Esempio n. 1
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def write_solver_file(solver_file, train_model, test_models, type, base_lr, momentum, weight_decay,
                      lr_policy, gamma, power, random_seed, max_iter, clip_gradients, snapshot_prefix,display=0):
    '''Writes a solver prototxt file with parameters set to the
    corresponding argument values. In particular, the train_net
    parameter is set to train_model, and a test_net parameter is
    added for each of test_models, which should be a list.'''
    param = SolverParameter()
    param.train_net = train_model
    for test_model in test_models:
        param.test_net.append(test_model)
        param.test_iter.append(0) #don't test automatically
    param.test_interval = max_iter
    param.type = type
    param.base_lr = base_lr
    param.momentum = momentum
    param.weight_decay = weight_decay
    param.lr_policy = lr_policy
    param.gamma = gamma
    param.power = power
    param.display = display #don't print solver iterations unless requested
    param.random_seed = random_seed
    param.max_iter = max_iter
    if clip_gradients > 0:
        param.clip_gradients = clip_gradients
    param.snapshot_prefix = snapshot_prefix
    print "WRITING",solver_file
    with open(solver_file,'w') as f:
        f.write(str(param))
Esempio n. 2
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def write_solver_file(solver_file, train_model, test_models, type, base_lr,
                      momentum, weight_decay, lr_policy, gamma, power,
                      random_seed, max_iter, clip_gradients, snapshot_prefix):
    '''Writes a solver prototxt file with parameters set to the
    corresponding argument values. In particular, the train_net
    parameter is set to train_model, and a test_net parameter is
    added for each of test_models, which should be a list.'''
    param = SolverParameter()
    param.train_net = train_model
    for test_model in test_models:
        param.test_net.append(test_model)
        param.test_iter.append(0)  #don't test automatically
    param.test_interval = max_iter
    param.type = type
    param.base_lr = base_lr
    param.momentum = momentum
    param.weight_decay = weight_decay
    param.lr_policy = lr_policy
    param.gamma = gamma
    param.power = power
    param.display = 0  #don't print solver iterations
    param.random_seed = random_seed
    param.max_iter = max_iter
    if clip_gradients > 0:
        param.clip_gradients = clip_gradients
    param.snapshot_prefix = snapshot_prefix
    print "WRITING", solver_file
    with open(solver_file, 'w') as f:
        f.write(str(param))
Esempio n. 3
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    def init_solver(self):
        """ Helper method to initialize the solver. """
        solver_param = SolverParameter()
        solver_param.snapshot_prefix = self._hyperparams['weights_file_prefix']
        solver_param.display = 0  # Don't display anything.
        solver_param.base_lr = self._hyperparams['lr']
        solver_param.lr_policy = self._hyperparams['lr_policy']
        solver_param.momentum = self._hyperparams['momentum']
        solver_param.weight_decay = self._hyperparams['weight_decay']
        solver_param.type = self._hyperparams['solver_type']
        solver_param.random_seed = self._hyperparams['random_seed']

        # Pass in net parameter either by filename or protostring.
        if isinstance(self._hyperparams['network_model'], basestring):
            self.solver = caffe.get_solver(self._hyperparams['network_model'])
        else:
            network_arch_params = self._hyperparams['network_arch_params']
            network_arch_params['dim_input'] = self._dO
            network_arch_params['dim_output'] = self._dU

            network_arch_params['batch_size'] = self.batch_size
            network_arch_params['phase'] = TRAIN
            solver_param.train_net_param.CopyFrom(
                self._hyperparams['network_model'](**network_arch_params)
            )

            # For running forward in python.
            network_arch_params['batch_size'] = 1
            network_arch_params['phase'] = TEST
            solver_param.test_net_param.add().CopyFrom(
                self._hyperparams['network_model'](**network_arch_params)
            )

            # For running forward on the robot.
            network_arch_params['batch_size'] = 1
            network_arch_params['phase'] = 'deploy'
            solver_param.test_net_param.add().CopyFrom(
                self._hyperparams['network_model'](**network_arch_params)
            )

            # These are required by Caffe to be set, but not used.
            solver_param.test_iter.append(1)
            solver_param.test_iter.append(1)
            solver_param.test_interval = 1000000

            f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
            f.write(MessageToString(solver_param))
            f.close()

            self.solver = caffe.get_solver(f.name)
    def init_solver(self):
        """ Helper method to initialize the solver. """
        solver_param = SolverParameter()
        solver_param.snapshot_prefix = self._hyperparams['weights_file_prefix']
        solver_param.display = 0  # Don't display anything.
        solver_param.base_lr = self._hyperparams['lr']
        solver_param.lr_policy = self._hyperparams['lr_policy']
        solver_param.momentum = self._hyperparams['momentum']
        solver_param.weight_decay = self._hyperparams['weight_decay']
        solver_param.type = self._hyperparams['solver_type']
        solver_param.random_seed = self._hyperparams['random_seed']

        # Pass in net parameter either by filename or protostring.
        if isinstance(self._hyperparams['network_model'], basestring):
            self.solver = caffe.get_solver(self._hyperparams['network_model'])
        else:
            network_arch_params = self._hyperparams['network_arch_params']
            network_arch_params['dim_input'] = self._dO
            network_arch_params['dim_output'] = self._dU

            network_arch_params['batch_size'] = self.batch_size
            network_arch_params['phase'] = TRAIN
            solver_param.train_net_param.CopyFrom(
                self._hyperparams['network_model'](**network_arch_params)
            )

            # For running forward in python.
            network_arch_params['batch_size'] = 1
            network_arch_params['phase'] = TEST
            solver_param.test_net_param.add().CopyFrom(
                self._hyperparams['network_model'](**network_arch_params)
            )

            # For running forward on the robot.
            network_arch_params['batch_size'] = 1
            network_arch_params['phase'] = 'deploy'
            solver_param.test_net_param.add().CopyFrom(
                self._hyperparams['network_model'](**network_arch_params)
            )

            # These are required by Caffe to be set, but not used.
            solver_param.test_iter.append(1)
            solver_param.test_iter.append(1)
            solver_param.test_interval = 1000000

            f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
            f.write(MessageToString(solver_param))
            f.close()

            self.solver = caffe.get_solver(f.name)
Esempio n. 5
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	def _init_solver(self):
		""" Helper method to initialize the solver. """
		solver_param = SolverParameter()
		solver_param.display = 0  # Don't display anything.
		solver_param.base_lr = self._hyperparams['lr']
		solver_param.lr_policy = self._hyperparams['lr_policy']
		solver_param.momentum = self._hyperparams['momentum']
		solver_param.weight_decay = self._hyperparams['weight_decay']
		solver_param.type = self._hyperparams['solver_type']
		solver_param.random_seed = self._hyperparams['random_seed']

		# Pass in net parameter by protostring (could add option to input prototxt file).
		network_arch_params = self._hyperparams['network_arch_params']

		network_arch_params['dim_input'] = self._dO
		network_arch_params['demo_batch_size'] = self._hyperparams['demo_batch_size']
		network_arch_params['sample_batch_size'] = self._hyperparams['sample_batch_size']
		network_arch_params['T'] = self._T
		network_arch_params['phase'] = TRAIN
		solver_param.train_net_param.CopyFrom(
			self._hyperparams['network_model'](**network_arch_params)
		)

		# For running forward in python.
		network_arch_params['phase'] = TEST
		solver_param.test_net_param.add().CopyFrom(
			self._hyperparams['network_model'](**network_arch_params)
		)

		network_arch_params['phase'] = 'forward_feat'
		solver_param.test_net_param.add().CopyFrom(
			self._hyperparams['network_model'](**network_arch_params)
		)

		# These are required by Caffe to be set, but not used.
		solver_param.test_iter.append(1)
		solver_param.test_iter.append(1)
		solver_param.test_interval = 1000000

		f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
		f.write(MessageToString(solver_param))
		f.close()
		self.solver = caffe.get_solver(f.name)