // 4)
self.pyramid6 = PyramidBlock(6, 2048, 2048 // 4)
# Pyramid pooling doubles feature maps via concatenation
self.logits = nn.Sequential(
nn.Conv2d(4096, 512, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, num_classes, kernel_size=1))
self.initialize()
from os.path import join
import torchvision, torch
from torchvision.models import resnet50
from collections import OrderedDict
ckptdir = r"E:\Cluster_Backup\torch"
resnetF = resnet50(False, num_classes=8631) # Face recognition Network's output is 8631 classes in VGG-Face Dataset.
#%%
import pickle as pkl
for pklname in ["resnet50_ft_weight.pkl", "resnet50_scratch_weight.pkl"]:
data = pkl.load(open(join(ckptdir, pklname), mode="rb")) #
state_dict = {k:torch.tensor(v) for k, v in data.items()}
resnetF.load_state_dict(state_dict)
torch.save(resnetF.state_dict(), join(ckptdir, pklname.split(".")[0]+".pt"))
#%%
ptname = "resnet50_scratch_weight.pt" #["resnet50_ft_weight.pkl", "resnet50_scratch_weight.pkl"]
resnetF.load_state_dict(torch.load(join(ckptdir, ptname)))
#%%
from layer_hook_utils import get_module_names
module_names, module_types, module_spec = get_module_names(resnetF, input_size=(3, 227, 227), device="cuda")
#%%
from grad_RF_estim import gradmap2RF_square, grad_RF_estimate
resnetF.cuda().eval()
for param in resnetF.parameters():
param.requires_grad_(False)
unit_list = [("resnet50-face_scratch", ".ReLUrelu", 5, 57, 57),
("resnet50-face_scratch", ".layer1.Bottleneck1", 5, 28, 28),
("resnet50-face_scratch", ".layer2.Bottleneck0", 5, 14, 14),
("resnet50-face_scratch", ".layer2.Bottleneck2", 5, 14, 14),
("resnet50-face_scratch", ".layer3.Bottleneck0", 5, 7, 7),
("resnet50-face_scratch", ".layer3.Bottleneck2", 5, 7, 7),
def main():
#=======================================
# 1. Load dataset
#=======================================
# CIFAR data
data_tf = transforms.Compose([transforms.ToTensor(), transforms.Normalize([
0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
train_set = datasets.CIFAR10(
'../../data', train=True, transform=data_tf, download=True)
train_loader = torch.utils.data.DataLoader(
train_set, batch_size=64, shuffle=True)
test_set = datasets.CIFAR10(
'../../data', train=False, transform=data_tf, download=True)
valid_loader = torch.utils.data.DataLoader(
test_set, batch_size=128, shuffle=False)
# custom datasets
# train_folders, valid_folders = get_folders(
# config.train_data, config.valid_data)
# train_datasets = MyDataSet(train_folders, transforms=None)
# valid_datasets = MyDataSet(valid_folders, transforms=None, train=False)
# train_loader = DataLoader(dataset=train_datasets,
# batch_size=config.batch_size, shuffle=True)
# valid_loader = DataLoader(dataset=valid_datasets,
# batch_size=config.batch_size, shuffle=True)
# print("Train numbers:{:d}".format(len(train_loader)))
# print("Test numbers:{:d}".format(len(valid_loader)))
#=======================================
# 2. Define network and Load model
#=======================================
if config.pretrained:
model = models.resnet50(num_classes=config.num_classes)
checkpoint = torch.load(config.model_path)
model.load_state_dict(checkpoint['model_state_dict'])
print("load model success")
else:
model = models.resnet50(pretrained=True)
# adjust last fc layer to class number
channel_in = model.fc.in_features
model.fc = nn.Linear(channel_in, config.num_classes)
model.to(device)
#=======================================
# 3. Define Loss function and optimizer
#=======================================
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=config.lr,
amsgrad=True, weight_decay=config.weight_decay)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
#=======================================
# 4. Train and Test the network
#=======================================
best_accuracy = 0.
epoch = 0
resume = False
# ====4.1 restart the training process====
if resume:
checkpoint = torch.load(config.model_path)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint["epoch"]
loss = checkpoint["loss"]
best_accuracy = checkpoint["best_accuracy"]
# print checkpoint
print('epoch:', epoch)
print('best_accuracy:', best_accuracy)
print("model's state_dict:")
for param_tensor in model.state_dict():
print(param_tensor, '\t', model.state_dict()[param_tensor].size())
for epoch in range(1, config.epochs + 1):
# ====4.2 start training====
print("======start training=====")
model.train()
torch.cuda.empty_cache()
start = time.time()
index = 1
sum_loss = 0.
correct = 0.
total = 0.
for images, labels in train_loader:
# clear the cuda cache
torch.cuda.empty_cache()
images = images.to(device)
labels = labels.to(device, dtype=torch.long)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and Optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
# print training loss and accuracy
_, predicted = torch.max(outputs.data, 1)
sum_loss += loss.item()
total += labels.size(0)
correct += (predicted == labels).sum()
if index % 10 == 0:
print("Iter: %d ===> Loss: %.8f | Acc: %.3f%%"
% (index, sum_loss / index, 100. * correct / total))
index += 1
end = time.time()
print("Epoch [%d/%d], Loss: %.8f, Time: %.1fsec!"
% (epoch, config.epochs, loss.item(), (end - start)))
vis.line(X=[epoch], Y=[loss.item()], win='loss',
opts=dict(title='train loss'), update='append')
# ====4.3 start evalidating====
model.eval()
correct_prediction = 0.
total = 0
with torch.no_grad():
for images, labels in valid_loader:
# clear the cuda cache
torch.cuda.empty_cache()
images = images.to(device)
labels = labels.to(device, dtype=torch.long)
# print prediction
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
# total labels number
total += labels.size(0)
# add correct
correct_prediction += (predicted == labels).sum().item()
# print("total correct number: ", correct_prediction)
accuracy = 100. * correct_prediction / total
print("Accuracy: %.4f%%" % accuracy)
scheduler.step()
if accuracy > best_accuracy:
if not os.path.exists(config.checkpoint):
os.mkdir(config.checkpoint)
# save networks
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss,
'best_accuracy': accuracy,
}, os.path.join("%s-%.3f.pth") % (config.best_models, accuracy))
print("save networks for epoch:", epoch)
best_accuracy = accuracy
def resnet50(num_classes, pretrained):
net = models.resnet50(pretrained)
net.fc = nn.Linear(512, num_classes)
return net
parser.add_argument('-r', '--saverate', default=5, help='The interval to save model states', type=int)
parser.add_argument('-w', '--workers', default=4, help='Number of workers for batch processing', type=int)
parser.add_argument('-n', '--notes', default="", help='Additional notes regarding the training')
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
training_stats = dnn.read_stats(args.dataloc + "train/stats")
val_stats = dnn.read_stats(args.dataloc + "val/stats")
train_dataset = dnn.DroneDataset(root_dir=args.dataloc + "train/", transform=transforms.Compose([ dnn.Resize(300), dnn.Corrupt(0.2), dnn.FlipHzt(0.1), dnn.ContrastBrightness(3.0, 100, 0.1), dnn.Rotate(50.0, 0.1), dnn.RandomCrop(224), dnn.Normalize(training_stats), dnn.ToTensor()]))
train_loader = DataLoader(train_dataset, batch_size=args.batch, shuffle=True, num_workers=args.workers)
val_dataset = dnn.DroneDataset(root_dir=args.dataloc + "val/", transform=transforms.Compose([ dnn.Resize(224), dnn.Normalize(val_stats), dnn.ToTensor()]))
val_loader = DataLoader(val_dataset, batch_size=args.batch, shuffle=True, num_workers=args.workers)
network = models.resnet50(pretrained=True)
#for param in network.parameters():
# param.requires_grad = False
num_ftrs = network.fc.in_features
network.fc = nn.Sequential(nn.Linear(num_ftrs, 1), nn.Sigmoid())
network = torch.nn.DataParallel(network).to(device)
#optimizer = torch.optim.Adam(network.parameters(), lr=args.lr)
optimizer = torch.optim.SGD(network.parameters(), lr=args.lr, momentum=args.momentum)
#sched_optimizer = lr_scheduler.ReduceLROnPlateau(optimizer, verbose=True)
criterion = nn.BCELoss()
print("Model created")
style_dir = "./image/"
data = dset.ImageFolder(style_dir,transform= transforms.Compose([
transforms.Scale(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
]))
print(data.class_to_idx)
img_list = []
for i in data.imgs:
img_list.append(i[0])
# import pretrained resnet-50 model
resnet = models.resnet50(pretrained=True)
class Resnet(nn.Module):
def __init__(self):
super(Resnet,self).__init__()
self.layer0 = nn.Sequential(*list(resnet.children())[0:1])
self.layer1 = nn.Sequential(*list(resnet.children())[1:4])
self.layer2 = nn.Sequential(*list(resnet.children())[4:5])
self.layer3 = nn.Sequential(*list(resnet.children())[5:6])
#self.layer4 = nn.Sequential(*list(resnet.children())[6:7])
#self.layer5 = nn.Sequential(*list(resnet.children())[7:8])
def forward(self,x):
out_0 = self.layer0(x)
out_1 = self.layer1(out_0)
out_2 = self.layer2(out_1)
