def __init__(self, hp):
super().__init__()
self._hp = hp
if self._hp.low_dim:
self.encoder = Linear(in_dim=self._hp.state_size * 2,
out_dim=128,
builder=self._hp.builder)
out_size = 128
else:
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()[0] * 5 * 5
self.mlp = torch.nn.Sequential()
self.mlp.add_module(
'linear_1',
Linear(in_dim=out_size, out_dim=128, builder=self._hp.builder))
for i in range(10):
self.mlp.add_module(
f'linear_{i+2}',
Linear(in_dim=128, out_dim=128, builder=self._hp.builder))
self.mlp.add_module(f'relu_{i+2}', torch.nn.ReLU())
self.mlp.add_module(
'linear_final',
Linear(in_dim=128,
out_dim=self._hp.action_size,
builder=self._hp.builder))
self.mlp.add_module('tanh', torch.nn.Tanh())
def __init__(self, hp, num_outputs):
super().__init__()
self._hp = hp
self.num_outputs = num_outputs
self.ll_size = ll_size = self._hp.linear_layer_size
self.fc_layers = torch.nn.ModuleList()
if self._hp.low_dim:
self.linear1 = Linear(in_dim=2 * self._hp.state_size,
out_dim=ll_size,
builder=self._hp.builder)
self.linear2 = Linear(in_dim=ll_size + self._hp.action_size,
out_dim=ll_size,
builder=self._hp.builder)
else:
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()
self.linear1 = Linear(in_dim=out_size[0] * out_size[1] *
out_size[2],
out_dim=ll_size,
builder=self._hp.builder)
self.linear2 = Linear(in_dim=ll_size + self._hp.action_size,
out_dim=ll_size,
builder=self._hp.builder)
for i in range(3):
self.fc_layers.append(
Linear(in_dim=ll_size,
out_dim=ll_size,
builder=self._hp.builder))
self.fc_layers.append(
Linear(in_dim=ll_size,
out_dim=self.num_outputs,
builder=self._hp.builder))
def build_network(self, build_encoder=True):
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()
self.spatial_softmax = SpatialSoftmax(
out_size[1], out_size[2], out_size[0]) # height, width, channel
self.linear = Linear(in_dim=out_size[0] * 2,
out_dim=1,
builder=self._hp.builder)
class ActorNetwork(torch.nn.Module):
def __init__(self, hp):
super().__init__()
self._hp = hp
if self._hp.low_dim:
self.encoder = Linear(in_dim=self._hp.state_size * 2,
out_dim=128,
builder=self._hp.builder)
out_size = 128
else:
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()[0] * 5 * 5
self.mlp = torch.nn.Sequential()
self.mlp.add_module(
'linear_1',
Linear(in_dim=out_size, out_dim=128, builder=self._hp.builder))
for i in range(10):
self.mlp.add_module(
f'linear_{i+2}',
Linear(in_dim=128, out_dim=128, builder=self._hp.builder))
self.mlp.add_module(f'relu_{i+2}', torch.nn.ReLU())
self.mlp.add_module(
'linear_final',
Linear(in_dim=128,
out_dim=self._hp.action_size,
builder=self._hp.builder))
self.mlp.add_module('tanh', torch.nn.Tanh())
def forward(self, image_pairs):
embeddings = self.encoder(image_pairs).reshape(image_pairs.size(0), -1)
return self.mlp(embeddings)
def __init__(self, hp):
super().__init__()
self._hp = hp
if self._hp.low_dim:
self.linear1 = Linear(in_dim=4,
out_dim=128,
builder=self._hp.builder)
else:
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()
self.linear1 = Linear(in_dim=64 * 5 * 5,
out_dim=128,
builder=self._hp.builder)
self.linear2 = Linear(in_dim=128 + self._hp.action_size,
out_dim=1,
builder=self._hp.builder)
class VAE(torch.nn.Module):
def __init__(self, hp):
super().__init__()
self._hp = hp
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()
out_flat_size = out_size[0] * out_size[1] * out_size[2]
self.linear1 = Linear(in_dim=out_flat_size,
out_dim=128,
builder=self._hp.builder)
self.linear2 = Linear(in_dim=128,
out_dim=self._hp.hidden_size * 2,
builder=self._hp.builder)
self.linear3 = Linear(in_dim=self._hp.hidden_size,
out_dim=128,
builder=self._hp.builder)
self.linear4 = Linear(in_dim=128,
out_dim=out_flat_size,
builder=self._hp.builder)
self.decoder = ConvDecoder(self._hp)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def encode(self, image):
embeddings = self.encoder(image).reshape(image.size(0), -1)
e = F.relu(self.linear1(embeddings))
z = self.linear2(e)
mu, logvar = z[:, :self._hp.hidden_size], z[:, self._hp.hidden_size:]
return mu, logvar
def decode(self, z):
e = F.relu(self.linear3(z))
e = F.relu(self.linear4(e))
e = e.view(*([e.size(0)] + list(self.encoder.get_output_size())))
im = F.sigmoid(self.decoder(e))
return im
def forward(self, image):
mu, logvar = self.encode(image)
z = self.reparameterize(mu, logvar)
im = self.decode(z)
return mu, logvar, z, im
def __init__(self, hp):
super().__init__()
self._hp = hp
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()
self.linear1 = Linear(in_dim=64 * 5 * 5,
out_dim=128,
builder=self._hp.builder)
self.linear2 = Linear(in_dim=128,
out_dim=self._hp.hidden_size * 2,
builder=self._hp.builder)
self.linear3 = Linear(in_dim=self._hp.hidden_size,
out_dim=128,
builder=self._hp.builder)
self.linear4 = Linear(in_dim=128,
out_dim=64 * 5 * 5,
builder=self._hp.builder)
self.decoder = ConvDecoder(self._hp)
def build_network(self, build_encoder=True):
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()
if self._hp.spatial_softmax:
self.spatial_softmax = SpatialSoftmax(
out_size[1], out_size[2],
out_size[0]) # height, width, channel
self.linear = Linear(in_dim=out_size[0] * 2,
out_dim=1,
builder=self._hp.builder)
else:
self.linear = Linear(in_dim=256,
out_dim=1,
builder=self._hp.builder)
self.fc_layers = []
self.fc_layers.append(
Linear(in_dim=out_size[0] * out_size[1] * out_size[2],
out_dim=256,
builder=self._hp.builder))
for i in range(3):
self.fc_layers.append(
Linear(in_dim=256, out_dim=256, builder=self._hp.builder))
class QNetwork(torch.nn.Module):
def __init__(self, hp, num_outputs):
super().__init__()
self._hp = hp
self.num_outputs = num_outputs
self.ll_size = ll_size = self._hp.linear_layer_size
self.fc_layers = torch.nn.ModuleList()
if self._hp.low_dim:
self.linear1 = Linear(in_dim=2 * self._hp.state_size,
out_dim=ll_size,
builder=self._hp.builder)
self.linear2 = Linear(in_dim=ll_size + self._hp.action_size,
out_dim=ll_size,
builder=self._hp.builder)
else:
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()
self.linear1 = Linear(in_dim=out_size[0] * out_size[1] *
out_size[2],
out_dim=ll_size,
builder=self._hp.builder)
self.linear2 = Linear(in_dim=ll_size + self._hp.action_size,
out_dim=ll_size,
builder=self._hp.builder)
for i in range(3):
self.fc_layers.append(
Linear(in_dim=ll_size,
out_dim=ll_size,
builder=self._hp.builder))
self.fc_layers.append(
Linear(in_dim=ll_size,
out_dim=self.num_outputs,
builder=self._hp.builder))
def forward(self, image_pairs, actions):
if self._hp.low_dim:
embeddings = image_pairs
else:
embeddings = self.encoder(image_pairs).reshape(
image_pairs.size(0), -1)
x = F.relu(self.linear1(embeddings))
x = torch.cat([x, actions], dim=1)
x = F.relu(self.linear2(x))
for layer in self.fc_layers:
x = F.relu(layer(x))
return x
class QNetwork(torch.nn.Module):
def __init__(self, hp):
super().__init__()
self._hp = hp
if self._hp.low_dim:
self.linear1 = Linear(in_dim=4,
out_dim=128,
builder=self._hp.builder)
else:
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()
self.linear1 = Linear(in_dim=64 * 5 * 5,
out_dim=128,
builder=self._hp.builder)
self.linear2 = Linear(in_dim=128 + self._hp.action_size,
out_dim=1,
builder=self._hp.builder)
# self.linear3 = Linear(in_dim=128, out_dim=128, builder=self._hp.builder)
# self.linear4 = Linear(in_dim=128, out_dim=128, builder=self._hp.builder)
# self.linear5 = Linear(in_dim=128, out_dim=128, builder=self._hp.builder)
# self.linear6 = Linear(in_dim=128, out_dim=1, builder=self._hp.builder)
def forward(self, image_pairs, actions):
if self._hp.low_dim:
embeddings = image_pairs
else:
embeddings = self.encoder(image_pairs).view(
image_pairs.size(0), -1)
e = F.relu(self.linear1(embeddings))
e = torch.cat([e, actions], dim=1)
# e = F.relu(self.linear2(e))
# e = F.relu(self.linear3(e))
# e = F.relu(self.linear4(e))
# e = F.relu(self.linear5(e))
qvalue = self.linear2(e) #self.linear6(e)
return qvalue
class TempdistRegressor(BaseModel):
def __init__(self, overrideparams, logger=None):
super().__init__(logger)
self._hp = self._default_hparams()
self.overrideparams = overrideparams
self.override_defaults(
overrideparams) # override defaults with config file
self.postprocess_params()
assert self._hp.batch_size != -1 # make sure that batch size was overridden
self.tdist_classifiers = []
self.build_network()
def _default_hparams(self):
default_dict = AttrDict({
'tmax_label':
10, # the highest label for temporal distance, values are clamped after that
'use_skips': False, #todo try resnet architecture!
'ngf': 8,
'nz_enc': 64,
'classifier_restore_path': None # not really needed here.
})
# add new params to parent params
parent_params = super()._default_hparams()
for k in default_dict.keys():
parent_params.add_hparam(k, default_dict[k])
return parent_params
@property
def singletempdistclassifier(self):
return SingleTempDistClassifier
def build_network(self, build_encoder=True):
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()
self.spatial_softmax = SpatialSoftmax(
out_size[1], out_size[2], out_size[0]) # height, width, channel
self.linear = Linear(in_dim=out_size[0] * 2,
out_dim=1,
builder=self._hp.builder)
def sample_image_pair(self, images):
tlen = images.shape[1]
# get positives:
t0 = np.random.randint(0, tlen, self._hp.batch_size)
t1 = np.array([
np.random.randint(t0[b], tlen, 1) for b in range(images.shape[0])
]).squeeze()
t0, t1 = torch.from_numpy(t0), torch.from_numpy(t1)
im_t0 = select_indices(images, t0)
im_t1 = select_indices(images, t1)
self.labels = torch.clamp_max(t1 - t0, self._hp.tmax_label).type(
torch.FloatTensor)
img_pair_stack = torch.stack([im_t0, im_t1], dim=1)
return img_pair_stack
def forward(self, inputs):
"""
forward pass at training time
:param
images shape = batch x time x channel x height x width
:return: model_output
"""
image_pairs = self.sample_image_pair(inputs.demo_seq_images)
self.img_pair = image_pairs
model_output = self.make_prediction(image_pairs)
return model_output
def make_prediction(self, image_pairs_stacked):
im_t0, im_t1 = image_pairs_stacked[:, 0], image_pairs_stacked[:, 1]
embeddings = self.encoder(torch.cat([im_t0, im_t1], dim=1))
embeddings = self.spatial_softmax(embeddings)
self.tdist_estimates = self.linear(embeddings)
model_output = AttrDict(tdist_estimates=self.tdist_estimates,
img_pair=image_pairs_stacked)
return model_output
def _log_outputs(self, model_output, inputs, losses, step, log_images,
phase):
if log_images:
self._logger.log_pair_predictions(self.img_pair,
self.tdist_estimates,
self.labels, 'tdist_regression',
step, phase)
def loss(self, model_output):
losses = AttrDict()
setattr(
losses, 'mse',
torch.nn.MSELoss()(model_output.tdist_estimates.squeeze(),
self.labels.to(self._hp.device)))
# compute total loss
losses.total_loss = torch.stack(list(losses.values())).sum()
return losses
def get_device(self):
return self._hp.device
class SingleTempDistClassifier(BaseModel):
def __init__(self, hp, tdist, logger):
super().__init__(logger)
self._hp = hp
self.tdist = tdist
self.build_network()
def build_network(self, build_encoder=True):
self.encoder = ConvEncoder(self._hp)
out_size = self.encoder.get_output_size()
self.spatial_softmax = SpatialSoftmax(
out_size[1], out_size[2], out_size[0]) # height, width, channel
self.linear = Linear(in_dim=out_size[0] * 2,
out_dim=1,
builder=self._hp.builder)
self.cross_ent_loss = nn.BCEWithLogitsLoss()
def forward(self, inputs):
"""
forward pass at training time
:param
images shape = batch x time x channel x height x width
:return: model_output
"""
import pdb
pdb.set_trace()
tlen = inputs.demo_seq_images.shape[1]
pos_pairs, neg_pairs = self.sample_image_pair(inputs.demo_seq_images,
tlen, self.tdist)
image_pairs = torch.cat([pos_pairs, neg_pairs], dim=0)
embeddings = self.encoder(image_pairs)
embeddings = self.spatial_softmax(embeddings)
logits = self.linear(embeddings)
self.out_sigmoid = torch.sigmoid(logits)
model_output = AttrDict(logits=logits,
out_sigmoid=self.out_sigmoid,
pos_pair=self.pos_pair,
neg_pair=self.neg_pair)
return model_output
def sample_image_pair(self, images, tlen, tdist):
# get positives:
t0 = np.random.randint(0, tlen - tdist - 1, self._hp.batch_size)
t1 = t0 + 1 + np.random.randint(0, tdist, self._hp.batch_size)
t0, t1 = torch.from_numpy(t0), torch.from_numpy(t1)
# print('t0', t0)
# print('t1', t1)
# print('t1 - t0', t1 - t0)
im_t0 = select_indices(images, t0)
im_t1 = select_indices(images, t1)
self.pos_pair = torch.stack([im_t0, im_t1], dim=1)
pos_pair_cat = torch.cat([im_t0, im_t1], dim=1)
# get negatives:
t0 = np.random.randint(0, tlen - tdist - 1, self._hp.batch_size)
t1 = [
np.random.randint(t0[b] + tdist + 1, tlen, 1)
for b in range(self._hp.batch_size)
]
t1 = np.array(t1).squeeze()
t0, t1 = torch.from_numpy(t0), torch.from_numpy(t1)
# print('--------------')
# print('t0', t0)
# print('t1', t1)
# print('t1 - t0', t1 - t0)
im_t0 = select_indices(images, t0)
im_t1 = select_indices(images, t1)
self.neg_pair = torch.stack([im_t0, im_t1], dim=1)
neg_pair_cat = torch.cat([im_t0, im_t1], dim=1)
# one means within range of tdist range, zero means outside of tdist range
self.labels = torch.cat([
torch.ones(self._hp.batch_size),
torch.zeros(self._hp.batch_size)
])
return pos_pair_cat, neg_pair_cat
def loss(self, model_output):
logits_ = model_output.logits[:, 0]
return self.cross_ent_loss(logits_, self.labels.to(self._hp.device))
def _log_outputs(self, model_output, inputs, losses, step, log_images,
phase):
out_sigmoid = self.out_sigmoid.data.cpu().numpy().squeeze()
predictions = np.zeros(out_sigmoid.shape)
predictions[np.where(out_sigmoid > 0.5)] = 1
labels = self.labels.data.cpu().numpy()
num_neg = np.sum(labels == 0)
false_positive_rate = np.sum(
predictions[np.where(labels == 0)]) / float(num_neg)
num_pos = np.sum(labels == 1)
false_negative_rate = np.sum(1 - predictions[np.where(
labels == 1)]) / float(num_pos)
self._logger.log_scalar(
false_positive_rate,
'tdist{}_false_postive_rate'.format(self.tdist), step, phase)
self._logger.log_scalar(
false_negative_rate,
'tdist{}_false_negative_rate'.format(self.tdist), step, phase)
if log_images:
self._logger.log_single_tdist_classifier_image(
self.pos_pair, self.neg_pair, self.out_sigmoid,
'tdist{}'.format(self.tdist), step, phase)
