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def test_agent_supertypes_in_env_1():
agents = [MockStrategicAgent('a1'), MockStrategicAgent('a2')]
network = ph.Network(agents)
s1 = MockSampler(0)
s2 = MockSampler(10)
agent_supertypes = {'a1': MockStrategicAgent.Supertype(type_value=s1), 'a2': MockStrategicAgent.Supertype(type_value=s2)}
en... |
def test_agent_supertypes_in_env_2():
agents = [MockStrategicAgent('a1'), MockStrategicAgent('a2')]
network = ph.Network(agents)
s1 = MockSampler(0)
s2 = MockSampler(10)
agent_supertypes = {'a1': {'type_value': s1}, 'a2': {'type_value': s2}}
env = ph.PhantomEnv(1, network, agent_supertypes=age... |
def test_agent_supertypes_in_env_bad():
agents = [MockStrategicAgent('a1'), MockStrategicAgent('a2')]
network = ph.Network(agents)
agent_supertypes = {'a1': {'wrong': 1.0}, 'a2': {}}
with pytest.raises(Exception):
ph.PhantomEnv(1, network, agent_supertypes=agent_supertypes)
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def test_env_supertype_in_env_1():
s1 = MockSampler(0)
env_supertype = MockEnv.Supertype(type_value=s1)
env = MockEnv(env_supertype=env_supertype)
assert (set(env._samplers) == set([s1]))
assert (env.env_type is None)
assert (env.env_supertype == MockEnv.Supertype(s1))
env.reset()
asse... |
def test_env_supertype_in_env_2():
s1 = MockSampler(0)
env_supertype = MockEnv.Supertype(type_value=s1)
env = MockEnv(env_supertype={'type_value': s1})
assert (set(env._samplers) == set([s1]))
assert (env.env_type is None)
assert (env.env_supertype == env_supertype)
env.reset()
assert ... |
def test_env_supertype_in_env_bad():
with pytest.raises(Exception):
MockEnv(env_supertype={'xxx': 0.0})
|
def test_env_type_passed_to_agent():
class MockAgent(ph.Agent):
def __init__(self, *args, num_steps=None, **kwargs):
super().__init__(*args, **kwargs)
self.num_steps = num_steps
self.param = 0.0
def generate_messages(self, ctx):
self.param = ctx.e... |
def test_telemetry(tmpdir):
ph.telemetry.logger.configure_print_logging(print_actions=True, print_observations=True, print_rewards=True, print_terminations=True, print_truncations=True, print_infos=True, print_messages=True, metrics={'step': ph.metrics.SimpleEnvMetric('current_step')})
env = MockEnv()
env... |
def test_uniform_range():
range_ = ph.utils.ranges.UniformRange(start=0.0, end=10.0, step=1.0)
assert (range_.values() == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])).all()
|
def test_linspace_range():
range_ = ph.utils.ranges.LinspaceRange(start=0.0, end=10.0, n=11)
assert (range_.values() == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])).all()
|
def test_unit_array_uniform_range():
range_ = ph.utils.ranges.UnitArrayUniformRange(start=0.0, end=10.0, step=1.0)
assert (range_.values() == [np.array([i]) for i in range(10)])
|
def test_unit_array_linspace_range():
range_ = ph.utils.ranges.UnitArrayLinspaceRange(start=0.0, end=10.0, n=11)
assert (range_.values() == [np.array([i]) for i in range(11)])
|
def test_rllib_train_rollout(tmpdir):
ph.utils.rllib.train(algorithm='PPO', env_class=MockEnv, policies={'mock_policy': MockStrategicAgent}, rllib_config={'disable_env_checking': True, 'num_rollout_workers': 1}, iterations=2, checkpoint_freq=2, results_dir=tmpdir)
results = ph.utils.rllib.rollout(directory=f'... |
def test_rllib_rollout_vectorized_fsm_env(tmpdir):
class Env(ph.FiniteStateMachineEnv):
def __init__(self):
agents = [MockStrategicAgent('A')]
network = ph.Network(agents)
super().__init__(num_steps=1, network=network, initial_stage='StageA')
@ph.FSMStage(sta... |
def test_rllib_rollout_bad():
with pytest.raises(AssertionError):
list(ph.utils.rllib.rollout(directory='', env_class=MockEnv, num_repeats=0))
with pytest.raises(AssertionError):
list(ph.utils.rllib.rollout(directory='', env_class=MockEnv, num_workers=(- 1)))
|
def test_rllib_train_no_checkpoint(tmpdir):
algo = ph.utils.rllib.train(algorithm='PPO', env_class=MockEnv, policies={'mock_policy': MockStrategicAgent}, rllib_config={'disable_env_checking': True, 'num_rollout_workers': 1}, iterations=1, checkpoint_freq=0, results_dir=tmpdir)
assert (not Path(algo.logdir, f'... |
def test_rllib_train_not_set_checkpoint_freq(tmpdir):
algo = ph.utils.rllib.train(algorithm='PPO', env_class=MockEnv, policies={'mock_policy': MockStrategicAgent}, rllib_config={'disable_env_checking': True, 'num_rollout_workers': 1}, iterations=2, checkpoint_freq=None, results_dir=tmpdir)
assert Path(algo.lo... |
def test_rollout_class():
rollout = ph.utils.rollout.Rollout(rollout_id=0, repeat_id=0, env_config={}, rollout_params={}, steps=[ph.utils.rollout.Step(i=0, observations={'agent': {'obs': 1}}, rewards={'agent': 1.0}, terminations={'agent': False}, truncations={'agent': False}, infos={'agent': {'info': 1}}, actions... |
@pytest.fixture
def float_sampler():
return UniformFloatSampler()
|
@pytest.fixture
def int_sampler():
return UniformIntSampler()
|
def test_comparison_with_float(float_sampler):
float_sampler._value = float_sampler.sample()
assert (float_sampler <= 1.0)
assert (float_sampler >= 0.0)
assert (float_sampler == float_sampler._value)
assert (float_sampler != (float_sampler._value + 0.1))
|
def test_comparison_with_int(int_sampler):
int_sampler._value = int_sampler.sample()
assert ((int_sampler == 0) or (int_sampler == 1))
assert (int_sampler == int_sampler._value)
assert (int_sampler != (int_sampler._value + 1))
|
def test_comparison_with_sampler(float_sampler):
float_sampler._value = 0.5
float_sampler2 = UniformFloatSampler()
float_sampler2._value = 0.5
assert (not (float_sampler == float_sampler2))
assert (float_sampler != float_sampler2)
|
def test_iterable():
sampler1 = UniformFloatSampler()
sampler1._value = 0.5
sampler2 = UniformFloatSampler()
sampler2._value = 0.5
sampler3 = UniformFloatSampler()
sampler3._value = 0.5
assert (sampler3 not in [sampler1, sampler2])
|
def test_lambda_sampler():
def _my_func(a_, b_=0):
return (a_ + b_)
a = 5
b = 1
sampler = LambdaSampler(_my_func, a, b_=b)
assert (sampler.sample() == 6)
assert (sampler.sample() == 6)
sampler = LambdaSampler(_my_func, a)
assert (sampler.sample() == 5)
assert (sampler.samp... |
def test_asserts():
with pytest.raises(AssertionError):
UniformFloatSampler(high=0.0, low=1.0)
with pytest.raises(AssertionError):
UniformIntSampler(high=0, low=1)
with pytest.raises(AssertionError):
UniformArraySampler(high=0.0, low=1.0)
|
def Deconv(inputs, f_dim_in, dim, net, batch_size, f_dim_out=None, stride=2):
if (f_dim_out is None):
f_dim_out = int((f_dim_in / 2))
return tl.layers.DeConv3dLayer(inputs, shape=[4, 4, 4, f_dim_out, f_dim_in], output_shape=[batch_size, dim, dim, dim, f_dim_out], strides=[1, stride, stride, stride, 1]... |
def Conv3D(inputs, f_dim_out, net, f_dim_in=None, batch_norm=False, is_train=True):
if (f_dim_in is None):
f_dim_in = int((f_dim_out / 2))
layer = tl.layers.Conv3dLayer(inputs, shape=[4, 4, 4, f_dim_in, f_dim_out], W_init=tf.random_normal_initializer(stddev=0.02), strides=[1, 2, 2, 2, 1], name=(('d/ne... |
def generator_64(inputs, is_train=True, reuse=False, batch_size=128, sig=False):
(output_size, half, forth, eighth, sixteenth) = (64, 32, 16, 8, 4)
gf_dim = 512
with tf.variable_scope('gen', reuse=reuse) as vs:
net_0 = tl.layers.InputLayer(inputs, name='g/net_0/in')
net_1 = tl.layers.Dense... |
def discriminator(inputs, output_size, sig=False, is_train=True, reuse=False, batch_size=128, output_units=1):
inputs = tf.reshape(inputs, [batch_size, output_size, output_size, output_size, 1])
df_dim = output_size
with tf.variable_scope('dis', reuse=reuse) as vs:
net_0 = tl.layers.InputLayer(inp... |
def make_inputs_raw(file_batch):
dt = np.dtype((np.uint8, (64, 64, 64)))
models = [np.fromfile(f, dtype=dt).reshape((64, 64, 64)) for f in file_batch]
models = np.array(models)
start_time = time.time()
return (models, start_time)
|
def load_networks(checkpoint_dir, sess, net_g, net_d, epoch=''):
print('[*] Loading checkpoints...')
if (len(epoch) >= 1):
epoch = ('_' + epoch)
net_g_name = os.path.join(checkpoint_dir, (('net_g' + epoch) + '.npz'))
net_d_name = os.path.join(checkpoint_dir, (('net_d' + epoch) + '.npz'))
i... |
def save_networks(checkpoint_dir, sess, net_g, net_d, epoch):
print('[*] Saving checkpoints...')
if (not os.path.exists(checkpoint_dir)):
os.makedirs(checkpoint_dir)
net_g_name = os.path.join(checkpoint_dir, 'net_g.npz')
net_d_name = os.path.join(checkpoint_dir, 'net_d.npz')
net_g_iter_nam... |
def save_voxels(save_dir, models, epock):
print('Saving the model')
np.save((save_dir + str(epock)), models[0])
|
def savitzky_golay(y, window_size, order, deriv=0, rate=1):
from math import factorial
try:
window_size = np.abs(np.int(window_size))
order = np.abs(np.int(order))
except ValueError:
raise ValueError('window_size and order have to be of type int')
if (((window_size % 2) != 1) o... |
def render_graphs(save_dir, epoch, track_g_loss, track_d_loss, epoch_arr):
if (not os.path.exists((save_dir + '/plots/'))):
os.makedirs((save_dir + '/plots/'))
if (len(track_d_loss) > 51):
plt.plot(epoch_arr, track_d_loss, color='blue', alpha=0.5)
plt.plot(epoch_arr, track_g_loss, colo... |
def save_values(save_dir, track_g_loss, track_d_loss, epoch_arr):
np.save((save_dir + '/plots/track_g_loss'), track_g_loss)
np.save((save_dir + '/plots/track_d_loss'), track_d_loss)
np.save((save_dir + '/plots/epochs'), epoch_arr)
|
def load_values(save_dir):
outputs = []
outputs.append(list(np.load((save_dir + '/plots/track_g_loss.npy'))))
outputs.append(list(np.load((save_dir + '/plots/track_d_loss.npy'))))
outputs.append(list(np.load((save_dir + '/plots/epochs.npy'))))
return outputs
|
def cal_acc(zeros, ones):
accuracy = 0.0
for example in zeros:
if (not np.isnan(example[0])):
if (example[0] < 0.5):
accuracy += 1.0
for example in ones:
if (not np.isnan(example[0])):
if (example[0] > 0.5):
accuracy += 1.0
accura... |
def Deconv(inputs, f_dim_in, dim, net, batch_size, f_dim_out=None, stride=2):
if (f_dim_out is None):
f_dim_out = int((f_dim_in / 2))
return tl.layers.DeConv3dLayer(inputs, shape=[4, 4, 4, f_dim_out, f_dim_in], output_shape=[batch_size, dim, dim, dim, f_dim_out], strides=[1, stride, stride, stride, 1]... |
def Conv3D(inputs, f_dim_out, net, f_dim_in=None, batch_norm=False, is_train=True):
if (f_dim_in is None):
f_dim_in = (f_dim_out / 2)
layer = tl.layers.Conv3dLayer(inputs, shape=[4, 4, 4, f_dim_in, f_dim_out], W_init=tf.random_normal_initializer(stddev=0.02), strides=[1, 2, 2, 2, 1], name=(('d/net_' +... |
def generator_64(inputs, is_train=True, reuse=False, batch_size=128, sig=False):
(output_size, half, forth, eighth, sixteenth) = (64, 32, 16, 8, 4)
gf_dim = 512
with tf.variable_scope('gen', reuse=reuse) as vs:
net_0 = tl.layers.InputLayer(inputs, name='g/net_0/in')
net_1 = tl.layers.Dense... |
def discriminator(inputs, output_size, improved=False, sig=False, is_train=True, reuse=False, batch_size=128, output_units=1):
inputs = tf.reshape(inputs, [batch_size, output_size, output_size, output_size, 1])
df_dim = output_size
with tf.variable_scope('dis', reuse=reuse) as vs:
net_0 = tl.layer... |
def make_inputs_raw(file_batch):
dt = np.dtype((np.uint8, (64, 64, 64)))
models = [np.fromfile(f, dtype=dt).reshape((64, 64, 64)) for f in file_batch]
start_time = time.time()
return (models, start_time)
|
def load_networks(checkpoint_dir, sess, net_g, net_d, epoch=''):
print('[*] Loading checkpoints...')
if (len(epoch) >= 1):
epoch = ('_' + epoch)
net_g_name = os.path.join(checkpoint_dir, (('net_g' + epoch) + '.npz'))
net_d_name = os.path.join(checkpoint_dir, (('net_d' + epoch) + '.npz'))
i... |
def save_networks(checkpoint_dir, sess, net_g, net_d, epoch):
print('[*] Saving checkpoints...')
if (not os.path.exists(checkpoint_dir)):
os.makedirs(checkpoint_dir)
net_g_name = os.path.join(checkpoint_dir, 'net_g.npz')
net_d_name = os.path.join(checkpoint_dir, 'net_d.npz')
net_g_iter_nam... |
def save_voxels(save_dir, models, epock):
print('Saving the model')
np.save((save_dir + str(epock)), models[0])
|
def savitzky_golay(y, window_size, order, deriv=0, rate=1):
from math import factorial
try:
window_size = np.abs(np.int(window_size))
order = np.abs(np.int(order))
except ValueError:
raise ValueError('window_size and order have to be of type int')
if (((window_size % 2) != 1) o... |
def render_graphs(save_dir, epoch, track_d_loss_iter, track_d_loss, epoch_arr):
if (not os.path.exists((save_dir + '/plots/'))):
os.makedirs((save_dir + '/plots/'))
if (len(track_d_loss) > 51):
smoothed_d_loss = savitzky_golay(track_d_loss, 51, 3)
plt.plot(epoch_arr, track_d_loss)
... |
def save_values(save_dir, track_d_loss_iter, track_d_loss, epoch_arr):
np.save((save_dir + '/plots/track_d_loss_iter'), track_d_loss_iter)
np.save((save_dir + '/plots/track_d_loss'), track_d_loss)
np.save((save_dir + '/plots/epochs'), epoch_arr)
|
def load_values(save_dir, valid=False):
outputs = []
outputs.append(list(np.load((save_dir + '/plots/track_d_loss_iter.npy'))))
outputs.append(list(np.load((save_dir + '/plots/track_d_loss.npy'))))
outputs.append(list(np.load((save_dir + '/plots/epochs.npy'))))
outputs.append(outputs[0][(- 1)])
... |
def Deconv(inputs, f_dim_in, dim, net, batch_size, f_dim_out=None, stride=2):
if (f_dim_out is None):
f_dim_out = int((f_dim_in / 2))
return tl.layers.DeConv3dLayer(inputs, shape=[4, 4, 4, f_dim_out, f_dim_in], output_shape=[batch_size, dim, dim, dim, f_dim_out], strides=[1, stride, stride, stride, 1]... |
def Conv3D(inputs, f_dim_out, net, f_dim_in=None, batch_norm=False, is_train=True):
if (f_dim_in is None):
f_dim_in = int((f_dim_out / 2))
layer = tl.layers.Conv3dLayer(inputs, shape=[4, 4, 4, f_dim_in, f_dim_out], W_init=tf.random_normal_initializer(stddev=0.02), strides=[1, 2, 2, 2, 1], name=(('d/ne... |
def generator_64(inputs, is_train=True, reuse=False, batch_size=128, sig=False):
(output_size, half, forth, eighth, sixteenth) = (64, 32, 16, 8, 4)
gf_dim = 512
with tf.variable_scope('gen', reuse=reuse) as vs:
net_0 = tl.layers.InputLayer(inputs, name='g/net_0/in')
net_1 = tl.layers.Dense... |
def discriminator(inputs, output_size, sig=False, is_train=True, reuse=False, batch_size=128, output_units=1):
inputs = tf.reshape(inputs, [batch_size, output_size, output_size, output_size, 1])
df_dim = output_size
with tf.variable_scope('dis', reuse=reuse) as vs:
net_0 = tl.layers.InputLayer(inp... |
def make_inputs_raw(file_batch):
dt = np.dtype((np.uint8, (64, 64, 64)))
models = [np.fromfile(f, dtype=dt).reshape((64, 64, 64)) for f in file_batch]
models = np.array(models)
models = models.astype(np.float32)
start_time = time.time()
return (models, start_time)
|
def load_networks(checkpoint_dir, sess, net_g, net_d, epoch=''):
print('[*] Loading checkpoints...')
if (len(epoch) >= 1):
epoch = ('_' + epoch)
net_g_name = os.path.join(checkpoint_dir, (('net_g' + epoch) + '.npz'))
net_d_name = os.path.join(checkpoint_dir, (('net_d' + epoch) + '.npz'))
i... |
def save_networks(checkpoint_dir, sess, net_g, net_d, epoch):
print('[*] Saving checkpoints...')
if (not os.path.exists(checkpoint_dir)):
os.makedirs(checkpoint_dir)
net_g_name = os.path.join(checkpoint_dir, 'net_g.npz')
net_d_name = os.path.join(checkpoint_dir, 'net_d.npz')
net_g_iter_nam... |
def save_voxels(save_dir, models, epock):
print('Saving the model')
np.save((save_dir + str(epock)), models[0])
|
def savitzky_golay(y, window_size, order, deriv=0, rate=1):
from math import factorial
try:
window_size = np.abs(np.int(window_size))
order = np.abs(np.int(order))
except ValueError:
raise ValueError('window_size and order have to be of type int')
if (((window_size % 2) != 1) o... |
def render_graphs(save_dir, epoch, track_g_loss, track_d_loss, epoch_arr):
if (not os.path.exists((save_dir + '/plots/'))):
os.makedirs((save_dir + '/plots/'))
if (len(track_d_loss) > 51):
smoothed_d_loss = savitzky_golay(track_d_loss, 51, 3)
smoothed_g_loss = savitzky_golay(track_g_lo... |
def save_values(save_dir, track_g_loss, track_d_loss, epoch_arr):
np.save((save_dir + '/plots/track_g_loss'), track_g_loss)
np.save((save_dir + '/plots/track_d_loss'), track_d_loss)
np.save((save_dir + '/plots/epochs'), epoch_arr)
|
def load_values(save_dir):
outputs = []
outputs.append(list(np.load((save_dir + '/plots/track_g_loss.npy'))))
outputs.append(list(np.load((save_dir + '/plots/track_d_loss.npy'))))
outputs.append(list(np.load((save_dir + '/plots/epochs.npy'))))
return outputs
|
def continue_download(is40=False):
queryStr = 'The ModelNet10.zip file is over 450 MB. Proceed to download (y/n): '
if is40:
queryStr = 'The ModelNet40.tar file is 2 GB and over 9 GB uncompressed. Proceed to download (y/n): '
while True:
reply = str(input(queryStr)).lower().strip()
... |
def query_dataset():
while True:
reply = str(input('Choose dataset, ModelNet10 (1) or manually aligned subset of the ModelNet40 (2):')).lower().strip()
if (reply[0] == '1'):
return True
if (reply[0] == '2'):
return False
else:
print('please reply... |
def camPosToQuaternion(cx, cy, cz):
camDist = math.sqrt((((cx * cx) + (cy * cy)) + (cz * cz)))
cx = (cx / camDist)
cy = (cy / camDist)
cz = (cz / camDist)
axis = ((- cz), 0, cx)
angle = math.acos(cy)
a = (math.sqrt(2) / 2)
b = (math.sqrt(2) / 2)
w1 = axis[0]
w2 = axis[1]
w3... |
def quaternionFromYawPitchRoll(yaw, pitch, roll):
c1 = math.cos((yaw / 2.0))
c2 = math.cos((pitch / 2.0))
c3 = math.cos((roll / 2.0))
s1 = math.sin((yaw / 2.0))
s2 = math.sin((pitch / 2.0))
s3 = math.sin((roll / 2.0))
q1 = (((c1 * c2) * c3) + ((s1 * s2) * s3))
q2 = (((c1 * c2) * s3) - ... |
def camRotQuaternion(cx, cy, cz, theta):
theta = ((theta / 180.0) * math.pi)
camDist = math.sqrt((((cx * cx) + (cy * cy)) + (cz * cz)))
cx = ((- cx) / camDist)
cy = ((- cy) / camDist)
cz = ((- cz) / camDist)
q1 = math.cos((theta * 0.5))
q2 = ((- cx) * math.sin((theta * 0.5)))
q3 = ((- ... |
def quaternionProduct(qx, qy):
a = qx[0]
b = qx[1]
c = qx[2]
d = qx[3]
e = qy[0]
f = qy[1]
g = qy[2]
h = qy[3]
q1 = ((((a * e) - (b * f)) - (c * g)) - (d * h))
q2 = ((((a * f) + (b * e)) + (c * h)) - (d * g))
q3 = ((((a * g) - (b * h)) + (c * e)) + (d * f))
q4 = ((((a *... |
def obj_centened_camera_pos(dist, azimuth_deg, elevation_deg):
phi = ((float(elevation_deg) / 180) * math.pi)
theta = ((float(azimuth_deg) / 180) * math.pi)
x = ((dist * math.cos(theta)) * math.cos(phi))
y = ((dist * math.sin(theta)) * math.cos(phi))
z = (dist * math.sin(phi))
return (x, y, z)... |
def dataloader_msrvtt_train(args, tokenizer):
msrvtt_dataset = MSRVTTDataset(subset='train', anno_path=args.anno_path, video_path=args.video_path, max_words=args.max_words, tokenizer=tokenizer, max_frames=args.max_frames, video_framerate=args.video_framerate, config=args)
try:
train_sampler = torch.ut... |
def dataloader_msrvtt_test(args, tokenizer, subset='test'):
msrvtt_testset = MSRVTTDataset(subset=subset, anno_path=args.anno_path, video_path=args.video_path, max_words=args.max_words, tokenizer=tokenizer, max_frames=args.max_frames, video_framerate=args.video_framerate, config=args)
try:
test_sample... |
def dataloader_activity_train(args, tokenizer):
activity_dataset = ActivityNetDataset(subset='train', data_path=args.anno_path, features_path=args.video_path, max_words=args.max_words, feature_framerate=args.video_framerate, tokenizer=tokenizer, max_frames=args.max_frames)
train_sampler = torch.utils.data.dis... |
def dataloader_activity_test(args, tokenizer, subset='test'):
activity_testset = ActivityNetDataset(subset=subset, data_path=args.anno_path, features_path=args.video_path, max_words=args.max_words, feature_framerate=args.video_framerate, tokenizer=tokenizer, max_frames=args.max_frames)
try:
test_sampl... |
def dataloader_didemo_train(args, tokenizer):
didemo_dataset = DiDeMoDataset(subset='train', data_path=args.anno_path, features_path=args.video_path, max_words=args.max_words, feature_framerate=args.video_framerate, tokenizer=tokenizer, max_frames=args.max_frames)
train_sampler = torch.utils.data.distributed.... |
def dataloader_didemo_test(args, tokenizer, subset='test'):
didemo_testset = DiDeMoDataset(subset=subset, data_path=args.anno_path, features_path=args.video_path, max_words=args.max_words, feature_framerate=args.video_framerate, tokenizer=tokenizer, max_frames=args.max_frames)
try:
test_sampler = torc... |
def dataloader_lsmdc_train(args, tokenizer):
lsmdc_dataset = LsmdcDataset(subset='train', anno_path=args.anno_path, video_path=args.video_path, max_words=args.max_words, tokenizer=tokenizer, max_frames=args.max_frames, video_framerate=args.video_framerate, config=args)
train_sampler = torch.utils.data.distrib... |
def dataloader_lsmdc_test(args, tokenizer, subset='test'):
lsmdc_testset = LsmdcDataset(subset=subset, anno_path=args.anno_path, video_path=args.video_path, max_words=args.max_words, tokenizer=tokenizer, max_frames=args.max_frames, video_framerate=args.video_framerate, config=args)
try:
test_sampler =... |
def dataloader_msvd_train(args, tokenizer):
msvd_dataset = MsvdDataset(subset='train', anno_path=args.anno_path, video_path=args.video_path, max_words=args.max_words, tokenizer=tokenizer, max_frames=args.max_frames, video_framerate=args.video_framerate, config=args)
train_sampler = torch.utils.data.distribute... |
def dataloader_msvd_test(args, tokenizer, subset='test'):
msvd_testset = MsvdDataset(subset=subset, anno_path=args.anno_path, video_path=args.video_path, max_words=args.max_words, tokenizer=tokenizer, max_frames=args.max_frames, video_framerate=args.video_framerate, config=args)
dataloader_msvd = DataLoader(m... |
class LsmdcDataset(RetrievalDataset):
'LSMDC dataset.'
def __init__(self, subset, anno_path, video_path, tokenizer, max_words=32, max_frames=12, video_framerate=1, image_resolution=224, mode='all', config=None):
super(LsmdcDataset, self).__init__(subset, anno_path, video_path, tokenizer, max_words, m... |
class MSRVTTDataset(RetrievalDataset):
'MSRVTT dataset.'
def __init__(self, subset, anno_path, video_path, tokenizer, max_words=32, max_frames=12, video_framerate=1, image_resolution=224, mode='all', config=None):
super(MSRVTTDataset, self).__init__(subset, anno_path, video_path, tokenizer, max_words... |
class MsvdDataset(RetrievalDataset):
'MSVD dataset loader.'
def __init__(self, subset, anno_path, video_path, tokenizer, max_words=32, max_frames=12, video_framerate=1, image_resolution=224, mode='all', config=None):
super(MsvdDataset, self).__init__(subset, anno_path, video_path, tokenizer, max_word... |
def _interpolation(kwargs):
interpolation = kwargs.pop('resample', Image.BILINEAR)
if isinstance(interpolation, (list, tuple)):
return random.choice(interpolation)
else:
return interpolation
|
def _check_args_tf(kwargs):
if (('fillcolor' in kwargs) and (_PIL_VER < (5, 0))):
kwargs.pop('fillcolor')
kwargs['resample'] = _interpolation(kwargs)
|
def shear_x(img, factor, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, factor, 0, 0, 1, 0), **kwargs)
|
def shear_y(img, factor, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, factor, 1, 0), **kwargs)
|
def translate_x_rel(img, pct, **kwargs):
pixels = (pct * img.size[0])
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
|
def translate_y_rel(img, pct, **kwargs):
pixels = (pct * img.size[1])
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
|
def translate_x_abs(img, pixels, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
|
def translate_y_abs(img, pixels, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
|
def rotate(img, degrees, **kwargs):
_check_args_tf(kwargs)
if (_PIL_VER >= (5, 2)):
return img.rotate(degrees, **kwargs)
elif (_PIL_VER >= (5, 0)):
(w, h) = img.size
post_trans = (0, 0)
rotn_center = ((w / 2.0), (h / 2.0))
angle = (- math.radians(degrees))
m... |
def auto_contrast(img, **__):
return ImageOps.autocontrast(img)
|
def invert(img, **__):
return ImageOps.invert(img)
|
def equalize(img, **__):
return ImageOps.equalize(img)
|
def solarize(img, thresh, **__):
return ImageOps.solarize(img, thresh)
|
def solarize_add(img, add, thresh=128, **__):
lut = []
for i in range(256):
if (i < thresh):
lut.append(min(255, (i + add)))
else:
lut.append(i)
if (img.mode in ('L', 'RGB')):
if ((img.mode == 'RGB') and (len(lut) == 256)):
lut = ((lut + lut) + l... |
def posterize(img, bits_to_keep, **__):
if (bits_to_keep >= 8):
return img
return ImageOps.posterize(img, bits_to_keep)
|
def contrast(img, factor, **__):
return ImageEnhance.Contrast(img).enhance(factor)
|
def color(img, factor, **__):
return ImageEnhance.Color(img).enhance(factor)
|
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