def _init_model(self):
self.model = ShellNet(in_channels=self.config['data']['in_channels'],
init_node_c=self.config['search']['init_node_c'],
out_channels=self.config['data']['out_channels'],
depth=self.config['search']['depth'],
n_nodes=self.config['search']['n_nodes']).to(self.device)
print('Param size = {:.3f} MB'.format(calc_param_size(self.model.parameters())))
self.loss = nn.CrossEntropyLoss().to(self.device)
self.optim_shell = Adam(self.model.alphas())
self.optim_kernel = Adam(self.model.kernel.parameters())
self.shell_scheduler = ReduceLROnPlateau(self.optim_shell,verbose=True,factor=0.5)
self.kernel_scheduler = ReduceLROnPlateau(self.optim_kernel,verbose=True,factor=0.5)
def _init_model(self):
self.model = ShellNet(in_channels=len(self.config['data']['all_mods']),
init_n_kernels=self.config['search']['init_n_kernels'],
out_channels=len(self.config['data']['labels']),
depth=self.config['search']['depth'],
n_nodes=self.config['search']['n_nodes'],
normal_w_share=self.config['search']['normal_w_share'],
channel_change=self.config['search']['channel_change']).to(self.device)
print('Param size = {:.3f} MB'.format(calc_param_size(self.model)))
self.loss = WeightedDiceLoss().to(self.device)
self.optim_shell = Adam(self.model.alphas()) # lr=3e-4
self.optim_kernel = Adam(self.model.kernel.parameters())
self.shell_scheduler = ReduceLROnPlateau(self.optim_shell,verbose=True,factor=0.5)
self.kernel_scheduler = ReduceLROnPlateau(self.optim_kernel,verbose=True,factor=0.5)
def _init_model(self):
geno_file = self.config['search']['geno_file']
with open(geno_file, 'rb') as f:
gene = eval(pickle.load(f)[0])
self.model = SearchedNet(
in_channels=len(self.config['data']['all_mods']),
init_n_kernels=self.config['search']['init_n_kernels'],
out_channels=len(self.config['data']['labels']),
depth=self.config['search']['depth'],
n_nodes=self.config['search']['n_nodes'],
channel_change=self.config['search']['channel_change'],
gene=gene).to(self.device)
print('Param size = {:.3f} MB'.format(calc_param_size(self.model)))
self.loss = WeightedDiceLoss().to(self.device)
self.optim = Adam(self.model.parameters())
self.scheduler = ReduceLROnPlateau(self.optim,
verbose=True,
factor=0.5)
def _init_model(self):
self.model = ShellNet(img_size=self.config['data']['img_size'],
in_channels=self.config['data']['in_channels'],
init_node_c=self.config['search']['init_node_c'],
out_channels=self.config['data']['out_channels'],
depth=self.config['search']['depth'],
n_nodes=self.config['search']['n_nodes'])
self.model(np.random.rand(1, 28, 28, 1).astype('float32'),
training=True)
print('Param size = {:.3f} MB'.format(
calc_param_size(self.model.trainable_variables)))
self.loss = lambda props, y_truth: tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(y_truth, props))
self.optim_shell = Adam()
self.optim_kernel = Adam()
self.shell_scheduler = ReduceLROnPlateau(self.optim_shell,
framework='tf')
self.kernel_scheduler = ReduceLROnPlateau(self.optim_kernel,
framework='tf')
def _init_model(self):
geno_file = os.path.join(self.log_path,
self.config['search']['geno_file'])
with open(geno_file, 'rb') as f:
gene = eval(pickle.load(f)[0])
self.model = SearchedNet(
gene=gene,
in_channels=self.config['data']['in_channels'],
init_node_c=self.config['search']['init_node_c'],
out_channels=self.config['data']['out_channels'],
depth=self.config['search']['depth'],
n_nodes=self.config['search']['n_nodes'],
drop_rate=self.config['train']['drop_rate'])
print('Param size = {:.3f} MB'.format(
calc_param_size(self.model.parameters())))
self.loss = lambda props, y_truth: fluid.layers.reduce_mean(
fluid.layers.softmax_with_cross_entropy(props, y_truth))
self.optim = Adam(parameter_list=self.model.parameters())
self.scheduler = ReduceLROnPlateau(self.optim)
def _init_model(self):
geno_file = os.path.join(self.log_path,
self.config['search']['geno_file'])
with open(geno_file, 'rb') as f:
gene = eval(pickle.load(f)[0])
self.model = SearchedNet(
gene=gene,
in_channels=self.config['data']['in_channels'],
init_node_c=self.config['search']['init_node_c'],
out_channels=self.config['data']['out_channels'],
depth=self.config['search']['depth'],
n_nodes=self.config['search']['n_nodes'],
drop_rate=self.config['train']['drop_rate']).to(self.device)
print('Param size = {:.3f} MB'.format(
calc_param_size(self.model.parameters())))
self.loss = nn.CrossEntropyLoss().to(self.device)
self.optim = Adam(self.model.parameters())
self.scheduler = ReduceLROnPlateau(self.optim,
verbose=True,
factor=0.5)
def _init_model(self):
geno_file = os.path.join(self.log_path,
self.config['search']['geno_file'])
with open(geno_file, 'rb') as f:
gene = eval(pickle.load(f)[0])
self.model = SearchedNet(
gene=gene,
in_channels=self.config['data']['in_channels'],
init_node_c=self.config['search']['init_node_c'],
out_channels=self.config['data']['out_channels'],
depth=self.config['search']['depth'],
n_nodes=self.config['search']['n_nodes'],
drop_rate=self.config['train']['drop_rate']).to(self.device)
print('Param size = {:.3f} MB'.format(
calc_param_size(self.model.parameters())))
self.loss = nn.CrossEntropyLoss().to(self.device)
state_dicts = torch.load(os.path.join(
self.log_path, self.config['train']['best_shot']),
map_location=self.device)
self.model.load_state_dict(state_dicts['model_param'])
self.model.eval()
def _init_model(self):
geno_file = os.path.join(self.search_log,
self.config['search']['geno_file'])
with open(geno_file, 'rb') as f:
gene = eval(pickle.load(f)[0])
self.model = SearchedNet(
gene=gene,
img_size=self.config['data']['img_size'],
in_channels=self.config['data']['in_channels'],
init_node_c=self.config['search']['init_node_c'],
out_channels=self.config['data']['out_channels'],
depth=self.config['search']['depth'],
n_nodes=self.config['search']['n_nodes'],
drop_rate=self.config['train']['drop_rate'])
self.model(np.random.rand(1, 28, 28, 1).astype('float32'),
training=True)
print('Param size = {:.3f} MB'.format(
calc_param_size(self.model.trainable_variables)))
self.loss = lambda props, y_truth: tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(y_truth, props))
self.optim = Adam()
self.scheduler = ReduceLROnPlateau(self.optim, framework='tf')
