def extract_tree_expression(self, node, index_mark='_'):
if node == None or node.data == None:
return self.expression_str
feature, parentheses, action, wl_scalar, wl_power, parentheses_bias, wl_activation, parentheses_activation, wl_bias, parentheses_power = Individual.get_all_merged_values(
node.data)
if parentheses == 1:
self.expression_str += utils.get_activation(
parentheses_activation) + '('
self.expression_str += utils.get_activation(wl_activation)
self.expression_str += '({}'.format(wl_scalar) + '*' ## add wl scalar
self.expression_str += '{}{}{}'.format(index_mark, feature, index_mark)
self.expression_str += '**{}'.format(wl_power) + '+{}'.format(wl_bias)
self.expression_str += ')'
self.expression_str += utils.get_action(action)
self.expression_str = self.extract_tree_expression(
node.left, index_mark)
self.expression_str = self.extract_tree_expression(
node.right, index_mark)
if parentheses == 1:
self.expression_str = self.expression_str[:-1] + '+{})'.format(
parentheses_bias) + '**{}'.format(
parentheses_power) + self.expression_str[
-1] ## put closing parentesis before action
return self.expression_str
def __init__(
self,
rating_vals,
user_in_units,
movie_in_units,
msg_units,
out_units,
dropout_rate=0.0,
agg="stack", # or 'sum'
agg_act=None,
out_act=None,
share_user_item_param=False,
):
super(GCMCLayer, self).__init__()
self.rating_vals = rating_vals
self.agg = agg
self.share_user_item_param = share_user_item_param
if agg == "stack":
# divide the original msg unit size by number of ratings to keep
# the dimensionality
assert msg_units % len(rating_vals) == 0
msg_units = msg_units // len(rating_vals)
with self.name_scope():
self.dropout = nn.Dropout(dropout_rate)
self.W_r = {}
for rating in rating_vals:
rating = str(rating)
if share_user_item_param and user_in_units == movie_in_units:
self.W_r[rating] = self.params.get(
"W_r_%s" % rating,
shape=(user_in_units, msg_units),
dtype=np.float32,
allow_deferred_init=True,
)
self.W_r["rev-%s" % rating] = self.W_r[rating]
else:
self.W_r[rating] = self.params.get(
"W_r_%s" % rating,
shape=(user_in_units, msg_units),
dtype=np.float32,
allow_deferred_init=True,
)
self.W_r["rev-%s" % rating] = self.params.get(
"revW_r_%s" % rating,
shape=(movie_in_units, msg_units),
dtype=np.float32,
allow_deferred_init=True,
)
self.ufc = nn.Dense(out_units)
if share_user_item_param:
self.ifc = self.ufc
else:
self.ifc = nn.Dense(out_units)
self.agg_act = get_activation(agg_act)
self.out_act = get_activation(out_act)
def train(self, batch, labels=None, loss="quadratic", learning_rate=0.01, epochs=1, mini_batch_size=1):
if labels is not None:
batch = np.c_[batch, labels]
amount_of_labels = len(set(batch[:, -1]))
for epoch in range(epochs):
print("Epoch: ", epoch, end=", ")
np.random.shuffle(batch) # avoids correlated mini batches or memorization of order
avg_loss_epoch = [] # average loss over all samples in batch for this epoch
sample_i = 0
while sample_i < (len(batch) - mini_batch_size):
mini_batch = batch[sample_i:sample_i + mini_batch_size]
input_values, labels = mini_batch[:, :-1], mini_batch[:, -1]
# one-hot-encoding of numerical labels:
labels = np.eye(amount_of_labels)[labels.astype(int)]
raw_outputs, activations, activated_outputs = \
self.inference(input_values, save_outputs=True)
''' Get loss function and its derivatives:
("dx_y" means partial derivative of y to x) '''
minibatch_loss = get_loss(loss)(activated_outputs[-1], labels)
avg_loss_epoch.append(minibatch_loss)
try:
da_loss = get_loss(loss, d="da_")(activated_outputs[-1], labels)
dz_a = get_activation(activations[-1], d="dz_")(raw_outputs[-1])
dz_loss = np.multiply(da_loss, dz_a) # Hadamard product
except AttributeError as e:
dz_loss = get_loss(loss, d="dz_")(activated_outputs[-1], labels)
for l in range(1, len(self.weights)):
m, n = activated_outputs[-l-1].shape
# faster than stacking ones to our activated outputs:
activated_outputs_with_ones = np.ones((m, n + 1))
activated_outputs_with_ones[:, :-1] = activated_outputs[-l-1]
dw_loss = np.matmul(activated_outputs_with_ones.T, dz_loss)
self.weights[-l] = self.weights[-l] - learning_rate * dw_loss / len(batch)
dz_a = get_activation(activations[-l-1], d="dz_")(raw_outputs[-l-1])
dz_loss = np.multiply(
np.matmul(dz_loss, self.weights[-l][:-1, :].T), # removed biases
dz_a
)
m, n = activated_outputs[0].shape
activated_outputs_with_ones = np.ones((m, n + 1))
activated_outputs_with_ones[:, :-1] = activated_outputs[0]
dw_loss = np.matmul(activated_outputs_with_ones.T, dz_loss)
self.weights[0] = self.weights[0] - learning_rate * dw_loss / len(batch)
sample_i += mini_batch_size
avg_loss_epoch = np.sum(np.array(avg_loss_epoch)) / np.array(avg_loss_epoch).size
print("Loss: ", avg_loss_epoch)
def __init__(self, config: Dict):
super().__init__(config)
torch.manual_seed(0)
default_config = {
"input_size": 33,
"num_actions": 4,
"activation": "relu",
"hidden_sizes": (64, 64),
}
self.config = with_default_config(config, default_config)
input_size: int = self.config.get("input_size")
num_actions: int = self.config.get("num_actions")
hidden_sizes: Tuple[int] = self.config.get("hidden_sizes")
self.activation: Callable = get_activation(self.config.get("activation"))
layer_sizes = (input_size,) + hidden_sizes
self.hidden_layers = nn.ModuleList([
nn.Linear(in_size, out_size)
for in_size, out_size in zip(layer_sizes, layer_sizes[1:])
])
self.policy_mu_head = nn.Linear(layer_sizes[-1], num_actions)
self.v_hidden_layers = nn.ModuleList([
nn.Linear(in_size, out_size)
for in_size, out_size in zip(layer_sizes, layer_sizes[1:])
])
self.std = nn.Parameter(torch.ones(1, num_actions))
self.value_head = nn.Linear(layer_sizes[-1], 1)
def __init__(self, config: Dict):
super().__init__(config)
default_config = {
"input_shape": (100, 100),
"num_actions": 5,
"activation": "relu",
}
self.config = with_default_config(config, default_config)
self.activation = get_activation(self.config["activation"])
input_shape: Tuple[int, int] = self.config["input_shape"]
self.conv_layers = nn.ModuleList([
nn.Conv2d(4, 32, kernel_size=8, stride=4), # 24x24x32
nn.Conv2d(32, 64, kernel_size=7, stride=3), # 6x6x64
nn.Conv2d(64, 64, kernel_size=3, stride=1)
]) # 4x4x64
_coords_i = torch.linspace(-1, 1, input_shape[0]).view(-1, 1).repeat(
1, input_shape[1])
_coords_j = torch.linspace(-1, 1, input_shape[1]).view(1, -1).repeat(
input_shape[0], 1)
self.coords = torch.stack([_coords_i, _coords_j])
# flatten
self.policy_head = nn.Linear(4 * 4 * 64, self.config["num_actions"])
self.value_head = nn.Linear(4 * 4 * 64, 1)
def DNN_regressor(params, model_dir, feature_columns, config):
'''Returns DNN estimator object'''
hidden_units = params['layers'] * [params['units']]
weight_column_name = utils.get_param(params, 'weight_column_name')
optimizer = utils.get_optimizer(utils.get_param(params, 'optimizer'),
params['learning_rate'])
activation_fn = utils.get_activation(
utils.get_param(params, 'activation_fn'))
dropout = float(utils.get_param(params, 'dropout'))
gradient_clip_norm = utils.get_param(params, 'gradient_clip_norm')
enable_centered_bias = False # keep false
feature_engineering_fn = utils.get_param(params, 'feature_engineering_fn')
embedding_lr_multipliers = utils.get_param(params,
'embedding_lr_multipliers')
input_layer_min_slice_size = utils.get_param(params,
'input_layer_min_slice_size')
label_keys = utils.get_param(params, 'label_keys')
return tf.contrib.learn.DNNRegressor(
hidden_units=hidden_units,
feature_columns=feature_columns,
model_dir=model_dir,
weight_column_name=weight_column_name,
optimizer=optimizer,
activation_fn=activation_fn,
dropout=dropout,
gradient_clip_norm=gradient_clip_norm,
enable_centered_bias=enable_centered_bias,
config=config,
feature_engineering_fn=feature_engineering_fn,
embedding_lr_multipliers=embedding_lr_multipliers,
input_layer_min_slice_size=input_layer_min_slice_size)
def __init__(self, config: Dict):
super().__init__(config)
default_config = {
"input_size": 15,
"num_actions": 5,
"hidden_sizes": (64, 64),
"activation": "leaky_relu",
}
self.config = with_default_config(config, default_config)
input_size: int = self.config.get("input_size")
num_actions: int = self.config.get("num_actions")
hidden_sizes: Tuple[int] = self.config.get("hidden_sizes")
self.activation: Callable = get_activation(
self.config.get("activation"))
layer_sizes = (input_size, ) + hidden_sizes
self.hidden_layers = nn.ModuleList([
nn.Linear(in_size, out_size)
for in_size, out_size in zip(layer_sizes, layer_sizes[1:])
])
self.policy_head = nn.Linear(layer_sizes[-1], num_actions)
self.value_head = nn.Linear(layer_sizes[-1], 1)
def __init__(self, config: Dict[str, Any]):
super().__init__()
default_config = {
"num_subgoals": 2,
"emb_size": 4,
"rel_hiddens": (16, 16, ),
"mlp_hiddens": (16, ),
"activation": "leaky_relu"
}
self.config = with_default_config(config, default_config)
self.activation: Callable[[Tensor], Tensor] = get_activation(self.config["activation"])
self.own_embedding = nn.Parameter(torch.randn(self.config["emb_size"])/10., requires_grad=True)
self.agent_embedding = nn.Parameter(torch.randn(self.config["emb_size"])/10., requires_grad=True)
self.subgoal_embedding = nn.Parameter(torch.randn(self.config["emb_size"])/10., requires_grad=True)
self.goal_embedding = nn.Parameter(torch.randn(self.config["emb_size"])/10., requires_grad=True)
rel_sizes = (2 * (self.config["emb_size"] + 3), ) + self.config["rel_hiddens"]
mlp_sizes = (self.config["rel_hiddens"][-1], ) + self.config["mlp_hiddens"]
self.relation_layers = nn.ModuleList([
nn.Linear(in_size, out_size)
for in_size, out_size in zip(rel_sizes, rel_sizes[1:])
])
self.mlp_layers = nn.ModuleList([
nn.Linear(in_size, out_size)
for in_size, out_size in zip(mlp_sizes, mlp_sizes[1:])
])
def __init__(self, config: Dict):
super().__init__(config)
default_config = {
"input_shape": (100, 100),
"num_actions": 5,
"activation": "relu",
}
self.config = with_default_config(config, default_config)
input_shape: Tuple[int, int] = self.config["input_shape"]
input_size: int = self.config.get("input_size")
num_actions: int = self.config.get("num_actions")
hidden_sizes: Tuple[int] = self.config.get("hidden_sizes")
self.activation: Callable = get_activation(
self.config.get("activation"))
self.conv = nn.Conv2d(3, 3, kernel_size=3, padding=1)
layer_sizes = (input_size, ) + hidden_sizes
self.hidden_layers = nn.ModuleList([
nn.Linear(in_size, out_size)
for in_size, out_size in zip(layer_sizes, layer_sizes[1:])
])
self.policy_head = nn.Linear(layer_sizes[-1], num_actions)
self.value_head = nn.Linear(layer_sizes[-1], 1)
def _propagate_one_layer(self, input_values, weights, activation="identity"):
m, n = input_values.shape
input_with_ones = np.ones((m, n + 1))
input_with_ones[:, :-1] = input_values # faster than stacking
raw_output = np.matmul(input_with_ones, weights)
activated_output = get_activation(activation)(raw_output)
return raw_output, activated_output
def __init__(self, args):
super(Net, self).__init__()
self._act = get_activation(args.model_activation)
self.encoder = nn.ModuleList()
self.encoder.append(
GCMCLayer(args.rating_vals,
args.src_in_units,
args.dst_in_units,
args.gcn_agg_units,
args.gcn_out_units,
args.gcn_dropout,
args.gcn_agg_accum,
agg_act=self._act,
share_user_item_param=args.share_param,
device=args.device))
self.gcn_agg_accum = args.gcn_agg_accum
self.rating_vals = args.rating_vals
self.device = args.device
self.gcn_agg_units = args.gcn_agg_units
self.src_in_units = args.src_in_units
for i in range(1, args.layers):
if args.gcn_agg_accum == 'stack':
gcn_out_units = args.gcn_out_units * len(args.rating_vals)
else:
gcn_out_units = args.gcn_out_units
self.encoder.append(
GCMCLayer(args.rating_vals,
args.gcn_out_units,
args.gcn_out_units,
gcn_out_units,
args.gcn_out_units,
args.gcn_dropout - i * 0.1,
args.gcn_agg_accum,
agg_act=self._act,
share_user_item_param=args.share_param,
ini=False,
device=args.device))
if args.decoder == "Bi":
self.decoder = BiDecoder(
in_units=args.gcn_out_units, #* args.layers,
num_classes=len(args.rating_vals),
num_basis=args.gen_r_num_basis_func)
'''
self.decoder2 = MLPDecoder(in_units= args.gcn_out_units * 2,
num_classes=len(args.rating_vals),
num_basis=args.gen_r_num_basis_func)
'''
elif args.decoder == "MLP":
if args.loss_func == "CE":
num_classes = len(args.rating_vals)
else:
num_classes = 1
self.decoder = MLPDecoder(in_units=args.gcn_out_units *
args.layers,
num_classes=num_classes,
num_basis=args.gen_r_num_basis_func)
self.rating_vals = args.rating_vals
def __init__(self, args, dev_id):
super(Net, self).__init__()
self._act = get_activation(args.model_activation)
self.encoder = nn.ModuleList()
self.encoder.append(
GCMCLayer(args.rating_vals,
args.src_in_units,
args.dst_in_units,
args.gcn_agg_units,
args.gcn_out_units,
args.gcn_dropout,
args.gcn_agg_accum,
agg_act=self._act,
share_user_item_param=args.share_param,
device=dev_id))
self.rating_vals = args.rating_vals
self.gcn_agg_accum = args.gcn_agg_accum
self.rating_vals = args.rating_vals
self.device = dev_id
self.gcn_agg_units = args.gcn_agg_units
self.src_in_units = args.src_in_units
self.batch_size = args.minibatch_size
for i in range(1, args.layers):
if args.gcn_agg_accum == 'stack':
gcn_out_units = args.gcn_out_units * len(args.rating_vals)
else:
gcn_out_units = args.gcn_out_units
self.encoder.append(
GCMCLayer(args.rating_vals,
args.gcn_out_units,
args.gcn_out_units,
gcn_out_units,
args.gcn_out_units,
args.gcn_dropout - i * 0.1,
args.gcn_agg_accum,
agg_act=self._act,
share_user_item_param=args.share_param,
ini=False,
device=dev_id))
if args.mix_cpu_gpu and args.use_one_hot_fea:
# if use_one_hot_fea, user and movie feature is None
# W can be extremely large, with mix_cpu_gpu W should be stored in CPU
self.encoder.partial_to(dev_id)
else:
self.encoder.to(dev_id)
self.decoder = BiDecoder(in_units=args.gcn_out_units,
num_classes=len(args.rating_vals),
num_basis=args.gen_r_num_basis_func)
self.decoder.to(dev_id)
def __init__(self,
rating_vals,
user_in_units,
movie_in_units,
msg_units,
out_units,
dropout_rate=0.0,
agg='stack', # or 'sum'
agg_act=None,
out_act=None,
share_user_item_param=False):
super(GCMCLayer, self).__init__()
self.rating_vals = rating_vals
self.agg = agg
self.share_user_item_param = share_user_item_param
self.ufc = nn.Linear(msg_units, out_units)
if share_user_item_param:
self.ifc = self.ufc
else:
self.ifc = nn.Linear(msg_units, out_units)
if agg == 'stack':
# divide the original msg unit size by number of ratings to keep
# the dimensionality
assert msg_units % len(rating_vals) == 0
msg_units = msg_units // len(rating_vals)
self.dropout = nn.Dropout(dropout_rate)
self.W_r = nn.ParameterDict()
for rating in rating_vals:
# PyTorch parameter name can't contain "."
rating = str(rating).replace('.', '_')
if share_user_item_param and user_in_units == movie_in_units:
self.W_r[rating] = nn.Parameter(th.randn(user_in_units, msg_units))
self.W_r['rev-%s' % rating] = self.W_r[rating]
else:
self.W_r[rating] = nn.Parameter(th.randn(user_in_units, msg_units))
self.W_r['rev-%s' % rating] = nn.Parameter(th.randn(movie_in_units, msg_units))
self.agg_act = get_activation(agg_act)
self.out_act = get_activation(out_act)
self.reset_parameters()
def __init__(
self,
src_key,
dst_key,
src_in_units,
dst_in_units,
agg_units,
out_units,
num_links,
dropout_rate=0.0,
agg_accum='stack',
agg_act=None,
out_act=None,
# agg_ordinal_sharing=False, share_agg_weights=False, share_out_fc_weights=False,
**kwargs):
super(GCMCLayer, self).__init__(**kwargs)
self._out_act = get_activation(out_act)
self._src_key = src_key
self._dst_key = dst_key
with self.name_scope():
self.dropout = nn.Dropout(dropout_rate)
self.aggregator = MultiLinkGCNAggregator(src_key=src_key,
dst_key=dst_key,
units=agg_units,
src_in_units=src_in_units,
dst_in_units=dst_in_units,
num_links=num_links,
dropout_rate=dropout_rate,
accum=agg_accum,
act=agg_act,
prefix='agg_')
self.user_out_fcs = nn.Dense(out_units,
flatten=False,
prefix='user_out_')
self.item_out_fcs = nn.Dense(out_units,
flatten=False,
prefix='item_out_')
self._out_act = get_activation(out_act)
def __init__(self, args):
super(Net, self).__init__()
self._act = get_activation(args.model_activation)
self.encoder = GCMCLayer(args.rating_vals,
args.src_in_units,
args.dst_in_units,
args.gcn_agg_units,
args.gcn_out_units,
args.gcn_dropout,
args.gcn_agg_accum,
agg_act=self._act,
share_user_item_param=args.share_param)
self.decoder = BiDecoder(args.rating_vals,
in_units=args.gcn_out_units,
num_basis_functions=args.gen_r_num_basis_func)
def __build_graph_propagation_model(self) -> tf.Tensor:
h_dim = self.params['hidden_size']
activation_fn = get_activation(self.params['graph_model_activation_function']) # tanh
# if the initial node feature size does not match the hidden size, we create a densely connected layer
# to project the features to the correct size (h_dim). This densely connected layer has
# h_dim nodes, and uses the activation function specified above (tanh).
if self.task.initial_node_feature_size != self.params['hidden_size']: # projects features to the specified hidden size
self.__ops['projected_node_features'] = \
tf.keras.layers.Dense(units=h_dim,
use_bias=False,
activation=activation_fn,
)(self.__ops['initial_node_features'])
else:
self.__ops['projected_node_features'] = self.__ops['initial_node_features']
cur_node_representations = self.__ops['projected_node_features']
last_residual_representations = tf.zeros_like(cur_node_representations)
for layer_idx in range(self.params['graph_num_layers']):
with tf.variable_scope('gnn_layer_%i' % layer_idx):
# with some probability, set neurons to zero in current node representations
cur_node_representations = \
tf.nn.dropout(cur_node_representations, rate=1.0 - self.__placeholders['graph_layer_input_dropout_keep_prob'])
# every 10000 layers, we add the previously saved node representation.
# this helps address vanishing or exploding gradients
if layer_idx % self.params['graph_residual_connection_every_num_layers'] == 0:
t = cur_node_representations
if layer_idx > 0:
cur_node_representations += last_residual_representations
cur_node_representations /= 2
last_residual_representations = t
# finally, we construct the gnn layer.
cur_node_representations = \
self._apply_gnn_layer(
cur_node_representations,
self.__ops['adjacency_lists'],
self.__ops['type_to_num_incoming_edges'],
self.params['graph_num_timesteps_per_layer'])
if self.params['graph_inter_layer_norm']:
cur_node_representations = tf.contrib.layers.layer_norm(cur_node_representations)
if layer_idx % self.params['graph_dense_between_every_num_gnn_layers'] == 0:
cur_node_representations = \
tf.keras.layers.Dense(units=h_dim,
use_bias=False,
activation=activation_fn,
name="Dense",
)(cur_node_representations)
self.__ops['final_node_representations'] = cur_node_representations
def __init__(self, args):
super(Net, self).__init__()
self._act = get_activation(args.model_activation)
self.encoder = GraphSageLayer(args.rating_vals,
args.src_in_units,
args.dst_in_units,
args.gcn_agg_units,
args.gcn_out_units,
args.gcn_dropout,
args.gcn_agg_accum,
agg_act=self._act,
share_user_item_param=args.share_param,
device=args.device)
self.decoder = DotProduct(args.gcn_agg_units,
args.gcn_out_units)
def __build_graph_propagation_model(self) -> tf.Tensor:
h_dim = self.params['hidden_size']
activation_fn = get_activation(
self.params['graph_model_activation_function'])
if self.task.initial_node_feature_size != self.params['hidden_size']:
self.__ops['projected_node_features'] = \
tf.keras.layers.Dense(units=h_dim,
use_bias=False,
activation=activation_fn,
)(self.__ops['initial_node_features'])
else:
self.__ops['projected_node_features'] = self.__ops[
'initial_node_features']
cur_node_representations = self.__ops['projected_node_features']
last_residual_representations = tf.zeros_like(cur_node_representations)
for layer_idx in range(self.params['graph_num_layers']):
with tf.variable_scope('gnn_layer_%i' % layer_idx):
cur_node_representations = \
tf.nn.dropout(cur_node_representations, rate=1.0 - self.__placeholders['graph_layer_input_dropout_keep_prob'])
if layer_idx % self.params[
'graph_residual_connection_every_num_layers'] == 0:
t = cur_node_representations
if layer_idx > 0:
cur_node_representations += last_residual_representations
cur_node_representations /= 2
last_residual_representations = t
cur_node_representations = \
self._apply_gnn_layer(
cur_node_representations,
self.__ops['adjacency_lists'],
self.__ops['type_to_num_incoming_edges'],
self.params['graph_num_timesteps_per_layer'])
if self.params['graph_inter_layer_norm']:
cur_node_representations = tf.contrib.layers.layer_norm(
cur_node_representations)
if layer_idx % self.params[
'graph_dense_between_every_num_gnn_layers'] == 0:
cur_node_representations = \
tf.keras.layers.Dense(units=h_dim,
use_bias=False,
activation=activation_fn,
name="Dense",
)(cur_node_representations)
self.__ops['final_node_representations'] = cur_node_representations
def get_masked_lm_output(bert_config, input_tensor, output_weights, positions,
label_ids, label_weights, prev_bplayers=None):
"""Get loss and log probs for the masked LM."""
input_tensor = gather_indexes(input_tensor, positions)
with tf.variable_scope("cls/predictions") as prediction_scope:
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with tf.variable_scope("transform"):
input_tensor = tf.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=utils.get_activation(bert_config.hidden_act),
kernel_initializer=utils.create_initializer(
bert_config.initializer_range))
input_tensor = utils.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
"output_bias",
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
loss_bplayer = BPLayer(loss, prediction_scope, backward_layers=prev_bplayers)
return (loss, per_example_loss, log_probs, loss_bplayer)
def __init__(self, n_in, n_out, n_layers, layer_factor,
act_fn='relu', dropout_p=0.0):
super(MLP, self).__init__()
n_h = int(n_in * layer_factor)
act_fn_class = utils.get_activation(act_fn)
# build the net
net = []
for i in range(n_layers):
if i == 0:
net.append(torch.nn.Linear(n_in, n_h))
else:
net.append(torch.nn.Linear(n_h, n_h))
net.append(act_fn_class())
net.append(torch.nn.BatchNorm1d(n_h))
if dropout_p > 0:
net.append(torch.nn.Dropout(p=dropout_p))
net.append(torch.nn.Linear(n_h, n_out))
# convert to sequential
self.net = torch.nn.Sequential(*net)
def __init__(self,
layer_sizes,
feature_extractor_needed=False,
use_dropout=False,
activation='relu',
dropoutv=0.5,
reshape_dims=None,
seed=0,
session_config=None,
it=None,
c=1.0,
xi=0.1,
lr=0.001):
assert (len(layer_sizes) == 4)
assert (session_config != None)
assert (it != None)
self.layer_sizes = layer_sizes
self.feature_extractor_needed = feature_extractor_needed
self.use_dropout = use_dropout
self.dropoutv = dropoutv
self.reshape_dims = reshape_dims
self.seed = seed
self.session_config = session_config
self.it = it
self.use_dropout = use_dropout
self.activation = utils.get_activation(activation)
print("Using feature extractor: %s" % self.feature_extractor_needed)
print("Using dropout, bn: %s, %f" % (self.use_dropout, self.dropoutv))
self.phs = {}
self.vars = {}
self.objs = {}
self.all_predictions = []
self.c = c
self.xi = xi
self.lr = float(lr)
def __init__(self, config: Dict):
super().__init__(config)
default_config = {
"input_shape": (100, 100),
"num_actions": 3,
"activation": "relu",
"field_threshold": 6,
"hidden_sizes": (64, 64),
}
self.config = with_default_config(config, default_config)
self.activation = get_activation(self.config["activation"])
self.field_threshold = self.config["field_threshold"]
hidden_sizes: Tuple[int] = self.config.get("hidden_sizes")
input_shape: Tuple[int, int] = self.config["input_shape"]
_coords_i = torch.linspace(-1, 1, input_shape[0]).view(-1, 1).repeat(
1, input_shape[1])
_coords_j = torch.linspace(-1, 1, input_shape[1]).view(1, -1).repeat(
input_shape[0], 1)
self.coords = torch.stack([_coords_i, _coords_j])
self.bilinear = nn.Bilinear(2, 2, 4)
self.pool1 = nn.AvgPool2d((100, self.field_threshold))
self.pool2 = nn.AvgPool2d((100, 100 - 2 * self.field_threshold))
self.pool3 = nn.AvgPool2d((100, self.field_threshold))
# concat + flatten to [B, 3*4]
layer_sizes = (12, ) + hidden_sizes
self.hidden_layers = nn.ModuleList([
nn.Linear(in_size, out_size)
for in_size, out_size in zip(layer_sizes, layer_sizes[1:])
])
self.policy_head = nn.Linear(layer_sizes[-1],
self.config["num_actions"])
self.value_head = nn.Linear(layer_sizes[-1], 1)
def __init__(self,
layer_sizes,
feature_extractor_needed=False,
use_dropout=False,
activation='relu',
dropoutv=0.5,
reshape_dims=None,
seed=0,
session_config=None,
it=None,
embedding=False):
assert (len(layer_sizes) == 4)
assert (session_config != None)
assert (it != None)
self.layer_sizes = layer_sizes
self.feature_extractor_needed = feature_extractor_needed
self.use_dropout = use_dropout
self.dropoutv = dropoutv
self.reshape_dims = reshape_dims
self.seed = seed
self.session_config = session_config
self.it = it
self.embedding = embedding # if true, then expect x data to be
# embeddings
if self.use_dropout:
self.glob_training_ph = tf.placeholder_with_default(False,
shape=())
self.training_ph = tf.placeholder_with_default(False, shape=())
self.activation = utils.get_activation(activation)
print("Using feature extractor: %s" % self.feature_extractor_needed)
print("Using dropout, bn: %s, %f" % (self.use_dropout, self.dropoutv))
self.phs = {}
self.vars = {}
self.objs = {}
self.all_predictions = []
def __init__(self, args, **kwargs):
super(Net, self).__init__(**kwargs)
self._act = get_activation(args.model_activation)
with self.name_scope():
self.encoder = GCMCLayer(src_key=args.src_key,
dst_key=args.dst_key,
src_in_units=args.src_in_units,
dst_in_units=args.dst_in_units,
agg_units=args.gcn_agg_units,
out_units=args.gcn_out_units,
num_links=args.nratings,
dropout_rate=args.gcn_dropout,
agg_accum=args.gcn_agg_accum,
agg_act=args.model_activation,
prefix='enc_')
if args.gen_r_use_classification:
self.gen_ratings = BiDecoder(
in_units=args.gcn_out_units,
out_units=args.nratings,
num_basis_functions=args.gen_r_num_basis_func,
prefix='gen_rating')
else:
self.gen_ratings = InnerProductLayer(prefix='gen_rating')
def __init__(self,
src_key,
dst_key,
units,
src_in_units,
dst_in_units,
num_links,
dropout_rate=0.0,
accum='stack',
act=None,
**kwargs):
super(MultiLinkGCNAggregator, self).__init__(**kwargs)
self._src_key = src_key
self._dst_key = dst_key
self._accum = accum
self._num_links = num_links
self._units = units
if accum == "stack":
assert units % num_links == 0, 'units should be divisible by the num_links '
self._units = self._units // num_links
with self.name_scope():
self.dropout = nn.Dropout(
dropout_rate) ### dropout before feeding the out layer
self.act = get_activation(act)
self.src_dst_weights = self.params.get('src_dst_weight',
shape=(num_links,
self._units,
src_in_units),
dtype=np.float32,
allow_deferred_init=True)
self.dst_src_weights = self.params.get('dst_dst_weight',
shape=(num_links,
self._units,
dst_in_units),
dtype=np.float32,
allow_deferred_init=True)
def __init__(self, args, dev_id):
super(Net, self).__init__()
self._act = get_activation(args.model_activation)
self.encoder = GCMCLayer(args.rating_vals,
args.src_in_units,
args.dst_in_units,
args.gcn_agg_units,
args.gcn_out_units,
args.gcn_dropout,
args.gcn_agg_accum,
agg_act=self._act,
share_user_item_param=args.share_param,
device=dev_id)
if args.mix_cpu_gpu and args.use_one_hot_fea:
# if use_one_hot_fea, user and movie feature is None
# W can be extremely large, with mix_cpu_gpu W should be stored in CPU
self.encoder.partial_to(dev_id)
else:
self.encoder.to(dev_id)
self.decoder = BiDecoder(in_units=args.gcn_out_units,
num_classes=len(args.rating_vals),
num_basis=args.gen_r_num_basis_func)
self.decoder.to(dev_id)
def sparse_rgcn_layer(
node_embeddings: tf.Tensor,
adjacency_lists: List[tf.Tensor],
type_to_num_incoming_edges: tf.Tensor,
state_dim: Optional[int],
num_timesteps: int = 1,
activation_function: Optional[str] = "tanh",
message_aggregation_function: str = "sum",
normalize_by_num_incoming: bool = True,
use_both_source_and_target: bool = False,
) -> tf.Tensor:
"""
Compute new graph states by neural message passing.
This implements the R-GCN model (Schlichtkrull et al., https://arxiv.org/pdf/1703.06103.pdf)
for the case of few relations / edge types, i.e., we do not use the dimensionality-reduction
tricks from section 2.2 of that paper.
For this, we assume existing node states h^t_v and a list of per-edge-type adjacency
matrices A_\ell.
We compute new states as follows:
h^{t+1}_v := \sigma(\sum_\ell
\sum_{(u, v) \in A_\ell}
1/c_{v,\ell} * (W_\ell * h^t_u))
c_{\v,\ell} is usually the number of \ell edges going into v.
The learnable parameters of this are the W_\ell \in R^{D,D}.
We use the following abbreviations in shape descriptions:
* V: number of nodes
* D: state dimension
* L: number of different edge types
* E: number of edges of a given edge type
Arguments:
node_embeddings: float32 tensor of shape [V, D], the original representation of
each node in the graph.
adjacency_lists: List of L adjacency lists, represented as int32 tensors of shape
[E, 2]. Concretely, adjacency_lists[l][k,:] == [v, u] means that the k-th edge
of type l connects node v to node u.
type_to_num_incoming_edges: float32 tensor of shape [L, V] representing the number
of incoming edges of a given type. Concretely, type_to_num_incoming_edges[l, v]
is the number of edge of type l connecting to node v.
state_dim: Optional size of output dimension of the GNN layer. If not set, defaults
to D, the dimensionality of the input. If different from the input dimension,
parameter num_timesteps has to be 1.
num_timesteps: Number of repeated applications of this message passing layer.
activation_function: Type of activation function used.
message_aggregation_function: Type of aggregation function used for messages.
normalize_by_num_incoming: Flag indicating if messages should be scaled by 1/(number
of incoming edges).
Returns:
float32 tensor of shape [V, state_dim]
"""
num_nodes = tf.shape(input=node_embeddings, out_type=tf.int32)[0]
if state_dim is None:
state_dim = tf.shape(input=node_embeddings, out_type=tf.int32)[1]
# === Prepare things we need across all timesteps:
activation_fn = get_activation(activation_function)
message_aggregation_fn = get_aggregation_function(
message_aggregation_function)
edge_type_to_message_transformation_layers = [
] # Layers to compute the message from a source state
edge_type_to_message_targets = [] # List of tensors of message targets
for edge_type_idx, adjacency_list_for_edge_type in enumerate(
adjacency_lists):
edge_type_to_message_transformation_layers.append(
tf.keras.layers.Dense(units=state_dim,
use_bias=False,
activation=None,
name="Edge_%i_Weight" % edge_type_idx))
edge_type_to_message_targets.append(adjacency_list_for_edge_type[:, 1])
# Let M be the number of messages (sum of all E):
message_targets = tf.concat(edge_type_to_message_targets,
axis=0) # Shape [M]
cur_node_states = node_embeddings
for _ in range(num_timesteps):
messages_per_type = [] # list of tensors of messages of shape [E, H]
# Collect incoming messages per edge type
for edge_type_idx, adjacency_list_for_edge_type in enumerate(
adjacency_lists):
edge_sources = adjacency_list_for_edge_type[:, 0]
edge_targets = adjacency_list_for_edge_type[:, 1]
edge_source_states = \
tf.nn.embedding_lookup(params=cur_node_states,
ids=edge_sources) # Shape [E, H]
if use_both_source_and_target:
edge_target_states = \
tf.nn.embedding_lookup(params=cur_node_states,
ids=edge_targets) # Shape [E, H]
edge_state_pairs = tf.concat(
[edge_source_states, edge_target_states],
axis=-1) # Shape [E, 2H]
messages = edge_type_to_message_transformation_layers[
edge_type_idx](edge_state_pairs) # Shape [E, H]
else:
messages = edge_type_to_message_transformation_layers[
edge_type_idx](edge_source_states) # Shape [E, H]
if normalize_by_num_incoming:
num_incoming_to_node_per_message = \
tf.nn.embedding_lookup(params=type_to_num_incoming_edges[edge_type_idx, :],
ids=edge_targets) # Shape [E, H]
messages = tf.expand_dims(
1.0 / (num_incoming_to_node_per_message + SMALL_NUMBER),
axis=-1) * messages
messages_per_type.append(messages)
cur_messages = tf.concat(messages_per_type, axis=0) # Shape [M, H]
aggregated_messages = \
message_aggregation_fn(data=cur_messages,
segment_ids=message_targets,
num_segments=num_nodes) # Shape [V, H]
new_node_states = activation_fn(aggregated_messages) # Shape [V, H]
cur_node_states = new_node_states
return cur_node_states
def sparse_rgat_layer(node_embeddings: tf.Tensor,
adjacency_lists: List[tf.Tensor],
state_dim: Optional[int],
num_heads: int = 4,
num_timesteps: int = 1,
activation_function: Optional[str] = "tanh"
) -> tf.Tensor:
"""
Compute new graph states by neural message passing using attention. This generalises
the original GAT model (Velickovic et al., https://arxiv.org/pdf/1710.10903.pdf)
to multiple edge types by using different weights for different edge types.
For this, we assume existing node states h^t_v and a list of per-edge-type adjacency
matrices A_\ell.
In the setting for a single attention head, we compute new states as follows:
h^t_{v, \ell} := W_\ell h^t_v
e_{u, \ell, v} := LeakyReLU(\alpha_\ell^T * concat(h^t_{u, \ell}, h^t_{v, \ell}))
a_v := softmax_{\ell, u with (u, v) \in A_\ell}(e_{u, \ell, v})
h^{t+1}_v := \sigma(\sum_{ell, (u, v) \in A_\ell}
a_v_{u, \ell} * h^_{u, \ell})
The learnable parameters of this are the W_\ell \in R^{D, D} and \alpha_\ell \in R^{2*D}.
In practice, we use K attention heads, computing separate, partial new states h^{t+1}_{v,k}
and compute h^{t+1}_v as the concatentation of the partial states.
For this, we reduce the shape of W_\ell to R^{D, D/K} and \alpha_\ell to R^{2*D/K}.
We use the following abbreviations in shape descriptions:
* V: number of nodes
* D: state dimension
* K: number of attention heads
* L: number of different edge types
* E: number of edges of a given edge type
Arguments:
node_embeddings: float32 tensor of shape [V, D], the original representation of
each node in the graph.
adjacency_lists: List of L adjacency lists, represented as int32 tensors of shape
[E, 2]. Concretely, adjacency_lists[l][k,:] == [v, u] means that the k-th edge
of type l connects node v to node u.
state_dim: Optional size of output dimension of the GNN layer. If not set, defaults
to D, the dimensionality of the input. If different from the input dimension,
parameter num_timesteps has to be 1.
num_heads: Number of attention heads to use.
num_timesteps: Number of repeated applications of this message passing layer.
activation_function: Type of activation function used.
Returns:
float32 tensor of shape [V, state_dim]
"""
num_nodes = tf.shape(input=node_embeddings, out_type=tf.int32)[0]
if state_dim is None:
state_dim = tf.shape(input=node_embeddings, out_type=tf.int32)[1]
per_head_dim = state_dim // num_heads
# === Prepare things we need across all timesteps:
activation_fn = get_activation(activation_function)
edge_type_to_state_transformation_layers = [] # Layers to compute the message from a source state
edge_type_to_attention_parameters = [] # Parameters for the attention mechanism
edge_type_to_message_targets = [] # List of tensors of message targets
for edge_type_idx, adjacency_list_for_edge_type in enumerate(adjacency_lists):
edge_type_to_state_transformation_layers.append(
tf.keras.layers.Dense(units=state_dim,
use_bias=False,
activation=None,
name="Edge_%i_Weight" % edge_type_idx))
edge_type_to_attention_parameters.append(
tf.compat.v1.get_variable(shape=(2 * state_dim),
name="Edge_%i_Attention_Parameters" % edge_type_idx))
edge_type_to_message_targets.append(adjacency_list_for_edge_type[:, 1])
# Let M be the number of messages (sum of all E):
message_targets = tf.concat(edge_type_to_message_targets, axis=0) # Shape [M]
cur_node_states = node_embeddings
for _ in range(num_timesteps):
edge_type_to_per_head_messages = [] # type: List[tf.Tensor] # list of lists of tensors of messages of shape [E, K, D/K]
edge_type_to_per_head_attention_coefficients = [] # type: List[tf.Tensor] # list of lists of tensors of shape [E, K]
# Collect incoming messages per edge type
# Note:
# We compute the state transformations (to make use of the wider, faster matrix multiplication),
# and then split into the individual attention heads via some reshapes:
for edge_type_idx, adjacency_list_for_edge_type in enumerate(adjacency_lists):
edge_sources = adjacency_list_for_edge_type[:, 0]
edge_targets = adjacency_list_for_edge_type[:, 1]
transformed_states = \
edge_type_to_state_transformation_layers[edge_type_idx](cur_node_states) # Shape [V, D]
edge_transformed_source_states = \
tf.nn.embedding_lookup(params=transformed_states, ids=edge_sources) # Shape [E, D]
edge_transformed_target_states = \
tf.nn.embedding_lookup(params=transformed_states, ids=edge_targets) # Shape [E, D]
per_edge_per_head_transformed_source_states = \
tf.reshape(edge_transformed_source_states, shape=(-1, num_heads, per_head_dim))
per_edge_per_head_transformed_states = \
tf.concat([per_edge_per_head_transformed_source_states,
tf.reshape(edge_transformed_target_states, shape=(-1, num_heads, per_head_dim))],
axis=-1) # Shape [E, K, 2*D/K]
per_head_attention_pars = tf.reshape(edge_type_to_attention_parameters[edge_type_idx],
shape=(num_heads, 2 * per_head_dim)) # Shape [K, 2*D/K]
per_edge_per_head_attention_coefficients = \
tf.nn.leaky_relu(tf.einsum('vki,ki->vk',
per_edge_per_head_transformed_states,
per_head_attention_pars)) # Shape [E, K]
edge_type_to_per_head_messages.append(per_edge_per_head_transformed_source_states)
edge_type_to_per_head_attention_coefficients.append(per_edge_per_head_attention_coefficients)
per_head_messages = tf.concat(edge_type_to_per_head_messages, axis=0)
per_head_attention_coefficients = tf.concat(edge_type_to_per_head_attention_coefficients, axis=0)
head_to_aggregated_messages = [] # list of tensors of shape [V, D/K]
for head_idx in range(num_heads):
# Compute the softmax over all the attention coefficients for all messages going to this state:
attention_coefficients = tf.concat(per_head_attention_coefficients[:, head_idx], axis=0) # Shape [M]
attention_values = \
tf.exp(unsorted_segment_log_softmax(logits=attention_coefficients,
segment_ids=message_targets,
num_segments=num_nodes)) # Shape [M]
messages = per_head_messages[:, head_idx, :] # Shape [M, D/K]
# Compute weighted sum per target node for this head:
head_to_aggregated_messages.append(
tf.math.unsorted_segment_sum(data=tf.expand_dims(attention_values, -1) * messages,
segment_ids=message_targets,
num_segments=num_nodes))
new_node_states = activation_fn(tf.concat(head_to_aggregated_messages, axis=-1))
cur_node_states = new_node_states
return cur_node_states
if not os.path.exists(args.result_dir):
os.mkdir(args.result_dir)
for set_cur in args.set_names:
if not os.path.exists(os.path.join(args.result_dir, set_cur)):
os.mkdir(os.path.join(args.result_dir, set_cur))
psnrs = []
ssims = []
for im in os.listdir(os.path.join(args.set_dir, set_cur)):
if im.endswith(".jpg") or im.endswith(".bmp") or im.endswith(
".png"):
# model.conv1.register_forward_hook(get_activation('conv1'))
model.dncnn.register_forward_hook(
get_activation('noise_level'))
x = np.array(imread(os.path.join(args.set_dir, set_cur, im)),
dtype=np.float32) / 255.0
np.random.seed(seed=0) # for reproducibility
y = x + np.random.normal(
0, args.sigma / 255.0,
x.shape) # Add Gaussian noise without clipping
y = y.astype(np.float32)
y_ = torch.from_numpy(y).view(1, -1, y.shape[0], y.shape[1])
torch.cuda.synchronize()
start_time = time.time()
y_ = y_.cuda()
x_ = model(y_) # inference
noise_level = activation['noise_level'].squeeze()
model = model.cuda()
if not os.path.exists(args.result_dir):
os.mkdir(args.result_dir)
for set_cur in args.set_names:
if not os.path.exists(os.path.join(args.result_dir, set_cur)):
os.mkdir(os.path.join(args.result_dir, set_cur))
psnrs = []
ssims = []
for im in os.listdir(os.path.join(args.set_dir, set_cur)):
if im.endswith(".jpg") or im.endswith(".bmp") or im.endswith(".png"):
# model.conv1.register_forward_hook(get_activation('conv1'))
model.dncnn.register_forward_hook(get_activation('noise_level'))
x = np.array(imread(os.path.join(args.set_dir, set_cur, im)), dtype=np.float32)/255.0
np.random.seed(seed=0) # for reproducibility
y = x + np.random.normal(0, args.sigma/255.0, x.shape) # Add Gaussian noise without clipping
y = y.astype(np.float32)
y_ = torch.from_numpy(y).view(1, -1, y.shape[0], y.shape[1])
High_origin, Low_origin = Decomposition(torch.from_numpy(x).unsqueeze(0))
High_noise, Low_noise = Decomposition(y_.squeeze(0))
torch.cuda.synchronize()
start_time = time.time()
y_ = y_.cuda()
