SoftMax = torch.nn.Softmax(dim=1).cuda()
class Linear_Classifier(torch.nn.Module):
def __init__(self, num_classes):
super(Linear_Classifier, self).__init__()
self.fc = torch.nn.Linear(feature_size, num_classes)
def forward(self, x):
return self.fc(x)
t1 = time.time()
cnn_model_supervised = net_model_from_lib(num_classes=number_of_known_classes) # timm library
cnn_model_moco_places = net_model_from_lib(num_classes=number_of_known_classes) # timm library
linear_model = Linear_Classifier(num_classes=number_of_known_classes)
assert os.path.isfile(cnn_path_supervised)
checkpoint = torch.load(cnn_path_supervised)
if "epoch" in checkpoint.keys():
state_dict_model = checkpoint["state_dict"]
else:
state_dict_model = checkpoint
from collections import OrderedDict
new_state_dict_model = OrderedDict()
for k, v in state_dict_model.items():
if "module." == k[:7]:
name = k[7:] # remove `module.`
img_pil = PIL.Image.fromarray(img)
x = self.transform(img_pil)
i = A.find("ImageNet/")
address = A[i:]
# y = int(L)
# return (x,y)
return (address, x)
dataset_test = csv_data_class(path=test_csv_path, transform=image_transform_val)
test_loader = DataLoader(dataset=dataset_test, batch_size=batch_size, shuffle=False, num_workers=n_cpu)
t1 = time.time()
cnn_model = net_model_from_lib(num_classes=number_of_known_classes) # timm library
assert os.path.isfile(cnn_path)
checkpoint = torch.load(cnn_path, map_location="cpu")
# rename moco pre-trained keys
print("keys = ", checkpoint.keys())
state_dict = checkpoint["state_dict"]
for k in list(state_dict.keys()):
# retain only encoder_q up to before the embedding layer
if k.startswith("module.encoder_q") and not k.startswith("module.encoder_q.fc"):
# remove prefix
state_dict[k[len("module.encoder_q.") :]] = state_dict[k]
# delete renamed or unused k
del state_dict[k]
msg = cnn_model.load_state_dict(state_dict, strict=False)
assert set(msg.missing_keys) == {"classifier.weight", "classifier.bias"}
x_moco = self.transform_moco(img_pil)
i = A.find("ImageNet/")
address = A[i:]
return (address, x_moco)
dataset_test = csv_data_class(path=test_csv_path,
transform_moco=image_transform_moco)
test_loader = DataLoader(dataset=dataset_test,
batch_size=batch_size,
shuffle=False,
num_workers=n_cpu)
t1 = time.time()
cnn_model_moco_imagenet = net_model_from_lib(
num_classes=number_of_known_classes) # timm library
cnn_model_moco_places = net_model_from_lib(
num_classes=number_of_known_classes) # timm library
assert os.path.isfile(cnn_path_moco_imagenet)
checkpoint = torch.load(cnn_path_moco_imagenet, map_location="cpu")
# rename moco pre-trained keys
print("keys = ", checkpoint.keys())
state_dict = checkpoint["state_dict"]
for k in list(state_dict.keys()):
# retain only encoder_q up to before the embedding layer
if k.startswith(
"module.encoder_q") and not k.startswith("module.encoder_q.fc"):
# remove prefix
state_dict[k[len("module.encoder_q."):]] = state_dict[k]
# delete renamed or unused k
interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
image_transform_moco = transforms.Compose([
transforms.Resize(size=(image_size, image_size),
interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]),
])
t1 = time.time()
model_supervised = net_model_from_lib(
num_classes=number_of_known_classes) # timm library
model_moco_imagenet = net_model_from_lib(
num_classes=number_of_known_classes) # timm library
assert os.path.isfile(cnn_path_supervised)
checkpoint = torch.load(cnn_path_supervised)
if "epoch" in checkpoint.keys():
state_dict_model = checkpoint["state_dict"]
else:
state_dict_model = checkpoint
from collections import OrderedDict
new_state_dict_model = OrderedDict()
for k, v in state_dict_model.items():
if "module." == k[:7]:
name = k[7:] # remove `module.`
