def load_network(self, config_dict):
out_file = self.out_file
model_type_str = config_dict['model_type']
pain_model = importlib.import_module('models.' + model_type_str)
network_base = self.load_base_network()
if 'network_params' in config_dict.keys():
network_pain = pain_model.PainHead(
base_network=network_base,
output_types=config_dict['output_types'],
**config_dict['network_params'])
else:
network_pain = pain_model.PainHead(
base_network=network_base,
output_types=config_dict['output_types'])
pretrained_states = torch.load(out_file, map_location=device)
utils_train.transfer_partial_weights(pretrained_states,
network_pain,
submodule=0) #
# todo transfer weights here
print(network_pain.to_pain)
# print (pretrained_states)
# s = input()
return network_pain
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
utils_train.transfer_partial_weights(
model_zoo.load_url(model_urls['resnet152']), model)
#model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
utils_train.transfer_partial_weights(
model_zoo.load_url(model_urls['resnet18']), model)
#model.load_state_dict( model_zoo.load_url(model_urls['resnet18']))
return model
def resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
print("resnet_low_level: Loading image net weights...")
utils_train.transfer_partial_weights(
model_zoo.load_url(model_urls['resnet101']), model)
print("resnet_low_level: Done loading image net weights...")
#model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNetTwoStream(Bottleneck, [3, 8, 36, 1], **kwargs)
if pretrained:
print("Loading image net weights...")
utils_train.transfer_partial_weights(
model_zoo.load_url(model_urls['resnet152']), model)
print("Done image net weights...")
#model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model
def load_network(self, config_dict):
network_base = self.load_base_network()
# fill it with saved params
pretrained_network_path = config_dict['pretrained_network_path']
pretrained_states = torch.load(pretrained_network_path, map_location=device)
utils_train.transfer_partial_weights(pretrained_states, network_base, submodule=0) # last argument is to remove "network.single" prefix in saved network
print("Done loading weights from config_dict['pretrained_network_path']", pretrained_network_path)
# s = input()
# define the pain model with pretrained encoder
model_type_str = config_dict['model_type']
pain_model = importlib.import_module('models.'+model_type_str)
if 'network_params' in config_dict.keys():
network_pain = pain_model.PainHead(base_network = network_base, output_types = config_dict['output_types'],**config_dict['network_params'])
else:
network_pain = pain_model.PainHead(base_network = network_base, output_types = config_dict['output_types'])
print (network_pain.to_pain)
# s = input()
return network_pain
def load_network(self, config_dict):
output_types = config_dict['output_types']
use_billinear_upsampling = config_dict.get('upsampling_bilinear',
False)
lower_billinear = 'upsampling_bilinear' in config_dict.keys(
) and config_dict['upsampling_bilinear'] == 'half'
upper_billinear = 'upsampling_bilinear' in config_dict.keys(
) and config_dict['upsampling_bilinear'] == 'upper'
from_latent_hidden_layers = config_dict.get(
'from_latent_hidden_layers', 0)
num_encoding_layers = config_dict.get('num_encoding_layers', 4)
num_cameras = 4
if config_dict['active_cameras']: # for H36M it is set to False
num_cameras = len(config_dict['active_cameras'])
if lower_billinear:
use_billinear_upsampling = False
if 'model_type' not in config_dict:
model_type_str = 'unet_encode3D_clean'
else:
model_type_str = config_dict['model_type']
unet_encode3D = importlib.import_module('models.' + model_type_str)
network_single = unet_encode3D.unet(
dimension_bg=config_dict['latent_bg'],
dimension_fg=config_dict['latent_fg'],
dimension_3d=config_dict['latent_3d'],
feature_scale=config_dict['feature_scale'],
shuffle_fg=config_dict['shuffle_fg'],
shuffle_3d=config_dict['shuffle_3d'],
latent_dropout=config_dict['latent_dropout'],
in_resolution=config_dict['inputDimension'],
encoderType=config_dict['encoderType'],
is_deconv=not use_billinear_upsampling,
upper_billinear=upper_billinear,
lower_billinear=lower_billinear,
from_latent_hidden_layers=from_latent_hidden_layers,
n_hidden_to3Dpose=config_dict['n_hidden_to3Dpose'],
num_encoding_layers=num_encoding_layers,
output_types=output_types,
subbatch_size=config_dict['use_view_batches'],
implicit_rotation=config_dict['implicit_rotation'],
skip_background=config_dict['skip_background'],
num_cameras=num_cameras,
)
if 'pretrained_network_path' in config_dict.keys(): # automatic
if config_dict['pretrained_network_path'] == 'MPII2Dpose':
pretrained_network_path = '/cvlabdata1/home/rhodin/code/humanposeannotation/output_save/CVPR18_H36M/TransferLearning2DNetwork/h36m_23d_crop_relative_s1_s5_aug_from2D_2017-08-22_15-52_3d_resnet/models/network_000000.pth'
print("Loading weights from MPII2Dpose")
pretrained_states = torch.load(pretrained_network_path,
map_location=device)
utils_train.transfer_partial_weights(
pretrained_states,
network_single,
submodule=0,
add_prefix='encoder.'
) # last argument is to remove "network.single" prefix in saved network
else:
print(
"Loading weights from config_dict['pretrained_network_path']"
)
pretrained_network_path = config_dict[
'pretrained_network_path']
pretrained_states = torch.load(pretrained_network_path,
map_location=device)
utils_train.transfer_partial_weights(
pretrained_states, network_single, submodule=0
) # last argument is to remove "network.single" prefix in saved network
print(
"Done loading weights from config_dict['pretrained_network_path']"
)
if 'pretrained_posenet_network_path' in config_dict.keys(
): # automatic
print(
"Loading weights from config_dict['pretrained_posenet_network_path']"
)
pretrained_network_path = config_dict[
'pretrained_posenet_network_path']
pretrained_states = torch.load(pretrained_network_path,
map_location=device)
utils_train.transfer_partial_weights(
pretrained_states, network_single.to_pose, submodule=0
) # last argument is to remove "network.single" prefix in saved network
print(
"Done loading weights from config_dict['pretrained_posenet_network_path']"
)
return network_single
