everyone!
I tried using step function in my own function, but it seems that step function only check global variable but not variables in function.
here is my example code :
library(tidyverse)
# simple test function
my_step_function <- function(model_data, formula) {
mod <- lm(formula, model_data, x = TRUE, y = TRUE)
step_mod <- step(mod, direction = "both", trace = FALSE)
summary(step_mod)
}
# test data
test <- tibble(
x1 = 1:100,
x2 = -49:50+9*rnorm(100),
x3 = 50+5*rnorm(100),
x4 = 10*rnorm(100),
x5 = sqrt(1:100),
y = 5*x1 + 2*x2 + 10*x5 + rnorm(100)
) %>% nest(data = everything())
# can't work in map() function, this is where I first find the problem
test %>%
mutate(RW = map(
data,
~ my_step_function(.x,formula = formula(y~.))
))
# error:can't find object 'model_data'
# can't work when used directly
my_step_function(test$data[[1]],formula = (y~.))
# error:can't find object 'model_data'
# still can't work when give a test variable name
test_data <- test$data[[1]]
my_step_function(test_data,formula = (y~.))
# error:can't find object 'model_data'
# work when the global variable name is same with the variable name in the function
model_data <- test$data[[1]]
my_step_function(model_data,formula = (y~.))
# success!
I will appreciate it if someone can solve my puzzle !Thank everyone!
Related
I am trying to run a model using the GPU, no problem with the CPU. I think somehow using measured boundary conditions is causing the issue but I am not sure. I am following this example: https://docs.sciml.ai/dev/modules/NeuralPDE/tutorials/gpu/. I am following this example for using measured boundary conditions: https://docs.sciml.ai/dev/modules/MethodOfLines/tutorials/icbc_sampled/
using Random
using NeuralPDE, Lux, CUDA, Random
using Optimization
using OptimizationOptimisers
using NNlib
import ModelingToolkit: Interval
using Interpolations
# Measured Boundary Conditions (Arbitrary For Example)
bc1 = 1.0:1:1001.0 .|> Float32
bc2 = 1.0:1:1001.0 .|> Float32
ic1 = zeros(101) .|> Float32
ic2 = zeros(101) .|> Float32;
# Interpolation Functions Registered as Symbolic
itp1 = interpolate(bc1, BSpline(Cubic(Line(OnGrid()))))
up_cond_1_f(t::Float32) = itp1(t)
#register_symbolic up_cond_1_f(t)
itp2 = interpolate(bc2, BSpline(Cubic(Line(OnGrid()))))
up_cond_2_f(t::Float32) = itp2(t)
#register_symbolic up_cond_2_f(t)
itp3 = interpolate(ic1, BSpline(Cubic(Line(OnGrid()))))
init_cond_1_f(x::Float32) = itp3(x)
#register_symbolic init_cond_1_f(x)
itp4 = interpolate(ic2, BSpline(Cubic(Line(OnGrid()))))
init_cond_2_f(x::Float32) = itp4(x)
#register_symbolic init_cond_2_f(x);
# Parameters and differentials
#parameters t, x
#variables u1(..), u2(..)
Dt = Differential(t)
Dx = Differential(x);
# Arbitrary Equations
eqs = [Dt(u1(t, x)) + Dx(u2(t, x)) ~ 0.,
Dt(u1(t, x)) * u1(t,x) + Dx(u2(t, x)) + 9.81 ~ 0.]
# Boundary Conditions with Measured Data
bcs = [
u1(t,1) ~ up_cond_1_f(t),
u2(t,1) ~ up_cond_2_f(t),
u1(1,x) ~ init_cond_1_f(x),
u2(1,x) ~ init_cond_2_f(x)
]
# Space and time domains
domains = [t ∈ Interval(1.0,1001.0),
x ∈ Interval(1.0,101.0)];
# Neural network
input_ = length(domains)
n = 10
chain = Chain(Dense(input_,n,NNlib.tanh_fast),Dense(n,n,NNlib.tanh_fast),Dense(n,4))
strategy = GridTraining(.25)
ps = Lux.setup(Random.default_rng(), chain)[1]
ps = ps |> Lux.ComponentArray |> gpu .|> Float32
discretization = PhysicsInformedNN(chain,
strategy,
init_params=ps)
# Model Setup
#named pdesystem = PDESystem(eqs,bcs,domains,[t,x],[u1(t, x),u2(t, x)])
prob = discretize(pdesystem,discretization);
sym_prob = symbolic_discretize(pdesystem,discretization);
# Losses and Callbacks
pde_inner_loss_functions = sym_prob.loss_functions.pde_loss_functions
bcs_inner_loss_functions = sym_prob.loss_functions.bc_loss_functions
callback = function (p, l)
println("loss: ", l)
println("pde_losses: ", map(l_ -> l_(p), pde_inner_loss_functions))
println("bcs_losses: ", map(l_ -> l_(p), bcs_inner_loss_functions))
return false
end;
# Train Model (Throws Error)
res = Optimization.solve(prob,Adam(0.01); callback = callback, maxiters=5000)
phi = discretization.phi;
I get the following error:
GPU broadcast resulted in non-concrete element type Union{}.
This probably means that the function you are broadcasting contains an error or type instability.
Please Advise.
I'm implementing a fail safe handover procedure in ROS and I'm using python scripts to do so.
I'm using the optical sensor from a mouse to keep under control the acceleration of the object so I can detect when is falling. Everything seems to works fine but now I want to give give a limit to the monitoring procedure (let's say 1000 times) before declaring the handover succeded. The problem is that the function read that I use for the mouse get stucked, if no movement are detected the next iteration is not performed. How can I read from the device without encountering this issue?
Here is the code I'm using to read from the mouse:
def getMouseEvent():
buf = file.read(3)
x, y = struct.unpack( "bb", buf[1:] ) # <--- X and Y deltas.
return [x , y]
Here the loop I want to implement
release_grasp()
rospy.loginfo( "Force detected -- Release mode active")
# If the object is falling regrasp it.
detected= False
trials = 0
while (not(detected) and trials < 1000):
trials = trials + 1
rospy.loginfo ("Acc monitored for the" + str(trials) + "th time"
if fall_test():
cilindrical_grasp()
rospy.loginfo("Fall detected -- Object regrasped")
detected = True
rate.sleep()
The output I get blocks to a given iteration until the mouse does not detect some kind of movement.
UPDATE: Here is the full code
#!/usr/bin/env python2
import rospy
import numpy
import struct
from reflex_sf_msgs.msg import SFPose
from matteo.msg import force
from matteo.msg import acc
# Defining force treshold in each direction ( to be completed and tuned )
rospy.init_node('DetectionFail')
xt = 0.5
yt = xt
zt = 0.3
# For the future try to handle the initialization.
fx = None
fy = None
fz = None
ax = None
ay = None
rate = rospy.Rate(100) # <--- Rate Hz
#-----------------------------MOUSE-----------------------------------#
# Open the mouse device. To be sure if it is "mouse2" type in the terminal: cat /proc/bus/input/devices, look for the device whose name is "Logitech optical USB mouse" and get the name of the handler. If you need root permissions type: sudo chmod 777 /dev/input/(handler)
file = open ("/dev/input/mouse3" , "rb")
#Defining the function to read mouse deltas.
def getMouseEvent():
buf = file.read(3);
x,y = struct.unpack( "bb", buf[1:] ); # <--- X and Y deltas.
return [x , y]
#Defining the function to estimate the acceleraton.
def acc_comp():
vx_old = 0
vy_old = 0
vx_new = getMouseEvent()[0]
vy_new = getMouseEvent()[1]
x_acc = (vx_old - vx_new)*100
y_acc = (vy_old - vy_new)*100
vx_old = vx_new
vy_old = vy_new
return [x_acc , y_acc]
#---------------------------------------------------------------------#
#Defining function fall test
def fall_test():
if ( acc_comp()[1] >= 3000 or acc_comp()[1] <= -3000 ):
return True
else:
return False
#---------------------------------------------------------------------#
# Initialize hand publisher.
hand_pub = rospy.Publisher('/reflex_sf/command', SFPose, queue_size=1)
rospy.sleep(0.5)
#---------------------------------------------------------------------#
# Defining sferical grasp.
def cilindrical_grasp():
hand_pub.publish ( 2.5 , 2.5 , 2.5, 0)
#---------------------------------------------------------------------#
# Define release position.
def release_grasp():
hand_pub.publish ( 2, 2 , 2 , 0)
#---------------------------------------------------------------------#
# Define test for the force measure
def force_treshold ( fx, fy , fz):
if ( fx > xt and fy > yt or fz > zt):
return True
else:
return False
#---------------------------------------------------------------------#
# Callback function to save the datas obtained by the force sensor
def callback_force(msg):
global fx
global fy
global fz
fx = msg.fx
fy = msg.fy
fz = msg.fz
# Main loop.
def main():
#Apply the sferical grasp.
rospy.loginfo("Applying grasp")
cilindrical_grasp()
while not(rospy.is_shutdown()):
rospy.Subscriber("/Forces", force, callback_force )
if force_treshold ( fx , fy , fz ):
release_grasp()
rospy.loginfo( "Force detected -- Release mode active")
# If the object is falling regrasp it.
detected= False
trials = 0
while (not(detected) and trials < 1000):
trials = trials +1
if fall_test():
cilindrical_grasp()
rospy.loginfo("Fall detected -- Object regrasped")
detected = True
rate.sleep()
if rospy.is_shutdown() :
break
Yesterday I came out with this code:
#!/usr/bin/env python
import struct
import rospy
from matteo.msg import acc
import struct
import os
import time
i = 0
# Mouse read with a non blocking structure, the problem is that does not provide the same output as
# mouse_clean.py, probably there is a problem with the unpacking or the reading.
while i < 1000:
i += 1
try:
file = os.open("/dev/input/mouse0", os.O_RDONLY | os.O_NONBLOCK)
time.sleep(0.1)
buf = os.read(file , 3)
x,y = struct.unpack( "bb", buf[1:] ) # <--- X and Y deltas.
print ( "X:" +str ( x ) + "---" +"Y:" +str ( y ) )
except OSError as err:
if err.errno == 11:
print ( "No motion detected")
continue
os.close(file)
It works fine, if there is no motion the message is printed out but, in case of motion the output I get is quite different from the "vanilla" mode.
I am learning Bender's decomposition method and I want to use that in a small instance. I started from "bendersatsp.py" example in CPLEX. When I run this example with my problem, I got the following error. Could you please let me know what the problem is and how I can fix it? In the following you can see the modifications in lazy constraints function. I have two decision variables in master problem "z_{ik}" and "u_{k}" that would incorporate as constant in the workerLp.
class BendersLazyConsCallback(LazyConstraintCallback):
def __call__(self):
print("shoma")
v = self.v
u = self.u
z = self.z
print ("u:", u)
print ("z:", z)
workerLP = self.workerLP
boxty = len(u)
#scenarios=self.scenarios2
ite=len(z)
print ("ite:", ite)
print ("boxty:", boxty)
# Get the current x solution
sol1 = []
sol2 = []
sol3 = []
print("okkkk")
for k in range(1, boxty+1):
sol1.append([])
sol1[k-1]= [self.get_values(u[k-1])];
print ("sol1:", sol1[k-1])
for i in range(1, ite+1):
sol2.append([])
sol2[i-1]= self.get_values(z[i-1]);
print ("sol2:", sol2[i-1])
for i in range(1, ite+1):
sol3.append([])
sol3[i-1]= self.get_values(v[i-1]);
#print ("sol3:", sol3[i-1])
# Benders' cut separation
if workerLP.separate(sol3,sol1,sol2,v,u,z):
self.add(cut = workerLP.cutLhs, sense = "G", rhs = workerLP.cutRhs)
CPLEX Error 1006: Error during callback.
benders(sys.argv[1][0], datafile)
cpx.solve()
_proc.mipopt(self._env._e, self._lp)
check_status(env, status)
raise callback_exception
TypeError: unsupported operand type(s) for +: 'int' and 'list'
I foound out something strange with the MDA of sellar problem on the doc page of OpenMDAO (http://openmdao.readthedocs.io/en/1.7.3/usr-guide/tutorials/sellar.html)
If I extract the code and only run the MDA (adding counters in the disciplines), I observe that the number of calls is differents between disciplines (twice the number of d2 for d1 discipline) which is not expected . Does someone has an answer ?
Here is the results
Coupling vars: 25.588303, 12.058488
Number of discipline 1 and 2 calls (10,5)
And here is the code
# For printing, use this import if you are running Python 2.x from __future__ import print_function
import numpy as np
from openmdao.api import Component from openmdao.api import ExecComp, IndepVarComp, Group, NLGaussSeidel, \
ScipyGMRES
class SellarDis1(Component):
"""Component containing Discipline 1."""
def __init__(self):
super(SellarDis1, self).__init__()
# Global Design Variable
self.add_param('z', val=np.zeros(2))
# Local Design Variable
self.add_param('x', val=0.)
# Coupling parameter
self.add_param('y2', val=1.0)
# Coupling output
self.add_output('y1', val=1.0)
self.execution_count = 0
def solve_nonlinear(self, params, unknowns, resids):
"""Evaluates the equation
y1 = z1**2 + z2 + x1 - 0.2*y2"""
z1 = params['z'][0]
z2 = params['z'][1]
x1 = params['x']
y2 = params['y2']
unknowns['y1'] = z1**2 + z2 + x1 - 0.2*y2
self.execution_count += 1
def linearize(self, params, unknowns, resids):
""" Jacobian for Sellar discipline 1."""
J = {}
J['y1','y2'] = -0.2
J['y1','z'] = np.array([[2*params['z'][0], 1.0]])
J['y1','x'] = 1.0
return J
class SellarDis2(Component):
"""Component containing Discipline 2."""
def __init__(self):
super(SellarDis2, self).__init__()
# Global Design Variable
self.add_param('z', val=np.zeros(2))
# Coupling parameter
self.add_param('y1', val=1.0)
# Coupling output
self.add_output('y2', val=1.0)
self.execution_count = 0
def solve_nonlinear(self, params, unknowns, resids):
"""Evaluates the equation
y2 = y1**(.5) + z1 + z2"""
z1 = params['z'][0]
z2 = params['z'][1]
y1 = params['y1']
# Note: this may cause some issues. However, y1 is constrained to be
# above 3.16, so lets just let it converge, and the optimizer will
# throw it out
y1 = abs(y1)
unknowns['y2'] = y1**.5 + z1 + z2
self.execution_count += 1
def linearize(self, params, unknowns, resids):
""" Jacobian for Sellar discipline 2."""
J = {}
J['y2', 'y1'] = .5*params['y1']**-.5
#Extra set of brackets below ensure we have a 2D array instead of a 1D array
# for the Jacobian; Note that Jacobian is 2D (num outputs x num inputs).
J['y2', 'z'] = np.array([[1.0, 1.0]])
return J
class SellarDerivatives(Group):
""" Group containing the Sellar MDA. This version uses the disciplines
with derivatives."""
def __init__(self):
super(SellarDerivatives, self).__init__()
self.add('px', IndepVarComp('x', 1.0), promotes=['x'])
self.add('pz', IndepVarComp('z', np.array([5.0, 2.0])), promotes=['z'])
self.add('d1', SellarDis1(), promotes=['z', 'x', 'y1', 'y2'])
self.add('d2', SellarDis2(), promotes=['z', 'y1', 'y2'])
self.add('obj_cmp', ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
z=np.array([0.0, 0.0]), x=0.0, y1=0.0, y2=0.0),
promotes=['obj', 'z', 'x', 'y1', 'y2'])
self.add('con_cmp1', ExecComp('con1 = 3.16 - y1'), promotes=['y1', 'con1'])
self.add('con_cmp2', ExecComp('con2 = y2 - 24.0'), promotes=['con2', 'y2'])
self.nl_solver = NLGaussSeidel()
self.nl_solver.options['atol'] = 1.0e-12
self.ln_solver = ScipyGMRES()
from openmdao.api import Problem, ScipyOptimizer
top = Problem() top.root = SellarDerivatives()
#top.driver = ScipyOptimizer()
#top.driver.options['optimizer'] = 'SLSQP'
#top.driver.options['tol'] = 1.0e-8
#
#top.driver.add_desvar('z', lower=np.array([-10.0, 0.0]),
# upper=np.array([10.0, 10.0]))
#top.driver.add_desvar('x', lower=0.0, upper=10.0)
#
#top.driver.add_objective('obj')
#top.driver.add_constraint('con1', upper=0.0)
#top.driver.add_constraint('con2', upper=0.0)
top.setup()
# Setting initial values for design variables top['x'] = 1.0 top['z'] = np.array([5.0, 2.0])
top.run()
print("\n")
print("Coupling vars: %f, %f" % (top['y1'], top['y2']))
count1 = top.root.d1.execution_count
count2 = top.root.d2.execution_count
print("Number of discipline 1 and 2 calls (%i,%i)"% (count1,count2))
This is a good observation. Whenever you have a cycle, the "head" component runs a second time. The reason is as follows:
If you have a model with components that contain implicit states, a single execution looks like this:
Call solve_nonlinear to execute components
Call apply_nonlinear to calculate the residuals.
We don't have any components with implicit states in this model, but we indirectly created the need for one by having a cycle. Our execution looks like this:
Call solve_nonlinear to execute all components.
Call apply_nonlinear (which caches the unknowns, calls solve_nolinear, and saves the difference in unknowns) on just the "head" component to generate a residual that we can converge.
Here, the head component is just the first component that is executed based on however it determines what order to run the cycle in. You can verify that only a single head component gets extra runs by building a cycle with more than 2 components.
I have a model that has been trained on CIFAR-10, but I don't realise how can I make a prediction in pycaffe.
I got an image from lmdb but I don't know how to load it in a net and get a predicted class.
My code:
net = caffe.Net('acc81/model.prototxt',
'acc81/cifar10_full_iter_70000.caffemodel.h5',
caffe.TEST)
lmdb_env = lmdb.open('cifar10_test_lmdb/')
lmdb_txn = lmdb_env.begin()
lmdb_cursor = lmdb_txn.cursor()
for key, value in lmdb_cursor:
datum = caffe.proto.caffe_pb2.Datum()
datum.ParseFromString(value)
image = caffe.io.datum_to_array(datum)
image = image.astype(np.uint8)
# What's next with the image variable?
# If i try:
# out = net.forward_all(data=np.asarray([image]))
# I get Exception: Input blob arguments do not match net inputs.
print("Image class is " + label)
Use this python script
# Run the script with anaconda-python
# $ /home/<path to anaconda directory>/anaconda/bin/python LmdbClassification.py
import sys
import numpy as np
import lmdb
import caffe
from collections import defaultdict
caffe.set_mode_gpu()
# Modify the paths given below
deploy_prototxt_file_path = '/home/<username>/caffe/examples/cifar10/cifar10_deploy.prototxt' # Network definition file
caffe_model_file_path = '/home/<username>/caffe/examples/cifar10/cifar10_iter_5000.caffemodel' # Trained Caffe model file
test_lmdb_path = '/home/<username>/caffe/examples/cifar10/cifar10_test_lmdb/' # Test LMDB database path
mean_file_binaryproto = '/home/<username>/caffe/examples/cifar10/mean.binaryproto' # Mean image file
# Extract mean from the mean image file
mean_blobproto_new = caffe.proto.caffe_pb2.BlobProto()
f = open(mean_file_binaryproto, 'rb')
mean_blobproto_new.ParseFromString(f.read())
mean_image = caffe.io.blobproto_to_array(mean_blobproto_new)
f.close()
# CNN reconstruction and loading the trained weights
net = caffe.Net(deploy_prototxt_file_path, caffe_model_file_path, caffe.TEST)
count = 0
correct = 0
matrix = defaultdict(int) # (real,pred) -> int
labels_set = set()
lmdb_env = lmdb.open(test_lmdb_path)
lmdb_txn = lmdb_env.begin()
lmdb_cursor = lmdb_txn.cursor()
for key, value in lmdb_cursor:
datum = caffe.proto.caffe_pb2.Datum()
datum.ParseFromString(value)
label = int(datum.label)
image = caffe.io.datum_to_array(datum)
image = image.astype(np.uint8)
out = net.forward_all(data=np.asarray([image]) - mean_image)
plabel = int(out['prob'][0].argmax(axis=0))
count += 1
iscorrect = label == plabel
correct += (1 if iscorrect else 0)
matrix[(label, plabel)] += 1
labels_set.update([label, plabel])
if not iscorrect:
print("\rError: key = %s, expected %i but predicted %i" % (key, label, plabel))
sys.stdout.write("\rAccuracy: %.1f%%" % (100.*correct/count))
sys.stdout.flush()
print("\n" + str(correct) + " out of " + str(count) + " were classified correctly")
print ""
print "Confusion matrix:"
print "(r , p) | count"
for l in labels_set:
for pl in labels_set:
print "(%i , %i) | %i" % (l, pl, matrix[(l,pl)])