def classification_model_resnet18_combine_last_var(**kwargs):
base_model = pretrainedmodels.resnet18()
return ClassificationModelResnetCombineLastVariable(
base_model,
base_model_features=512,
nb_features=6,
base_model_l1_outputs=64,
**kwargs)
def segmentation_model_resnet18_bn_filters8_masked(**kwargs):
base_model = pretrainedmodels.resnet18()
return ResnetWeightedSegmentatation(base_model,
DecoderBlock=DecoderBlockBN,
nb_features=6,
base_model_l1_outputs=64,
filters=8,
**kwargs)
def segmentation_model_resnet18_bn_filters8(**kwargs):
base_model = pretrainedmodels.resnet18()
return ClassificationModelResnetCombineLastVariable3(
base_model,
DecoderBlock=DecoderBlockBN,
nb_features=6,
base_model_l1_outputs=64,
filters=8,
**kwargs)
def __init__(self, weight='imagenet'):
super(DiscriminatorModel, self).__init__()
self.layer = nn.Conv2d(1, 3, (2, 2), padding=2)
model = cm.resnet18(num_classes=1000, pretrained=weight)
model.last_linear = nn.Linear(in_features=512,
out_features=1,
bias=True)
self.model = model
def __init__(self, num_classes, pretrained="imagenet"):
super().__init__()
self.model = resnet18(pretrained=pretrained)
self.model.avg_pool = nn.AdaptiveAvgPool2d(1)
new_last_linear = nn.Linear(self.model.last_linear.in_features,
num_classes)
new_last_linear.weight.data = self.model.last_linear.weight.data[:
num_classes]
new_last_linear.bias.data = self.model.last_linear.bias.data[:
num_classes]
self.model.last_linear = new_last_linear
def __init__(self):
#扩展的VGG
super(VGG, self).__init__()
self.model = pretrainedmodels.resnet18()
in_dim = self.model.last_linear.in_features
classifier = torch.nn.Sequential(torch.nn.Linear(in_dim, 448),
torch.nn.ReLU(),
torch.nn.Dropout(0.2),
torch.nn.Linear(448, 15))
self.model.last_linear = classifier
for param in self.parameters():
param.require_grad = True
def __init__(self):
super().__init__()
resnet = pretrainedmodels.resnet18()
self.a = nn.Sequential(
resnet.conv1,
resnet.bn1,
resnet.relu,
)
self.b = nn.Sequential(
resnet.maxpool,
resnet.layer1,
)
self.c = resnet.layer2
self.d = resnet.layer3
def load_models(conf_file):
out = {}
with open(conf_file) as f:
doc = yaml.full_load(f)
conf = doc['config']
for k, stat in conf.items():
if stat['net'] == 'inception_v4':
out[k] = (pretrainedmodels.inceptionv4(pretrained=None),
stat['sparisity'])
elif stat['net'] == 'resnet18':
out[k] = (pretrainedmodels.resnet18(pretrained=None),
stat['sparisity'])
elif stat['net'] == 'mobilenet_v2':
out[k] = (models.mobilenet_v2(), stat['sparisity'])
else:
raise "Unknown net"
return out
def _get_branch_net(self, channels, num_classes):
model = resnet18(pretrained="imagenet")
model.global_pool = nn.AdaptiveAvgPool2d(1)
model.conv1_7x7_s2 = nn.Conv2d(channels,
64,
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3))
model.last_linear = nn.Sequential(
nn.BatchNorm1d(1024),
nn.Dropout(0.5),
nn.Linear(1024, num_classes),
)
# print('Model architecture:')
# print(model)
# total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
# print(f'\n\n\n\nModel trainable parameters {total_params}')
return model
def resnet18(input_size=(3, 224, 224), num_classes=1000, pretrained=None):
model = models.resnet18(pretrained=pretrained)
model = add_instances_to_torchvisionmodel(model)
# Change the First Convol2D layer into new input shape
if input_size != (3, 224, 224):
model.conv1 = nn.Conv2d(input_size[0],
64,
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3),
bias=False)
model.input_size = input_size
del model.fc
del model.avgpool
# calc kernel_size on new_avgpool2d layer
test_tensor = torch.randn((1, input_size[0], input_size[1], input_size[2]))
features = model.features(test_tensor)
# print(features, features.shape[2], features.shape[3])
avg_pool2d_kernel_size = (features.shape[2], features.shape[3])
# calc last linear size
x = F.avg_pool2d(features, kernel_size=avg_pool2d_kernel_size)
x = x.view(x.size(0), -1).shape[1]
model.last_linear = nn.Linear(in_features=x, out_features=num_classes)
##del model.logits
##del model.forward
def logits(self, features):
x = F.relu(features, inplace=False)
x = F.avg_pool2d(x, kernel_size=avg_pool2d_kernel_size, stride=1)
x = x.view(x.size(0), -1)
x = self.last_linear(x)
return x
def forward(self, input):
x = self.features(input)
x = self.logits(x)
return x
model.logits = types.MethodType(logits, model)
model.forward = types.MethodType(forward, model)
return model
def get_resnet18():
model = resnet18()
w = model.conv1.weight
w = torch.nn.Parameter(
torch.cat((w, torch.mean(w, dim=1).unsqueeze(1)), dim=1))
model.conv1 = nn.Conv2d(4,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
model.conv1.weight = w
model.avgpool = nn.Sequential(nn.AvgPool2d(9, stride=1),
nn.AvgPool2d(7, stride=1))
model.fc = nn.Sequential(nn.Dropout(), nn.Linear(2048, 28))
return model
def __init__(self, nb_embeddings=config.NB_EMBEDDINGS):
super().__init__()
self.base_model = pretrainedmodels.resnet18()
self.fc = nn.Linear(2048, nb_embeddings)
params['feat_path'], params['logit_name'] +
('' if '.hdf5' in params['logit_name'] else '.hdf5'))
print('Model: %s' % params['model'])
print('The extracted features will be saved to --> %s' %
params['feat_dir'])
if params['model'] == 'resnet101':
C, H, W = 3, 224, 224
model = pretrainedmodels.resnet101(pretrained='imagenet')
elif params['model'] == 'resnet152':
C, H, W = 3, 224, 224
model = pretrainedmodels.resnet152(pretrained='imagenet')
elif params['model'] == 'resnet18':
C, H, W = 3, 224, 224
model = pretrainedmodels.resnet18(pretrained='imagenet')
elif params['model'] == 'resnet34':
C, H, W = 3, 224, 224
model = pretrainedmodels.resnet34(pretrained='imagenet')
elif params['model'] == 'inceptionresnetv2':
C, H, W = 3, 299, 299
model = pretrainedmodels.inceptionresnetv2(
num_classes=1001, pretrained='imagenet+background')
elif params['model'] == 'googlenet':
C, H, W = 3, 224, 224
model = googlenet(pretrained=True)
print(model)
else:
print("doesn't support %s" % (params['model']))
if params['model'] != 'googlenet':
def __init__(self,
backbone,
heads,
head_conv=128,
num_filters=[256, 256, 256],
pretrained=True,
dcn=False,
gn=False,
ws=False,
freeze_bn=False,
after_non_local='layer1',
non_local_hidden_channels=None):
super().__init__()
self.heads = heads
if backbone == 'resnet18':
pretrained = 'imagenet' if pretrained else None
self.backbone = pretrainedmodels.resnet18(pretrained=pretrained)
num_bottleneck_filters = 512
elif backbone == 'resnet34':
pretrained = 'imagenet' if pretrained else None
self.backbone = pretrainedmodels.resnet34(pretrained=pretrained)
num_bottleneck_filters = 512
elif backbone == 'resnet50':
pretrained = 'imagenet' if pretrained else None
self.backbone = pretrainedmodels.resnet50(pretrained=pretrained)
num_bottleneck_filters = 2048
elif backbone == 'resnet101':
pretrained = 'imagenet' if pretrained else None
self.backbone = pretrainedmodels.resnet101(pretrained=pretrained)
num_bottleneck_filters = 2048
elif backbone == 'resnet152':
pretrained = 'imagenet' if pretrained else None
self.backbone = pretrainedmodels.resnet152(pretrained=pretrained)
num_bottleneck_filters = 2048
elif backbone == 'se_resnext50_32x4d':
pretrained = 'imagenet' if pretrained else None
self.backbone = pretrainedmodels.se_resnext50_32x4d(
pretrained=pretrained)
num_bottleneck_filters = 2048
elif backbone == 'se_resnext101_32x4d':
pretrained = 'imagenet' if pretrained else None
self.backbone = pretrainedmodels.se_resnext101_32x4d(
pretrained=pretrained)
num_bottleneck_filters = 2048
elif backbone == 'resnet34_v1b':
self.backbone = timm.create_model('gluon_resnet34_v1b',
pretrained=pretrained)
convert_to_inplace_relu(self.backbone)
num_bottleneck_filters = 512
elif backbone == 'resnet50_v1d':
self.backbone = timm.create_model('gluon_resnet50_v1d',
pretrained=pretrained)
convert_to_inplace_relu(self.backbone)
num_bottleneck_filters = 2048
elif backbone == 'resnet101_v1d':
self.backbone = timm.create_model('gluon_resnet101_v1d',
pretrained=pretrained)
convert_to_inplace_relu(self.backbone)
num_bottleneck_filters = 2048
elif backbone == 'resnext50_32x4d':
self.backbone = timm.create_model('resnext50_32x4d',
pretrained=pretrained)
convert_to_inplace_relu(self.backbone)
num_bottleneck_filters = 2048
elif backbone == 'resnext50d_32x4d':
self.backbone = timm.create_model('resnext50d_32x4d',
pretrained=pretrained)
convert_to_inplace_relu(self.backbone)
num_bottleneck_filters = 2048
elif backbone == 'seresnext26_32x4d':
self.backbone = timm.create_model('seresnext26_32x4d',
pretrained=pretrained)
convert_to_inplace_relu(self.backbone)
num_bottleneck_filters = 2048
elif backbone == 'resnet18_ctdet':
self.backbone = models.resnet18()
state_dict = torch.load(
'pretrained_weights/ctdet_coco_resdcn18.pth')['state_dict']
self.backbone.load_state_dict(state_dict, strict=False)
num_bottleneck_filters = 512
elif backbone == 'resnet50_maskrcnn':
self.backbone = models.detection.maskrcnn_resnet50_fpn(
pretrained=pretrained).backbone.body
print(self.backbone)
num_bottleneck_filters = 2048
else:
raise NotImplementedError
if after_non_local is not None:
self.after_non_local = after_non_local
in_channels = getattr(self.backbone,
after_non_local)[0].conv1.in_channels
if non_local_hidden_channels is None:
non_local_hidden_channels = in_channels // 2
self.non_local = NonLocal2d(in_channels, non_local_hidden_channels)
if freeze_bn:
for m in self.backbone.modules():
if isinstance(m, nn.BatchNorm2d):
m.weight.requires_grad = False
m.bias.requires_grad = False
self.lateral4 = nn.Sequential(
Conv2d(num_bottleneck_filters,
num_filters[0],
kernel_size=1,
bias=False,
ws=ws),
nn.GroupNorm(32, num_filters)
if gn else nn.BatchNorm2d(num_filters[0]), nn.ReLU(inplace=True))
self.lateral3 = nn.Sequential(
Conv2d(num_bottleneck_filters // 2,
num_filters[0],
kernel_size=1,
bias=False,
ws=ws),
nn.GroupNorm(32, num_filters[0])
if gn else nn.BatchNorm2d(num_filters[0]), nn.ReLU(inplace=True))
self.lateral2 = nn.Sequential(
Conv2d(num_bottleneck_filters // 4,
num_filters[1],
kernel_size=1,
bias=False,
ws=ws),
nn.GroupNorm(32, num_filters[1])
if gn else nn.BatchNorm2d(num_filters[1]), nn.ReLU(inplace=True))
self.lateral1 = nn.Sequential(
Conv2d(num_bottleneck_filters // 8,
num_filters[2],
kernel_size=1,
bias=False,
ws=ws),
nn.GroupNorm(32, num_filters)
if gn else nn.BatchNorm2d(num_filters[2]), nn.ReLU(inplace=True))
self.decode3 = nn.Sequential(
DCN(num_filters[0], num_filters[1],
kernel_size=3, padding=1, stride=1) if dcn else \
Conv2d(num_filters[0], num_filters[1],
kernel_size=3, padding=1, bias=False, ws=ws),
nn.GroupNorm(32, num_filters[1]) if gn else nn.BatchNorm2d(num_filters[1]),
nn.ReLU(inplace=True))
self.decode2 = nn.Sequential(
Conv2d(num_filters[1],
num_filters[2],
kernel_size=3,
padding=1,
bias=False,
ws=ws),
nn.GroupNorm(32, num_filters[2])
if gn else nn.BatchNorm2d(num_filters[2]), nn.ReLU(inplace=True))
self.decode1 = nn.Sequential(
Conv2d(num_filters[2],
num_filters[2],
kernel_size=3,
padding=1,
bias=False,
ws=ws),
nn.GroupNorm(32, num_filters[2])
if gn else nn.BatchNorm2d(num_filters[2]), nn.ReLU(inplace=True))
for head in sorted(self.heads):
num_output = self.heads[head]
fc = nn.Sequential(
Conv2d(num_filters[2],
head_conv,
kernel_size=3,
padding=1,
bias=False,
ws=ws),
nn.GroupNorm(32, head_conv)
if gn else nn.BatchNorm2d(head_conv), nn.ReLU(inplace=True),
nn.Conv2d(head_conv, num_output, kernel_size=1))
if 'hm' in head:
fc[-1].bias.data.fill_(-2.19)
else:
fill_fc_weights(fc)
self.__setattr__(head, fc)
def Model_builder(configer):
model_name = configer.model['name']
No_classes = configer.dataset_cfg["id_cfg"]["num_classes"]
model_pretrained = configer.model['pretrained']
model_dataparallel = configer.model["DataParallel"]
model_gpu_replica = configer.model["Multi_GPU_replica"]
gpu_ids = configer.train_cfg["gpu"]
if model_name == "Inceptionv3":
model = PM.inceptionv3(num_classes=1000, pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "Xception":
model = PM.xception(num_classes=1000, pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "VGG_19":
model = PM.vgg19(num_classes=1000, pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "Resnet18":
model = PM.resnet18(num_classes=1000, pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "Resnet50":
model = PM.resnet50(num_classes=1000, pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "Resnet101":
model = PM.resnet101(num_classes=1000, pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "Resnet152":
model = PM.resnet152(num_classes=1000, pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "Resnet34":
model = PM.resnet34(num_classes=1000, pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "Densenet121":
model = PM.densenet121(num_classes=1000, pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "ResNeXt101-32":
model = PM.resnext101_32x4d(num_classes=1000,
pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "ResNeXt101-64":
model = PM.resnext101_64x4d(num_classes=1000,
pretrained=model_pretrained)
d = model.last_linear.in_features
model.last_linear = nn.Linear(d, No_classes)
elif model_name == "MobilenetV2":
model = MobileNetV2(n_class=No_classes)
else:
raise ImportError("Model Architecture not supported")
# Performing Data Parallelism if configured
if model_dataparallel:
model = torch.nn.DataParallel(model.to(device), device_ids=gpu_ids)
elif model_gpu_replica:
torch.distributed.init_process_group(backend='nccl',
world_size=1,
rank=1)
model = torch.nn.DistributedDataParallel(model.to(device),
device_ids=gpu_ids)
else:
model = model.to(device)
print('---------- Model Loaded')
return model
def get_base_model(config):
model_name = config.backbone
pretrained = config.pretrained
if pretrained is not None and pretrained != 'imagenet':
weights_path = pretrained
pretrained = None
else:
weights_path = None
if config.multibranch:
input_channels = config.multibranch_input_channels
else:
input_channels = config.num_slices
if hasattr(config, 'append_masks') and config.append_masks:
input_channels *= 2
_available_models = ['senet154', 'se_resnext50', 'resnet34', 'resnet18']
if model_name == 'senet154':
cut_point = -3
model = nn.Sequential(
*list(pretrainedmodels.senet154(
pretrained=pretrained).children())[:cut_point])
num_features = 2048
elif model_name == 'se_resnext50':
cut_point = -2
model = nn.Sequential(*list(
pretrainedmodels.se_resnext50_32x4d(
pretrained=pretrained).children())[:cut_point])
num_features = 2048
elif model_name == 'resnet34':
cut_point = -2
model = nn.Sequential(
*list(pretrainedmodels.resnet34(
pretrained=pretrained).children())[:cut_point])
num_features = 512
elif model_name == 'resnet18':
cut_point = -2
model = nn.Sequential(
*list(pretrainedmodels.resnet18(
pretrained=pretrained).children())[:cut_point])
num_features = 512
else:
raise ValueError('Unavailable backbone, choose one from {}'.format(
_available_models))
if model_name in ['senet154', 'se_resnext50']:
conv1 = model[0].conv1
else:
conv1 = model[0]
if input_channels != 3:
conv1_weights = deepcopy(conv1.weight)
new_conv1 = nn.Conv2d(input_channels,
conv1.out_channels,
kernel_size=conv1.kernel_size,
stride=conv1.stride,
padding=conv1.padding,
bias=conv1.bias)
if weights_path is None:
if input_channels == 1:
new_conv1.weight.data.fill_(0.)
new_conv1.weight[:, 0, :, :].data.copy_(conv1_weights[:,
0, :, :])
elif input_channels > 3:
diff = (input_channels - 3) // 2
new_conv1.weight.data.fill_(0.)
new_conv1.weight[:,
diff:diff + 3, :, :].data.copy_(conv1_weights)
if model_name in ['senet154', 'se_resnext50']:
model[0].conv1 = new_conv1
else:
model[0] = new_conv1
if weights_path is not None:
if model_name in ['senet154', 'se_resnext50']:
conv1_str = '0.conv1.weight'
else:
conv1_str = '0.weight'
weights = load_base_weights(weights_path, input_channels, conv1_str)
model.load_state_dict(weights)
return model, num_features
def __init__(self,
config_file: Optional[str] = None,
override_list: List[Any] = []):
_C = CN()
_C.VALID_IMAGES = [
'CXR1576_IM-0375-2001.png', 'CXR1581_IM-0378-2001.png',
'CXR3177_IM-1497-2001.png', 'CXR2585_IM-1082-1001.png',
'CXR1125_IM-0082-1001.png', 'CXR3_IM-1384-2001.png',
'CXR1565_IM-0368-1001.png', 'CXR1105_IM-0072-1001-0001.png',
'CXR2874_IM-1280-1001.png', 'CXR1886_IM-0574-1001.png'
]
_C.MODELS = [{
'resnet18': (pretrainedmodels.resnet18(pretrained=None), 512, 224),
'resnet50':
(pretrainedmodels.resnet50(pretrained=None), 2048, 224),
'resnet101':
(pretrainedmodels.resnet101(pretrained=None), 2048, 224),
'resnet152':
(pretrainedmodels.resnet152(pretrained=None), 2048, 224),
'inception_resnet_v2':
(pretrainedmodels.inceptionresnetv2(pretrained=None), 1536, 299)
}]
# _C.MODELS_FEATURE_SIZE = {'resnet18':512, 'resnet50':2048, 'resnet101':2048, 'resnet152':2048,
# 'inception_v3':2048, 'inception_resnet_v2':1536}
# Random seed for NumPy and PyTorch, important for reproducibility.
_C.RANDOM_SEED = 42
# Opt level for mixed precision training using NVIDIA Apex. This can be
# one of {0, 1, 2}. Refer NVIDIA Apex docs for their meaning.
_C.FP16_OPT = 2
# Path to the dataset root, which structure as per README. Path is
# assumed to be relative to project root.
_C.IMAGE_PATH = '/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/Images_2'
_C.TRAIN_JSON_PATH = '/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/iu_xray_train_2.json'
_C.VAL_JSON_PATH = '/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/iu_xray_val_2.json'
_C.TEST_JSON_PATH = '/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/iu_xray_test_2.json'
_C.PRETRAINED_EMDEDDING = False
# Path to .vocab file generated by ``sentencepiece``.
_C.VOCAB_FILE_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/Vocab/indiana.vocab"
# Path to .model file generated by ``sentencepiece``.
_C.VOCAB_MODEL_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/Vocab/indiana.model"
_C.VOCAB_SIZE = 3000
_C.EPOCHS = 1024
_C.BATCH_SIZE = 10
_C.TEST_BATCH_SIZE = 100
_C.ITERATIONS_PER_EPOCHS = 1
_C.WEIGHT_DECAY = 1e-5
_C.NUM_LABELS = 41
_C.IMAGE_SIZE = 299
_C.MAX_SEQUENCE_LENGTH = 130
_C.DROPOUT_RATE = 0.1
_C.D_HEAD = 64
_C.TRAIN_DATASET_LENGTH = 25000
_C.INFERENCE_TIME = False
_C.COMBINED_N_LAYERS = 1
_C.BEAM_SIZE = 50
_C.PADDING_INDEX = 0
_C.EOS_INDEX = 3
_C.SOS_INDEX = 2
_C.USE_BEAM_SEARCH = True
_C.EXTRACTED_FEATURES = False
_C.IMAGE_MODEL_PATH = '/netscratch/gsingh/MIMIC_CXR/Results/Image_Feature_Extraction/MIMIC_CXR_No_ES/model.pth'
_C.EMBEDDING_DIM = 8192
_C.CONTEXT_SIZE = 1024
_C.LR_COMBINED = 1e-4
_C.MAX_LR = 1e-1
_C.SAVED_DATASET = False
_C.MODEL_NAME = 'inception_resnet_v2'
INIT_PATH = '/netscratch/gsingh/MIMIC_CXR/Results/Modified_Transformer/Indiana_15_10_2020_2/'
_C.SAVED_DATASET_PATH_TRAIN = INIT_PATH + 'DataSet/train_dataloader.pth'
_C.SAVED_DATASET_PATH_VAL = INIT_PATH + 'DataSet/val_dataloader.pth'
_C.SAVED_DATASET_PATH_TEST = INIT_PATH + 'DataSet/test_dataloader.pth'
_C.CHECKPOINT_PATH = INIT_PATH + 'CheckPoints'
_C.MODEL_PATH = INIT_PATH + 'combined_model.pth'
_C.MODEL_STATE_DIC = INIT_PATH + 'combined_model_state_dic.pth'
_C.FIGURE_PATH = INIT_PATH + 'Graphs'
_C.CSV_PATH = INIT_PATH
_C.TEST_CSV_PATH = INIT_PATH + 'test_output_image_feature_input.json'
self._C = _C
if config_file is not None:
self._C.merge_from_file(config_file)
self._C.merge_from_list(override_list)
self.add_derived_params()
# Make an instantiated object of this class immutable.
self._C.freeze()
# 指定RGB三个通道的均值和方差来将图像通道归一化
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_augs = transforms.Compose([
transforms.RandomResizedCrop(size=224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
])
test_augs = transforms.Compose([
transforms.Resize(size=256),
transforms.CenterCrop(size=224),
transforms.ToTensor(), normalize
])
pretrained_net = pretrainedmodels.resnet18()
# fc是输出层函数
# 源码：self.fc = nn.Linear(512 * block.expansion, num_classes)
pretrained_net.fc = nn.Linear(512, 2)
output_params = list(map(id, pretrained_net.fc.parameters()))
feature_params = filter(lambda p: id(p) not in output_params,
pretrained_net.parameters())
lr = 0.01
optimizer = optim.SGD([{
'params': feature_params
}, {
'params': pretrained_net.fc.parameters(),
'lr': lr * 10
}],
