def question_1h_sanity_check():
""" Sanity check for highway.py
designed by Kortez
"""
test_model0 = highway.Highway(e_word=256)
x_test0 = torch.zeros(BATCH_SIZE, 256)
assert(test_model0.forward(x_test0).shape == x_test0.shape), \
"Shape of forward() should be {} but is: {}".format(x_test0.shape, test_model.forward(x_test0).shape)
print("-" * 80)
print("Shape of forward() passed Sanity Check!")
print("-" * 80)
x_test1 = torch.tensor([[[-1, 3, 2, 0], [100, 10, 1, 0.1]],
[[1, 1, 1, 1], [0, 0, 0, 0]]])
test_model1 = highway.Highway(e_word=4)
test_model1.load_state_dict({'conv_out_to_proj_hidden.weight':torch.tensor([[1,-1,0,1],[1,-1,0,1],[1,-1,1,0],[1,-1,1,0]]), \
'conv_out_to_proj_hidden.bias':torch.tensor([1,1,1,1]), \
'conv_out_to_gate_hidden.weight':torch.tensor([[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]), \
'conv_out_to_gate_hidden.bias':torch.tensor([0,0,0,0])})
gold_p1 = [[[-0.5, 1.5, 1, 0], [95.55, 50.55, 46.5, 46.05]],
[[1.5, 1.5, 1.5, 1.5], [0.5, 0.5, 0.5, 0.5]]]
out_p1 = [[[round(x.item(), 2) for x in y] for y in z]
for z in test_model1.forward(x_test1)]
assert (
out_p1 == gold_p1), "Output of part1 should be {} but is: {}".format(
gold_p1, out_p1)
print("Part 1 passed Sanity Check!")
print("-" * 80)
x_test2 = torch.rand((3, 5, 4)) - torch.full((3, 5, 4), 0.5)
test_model2 = highway.Highway(e_word=4)
w_p, b_p = test_model2.state_dict(
)['conv_out_to_proj_hidden.weight'], test_model2.state_dict(
)['conv_out_to_proj_hidden.bias']
w_g, b_g = test_model2.state_dict(
)['conv_out_to_gate_hidden.weight'], test_model2.state_dict(
)['conv_out_to_gate_hidden.bias']
b_w_p = torch.unsqueeze(torch.t(w_p), 0)
b_b_p = torch.unsqueeze(torch.unsqueeze(b_p, 0), 0)
b_w_g = torch.unsqueeze(torch.t(w_g), 0)
b_b_g = torch.unsqueeze(torch.unsqueeze(b_g, 0), 0)
b_w_p = torch.cat((b_w_p, b_w_p, b_w_p), 0)
b_b_p = torch.cat((b_b_p, b_b_p, b_b_p), 0)
b_w_g = torch.cat((b_w_g, b_w_g, b_w_g), 0)
b_b_g = torch.cat((b_b_g, b_b_g, b_b_g), 0)
x_p = torch.nn.functional.relu(torch.bmm(x_test2, b_w_p) + b_b_p)
x_g = torch.sigmoid(torch.bmm(x_test2, b_w_g) + b_b_g)
print("+" * 10 + "Should be:" + "+" * 10)
print(torch.mul(x_g, x_p) + torch.mul(1 - x_g, x_test2))
print("+" * 10 + "Yours:" + "+" * 10)
print(test_model2.forward(x_test2))
print("End of Part 2!")
print("Sanity Check Passed for Question 1h Highway!")
print("-" * 80)
def question_1f_sanity_check():
""" Sanity check for highway.py
basic shape check
"""
print("-" * 80)
print("Running Sanity Check for Question 1f: HIGHWAY CLASS ")
print("-" * 80)
inpt = torch.zeros(BATCH_SIZE, EMBED_SIZE)
HIGH_source = highway.Highway(EMBED_SIZE)
output = HIGH_source.forward(inpt)
output_expected_size = [BATCH_SIZE, EMBED_SIZE]
assert (
list(output.size()) == output_expected_size
), "output shape is incorrect: it should be:\n {} but is:\n{}".format(
output_expected_size, list(output.size()))
print("Sanity Check Passed for Question 1f: HIGHWAY CLASS!")
print("-" * 80)
def run(self, iteration_num, interval, duration, jam_start, jam_end):
iteration = iteration_num
acc_start = jam_start
acc_stop = jam_end
hwy = highway.Highway ()
basemap = settings.UI_BASEMAP
accident = "ON" # toggle between ON and OFF
traffic_light = "ON" # toggle between ON and OFF
if interval == -1:
traffic_light = "OFF"
if jam_start == -1:
accident = "OFF"
traff_intv = interval
traff_dura = duration
for itr in range (iteration):
if accident == "ON":
# Interface for calling traffic accidents: hwy.update_states(itr, flag)
# flag = 1: traffic accident; flag = 0: no accident
if itr > acc_start or itr < acc_stop:
hwy.update_states(itr, 0.3, 1)
else:
hwy.update_states(itr, 0.3, 0)
if traffic_light == "ON":
if itr % (traff_intv + traff_dura) == 0:
hwy.mergelane.e_prob1 = 0
elif itr % (traff_intv + traff_dura) == traff_intv:
hwy.mergelane.e_prob1 = 0.7
if itr == iteration - 1:
hwy.mergelane.e_prob1 = 0.5
if accident == "OFF":
hwy.update_states (itr, 0.3, 0)
res1 = hwy.multiway.lanes
res2 = hwy.mergelane.lanes
res3 = hwy.exitway.lanes
res = [[] for _ in range (8)]
res[:5] = res1
res[5:7] = res2
res[7] = res3
x = [[] for _ in range (8)]
y = [[] for _ in range (8)]
for i, lanex in enumerate (res):
for j, c in enumerate (lanex.cells):
if c != None:
id = c.id
xi, yi = basemap[i][j]
x[id].append (xi)
y[id].append (yi)
if itr % 30 == 0 or itr % 30 == 1 or itr % 30 == 2:
print "\n----------------- The %dth Run: -----------------" % (itr)
for i, lane in enumerate (res):
count = 0
v_sum = 0
for j, c in enumerate (lane.cells):
if c != None:
count += 1
v_sum += c.speed
print "Num of vehicles on Lane %d is %d" % (i, count)
print "Average speed of vehicles on Lane %d is %f" % (i, float(10*v_sum)/count)
count_enter = 0
v_sum_enter = 0
count_exit = 0
v_sum_exit= 0
if i in range(5):
a, b, c = 100, 151, 50
else:
a, b, c = 30, 51, 0
d = b - a - 1
for j in range(a, b):
if lane.cells[j] != None:
count_enter += 1
v_sum_enter += lane.cells[j].speed
if lane.cells[c-j] != None:
count_exit += 1
v_sum_exit += lane.cells[c-j].speed
print "Sparsity(at entrance): %f" % (float(count_enter)/d)
print "Sparsity(at exit): %f" % (float(count_exit)/d)
if count_enter == 0:
print "No Entrance"
else:
print "Enter Speed (at entrance): %f" % (float(v_sum_enter)/count_enter)
if count_exit == 0:
print "No Exit"
else:
print "Exit Speed (at exit): %f" % (float (v_sum_exit)/count_exit)
print
# send data to UI
self.sendMessage (x, y, itr)
if not self.masterMessageQueue.empty():
return