def test_read_direct():
dset = ReplayDataset("test.hdf5", STATE_SHAPE, dset_size=1000,
overwrite=True)
batch_size = 32
s = np.zeros((batch_size,)+STATE_SHAPE)
a = np.zeros(batch_size)
r = np.zeros(batch_size)
sp = np.zeros((batch_size,)+STATE_SHAPE)
states = random_fill(dset, 100)
dset.sample_direct(s, a, r, sp, batch_size)
diff = [np.linalg.norm(s[0] - states[i]) for i in range(100)]
assert(min(diff) == 0)
def test_read_direct():
dset = ReplayDataset("test.hdf5",
STATE_SHAPE,
dset_size=1000,
overwrite=True)
batch_size = 32
s = np.zeros((batch_size, ) + STATE_SHAPE)
a = np.zeros(batch_size)
r = np.zeros(batch_size)
sp = np.zeros((batch_size, ) + STATE_SHAPE)
states = random_fill(dset, 100)
dset.sample_direct(s, a, r, sp, batch_size)
diff = [np.linalg.norm(s[0] - states[i]) for i in range(100)]
assert (min(diff) == 0)
def test_correctness():
dset = ReplayDataset("test.hdf5",
STATE_SHAPE,
overwrite=True,
dset_size=10)
print "Initially..."
print "------------------------"
print "Head: ", dset.head
print "Valid: ", dset.valid
try:
sample = dset.sample(1)
except ValueError as e:
print e.message
print "Correctly triggered exception"
# Add one experience to dataset, then try sampling again
dset.add_experience(0, 10, np.random.randint(0, 256, size=(4, 128, 128)))
print "Added one experience"
try:
sample = dset.sample(1)
print "Correctly avoided exception when sampling"
except ValueError as e:
print e.message
for _ in xrange(13):
state = np.random.randint(0, 256, size=(4, 128, 128))
action = random.choice(range(0, 10))
reward = random.choice(range(-5, 6))
dset.add_experience(action, reward, state)
assert (dset.head == (13 + 1) % 10)
assert (dset.valid == 10)
print
print "Current state of dataset:"
print "----------------------------"
print "Action:", dset.action
print "Reward:", dset.reward
print "State:", dset.state
print "State[0,0,0]:", [dset.state[i, 0, 0, 0] for i in range(10)]
print
print "Draw full sample:"
s, a, r, ns = dset.sample(9)
print "S (0,0,0):", [s[i, 0, 0, 0] for i in range(9)]
print "S'(0,0,0):", [ns[i, 0, 0, 0] for i in range(9)]
def test_correctness():
dset = ReplayDataset("test.hdf5", STATE_SHAPE,
overwrite=True, dset_size=10)
print "Initially..."
print "------------------------"
print "Head: ", dset.head
print "Valid: ", dset.valid
try:
sample = dset.sample(1)
except ValueError as e:
print e.message
print "Correctly triggered exception"
# Add one experience to dataset, then try sampling again
dset.add_experience(0, 10, np.random.randint(0, 256, size=(4, 128, 128)))
print "Added one experience"
try:
sample = dset.sample(1)
print "Correctly avoided exception when sampling"
except ValueError as e:
print e.message
for _ in xrange(13):
state = np.random.randint(0, 256, size=(4, 128, 128))
action = random.choice(range(0, 10))
reward = random.choice(range(-5, 6))
dset.add_experience(action, reward, state)
assert(dset.head == (13 + 1) % 10)
assert(dset.valid == 10)
print
print "Current state of dataset:"
print "----------------------------"
print "Action:", dset.action
print "Reward:", dset.reward
print "State:", dset.state
print "State[0,0,0]:", [dset.state[i, 0, 0, 0] for i in range(10)]
print
print "Draw full sample:"
s, a, r, ns = dset.sample(9)
print "S (0,0,0):", [s[i, 0, 0, 0] for i in range(9)]
print "S'(0,0,0):", [ns[i, 0, 0, 0] for i in range(9)]
def test_timing(dset_size=10000, num_write=20000,
num_samples=1000, sample_size=32):
""" Speed test for replay dataset storage scheme. """
import matplotlib.pyplot as plt
if os.path.exists("test.hdf5"):
os.remove("test.hdf5")
if not os.path.isdir("evaluation"):
os.makedirs("evaluation")
results_file = open("evaluation/results.txt", 'w')
# Create and fill dataset
dset = ReplayDataset("test.hdf5", STATE_SHAPE, dset_size=dset_size)
# Time writing speed
start = time.time()
tocs = []
for _ in xrange(num_write):
state = np.ones((4, 128, 128), dtype=np.float32)
dset.add_experience(0, 0, state)
toc = time.time()
tocs.append(toc)
cum_time = np.array(tocs) - start
plt.figure(1)
plt.plot(cum_time)
plt.title("Writing Performance")
plt.xlabel("number of states written")
plt.ylabel("cumulative run time (s)")
plt.savefig("evaluation/write.png")
print >> results_file, \
"Time to write %d samples: %0.2f milliseconds" \
% (num_write, 1000*(tocs[-1] - start))
# Time sampling speed
times = []
for _ in xrange(num_samples):
tic = time.time()
sample = dset.sample(sample_size)
toc = time.time()
times.append(1000 * (toc - tic))
mean_sample_time = np.mean(times)
std_sample_time = np.std(times)
print >> results_file, \
"Mean sample time: %0.3f milliseconds" % np.mean(times)
print >> results_file, \
"Std. dev. sample time: %0.3f milliseconds" % np.std(times)
plt.figure(2)
plt.hist(times, bins=np.linspace(0, mean_sample_time + std_sample_time),
alpha=0.5, color="green")
plt.title("Distribution of Sampling Times (Batch Size = %d)" %
sample_size)
plt.xlabel("sampling time (ms)")
plt.ylabel("frequency")
plt.savefig("evaluation/sample.png")
def test_timing(dset_size=10000,
num_write=20000,
num_samples=1000,
sample_size=32):
""" Speed test for replay dataset storage scheme. """
import matplotlib.pyplot as plt
if os.path.exists("test.hdf5"):
os.remove("test.hdf5")
if not os.path.isdir("evaluation"):
os.makedirs("evaluation")
results_file = open("evaluation/results.txt", 'w')
# Create and fill dataset
dset = ReplayDataset("test.hdf5", STATE_SHAPE, dset_size=dset_size)
# Time writing speed
start = time.time()
tocs = []
for _ in xrange(num_write):
state = np.ones((4, 128, 128), dtype=np.float32)
dset.add_experience(0, 0, state)
toc = time.time()
tocs.append(toc)
cum_time = np.array(tocs) - start
plt.figure(1)
plt.plot(cum_time)
plt.title("Writing Performance")
plt.xlabel("number of states written")
plt.ylabel("cumulative run time (s)")
plt.savefig("evaluation/write.png")
print >> results_file, \
"Time to write %d samples: %0.2f milliseconds" \
% (num_write, 1000*(tocs[-1] - start))
# Time sampling speed
times = []
for _ in xrange(num_samples):
tic = time.time()
sample = dset.sample(sample_size)
toc = time.time()
times.append(1000 * (toc - tic))
mean_sample_time = np.mean(times)
std_sample_time = np.std(times)
print >> results_file, \
"Mean sample time: %0.3f milliseconds" % np.mean(times)
print >> results_file, \
"Std. dev. sample time: %0.3f milliseconds" % np.std(times)
plt.figure(2)
plt.hist(times,
bins=np.linspace(0, mean_sample_time + std_sample_time),
alpha=0.5,
color="green")
plt.title("Distribution of Sampling Times (Batch Size = %d)" % sample_size)
plt.xlabel("sampling time (ms)")
plt.ylabel("frequency")
plt.savefig("evaluation/sample.png")
