def verify_loss(self, f):
p = 0
n_pair = f.size(1)
for i in range(n_pair):
for t in range(n_pair):
p += torch.dot(f[:, i], f[:, t]) / torch.dot(f[:, i], f)
return p.mean()
def test_local_var_binary_methods(self):
''' Unit tests for methods mentioned on issue 1385
https://github.com/OpenMined/PySyft/issues/1385'''
x = torch.FloatTensor([1, 2, 3, 4])
y = torch.FloatTensor([[1, 2, 3, 4]])
z = torch.matmul(x, y.t())
assert (torch.equal(z, torch.FloatTensor([30])))
z = torch.add(x, y)
assert (torch.equal(z, torch.FloatTensor([[2, 4, 6, 8]])))
x = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
y = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
z = torch.cross(x, y, dim=1)
assert (torch.equal(z, torch.FloatTensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]])))
x = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
y = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
z = torch.dist(x, y)
t = torch.FloatTensor([z])
assert (torch.equal(t, torch.FloatTensor([0.])))
x = torch.FloatTensor([1, 2, 3])
y = torch.FloatTensor([1, 2, 3])
z = torch.dot(x, y)
t = torch.FloatTensor([z])
assert torch.equal(t, torch.FloatTensor([14]))
z = torch.eq(x, y)
assert (torch.equal(z, torch.ByteTensor([1, 1, 1])))
z = torch.ge(x, y)
assert (torch.equal(z, torch.ByteTensor([1, 1, 1])))
def test_remote_var_binary_methods(self):
''' Unit tests for methods mentioned on issue 1385
https://github.com/OpenMined/PySyft/issues/1385'''
hook = TorchHook(verbose=False)
local = hook.local_worker
remote = VirtualWorker(hook, 1)
local.add_worker(remote)
x = Var(torch.FloatTensor([1, 2, 3, 4])).send(remote)
y = Var(torch.FloatTensor([[1, 2, 3, 4]])).send(remote)
z = torch.matmul(x, y.t())
assert (torch.equal(z.get(), Var(torch.FloatTensor([30]))))
z = torch.add(x, y)
assert (torch.equal(z.get(), Var(torch.FloatTensor([[2, 4, 6, 8]]))))
x = Var(torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])).send(remote)
y = Var(torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])).send(remote)
z = torch.cross(x, y, dim=1)
assert (torch.equal(z.get(), Var(torch.FloatTensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]]))))
x = Var(torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])).send(remote)
y = Var(torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])).send(remote)
z = torch.dist(x, y)
assert (torch.equal(z.get(), Var(torch.FloatTensor([0.]))))
x = Var(torch.FloatTensor([1, 2, 3])).send(remote)
y = Var(torch.FloatTensor([1, 2, 3])).send(remote)
z = torch.dot(x, y)
print(torch.equal(z.get(), Var(torch.FloatTensor([14]))))
z = torch.eq(x, y)
assert (torch.equal(z.get(), Var(torch.ByteTensor([1, 1, 1]))))
z = torch.ge(x, y)
assert (torch.equal(z.get(), Var(torch.ByteTensor([1, 1, 1]))))
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
if group['weight_decay'] != 0:
grad = grad.add(group['weight_decay'], p.data)
if state['step'] > 1:
prev_bias_correction1 = 1 - beta1 ** (state['step'] - 1)
prev_bias_correction2 = 1 - beta2 ** (state['step'] - 1)
# Hypergradient for Adam:
h = torch.dot(grad.view(-1), torch.div(exp_avg, exp_avg_sq.sqrt().add_(group['eps'])).view(-1)) * math.sqrt(prev_bias_correction2) / prev_bias_correction1
# Hypergradient descent of the learning rate:
tmp = group['hypergrad_lr'] * h
group['lr'] += tmp.double().cpu()
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
return loss
def cal_angle(vec1, vec2):
""" Calculate cosine similarities between two torch tensors or two ndarraies
Args:
vec1, vec2: two tensors or numpy ndarraies
"""
if isinstance(vec1, torch.Tensor) and isinstance(vec1, torch.Tensor):
return torch.dot(vec1, vec2)/(vec1.norm()*vec2.norm()).item()
elif isinstance(vec1, np.ndarray) and isinstance(vec2, np.ndarray):
return np.ndarray.dot(vec1, vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2))
def updateOutput(self, input, target):
# - log(input) * target - log(1 - input) * (1 - target)
if input.nelement() != target.nelement():
raise RuntimeError("input and target size mismatch")
if self.buffer is None:
self.buffer = input.new()
buffer = self.buffer
weights = self.weights
buffer.resize_as_(input)
if weights is not None and target.dim() != 1:
weights = self.weights.view(1, target.size(1)).expand_as(target)
# log(input) * target
torch.add(input, self.eps, out=buffer).log_()
if weights is not None:
buffer.mul_(weights)
target_1d = target.contiguous().view(-1)
# don't save a 1-d view of buffer: it should already be contiguous, and it's
# used as non-1d tensor later.
output = torch.dot(target_1d, buffer.contiguous().view(-1))
# log(1 - input) * (1 - target)
torch.mul(input, -1, out=buffer).add_(1 + self.eps).log_()
if weights is not None:
buffer.mul_(weights)
output = output + torch.sum(buffer)
output = output - torch.dot(target_1d, buffer.contiguous().view(-1))
if self.sizeAverage:
output = output / input.nelement()
self.output = - output.item()
return self.output
def project_1D(w, d):
""" Project vector w to vector d and get the length of the projection.
Args:
w: vectorized weights
d: vectorized direction
Returns:
the projection scalar
"""
assert len(w) == len(d), 'dimension does not match for w and '
scale = torch.dot(w, d)/d.norm()
return scale.item()
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
assert len(self.param_groups) == 1
loss = None
if closure is not None:
loss = closure()
group = self.param_groups[0]
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
grad = self._gather_flat_grad_with_weight_decay(weight_decay)
# NOTE: SGDHD has only global state, but we register it as state for
# the first param, because this helps with casting in load_state_dict
state = self.state[self._params[0]]
# State initialization
if len(state) == 0:
state['grad_prev'] = torch.zeros_like(grad)
grad_prev = state['grad_prev']
# Hypergradient for SGD
h = torch.dot(grad, grad_prev)
# Hypergradient descent of the learning rate:
group['lr'] += group['hypergrad_lr'] * h
if momentum != 0:
if 'momentum_buffer' not in state:
buf = state['momentum_buffer'] = torch.zeros_like(grad)
buf.mul_(momentum).add_(grad)
else:
buf = state['momentum_buffer']
buf.mul_(momentum).add_(1 - dampening, grad)
if nesterov:
grad.add_(momentum, buf)
else:
grad = buf
state['grad_prev'] = grad
self._add_grad(-group['lr'], grad)
return loss
def compute_weight(self, module):
weight = module._parameters[self.name + '_org']
u = module._buffers[self.name + '_u']
height = weight.size(0)
weight_mat = weight.view(height, -1)
for _ in range(self.n_power_iterations):
# Spectral norm of weight equals to `u^T W v`, where `u` and `v`
# are the first left and right singular vectors.
# This power iteration produces approximations of `u` and `v`.
v = normalize(torch.matmul(weight_mat.t(), u), dim=0, eps=self.eps)
u = normalize(torch.matmul(weight_mat, v), dim=0, eps=self.eps)
sigma = torch.dot(u, torch.matmul(weight_mat, v))
weight.data /= sigma
return weight, u
def compute_weight(self, module):
weight = getattr(module, self.name + '_org')
u = getattr(module, self.name + '_u')
height = weight.size(0)
weight_mat = weight.view(height, -1)
with torch.no_grad():
for _ in range(self.n_power_iterations):
# Spectral norm of weight equals to `u^T W v`, where `u` and `v`
# are the first left and right singular vectors.
# This power iteration produces approximations of `u` and `v`.
v = normalize(torch.matmul(weight_mat.t(), u), dim=0, eps=self.eps)
u = normalize(torch.matmul(weight_mat, v), dim=0, eps=self.eps)
sigma = torch.dot(u, torch.matmul(weight_mat, v))
weight = weight / sigma
return weight, u
def compute_weight(self, module):
weight = getattr(module, self.name + '_orig')
u = getattr(module, self.name + '_u')
weight_mat = weight
if self.dim != 0:
# permute dim to front
weight_mat = weight_mat.permute(self.dim,
*[d for d in range(weight_mat.dim()) if d != self.dim])
height = weight_mat.size(0)
weight_mat = weight_mat.reshape(height, -1)
with torch.no_grad():
for _ in range(self.n_power_iterations):
v = F.normalize(torch.matmul(weight_mat.t(), u), dim=0, eps=self.eps)
u = F.normalize(torch.matmul(weight_mat, v), dim=0, eps=self.eps)
sigma = torch.dot(u, torch.matmul(weight_mat, v))
weight = weight / sigma
return weight, u
def test(self, dataset):
self.model.eval()
total_loss = 0
predictions = torch.zeros(len(dataset))
indices = torch.arange(1, dataset.num_classes + 1)
for idx in tqdm(range(len(dataset)),desc='Testing epoch ' + str(self.epoch) + ''):
ltree, lsent, rtree, rsent, label = dataset[idx]
linput, rinput = Var(lsent, volatile=True), Var(rsent, volatile=True)
target = Var(map_label_to_target(label, dataset.num_classes), volatile=True)
if self.args.cuda:
linput, rinput = linput.cuda(), rinput.cuda()
target = target.cuda()
output = self.model(ltree, linput, rtree, rinput)
loss = self.criterion(output, target)
total_loss += loss.data[0]
output = output.data.squeeze().cpu()
predictions[idx] = torch.dot(indices, torch.exp(output))
return total_loss / len(dataset), predictions
def lovasz_hinge_flat(logits, labels):
"""
Binary Lovasz hinge loss
logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
labels: [P] Tensor, binary ground truth labels (0 or 1)
ignore: label to ignore
"""
if len(labels) == 0:
# only void pixels, the gradients should be 0
return logits.sum() * 0.
signs = 2. * labels.float() - 1.
errors = (1. - logits * Variable(signs))
errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
perm = perm.data
gt_sorted = labels[perm]
grad = lovasz_grad(gt_sorted)
loss = torch.dot(F.relu(errors_sorted), Variable(grad))
return loss
def backward(self, gradient, image):
# lazy import
import torch
from torch.autograd import Variable
assert gradient.ndim == 1
gradient = torch.from_numpy(gradient)
if self.cuda: # pragma: no cover
gradient = gradient.cuda()
gradient = Variable(gradient)
image = self._process_input(image)
assert image.ndim == 3
images = image[np.newaxis]
images = torch.from_numpy(images)
if self.cuda: # pragma: no cover
images = images.cuda()
images = Variable(images, requires_grad=True)
predictions = self._model(images)
print(predictions.size())
predictions = predictions[0]
assert gradient.dim() == 1
assert predictions.dim() == 1
assert gradient.size() == predictions.size()
loss = torch.dot(predictions, gradient)
loss.backward()
# should be the same as predictions.backward(gradient=gradient)
grad = images.grad
grad = grad.data
if self.cuda: # pragma: no cover
grad = grad.cpu()
grad = grad.numpy()
grad = self._process_gradient(grad)
grad = np.squeeze(grad, axis=0)
assert grad.shape == image.shape
return grad
def lovasz_softmax_flat(probas, labels, only_present=False):
"""
Multi-class Lovasz-Softmax loss
probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
labels: [P] Tensor, ground truth labels (between 0 and C - 1)
only_present: average only on classes present in ground truth
"""
C = probas.size(1)
losses = []
for c in range(C):
fg = (labels == c).float() # foreground for class c
if only_present and fg.sum() == 0:
continue
errors = (Variable(fg) - probas[:, c]).abs()
errors_sorted, perm = torch.sort(errors, 0, descending=True)
perm = perm.data
fg_sorted = fg[perm]
losses.append(torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))
return mean(losses)
def test_remote_tensor_binary_methods(self):
hook = TorchHook(verbose = False)
local = hook.local_worker
remote = VirtualWorker(hook, 0)
local.add_worker(remote)
x = torch.FloatTensor([1, 2, 3, 4, 5]).send(remote)
y = torch.FloatTensor([1, 2, 3, 4, 5]).send(remote)
assert (x.add_(y).get() == torch.FloatTensor([2,4,6,8,10])).all()
x = torch.FloatTensor([1, 2, 3, 4]).send(remote)
y = torch.FloatTensor([[1, 2, 3, 4]]).send(remote)
z = torch.matmul(x, y.t())
assert (torch.equal(z.get(), torch.FloatTensor([30])))
z = torch.add(x, y)
assert (torch.equal(z.get(), torch.FloatTensor([[2, 4, 6, 8]])))
x = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]]).send(remote)
y = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]]).send(remote)
z = torch.cross(x, y, dim=1)
assert (torch.equal(z.get(), torch.FloatTensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]])))
x = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]]).send(remote)
y = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]]).send(remote)
z = torch.dist(x, y)
t = torch.FloatTensor([z])
assert (torch.equal(t, torch.FloatTensor([0.])))
x = torch.FloatTensor([1, 2, 3]).send(remote)
y = torch.FloatTensor([1, 2, 3]).send(remote)
z = torch.dot(x, y)
t = torch.FloatTensor([z])
assert torch.equal(t, torch.FloatTensor([14]))
z = torch.eq(x, y)
assert (torch.equal(z.get(), torch.ByteTensor([1, 1, 1])))
z = torch.ge(x, y)
assert (torch.equal(z.get(), torch.ByteTensor([1, 1, 1])))
def test_local_tensor_binary_methods(self):
''' Unit tests for methods mentioned on issue 1385
https://github.com/OpenMined/PySyft/issues/1385'''
x = torch.FloatTensor([1, 2, 3, 4])
y = torch.FloatTensor([[1, 2, 3, 4]])
z = torch.matmul(x, y.t())
assert (torch.equal(z, torch.FloatTensor([30])))
z = torch.add(x, y)
assert (torch.equal(z, torch.FloatTensor([[2, 4, 6, 8]])))
x = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
y = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
z = torch.cross(x, y, dim=1)
assert (torch.equal(z, torch.FloatTensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]])))
x = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
y = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
z = torch.dist(x, y)
assert (torch.equal(torch.FloatTensor([z]), torch.FloatTensor([0])))
x = torch.FloatTensor([1, 2, 3])
y = torch.FloatTensor([1, 2, 3])
z = torch.dot(x, y)
# There is an issue with some Macs getting 0.0 instead
# Solved here: https://github.com/pytorch/pytorch/issues/5609
assert torch.equal(torch.FloatTensor([z]), torch.FloatTensor([14]))
z = torch.eq(x, y)
assert (torch.equal(z, torch.ByteTensor([1, 1, 1])))
z = torch.ge(x, y)
assert (torch.equal(z, torch.ByteTensor([1, 1, 1])))
x = torch.FloatTensor([1, 2, 3, 4, 5])
y = torch.FloatTensor([1, 2, 3, 4, 5])
assert (x.add_(y) == torch.FloatTensor([2, 4, 6, 8, 10])).all()
def step(self, closure, b=None, M_inv=None):
"""
Performs a single optimization step.
Arguments:
closure (callable): A closure that re-evaluates the model
and returns a tuple of the loss and the output.
b (callable, optional): A closure that calculates the vector b in
the minimization problem x^T . A . x + x^T b.
M (callable, optional): The INVERSE preconditioner of A
"""
assert len(self.param_groups) == 1
group = self.param_groups[0]
alpha = group['alpha']
delta_decay = group['delta_decay']
cg_max_iter = group['cg_max_iter']
damping = group['damping']
use_gnm = group['use_gnm']
verbose = group['verbose']
state = self.state[self._params[0]]
state.setdefault('func_evals', 0)
state.setdefault('n_iter', 0)
loss_before, output = closure()
current_evals = 1
state['func_evals'] += 1
# Gather current parameters and respective gradients
flat_params = parameters_to_vector(self._params)
flat_grad = self._gather_flat_grad()
# Define linear operator
if use_gnm:
# Generalized Gauss-Newton vector product
def A(x):
return self._Gv(loss_before, output, x, damping)
else:
# Hessian-vector product
def A(x):
return self._Hv(flat_grad, x, damping)
if M_inv is not None:
m_inv = M_inv()
# Preconditioner recipe (Section 20.13)
if m_inv.dim() == 1:
m = (m_inv + damping) ** (-0.85)
def M(x):
return m * x
else:
m = torch.inverse(m_inv + damping * torch.eye(*m_inv.shape))
def M(x):
return m @ x
else:
M = None
b = flat_grad.detach() if b is None else b().detach().flatten()
# Initializing Conjugate-Gradient (Section 20.10)
if state.get('init_delta') is not None:
init_delta = delta_decay * state.get('init_delta')
else:
init_delta = torch.zeros_like(flat_params)
eps = torch.finfo(b.dtype).eps
# Conjugate-Gradient
deltas, Ms = self._CG(A=A, b=b.neg(), x0=init_delta,
M=M, max_iter=cg_max_iter,
tol=1e1 * eps, eps=eps, martens=True)
# Update parameters
delta = state['init_delta'] = deltas[-1]
M = Ms[-1]
vector_to_parameters(flat_params + delta, self._params)
loss_now = closure()[0]
current_evals += 1
state['func_evals'] += 1
# Conjugate-Gradient backtracking (Section 20.8.7)
if verbose:
print("Loss before CG: {}".format(float(loss_before)))
print("Loss before BT: {}".format(float(loss_now)))
for (d, m) in zip(reversed(deltas[:-1][::2]), reversed(Ms[:-1][::2])):
vector_to_parameters(flat_params + d, self._params)
loss_prev = closure()[0]
if float(loss_prev) > float(loss_now):
break
delta = d
M = m
loss_now = loss_prev
if verbose:
print("Loss after BT: {}".format(float(loss_now)))
# The Levenberg-Marquardt Heuristic (Section 20.8.5)
reduction_ratio = (float(loss_now) -
float(loss_before)) / M if M != 0 else 1
if reduction_ratio < 0.25:
group['damping'] *= 3 / 2
elif reduction_ratio > 0.75:
group['damping'] *= 2 / 3
if reduction_ratio < 0:
group['init_delta'] = 0
# Line Searching (Section 20.8.8)
beta = 0.8
c = 1e-2
min_improv = min(c * torch.dot(b, delta), 0)
for _ in range(60):
if float(loss_now) <= float(loss_before) + alpha * min_improv:
break
alpha *= beta
vector_to_parameters(flat_params + alpha * delta, self._params)
loss_now = closure()[0]
else: # No good update found
alpha = 0.0
loss_now = loss_before
# Update the parameters (this time fo real)
vector_to_parameters(flat_params + alpha * delta, self._params)
if verbose:
print("Loss after LS: {0} (lr: {1:.3f})".format(
float(loss_now), alpha))
print("Tikhonov damping: {0:.3f} (reduction ratio: {1:.3f})".format(
group['damping'], reduction_ratio), end='\n\n')
return loss_now
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--exp_name', default='ijba_eval')
parser.add_argument('-g', '--gpu', type=int, default=0)
parser.add_argument('-d', '--data_dir',
default='/home/renyi/arunirc/data1/datasets/CS2')
parser.add_argument('-p', '--protocol_dir',
default='/home/renyi/arunirc/data1/datasets/IJB-A/IJB-A_11_sets/')
parser.add_argument('--fold', type=int, default=1, choices=[1,10])
parser.add_argument('--sqrt', action='store_true', default=False,
help='Add signed sqrt normalization')
parser.add_argument('--cosine', action='store_true', default=False,
help='Use cosine similarity instead of L2 distance')
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('-m', '--model_path',
default=MODEL_PATH,
help='Path to pre-trained model')
parser.add_argument('--model_type', default=MODEL_TYPE,
choices=['resnet50', 'resnet101', 'resnet101-512d', 'resnet101-512d-norm'])
args = parser.parse_args()
# CUDA setup
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
cuda = torch.cuda.is_available()
torch.manual_seed(1337)
if cuda:
torch.cuda.manual_seed(1337)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True # enable if all images are same size
# -----------------------------------------------------------------------------
# 1. Model
# -----------------------------------------------------------------------------
num_class = 8631
if args.model_type == 'resnet50':
model = torchvision.models.resnet50(pretrained=False)
model.fc = torch.nn.Linear(2048, num_class)
elif args.model_type == 'resnet101':
model = torchvision.models.resnet101(pretrained=False)
model.fc = torch.nn.Linear(2048, num_class)
elif args.model_type == 'resnet101-512d':
model = torchvision.models.resnet101(pretrained=False)
layers = []
layers.append(torch.nn.Linear(2048, 512))
layers.append(torch.nn.Linear(512, num_class))
model.fc = torch.nn.Sequential(*layers)
elif args.model_type == 'resnet101-512d-norm':
model = torchvision.models.resnet101(pretrained=False)
layers = []
layers.append(torch.nn.Linear(2048, 512))
layers.append(models.NormFeat(scale_factor=50.0))
layers.append(torch.nn.Linear(512, num_class))
model.fc = torch.nn.Sequential(*layers)
else:
raise NotImplementedError
checkpoint = torch.load(args.model_path)
if checkpoint['arch'] == 'DataParallel':
model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3, 4])
model.load_state_dict(checkpoint['model_state_dict'])
model = model.module # get network module from inside its DataParallel wrapper
else:
model.load_state_dict(checkpoint['model_state_dict'])
if cuda:
model = model.cuda()
# Convert the trained network into a "feature extractor"
feature_map = list(model.children())
if args.model_type == 'resnet101-512d' or args.model_type == 'resnet101-512d-norm':
model.eval()
extractor = model
extractor.fc = nn.Sequential(extractor.fc[0])
else:
feature_map.pop()
extractor = nn.Sequential(*feature_map)
extractor.eval() # ALWAYS set to evaluation mode (fixes BatchNorm, dropout, etc.)
# -----------------------------------------------------------------------------
# 2. Dataset
# -----------------------------------------------------------------------------
fold_id = 1
file_ext = '.jpg'
RGB_MEAN = [ 0.485, 0.456, 0.406 ]
RGB_STD = [ 0.229, 0.224, 0.225 ]
test_transform = transforms.Compose([
# transforms.Scale(224),
# transforms.CenterCrop(224),
transforms.Scale((224,224)),
transforms.ToTensor(),
transforms.Normalize(mean = RGB_MEAN,
std = RGB_STD),
])
pairs_path = osp.join(args.protocol_dir, 'split%d' % fold_id,
'verify_comparisons_%d.csv' % fold_id)
pairs = utils.read_ijba_pairs(pairs_path)
protocol_file = osp.join(args.protocol_dir, 'split%d' % fold_id,
'verify_metadata_%d.csv' % fold_id)
metadata = utils.get_ijba_1_1_metadata(protocol_file) # dict
assert np.all(np.unique(pairs) == np.unique(metadata['template_id'])) # sanity-check
path_list = np.array([osp.join(args.data_dir, str(x)+file_ext)
for x in metadata['sighting_id'] ]) # face crops saved as <sighting_id.jpg>
# Create data loader
test_loader = torch.utils.data.DataLoader(
data_loader.IJBADataset(
path_list, test_transform, split=fold_id),
batch_size=args.batch_size, shuffle=False )
# testing
# for i in range(len(test_loader.dataset)):
# img = test_loader.dataset.__getitem__(i)
# sz = img.shape
# if sz[0] != 3:
# print sz
# -----------------------------------------------------------------------------
# 3. Feature extraction
# -----------------------------------------------------------------------------
print 'Feature extraction...'
cache_dir = osp.join(here, 'cache-' + args.model_type)
if not osp.exists(cache_dir):
os.makedirs(cache_dir)
feat_path = osp.join(cache_dir, 'feat-fold-%d.mat' % fold_id)
if not osp.exists(feat_path):
features = []
for batch_idx, images in tqdm.tqdm(enumerate(test_loader),
total=len(test_loader),
desc='Extracting features'):
x = Variable(images, volatile=True) # test-time memory conservation
if cuda:
x = x.cuda()
feat = extractor(x)
if cuda:
feat = feat.data.cpu() # free up GPU
else:
feat = feat.data
features.append(feat)
features = torch.cat(features, dim=0) # (n_batch*batch_sz) x 512
sio.savemat(feat_path, {'feat': features.cpu().numpy() })
else:
dat = sio.loadmat(feat_path)
features = torch.FloatTensor(dat['feat'])
del dat
print 'Loaded.'
# -----------------------------------------------------------------------------
# 4. Verification
# -----------------------------------------------------------------------------
scores = []
labels = []
# labels: is_same_subject
print 'Computing pair labels . . . '
for pair in tqdm.tqdm(pairs): # TODO - check tqdm
sel_t0 = np.where(metadata['template_id'] == pair[0])
sel_t1 = np.where(metadata['template_id'] == pair[1])
subject0 = np.unique(metadata['subject_id'][sel_t0])
subject1 = np.unique(metadata['subject_id'][sel_t1])
labels.append(int(subject0 == subject1))
labels = np.array(labels)
print 'done'
# templates: average pool, then L2-normalize
print 'Pooling templates . . . '
pooled_features = []
template_set = np.unique(metadata['template_id'])
for tid in tqdm.tqdm(template_set):
sel = np.where(metadata['template_id'] == tid)
# pool template: 1 x n x 512 -> 1 x 512
feat = features[sel,:].mean(1)
if args.sqrt: # signed-square-root normalization
feat = torch.mul(torch.sign(feat),torch.sqrt(torch.abs(feat)+1e-12))
pooled_features.append(F.normalize(feat, p=2, dim=1) )
pooled_features = torch.cat(pooled_features, dim=0) # (n_batch*batch_sz) x 512
print 'done'
print 'Computing pair distances . . . '
for pair in tqdm.tqdm(pairs):
sel_t0 = np.where(template_set == pair[0])
sel_t1 = np.where(template_set == pair[1])
if args.cosine:
feat_dist = torch.dot(torch.squeeze(pooled_features[sel_t0]),
torch.squeeze(pooled_features[sel_t1]))
else:
feat_dist = (pooled_features[sel_t0] - pooled_features[sel_t1]).norm(p=2, dim=1)
feat_dist = -torch.squeeze(feat_dist)
feat_dist = feat_dist.numpy()
scores.append(feat_dist) # score: negative of L2-distance
scores = np.array(scores)
# Metrics: TAR (tpr) at FAR (fpr)
fpr, tpr, thresholds = sklearn.metrics.roc_curve(labels, scores)
fpr_levels = [0.0001, 0.001, 0.01, 0.1]
f_interp = interpolate.interp1d(fpr, tpr)
tpr_at_fpr = [ f_interp(x) for x in fpr_levels ]
for (far, tar) in zip(fpr_levels, tpr_at_fpr):
print 'TAR @ FAR=%.4f : %.4f' % (far, tar)
res = {}
res['TAR'] = tpr_at_fpr
res['FAR'] = fpr_levels
with open( osp.join(cache_dir, 'result-1-1-fold-%d.yaml' % fold_id),
'w') as f:
yaml.dump(res, f, default_flow_style=False)
sio.savemat(osp.join(cache_dir, 'roc-1-1-fold-%d.mat' % fold_id),
{'fpr': fpr, 'tpr': tpr, 'thresholds': thresholds,
'tpr_at_fpr': tpr_at_fpr})
def __getitem__(self, index):
scene_id = self.scene_index[index // NUM_SAMPLE_PER_SCENE]
sample_id = index % NUM_SAMPLE_PER_SCENE
sample_path = os.path.join(self.image_folder, 'scene_' + str(scene_id),
'sample_' + str(sample_id))
images = []
for image_name in image_names:
image_path = os.path.join(sample_path, image_name)
image = Image.open(image_path)
image.load()
images.append(self.transform["image"](image))
image_tensor = torch.stack(images)
data_entries = self.annotation_dataframe[
(self.annotation_dataframe['scene'] == scene_id)
& (self.annotation_dataframe['sample'] == sample_id)]
corners = data_entries[[
'fl_x', 'fr_x', 'bl_x', 'br_x', 'fl_y', 'fr_y', 'bl_y', 'br_y'
]].to_numpy()
categories = data_entries.category_id.to_numpy()
ego_path = os.path.join(sample_path, 'ego.png')
ego_image = Image.open(ego_path)
ego_image.load()
ego_image = torchvision.transforms.functional.to_tensor(ego_image)
road_image = convert_map_to_road_map(ego_image)
road_image = self.transform["road"](road_image.type(torch.FloatTensor))
# print(torch.as_tensor(corners).view(-1, 2, 4).transpose(1,2).flatten(1,2))
bounding_box = torch.as_tensor(corners).view(
-1, 2, 4) #.transpose(1,2)#.flatten(1,2)
bounding_box[:, 0] = (bounding_box[:, 0] * 10) + 400
bounding_box[:, 1] = (-bounding_box[:, 1] * 10) + 400
bounding_box = (bounding_box * 256) / 800
bounding_box = bounding_box.transpose(1, 2)
# print(bounding_box[:, :, 0].shape)
bbox = torch.zeros(bounding_box.shape[0], 4)
# print(bbox.shape, bounding_box.shape)
# bbox = (bbox * 256)/800
bbox_new = torch.zeros(bounding_box.shape[0], 5)
# print(bbox.shape, bounding_box.shape)
# bbox[:, 0] = bounding_box[:, :, 0].min(dim=1)[0]
# bbox[:, 1] = bounding_box[:, :, 1].min(dim=1)[0]
# bbox[:, 2] = bounding_box[:, :, 0].max(dim=1)[0]
# bbox[:, 3] = bounding_box[:, :, 1].max(dim=1)[0]
# Computre rotate angle from center point
for i, box in enumerate(bounding_box):
if box[0][0] <= box[2][0] and box[0][1] >= box[1][1]:
br = box[0]
bl = box[1]
fr = box[2]
fl = box[3]
else:
fl = box[0]
fr = box[1]
bl = box[2]
br = box[3]
# print("before:",box)
centerpoint = (fl + br) / 2
if fl[0] > fr[0]: # negative angle
if fr[0] != centerpoint[0]:
theta = torch.atan(
(fr[1] - centerpoint[1]) / abs(fr[0] - centerpoint[0]))
else:
theta = (np.pi / 2)
a = bl - centerpoint
b = fl - centerpoint
tempangle = torch.acos(
torch.dot(a, b) / (torch.norm(a, 2) * torch.norm(b, 2)))
beta = (np.pi - tempangle) / 2
if fr[0] > centerpoint[0]:
gamma = -(theta - beta)
else:
gamma = -(np.pi - theta - beta)
# print ("-----test----")
# print (torch.norm(a, 2))
# print (torch.norm(b, 2))
# print (theta)
# print (beta)
# print (gamma)
elif fl[0] < fr[0]: # positive angle
if centerpoint[0] != br[0]:
theta = torch.atan(
(br[1] - centerpoint[1]) / abs(centerpoint[0] - br[0]))
else:
theta = np.pi / 2
a = fl - centerpoint
b = bl - centerpoint
tempangle = torch.acos(
torch.dot(a, b) / (torch.norm(a, 2) * torch.norm(b, 2)))
beta = (np.pi - tempangle) / 2
if br[0] > centerpoint[0]:
gamma = (theta - beta)
else:
gamma = (np.pi - theta - beta)
else:
gamma = 0
# print((gamma*180)/np.pi)
#theta = np.arctan((fr[1] - br[1])/(fr[0]-br[0]))
bbox_new[i, 4] = gamma
translation_matrix = torch.tensor([[1, 0, centerpoint[0]],
[0, 1, centerpoint[1]],
[0, 0, 1]])
reverse_translation_matrix = torch.tensor([[1, 0, -centerpoint[0]],
[0, 1, -centerpoint[1]],
[0, 0, 1]])
rotation_matrix = torch.tensor(
[[torch.cos(-gamma), -torch.sin(-gamma), 0],
[torch.sin(-gamma), torch.cos(-gamma), 0], [0, 0, 1]])
# print(translation_matrix,reverse_translation_matrix,rotation_matrix)
# print(box.shape)
box = torch.cat([
box.transpose(0, 1),
torch.ones(box.shape[0]).type(torch.DoubleTensor).unsqueeze(0)
],
dim=0)
# print(box)
bbox_rotated = torch.matmul(
translation_matrix,
torch.matmul(rotation_matrix,
torch.matmul(reverse_translation_matrix,
box)))[:2]
# print(bbox_rotated)
# print("\nrotation matrix shape:",rotation_matrix.shape)
# rotation_matrix = torch.from_numpy(rotation_matrix)
# bbox_rotated = torch.matmul(rotation_matrix, torch.transpose(box, 0, 1))
# print("\nbbox_rotated shape:",bbox_rotated.shape)
# print("\nrotated_bbox:", bbox_rotated)
# print("\nbbox new shape:",bbox_new.shape)
if box[0][0] <= box[2][0] and box[0][1] >= box[1][1]:
bbox_new[i, 0] = bbox_rotated[0, 1]
bbox_new[i, 1] = bbox_rotated[1, 1]
bbox_new[i, 2] = bbox_rotated[0, 2]
bbox_new[i, 3] = bbox_rotated[1, 2]
else:
bbox_new[i, 0] = bbox_rotated[0, 0]
bbox_new[i, 1] = bbox_rotated[1, 0]
bbox_new[i, 2] = bbox_rotated[0, 3]
bbox_new[i, 3] = bbox_rotated[1, 3]
# print("\nafter:",bbox_new[i])
# if len(bbox_rotated[bbox_rotated<0])>0:
# print(bbox[0])
# print(scene_id, sample_id, bounding_box.shape)
classes = torch.as_tensor(categories).view(-1, 1)
# print(bbox_new.shape,classes.shape)
if self.args.gen_semantic_map:
semantic_map_path = os.path.join(sample_path, "semantic_map.npy")
semantic_map = np.load(semantic_map_path)
semantic_map = F.one_hot(
torch.tensor(semantic_map).to(torch.int64), 11)
else: # self.args.gen_object_map:
semantic_map_path = os.path.join(sample_path, "object_map.npy")
semantic_map = np.load(semantic_map_path)
semantic_map = F.one_hot(
torch.tensor(semantic_map).to(torch.int64), 3)
semantic_map = semantic_map.transpose(1, 2).transpose(0, 1)
# plt.imshow(semantic_map)
if self.extra_info:
actions = data_entries.action_id.to_numpy()
# You can change the binary_lane to False to get a lane with
lane_image = convert_map_to_lane_map(ego_image, binary_lane=True)
action = torch.as_tensor(actions)
ego = self.transform["road"](ego_image)
road = lane_image
# print(scene_id, sample_id, bounding_box[0])
# print(bounding_box.shape,classes.shape)
# print(classes)
# exit(0)
return index, image_tensor, bbox_new, classes, action, ego, road_image, semantic_map
else:
return index, image_tensor, bbox_new, classes
# -- Matrix Multiplication --
x1 = torch.rand((2, 5))
x2 = torch.rand((5, 3))
x3 = torch.mm(x1, x2) # Matrix multiplication of x1 and x2, out shape: 2x3
x3 = x1.mm(x2) # Similar as line above
# -- Matrix Exponentiation --
matrix_exp = torch.rand(5, 5)
print(matrix_exp.matrix_power(
3)) # is same as matrix_exp (mm) matrix_exp (mm) matrix_exp
# -- Element wise Multiplication --
z = x * y # z = [9, 16, 21] = [1*9, 2*8, 3*7]
# -- Dot product --
z = torch.dot(x, y) # Dot product, in this case z = 1*9 + 2*8 + 3*7
# -- Batch Matrix Multiplication --
batch = 32
n = 10
m = 20
p = 30
tensor1 = torch.rand((batch, n, m))
tensor2 = torch.rand((batch, m, p))
out_bmm = torch.bmm(tensor1, tensor2) # Will be shape: (b x n x p)
# -- Example of broadcasting --
x1 = torch.rand((5, 5))
x2 = torch.ones((1, 5))
z = (
x1 - x2
#--------------------------------------------------------------------
# Matrix modes_to_nodes
val_r_inv = torch.inverse(val_r)
# Computes coordiantes modes
coords_modes = torch.mm(val_r_inv, coords)
# Initialized coordiantes
interp_coords = torch.mm(val_i, coords_modes)
# Initialized jacobian
jacobian = torch.empty(3, 3, nnodes_if, dtype=torch.float64)
for inode in range(0, nnodes_if):
jacobian[0, 0, inode] = torch.dot(ddxi_i[inode, :], coords_modes[:, 0])
jacobian[0, 1, inode] = torch.dot(ddeta_i[inode, :], coords_modes[:, 0])
jacobian[0, 2, inode] = torch.dot(ddzeta_i[inode, :], coords_modes[:, 0])
jacobian[1, 0, inode] = torch.dot(ddxi_i[inode, :], coords_modes[:, 1])
jacobian[1, 1, inode] = torch.dot(ddeta_i[inode, :], coords_modes[:, 1])
jacobian[1, 2, inode] = torch.dot(ddzeta_i[inode, :], coords_modes[:, 1])
jacobian[2, 0, inode] = torch.dot(ddxi_i[inode, :], coords_modes[:, 2])
jacobian[2, 1, inode] = torch.dot(ddeta_i[inode, :], coords_modes[:, 2])
jacobian[2, 2, inode] = torch.dot(ddzeta_i[inode, :], coords_modes[:, 2])
update_progress("Computing Jacobian ",
inode / (nnodes_if - 1))
if coord_sys == 'CYLINDRICAL':
scaling_factor = torch.mm(val_i, coords_modes[:, 0])
for inode in range(0, nnodes_if):
jacobian[1, 0, inode] = jacobian[1, 0, inode] * scaling_factor[inode]
jacobian[1, 1, inode] = jacobian[1, 1, inode] * scaling_factor[inode]
def train(self, num_episodes, save_path, batch_size):
loss_file = open("results/losses.txt", "w")
reward_file = open("results/rewards.txt", "w")
step = 0
for e in tqdm.tqdm(range(1, num_episodes + 1)):
state = self.env.reset()
done = False
episode_reward = 0
if self.prioritized_sample:
gradient_accum = [
torch.zeros(param.shape).to(self.device)
for param in self.training_model.parameters()
]
while not done:
step += 1
self.epsilon = max(self.epsilon_min,
self.epsilon * self.epsilon_decay)
action = self.get_action(state)
# self.env.render()
new_state, reward, done, _ = self.env.step(action.item())
self.replay_buffer.push(
[state, action, new_state if not done else None, reward])
episode_reward += reward
state = new_state
if len(self.replay_buffer) > self.minimum_buffer_size:
if self.prioritized_sample:
states, actions, new_states, rewards, mask, importance_weights, indices = self.replay_buffer.sample(
batch_size)
importance_weights = importance_weights.to(self.device)
else:
states, actions, new_states, rewards, mask = self.replay_buffer.sample(
batch_size)
rewards = rewards.to(self.device)
mask = mask.to(self.device)
best_actions = self.get_best_actions(new_states)
target_q_values = self.get_target_q_value(
new_states, best_actions) * mask
expected_q_values = self.get_training_q_value(
states, actions)
errors = (rewards + self.gamma * target_q_values -
expected_q_values).pow(2)
if self.prioritized_sample:
self.replay_buffer.update(errors.detach().cpu(),
indices)
loss = errors.mean().sqrt()
loss_file.write("{}\n".format(loss.item()))
self.optimizer.zero_grad()
loss.backward()
if self.prioritized_sample:
with torch.no_grad():
for i, param in enumerate(
self.training_model.parameters()):
gradient_accum[i] += torch.dot(
importance_weights, errors) * param.grad
with torch.no_grad():
for param in self.training_model.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
if (self.transfer_frequency >
0) and (step % self.transfer_frequency == 0):
self.target_model.load_state_dict(
self.training_model.state_dict())
if self.prioritized_sample:
with torch.no_grad():
for i, param in enumerate(
self.training_model.parameters()):
new_param = param + gradient_accum[i]
param.data.copy_(new_param)
reward_file.write("{}\t{}\n".format(e, episode_reward))
self.target_model.load_state_dict(self.training_model.state_dict())
torch.save(self.target_model.state_dict(), save_path)
loss_file.close()
reward_file.close()
self.env.close()
def compute_weight(self, module, do_power_iteration):
# NB: If `do_power_iteration` is set, the `u` and `v` vectors are
# updated in power iteration **in-place**. This is very important
# because in `DataParallel` forward, the vectors (being buffers) are
# broadcast from the parallelized module to each module replica,
# which is a new module object created on the fly. And each replica
# runs its own spectral norm power iteration. So simply assigning
# the updated vectors to the module this function runs on will cause
# the update to be lost forever. And the next time the parallelized
# module is replicated, the same randomly initialized vectors are
# broadcast and used!
#
# Therefore, to make the change propagate back, we rely on two
# important bahaviors (also enforced via tests):
# 1. `DataParallel` doesn't clone storage if the broadcast tensor
# is alreay on correct device; and it makes sure that the
# parallelized module is already on `device[0]`.
# 2. If the out tensor in `out=` kwarg has correct shape, it will
# just fill in the values.
# Therefore, since the same power iteration is performed on all
# devices, simply updating the tensors in-place will make sure that
# the module replica on `device[0]` will update the _u vector on the
# parallized module (by shared storage).
#
# However, after we update `u` and `v` in-place, we need to **clone**
# them before using them to normalize the weight. This is to support
# backproping through two forward passes, e.g., the common pattern in
# GAN training: loss = D(real) - D(fake). Otherwise, engine will
# complain that variables needed to do backward for the first forward
# (i.e., the `u` and `v` vectors) are changed in the second forward.
weight = getattr(module, self.name + '_orig')
u = getattr(module, self.name + '_u')
v = getattr(module, self.name + '_v')
sigma_log = getattr(module, self.name + '_sigma') # for logging
# get settings from conv-module (for transposed convolution)
stride = module.stride
padding = module.padding
if do_power_iteration:
with torch.no_grad():
for _ in range(self.n_power_iterations):
v_s = conv_transpose2d(u.view(self.out_shape),
weight,
stride=stride,
padding=padding,
output_padding=0)
# Note: out flag for in-place changes
v = normalize(v_s.view(-1), dim=0, eps=self.eps, out=v)
u_s = conv2d(v.view(self.input_dim),
weight,
stride=stride,
padding=padding,
bias=None)
u = normalize(u_s.view(-1), dim=0, eps=self.eps, out=u)
if self.n_power_iterations > 0:
# See above on why we need to clone
u = u.clone()
v = v.clone()
weight_v = conv2d(v.view(self.input_dim),
weight,
stride=stride,
padding=padding,
bias=None)
weight_v = weight_v.view(-1)
sigma = torch.dot(u.view(-1), weight_v)
# enforce spectral norm only as constraint
factorReverse = torch.max(
torch.ones(1).to(weight.device), sigma / self.coeff)
# for logging
weight_v_det = weight_v.detach()
u_det = u.detach()
torch.max(torch.dot(u_det.view(-1), weight_v_det),
torch.dot(u_det.view(-1), weight_v_det),
out=sigma_log)
# rescaling
weight = weight / (factorReverse + 1e-5) # for stability
return weight
def compute_weight(self, module, do_power_iteration):
# NB: If `do_power_iteration` is set, the `u` and `v` vectors are
# updated in power iteration **in-place**. This is very important
# because in `DataParallel` forward, the vectors (being buffers) are
# broadcast from the parallelized module to each module replica,
# which is a new module object created on the fly. And each replica
# runs its own spectral norm power iteration. So simply assigning
# the updated vectors to the module this function runs on will cause
# the update to be lost forever. And the next time the parallelized
# module is replicated, the same randomly initialized vectors are
# broadcast and used!
#
# Therefore, to make the change propagate back, we rely on two
# important bahaviors (also enforced via tests):
# 1. `DataParallel` doesn't clone storage if the broadcast tensor
# is alreay on correct device; and it makes sure that the
# parallelized module is already on `device[0]`.
# 2. If the out tensor in `out=` kwarg has correct shape, it will
# just fill in the values.
# Therefore, since the same power iteration is performed on all
# devices, simply updating the tensors in-place will make sure that
# the module replica on `device[0]` will update the _u vector on the
# parallized module (by shared storage).
#
# However, after we update `u` and `v` in-place, we need to **clone**
# them before using them to normalize the weight. This is to support
# backproping through two forward passes, e.g., the common pattern in
# GAN training: loss = D(real) - D(fake). Otherwise, engine will
# complain that variables needed to do backward for the first forward
# (i.e., the `u` and `v` vectors) are changed in the second forward.
weight = getattr(module, self.name + '_orig')
u = getattr(module, self.name + '_u')
v = getattr(module, self.name + '_v')
sigma_log = getattr(module, self.name + '_sigma') # for logging
weight_mat = self.reshape_weight_to_matrix(weight)
if do_power_iteration:
with torch.no_grad():
for _ in range(self.n_power_iterations):
# Spectral norm of weight equals to `u^T W v`, where `u` and `v`
# are the first left and right singular vectors.
# This power iteration produces approximations of `u` and `v`.
v = normalize(torch.mv(weight_mat.t(), u),
dim=0,
eps=self.eps,
out=v)
u = normalize(torch.mv(weight_mat, v),
dim=0,
eps=self.eps,
out=u)
if self.n_power_iterations > 0:
# See above on why we need to clone
u = u.clone()
v = v.clone()
sigma = torch.dot(u, torch.mv(weight_mat, v))
# soft normalization: only when sigma larger than coeff
factor = torch.max(torch.ones(1).to(weight.device), sigma / self.coeff)
weight = weight / factor
# for logging
sigma_det = sigma.detach()
torch.max(torch.ones(1).to(weight.device),
sigma_det / self.coeff,
out=sigma_log)
return weight
def Bearing(xA_4d, xB_4d):
dp = xA_4d[:2] - xB_4d[:2]
v = xA_4d[2:]
cos_theta = torch.dot(dp, v) / (torch.norm(dp) * torch.norm(v) + 1E-6)
return cos_theta
def compute_nll_from_model(data,
pathmodel,
pathweights,
image_shape,
num_classes,
nb_step=5,
optim_default=partial(optim.SGD,
lr=5e-5,
momentum=0.),
dataloader=False):
print("Compute NLL from Model")
torch.random.manual_seed(0)
np.random.seed(0)
lls = {}
grad_total = {}
grad_stat_total = {}
likelihood_ratio_statistic = {}
grad_total[0] = []
for k in range(nb_step + 1):
lls[k] = []
# grad_total[k] = []
grad_stat_total[k] = []
likelihood_ratio_statistic[k] = []
model = load_model_from_param(pathmodel, pathweights, num_classes,
image_shape).cuda()
if not dataloader:
dataloader_aux = [(tqdm.tqdm(data), None)]
else:
dataloader_aux = tqdm.tqdm(iter(data))
for data_list, _ in dataloader_aux:
for x in data_list:
# load weights. print the weights.
model_copy = load_model_from_param(pathmodel, pathweights,
num_classes,
image_shape).cuda()
optimizer = optim_default(model_copy.parameters())
for param_group in optimizer.param_groups:
lr = param_group['lr']
break
model_copy.zero_grad()
grads = []
diff_param = []
x = x.to(device_test).unsqueeze(0)
_, nll, _ = model_copy(x, y_onehot=None)
nll.backward()
lls[0].append(-nll.detach().cpu().item())
optimizer.step()
for name_copy, param_copy in model_copy.named_parameters():
if param_copy.grad is not None:
grads.append(-param_copy.grad.view(-1))
grad_total[0].append(
torch.sum(lr * (torch.cat(grads)**2)).detach().cpu().item())
for (name_copy,
param_copy), (name,
param) in zip(model_copy.named_parameters(),
model.named_parameters()):
assert (name_copy == name)
if param_copy.grad is not None:
aux_diff_param = param_copy.data.detach(
) - param.data.detach()
diff_param.append(aux_diff_param.view(-1))
grads = torch.flatten(torch.cat(grads))
diff_param = torch.flatten(torch.cat(diff_param))
if not torch.isinf(torch.abs(torch.dot(grads, diff_param))).any():
grad_stat_total[0].append(
torch.abs(torch.dot(grads,
diff_param)).detach().cpu().item())
for k in range(1, nb_step + 1):
model_copy.zero_grad()
diff_param = []
_, nll, _ = model_copy(x, y_onehot=None)
nll.backward()
if not torch.isinf(-nll).any():
lls[k].append(-nll.detach().cpu().item())
else:
print("INF NLL")
lls[k].append(torch.sign(nll).detach().cpu().item() * 1e8)
optimizer.step()
for (name_copy,
param_copy), (name,
param) in zip(model_copy.named_parameters(),
model.named_parameters()):
assert (name_copy == name)
if param_copy.grad is not None:
aux_diff_param = param_copy.data.detach(
) - param.data.detach()
diff_param.append(aux_diff_param.view(-1))
diff_param = torch.flatten(torch.cat(diff_param))
if not torch.isinf(torch.abs(torch.dot(grads,
diff_param))).any():
grad_stat_total[k].append(
torch.abs(torch.dot(grads,
diff_param)).detach().cpu().item())
else:
print("Inf grad stat")
grad_total[0] = np.array(grad_total[0])
for key in grad_stat_total.keys():
lls[key] = np.array(lls[key])
likelihood_ratio_statistic[key] = lls[key] - lls[0]
likelihood_ratio_statistic[key] = likelihood_ratio_statistic[key][
np.where(np.abs(likelihood_ratio_statistic[key]) < 1e7)]
grad_stat_total[key] = np.array(grad_stat_total[key])
return lls, grad_total, grad_stat_total, likelihood_ratio_statistic
def compute_nll(data,
model,
nb_step=1,
optim_default=partial(optim.SGD, lr=1e-5, momentum=0.),
dataloader=False):
print("Compute NLL")
torch.random.manual_seed(0)
np.random.seed(0)
lls = {}
grad_total = {}
grad_stat_total = {}
likelihood_ratio_statistic = {}
for k in range(nb_step + 1):
lls[k] = []
grad_total[k] = []
grad_stat_total[k] = []
likelihood_ratio_statistic[k] = []
if not dataloader:
dataloader_aux = [(tqdm.tqdm(data), None)]
else:
dataloader_aux = tqdm.tqdm(iter(data))
for data_list, _ in dataloader_aux:
for x in data_list:
# load weights. print the weights.
model_copy = copy.deepcopy(model).to(device_test)
optimizer = optim_default(model_copy.parameters())
for param_group in optimizer.param_groups:
lr = param_group['lr']
break
model_copy.zero_grad()
grads = []
diff_param = []
x = x.to(device_test).unsqueeze(0)
_, nll, _ = model_copy(x, y_onehot=None)
nll.backward()
lls[0].append(-nll.detach().cpu().item())
optimizer.step()
for name_copy, param_copy in model_copy.named_parameters():
if param_copy.grad is not None:
grads.append(-param_copy.grad.view(-1))
grad_total[0].append(
torch.sum(lr * (torch.cat(grads)**2)).detach().cpu().item())
for (name_copy,
param_copy), (name,
param) in zip(model_copy.named_parameters(),
model.named_parameters()):
assert (name_copy == name)
if param_copy.grad is not None:
aux_diff_param = param_copy.data - param.data
diff_param.append(aux_diff_param.view(-1))
grads = torch.flatten(torch.cat(grads))
diff_param = torch.flatten(torch.cat(diff_param))
grad_stat_total[0].append(
torch.abs(torch.dot(grads, diff_param)).detach().cpu().item())
for k in range(1, nb_step + 1):
model_copy.zero_grad()
diff_param = []
_, nll, _ = model_copy(x, y_onehot=None)
nll.backward()
lls[k].append(-nll.detach().cpu().item())
optimizer.step()
for (name_copy,
param_copy), (name,
param) in zip(model_copy.named_parameters(),
model.named_parameters()):
assert (name_copy == name)
if param_copy.grad is not None:
aux_diff_param = param_copy.data - param.data
diff_param.append(aux_diff_param.view(-1))
grad_total[k].append(
torch.sum((grads**2) * lr).detach().cpu().item())
diff_param = torch.flatten(torch.cat(diff_param))
grad_stat_total[k].append(
torch.abs(torch.dot(grads,
diff_param)).detach().cpu().item())
for key in grad_total.keys():
grad_total[key] = np.array(grad_total[key])
lls[key] = np.array(lls[key])
likelihood_ratio_statistic[key] = lls[key] - lls[0]
grad_stat_total[key] = np.array(grad_stat_total[key])
return lls, grad_total, grad_stat_total, likelihood_ratio_statistic
def estimate_metrics(pred, random_query, binary_target, sup_net, switch_vec):
query_pred = torch.gather(pred, 1, random_query.view(-1, 1)).squeeze(1)
num_s = torch.tensor(np.sum(switch_vec).item(),
dtype=torch.float32).to(device)
# ipdb.set_trace()
_, class_pred = torch.max(pred, dim=1)
binary_pred = class_pred.eq(random_query).type(torch.cuda.LongTensor)
correct = binary_target.eq(binary_pred).sum()
accuracy = correct.type(torch.cuda.FloatTensor) / binary_target.size(0)
bce_loss = bce(query_pred, binary_target.type(torch.cuda.FloatTensor))
metrics = {}
s_hist = {}
ortho_mtrx = {}
metrics['accuracy'] = accuracy
metrics['bce_loss'] = bce_loss * args.lambda_bce
metrics['l1_loss_total'] = torch.from_numpy(np.float32([0.])).to(device)
metrics['orthogonality_loss_total'] = torch.from_numpy(np.float32(
[0.])).to(device)
metrics['quantization_loss_total'] = torch.from_numpy(np.float32(
[0.])).to(device)
metrics['total_loss'] = torch.from_numpy(np.float32([0.])).to(device)
# ipdb.set_trace()
metrics['total_loss'] = metrics['total_loss'] + metrics['bce_loss']
one_hot = torch.zeros((10, 10)).fill_(1).to(device)
s_one_hot = torch.zeros(10, 10).type(torch.cuda.FloatTensor)
s_queries = torch.from_numpy(np.array(list(range(10)))).to(device)
s_one_hot = s_one_hot.scatter_(dim=1,
index=s_queries.view(-1, 1),
src=one_hot)
s_vectors_all = sup_net(s_one_hot)
for k in range(len(switch_vec)):
if switch_vec[k]:
s_vectors = s_vectors_all[k]
for i in range(10):
s_hist['s_layer_{}_class_{}'.format(
k, i)] = s_vectors[i].cpu().data.numpy()
sparsity_loss = l1(s_vectors,
torch.zeros_like(s_vectors).to(device))
orth_loss = torch.from_numpy(np.float32([0.])).to(device)
for i in range(10):
for j in range(i, 10):
orth_loss = orth_loss + torch.dot(s_vectors[i],
s_vectors[j])
ortho_mtrx['layer_{}'.format(k)] = np.zeros((10, 10))
for i in range(10):
for j in range(10):
ortho_mtrx['layer_{}'.format(k)][i][j] = torch.dot(
s_vectors[i], s_vectors[j]).cpu().data.numpy()
quantization_target = s_vectors.detach() > 0.5
quantization_loss = mse(
s_vectors, quantization_target.type(torch.cuda.FloatTensor))
orth_loss = orth_loss / 45
# ipdb.set_trace()
metrics['l1_loss_{}'.format(k)] = sparsity_loss * args.lambda_l1
metrics['l1_loss_total'] = metrics['l1_loss_total'] + metrics[
'l1_loss_{}'.format(k)]
metrics['orthogonality_loss_{}'.format(
k)] = orth_loss * args.lambda_ortho
metrics['orthogonality_loss_total'] = metrics[
'orthogonality_loss_total'] + metrics[
'orthogonality_loss_{}'.format(k)]
metrics['quantization_loss_{}'.format(
k)] = quantization_loss * args.lambda_quant
metrics['quantization_loss_total'] = metrics[
'quantization_loss_total'] + metrics[
'quantization_loss_{}'.format(k)]
# ipdb.set_trace()
metrics['total_loss'] = metrics['total_loss'] + metrics['l1_loss_total']/num_s + \
metrics['orthogonality_loss_total']/num_s + \
metrics['quantization_loss_total']/num_s
return metrics, s_hist, ortho_mtrx
def train_model(num_epochs, dataset_name, datadir, feature, model_name,
fraction, select_every, optim_type, learning_rate, run, device,
log_dir, trn_batch_size, strategy):
# Loading the Dataset
trainset, validset, testset, num_cls = load_dataset_custom(
datadir, dataset_name, feature)
N = len(trainset)
val_batch_size = 1000
tst_batch_size = 1000
# Creating the Data Loaders
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=trn_batch_size,
shuffle=False,
pin_memory=True)
valloader = torch.utils.data.DataLoader(validset,
batch_size=val_batch_size,
shuffle=False,
pin_memory=True)
testloader = torch.utils.data.DataLoader(testset,
batch_size=tst_batch_size,
shuffle=False,
pin_memory=True)
# Budget for subset selection
bud = int(fraction * N)
print("Budget, fraction and N:", bud, fraction, N)
# Subset Selection and creating the subset data loader
start_idxs = np.random.choice(N, size=bud, replace=False)
idxs = start_idxs
data_sub = Subset(trainset, idxs)
subset_trnloader = torch.utils.data.DataLoader(data_sub,
batch_size=trn_batch_size,
shuffle=False,
pin_memory=True)
# Variables to store accuracies
gammas = torch.ones(len(idxs)).to(device)
substrn_losses = np.zeros(num_epochs)
val_losses = np.zeros(num_epochs)
timing = np.zeros(num_epochs)
val_acc = np.zeros(num_epochs)
tst_acc = np.zeros(num_epochs)
subtrn_acc = np.zeros(num_epochs)
# Results logging file
print_every = 3
all_logs_dir = log_dir + '/' + str(uuid.uuid4())
while os.path.exists(all_logs_dir):
all_logs_dir = log_dir + '/' + str(uuid.uuid4())
print(all_logs_dir)
subprocess.run(["mkdir", "-p", all_logs_dir])
path_logfile = os.path.join(all_logs_dir, 'log.txt')
logfile = open(path_logfile, 'w')
exp_name = dataset_name + '_fraction:' + str(fraction) + '_epochs:' + str(num_epochs) + \
'_selEvery:' + str(select_every) + '_variant' + '_runs' + str(run)
print(exp_name)
# Model Creation
model = create_model(model_name, num_cls, device)
model1 = create_model(model_name, num_cls, device)
# Loss Functions
criterion, criterion_nored = loss_function()
# Getting the optimizer and scheduler
optimizer, scheduler = optimizer_with_scheduler(optim_type, model,
num_epochs, learning_rate)
if strategy == 'GradMatch':
# OMPGradMatch Selection strategy
setf_model = OMPGradMatchStrategy(trainloader,
valloader,
model1,
criterion,
learning_rate,
device,
num_cls,
True,
'PerClassPerGradient',
False,
lam=0.5,
eps=1e-100)
elif strategy == 'GradMatchPB':
setf_model = OMPGradMatchStrategy(trainloader,
valloader,
model1,
criterion,
learning_rate,
device,
num_cls,
True,
'PerBatch',
False,
lam=0,
eps=1e-100)
elif strategy == 'GradMatch-Explore':
# OMPGradMatch Selection strategy
setf_model = OMPGradMatchStrategy(trainloader,
valloader,
model1,
criterion,
learning_rate,
device,
num_cls,
True,
'PerClassPerGradient',
False,
lam=0.5,
eps=1e-100)
# Random-Online Selection strategy
rand_setf_model = RandomStrategy(trainloader, online=True)
elif strategy == 'GradMatchPB-Explore':
# OMPGradMatch Selection strategy
setf_model = OMPGradMatchStrategy(trainloader,
valloader,
model1,
criterion,
learning_rate,
device,
num_cls,
True,
'PerBatch',
False,
lam=0,
eps=1e-100)
# Random-Online Selection strategy
rand_setf_model = RandomStrategy(trainloader, online=True)
elif strategy == 'Random':
# Random Selection strategy
setf_model = RandomStrategy(trainloader, online=False)
elif strategy == 'Random-Online':
# Random-Online Selection strategy
setf_model = RandomStrategy(trainloader, online=True)
print("=======================================", file=logfile)
kappa_epochs = int(0.5 * num_epochs)
full_epochs = floor(kappa_epochs / int(fraction * 100))
for i in range(num_epochs):
subtrn_loss = 0
subtrn_correct = 0
subtrn_total = 0
subset_selection_time = 0
if (strategy in [
'GLISTER', 'GradMatch', 'GradMatchPB', 'CRAIG', 'CRAIGPB'
]) and (((i + 1) % select_every) == 0):
start_time = time.time()
cached_state_dict = copy.deepcopy(model.state_dict())
clone_dict = copy.deepcopy(model.state_dict())
if strategy in ['CRAIG', 'CRAIGPB']:
subset_idxs, gammas = setf_model.select(
int(bud), clone_dict, 'lazy')
else:
subset_idxs, gammas = setf_model.select(int(bud), clone_dict)
model.load_state_dict(cached_state_dict)
idxs = subset_idxs
if strategy in ['GradMatch', 'GradMatchPB', 'CRAIG', 'CRAIGPB']:
gammas = torch.from_numpy(np.array(gammas)).to(device).to(
torch.float32)
subset_selection_time += (time.time() - start_time)
elif (strategy in [
'GLISTER-Explore', 'GradMatch-Explore', 'GradMatchPB-Explore',
'CRAIG-Explore', 'CRAIGPB-Explore'
]):
start_time = time.time()
if i < full_epochs:
subset_idxs, gammas = rand_setf_model.select(int(bud))
idxs = subset_idxs
gammas = gammas.to(device)
elif ((i % select_every == 0) and (i >= kappa_epochs)):
cached_state_dict = copy.deepcopy(model.state_dict())
clone_dict = copy.deepcopy(model.state_dict())
if strategy in ['CRAIG-Explore', 'CRAIGPB-Explore']:
subset_idxs, gammas = setf_model.select(
int(bud), clone_dict, 'lazy')
else:
subset_idxs, gammas = setf_model.select(
int(bud), clone_dict)
model.load_state_dict(cached_state_dict)
idxs = subset_idxs
if strategy in [
'GradMatch-Explore', 'GradMatchPB-Explore',
'CRAIG-Explore', 'CRAIGPB-Explore'
]:
gammas = torch.from_numpy(np.array(gammas)).to(device).to(
torch.float32)
subset_selection_time += (time.time() - start_time)
print("selEpoch: %d, Selection Ended at:" % (i),
str(datetime.datetime.now()))
data_sub = Subset(trainset, idxs)
subset_trnloader = torch.utils.data.DataLoader(
data_sub,
batch_size=trn_batch_size,
shuffle=False,
pin_memory=True)
model.train()
batch_wise_indices = list(subset_trnloader.batch_sampler)
if strategy in ['CRAIG', 'CRAIGPB', 'GradMatch', 'GradMatchPB']:
start_time = time.time()
for batch_idx, (inputs, targets) in enumerate(subset_trnloader):
inputs, targets = inputs.to(device), targets.to(
device,
non_blocking=True) # targets can have non_blocking=True.
optimizer.zero_grad()
outputs = model(inputs)
losses = criterion_nored(outputs, targets)
loss = torch.dot(
losses, gammas[batch_wise_indices[batch_idx]]) / (
gammas[batch_wise_indices[batch_idx]].sum())
loss.backward()
subtrn_loss += loss.item()
optimizer.step()
_, predicted = outputs.max(1)
subtrn_total += targets.size(0)
subtrn_correct += predicted.eq(targets).sum().item()
train_time = time.time() - start_time
elif strategy in [
'CRAIGPB-Explore', 'CRAIG-Explore', 'GradMatch-Explore',
'GradMatchPB-Explore'
]:
start_time = time.time()
if i < full_epochs:
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(
device, non_blocking=True
) # targets can have non_blocking=True.
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
subtrn_loss += loss.item()
optimizer.step()
_, predicted = outputs.max(1)
subtrn_total += targets.size(0)
subtrn_correct += predicted.eq(targets).sum().item()
elif i >= kappa_epochs:
for batch_idx, (inputs,
targets) in enumerate(subset_trnloader):
inputs, targets = inputs.to(device), targets.to(
device, non_blocking=True
) # targets can have non_blocking=True.
optimizer.zero_grad()
outputs = model(inputs)
losses = criterion_nored(outputs, targets)
loss = torch.dot(
losses, gammas[batch_wise_indices[batch_idx]]) / (
gammas[batch_wise_indices[batch_idx]].sum())
loss.backward()
subtrn_loss += loss.item()
optimizer.step()
_, predicted = outputs.max(1)
subtrn_total += targets.size(0)
subtrn_correct += predicted.eq(targets).sum().item()
train_time = time.time() - start_time
elif strategy in ['Full']:
start_time = time.time()
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(
device,
non_blocking=True) # targets can have non_blocking=True.
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
subtrn_loss += loss.item()
optimizer.step()
_, predicted = outputs.max(1)
subtrn_total += targets.size(0)
subtrn_correct += predicted.eq(targets).sum().item()
train_time = time.time() - start_time
scheduler.step()
timing[i] = train_time + subset_selection_time
# print("Epoch timing is: " + str(timing[i]))
val_loss = 0
val_correct = 0
val_total = 0
tst_correct = 0
tst_total = 0
tst_loss = 0
model.eval()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(valloader):
# print(batch_idx)
inputs, targets = inputs.to(device), targets.to(
device, non_blocking=True)
outputs = model(inputs)
loss = criterion(outputs, targets)
val_loss += loss.item()
_, predicted = outputs.max(1)
val_total += targets.size(0)
val_correct += predicted.eq(targets).sum().item()
for batch_idx, (inputs, targets) in enumerate(testloader):
# print(batch_idx)
inputs, targets = inputs.to(device), targets.to(
device, non_blocking=True)
outputs = model(inputs)
loss = criterion(outputs, targets)
tst_loss += loss.item()
_, predicted = outputs.max(1)
tst_total += targets.size(0)
tst_correct += predicted.eq(targets).sum().item()
val_acc[i] = val_correct / val_total
tst_acc[i] = tst_correct / tst_total
subtrn_acc[i] = subtrn_correct / subtrn_total
substrn_losses[i] = subtrn_loss
val_losses[i] = val_loss
print('Epoch:', i + 1, 'Validation Accuracy: ', val_acc[i],
'Test Accuracy: ', tst_acc[i], 'Train Accuracy:', subtrn_acc[i],
'Time: ', timing[i])
print(strategy + " Selection Run---------------------------------")
print("Final SubsetTrn:", subtrn_loss)
print("Validation Loss and Accuracy:", val_loss, val_acc.max())
print("Test Data Loss and Accuracy:", tst_loss, tst_acc.max())
print('-----------------------------------')
# Results logging into the file
print(strategy, file=logfile)
print(
'---------------------------------------------------------------------',
file=logfile)
val = "Validation Accuracy, "
tst = "Test Accuracy, "
trn = "Train Accuracy, "
time_str = "Time, "
for i in range(num_epochs):
time_str = time_str + "," + str(timing[i])
val = val + "," + str(val_acc[i])
trn = trn + "," + str(subtrn_acc[i])
tst = tst + "," + str(tst_acc[i])
print(timing, file=logfile)
print(val, file=logfile)
print(trn, file=logfile)
print(tst, file=logfile)
omp_timing = np.array(timing)
omp_cum_timing = list(generate_cumulative_timing(omp_timing))
omp_tst_acc = list(filter(tst_acc))
print("Total time taken by " + strategy + " = " + str(omp_cum_timing[-1]))
logfile.close()
return {
'loss': -tst_acc.max(),
'max_val_acc': val_acc.max(),
'train_acc': subtrn_acc.max(),
'status': STATUS_OK
}
def f(x):
rewards = np.zeros(n_cats)
rewards[0] = 1.
rewards = torch.tensor(rewards).float()
# print (x, rewards)
return torch.dot(x,rewards)
def calc_potential_energy(self, xx):
xx = xx - self.bias
potential_energy = torch.dot(xx, torch.matmul(self.weight_matrix, xx))
return potential_energy
def get_coordinates(v, basis_vectors, offset_v):
adjusted_v = v - offset_v
coeffs = [float(torch.dot(adjusted_v, b) / torch.norm(b)) for b in basis_vectors]
return coeffs
import os import torch A = torch.tensor([1, 2, 3], dtype=torch.float) B = torch.tensor([4, 5, 6], dtype=torch.float) result = torch.dot(A, B) print(result) print(result.item())
def train(self, env, expert, render=False):
num_iters = self.train_config["num_iters"]
num_steps_per_iter = self.train_config["num_steps_per_iter"]
horizon = self.train_config["horizon"]
lambda_ = self.train_config["lambda"]
gae_gamma = self.train_config["gae_gamma"]
gae_lambda = self.train_config["gae_lambda"]
eps = self.train_config["epsilon"]
max_kl = self.train_config["max_kl"]
cg_damping = self.train_config["cg_damping"]
normalize_advantage = self.train_config["normalize_advantage"]
opt_d = torch.optim.Adam(self.d.parameters())
exp_rwd_iter = []
exp_obs = []
exp_acts = []
steps = 0
while steps < num_steps_per_iter:
ep_obs = []
ep_rwds = []
t = 0
done = False
ob = env.reset()
while not done and steps < num_steps_per_iter:
act = expert.act(ob)
ep_obs.append(ob)
exp_obs.append(ob)
exp_acts.append(act)
if render:
env.render()
ob, rwd, done, info = env.step(act)
ep_rwds.append(rwd)
t += 1
steps += 1
if horizon is not None:
if t >= horizon:
break
if done:
exp_rwd_iter.append(np.sum(ep_rwds))
ep_obs = FloatTensor(ep_obs)
ep_rwds = FloatTensor(ep_rwds)
exp_rwd_mean = np.mean(exp_rwd_iter)
print("Expert Reward Mean: {}".format(exp_rwd_mean))
exp_obs = FloatTensor(exp_obs)
exp_acts = FloatTensor(np.array(exp_acts))
rwd_iter_means = []
for i in range(num_iters):
rwd_iter = []
obs = []
acts = []
rets = []
advs = []
gms = []
steps = 0
while steps < num_steps_per_iter:
ep_obs = []
ep_acts = []
ep_rwds = []
ep_costs = []
ep_disc_costs = []
ep_gms = []
ep_lmbs = []
t = 0
done = False
ob = env.reset()
while not done and steps < num_steps_per_iter:
act = self.act(ob)
ep_obs.append(ob)
obs.append(ob)
ep_acts.append(act)
acts.append(act)
if render:
env.render()
ob, rwd, done, info = env.step(act)
ep_rwds.append(rwd)
ep_gms.append(gae_gamma**t)
ep_lmbs.append(gae_lambda**t)
t += 1
steps += 1
if horizon is not None:
if t >= horizon:
break
if done:
rwd_iter.append(np.sum(ep_rwds))
ep_obs = FloatTensor(ep_obs)
# ep_acts = FloatTensor(np.array(ep_acts)).to(torch.device("cuda"))
ep_acts = FloatTensor(np.array(ep_acts))
ep_rwds = FloatTensor(ep_rwds)
# ep_disc_rwds = FloatTensor(ep_disc_rwds)
ep_gms = FloatTensor(ep_gms)
ep_lmbs = FloatTensor(ep_lmbs)
ep_costs = (-1) * torch.log(self.d(ep_obs, ep_acts))\
.squeeze().detach()
ep_disc_costs = ep_gms * ep_costs
ep_disc_rets = FloatTensor(
[sum(ep_disc_costs[i:]) for i in range(t)])
ep_rets = ep_disc_rets / ep_gms
rets.append(ep_rets)
self.v.eval()
curr_vals = self.v(ep_obs).detach()
next_vals = torch.cat(
(self.v(ep_obs)[1:], FloatTensor([[0.]]))).detach()
ep_deltas = ep_costs.unsqueeze(-1)\
+ gae_gamma * next_vals\
- curr_vals
ep_advs = torch.FloatTensor([
((ep_gms * ep_lmbs)[:t - j].unsqueeze(-1) *
ep_deltas[j:]).sum() for j in range(t)
])
advs.append(ep_advs)
gms.append(ep_gms)
rwd_iter_means.append(np.mean(rwd_iter))
print("Iterations: {}, Reward Mean: {}".format(
i + 1, np.mean(rwd_iter)))
obs = FloatTensor(obs)
# acts = FloatTensor(np.array(acts)).to(torch.device("cuda"))
acts = FloatTensor(np.array(acts))
rets = torch.cat(rets)
advs = torch.cat(advs)
gms = torch.cat(gms)
if normalize_advantage:
advs = (advs - advs.mean()) / advs.std()
self.d.train()
exp_scores = self.d.get_logits(exp_obs, exp_acts)
nov_scores = self.d.get_logits(obs, acts)
opt_d.zero_grad()
loss = torch.nn.functional.binary_cross_entropy_with_logits(
exp_scores, torch.zeros_like(exp_scores)
) \
+ torch.nn.functional.binary_cross_entropy_with_logits(
nov_scores, torch.ones_like(nov_scores)
)
loss.backward()
opt_d.step()
self.v.train()
old_params = get_flat_params(self.v).detach()
old_v = self.v(obs).detach()
def constraint():
return ((old_v - self.v(obs))**2).mean()
grad_diff = get_flat_grads(constraint(), self.v)
def Hv(v):
hessian = get_flat_grads(torch.dot(grad_diff, v), self.v)\
.detach()
return hessian
g = get_flat_grads(
((-1) * (self.v(obs).squeeze() - rets)**2).mean(),
self.v).detach()
s = conjugate_gradient(Hv, g).detach()
Hs = Hv(s).detach()
alpha = torch.sqrt(2 * eps / torch.dot(s, Hs))
new_params = old_params + alpha * s
set_params(self.v, new_params)
self.pi.train()
old_params = get_flat_params(self.pi).detach()
old_distb = self.pi(obs)
def L():
distb = self.pi(obs)
return (advs.to(torch.device("cuda")) * torch.exp(
distb.log_prob(acts) - old_distb.log_prob(acts).detach())
).mean()
def kld():
distb = self.pi(obs)
if self.discrete:
old_p = old_distb.probs.detach()
p = distb.probs
return (old_p * (torch.log(old_p) - torch.log(p)))\
.sum(-1)\
.mean()
else:
old_mean = old_distb.mean.detach()
old_cov = old_distb.covariance_matrix.sum(-1).detach()
mean = distb.mean
cov = distb.covariance_matrix.sum(-1)
return (0.5) * ((old_cov / cov).sum(-1) +
(((old_mean - mean)**2) / cov).sum(-1) -
self.action_dim + torch.log(cov).sum(-1) -
torch.log(old_cov).sum(-1)).mean()
grad_kld_old_param = get_flat_grads(kld(), self.pi)
def Hv(v):
hessian = get_flat_grads(torch.dot(grad_kld_old_param, v),
self.pi).detach()
return hessian + cg_damping * v
g = get_flat_grads(L(), self.pi).detach()
s = conjugate_gradient(Hv, g).detach()
Hs = Hv(s).detach()
new_params = rescale_and_linesearch(g, s, Hs, max_kl, L, kld,
old_params, self.pi)
disc_causal_entropy = ((-1) * gms * self.pi(obs).log_prob(acts))\
.mean()
grad_disc_causal_entropy = get_flat_grads(disc_causal_entropy,
self.pi)
new_params += lambda_ * grad_disc_causal_entropy
set_params(self.pi, new_params)
return exp_rwd_mean, rwd_iter_means
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
assert len(self.param_groups) == 1
loss = None
if closure is not None:
loss = closure()
group = self.param_groups[0]
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
grad = self._gather_flat_grad_with_weight_decay(weight_decay)
# NOTE: op_Sgd_lop_Sgdn has only global state, but we register it as state for
# the first param, because this helps with casting in load_state_dict
state = self.state[self._params[0]]
# State initialization
if len(state) == 0:
state['step'] = 0
state['grad_prev'] = torch.zeros_like(grad)
# Accumulated momentum for the hypergradient
state['momentum_buffer_h'] = grad.new_tensor(0)
state['step'] += 1
grad_prev = state['grad_prev']
# Hypergradient for SGD optimizer
h = torch.dot(grad, grad_prev)
h = -h
''' Hypergradient descent with momentum (HD-momentum) coefficients
Parameters
-----------
momentum_h : momentum coefficient for the hypergradient
dampening_h : dampening coefficient for the hypergradient
nesterov_h : bool, if true : use nesterov momentum for the l.r update, else use sgd + momemtum
'''
momentum_h = group['momentum_h']
dampening_h = group['dampening_h']
nesterov_h = group['nesterov_h']
# Hypergradient descent with momentum (HD momentum) for the learning rate
if momentum_h and state['step'] > 1:
buf_h = state['momentum_buffer_h']
buf_h.mul_(momentum_h).add_(1 - dampening_h, h)
state['momentum_buffer_h'] = buf_h
if nesterov_h:
h.add_(momentum_h, buf_h)
else:
h = buf_h
group['lr'] -= group['hypergrad_lr'] * h
if momentum != 0:
if 'momentum_buffer' not in state:
buf = state['momentum_buffer'] = torch.zeros_like(grad)
buf.mul_(momentum).add_(grad)
else:
buf = state['momentum_buffer']
buf.mul_(momentum).add_(1 - dampening, grad)
if nesterov:
grad.add_(momentum, buf)
else:
grad = buf
state['grad_prev'] = grad
self._add_grad(-group['lr'], grad)
return loss
def get_pure_mspbe(self):
A_theta_minus_b = torch.mv(self.A, self.theta) - self.b
return (1/2) * torch.dot(A_theta_minus_b, torch.mv(self.C_inv, A_theta_minus_b))
def test(learner, args, train_envs, test_envs, log_dir):
batch_sampler = sampler(args.batch_size, args.num_bandits)
batch_sampler.build(args.num_tasks_train, train_envs, args.batch_size)
max_kl = args.max_kl
cg_iters = args.cg_iters
cg_damping = args.cg_damping
ls_max_steps = args.ls_max_steps
ls_backtrack_ratio = args.ls_backtrack_ratio
train_rew = []
test_rew = []
for i in range(args.num_updates):
#print(i)
adapt_params = []
inner_losses = []
adapt_episodes = []
rew_rem = []
rew_rem_test = []
for j in range(args.num_tasks_test):
e = batch_sampler.sample(test_envs[j], learner)
inner_loss = learner.cal_loss(e.s, e.a, e.r)
params = learner.update_params(inner_loss, args.inner_lr,
args.first_order)
a_e = batch_sampler.sample_policy(test_envs[j], learner, params)
mean_rew = torch.mean(a_e.r).data.numpy()
rew_rem_test.append(mean_rew)
for j in range(args.num_tasks_train):
e = batch_sampler.sample(train_envs[j], learner)
inner_loss = learner.cal_loss(e.s, e.a, e.r)
params = learner.update_params(inner_loss, args.inner_lr,
args.first_order)
a_e = batch_sampler.sample_policy(train_envs[j], learner, params)
adapt_params.append(params)
adapt_episodes.append(a_e)
inner_losses.append(inner_loss)
mean_rew = torch.mean(a_e.r).data.numpy()
rew_rem.append(mean_rew)
print(i, np.mean(rew_rem), np.mean(rew_rem_test))
#print(batch_sampler.poss/batch_sampler.cnt)
train_rew.append(np.mean(rew_rem))
test_rew.append(np.mean(rew_rem_test))
old_loss, _, old_pis = learner.surrogate_loss(adapt_episodes,
inner_losses)
grads = torch.autograd.grad(old_loss,
learner.parameters(),
retain_graph=True)
grads = parameters_to_vector(grads)
# Compute the step direction with Conjugate Gradient
hessian_vector_product = learner.hessian_vector_product(
adapt_episodes, inner_losses, damping=cg_damping)
stepdir = conjugate_gradient(hessian_vector_product,
grads,
cg_iters=cg_iters)
# Compute the Lagrange multiplier
shs = 0.5 * torch.dot(stepdir, hessian_vector_product(stepdir))
lagrange_multiplier = torch.sqrt(shs / max_kl)
step = stepdir / lagrange_multiplier
# Save the old parameters
old_params = parameters_to_vector(learner.parameters())
# Line search
step_size = 1.0
for _ in range(ls_max_steps):
vector_to_parameters(old_params - step_size * step,
learner.parameters())
loss, kl, _ = learner.surrogate_loss(adapt_episodes,
inner_losses,
old_pis=old_pis)
improve = loss - old_loss
if (improve.item() < 0.0) and (kl.item() < max_kl):
break
step_size *= ls_backtrack_ratio
else:
vector_to_parameters(old_params, learner.parameters())
if (i + 1) % 10 == 0:
test_input = torch.FloatTensor([[1]])
test_output = learner.forward(test_input).data.numpy()[0]
plt.figure()
plt.bar(np.arange(len(test_output)), test_output)
plt.savefig(log_dir + 'figures/before%i.png' % i)
plt.close()
for j in range(args.num_tasks_train):
test_output = learner.forward(test_input,
adapt_params[j]).data.numpy()[0]
plt.figure()
plt.bar(np.arange(len(test_output)), test_output)
plt.savefig(log_dir + 'figures/after%i_%i.png' % (j, i))
plt.close()
np.save(log_dir + 'train_rew' + str(args.inner_lr) + '.npy', train_rew)
np.save(log_dir + 'test_rew' + str(args.inner_lr) + '.npy', test_rew)
plt.figure()
plt.plot(train_rew)
plt.show()
plt.figure()
plt.plot(train_rew)
plt.savefig(log_dir + 'train_rew.png')
plt.close()
plt.figure()
plt.plot(test_rew)
plt.savefig(log_dir + 'test_rew.png')
plt.figure()
plt.bar(np.arange(len(batch_sampler.poss)),
batch_sampler.poss / batch_sampler.cnt)
plt.savefig(log_dir + 'sample.png')
plt.close()
return
def adj_broyden_correl(opa_freq,
n_runs=1,
random_prescribed=True,
dataset='imagenet',
model_size='LARGE'):
# setup
model = setup_model(opa_freq is not None, dataset, model_size)
if dataset == 'imagenet':
traindir = os.path.join(config.DATASET.ROOT + '/images',
config.DATASET.TRAIN_SET)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform_train = transforms.Compose([
transforms.RandomResizedCrop(config.MODEL.IMAGE_SIZE[0]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
train_dataset = datasets.ImageFolder(traindir, transform_train)
else:
normalize = transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
augment_list = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip()
] if config.DATASET.AUGMENT else []
transform_train = transforms.Compose(augment_list + [
transforms.ToTensor(),
normalize,
])
train_dataset = datasets.CIFAR10(root=f'{config.DATASET.ROOT}',
train=True,
download=True,
transform=transform_train)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=32,
shuffle=True,
num_workers=10,
pin_memory=True,
worker_init_fn=partial(worker_init_fn, seed=42),
)
methods_results = {
method_name: {
'correl': [],
'ratio': []
}
for method_name in ['shine-adj-br', 'shine', 'shine-opa', 'fpn']
}
methods_solvers = {
'shine': broyden,
'shine-adj-br': adj_broyden,
'shine-opa': adj_broyden,
'fpn': broyden,
}
random_results = {'correl': [], 'ratio': []}
iter_loader = iter(train_loader)
for i_run in range(n_runs):
input, target = next(iter_loader)
target = target.cuda(non_blocking=True)
x_list, z_list = model.feature_extraction(input.cuda())
model.fullstage._reset(z_list)
model.fullstage_copy._copy(model.fullstage)
# fixed point solving
x_list = [x.clone().detach().requires_grad_() for x in x_list]
cutoffs = [(elem.size(1), elem.size(2), elem.size(3))
for elem in z_list]
args = (27, int(1e9), None)
nelem = sum([elem.nelement() for elem in z_list])
eps = 1e-5 * np.sqrt(nelem)
z1_est = DEQFunc2d.list2vec(z_list)
directions_dir = {
'random': torch.randn(z1_est.shape),
'prescribed': torch.randn(z1_est.shape),
}
for method_name in methods_results.keys():
z1_est = torch.zeros_like(z1_est)
g = lambda x: DEQFunc2d.g(model.fullstage_copy, x, x_list, cutoffs,
*args)
if random_prescribed:
inverse_direction_fun = lambda x: directions_dir['prescribed']
else:
model.copy_modules()
loss_function = lambda y_est: model.get_fixed_point_loss(
y_est, target)
def inverse_direction_fun_vec(x):
x_temp = x.clone().detach().requires_grad_()
with torch.enable_grad():
x_list = DEQFunc2d.vec2list(x_temp, cutoffs)
loss = loss_function(x_list)
loss.backward()
dl_dx = x_temp.grad
return dl_dx
inverse_direction_fun = inverse_direction_fun_vec
solver = methods_solvers[method_name]
if 'opa' in method_name:
add_kwargs = dict(
inverse_direction_freq=opa_freq,
inverse_direction_fun=inverse_direction_fun
if opa_freq is not None else None,
)
else:
add_kwargs = {}
result_info = solver(
g,
z1_est,
threshold=config.MODEL.F_THRES,
eps=eps,
name="forward",
**add_kwargs,
)
z1_est = result_info['result']
Us = result_info['Us']
VTs = result_info['VTs']
nstep = result_info['lowest_step']
if opa_freq is not None:
nstep += (nstep - 1) // opa_freq
# compute true incoming gradient if needed
if not random_prescribed:
directions_dir['prescribed'] = inverse_direction_fun_vec(
z1_est)
# making sure the random direction norm is not unrealistic
directions_dir[
'random'] = directions_dir['random'] * torch.norm(
directions_dir['prescribed']) / torch.norm(
directions_dir['random'])
# inversion on random gradients
z1_temp = z1_est.clone().detach().requires_grad_()
with torch.enable_grad():
y = DEQFunc2d.g(model.fullstage_copy, z1_temp, x_list, cutoffs,
*args)
eps = 2e-10
for direction_name, direction in directions_dir.items():
def g(x):
y.backward(x, retain_graph=True)
res = z1_temp.grad + direction
z1_temp.grad.zero_()
return res
result_info_inversion = broyden(
g,
direction, # we initialize Jacobian Free style
# in order to accelerate the convergence
threshold=35,
eps=eps,
name="backward",
)
true_inv = result_info_inversion['result']
inv_dir = {
'fpn':
direction,
'shine':
-rmatvec(Us[:, :, :, :nstep - 1], VTs[:, :nstep - 1],
direction),
}
inv_dir['shine-opa'] = inv_dir['shine']
inv_dir['shine-adj-br'] = inv_dir['shine']
approx_inv = inv_dir[method_name]
correl = torch.dot(
torch.flatten(true_inv),
torch.flatten(approx_inv),
)
scaling = torch.norm(true_inv) * torch.norm(approx_inv)
correl = correl / scaling
ratio = torch.norm(true_inv) / torch.norm(approx_inv)
if direction_name == 'prescribed':
methods_results[method_name]['correl'].append(
correl.item())
methods_results[method_name]['ratio'].append(ratio.item())
else:
if method_name == 'fpn':
random_results['correl'].append(correl.item())
random_results['ratio'].append(ratio.item())
y.backward(torch.zeros_like(true_inv), retain_graph=False)
return methods_results, random_results
def get_att_score(
self, dec_output, enc_output
): # enc_outputs [batch_size, num_directions(=1) * n_hidden]
score = self.attn(enc_output) # score : [batch_size, n_hidden]
return torch.dot(dec_output.view(-1),
score.view(-1)) # inner product make scalar value
def main(
data_path,
model_path,
w2i_path,
hidden_size,
classifier_path=None,
intervention=False,
learning_rate=None,
component_names=None,
generate_labels=False):
# load word-to-index vocabulary
with open(w2i_path, 'r') as f:
vocab_lines = f.readlines()
w2i = {}
for i, line in enumerate(vocab_lines):
w2i[line.strip()] = i
unk_idx = w2i['<unk>']
vocab_size = len(w2i)
# load and initialise model
lstm = Forward_LSTM(vocab_size,
hidden_size,
hidden_size,
vocab_size,
w2i_path,
model_path)
# initialise hidden state for time step -1
# the hidden state will not be reset for each sentence
relevant_activations = {}
relevant_activations['hx_l0'] = torch.Tensor(torch.zeros(hidden_size))
relevant_activations['cx_l0'] = torch.Tensor(torch.zeros(hidden_size))
relevant_activations['hx_l1'] = torch.Tensor(torch.zeros(hidden_size))
relevant_activations['cx_l1'] = torch.Tensor(torch.zeros(hidden_size))
# load testing data
with open(data_path, 'r') as f_in:
test_set = f_in.readlines()[1:]
# collect scores for all subcategories
scores_original_nvv = []
scores_nonce_nvv = []
scores_original_vnpcv = []
scores_nonce_vnpcv = []
scores_original = []
scores_nonce = []
scores = []
if intervention:
classifiers = defaultdict()
# load diagnostic classifiers for the intervention
for act in component_names:
with open("{}/{}.pickle".format(classifier_path, act), 'rb') as trained_classifier:
classifiers[act] = pickle.load(trained_classifier)
# process sentences
for line_idx in range(0, len(test_set), 2):
# read two consecutive lines with the following structure:
# 0:pattern 1:constr_id 2:sent_id 3:correct_number 4:form 5:class 6:type
# 7:prefix 8:n_attr 9:punct 10:freq 11:len_context 12:len_prefix 13:sent
sent_data1 = test_set[line_idx].split('\t')
sent_data2 = test_set[line_idx + 1].split('\t')
# L__NOUN_VERB_VERB or R__VERB_NOUN_CCONJ_VERB
pattern1 = sent_data1[0]
pattern2 = sent_data2[0]
assert(pattern1[0] in ['R', 'L'] and pattern2[0] in ['R', 'L'])
assert(pattern1[0] == pattern2[0])
construction_id = 0 if pattern1[0] == 'R' else 1
assert(sent_data1[3] == sent_data2[3])
if not generate_labels:
label = 0 if sent_data1[3].strip() == 'sing' else 1
assert(sent_data1[5] != sent_data2[5])
if sent_data1[5] == 'correct':
correct_form = sent_data1[4]
wrong_form = sent_data2[4]
else:
correct_form = sent_data2[4]
wrong_form = sent_data1[4]
assert(sent_data1[6] == sent_data2[6])
type_of_sent = sent_data1[6]
assert(sent_data1[11] == sent_data2[11])
context_length = int(sent_data1[11])
assert(sent_data1[12] == sent_data2[12])
target_idx = int(sent_data1[12])
subject_idx = target_idx - context_length
assert(sent_data1[13] == sent_data2[13])
sentence = sent_data1[13].split()
# process sentence
for t, word in enumerate(sentence):
output, layer0, layer1 = lstm(word,
relevant_activations['hx_l0'],
relevant_activations['cx_l0'],
relevant_activations['hx_l1'],
relevant_activations['cx_l1'])
relevant_activations['hx_l0'] = layer0[0]
relevant_activations['hx_l1'] = layer1[0]
relevant_activations['cx_l0'] = layer0[1]
relevant_activations['cx_l1'] = layer1[1]
if t == target_idx - 1:
vocab_probs = F.log_softmax(
output.view(-1, len(w2i)), dim=1)[0]
# intervention at subject timestep
if intervention and t == subject_idx:
for act in component_names:
weight, bias = classifiers[act]
weight = Variable(torch.tensor(
weight, dtype=torch.double).squeeze(0), requires_grad=False)
bias = Variable(torch.tensor(
bias, dtype=torch.double), requires_grad=False)
current_activation = Variable(torch.tensor(
relevant_activations[act], dtype=torch.double), requires_grad=True)
total_prob = torch.tensor(1.0, dtype=torch.double)
class_1_prob = torch.dot(weight, current_activation) + bias
class_1_prob = F.sigmoid(class_1_prob)
class_0_prob = total_prob - class_1_prob
class_0_log_prob = torch.log(class_0_prob)
class_1_log_prob = torch.log(class_1_prob)
params = [current_activation]
optimiser = torch.optim.SGD(params, lr=learning_rate)
optimiser.zero_grad()
prediction = (class_0_log_prob, class_1_log_prob)
prediction = torch.tensor(
torch.cat(prediction)).unsqueeze(0)
# unsupervised intervention requires generated labels
if generate_labels:
label = 0 if class_0_prob > class_1_prob else 1
gold_label = torch.tensor(label).unsqueeze(0)
criterion = nn.NLLLoss()
loss = criterion(prediction, gold_label)
loss.backward()
optimiser.step()
relevant_activations[act] = torch.tensor(current_activation, dtype=torch.float)
correct_form_score = vocab_probs[w2i[correct_form]].data
wrong_form_score = vocab_probs[w2i[wrong_form]].data
if (correct_form_score > wrong_form_score).all():
score = 1
else:
score = 0
scores.append(score)
if construction_id == 0 and type_of_sent == 'original':
scores_original_vnpcv.append(score)
scores_original.append(score)
if construction_id == 1 and type_of_sent == 'original':
scores_original_nvv.append(score)
scores_original.append(score)
if construction_id == 0 and type_of_sent == 'generated':
scores_nonce_vnpcv.append(score)
scores_nonce.append(score)
if construction_id == 1 and type_of_sent == 'generated':
scores_nonce_nvv.append(score)
scores_nonce.append(score)
assert(len(scores) == len(test_set) / 2)
# Print accuracy results
print ('Original V NP Conj V ', np.sum(
scores_original_vnpcv) / len(scores_original_vnpcv))
print ('Nonce V NP Conj V ', np.sum(
scores_nonce_vnpcv) / len(scores_nonce_vnpcv))
print ('Original N V V ', np.sum(
scores_original_nvv) / len(scores_original_nvv))
print ('Nonce N V V ', np.sum(
scores_nonce_nvv) / len(scores_nonce_nvv))
print ('Original Overall ', np.sum(
scores_original) / len(scores_original))
print ('Nonce Overall ', np.sum(scores_nonce) / len(scores_nonce))
print ('Overall ', np.sum(scores) / len(scores))
def cosine(a, b):
numerator = torch.dot(a, b)
denominator = torch.norm(a, 2) * torch.norm(b, 2)
return float(numerator / denominator)
def get_att_score(self, hidden, encoder_hidden):
score = self.attn(encoder_hidden)
return torch.dot(hidden.view(-1), score.view(-1))
# print(max_1) # print(value) # print(index) # print(max_1_0) # print(max_1_1) ''' (tensor([2., 4., 6., 8.]), tensor([1, 1, 1, 1])) tensor([2., 4., 6., 8.]) tensor([1, 1, 1, 1]) tensor([2., 4., 6., 8.]) tensor([1, 1, 1, 1]) ''' ### Dot product tensor = torch.Tensor([1, 2, 3, 4, 5]) dot = torch.dot(tensor, tensor) # print(dot) ''' tensor(55.) ''' ### Mathematical functions tensor = torch.Tensor([[1, 2, 3, 4], [5, 6, 7, 8]]) sqrt = torch.sqrt(tensor) exp = torch.exp(tensor) log = torch.log(tensor) # print(sqrt) # print(exp) # print(log) '''
def matrix_factorization(R, K, steps=100, lr=0.002):
"""
Input
-----
R - Tensor: Ratings matrix_factorization:
Dimensions: N-users by M-items
K - Int: Number of latent features
Output
------
P: User-feature matrix
Dimensions: N-users by K-features
Qt: Transpose of Item-feature matrix
Dimensions: K-features by M-items
Reference
---------
https://towardsdatascience.com/recommendation-system-matrix-factorization-d61978660b4b
"""
# Initialize matrices P and Qt with random values between 1 and 0
N_users, M_items = R.size()
P = torch.randint(1, 5, (N_users, K))
Qt = torch.randint(1, 5, (K, M_items))
beta = 0.02
prev_e = float("inf")
# Setup training loop
for step in range(steps):
# Calculate and apply gradients
for user_i in range(N_users):
for item_j in range(M_items):
if R[user_i][item_j] > 0:
pred = torch.dot(P[user_i,:], Qt[:,item_j])
err_ij = R[user_i][item_j] - pred
for k in range(K):
# Calculate and shift the gradient
P[user_i][k] = P[user_i][k] + lr * (2*err_ij*Qt[k][item_j] - beta*P[user_i][k])
Qt[k][item_j] = Qt[k][item_j] + lr * (2*err_ij*P[user_i][k] - beta*Qt[k][item_j])
# Calculate difference in loss
e = 0.0
for user_i in range(N_users):
for item_j in range(M_items):
if R[user_i][item_j] > 0:
e = e + pow(R[user_i][item_j] - torch.dot(P[user_i,:],Qt[:,item_j]), 2)
for k in range(K):
e = e + (beta/2) * (pow(P[user_i][k],2) + pow(Qt[k][item_j],2))
if 0 < (prev_e - e) < 50:
break
prev_e = e
if step % 1 == 0:
print("step: %s, loss: %s" % (step+1, e))
return P, Qt
# Calculate Root mean squared error for all values of R and P*Qt
# Calculate gradients for all values of R and P*Qt
# Apply gradients to P and Q
# Return P and Qt
def Hv(v):
hessian = get_flat_grads(torch.dot(grad_kld_old_param, v),
self.pi).detach()
return hessian + cg_damping * v
def main(dom="driving",
reptype="wordfeat",
splittype="LOOtask",
excludeid=2,
taskrepsize=2,
modeltype="neural",
gpmode=0,
pval=0.1,
seed=0,
nfolds=10):
modelname = modeltype + "_" + str(taskrepsize) + "_" + str(
gpmode) + "_" + dom + "_" + splittype + "_" + str(excludeid)
# check what kind of modifications to the GP we are using
print("Modelname: ", modelname)
usepriormean, usepriorpoints = getGPParams(gpmode)
verbose = False
torch.manual_seed(seed) # set up our seed for reproducibility
np.random.seed(seed)
# load the data
data, nparts = loadData(dom)
# print(data)
# recreate word vectors if needed
# e.g., when you download new word features from glove.
recreate_word_vectors = False
if recreate_word_vectors:
recreateWordVectors()
# load word features
wordfeatures = loadWordFeatures(dom, loadpickle=True)
print(wordfeatures.shape)
# in the experiments in the paper, we use the word features directly. However,
# you can also use tsne or pca dim-reduced features.
tsnefeatures = computeTSNEFeatures(wordfeatures)
pcafeatures = computePCAFeatures(wordfeatures)
allfeatures = {
"wordfeat": wordfeatures,
"tsne": tsnefeatures,
"pca": pcafeatures
}
# create primary dataset
dataset = createDataset(data, reptype, allfeatures)
# create dataset splits
expdata = getTrainTestValSplit(data,
dataset,
splittype,
excludeid=excludeid,
pval=pval,
nfolds=nfolds)
nfeats = allfeatures[reptype].shape[1]
# we don't use an initial projection matrix. You can substitute one here if you like
Ainit = None
inptasksobs = Variable(dtype(expdata["tasksobsfeats_train"]),
requires_grad=False)
inptasksperf = Variable(dtype(expdata["tasksobsperf_train"]),
requires_grad=False)
inptaskspred = Variable(dtype(expdata["taskspredfeats_train"]),
requires_grad=False)
outtrustpred = Variable(dtype(expdata["trustpred_train"]),
requires_grad=False)
inptasksobs_val = Variable(dtype(expdata["tasksobsfeats_val"]),
requires_grad=False)
inptasksperf_val = Variable(dtype(expdata["tasksobsperf_val"]),
requires_grad=False)
inptaskspred_val = Variable(dtype(expdata["taskspredfeats_val"]),
requires_grad=False)
outtrustpred_val = Variable(dtype(expdata["trustpred_val"]),
requires_grad=False)
inptasksobs_test = Variable(dtype(expdata["tasksobsfeats_test"]),
requires_grad=False)
inptasksperf_test = Variable(dtype(expdata["tasksobsperf_test"]),
requires_grad=False)
inptaskspred_test = Variable(dtype(expdata["taskspredfeats_test"]),
requires_grad=False)
outtrustpred_test = Variable(dtype(expdata["trustpred_test"]),
requires_grad=False)
learning_rate = 1e-3
if modeltype == "gp":
learning_rate = 1e-1
usepriormean = usepriormean
obsseqlen = 2
phiinit = 1.0
weight_decay = 0.01 #0.01
modelparams = {
"inputsize": inptasksobs.shape[2],
"reptype": reptype,
"taskrepsize": taskrepsize,
"phiinit": phiinit,
"Ainit": None, # np.array(Ainit),
"obsseqlen": obsseqlen,
"verbose": verbose,
"usepriormean": usepriormean,
"usepriorpoints": usepriorpoints
}
elif modeltype == "neural":
perfrepsize = taskrepsize
numGRUlayers = 2
nperf = 2
weight_decay = 0.00
modelparams = {
"perfrepsize": perfrepsize,
"numGRUlayers": numGRUlayers,
"nperf": nperf,
"verbose": verbose,
"taskrepsize": taskrepsize,
"Ainit": None, #np.array(Ainit),
"nfeats": inptasksobs.shape[2]
}
elif modeltype == "lineargaussian":
obsseqlen = 2
weight_decay = 0.01
modelparams = {
"inputsize": inptasksobs.shape[2],
"obsseqlen": obsseqlen,
}
elif modeltype == "constant":
obsseqlen = 2
weight_decay = 0.01
modelparams = {
"inputsize": inptasksobs.shape[2],
"obsseqlen": obsseqlen,
}
else:
raise ValueError("No such model")
verbose = False
reportperiod = 1
# these two parameters control the early stopping
# we save the stopcount-th model after the best validation is achived
# but keep the model running for burnin longer in case a better
# model is attained
if splittype == "3participant":
stopcount = 3
burnin = 50
elif splittype == "LOOtask":
stopcount = 3
burnin = 50
t0 = time.time()
bestvalloss = 1e10
modeldir = "savedmodels"
for rep in range(1):
print("REP", rep)
model = initModel(modeltype, modelname, parameters=modelparams)
# optimizer = torch.optim.RMSprop(model.parameters(), lr=learning_rate)
#if modeltype == "neural"
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=weight_decay)
#optimizer = torch.optim.LBFGS(model.parameters(), lr=learning_rate, max_iter=10, max_eval=20)
#optimizer = torch.optim.LBFGS(model.parameters(), lr=learning_rate)
counter = 0
torch.save(model, os.path.join(modeldir, model.modelname + ".pth"))
restartopt = False
t = 1
#l2comp = nn.L2Loss()
while t < 500:
def closure():
N = inptaskspred.shape[0]
predtrust = model(inptasksobs, inptasksperf, inptaskspred)
predtrust = torch.squeeze(predtrust)
# logloss = torch.mean(torch.pow(predtrust - outtrustpred, 2.0)) # / 2*torch.exp(obsnoise))
loss = -(
torch.dot(outtrustpred, torch.log(predtrust)) + torch.dot(
(1 - outtrustpred), torch.log(1.0 - predtrust))) / N
optimizer.zero_grad()
loss.backward()
return loss
optimizer.step(closure)
if t % reportperiod == 0:
# compute training loss
predtrust = model(inptasksobs, inptasksperf, inptaskspred)
predtrust = torch.squeeze(predtrust)
loss = -(torch.dot(outtrustpred, torch.log(predtrust)) +
torch.dot(
(1 - outtrustpred), torch.log(1.0 - predtrust))
) / inptaskspred.shape[0]
# compute validation loss
predtrust_val = model(inptasksobs_val, inptasksperf_val,
inptaskspred_val)
predtrust_val = torch.squeeze(predtrust_val)
valloss = -(torch.dot(
outtrustpred_val, torch.log(predtrust_val)) + torch.dot(
(1 - outtrustpred_val), torch.log(1.0 - predtrust_val))
) / predtrust_val.shape[0]
# compute prediction loss
predtrust_test = torch.squeeze(
model(inptasksobs_test, inptasksperf_test,
inptaskspred_test))
predloss = -(torch.dot(
outtrustpred_test, torch.log(predtrust_test)) + torch.dot(
(1 - outtrustpred_test), torch.log(
1.0 - predtrust_test))) / predtrust_test.shape[0]
#print(model.wb, model.wtp, model.trust0, model.sigma0)
#check for nans
checkval = np.sum(np.array(predtrust_test.data))
if np.isnan(checkval) or np.isinf(checkval):
# check if we have already restarted once
if restartopt:
#we've already done this, fail out.
#break out.
print("Already restarted once. Quitting")
break
# reinitialize model and switch optimizer
print("NaN value encountered. Restarting opt")
model = initModel(modeltype,
modelname,
parameters=modelparams)
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate)
t = 1
counter = 0
restartopt = True
else:
# print(predtrust_test.data, outtrustpred_test.data)
mae = metrics.mean_absolute_error(predtrust_test.data,
outtrustpred_test.data)
print(t, loss.data[0], valloss.data[0], predloss.data[0],
mae)
optimizer.zero_grad()
# if validation loss has increased for stopcount iterations
augname = model.modelname + "_" + str(excludeid) + ".pth"
if valloss.data[0] <= bestvalloss:
torch.save(model, os.path.join(modeldir, augname))
print(valloss.data[0], bestvalloss, "Model saved")
bestvalloss = valloss.data[0]
counter = 0
else:
if counter < stopcount and (valloss.data[0] -
bestvalloss) <= 0.1:
torch.save(model, os.path.join(modeldir, augname))
print(valloss.data[0], bestvalloss,
"Model saved : POST", counter)
counter += 1
if counter >= stopcount and t > burnin:
#torch.save(model, modeldir+ model.modelname + ".pth")
break
t = t + 1
t1 = time.time()
print("Total time: ", t1 - t0)
model = torch.load(
os.path.join(modeldir, modelname + "_" + str(excludeid) + ".pth"))
# make predictions using trained model and compute metrics
predtrust_test = torch.squeeze(
model(inptasksobs_test, inptasksperf_test, inptaskspred_test))
res = np.zeros((predtrust_test.shape[0], 2))
res[:, 0] = predtrust_test.data[:]
res[:, 1] = outtrustpred_test.data[:]
print(res)
mae = metrics.mean_absolute_error(predtrust_test.data,
outtrustpred_test.data)
predloss = -(torch.dot(outtrustpred_test, torch.log(predtrust_test)) + torch.dot((1 - outtrustpred_test),
torch.log(1.0 - predtrust_test))) / \
predtrust_test.shape[0]
predloss = predloss.data[0]
return (mae, predloss, res)
def Hv(v):
hessian = get_flat_grads(torch.dot(grad_diff, v), self.v)\
.detach()
return hessian
def regularize_expectation(loss):
u = loss / loss.sum()
return torch.dot(loss, u)
def get_att_score(self, dec_output, enc_output):
score = self.attn(enc_output) # score : [batch_size, n_hidden]
return torch.dot(dec_output.view(-1), score.view(-1)) # 标量值
def matmul(tensor1, tensor2, out=None):
r"""Matrix product of two tensors.
The behavior depends on the dimensionality of the tensors as follows:
- If both tensors are 1-dimensional, the dot product (scalar) is returned.
- If both arguments are 2-dimensional, the matrix-matrix product is returned.
- If the first argument is 1-dimensional and the second argument is 2-dimensional,
a 1 is prepended to its dimension for the purpose of the matrix multiply.
After the matrix multiply, the prepended dimension is removed.
- If the first argument is 2-dimensional and the second argument is 1-dimensional,
the matrix-vector product is returned.
- If both arguments are at least 1-dimensional and at least one argument is
N-dimensional (where N > 2), then a batched matrix multiply is returned. If the first
argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the
batched matrix multiply and removed after. If the second argument is 1-dimensional, a
1 is appended to its dimension for the purpose of the batched matrix multiple and removed after.
The non-matrix (i.e. batch) dimensions are :ref:`broadcasted <broadcasting-semantics>` (and thus
must be broadcastable). For example, if :attr:`tensor1` is a
:math:`(j \times 1 \times n \times m)` tensor and :attr:`tensor2` is a :math:`(k \times m \times p)`
tensor, :attr:`out` will be an :math:`(j \times k \times n \times p)` tensor.
.. note::
The 1-dimensional dot product version of this function does not support an :attr:`out` parameter.
Arguments:
tensor1 (Tensor): the first tensor to be multiplied
tensor2 (Tensor): the second tensor to be multiplied
out (Tensor, optional): the output tensor
"""
dim_tensor1 = tensor1.dim()
dim_tensor2 = tensor2.dim()
if dim_tensor1 == 1 and dim_tensor2 == 1:
if out is None:
return torch.dot(tensor1, tensor2)
else:
raise ValueError("out must be None for 1-d tensor matmul, returns a scalar")
if dim_tensor1 == 2 and dim_tensor2 == 1:
if out is None:
return torch.mv(tensor1, tensor2)
else:
return torch.mv(tensor1, tensor2, out=out)
elif dim_tensor1 == 1 and dim_tensor2 == 2:
if out is None:
return torch.mm(tensor1.unsqueeze(0), tensor2).squeeze_(0)
else:
return torch.mm(tensor1.unsqueeze(0), tensor2, out=out).squeeze_(0)
elif dim_tensor1 == 2 and dim_tensor2 == 2:
if out is None:
return torch.mm(tensor1, tensor2)
else:
return torch.mm(tensor1, tensor2, out=out)
elif dim_tensor1 >= 3 and (dim_tensor2 == 1 or dim_tensor2 == 2):
# optimization: use mm instead of bmm by folding tensor1's batch into
# its leading matrix dimension.
if dim_tensor2 == 1:
tensor2 = tensor2.unsqueeze(-1)
size1 = tensor1.size()
size2 = tensor2.size()
output_size = size1[:-1] + size2[-1:]
# fold the batch into the first dimension
tensor1 = tensor1.contiguous().view(-1, size1[-1])
if out is None or not out.is_contiguous():
output = torch.mm(tensor1, tensor2)
else:
output = torch.mm(tensor1, tensor2, out=out)
output = output.view(output_size)
if dim_tensor2 == 1:
output = output.squeeze(-1)
if out is not None:
out.set_(output)
return out
return output
elif (dim_tensor1 >= 1 and dim_tensor2 >= 1) and (dim_tensor1 >= 3 or dim_tensor2 >= 3):
# ensure each tensor size is at least 3-dimensional
tensor1_exp_size = torch.Size((1,) * max(3 - tensor1.dim(), 0) + tensor1.size())
# rhs needs to be a separate case since we can't freely expand 1s on the rhs, but can on lhs
if dim_tensor2 == 1:
tensor2 = tensor2.unsqueeze(1)
tensor2_exp_size = torch.Size((1,) * max(3 - tensor2.dim(), 0) + tensor2.size())
# expand the batch portion (i.e. cut off matrix dimensions and expand rest)
expand_batch_portion = torch._C._infer_size(tensor1_exp_size[:-2], tensor2_exp_size[:-2])
# flatten expanded batches
tensor1_expanded = tensor1.expand(*(expand_batch_portion + tensor1_exp_size[-2:])) \
.contiguous().view(reduce(mul, expand_batch_portion), *tensor1_exp_size[-2:])
tensor2_expanded = tensor2.expand(*(expand_batch_portion + tensor2_exp_size[-2:])) \
.contiguous().view(reduce(mul, expand_batch_portion), *tensor2_exp_size[-2:])
# reshape batches back into result
total_expansion = expand_batch_portion + (tensor1_exp_size[-2], tensor2_exp_size[-1])
def maybeSqueeze(tensor):
if dim_tensor1 == 1:
return tensor.squeeze(-2)
elif dim_tensor2 == 1:
return tensor.squeeze(-1)
else:
return tensor
if out is None or not out.is_contiguous():
output = torch.bmm(tensor1_expanded, tensor2_expanded)
else:
output = torch.bmm(tensor1_expanded, tensor2_expanded, out=out)
output = maybeSqueeze(output.view(total_expansion))
if out is not None:
out.set_(output)
return out
return output
raise ValueError("both arguments to __matmul__ need to be at least 1D, "
"but they are {}D and {}D".format(dim_tensor1, dim_tensor2))
def regularize_expectation_exp(loss):
u = loss.exp() / loss.exp().sum()
return torch.dot(loss, u)
