def load_model():
# this model has a last conv feature map as 14x14
model_file = 'wideresnet18_places365.pth.tar'
if not os.access(model_file, os.W_OK):
os.system('wget http://places2.csail.mit.edu/models_places365/' +
model_file)
os.system(
'wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py'
)
import wideresnet
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
model.eval()
model.eval()
# hook the feature extractor
features_names = ['layer4', 'avgpool'
] # this is the last conv layer of the resnet
for name in features_names:
model._modules.get(name).register_forward_hook(hook_feature)
return model
def __init__(this):
this.features_blobs = []
file_name_W = 'W_sceneattribute_wideresnet18.npy'
if not os.access(file_name_W, os.W_OK):
synset_url = 'http://places2.csail.mit.edu/models_places365/W_sceneattribute_wideresnet18.npy'
os.system('wget ' + synset_url)
this.W_attribute = np.load(file_name_W)
model_file = 'wideresnet18_places365.pth.tar'
if not os.access(model_file, os.W_OK):
os.system('wget http://places2.csail.mit.edu/models_places365/' +
model_file)
os.system(
'wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py'
)
import wideresnet
this.model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
this.model.load_state_dict(state_dict)
this.model.eval()
features_names = ['layer4', 'avgpool']
for name in features_names:
this.model._modules.get(name).register_forward_hook(
this.__hook_feature)
this.tf = trn.Compose([
trn.Resize((224, 224)),
trn.ToTensor(),
trn.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
def load_model_wideresnet18():
# this model has a last conv feature map as 14x14
model_file = 'wideresnet18_places365.pth.tar'
if not os.access(model_file, os.W_OK):
wget.download("http://places2.csail.mit.edu/models_places365/" +
model_file)
wget.download(
"https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py"
)
import wideresnet
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
# hacky way to deal with the upgraded batchnorm2D and avgpool layers...
for i, (name, module) in enumerate(model._modules.items()):
module = recursion_change_bn(model)
model.avgpool = torch.nn.AvgPool2d(kernel_size=14, stride=1, padding=0)
model.eval()
# hook the feature extractor
features_names = ['layer4',
'avgpool'] # this is the last conv layer of the resnet
for name in features_names:
model._modules.get(name).register_forward_hook(hook_feature)
return model
def load_model():
# this model has a last conv feature map as 14x14
model_file = 'models/wideresnet_best_attn.pth.tar'
import wideresnet
model = wideresnet.resnet18(num_classes=7)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
model.eval()
# the following is deprecated, everything is migrated to python36
## if you encounter the UnicodeDecodeError when use python3 to load the model, add the following line will fix it. Thanks to @soravux
#from functools import partial
#import pickle
#pickle.load = partial(pickle.load, encoding="latin1")
#pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
#model = torch.load(model_file, map_location=lambda storage, loc: storage, pickle_module=pickle)
# hook the feature extractor
features_names = ['layer4', 'avgpool'
] # this is the last conv layer of the resnet
for name in features_names:
model._modules.get(name).register_forward_hook(hook_feature)
return model
def load_model():
# this model has a last conv feature map as 14x14
model_file = 'wideresnet18_places365.pth.tar'
if not os.access(model_file, os.W_OK):
os.system('wget http://places2.csail.mit.edu/models_places365/' +
model_file)
os.system(
'wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py'
)
import wideresnet
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
# hacky way to deal with the upgraded batchnorm2D and avgpool layers...
for i, (name, module) in enumerate(model._modules.items()):
module = recursion_change_bn(model)
model.avgpool = torch.nn.AvgPool2d(kernel_size=14, stride=1, padding=0)
model.eval()
model.cuda()
return model
def load_model():
# fetch the pretrained weights of the model if not already present
model_file = 'wideresnet18_places365.pth.tar'
if not os.access(model_file, os.W_OK):
os.system('wget http://places2.csail.mit.edu/models_places365/' +
model_file)
os.system(
'wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py'
)
# imported here since this model file may not be present in the beginning
# and has been downloaded right before this
# makes the model ready to run the forward pass
import wideresnet
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
model.eval()
# hook the feature extractor
features_names = ['layer4',
'avgpool'] # this is the last conv layer of the resnet
for name in features_names:
model._modules.get(name).register_forward_hook(hook_feature)
return model
def load_model():
# this model has a last conv feature map as 14x14
file = 'wideresnet18_places365.pth.tar'
model_file = './utils/wideresnet18_places365.pth.tar'
prefix_path = ' -P ./utils/'
if not os.access(model_file, os.W_OK):
os.system('wget http://places2.csail.mit.edu/models_places365/' + file + prefix_path)
os.system('wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py' + prefix_path)
import wideresnet
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
model.load_state_dict(state_dict)
model.eval()
# the following is deprecated, everything is migrated to python36
## if you encounter the UnicodeDecodeError when use python3 to load the model, add the following line will fix it. Thanks to @soravux
#from functools import partial
#import pickle
#pickle.load = partial(pickle.load, encoding="latin1")
#pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
#model = torch.load(model_file, map_location=lambda storage, loc: storage, pickle_module=pickle)
model.eval()
# hook the feature extractor
features_names = ['layer4','avgpool'] # this is the last conv layer of the resnet
for name in features_names:
model._modules.get(name).register_forward_hook(hook_feature)
return model
def load_model(model_file):
if not os.access(model_file, os.W_OK):
os.system('wget http://places2.csail.mit.edu/models_places365/' +
model_file)
os.system(
'wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py'
+ ' -P ./utils')
import wideresnet
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
model.eval()
for param in model.parameters():
param.requires_grad = False
params = list(model.parameters())
weight_softmax = params[-2].clone().numpy()
weight_softmax[weight_softmax < 0] = 0
conv_model = nn.Sequential(*list(model.children())[:-2])
linear_layer = model.fc
conv_model.eval()
linear_layer.eval()
return linear_layer, conv_model, weight_softmax
def load_model():
# this model has a last conv feature map as 14x14
model_file = 'wideresnet18_places365.pth.tar'
if not os.access(model_file, os.W_OK):
os.system('wget http://places2.csail.mit.edu/models_places365/' + model_file)
os.system('wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py')
import wideresnet
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
model.load_state_dict(state_dict)
model.eval()
# the following is deprecated, everything is migrated to python36
## if you encounter the UnicodeDecodeError when use python3 to load the model, add the following line will fix it. Thanks to @soravux
#from functools import partial
#import pickle
#pickle.load = partial(pickle.load, encoding="latin1")
#pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
#model = torch.load(model_file, map_location=lambda storage, loc: storage, pickle_module=pickle)
model.eval()
# hook the feature extractor
features_names = ['layer4','avgpool'] # this is the last conv layer of the resnet
for name in features_names:
model._modules.get(name).register_forward_hook(hook_feature)
return model
def main():
global args, best_prec1, globaliter_train, globaliter_val
globaliter_train = 0
globaliter_val = 0
args = parser.parse_args()
print(args)
# create model
num_classes = args.num_classes
if args.transfer != "":
num_classes = 365
print("=> creating model '{}'".format(args.arch))
if args.arch.lower().startswith('wideresnet'):
# a customized resnet model with last feature map size as 14x14 for better class activation mapping
if args.arch.lower() == 'wideresnet50':
model = wideresnet.resnet50(num_classes=num_classes)
if args.arch.lower() == 'wideresnet18':
model = wideresnet.resnet18(num_classes=num_classes)
else:
model = models.__dict__[args.arch](num_classes=num_classes)
print(model)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if args.transfer != "":
if os.path.isfile(args.transfer):
print("=> loading checkpoint '{}'".format(args.transfer))
checkpoint = torch.load(args.transfer, map_location=lambda storage, loc: storage)
state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
model.load_state_dict(state_dict)
# print("=> loaded checkpoint '{}' (epoch {})"
# .format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.transfer))
for param in model.parameters():
param.requires_grad = False
if freze == 2:
for param in model.layer4.parameters():
param.requires_grad = True
args.arch.lower() == 'wideresnet50':
model.fc = nn.Linear(2040 , 2)
else:
model.fc = nn.Linear(512 , 2)
model.fc.reset_parameters()
def load_model():
# this model has a last conv feature map as 14x14
model_file = 'wideresnet18_places365.pth.tar'
model_file_path = Path(model_file)
weight_url = 'http://places2.csail.mit.edu/models_places365/' + model_file
if not os.access(model_file, os.W_OK):
# os.system('wget http://places2.csail.mit.edu/models_places365/' + model_file)
print('Downloading...', end=' ')
resp = requests.get(weight_url)
with model_file_path.open('wb') as f:
f.write(resp.content)
print('Done!')
# os.system('wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py')
widersnet_url = 'https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py'
widersnet_name = 'wideresnet.py'
widersnet_name_path = Path(widersnet_name)
print('Downloading...', end=' ')
resp = requests.get(widersnet_url)
with widersnet_name_path.open('wb') as f:
f.write(resp.content)
print('Done!')
import wideresnet
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
model.load_state_dict(state_dict)
# hacky way to deal with the upgraded batchnorm2D and avgpool layers...
for i, (name, module) in enumerate(model._modules.items()):
module = recursion_change_bn(model)
model.avgpool = torch.nn.AvgPool2d(kernel_size=14, stride=1, padding=0)
model.eval()
# the following is deprecated, everything is migrated to python36
## if you encounter the UnicodeDecodeError when use python3 to load the model, add the following line will fix it. Thanks to @soravux
#from functools import partial
#import pickle
#pickle.load = partial(pickle.load, encoding="latin1")
#pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
#model = torch.load(model_file, map_location=lambda storage, loc: storage, pickle_module=pickle)
model.eval()
# hook the feature extractor
features_names = ['layer4','avgpool'] # this is the last conv layer of the resnet
for name in features_names:
model._modules.get(name).register_forward_hook(hook_feature)
return model
def load_model(model_file):
if args.model == 'resnet18':
model = wideresnet.resnet18(num_classes=365)
if args.model == 'resnet50':
model = wideresnet.resnet50(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
model.eval()
return model
def load_model():
# this model has a last conv feature map as 14x14
# model_file = os.path.join(dir_places365_model,'whole_wideresnet18_places365_python36.pth.tar')
model_file = os.path.join(dir_places365_model,'wideresnet18_places365.pth.tar')
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
model.load_state_dict(state_dict)
model.eval()
# hook the feature extractor
features_names = ['layer4','avgpool'] # this is the last conv layer of the resnet
for name in features_names:
model._modules.get(name).register_forward_hook(hook_feature)
return model
def load_model():
model_file = 'wideresnet18_places365.pth.tar'
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
model.eval()
model.eval()
features_names = ['layer4', 'avgpool']
for name in features_names:
model._modules.get(name).register_forward_hook(hook_feature)
return model
def load_model(path, model):
model_file = path
if model == 'resnet18':
model = wideresnet.resnet18(num_classes=365)
if model == 'resnet50':
model = wideresnet.resnet50(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
#model.load_state_dict(checkpoint['state_dict'])
model.load_state_dict(state_dict)
for i, (name, module) in enumerate(model._modules.items()):
module = recursion_change_bn(model)
model.eval()
return model
def initialize(self, ctx):
properties = ctx.system_properties
model_dir = properties.get("model_dir")
self.model_dir = model_dir
self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
# Read model serialize/pt file
model_file = os.path.join(model_dir, 'wideresnet18_places365.pth.tar')
self.model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
self.model.load_state_dict(state_dict)
self.model.eval()
self.tf = self.returnTF()
self.classes, self.labels_IO, self.labels_attribute, self.W_attribute = self.load_labels()
self.initialized = True
def load(self):
model_file_dir = './pytorchserver/example_model/'
model_file = os.path.join(model_file_dir, PYTORCH_FILE)
py_files = []
#for filename in os.listdir(model_file_dir):
# if filename.endswith('.py'):
# py_files.append(filename)
#if len(py_files) == 1:
# model_class_file = os.path.join(model_file_dir, py_files[0])
#elif len(py_files) == 0:
# raise Exception('Missing PyTorch Model Class File.')
#else:
# raise Exception('More than one Python file is detected',
# 'Only one Python file is allowed within model_dir.')
#model_class_name = self.model_class_name
# Load the python class into memory
#sys.path.append(os.path.dirname(model_class_file))
#modulename = os.path.basename(model_class_file).split('.')[0].replace('-', '_')
#model_class = getattr(importlib.import_module(modulename), model_class_name)
# Make sure the model weight is transform with the right device in this machine
#self.model = model_class().to(self.device)
#self.model.load_state_dict(torch.load(model_file, map_location=self.device))
#outfile = open('output.csv','a')
import wideresnet
self.model = wideresnet.resnet18(num_classes=365)
self.model.load_state_dict(torch.load(model_file))
#model.load_state_dict(torch.load('./models/wideresnet18_places365.pth'))
#params = list(model.parameters())
#from UncertaintySampling import UncertaintySampling
#device = torch.device('cuda:1')
if torch.cuda.is_available():
self.device = torch.device('cuda:1')
print("Train on GPU.")
else:
print("No cuda available")
self.device = torch.device('cpu')
self.model.to(self.device)
self.model.eval()
self.ready = True
def load_model(
): # TODO Should the model be reloaded for every photo? Wouldn't it be better to do that once?
# this model has a last conv feature map as 14x14
model_file = os.path.join(dir_places365_model,
'wideresnet18_places365.pth.tar')
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
model.eval()
# hook the feature extractor
features_names = ['layer4', 'avgpool'
] # this is the last conv layer of the resnet
for name in features_names:
model._modules.get(name).register_forward_hook(hook_feature)
return model
def load_model():
# this model has a last conv feature map as 14x14
model_file = 'wideresnet18_places365.pth.tar'
if not os.access(model_file, os.W_OK):
os.system('wget http://places2.csail.mit.edu/models_places365/' +
model_file)
os.system(
'wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py'
)
import wideresnet
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
# hacky way to deal with the upgraded batchnorm2D and avgpool layers...
for i, (name, module) in enumerate(model._modules.items()):
module = recursion_change_bn(model)
model.avgpool = torch.nn.AvgPool2d(kernel_size=14, stride=1, padding=0)
model.eval()
# the following is deprecated, everything is migrated to python36
## if you encounter the UnicodeDecodeError when use python3 to load the model, add the following line will fix it. Thanks to @soravux
#from functools import partial
#import pickle
#pickle.load = partial(pickle.load, encoding="latin1")
#pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
#model = torch.load(model_file, map_location=lambda storage, loc: storage, pickle_module=pickle)
return model
def load_model(model_file):
# this model has a last conv feature map as 14x14
#model_file = 'wideresnet18_places365.pth.tar'
if not os.access(model_file, os.W_OK):
os.system('wget http://places2.csail.mit.edu/models_places365/' +
model_file)
os.system(
'wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py'
)
import wideresnet
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
model.eval()
return model
parser.add_argument('--weights',
'-w',
metavar='weights',
default='resnet18_best.pth.tar',
help='model architecture:')
args = parser.parse_args()
# th architecture to use
arch = args.arch
# arch = 'resnet18'
# load the pre-trained weights
model_file = args.weights
if args.arch.lower().startswith('wideresnet'):
# a customized resnet model with last feature map size as 14x14 for better class activation mapping
model = wideresnet.resnet18(num_classes=args.num_classes)
else:
model = models.__dict__[arch](num_classes=args.num_classes)
# model = models.__dict__[arch](num_classes=args.num_classes)
checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
# model = torch.nn.DataParallel(model)
model.cpu()
model.eval()
# load the image transformer
def main():
global args, best_prec1
args = parser.parse_args()
print args
# create model
print("=> creating model '{}'".format(args.arch))
if args.arch.lower().startswith('wideresnet'):
# a customized resnet model with last feature map size as 14x14 for better class activation mapping
model = wideresnet.resnet18(num_classes=args.num_classes)
else:
# model = models.__dict__[args.arch](num_classes=args.num_classes)
model = resnet.resnet18(num_classes=args.num_classes)
if args.arch.lower().startswith('alexnet') or args.arch.lower().startswith(
'vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
print(model)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
data_dir = places_dir + '/places365_standard_home'
traindir = os.path.join(data_dir, 'train')
valdir = os.path.join(data_dir, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_loader = torch.utils.data.DataLoader(datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(datasets.ImageFolder(
valdir,
transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# define loss function (criterion) and pptimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.evaluate:
validate(val_loader, model, criterion)
return
accuracies_list = []
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion)
accuracies_list.append("%.2f" % prec1.tolist())
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best, args.arch.lower())
print("The best accuracy obtained during training is = {}".format(
best_prec1))
def main():
global args, best_prec1
args = parser.parse_args()
# print (args)
# create model
print("=> creating model '{}'".format(args.arch))
if args.arch.lower().startswith('wideresnet_freeze'):
model_file = 'wideresnet18_places365_1.pth.tar'
model = wideresnet.resnet18(num_classes=365)
checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
model.load_state_dict(state_dict)
model.eval()
for name,param in model.named_parameters():
# print(name)
if "fc" not in name:
param.requires_grad = False
else:
if args.arch.lower().startswith('wideresnet'):
# a customized resnet model with last feature map size as 14x14 for better class activation mapping
model = wideresnet.resnet50(num_classes=args.num_classes)
else:
model = models.__dict__[args.arch](num_classes=args.num_classes)
if args.arch.lower().startswith('alexnet') or args.arch.lower().startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# print (model)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
print("DATA",args.data)
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(traindir, transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# define loss function (criterion) and pptimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.evaluate:
validate(val_loader, model, criterion)
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best, args.arch.lower())
help='num of class in the model')
args = parser.parse_args()
# th architecture to use
arch = args.arch
# arch = 'resnet18'
# load the pre-trained weights
model_file = '%s_places365.pth.tar' % arch
if not os.access(model_file, os.W_OK):
weight_url = 'http://places2.csail.mit.edu/models_places365/' + model_file
os.system('wget ' + weight_url)
if args.arch.lower().startswith('wideresnet'):
# a customized resnet model with last feature map size as 14x14 for better class activation mapping
model = wideresnet.resnet18(num_classes=365)
else:
model = models.__dict__[arch](num_classes=args.num_classes)
# model.cuda()
checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
model.load_state_dict(state_dict)
# model = torch.nn.DataParallel(model)
model.cpu()
model.eval()
# load the image transformer
centre_crop = trn.Compose([
