def __init__(self):
super(Net, self).__init__()
#name
self.name = "DirCNNh5"
#optimizer
self.lr = 0.001
self.optimizer_name = 'Adam-Exp'
#data
#self.data_name = "ModelNet10"
self.data_name = "Geometry"
self.batch_size = 20
self.nr_points = 1024
self.nr_classes = 10 if self.data_name == 'ModelNet10' else 40
#train_info
self.max_epochs = 301
self.save_every = 100
#model
self.k = 20
self.l = 7
# DD1
self.in_size = 3
self.out_size = 64
layers = []
layers.append(Linear(self.in_size, 64))
layers.append(ReLU())
layers.append(torch.nn.BatchNorm1d(64))
layers.append(Linear(64 , 64))
layers.append(ReLU())
layers.append(torch.nn.BatchNorm1d(64))
layers.append(Linear(64, self.out_size))
layers.append(ReLU())
layers.append(torch.nn.BatchNorm1d(self.out_size))
dense3dnet = Sequential(*layers)
self.dd = DD(l = self.l,
k = self.k,
mlp = dense3dnet,
conv_p = True,
conv_fc = False,
conv_fn = False,
out_3d = True)
# DD2
self.in_size_2 = 64*3
self.out_size_2 = 128
layers2 = []
layers2.append(Linear(self.in_size_2, self.out_size_2))
layers2.append(ReLU())
layers2.append(torch.nn.BatchNorm1d(self.out_size_2))
dense3dnet2 = Sequential(*layers2)
self.dd2 = DD(l = self.l,
k = self.k,
mlp = dense3dnet2,
conv_p = False,
conv_fc = False,
conv_fn = True,
out_3d = False)
self.nn1 = torch.nn.Linear(self.out_size_2, 1024)
self.bn1 = torch.nn.BatchNorm1d(1024)
self.nn2 = torch.nn.Linear(1024, 512)
self.bn2 = torch.nn.BatchNorm1d(512)
self.nn3 = torch.nn.Linear(512, 265)
self.bn3 = torch.nn.BatchNorm1d(265)
self.nn4 = torch.nn.Linear(265, self.nr_classes)
self.sm = torch.nn.LogSoftmax(dim=1)
def __init__(self, in_features, out_features):
super(ComplexLinear, self).__init__()
self.fc_r = Linear(in_features, out_features)
self.fc_i = Linear(in_features, out_features)
def make_model_and_optim():
model = Linear(in_dim, 2, bias=False)
model = model.cuda()
optim = AdaScale(SGD(model.parameters(), lr=0.1, momentum=0.9),
num_gradients_to_accumulate=accum_steps)
return model, optim
def __init__(
self,
embed_dim=None, # type: Optional[int]
num_heads=1, # type: int
dropout=0.0, # type: float
bias=True, # type: bool
add_bias_kv=False, # type: bool
add_zero_attn=False, # type: bool
kdim=None, # type: Optional[int]
vdim=None, # type: Optional[int]
head_dim=None, # type: Optional[int]
pattern_dim=None, # type: Optional[int]
out_dim=None, # type: Optional[int]
disable_out_projection=False, # type: bool
key_as_static=False, # type: bool
query_as_static=False, # type: bool
value_as_static=False, # type: bool
value_as_connected=False, # type: bool
normalize_pattern=False, # type: bool
normalize_pattern_affine=False # type: bool
):
super(HopfieldCore, self).__init__()
assert (type(key_as_static)
== bool) and (type(query_as_static)
== bool) and (type(value_as_static) == bool)
self.key_as_static, self.query_as_static, self.value_as_static = key_as_static, query_as_static, value_as_static
num_non_static = 3 - (self.key_as_static + self.query_as_static +
self.value_as_static)
assert 0 <= num_non_static < 4
self.value_as_connected = value_as_connected
self.normalize_pattern, self.normalize_pattern_affine = normalize_pattern, normalize_pattern_affine
self.disable_out_projection = disable_out_projection
# In case of a static-only executions, check corresponding projections and normalizations.
self.static_execution = self._check_execution_mode()
if self.static_execution:
embed_dim, kdim, vdim = None, None, None
if embed_dim is None:
assert self.static_execution, r'static-only execution requires all projections to be deactivated.'
# Check and set all other properties, conditioned on <static_execution>.
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self._qkv_same_embed_dim = all(
(self.kdim == embed_dim, self.vdim == embed_dim,
pattern_dim is None, not self.value_as_connected))
assert (not self.value_as_connected) or (
self.kdim
== self.vdim), r'key and value need to be of same dimension.'
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = None
self.pattern_dim = pattern_dim
self.virtual_hopfield_dim = None
self.virtual_pattern_dim = None
if not self.static_execution:
if head_dim is None:
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads."
else:
assert head_dim > 0, "dimension of the association space has to be positive."
self.head_dim = head_dim
if self.pattern_dim is None:
self.pattern_dim = self.head_dim
self.virtual_hopfield_dim = self.num_heads * self.head_dim
self.virtual_pattern_dim = self.num_heads * self.pattern_dim
self.out_dim = embed_dim if out_dim is None else out_dim
assert disable_out_projection or (
self.out_dim >
0), "output projection dimension has to be positive."
if normalize_pattern_affine:
assert normalize_pattern, "affine pattern normalization without pattern normalization has no effect."
self.p_norm_weight = Parameter(torch.Tensor(head_dim))
self.p_norm_bias = Parameter(torch.Tensor(head_dim))
else:
self.register_parameter('p_norm_weight', None)
self.register_parameter('p_norm_bias', None)
if self._qkv_same_embed_dim is False:
if query_as_static:
self.register_parameter('q_proj_weight', None)
else:
self.q_proj_weight = Parameter(
torch.Tensor(self.virtual_hopfield_dim, embed_dim))
if key_as_static:
self.register_parameter('k_proj_weight', None)
else:
self.k_proj_weight = Parameter(
torch.Tensor(self.virtual_hopfield_dim, self.kdim))
if value_as_static:
self.register_parameter('v_proj_weight', None)
else:
self.v_proj_weight = Parameter(
torch.Tensor(
self.virtual_pattern_dim, self.virtual_hopfield_dim if
(value_as_connected
and not key_as_static) else self.vdim))
self.register_parameter('in_proj_weight', None)
else:
if num_non_static > 0:
self.in_proj_weight = Parameter(
torch.empty(
(not query_as_static) * self.virtual_hopfield_dim +
(not key_as_static) * self.virtual_hopfield_dim +
(not value_as_static) * self.virtual_pattern_dim,
embed_dim))
else:
self.register_parameter('in_proj_weight', None)
self.register_parameter('q_proj_weight', None)
self.register_parameter('k_proj_weight', None)
self.register_parameter('v_proj_weight', None)
if bias and (num_non_static > 0):
self.in_proj_bias = Parameter(
torch.empty((not query_as_static) * self.virtual_hopfield_dim +
(not key_as_static) * self.virtual_hopfield_dim +
self.virtual_pattern_dim))
else:
self.register_parameter('in_proj_bias', None)
if disable_out_projection:
self.register_parameter('out_proj', None)
else:
if bias and _LinearWithBias is not None:
self.out_proj = _LinearWithBias(self.virtual_pattern_dim,
self.out_dim)
else:
self.out_proj = Linear(self.virtual_pattern_dim,
self.out_dim,
bias=bias)
self.bias_k, self.bias_v = None, None
if add_bias_kv:
if not key_as_static:
self.bias_k = Parameter(
torch.empty(1, 1, self.virtual_hopfield_dim))
if not value_as_static:
self.bias_v = Parameter(
torch.empty(1, 1, self.virtual_hopfield_dim))
assert not (self.bias_k is None and self.bias_v is None
), r'cannot set key/value bias if both are static.'
self.add_zero_attn = add_zero_attn
self.reset_parameters()
Compute the gradient with PyTorch and the gradient variance with BackPACK.
"""
from torch.nn import CrossEntropyLoss, Flatten, Linear, Sequential
from backpack import backpack, extend, extensions
from backpack.utils.examples import load_mnist_data
B = 4
X, y = load_mnist_data(B)
print("# Gradient with PyTorch, gradient variance with BackPACK | B =", B)
model = Sequential(
Flatten(),
Linear(784, 10),
)
lossfunc = CrossEntropyLoss()
model = extend(model)
lossfunc = extend(lossfunc)
loss = lossfunc(model(X), y)
with backpack(extensions.Variance()):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".variance.shape: ", param.variance.shape)
def __init__(self, D_key, D_query):
super(AttentionLayer, self).__init__()
self.W_k = Linear(D_key, D_query, bias=False)
self.W_q = Linear(D_key + D_query, D_query, bias=False)
def __init__(self, state_dim, action_dim):
super().__init__()
self.model = Sequential(Linear(state_dim + action_dim, 64),
LeakyReLU(), Linear(64, 32), LeakyReLU(),
Linear(32, 1))
import torch
from torch.optim import Adam
from torch.nn.functional import cross_entropy
from collections import OrderedDict
from torch.nn import Linear, ReLU, Sequential
def classify_target(x, y):
return (y > (x * 3).sin()).long()
mlp = torch.nn.Sequential(OrderedDict([
('layer1', Sequential(Linear(2, 20), ReLU())),
('layer2', Sequential(Linear(20, 20), ReLU())),
('layer3', Sequential(Linear(20, 2)))
]))
mlp.cuda()
optimizer = Adam(mlp.parameters(), lr=0.01)
for iteration in range(1024):
in_batch = torch.randn(10000, 2, device='cuda')
target_batch = classify_target(in_batch[:,0], in_batch[:,1])
out_batch = mlp(in_batch)
loss = cross_entropy(out_batch, target_batch)
if iteration > 0:
mlp.zero_grad()
loss.backward()
optimizer.step()
if iteration == 2 ** iteration.bit_length() - 1:
pred_batch = out_batch.max(1)[1]
accuracy = (pred_batch == target_batch).float().sum() / len(in_batch)
print(f'Iteration {iteration} accuracy: {accuracy}')
def __init__(self):
super(Net, self).__init__()
self.hidden_layer = Linear(1, 20)
self.out_layer = Linear(20, 1)
def __init__(self, in_channels):
super().__init__()
self.lin_src = Linear(in_channels, in_channels)
self.lin_dst = Linear(in_channels, in_channels)
self.lin_final = Linear(in_channels, 1)
print("b shape", b.shape)
def forward(x):
yhat = w * x + b
return yhat
x = torch.tensor([[1.0], [2.0], [3.0]])
yhat = forward(x)
print("The Prediction: ", yhat)
print("Y size", yhat.shape)
torch.manual_seed(1)
lr = Linear(in_features=1, out_features=1, bias=True)
print("Parameters w and b: ", list(lr.parameters()))
print("Python Dictionary", lr.state_dict())
print("keys:", lr.state_dict().keys())
print("values:", lr.state_dict().values())
print("weight:", lr.weight)
print("bias:", lr.bias)
x = torch.tensor([[1.0]])
yhat = lr(x)
print("The prediction: ", yhat)
x = torch.tensor([[1.0], [2.0]])
yhat = lr(x)
def __init__(self,
pretrained="",
checkpoint_path=None,
freeze_nlayers=0,
round_at: float = None,
demo_mode=False):
super(S20DeconvToDrySpotEff2, self).__init__()
self.ct1 = ConvTranspose2d(1, 256, 3, stride=2)
self.ct2 = ConvTranspose2d(256, 128, 5, stride=2)
self.ct3 = ConvTranspose2d(128, 64, 10, stride=2)
self.ct4 = ConvTranspose2d(64, 16, 17, stride=2)
self.details = Conv2d(16, 8, 5)
# ^ Pretrained ^
self.c2 = Conv2d(8, 16, 13)
self.c3 = Conv2d(16, 64, 7)
self.c4 = Conv2d(64, 128, 3)
self.c5 = Conv2d(128, 256, 3)
self.c6 = Conv2d(256, 512, 3)
self.c7 = Conv2d(512, 512, 1)
self.maxpool = nn.MaxPool2d(2, 2)
self.lin1 = Linear(1024, 256)
self.lin2 = Linear(256, 1)
self.dropout = nn.Dropout(0.3)
# self.bn8 = nn.BatchNorm2d(8)
# self.bn512 = nn.BatchNorm2d(512)
self.round_at = round_at
self.demo_mode = demo_mode
if pretrained == "deconv_weights":
logger = logging.getLogger(__name__)
weights = load_model_layers_from_path(
path=checkpoint_path,
layer_names={'ct1', 'ct2', 'ct3', 'ct4', 'details'})
incomp = self.load_state_dict(weights, strict=False)
logger.debug(f'All layers: {self.state_dict().keys()}')
logger.debug(f'Loaded weights but the following: {incomp}')
if pretrained == "all":
logger = logging.getLogger(__name__)
weights = load_model_layers_from_path(path=checkpoint_path,
layer_names={
'ct1', 'ct2', 'ct3',
'ct4', 'details', 'c2',
'c3', 'c4', 'c5', 'c6',
'c7', 'lin1', 'lin2'
})
incomp = self.load_state_dict(weights, strict=False)
logger.debug(f'All layers: {self.state_dict().keys()}')
logger.debug(f'Loaded weights but the following: {incomp}')
if freeze_nlayers == 0:
return
for i, c in enumerate(self.children()):
logger = logging.getLogger(__name__)
logger.info(f'Freezing: {c}')
for param in c.parameters():
param.requires_grad = False
if i == freeze_nlayers - 1:
break
def __init__(self,
input_size: int,
input_module_class: Callable,
rnn_module_class: Callable,
output_size: int,
option_size: int,
rnn_size: int,
intra_option_policy: str,
intra_option_kwargs: [dict, None] = None,
input_module_kwargs: [dict, None] = None,
use_interest: bool = False,
use_diversity: bool = False,
use_attention: bool = False,
baselines_init: bool = True,
prev_action: np.ndarray = np.ones(5, dtype=bool),
prev_reward: np.ndarray = np.ones(5, dtype=bool),
prev_option: np.ndarray = np.zeros(5, dtype=bool),
NORM_EPS: float = 1e-6):
super().__init__()
if input_module_kwargs is None:
input_module_kwargs = {
} # Assume module has all necessary arguments
if intra_option_kwargs is None: intra_option_kwargs = {}
input_module_kwargs = {
**input_module_kwargs,
**{
'input_size': input_size
}
} # Add input size
intra_option_kwargs = {**intra_option_kwargs}
self.use_interest = use_interest
self.use_diversity = use_diversity
self.use_attention = use_attention
self.NORM_EPS = NORM_EPS
pi_class = DiscreteIntraOptionPolicy if intra_option_policy == 'discrete' else ContinuousIntraOptionPolicy
# Instantiate independent preprocessors for pi, pi_omega, q (and entropy), interest, and termination heads
self.pi_proc, self.pi_omega_proc, self.q_proc, self.beta_proc = [
input_module_class(**input_module_kwargs) for _ in range(4)
]
self.int_proc = input_module_class(
**input_module_kwargs) if use_interest else Dummy(option_size)
if baselines_init:
self.pi_proc.apply(apply_init)
self.pi_omega_proc.apply(apply_init)
self.q_proc.apply(apply_init)
self.int_proc.apply(apply_init)
self.beta_proc.apply(apply_init)
input_size = self.pi_proc.output_size
rnn_input_sizes = [
input_size + prev_option[i] * option_size +
prev_action[i] * output_size + prev_reward[i] for i in range(4)
]
self.pi_rnn, self.beta_rnn, self.q_rnn, self.pi_omega_rnn = [
rnn_module_class(s, rnn_size) for s in rnn_input_sizes
]
self.int_rnn = rnn_module_class(
input_size + prev_option[-1] * option_size +
prev_action[-1] * output_size +
prev_reward[-1], rnn_size) if use_interest else None
if baselines_init:
lstm_init = partial(apply_init, gain=O_INIT_VALUES['lstm'])
self.pi_rnn.apply(lstm_init)
self.pi_omega_rnn.apply(lstm_init)
self.q_rnn.apply(lstm_init)
self.beta_rnn.apply(lstm_init)
if use_interest: self.int_rnn.apply(lstm_init)
self.pi = Sequential(
nn.ReLU(),
pi_class(rnn_size,
option_size,
output_size,
ortho_init=baselines_init,
**intra_option_kwargs))
self.beta = Sequential(nn.ReLU(), Linear(rnn_size, option_size),
nn.Sigmoid())
self.q = Sequential(nn.ReLU(), Linear(rnn_size, option_size))
self.q_ent = Sequential(nn.ReLU(), Linear(
rnn_size, option_size)) if use_diversity else Dummy(option_size,
out_value=0.)
self.pi_omega = Sequential(nn.ReLU(), Linear(rnn_size, option_size),
nn.Softmax(-1))
self.interest = Sequential(
nn.ReLU(), Linear(rnn_size, option_size),
nn.Sigmoid()) if use_interest else Dummy(option_size)
self.p_a, self.p_o, self.p_r = prev_action, prev_option, prev_reward
if baselines_init:
init_v, init_pi = O_INIT_VALUES['v'], O_INIT_VALUES['pi']
self.beta[1].apply(apply_init)
self.pi_omega[1].apply(partial(apply_init, gain=init_pi))
self.q[1].apply(partial(apply_init, gain=init_v))
if use_interest: self.interest[0].apply(apply_init)
if use_diversity: self.q_ent.apply(apply_init)
def __init__(self,
input_size: int,
input_module_class: Callable,
output_size: int,
option_size: int,
intra_option_policy: str,
intra_option_kwargs: [dict, None] = None,
input_module_kwargs: [dict, None] = None,
use_interest: bool = False,
use_diversity: bool = False,
use_attention: bool = False,
baselines_init: bool = True,
NORM_EPS: float = 1e-6):
super().__init__()
if input_module_kwargs is None:
input_module_kwargs = {
} # Assume module has all necessary arguments
if intra_option_kwargs is None: intra_option_kwargs = {}
input_module_kwargs = {
**input_module_kwargs,
**{
'input_size': input_size
}
} # Add input size
intra_option_kwargs = {**intra_option_kwargs}
self.use_interest = use_interest
self.use_diversity = use_diversity
self.use_attention = use_attention
self.NORM_EPS = NORM_EPS
pi_class = DiscreteIntraOptionPolicy if intra_option_policy == 'discrete' else ContinuousIntraOptionPolicy
# Instantiate independent preprocessors for pi, pi_omega, q (and entropy), interest, and termination heads
pi_proc, pi_omega_proc, q_proc, int_proc, beta_proc = [
input_module_class(**input_module_kwargs) for _ in range(5)
]
if baselines_init:
pi_proc.apply(apply_init)
pi_omega_proc.apply(apply_init)
q_proc.apply(apply_init)
int_proc.apply(apply_init)
beta_proc.apply(apply_init)
input_size = pi_proc.output_size
self.pi = Sequential(
pi_proc,
pi_class(input_size,
option_size,
output_size,
ortho_init=baselines_init,
**intra_option_kwargs))
self.beta = Sequential(beta_proc, Linear(input_size, option_size),
nn.Sigmoid())
self.q = Sequential(q_proc, Linear(input_size, option_size))
self.q_ent = Sequential(q_proc, Linear(
input_size, option_size)) if use_diversity else Dummy(option_size,
out_value=0.)
self.pi_omega = Sequential(pi_omega_proc,
Linear(input_size, option_size),
nn.Softmax(-1))
self.interest = Sequential(
int_proc, Linear(input_size, option_size),
nn.Sigmoid()) if use_interest else Dummy(option_size)
if baselines_init:
init_v, init_pi = O_INIT_VALUES['v'], O_INIT_VALUES['pi']
self.beta[1].apply(apply_init)
self.pi_omega[1].apply(partial(apply_init, gain=init_pi))
self.q[1].apply(partial(apply_init, gain=init_v))
if use_interest: self.interest[1].apply(apply_init)
if use_diversity: self.q_ent[1].apply(apply_init)
x_train, x_test, y_train, y_test = train_test_split(x,
y,
train_size=0.8,
random_state=1)
order = y_train.argsort(axis=0)
y_train = y_train.values[order]
y_train = np.reshape(y_train, newshape=(y_train.shape[0], 1))
x_train = x_train.values[order, :]
x_train = torch.FloatTensor(x_train)
y_train = torch.FloatTensor(y_train)
Net = Sequential(
# BatchNorm1d(num_features=2),
Linear(in_features=2, out_features=10),
ReLU(inplace=True),
Linear(in_features=10, out_features=1),
)
optimizer = RMSprop(Net.parameters(), lr=0.001)
loss_func = MSELoss()
x_data, y_data = Variable(x_train), Variable(y_train)
bar = ProgressBar(1, STEPS, "train_loss:%.9f")
predict = []
myloss = []
for step in range(STEPS):
prediction = Net(x_data)
def __init__(self):
super(LinearClassifier, self).__init__()
self.fully_connected = Linear(2, 1)
)
from backpack import convert_module_to_backpack
from backpack.custom_module.branching import Parallel
SQRT_GGN_SETTINGS = SECONDORDER_SETTINGS
###############################################################################
# Embedding #
###############################################################################
SQRT_GGN_SETTINGS += [
{
"input_fn": lambda: randint(0, 5, (6, )),
"module_fn": lambda: Sequential(
Embedding(5, 3),
Linear(3, 4),
),
"loss_function_fn": lambda: CrossEntropyLoss(reduction="mean"),
"target_fn": lambda: classification_targets((6, ), 4),
},
{
"input_fn": lambda: randint(0, 3, (3, 2, 2)),
"module_fn": lambda: Sequential(
Embedding(3, 2),
Flatten(),
),
"loss_function_fn": lambda: CrossEntropyLoss(reduction="mean"),
"target_fn": lambda: classification_targets((3, ), 2 * 2),
"seed": 1,
},
]
def _test_create_supervised_trainer(
model_device: Optional[str] = None,
trainer_device: Optional[str] = None,
trace: bool = False,
amp_mode: str = None,
scaler: Union[bool, "torch.cuda.amp.GradScaler"] = False,
):
model = Linear(1, 1)
if model_device:
model.to(model_device)
model.weight.data.zero_()
model.bias.data.zero_()
optimizer = SGD(model.parameters(), 0.1)
if trace:
example_input = torch.randn(1, 1)
model = torch.jit.trace(model, example_input)
if amp_mode == "apex" and model_device == trainer_device == "cuda":
from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level="O2")
trainer = create_supervised_trainer(
model,
optimizer,
mse_loss,
device=trainer_device,
output_transform=lambda x, y, y_pred, loss: (y_pred, loss.item()),
amp_mode=amp_mode,
scaler=scaler,
)
x = torch.tensor([[0.1], [0.2]])
y = torch.tensor([[0.3], [0.5]])
data = [(x, y)]
assert model.weight.data[0, 0].item() == approx(0.0)
assert model.bias.item() == approx(0.0)
if model_device == trainer_device or ((model_device == "cpu") ^
(trainer_device == "cpu")):
state = trainer.run(data)
assert state.output[-1] == approx(0.17), state.output[-1]
assert round(model.weight.data[0, 0].item(),
3) == approx(0.013), model.weight.item()
assert round(model.bias.item(), 3) == approx(0.08), model.bias.item()
if amp_mode == "amp":
assert state.output[0].dtype is torch.half
if scaler and isinstance(scaler, bool):
assert hasattr(state, "scaler")
else:
assert not hasattr(state, "scaler")
else:
if LooseVersion(torch.__version__) >= LooseVersion("1.7.0"):
# This is broken in 1.6.0 but will be probably fixed with 1.7.0
with pytest.raises(
RuntimeError,
match=r"is on CPU, but expected them to be on GPU"):
trainer.run(data)
def __init__(self, observable_dim: int, delay: int,
latent_dim: int) -> None:
super().__init__()
self.linear_embedder = Linear(in_features=observable_dim * delay,
out_features=latent_dim)
def __init__(self, input_dim: int, hidden_dim: int, output_dim: int) -> None:
super().__init__()
self.fc1 = Linear(input_dim, hidden_dim)
self.fc2 = Linear(hidden_dim, output_dim)
def __init__(self, dim):
super(NetGIN, self).__init__()
self.node_attribute_encoder = Sequential(Linear(2 * 13, dim),
torch.nn.BatchNorm1d(dim),
ReLU(), Linear(dim, dim),
torch.nn.BatchNorm1d(dim),
ReLU())
self.type_encoder = Sequential(Linear(3, dim),
torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim),
torch.nn.BatchNorm1d(dim), ReLU())
self.edge_encoder = Sequential(Linear(4 + 1, dim),
torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim),
torch.nn.BatchNorm1d(dim), ReLU())
self.mlp = Sequential(Linear(3 * dim, dim), torch.nn.BatchNorm1d(dim),
ReLU(), Linear(dim, dim),
torch.nn.BatchNorm1d(dim), ReLU())
nn1_1 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
nn1_2 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
self.conv1_1 = GINConv(nn1_1, train_eps=True)
self.conv1_2 = GINConv(nn1_2, train_eps=True)
self.mlp_1 = Sequential(Linear(2 * dim, dim),
torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim),
ReLU())
nn2_1 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
nn2_2 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
self.conv2_1 = GINConv(nn2_1, train_eps=True)
self.conv2_2 = GINConv(nn2_2, train_eps=True)
self.mlp_2 = Sequential(Linear(2 * dim, dim),
torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim),
ReLU())
nn3_1 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
nn3_2 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
self.conv3_1 = GINConv(nn3_1, train_eps=True)
self.conv3_2 = GINConv(nn3_2, train_eps=True)
self.mlp_3 = Sequential(Linear(2 * dim, dim),
torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim),
ReLU())
nn4_1 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
nn4_2 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
self.conv4_1 = GINConv(nn4_1, train_eps=True)
self.conv4_2 = GINConv(nn4_2, train_eps=True)
self.mlp_4 = Sequential(Linear(2 * dim, dim),
torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim),
ReLU())
nn5_1 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
nn5_2 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
self.conv5_1 = GINConv(nn5_1, train_eps=True)
self.conv5_2 = GINConv(nn5_2, train_eps=True)
self.mlp_5 = Sequential(Linear(2 * dim, dim),
torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim),
ReLU())
nn6_1 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
nn6_2 = Sequential(Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim), ReLU())
self.conv6_1 = GINConv(nn6_1, train_eps=True)
self.conv6_2 = GINConv(nn6_2, train_eps=True)
self.mlp_6 = Sequential(Linear(2 * dim, dim),
torch.nn.BatchNorm1d(dim), ReLU(),
Linear(dim, dim), torch.nn.BatchNorm1d(dim),
ReLU())
self.set2set = Set2Set(1 * dim, processing_steps=6)
self.fc1 = Linear(2 * dim, dim)
self.fc4 = Linear(dim, 12)
def __init__(self, embedding_size, out_h, out_w):
super(MobileFaceNet, self).__init__()
self.conv1 = Conv_block(3,
64,
kernel=(3, 3),
stride=(2, 2),
padding=(1, 1))
self.conv2_dw = Conv_block(64,
64,
kernel=(3, 3),
stride=(1, 1),
padding=(1, 1),
groups=64)
self.conv_23 = Depth_Wise(64,
64,
kernel=(3, 3),
stride=(2, 2),
padding=(1, 1),
groups=128)
self.conv_3 = Residual(64,
num_block=4,
groups=128,
kernel=(3, 3),
stride=(1, 1),
padding=(1, 1))
self.conv_34 = Depth_Wise(64,
128,
kernel=(3, 3),
stride=(2, 2),
padding=(1, 1),
groups=256)
self.conv_4 = Residual(128,
num_block=6,
groups=256,
kernel=(3, 3),
stride=(1, 1),
padding=(1, 1))
self.conv_45 = Depth_Wise(128,
128,
kernel=(3, 3),
stride=(2, 2),
padding=(1, 1),
groups=512)
self.conv_5 = Residual(128,
num_block=2,
groups=256,
kernel=(3, 3),
stride=(1, 1),
padding=(1, 1))
self.conv_6_sep = Conv_block(128,
512,
kernel=(1, 1),
stride=(1, 1),
padding=(0, 0))
#self.conv_6_dw = Linear_block(512, 512, groups=512, kernel=(7,7), stride=(1, 1), padding=(0, 0))
#self.conv_6_dw = Linear_block(512, 512, groups=512, kernel=(4,7), stride=(1, 1), padding=(0, 0))
self.conv_6_dw = Linear_block(512,
512,
groups=512,
kernel=(out_h, out_w),
stride=(1, 1),
padding=(0, 0))
self.conv_6_flatten = Flatten()
self.linear = Linear(512, embedding_size, bias=False)
self.bn = BatchNorm1d(embedding_size)
def __init__(self):
super().__init__()
self.layer = Linear(4, 4)
def __init__(self, in_channels, hidden_channels, out_channels):
super(Block, self).__init__()
self.conv1 = DenseGCNConv(in_channels, hidden_channels)
self.conv2 = DenseGCNConv(hidden_channels, hidden_channels)
self.lin = Linear(hidden_channels + hidden_channels, out_channels)
def __init__(self, dim_in, dim_out, dim_ctx):
super(ConcatSquashLinear, self).__init__()
self._layer = Linear(dim_in, dim_out)
self._hyper_bias = Linear(dim_ctx, dim_out, bias=False)
self._hyper_gate = Linear(dim_ctx, dim_out)
def __init__(self):
super().__init__()
self.inner = FSDP(Linear(4, 4), **fsdp_config)
self.outer = Linear(4, 5)
def __init__(self, vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
use_attention: bool,
seq2seq_encoder: Seq2SeqEncoder,
seq2vec_encoder: Seq2VecEncoder,
span_end_encoder_after: Seq2SeqEncoder,
use_decoder_trainer: bool,
decoder_beam_search: BeamSearch,
kb_configs: dict,
other_configs: dict,
initializer: InitializerApplicator) -> None:
super(ProStructModel, self).__init__(vocab)
self.text_field_embedder = text_field_embedder
self.num_actions = len(Action) # number of actions is hardcoded here.
# They are defined in Action enum in propara_dataset_reader.py
self.other_configs = other_configs
# kb_coefficient * kb_score + (1-kb_coefficient) * model_score
self.kb_coefficient = torch.nn.Parameter(torch.ones(1).mul(kb_configs.get('kb_coefficient', 0.5)))
self.use_attention = use_attention
self.use_decoder_trainer = use_decoder_trainer
if self.use_attention:
self.seq2seq_encoder = seq2seq_encoder
self.time_distributed_seq2seq_encoder = TimeDistributed(TimeDistributed(self.seq2seq_encoder))
self.time_distributed_attention_layer = \
TimeDistributed(TimeDistributed(
Attention(similarity_function=BilinearSimilarity(2 * seq2seq_encoder.get_output_dim(),
seq2seq_encoder.get_output_dim()),
normalize=True)))
self.aggregate_feedforward = Linear(seq2seq_encoder.get_output_dim(),
self.num_actions)
else:
self.seq2vec_encoder = seq2vec_encoder
self.time_distributed_seq2vec_encoder = TimeDistributed(TimeDistributed(self.seq2vec_encoder))
self.aggregate_feedforward = Linear(seq2vec_encoder.get_output_dim(),
self.num_actions)
self.span_end_encoder_after = span_end_encoder_after
# per step per participant
self.time_distributed_encoder_span_end_after = TimeDistributed(TimeDistributed(self.span_end_encoder_after))
# Fixme: dimensions
self._span_start_predictor_after = TimeDistributed(TimeDistributed(torch.nn.Linear(2 + 2*seq2seq_encoder.get_output_dim(), 1)))
self._span_end_predictor_after = TimeDistributed(TimeDistributed(torch.nn.Linear(span_end_encoder_after.get_output_dim(), 1)))
self._type_accuracy = BooleanAccuracy()
self._loss = torch.nn.CrossEntropyLoss(ignore_index=-1) # Fixme: This is less robust. If the masking value
# Fixme: add a metric for location span strings
self.span_metric = SquadEmAndF1()
if self.use_decoder_trainer:
self.decoder_trainer = MaximumMarginalLikelihood()
if kb_configs['kb_to_use'] == 'lexicalkb':
kb = KBLexical(
lexical_kb_path=kb_configs['lexical_kb_path'],
fullgrid_prompts_load_path=kb_configs['fullgrid_prompts_load_path']
)
# Makeshift arrangement to get number of participants in tiny.tsv .
self.commonsense_based_action_generator = CommonsenseBasedActionGenerator(self.num_actions)
self.rules_activated = [int(rule_val.strip()) > 0
for rule_val in self.other_configs.get('constraint_rules_to_turn_on', '0,0,0,1')
.split(",")]
self.rule_2_fraction_participants = self.other_configs.get('rule_2_fraction_participants', 0.5)
self.rule_3_fraction_steps = self.other_configs.get('rule_3_fraction_steps', 0.5)
self.commonsense_based_action_generator.set_rules_used(self.rules_activated,
self.rule_2_fraction_participants,
self.rule_3_fraction_steps)
# [self.rules_activated[0], # C/D/C/D cannot happen
# self.rules_activated[1], # > 1/2 partic
# self.rules_activated[2], # > 1/2 steps cannot change
# self.rules_activated[3] # until mentioned
# ])
self.decoder_step = ProParaDecoderStep(KBBasedActionScorer(kb=kb, kb_coefficient=self.kb_coefficient),
valid_action_generator=self.commonsense_based_action_generator)
self.beam_search = decoder_beam_search
initializer(self)
shuffle=True)
images, label = next(iter(trainloader))
images.size()
im1 = images[0]
im1.size()
im1_plt = np.squeeze(im1)
plt.imshow(im1_plt)
for image, label in trainloader:
pass
#Apply your DL on the dataset.
############################################ Linear transformation
from torch.nn import Linear
## linear layer
l1 = Linear(in_features=10, out_features=5, bias=True)
## inputs
inp = Variable(torch.randn(1, 10))
## Apply linear transformation to the inputs
l1(inp).size()
## accessing the trainable parameters
l1.weight ## size of the l1 weight layer would be such that that the mat mul will give out_features so 10X1.T.dot(10X5)-->
l1.weight.size()
l1.bias
## super is a shortcut to access a base class without having to know its type or name
## here super is used to pass arguments of child class to parents class
## sample network code
def test_add_param_group(debias_ewma):
"""Test AdaScale supports add_param_group() API."""
model1 = Linear(2, 2, bias=True)
with torch.no_grad():
# make weights and bias deterministic, which is needed for
# multi-layer models. For them, adascale gain is affected by
# parameters from other layers.
model1.weight.copy_(Tensor([1.0, 2.0, 3.0, 4.0]).reshape(2, 2))
model1.bias.fill_(0.1)
optim = AdaScale(SGD(model1.parameters(), lr=0.1),
num_gradients_to_accumulate=2,
debias_ewma=debias_ewma)
assert len(optim._hook_handles) == 2
model2 = Linear(2, 3, bias=True)
with torch.no_grad():
# make weights and bias deterministic
model2.weight.copy_(
Tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).reshape(3, 2))
model2.bias.fill_(0.2)
optim.add_param_group({"params": model2.parameters()})
assert len(optim._hook_handles) == 4
# make sure we can run the model.
model = Sequential(model1, model2).cuda()
in_data_0 = Tensor([1.0, 2.0]).cuda()
out = model(in_data_0)
out.sum().backward()
in_data_1 = Tensor([3.0, 4.0]).cuda()
out = model(in_data_1)
out.sum().backward()
# make sure the gains are right and we can step.
# since this is the first step, debias_ewma doesn't affect the value.
assert np.allclose(optim.gain(), 1.1440223454935758), optim.gain()
assert np.allclose(optim.gain(0), 1.1428571428571428), optim.gain(0)
assert np.allclose(optim.gain(1), 1.1471258476157762), optim.gain(1)
optim.step()
optim.zero_grad()
# make sure we can add a PG again after stepping.
model3 = Linear(3, 4, bias=True)
with torch.no_grad():
# make weights and bias deterministic
model3.weight.copy_(
Tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0] * 2).reshape(4, 3))
model3.bias.fill_(0.2)
optim.add_param_group({"params": model3.parameters()})
assert len(optim._hook_handles) == 6
# make sure we can run the model.
model = Sequential(model1, model2, model3).cuda()
in_data_0 = Tensor([1.0, 2.0]).cuda()
out = model(in_data_0)
out.sum().backward()
in_data_1 = Tensor([3.0, 4.0]).cuda()
out = model(in_data_1)
out.sum().backward()
# make sure gains are right and we can step.
# the last PG's gain is not affected by debias_ewma since it is the first step for that PG.
assert np.allclose(
optim.gain(), 1.1191193589460822
if debias_ewma else 1.1192783954732368), optim.gain()
assert np.allclose(
optim.gain(0), 1.1428571880897151
if debias_ewma else 1.142857188085096), optim.gain(0)
assert np.allclose(
optim.gain(1), 1.1167103578364508
if debias_ewma else 1.1167104954034948), optim.gain(1)
assert np.allclose(optim.gain(2), 1.117381091722702), optim.gain(2)
optim.step()
optim.zero_grad()
"""
Compute the gradient with PyTorch and the KFLR approximation with BackPACK.
"""
from torch.nn import CrossEntropyLoss, Flatten, Linear, Sequential
from backpack import backpack, extend, extensions
from backpack.utils.examples import load_mnist_data
B = 4
X, y = load_mnist_data(B)
print("# Gradient with PyTorch, KFLR approximation with BackPACK | B =", B)
model = Sequential(Flatten(), Linear(784, 10),)
lossfunc = CrossEntropyLoss()
model = extend(model)
lossfunc = extend(lossfunc)
loss = lossfunc(model(X), y)
with backpack(extensions.KFLR()):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".kflr (shapes): ", [kflr.shape for kflr in param.kflr])
