def main(args):
# read all the images in the folder
image_list = glob.glob(args.data_dir + os.sep + '*.' + args.img_extn)
up = None
if args.modelType == 2:
up = torch.nn.Upsample(scale_factor=16, mode='bilinear')
else:
up = torch.nn.Upsample(scale_factor=2, mode='bilinear')
if args.gpu:
up = up.cuda()
p = args.p
q = args.q
classes = args.classes
if args.modelType == 2:
modelA = Net.ESPNet_Encoder(
classes, p, q) # Net.Mobile_SegNetDilatedIA_C_stage1(20)
model_weight_file = args.weightsDir + os.sep + 'encoder' + os.sep + 'espnet_p_' + str(
p) + '_q_' + str(q) + '.pth'
if not os.path.isfile(model_weight_file):
print(
'Pre-trained model file does not exist. Please check ../pretrained/encoder folder'
)
exit(-1)
modelA.load_state_dict(torch.load(model_weight_file))
elif args.modelType == 1:
modelA = Net.ESPNet(classes, p,
q) # Net.Mobile_SegNetDilatedIA_C_stage1(20)
model_weight_file = args.weightsDir + os.sep + 'decoder' + os.sep + 'espnet_p_' + str(
p) + '_q_' + str(q) + '.pth'
if not os.path.isfile(model_weight_file):
print(
'Pre-trained model file does not exist. Please check ../pretrained/decoder folder'
)
exit(-1)
modelA.load_state_dict(torch.load(model_weight_file))
else:
print('Model not supported')
# modelA = torch.nn.DataParallel(modelA)
if args.gpu:
modelA = modelA.cuda()
# set to evaluation mode
modelA.eval()
if not os.path.isdir(args.savedir):
os.mkdir(args.savedir)
evaluateModel(args, modelA, up, image_list)
def model2(self):
input_dim = 4
output_dim = 1
model = Model2.LinearRegressionModel(input_dim, output_dim)
criterion = nn.MSELoss() # 损失函数为均方差
learning_rate = 0.01
self.optimizer21 = torch.optim.SGD(model.parameters(),
lr=learning_rate)
'''训练网络'''
for epoch in range(self.epochs21):
epoch += 1
# 清空梯度参数
self.optimizer21.zero_grad()
# 获得输出
outputs = model(self.x)
# 计算损失
loss = criterion(outputs, self.y)
# 反向传播
loss.backward()
# 更新参数
self.optimizer21.step()
return model
def __init__(self):
self.train_number = 200
self.model11 = Model1.NonLinearRegression()
self.model21 = Model2.LinearRegressionModel(4, 1)
learning_rate = 0.01
self.optimizer11 = torch.optim.SGD(self.model11.parameters(),
lr=learning_rate)
self.optimizer21 = torch.optim.SGD(self.model21.parameters(),
lr=learning_rate)
self.max = 0
self.min = 0
def SolveProblem(problem, constructive=True, aco=True, Model=False):
nt = len([(v, i) for v in range(1, problem.numVertices)
for i in range(problem.numTasks[problem.request[v]])])
print('num tasks', nt)
if constructive:
ts = time.time()
sol = Constructive.Constructive(problem)
te = time.time()
print('Constructive heuristic')
print(' Solution:', len(sol), 'days')
print(' Time:', te - ts)
if aco:
ts = time.time()
sol = ACO.Main.Solve(problem)
te = time.time()
print('Ant Colony Algorithm')
print(' Solution:', len(sol), 'days')
print(' Time:', te - ts)
if Model:
print('Model')
info, _ = Model2.Solve(problem, sol, integer=False)
lbroot = info['lb']
ts = time.time()
info, sol = Model2.Solve(problem, sol, integer=True)
te = time.time()
print(' Upper bound:', info['ub'])
if info['status'] == 'MIP_optimal':
print(' Lower bound:', info['ub'])
else:
print(' Lower bound:', info['lb'])
print(' Root lower bound:', lbroot)
print(' Root lower bound (with cplex cuts):', info['lbroot'])
print(' Number of nodes:', info['numnodes'])
print(' Time', te - ts)
def inference(self):
if self.arch == "Model1":
model = Model1.build(self.x, n_classes=self.n_classes)
return model
elif self.arch == "Model2":
#raise NotImplementedError
model = Model2.build(self.x, n_classes=self.n_classes)
print("model shape:", model.shape)
return model
elif self.arch == "Model3":
#raise NotImplementedError
model = Model3.build(self.x, n_classes=self.n_classes)
print("model shape:", model.shape)
return model
else:
model = Model1.build(self.x, n_classes=self.n_classes)
print("model shape:", model.shape)
return model
import Model1 import Model2 import Model3 import Model4 ##test = Model1.ML_Model1() test1 = Model2.ML_Model1() test2 = Model3.ML_Model1() test3 = Model4.ML_Model1() print(Model1.ML_Model1().Load_Model_Predict([1, 1, 1])) print(test1.Load_Model_Predict([1, 1, 7])) print(test2.Load_Model_Predict([1, 3, 12])) print(test3.Load_Model_Predict([1, 7, 10]))
def do_click():
global prevTableTheta
#init dictionary of users
global d
#add artificial delay
time.sleep(0.5)
#manually set value
totalPicsNum = 19
survey_duration = 10*60*60 #10 hours to prevent retaking
#get the data that the buttonClicked posted
requestData = json.loads(request.body.getvalue())
sessionData = requestData["sessionData"]
if "toSurvey" in sessionData:
return json.dumps({"toSurvey":True})
#init log variable
global data
#go to next/prev pic according to button clicked
buttonClicked = requestData["buttonID"]
if sessionData["picCount"]<5:
if buttonClicked==0:
sessionData["picCount"] -= 1
elif buttonClicked==1:
sessionData["picCount"] += 1
if sessionData["picCount"]==1:
ret = {"imageURL": "images/Slide1.JPG",
"buttonLabels": ["null", "Next"],
"instructionText": "Instructions 1/3",
"sessionData": sessionData,
"buttonClass": "btn-primary"}
return json.dumps(ret)
if sessionData["picCount"]==2:
ret = {"imageURL": "images/Slide2.JPG",
"buttonLabels": ["null", "Next"],
"instructionText": " ",
"sessionData": sessionData,
"buttonClass": "btn-primary"}
return json.dumps(ret)
if sessionData["picCount"]==3:
ret = {"imageURL": "images/Slide3.JPG",
"buttonLabels": ["Prev", "Next"],
"instructionText": " ",
"sessionData": sessionData}
return json.dumps(ret)
if sessionData["picCount"]==4:
ret = {"imageURL": "images/Slide4.JPG",
"buttonLabels": ["Prev", "START"],
"instructionText": " ",
"sessionData": sessionData}
return json.dumps(ret)
if sessionData["picCount"]==5:
#generate a cookie with user's ID
gen_id = ''.join(random.choice(string.ascii_uppercase +
string.digits) for _ in range(6))
response.set_cookie('mturk_id', gen_id, max_age=survey_duration, path='/')
data[gen_id] = []
#get ip
ip = request.environ.get('REMOTE_ADDR')
data[gen_id].append(ip)
#timestamp
startTime = datetime.datetime.now()
data[gen_id].append("start: "+ str(startTime))
timestart1[gen_id] = startTime
sessionData["playVideo"] = 0
sessionData["playedLong"] = 0
ret = {"imageURL": "images/T100.jpg",
"buttonLabels": ['<i class="fa fa-2x fa-rotate-right fa-rotate-225"></i>',
'<i class="fa fa-2x fa-rotate-left fa-rotate-135"></i>'],
"instructionText": "Choose how you would like to rotate the table.",
"sessionData": sessionData,
"buttonClass": "btn-success"}
sessionData["picCount"]+=1
return json.dumps(ret)
#following code may need mturk_id, so get it once now
mturk_id = request.cookies.get('mturk_id','NOT SET')
if sessionData["picCount"]==7:
sessionData["playVideo"] = 0
ret = {"imageURL": "images/Slide5.JPG",
"buttonLabels": ["null", "START"],
"instructionText": " ",
"sessionData": sessionData,
"buttonClass": "btn-primary"}
data[mturk_id].append("round two")
sessionData["picCount"]+=1
#timestamp
firstFinish = datetime.datetime.now()
data[mturk_id].append("firstFinish: "+ str(firstFinish))
timeDelta = firstFinish-timestart1[mturk_id]
data[mturk_id].append("timeDelta: "+ str(timeDelta.total_seconds()))
return json.dumps(ret)
if sessionData["picCount"]==8:
sessionData["playVideo"] = 0
Model2.restartTask(d,request.cookies.get('mturk_id','NOT SET'))
ret = {"imageURL": "images/T100.jpg",
"buttonLabels": ['<i class="fa fa-2x fa-rotate-right fa-rotate-225"></i>',
'<i class="fa fa-2x fa-rotate-left fa-rotate-135"></i>'],
"instructionText": "Choose how you would like to rotate the table.",
"sessionData": sessionData,
"buttonClass": "btn-success"}
#timestamp
secondStart = datetime.datetime.now()
data[mturk_id].append("secondStart: "+ str(secondStart))
timestart2[mturk_id] = secondStart
sessionData["picCount"]+=1
return json.dumps(ret)
#record in log
data[mturk_id].append(buttonClicked)
#get next move
currTableTheta, oldTableTheta, message = \
Model2.getMove(d,request.cookies.get('mturk_id','NOT SET'),buttonClicked)
#play the long video if the human-robot actions
# are the same and it's the first time this is happening
suffix=""
if oldTableTheta==currTableTheta and sessionData["playedLong"]==0:
suffix="l"
sessionData["playedLong"]=1
videoLink = "videos/{}to{}{}.mp4".format(oldTableTheta, currTableTheta,suffix)
imageLink = "images/T{}.jpg".format(currTableTheta)
if currTableTheta==0 or currTableTheta==180:
if sessionData["picCount"]==6:
Model2.setPrevGoalStateTheta(d,request.cookies.get('mturk_id','NOT SET'), currTableTheta)
sessionData["picCount"]+=1
elif sessionData["picCount"]==9:
sessionData["toSurvey"] = True
#timestamp
secondFinish = datetime.datetime.now()
data[mturk_id].append("secondFinish: "+ str(secondFinish))
timeDelta = secondFinish-timestart2[mturk_id]
data[mturk_id].append("timeDelta2: "+ str(timeDelta.total_seconds()))
ret = {"videoURL": videoLink,
"imageURL": imageLink,
"buttonLabels": ["null","Next"],
"instructionText": "The table is in a horizontal position. You finished the task!",
"sessionData": sessionData}
return json.dumps(ret)
else:
sessionData["playVideo"] = 1
ret = {"videoURL": videoLink,
"imageURL":imageLink,
"buttonLabels": ['<i class="fa fa-2x fa-rotate-right fa-rotate-225"></i>',
'<i class="fa fa-2x fa-rotate-left fa-rotate-135"></i>'],
"instructionText": "<br>",
"sessionData": sessionData,
"buttonClass": "btn-success"}
return json.dumps(ret)
"instructionText": "<br>",
"sessionData": sessionData,
"buttonClass": "btn-success"}
return json.dumps(ret)
#when the survey is approved by surveyhandler.js, the button requests this url
#handle_survey records the responses and gives a one line html page in response
#web browsers automatically add head/body syntax for this case
@app.post('/submit_survey')
def handle_survey():
mturk_id = request.cookies.get('mturk_id', 'EXPIRED')
for i in xrange(1,17):
data[mturk_id].append(request.forms.get(str(i)))
with open('output/log.json', 'w') as outfile:
json.dump(data, outfile)
print("User {} submitted the survey".format(mturk_id))
return "<p> Your answers have been submitted. ID for mturk: {}".format(mturk_id)
#the server only writes to log.json, so if there's some data there already,
#we'll copy it to another file
def backupLog():
i=1
while (os.path.isfile("output/log-backup-{}.json".format(i))):
i+=1
shutil.copy("output/log.json","output/log-backup-{}.json".format(i))
Model2.globalsInit()
backupLog()
run(app, host='0.0.0.0', port=2223)
def do_click():
global prevTableTheta
#init dictionary of users
global d
#add artificial delay
time.sleep(0.5)
#manually set value
totalPicsNum = 19
survey_duration = 10 * 60 * 60 #10 hours to prevent retaking
#get the data that the buttonClicked posted
requestData = json.loads(request.body.getvalue())
sessionData = requestData["sessionData"]
if "toSurvey" in sessionData:
return json.dumps({"toSurvey": True})
#init log variable
global data
#go to next/prev pic according to button clicked
buttonClicked = requestData["buttonID"]
if sessionData["picCount"] < 8:
if buttonClicked == 0:
sessionData["picCount"] -= 1
elif buttonClicked == 1:
sessionData["picCount"] += 1
if sessionData["picCount"] == 1:
ret = {
"imageURL": "images/Slide1.JPG",
"buttonLabels": ["null", "Next"],
"instructionText": "Instructions 1/3",
"sessionData": sessionData,
"buttonClass": "btn-primary"
}
return json.dumps(ret)
if sessionData["picCount"] == 2:
#generate a cookie with user's ID
gen_id = ''.join(
random.choice(string.ascii_uppercase + string.digits)
for _ in range(6))
response.set_cookie('mturk_id',
gen_id,
max_age=survey_duration,
path='/')
data[gen_id] = []
#get ip
ip = request.environ.get('REMOTE_ADDR')
data[gen_id].append(ip)
ret = {
"imageURL": "images/Slide2.JPG",
"buttonLabels": ["Prev", "Next"],
"instructionText": " ",
"sessionData": sessionData,
"buttonClass": "btn-primary"
}
return json.dumps(ret)
#following code may need mturk_id, so get it once now
mturk_id = request.cookies.get('mturk_id', 'NOT SET')
if sessionData["picCount"] == 3:
trialIndx[mturk_id] = 1
ret = {
"imageURL": "images/Slide3.JPG",
"buttonLabels": ["Prev", "Next"],
"instructionText": " ",
"sessionData": sessionData,
"buttonClass": "btn-primary"
}
return json.dumps(ret)
if sessionData["picCount"] == 4:
ret = {
"imageURL": "images/Slide4.JPG",
"buttonLabels": ["Prev", "Next"],
"instructionText": " ",
"sessionData": sessionData
}
return json.dumps(ret)
if sessionData["picCount"] == 5:
if "radioChoice" in requestData.keys():
data[mturk_id].append("radioChoice: " + requestData["radioChoice"])
ret = {
"imageURL": "images/HERBspeaks.JPG",
"buttonLabels": ["Prev", "Next"],
"instructionText": " ",
"sessionData": sessionData
}
return json.dumps(ret)
if sessionData["picCount"] == 6:
# we got the results from slide4 radio
ret = {
"imageURL": "",
"buttonLabels": ["Prev", "Next"],
"instructionText": " ",
"sessionData": sessionData
}
return json.dumps(ret)
if sessionData["picCount"] == 7:
data[mturk_id].append("trustRate1: " + requestData["trustRate1"])
ret = {
"imageURL": "images/Slide5.JPG",
"buttonLabels": ["Prev", "START"],
"instructionText": " ",
"sessionData": sessionData
}
return json.dumps(ret)
if sessionData["picCount"] == 8:
#timestamp
startTime = datetime.datetime.now()
data[mturk_id].append("start: " + str(startTime))
timestart1[mturk_id] = startTime
sessionData["playVideo"] = 0
sessionData["playedLong"] = 0
ret = {
"imageURL":
"images/T100.jpg",
"buttonLabels": [
'<i class="fa fa-2x fa-rotate-right fa-rotate-225"></i>',
'<i class="fa fa-2x fa-rotate-left fa-rotate-135"></i>'
],
"instructionText":
"Choose how you would like to rotate the table.",
"sessionData":
sessionData,
"buttonClass":
"btn-success"
}
sessionData["picCount"] += 1
return json.dumps(ret)
if sessionData["picCount"] == 10:
sessionData["playVideo"] = 0
ret = {
"imageURL": "",
"buttonLabels": ["null", "Next"],
"instructionText": " ",
"sessionData": sessionData,
"buttonClass": "btn-primary"
}
sessionData["picCount"] += 1
#timestamp
firstFinish = datetime.datetime.now()
data[mturk_id].append("firstFinish: " + str(firstFinish))
timeDelta = firstFinish - timestart1[mturk_id]
data[mturk_id].append("timeDelta: " + str(timeDelta.total_seconds()))
return json.dumps(ret)
if sessionData["picCount"] == 11:
data[mturk_id].append("trustRate2: " + requestData["trustRate2"])
sessionData["playVideo"] = 0
ret = {
"imageURL": "images/Slide6.JPG",
"buttonLabels": ["null", "START"],
"instructionText": " ",
"sessionData": sessionData,
"buttonClass": "btn-primary"
}
data[mturk_id].append("round two")
sessionData["picCount"] += 1
#timestamp
return json.dumps(ret)
if sessionData["picCount"] == 12:
sessionData["playVideo"] = 0
Model2.restartTask(d, request.cookies.get('mturk_id', 'NOT SET'))
ret = {
"imageURL":
"images/T100.jpg",
"buttonLabels": [
'<i class="fa fa-2x fa-rotate-right fa-rotate-225"></i>',
'<i class="fa fa-2x fa-rotate-left fa-rotate-135"></i>'
],
"instructionText":
"Choose how you would like to rotate the table.",
"sessionData":
sessionData,
"buttonClass":
"btn-success"
}
#timestamp
secondStart = datetime.datetime.now()
data[mturk_id].append("secondStart: " + str(secondStart))
timestart2[mturk_id] = secondStart
sessionData["picCount"] += 1
return json.dumps(ret)
#record in log
data[mturk_id].append(buttonClicked)
#get next move
currTableTheta, oldTableTheta, resultBelief, resultHAction, message = \
Model2.getMove(d,request.cookies.get('mturk_id','NOT SET'),buttonClicked, prior)
#debugging
#print "Belief is: {}".format(resultBelief)
#play the long video if the human-robot actions
# are the same and it's the first time this is happening
suffix = ""
if oldTableTheta == currTableTheta and sessionData["playedLong"] == 0:
suffix = "l"
sessionData["playedLong"] = 1
if (resultHAction == 'ROTATE_COUNTER_CLOCKWISE'):
suffix = "l"
videoLink = "videos/{}to{}{}.mp4".format(oldTableTheta, currTableTheta,
suffix)
else:
videoLink = "videos/{}to{}.mp4".format(oldTableTheta, currTableTheta)
#print "VIDEOLINK: " + videoLink
imageLink = "images/T{}.jpg".format(currTableTheta)
if currTableTheta == 0 or currTableTheta == 180:
if sessionData["picCount"] == 9:
Model2.setPrevGoalStateTheta(
d, request.cookies.get('mturk_id', 'NOT SET'), currTableTheta)
sessionData["picCount"] += 1
elif sessionData["picCount"] == 13:
sessionData["toSurvey"] = True
#timestamp
secondFinish = datetime.datetime.now()
data[mturk_id].append("secondfinish: " + str(secondFinish))
timeDelta = secondFinish - timestart2[mturk_id]
data[mturk_id].append("timeDelta2: " +
str(timeDelta.total_seconds()))
data[mturk_id].append("trial" + str(trialIndx[mturk_id]) + "belief0:" +
str(resultBelief[0][0]))
data[mturk_id].append("trial" + str(trialIndx[mturk_id]) + "belief1:" +
str(resultBelief[1][0]))
data[mturk_id].append("trial" + str(trialIndx[mturk_id]) + "belief2:" +
str(resultBelief[2][0]))
data[mturk_id].append("trial" + str(trialIndx[mturk_id]) + "belief3:" +
str(resultBelief[3][0]))
data[mturk_id].append("trial" + str(trialIndx[mturk_id]) + "belief4:" +
str(resultBelief[4][0]))
trialIndx[mturk_id] = trialIndx[mturk_id] + 1
ret = {
"videoURL": videoLink,
"imageURL": imageLink,
"buttonLabels": ["null", "Next"],
"instructionText":
"The table is in a horizontal position. You finished the task!",
"sessionData": sessionData
}
return json.dumps(ret)
else:
sessionData["playVideo"] = 1
ret = {
"videoURL":
videoLink,
"imageURL":
imageLink,
"buttonLabels": [
'<i class="fa fa-2x fa-rotate-right fa-rotate-225"></i>',
'<i class="fa fa-2x fa-rotate-left fa-rotate-135"></i>'
],
"instructionText":
"<br>",
"sessionData":
sessionData,
"buttonClass":
"btn-success"
}
return json.dumps(ret)
#when the survey is approved by surveyhandler.js, the button requests this url
#handle_survey records the responses and gives a one line html page in response
#web browsers automatically add head/body syntax for this case
@app.post('/submit_survey')
def handle_survey():
mturk_id = request.cookies.get('mturk_id', 'EXPIRED')
for i in xrange(1, 17):
data[mturk_id].append(request.forms.get(str(i)))
#data[mturk_id].append(request.forms.get("t3"))
with open('output/log.json', 'w') as outfile:
json.dump(data, outfile)
print("User {} submitted the survey".format(mturk_id))
return "<p> Your answers have been submitted. ID for mturk: {}".format(
mturk_id)
#the server only writes to log.json, so if there's some data there already,
#we'll copy it to another file
def backupLog():
i = 1
while (os.path.isfile("output/log-backup-{}.json".format(i))):
i += 1
shutil.copy("output/log.json", "output/log-backup-{}.json".format(i))
Model2.globalsInit()
backupLog()
run(app, host='0.0.0.0', port=8085)
with open('xtrain_gray.npy', mode="rb") as f:
xtrain = np.load(f)
with open('ytrain_gray.npy', mode="rb") as f:
ytrain = np.load(f)
idxs = np.arange(len(xtrain)) # -> array([0, 1, ..., 9999])
np.random.shuffle(idxs)
x_train = []
y_train = []
for i in idxs:
x_train.append(xtrain[i])
y_train.append(ytrain[i])
x_train = np.array(x_train)
y_train = np.array(y_train)
model = Model2.Model()
lr = 0.01
mn = 0.9
wd = 5e-04
optimization = Adam(model.parameters, learning_rate=lr)
batch_size = 1000
idxs = np.arange(len(xtrain)) # -> array([0, 1, ..., 9999])
np.random.shuffle(idxs)
for batch_cnt in range(0, len(xtrain) // batch_size):
batch_indices = idxs[batch_cnt * batch_size: (batch_cnt + 1) * batch_size]
batch = xtrain[batch_indices] # random batch of our training data
# compute the predictions for this batch by calling on model
def main():
try:
global args
global canvas
global settings
controller = None
filemerge = None
view = None
model = None
settings.initialize_prefs(canvas.max_colors())
# Only POSIX systems are supported at this point
fileops = FileOpsPOSIX.FileOpsPOSIX()
if len(args) == 2:
left = args[0]
right = args[1]
left = os.path.realpath(left)
right = os.path.realpath(right)
# Instantiate the classes and link them together.
model = Model2.Model(fileops, left, right)
view = View2.View(canvas, model, settings)
else:
# len(args) == 3
left = args[0]
middle = args[1]
right = args[2]
left = os.path.realpath(left)
middle = os.path.realpath(middle)
right = os.path.realpath(right)
# Instantiate the classes and link them together.
model = Model3.Model(fileops, left, middle, right)
view = View3.View(canvas, model, settings)
requested_filemerge = settings.get_value('file_merge_program')
# print('file_merge_program:', requested_filemerge)
if requested_filemerge == 'vim':
filemerge = FileMergeVim.FileMergeVim()
else:
filemerge = FileMergeEmacs.FileMergeEmacs()
controller = Controller.Controller(model, view, canvas, filemerge)
# Start the model. It will enumerate and then start comparing.
model.start_enumerate()
# Main event loop
while not controller.need_to_quit:
if canvas.need_to_resize:
view.resize()
model.render_again = False
view.render()
if ((model.state() == Model2.STATE_NORMAL
or model.state() == Model3.STATE_NORMAL)
and not model.render_again and not canvas.need_to_resize):
input = canvas.get_input(0) # wait indefinitely
else:
input = canvas.get_input(1) # timeout in 1/10 second
controller.process_input(input)
finally:
# print("quitting")
if controller is not None:
controller.destroy()
if filemerge is not None:
filemerge.destroy()
if view is not None:
view.destroy()
if model is not None:
model.destroy()
#coding=gbk
from matplotlib import pyplot as plt
import Model2
import Cal_para
args_model = [0, 0.6, -0.21]
args_time = [-1.55, 0, 17, 10000]
args_steps = [-1.80, 5]
args_grid = [[20, 20], [10, 2, 10, 2], 200]
time = 10000
route = []
newModel = Model2.Normal_Model(args_model=args_model,
args_grid=args_grid,
args_step=args_steps,
args_t=args_time,
simulate_time=time)
route = newModel.get_route()
draw = Cal_para.draw(route, newModel)
#draw.draw_visit_location_number_disput()
#draw.draw_location_disput()
draw.draw_location_disput_raster()
def main(args):
classes = args.classes
p = args.p
q = args.q
if args.modelType == 2:
model = Net.ESPNet_Encoder(
classes, p, q) # Net.Mobile_SegNetDilatedIA_C_stage1(20)
model_weight_file = args.weightsDir + os.sep + 'encoder' + os.sep + 'espnet_p_' + str(
p) + '_q_' + str(q) + '.pth'
if not os.path.isfile(model_weight_file):
print(
'Pre-trained model file does not exist. Please check ../pretrained/encoder folder'
)
exit(-1)
model.load_state_dict(torch.load(model_weight_file))
elif args.modelType == 1:
model = Net.ESPNet(classes, p,
q) # Net.Mobile_SegNetDilatedIA_C_stage1(20)
model_weight_file = args.weightsDir + os.sep + 'decoder' + os.sep + 'espnet_p_' + str(
p) + '_q_' + str(q) + '.pth'
if not os.path.isfile(model_weight_file):
print(
'Pre-trained model file does not exist. Please check ../pretrained/decoder folder'
)
exit(-1)
model.load_state_dict(torch.load(model_weight_file))
else:
print('Model not supported')
if (not args.cpu):
model = model.cuda(
) #.half() #HALF seems to be doing slower for some reason
#model = torch.nn.DataParallel(model).cuda()
model.eval()
images = torch.randn(args.batch_size, args.num_channels, args.height,
args.width)
if (not args.cpu):
images = images.cuda() #.half()
time_train = []
i = 0
while (True):
#for step, (images, labels, filename, filenameGt) in enumerate(loader):
start_time = time.time()
inputs = Variable(images, volatile=True)
outputs = model(inputs)
#preds = outputs.cpu()
if (not args.cpu):
torch.cuda.synchronize(
) #wait for cuda to finish (cuda is asynchronous!)
if i != 0: #first run always takes some time for setup
fwt = time.time() - start_time
time_train.append(fwt)
print("Forward time per img (b=%d): %.3f (Mean: %.3f)" %
(args.batch_size, fwt / args.batch_size,
sum(time_train) / len(time_train) / args.batch_size))
time.sleep(1) #to avoid overheating the GPU too much
i += 1
def model2():
return Model2.Model2()