def bbox_iou(box1, box2, x1y1x2y2=True):
"""
Returns the IoU of two bounding boxes
"""
if not x1y1x2y2:
# Transform from center and width to exact coordinates
b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
else:
# Get the coordinates of bounding boxes
b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
# get the corrdinates of the intersection rectangle
inter_rect_x1 = torch.max(b1_x1, b2_x1)
inter_rect_y1 = torch.max(b1_y1, b2_y1)
inter_rect_x2 = torch.min(b1_x2, b2_x2)
inter_rect_y2 = torch.min(b1_y2, b2_y2)
# Intersection area
inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(
inter_rect_y2 - inter_rect_y1 + 1, min=0
)
# Union Area
b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)
iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)
return iou
def bbox_iou(box1, box2):
"""
Returns the IoU of two bounding boxes
"""
#Get the coordinates of bounding boxes
b1_x1, b1_y1, b1_x2, b1_y2 = box1[:,0], box1[:,1], box1[:,2], box1[:,3]
b2_x1, b2_y1, b2_x2, b2_y2 = box2[:,0], box2[:,1], box2[:,2], box2[:,3]
#get the corrdinates of the intersection rectangle
inter_rect_x1 = torch.max(b1_x1, b2_x1)
inter_rect_y1 = torch.max(b1_y1, b2_y1)
inter_rect_x2 = torch.min(b1_x2, b2_x2)
inter_rect_y2 = torch.min(b1_y2, b2_y2)
#Intersection area
inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(inter_rect_y2 - inter_rect_y1 + 1, min=0)
#Union Area
b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)
b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)
iou = inter_area / (b1_area + b2_area - inter_area)
return iou
def bbox_overlaps(boxes, query_boxes):
"""
Parameters
----------
boxes: (N, 4) ndarray or tensor or variable
query_boxes: (K, 4) ndarray or tensor or variable
Returns
-------
overlaps: (N, K) overlap between boxes and query_boxes
"""
if isinstance(boxes, np.ndarray):
boxes = torch.from_numpy(boxes)
query_boxes = torch.from_numpy(query_boxes)
out_fn = lambda x: x.numpy() # If input is ndarray, turn the overlaps back to ndarray when return
else:
out_fn = lambda x: x
box_areas = (boxes[:, 2] - boxes[:, 0] + 1) * \
(boxes[:, 3] - boxes[:, 1] + 1)
query_areas = (query_boxes[:, 2] - query_boxes[:, 0] + 1) * \
(query_boxes[:, 3] - query_boxes[:, 1] + 1)
iw = (torch.min(boxes[:, 2:3], query_boxes[:, 2:3].t()) - torch.max(
boxes[:, 0:1], query_boxes[:, 0:1].t()) + 1).clamp(min=0)
ih = (torch.min(boxes[:, 3:4], query_boxes[:, 3:4].t()) - torch.max(
boxes[:, 1:2], query_boxes[:, 1:2].t()) + 1).clamp(min=0)
ua = box_areas.view(-1, 1) + query_areas.view(1, -1) - iw * ih
overlaps = iw * ih / ua
return out_fn(overlaps)
def bbox_ious(boxes1, boxes2, x1y1x2y2=True):
if x1y1x2y2:
mx = torch.min(boxes1[0], boxes2[0])
Mx = torch.max(boxes1[2], boxes2[2])
my = torch.min(boxes1[1], boxes2[1])
My = torch.max(boxes1[3], boxes2[3])
w1 = boxes1[2] - boxes1[0]
h1 = boxes1[3] - boxes1[1]
w2 = boxes2[2] - boxes2[0]
h2 = boxes2[3] - boxes2[1]
else:
mx = torch.min(boxes1[0]-boxes1[2]/2.0, boxes2[0]-boxes2[2]/2.0)
Mx = torch.max(boxes1[0]+boxes1[2]/2.0, boxes2[0]+boxes2[2]/2.0)
my = torch.min(boxes1[1]-boxes1[3]/2.0, boxes2[1]-boxes2[3]/2.0)
My = torch.max(boxes1[1]+boxes1[3]/2.0, boxes2[1]+boxes2[3]/2.0)
w1 = boxes1[2]
h1 = boxes1[3]
w2 = boxes2[2]
h2 = boxes2[3]
uw = Mx - mx
uh = My - my
cw = w1 + w2 - uw
ch = h1 + h2 - uh
mask = ((cw <= 0) + (ch <= 0) > 0)
area1 = w1 * h1
area2 = w2 * h2
carea = cw * ch
carea[mask] = 0
uarea = area1 + area2 - carea
return carea/uarea
def updateOutput(self, input):
self._lazyInit()
dimension = self._getPositiveDimension(input)
torch.min(input, dimension, out=(self._output, self._indices), keepdim=True)
if input.dim() > 1:
self.output.set_(self._output.select(dimension, 0))
else:
self.output.set_(self._output)
return self.output
def forward(self, v, u, d): """ @param v [batch_size, embedding_size] matrix to push @param u [batch_size,] vector of pop signals in (0, 1) @param d [batch_size,] vector of push signals in (0, 1) @return [batch_size, embedding_size] or [batch_size, self.k, embedding_size] read matrix """ # update V, which is of size [t, bach_size, embedding_size] v = v.view(1, self.batch_size, self.embedding_size) self.V = torch.cat([self.V, v], 0) if len(self.V.data) != 0 else v # TODO append to self.s so we can terminate lower loop early? # TODO initialize stack to fixed size # update s, which is of size [t, batch_size] old_t = self.s.data.shape[0] if self.s.data.shape else 0 s = Variable(torch.FloatTensor(old_t + 1, self.batch_size)) w = u for i in reversed(xrange(old_t)): s_ = F.relu(self.s[i,:] - w) w = F.relu(w - self.s[i,:]) s[i,:] = s_ s[old_t,:] = d self.s = s if self.k is None: # calculate r, which is of size [batch_size, embedding_size] r = Variable(torch.zeros([self.batch_size, self.embedding_size])) for i in reversed(xrange(old_t + 1)): used = torch.sum(self.s[i + 1:old_t + 1,:], 0) if i < old_t else self.zero coeffs = torch.min(self.s[i,:], F.relu(1 - used)) # reformating coeffs into a matrix that can be multiplied element-wise r += coeffs.view(self.batch_size, 1).repeat(1, self.embedding_size) * self.V[i,:,:] return r else: # calculate k read vectors # TODO can probably make this more efficient r = Variable(torch.zeros([self.batch_size, self.k, self.embedding_size])) for k in xrange(self.k): for i in reversed(xrange(old_t + 1)): used = torch.sum(self.s[i + 1:old_t + 1,:], 0) if i < old_t else self.zero coeffs = torch.min(self.s[i,:], F.relu(1 + k - used)) r[:,k,:] = r[:,k,:] + coeffs.view(self.batch_size, 1).repeat(1, self.embedding_size) * self.V[i,:,:] for k in reversed(xrange(1, self.k)): r[:,k,:] = r[:,k,:] - r[:,k - 1,:] return r
def bbox_transform(self, boxes, deltas, weights=(1.0, 1.0, 1.0, 1.0), clip_value=4.135166556742356):
"""Forward transform that maps proposal boxes to predicted ground-truth
boxes using bounding-box regression deltas. See bbox_transform_inv for a
description of the weights argument.
"""
if boxes.size(0) == 0:
return None
#return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)
# get boxes dimensions and centers
widths = boxes[:, 2] - boxes[:, 0] + 1.0
heights = boxes[:, 3] - boxes[:, 1] + 1.0
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
wx, wy, ww, wh = weights
dx = deltas[:, 0::4] / wx
dy = deltas[:, 1::4] / wy
dw = deltas[:, 2::4] / ww
dh = deltas[:, 3::4] / wh
clip_value = Variable(torch.FloatTensor([clip_value]))
if boxes.is_cuda:
clip_value = clip_value.cuda()
# Prevent sending too large values into np.exp()
dw = torch.min(dw,clip_value)
dh = torch.min(dh,clip_value)
pred_ctr_x = dx * widths.unsqueeze(1) + ctr_x.unsqueeze(1)
pred_ctr_y = dy * heights.unsqueeze(1) + ctr_y.unsqueeze(1)
pred_w = torch.exp(dw) * widths.unsqueeze(1)
pred_h = torch.exp(dh) * heights.unsqueeze(1)
# pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
# x1
pred_boxes_x1 = pred_ctr_x - 0.5 * pred_w
# y1
pred_boxes_y1 = pred_ctr_y - 0.5 * pred_h
# x2 (note: "- 1" is correct; don't be fooled by the asymmetry)
pred_boxes_x2 = pred_ctr_x + 0.5 * pred_w - 1
# y2 (note: "- 1" is correct; don't be fooled by the asymmetry)
pred_boxes_y2 = pred_ctr_y + 0.5 * pred_h - 1
pred_boxes = torch.cat((pred_boxes_x1,
pred_boxes_y1,
pred_boxes_x2,
pred_boxes_y2),1)
return pred_boxes
def forward(self, v, u, d): """ @param v [batch_size, embedding_size] matrix to push @param u [batch_size,] vector of pop signals in (0, 1) @param d [batch_size,] vector of push signals in (0, 1) @return [batch_size, embedding_size] read matrix """ # update V, which is of size [t, bach_size, embedding_size] v = v.view(1, self.batch_size, self.embedding_size) self.V = torch.cat([self.V, v], 0) if len(self.V.data) != 0 else v # TODO initialize queue to fixed size # update s, which is of size [t, batch_size] old_t = self.s.size(0) if self.s.size() else 0 s = Variable(torch.FloatTensor(old_t + 1, self.batch_size)) w = u for i in xrange(old_t): s_ = F.relu(self.s[i,:] - w) w = F.relu(w - self.s[i,:]) s[i,:] = s_ # if len(torch.nonzero(w.data)) == 0: break # TODO does this if work properly now? s[old_t,:] = d self.s = s # calculate r, which is of size [batch_size, embedding_size] r = Variable(torch.zeros([self.batch_size, self.embedding_size])) for i in xrange(old_t + 1): used = torch.sum(self.s[:i,:], 0) if i > 0 else self.zero coeffs = torch.min(self.s[i,:], F.relu(1 - used)) # reformating coeffs into a matrix that can be multiplied element-wise r += coeffs.view(self.batch_size, 1).repeat(1, self.embedding_size) * self.V[i,:,:] return r
def my_loss_function(reconstructed_x, z_patches, prototypes, padding_idx, x, lambda_=0.01):
"""
reconstructed_x : batch_size, channels, height, width
z_patches : batch_size, num_patches, embedding_dim
prototypes : batch_size, num_prototypes, embedding_dim
padding_idx : batch_size
x : batch_size, channels, height, width
"""
assert not x.requires_grad
batch_size = x.size(0)
loss = F.mse_loss(reconstructed_x, x, size_average=False)
for i in range(batch_size):
image_patches = z_patches[i]
image_prototypes = prototypes[i][:padding_idx[i]]
dists = pairwise_squared_euclidean_distances(
image_prototypes, image_patches)
min_dists = torch.min(dists, dim=1)[0]
prototype_loss = torch.sum(min_dists)
loss = loss + (lambda_ * prototype_loss)
loss = loss / batch_size
return loss
def bisect_demo():
""" Bisect the LB/UB on specified columns.
The key is to use scatter_() to convert indices into one-hot encodings.
"""
t1t2 = torch.stack((torch.randn(5, 4), torch.randn(5, 4)), dim=-1)
lb, _ = torch.min(t1t2, dim=-1)
ub, _ = torch.max(t1t2, dim=-1)
print('LB:', lb)
print('UB:', ub)
# random idxs for testing
idxs = torch.randn_like(lb)
_, idxs = idxs.max(dim=-1) # <Batch>
print('Split idxs:', idxs)
idxs = idxs.unsqueeze(dim=-1) # Batch x 1
idxs = torch.zeros_like(lb).byte().scatter_(-1, idxs, 1) # convert into one-hot encoding
print('Reorg idxs:', idxs)
mid = (lb + ub) / 2.0
lefts_lb = lb
lefts_ub = torch.where(idxs, mid, ub) # use the one-hot encoding to call torch.where()
rights_lb = torch.where(idxs, mid, lb) # definitely faster than element-wise reassignment
rights_ub = ub
print('LEFT LB:', lefts_lb)
print('LEFT UB:', lefts_ub)
print('RIGHT LB:', rights_lb)
print('RIGHT UB:', rights_ub)
newlb = torch.cat((lefts_lb, rights_lb), dim=0)
newub = torch.cat((lefts_ub, rights_ub), dim=0)
return newlb, newub
def eval_one_batch(self, batch):
enc_batch, enc_padding_mask, enc_lens, enc_batch_extend_vocab, extra_zeros, c_t_1, coverage = \
get_input_from_batch(batch, use_cuda)
dec_batch, dec_padding_mask, max_dec_len, dec_lens_var, target_batch = \
get_output_from_batch(batch, use_cuda)
encoder_outputs, encoder_hidden, max_encoder_output = self.model.encoder(enc_batch, enc_lens)
s_t_1 = self.model.reduce_state(encoder_hidden)
if config.use_maxpool_init_ctx:
c_t_1 = max_encoder_output
step_losses = []
for di in range(min(max_dec_len, config.max_dec_steps)):
y_t_1 = dec_batch[:, di] # Teacher forcing
final_dist, s_t_1, c_t_1,attn_dist, p_gen, coverage = self.model.decoder(y_t_1, s_t_1,
encoder_outputs, enc_padding_mask, c_t_1,
extra_zeros, enc_batch_extend_vocab, coverage)
target = target_batch[:, di]
gold_probs = torch.gather(final_dist, 1, target.unsqueeze(1)).squeeze()
step_loss = -torch.log(gold_probs + config.eps)
if config.is_coverage:
step_coverage_loss = torch.sum(torch.min(attn_dist, coverage), 1)
step_loss = step_loss + config.cov_loss_wt * step_coverage_loss
step_mask = dec_padding_mask[:, di]
step_loss = step_loss * step_mask
step_losses.append(step_loss)
sum_step_losses = torch.sum(torch.stack(step_losses, 1), 1)
batch_avg_loss = sum_step_losses / dec_lens_var
loss = torch.mean(batch_avg_loss)
return loss.data[0]
def coverage_wu(self, beam, cov, beta=0.):
"""
NMT coverage re-ranking score from
"Google's Neural Machine Translation System" :cite:`wu2016google`.
"""
penalty = -torch.min(cov, cov.clone().fill_(1.0)).log().sum(1)
return beta * penalty
def forward(self, y_pred, y_true, eps=1e-6):
return NotImplementedError
torch.nn.modules.loss._assert_no_grad(y_true)
assert y_pred.shape[1] == 2
same_left = torch.stack([y_true[:, 0], y_pred[:, 0]], dim=1)
same_left, _ = torch.max(same_left, dim=1)
same_right = torch.stack([y_true[:, 1], y_pred[:, 1]], dim=1)
same_right, _ = torch.min(same_right, dim=1)
same_len = same_right - same_left + 1 # (batch_size,)
same_len = torch.stack([same_len, torch.zeros_like(same_len)], dim=1)
same_len, _ = torch.max(same_len, dim=1)
same_len = same_len.type(torch.float)
pred_len = (y_pred[:, 1] - y_pred[:, 0] + 1).type(torch.float)
true_len = (y_true[:, 1] - y_true[:, 0] + 1).type(torch.float)
pre = same_len / (pred_len + eps)
rec = same_len / (true_len + eps)
f1 = 2 * pre * rec / (pre + rec + eps)
return -torch.mean(f1)
def shortest_dist(dist_mat):
"""Parallel version.
Args:
dist_mat: pytorch Variable, available shape:
1) [m, n]
2) [m, n, N], N is batch size
3) [m, n, *], * can be arbitrary additional dimensions
Returns:
dist: three cases corresponding to `dist_mat`:
1) scalar
2) pytorch Variable, with shape [N]
3) pytorch Variable, with shape [*]
"""
m, n = dist_mat.size()[:2]
# Just offering some reference for accessing intermediate distance.
dist = [[0 for _ in range(n)] for _ in range(m)]
for i in range(m):
for j in range(n):
if (i == 0) and (j == 0):
dist[i][j] = dist_mat[i, j]
elif (i == 0) and (j > 0):
dist[i][j] = dist[i][j - 1] + dist_mat[i, j]
elif (i > 0) and (j == 0):
dist[i][j] = dist[i - 1][j] + dist_mat[i, j]
else:
dist[i][j] = torch.min(dist[i - 1][j], dist[i][j - 1]) + dist_mat[i, j]
dist = dist[-1][-1]
return dist
def __call__(self, reconstructed_x, z_patches, prototypes, padding_idx, x):
"""
reconstructed_x : batch_size, channels, height, width
z_patches : batch_size, num_patches, embedding_dim
prototypes : batch_size, num_prototypes, embedding_dim
padding_idx : batch_size
x : batch_size, channels, height, width
"""
assert not x.requires_grad
lambda_val = self._lambda_val
batch_size = x.size(0)
loss = lambda_val * self._reconstruction_loss(
reconstructed_x, x, size_average=False)
for i in range(batch_size):
image_patches = z_patches[i]
associated_prototypes = prototypes[i][:padding_idx[i]]
dists = pairwise_squared_euclidean_distances(
associated_prototypes, image_patches)
nearest_patch_idxs = torch.min(dists, dim=1)[1]
loss += self._xentropy_loss(-dists, nearest_patch_idxs)
loss = loss / batch_size
return loss
def generate_smooth_grad(Backprop, prep_img, target_class, param_n, param_sigma_multiplier):
"""
Generates smooth gradients of given Backprop type. You can use this with both vanilla
and guided backprop
Args:
Backprop (class): Backprop type
prep_img (torch Variable): preprocessed image
target_class (int): target class of imagenet
param_n (int): Amount of images used to smooth gradient
param_sigma_multiplier (int): Sigma multiplier when calculating std of noise
"""
# Generate an empty image/matrix
smooth_grad = np.zeros(prep_img.size()[1:])
mean = 0
sigma = param_sigma_multiplier / (torch.max(prep_img) - torch.min(prep_img)).data[0]
for x in range(param_n):
# Generate noise
noise = Variable(prep_img.data.new(prep_img.size()).normal_(mean, sigma**2))
# Add noise to the image
noisy_img = prep_img + noise
# Calculate gradients
vanilla_grads = Backprop.generate_gradients(noisy_img, target_class)
# Add gradients to smooth_grad
smooth_grad = smooth_grad + vanilla_grads
# Average it out
smooth_grad = smooth_grad / param_n
return smooth_grad
def box_iou(box1, box2):
"""
计算两个box之间的IOU，其中box1为default_box_xyxy(format 为xyxy)，box2为bounding box
:param box1: default_boxes，[#default_boxes, 4]
:param box2: bounding_boxes，[#bounding_boxes, 4]
:return:
iou，sized [#default_boxes, #bounding_boxes]
"""
# print('box1.size():{}'.format(box1.size()))
# print('box2.size():{}'.format(box2.size()))
lt = torch.max(box1[:, None, :2], box2[:, :2]) # [#default_boxes, #bounding_boxes, 2]
rb = torch.min(box1[:, None, 2:], box2[:, 2:]) # [#default_boxes, #bounding_boxes, 2]
# print('lt:{}'.format(lt))
# print('rb:{}'.format(rb))
wh = (rb-lt).clamp(min=0) # [#default_boxes, #bounding_boxes, 2]
inter = wh[:, :, 0] * wh[:, :, 1] # [#default_boxes, #bounding_boxes]
# print('inter:{}'.format(inter))
area1 = (box1[:, 2]-box1[:, 0])*(box1[:, 3]-box1[:, 1]) # [#default_boxes]
area2 = (box2[:, 2]-box2[:, 0])*(box2[:, 3]-box2[:, 1]) # [#bounding_boxes]
# print('area1:{}'.format(area1))
# print('area2:{}'.format(area2))
iou = inter / (area1[:, None] + area2 - inter)
# print('iou:{}'.format(iou))
return iou
def read(self, strength):
"""
The read operation looks at the first few items on the stack, in
the order determined by self._read_indices, such that the total
strength of these items is equal to the value of the strength
parameter. If necessary, the strength of the last vector is
reduced so that the total strength of the items read is exactly
equal to the strength parameter. The output of the read
operation is computed by taking the sum of all the vectors
looked at, weighted by their strengths.
:type strength: float
:param strength: The total amount of vectors to look at,
measured by their strengths
:rtype: Variable
:return: The output of the read operation, described above
"""
r = Variable(torch.zeros([self.batch_size, self.embedding_size]))
str_used = Variable(torch.zeros(self.batch_size))
for i in self._read_indices():
str_i = self.strengths[i, :]
str_weights = torch.min(str_i, relu(1 - str_used))
str_weights = str_weights.view(self.batch_size, 1)
str_weights = str_weights.repeat(1, self.embedding_size)
r += str_weights * self.contents[i, :, :]
str_used = str_used + str_i
return r
def box_iou(box1, box2, order='xyxy'):
'''Compute the intersection over union of two set of boxes.
The default box order is (xmin, ymin, xmax, ymax).
Args:
box1: (tensor) bounding boxes, sized [N,4].
box2: (tensor) bounding boxes, sized [M,4].
order: (str) box order, either 'xyxy' or 'xywh'.
Return:
(tensor) iou, sized [N,M].
Reference:
https://github.com/chainer/chainercv/blob/master/chainercv/utils/bbox/bbox_iou.py
'''
if order == 'xywh':
box1 = change_box_order(box1, 'xywh2xyxy')
box2 = change_box_order(box2, 'xywh2xyxy')
N = box1.size(0)
M = box2.size(0)
lt = torch.max(box1[:,None,:2], box2[:,:2]) # [N,M,2]
rb = torch.min(box1[:,None,2:], box2[:,2:]) # [N,M,2]
wh = (rb-lt+1).clamp(min=0) # [N,M,2]
inter = wh[:,:,0] * wh[:,:,1] # [N,M]
area1 = (box1[:,2]-box1[:,0]+1) * (box1[:,3]-box1[:,1]+1) # [N,]
area2 = (box2[:,2]-box2[:,0]+1) * (box2[:,3]-box2[:,1]+1) # [M,]
iou = inter / (area1[:,None] + area2 - inter)
return iou
def forward(self, batch):
X_data, X_padding_mask, X_lens, X_batch_extend_vocab, X_extra_zeros, context, coverage = self.get_input_from_batch(batch)
y_data, y_padding_mask, y_max_len, y_lens_var, target_data = self.get_output_from_batch(batch)
encoder_outputs, encoder_hidden, max_encoder_output = self.encoder(X_data, X_lens)
s_t_1 = self.reduce_state(encoder_hidden)
if config.use_maxpool_init_ctx:
context = max_encoder_output
step_losses = []
for di in range(min(y_max_len, self.args.max_decoder_steps)):
y_t_1 = y_data[:, di] # Teacher forcing
final_dist, s_t_1, context, attn_dist, p_gen, coverage = self.decoder(y_t_1, s_t_1,
encoder_outputs, X_padding_mask, context,
X_extra_zeros, X_batch_extend_vocab,
coverage)
target = target_data[:, di]
gold_probs = torch.gather(final_dist, 1, target.unsqueeze(1)).squeeze()
step_loss = -torch.log(gold_probs + self.args.eps)
if self.args.is_coverage:
step_coverage_loss = torch.sum(torch.min(attn_dist, coverage), 1)
step_loss = step_loss + config.cov_loss_wt * step_coverage_loss
step_mask = y_padding_mask[:, di]
step_loss = step_loss * step_mask
step_losses.append(step_loss)
sum_losses = torch.sum(torch.stack(step_losses, 1), 1)
batch_avg_loss = sum_losses / y_lens_var
loss = torch.mean(batch_avg_loss)
return loss
def clip_boxes_graph(boxes, window):
"""
boxes: [N, 4] each row is y1, x1, y2, x2
window: [4] in the form y1, x1, y2, x2
"""
# Split corners
wy1, wx1, wy2, wx2 = window
y1, x1, y2, x2 = boxes
# Clip
y1 = torch.max(torch.min(y1, wy2), wy1)
x1 = torch.max(torch.min(x1, wx2), wx1)
y2 = torch.max(torch.min(y2, wy2), wy1)
x2 = torch.max(torch.min(x2, wx2), wx1)
clipped = torch.stack([x1, y1, x2, y2], dim=2)
return clipped
def train_actor_critic(actor, critic, memory, actor_optim, critic_optim, args):
memory = np.array(memory)
states = np.vstack(memory[:, 0])
actions = list(memory[:, 1])
rewards = list(memory[:, 2])
masks = list(memory[:, 3])
old_values = critic(torch.Tensor(states))
returns, advants = get_gae(rewards, masks, old_values, args)
mu, std = actor(torch.Tensor(states))
old_policy = log_prob_density(torch.Tensor(actions), mu, std)
criterion = torch.nn.MSELoss()
n = len(states)
arr = np.arange(n)
for _ in range(args.ppo_update_num):
np.random.shuffle(arr)
for i in range(n // args.batch_size):
batch_index = arr[args.batch_size * i : args.batch_size * (i + 1)]
batch_index = torch.LongTensor(batch_index)
inputs = torch.Tensor(states)[batch_index]
actions_samples = torch.Tensor(actions)[batch_index]
returns_samples = returns.unsqueeze(1)[batch_index]
advants_samples = advants.unsqueeze(1)[batch_index]
oldvalue_samples = old_values[batch_index].detach()
values = critic(inputs)
clipped_values = oldvalue_samples + \
torch.clamp(values - oldvalue_samples,
-args.clip_param,
args.clip_param)
critic_loss1 = criterion(clipped_values, returns_samples)
critic_loss2 = criterion(values, returns_samples)
critic_loss = torch.max(critic_loss1, critic_loss2).mean()
loss, ratio, entropy = surrogate_loss(actor, advants_samples, inputs,
old_policy.detach(), actions_samples,
batch_index)
clipped_ratio = torch.clamp(ratio,
1.0 - args.clip_param,
1.0 + args.clip_param)
clipped_loss = clipped_ratio * advants_samples
actor_loss = -torch.min(loss, clipped_loss).mean()
loss = actor_loss + 0.5 * critic_loss - 0.001 * entropy
critic_optim.zero_grad()
loss.backward(retain_graph=True)
critic_optim.step()
actor_optim.zero_grad()
loss.backward()
actor_optim.step()
def _action_history_match(predicted: List[int], targets: torch.LongTensor) -> int:
# TODO(mattg): this could probably be moved into a FullSequenceMatch metric, or something.
# Check if target is big enough to cover prediction (including start/end symbols)
if len(predicted) > targets.size(1):
return 0
predicted_tensor = targets.new_tensor(predicted)
targets_trimmed = targets[:, :len(predicted)]
# Return 1 if the predicted sequence is anywhere in the list of targets.
return torch.max(torch.min(targets_trimmed.eq(predicted_tensor), dim=1)[0]).item()
def triplet_margin_loss(anchor, positive, negative, margin=1.0, p=2, eps=1e-6, swap=False):
r"""Creates a criterion that measures the triplet loss given an input
tensors x1, x2, x3 and a margin with a value greater than 0.
This is used for measuring a relative similarity between samples. A triplet
is composed by `a`, `p` and `n`: anchor, positive examples and negative
example respectively. The shape of all input variables should be
:math:`(N, D)`.
The distance swap is described in detail in the paper `Learning shallow
convolutional feature descriptors with triplet losses`_ by
V. Balntas, E. Riba et al.
.. math::
L(a, p, n) = \frac{1}{N} \left( \sum_{i=1}^N \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} \right)
where :math:`d(x_i, y_i) = \| {\bf x}_i - {\bf y}_i \|_2^2`.
Args:
anchor: anchor input tensor
positive: positive input tensor
negative: negative input tensor
margin: the margin value. Default: 1
p: the norm degree. Default: 2
eps: small epsilon value to avoid numerical issues. Default: 1e-6
swap: compute distance swap. Default: False
Shape:
- Input: :math:`(N, D)` where `D = vector dimension`
- Output: :math:`(N, 1)`
Example::
>>> input1 = autograd.Variable(torch.randn(100, 128))
>>> input2 = autograd.Variable(torch.randn(100, 128))
>>> input3 = autograd.Variable(torch.randn(100, 128))
>>> output = F.triplet_margin_loss(input1, input2, input3, p=2)
>>> output.backward()
.. _Learning shallow convolutional feature descriptors with triplet losses:
http://www.iis.ee.ic.ac.uk/%7Evbalnt/shallow_descr/TFeat_paper.pdf
"""
assert anchor.size() == positive.size(), "Input sizes between positive and negative must be equal."
assert anchor.size() == negative.size(), "Input sizes between anchor and negative must be equal."
assert positive.size() == negative.size(), "Input sizes between positive and negative must be equal."
assert anchor.dim() == 2, "Inputd must be a 2D matrix."
assert margin > 0.0, 'Margin should be positive value.'
d_p = pairwise_distance(anchor, positive, p, eps)
d_n = pairwise_distance(anchor, negative, p, eps)
if swap:
d_s = pairwise_distance(positive, negative, p, eps)
d_n = torch.min(d_n, d_s)
dist_hinge = torch.clamp(margin + d_p - d_n, min=0.0)
loss = torch.mean(dist_hinge)
return loss
def hard_example_mining(dist_mat, labels, return_inds=False):
"""For each anchor, find the hardest positive and negative sample.
Args:
dist_mat: pytorch Variable, pair wise distance between samples, shape [N, N]
labels: pytorch LongTensor, with shape [N]
return_inds: whether to return the indices. Save time if `False`(?)
Returns:
dist_ap: pytorch Variable, distance(anchor, positive); shape [N]
dist_an: pytorch Variable, distance(anchor, negative); shape [N]
p_inds: pytorch LongTensor, with shape [N];
indices of selected hard positive samples; 0 <= p_inds[i] <= N - 1
n_inds: pytorch LongTensor, with shape [N];
indices of selected hard negative samples; 0 <= n_inds[i] <= N - 1
NOTE: Only consider the case in which all labels have same num of samples,
thus we can cope with all anchors in parallel.
"""
assert len(dist_mat.size()) == 2
assert dist_mat.size(0) == dist_mat.size(1)
N = dist_mat.size(0)
# shape [N, N]
is_pos = labels.expand(N, N).eq(labels.expand(N, N).t())
is_neg = labels.expand(N, N).ne(labels.expand(N, N).t())
# `dist_ap` means distance(anchor, positive)
# both `dist_ap` and `relative_p_inds` with shape [N, 1]
dist_ap, relative_p_inds = torch.max(
dist_mat[is_pos].contiguous().view(N, -1), 1, keepdim=True)
# `dist_an` means distance(anchor, negative)
# both `dist_an` and `relative_n_inds` with shape [N, 1]
dist_an, relative_n_inds = torch.min(
dist_mat[is_neg].contiguous().view(N, -1), 1, keepdim=True)
# shape [N]
dist_ap = dist_ap.squeeze(1)
dist_an = dist_an.squeeze(1)
if return_inds:
# shape [N, N]
ind = (labels.new().resize_as_(labels)
.copy_(torch.arange(0, N).long())
.unsqueeze( 0).expand(N, N))
# shape [N, 1]
p_inds = torch.gather(
ind[is_pos].contiguous().view(N, -1), 1, relative_p_inds.data)
n_inds = torch.gather(
ind[is_neg].contiguous().view(N, -1), 1, relative_n_inds.data)
# shape [N]
p_inds = p_inds.squeeze(1)
n_inds = n_inds.squeeze(1)
return dist_ap, dist_an, p_inds, n_inds
return dist_ap, dist_an
def bbox_overlap(boxes, query_boxes):
"""
:param boxes <torch.Tensor>: (N, 6)
:param query_boxes <torch.Tensor>: (K, 6)
:return: overlaps (N, K) overlap between boxes and query boxes
"""
out_fn = lambda x: x
box_ares = (boxes[:, 3] - boxes[:, 0]) * (boxes[:, 4] - boxes[:, 1]) * (boxes[:, 5] - boxes[:, 2])
query_ares = (query_boxes[:, 3] - query_boxes[:, 0]) * (query_boxes[:, 4] - query_boxes[:, 1]) *\
(query_boxes[:, 5] - query_boxes[:, 2])
iw = (torch.min(boxes[:, 3:4], query_boxes[:, 3:4].t()) - torch.max(boxes[:, 0:1], query_boxes[:, 0:1].t())).clamp(min=0)
ih = (torch.min(boxes[:, 4:5], query_boxes[:, 4:5].t()) - torch.max(boxes[:, 1:2], query_boxes[:, 1:2].t())).clamp(min=0)
il = (torch.min(boxes[:, 5:6], query_boxes[:, 5:6].t()) - torch.max(boxes[:, 2:3], query_boxes[:, 2:3].t())).clamp(min=0)
ua = box_ares.view(-1, 1) + query_ares.view(1, -1) - iw*ih*il
overlaps = iw*ih*il / ua
return out_fn(overlaps)
def coverage_wu(self, cov, beta=0.):
"""GNMT coverage re-ranking score.
See "Google's Neural Machine Translation System" :cite:`wu2016google`.
``cov`` is expected to be sized ``(*, seq_len)``, where ``*`` is
probably ``batch_size x beam_size`` but could be several
dimensions like ``(batch_size, beam_size)``. If ``cov`` is attention,
then the ``seq_len`` axis probably sums to (almost) 1.
"""
penalty = -torch.min(cov, cov.clone().fill_(1.0)).log().sum(-1)
return beta * penalty
def intersection_area(yx_min1, yx_max1, yx_min2, yx_max2):
"""
Calculates the intersection area of two lists of bounding boxes.
:author 申瑞珉 (Ruimin Shen)
:param yx_min1: The top left coordinates (y, x) of the first list (size [N1, 2]) of bounding boxes.
:param yx_max1: The bottom right coordinates (y, x) of the first list (size [N1, 2]) of bounding boxes.
:param yx_min2: The top left coordinates (y, x) of the second list (size [N2, 2]) of bounding boxes.
:param yx_max2: The bottom right coordinates (y, x) of the second list (size [N2, 2]) of bounding boxes.
:return: The matrix (size [N1, N2]) of the intersection area.
"""
ymin1, xmin1 = torch.split(yx_min1, 1, -1)
ymax1, xmax1 = torch.split(yx_max1, 1, -1)
ymin2, xmin2 = torch.split(yx_min2, 1, -1)
ymax2, xmax2 = torch.split(yx_max2, 1, -1)
max_ymin = torch.max(ymin1.repeat(1, ymin2.size(0)), torch.transpose(ymin2, 0, 1).repeat(ymin1.size(0), 1)) # PyTorch's bug
min_ymax = torch.min(ymax1.repeat(1, ymax2.size(0)), torch.transpose(ymax2, 0, 1).repeat(ymax1.size(0), 1)) # PyTorch's bug
height = torch.clamp(min_ymax - max_ymin, min=0)
max_xmin = torch.max(xmin1.repeat(1, xmin2.size(0)), torch.transpose(xmin2, 0, 1).repeat(xmin1.size(0), 1)) # PyTorch's bug
min_xmax = torch.min(xmax1.repeat(1, xmax2.size(0)), torch.transpose(xmax2, 0, 1).repeat(xmax1.size(0), 1)) # PyTorch's bug
width = torch.clamp(min_xmax - max_xmin, min=0)
return height * width
def forward(self, sent_tuple):
# sent_len: [max_len, ..., min_len] (batch)
# sent: Variable(seqlen x batch x worddim)
sent, sent_len = sent_tuple
bsize = sent.size(1)
self.init_lstm = self.init_lstm if bsize == self.init_lstm.size(1) else \
Variable(torch.FloatTensor(2, bsize, self.enc_lstm_dim).zero_()).cuda()
# Sort by length (keep idx)
sent_len, idx_sort = np.sort(sent_len)[::-1], np.argsort(-sent_len)
sent = sent.index_select(1, Variable(torch.cuda.LongTensor(idx_sort)))
# Handling padding in Recurrent Networks
sent_packed = nn.utils.rnn.pack_padded_sequence(sent, sent_len)
sent_output = self.enc_lstm(sent_packed,
(self.init_lstm, self.init_lstm))[0]
# seqlen x batch x 2*nhid
sent_output = nn.utils.rnn.pad_packed_sequence(sent_output)[0]
# Un-sort by length
idx_unsort = np.argsort(idx_sort)
sent_output = sent_output.index_select(1,
Variable(torch.cuda.LongTensor(idx_unsort)))
sent_output = sent_output.transpose(0,1).contiguous()
sent_output_proj = self.proj_lstm(sent_output.view(-1,
2*self.enc_lstm_dim)).view(bsize, -1, 2*self.enc_lstm_dim)
sent_keys = self.proj_enc(sent_output.view(-1,
2*self.enc_lstm_dim)).view(bsize, -1, 2*self.enc_lstm_dim)
sent_max = torch.max(sent_output, 1)[0].squeeze(1) # (bsize, 2*nhid)
sent_summary = self.proj_query(
sent_max).unsqueeze(1).expand_as(sent_keys)
# (bsize, seqlen, 2*nhid)
sent_M = torch.tanh(sent_keys + sent_summary)
# (bsize, seqlen, 2*nhid) YANG : M = tanh(Wh_i + Wh_avg
sent_w = self.query_embedding(Variable(torch.LongTensor(
bsize*[0]).cuda())).unsqueeze(2) # (bsize, 2*nhid, 1)
sent_alphas = self.softmax(sent_M.bmm(sent_w).squeeze(2)).unsqueeze(1)
# (bsize, 1, seqlen)
if int(time.time()) % 200 == 0:
print('w', torch.max(sent_w[0]), torch.min(sent_w[0]))
print('alphas', sent_alphas[0][0][0:sent_len[0]])
# Get attention vector
emb = sent_alphas.bmm(sent_output_proj).squeeze(1)
return emb
def forward(self, x):
# feed through neural network
h = F.relu(self.fc1(x))
# h = F.relu(self.fc2(h))
fudge_lower_bdd = torch.Tensor([-8])
h = torch.max(self.fc3(h), fudge_lower_bdd)
h = torch.min(h, - fudge_lower_bdd)
log_class_weights = self.log_softmax(h)
return log_class_weights
def update(self, replay_buffer, n_iter):
for i in range(n_iter):
state, action, reward, next_state, done = replay_buffer.sample(
batch_size)
state = torch.FloatTensor(state).to(device)
action = torch.FloatTensor(action).to(device)
reward = torch.FloatTensor(reward).to(device)
next_state = torch.FloatTensor(next_state).to(device)
done = torch.FloatTensor(done).to(device)
# Select next action according to target policy:
noise = torch.empty_like(action).data.normal_(
0, policy_noise).to(device)
noise = noise.clamp(-noise_clip, noise_clip)
next_action = (self.actor_target(next_state) + noise)
next_action = next_action.clamp(-self.max_action, self.max_action)
# Compute target Q-value:
target_Q1 = self.critic_1_target(next_state, next_action)
target_Q2 = self.critic_2_target(next_state, next_action)
target_Q = torch.min(target_Q1, target_Q2)
target_Q = reward + ((1 - done) * gamma * target_Q).detach()
# Optimize Critic 1:
current_Q1 = self.critic_1(state, action)
loss_Q1 = F.mse_loss(current_Q1, target_Q)
self.critic_1_optimizer.zero_grad()
loss_Q1.backward()
self.critic_1_optimizer.step()
# Optimize Critic 2:
current_Q2 = self.critic_2(state, action)
loss_Q2 = F.mse_loss(current_Q2, target_Q)
self.critic_2_optimizer.zero_grad()
loss_Q2.backward()
self.critic_2_optimizer.step()
# Delayed policy updates:
if i % policy_delay == 0:
# Compute actor loss:
actor_loss = -self.critic_1(state, self.actor(state)).mean()
# Optimize the actor
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# Polyak averaging update:
for param, target_param in zip(self.actor.parameters(),
self.actor_target.parameters()):
target_param.data.copy_((polyak * target_param.data) +
((1 - polyak) * param.data))
for param, target_param in zip(
self.critic_1.parameters(),
self.critic_1_target.parameters()):
target_param.data.copy_((polyak * target_param.data) +
((1 - polyak) * param.data))
for param, target_param in zip(
self.critic_2.parameters(),
self.critic_2_target.parameters()):
target_param.data.copy_((polyak * target_param.data) +
((1 - polyak) * param.data))
def train(self,
replay_buffer,
iterations,
batch_size=30,
discount=0.99,
tau=0.005,
policy_noise=0.2,
noise_clip=0.5,
policy_freq=2):
# self.critic_scheduler.step(critic_loss)
# self.actor_scheduler.step(actor_loss)
for it in range(iterations):
# Step 4: We sample a batch of transitions (s, s’, a, r) from the memory
batch_states1, batch_states2, batch_next_states1, batch_next_states2, batch_actions, batch_rewards, batch_dones = replay_buffer.sample(
batch_size)
state1 = torch.Tensor(batch_states1).to(device)
state2 = torch.Tensor(batch_states2).to(device)
next_state1 = torch.Tensor(batch_next_states1).to(device)
next_state2 = torch.Tensor(batch_next_states2).to(device)
action = torch.Tensor(batch_actions).to(device).unsqueeze(1)
reward = torch.Tensor(batch_rewards).to(device)
dones = torch.Tensor(batch_dones).to(device)
# Step 5: From the next state s’, the Actor target plays the next action a’
next_action = self.actor_target(next_state1, next_state2)
# Step 6: We add Gaussian noise to this next action a’ and we clamp it in a range of values supported by the environment
noise = torch.Tensor(batch_actions).data.normal_(
0, policy_noise).to(device)
noise = noise.clamp(-noise_clip, noise_clip).unsqueeze(1)
next_action = (next_action + noise).clamp(-self.max_action,
self.max_action)
# Step 7: The two Critic targets take each the couple (s’, a’) as input and return two Q-values Qt1(s’,a’) and Qt2(s’,a’) as outputs
target_Q1, target_Q2 = self.critic_target(next_state1, next_state2,
next_action)
# Step 8: We keep the minimum of these two Q-values: min(Qt1, Qt2)
target_Q = torch.min(target_Q1, target_Q2)
# Step 9: We get the final target of the two Critic models, which is: Qt = r + γ * min(Qt1, Qt2), where γ is the discount factor
target_Q = reward + ((1 - dones) * discount * target_Q).detach()
# Step 10: The two Critic models take each the couple (s, a) as input and return two Q-values Q1(s,a) and Q2(s,a) as outputs
current_Q1, current_Q2 = self.critic(state1, state2, action)
# Step 11: We compute the loss coming from the two Critic models: Critic Loss = MSE_Loss(Q1(s,a), Qt) + MSE_Loss(Q2(s,a), Qt)
critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(
current_Q2, target_Q)
if it % 20 == 0:
print("_________________________")
print("iteration number", it)
print("critic loss", critic_loss)
# Step 12: We backpropagate this Critic loss and update the parameters of the two Critic models with a SGD optimizer
self.critic_optimizer.zero_grad()
critic_loss.backward()
# self.critic_scheduler.step()
# self.critic_scheduler.step(critic_loss)
self.critic_optimizer.step()
# Step 13: Once every two iterations, we update our Actor model by performing gradient ascent on the output of the first Critic model
if it % policy_freq == 0:
actor_loss = -self.critic.Q1(
state1, state2, self.actor(state1, state2)).mean()
if it % 20 == 0:
print("actor loss", actor_loss)
self.actor_optimizer.zero_grad()
actor_loss.backward()
# self.actor_scheduler.step()
# self.actor_scheduler.step(actor_loss)
self.actor_optimizer.step()
# if it % 2*policy_freq == 0:
# Step 14: Still once every two iterations, we update the weights of the Actor target by polyak averaging
for param, target_param in zip(self.actor.parameters(),
self.actor_target.parameters()):
target_param.data.copy_(tau * param.data +
(1 - tau) * target_param.data)
# Step 15: Still once every two iterations, we update the weights of the Critic target by polyak averaging
for param, target_param in zip(
self.critic.parameters(),
self.critic_target.parameters()):
target_param.data.copy_(tau * param.data +
(1 - tau) * target_param.data)
},
"training": {
"epochs": 10000,
"batchsize": 64,
"savefile": "../keras_models/fully_trained_stamp"
},
"directory": "./test_data/",
"imgtype": "png",
"scale_integer": False,
"shape": [201, 201],
"noise": 0.05,
"ext_noise" : 0.01,
"train": {"nframes": 10},
"test": {"nframes": 10},
"eval": {"nframes": 10},
"overwrite": True,
"max_overlaps": 2,
"scale_integer": False,
"delete_files_after_training": False
}
makedata(config)
dl = EstimatorDataset(config)
for i in range(len(dl)):
print(torch.max(dl[i][0]),torch.min(dl[i][0]))
print(dl[i][0].shape)
print(type(dl[i][0]))
def m2min(x, y):
return torch.min(x.unsqueeze(1), y)
def mmin2(x):
return torch.min(x, dim=2)
def interpolate_dense_features(pos, dense_features, return_corners=False):
device = pos.device
ids = torch.arange(0, pos.size(1), device=device)
_, h, w = dense_features.size()
i = pos[0, :]
j = pos[1, :]
# Valid corners
i_top_left = torch.floor(i).long()
j_top_left = torch.floor(j).long()
valid_top_left = torch.min(i_top_left >= 0, j_top_left >= 0)
i_top_right = torch.floor(i).long()
j_top_right = torch.ceil(j).long()
valid_top_right = torch.min(i_top_right >= 0, j_top_right < w)
i_bottom_left = torch.ceil(i).long()
j_bottom_left = torch.floor(j).long()
valid_bottom_left = torch.min(i_bottom_left < h, j_bottom_left >= 0)
i_bottom_right = torch.ceil(i).long()
j_bottom_right = torch.ceil(j).long()
valid_bottom_right = torch.min(i_bottom_right < h, j_bottom_right < w)
valid_corners = torch.min(torch.min(valid_top_left, valid_top_right),
torch.min(valid_bottom_left, valid_bottom_right))
i_top_left = i_top_left[valid_corners]
j_top_left = j_top_left[valid_corners]
i_top_right = i_top_right[valid_corners]
j_top_right = j_top_right[valid_corners]
i_bottom_left = i_bottom_left[valid_corners]
j_bottom_left = j_bottom_left[valid_corners]
i_bottom_right = i_bottom_right[valid_corners]
j_bottom_right = j_bottom_right[valid_corners]
ids = ids[valid_corners]
if ids.size(0) == 0:
raise EmptyTensorError
# Interpolation
i = i[ids]
j = j[ids]
dist_i_top_left = i - i_top_left.float()
dist_j_top_left = j - j_top_left.float()
w_top_left = (1 - dist_i_top_left) * (1 - dist_j_top_left)
w_top_right = (1 - dist_i_top_left) * dist_j_top_left
w_bottom_left = dist_i_top_left * (1 - dist_j_top_left)
w_bottom_right = dist_i_top_left * dist_j_top_left
descriptors = (
w_top_left * dense_features[:, i_top_left, j_top_left] +
w_top_right * dense_features[:, i_top_right, j_top_right] +
w_bottom_left * dense_features[:, i_bottom_left, j_bottom_left] +
w_bottom_right * dense_features[:, i_bottom_right, j_bottom_right])
pos = torch.cat([i.view(1, -1), j.view(1, -1)], dim=0)
if not return_corners:
return [descriptors, pos, ids]
else:
corners = torch.stack([
torch.stack([i_top_left, j_top_left], dim=0),
torch.stack([i_top_right, j_top_right], dim=0),
torch.stack([i_bottom_left, j_bottom_left], dim=0),
torch.stack([i_bottom_right, j_bottom_right], dim=0)
],
dim=0)
return [descriptors, pos, ids, corners]
def initialize(self, image, info: dict) -> dict:
# Initialize some stuff
self.frame_num = 1
if not self.params.has('device'):
self.params.device = 'cuda' if self.params.use_gpu else 'cpu'
# Initialize network
self.initialize_features()
# The DiMP network
# self.net = self.params.net
self.net_rgb = self.params.net_rgb
self.net_d = self.params.net_d
# Time initialization
tic = time.time()
# Convert image
im = image[:, :, :3]
dp = image[:, :, 3:]
im = numpy_to_torch(im) #
dp = numpy_to_torch(dp)
# Get target position and size
state = info['init_bbox']
self.pos = torch.Tensor([state[1] + (state[3] - 1)/2, state[0] + (state[2] - 1)/2])
self.target_sz = torch.Tensor([state[3], state[2]])
# Get object id
self.object_id = info.get('object_ids', [None])[0]
self.id_str = '' if self.object_id is None else ' {}'.format(self.object_id)
# Set sizes
self.image_sz = torch.Tensor([im.shape[2], im.shape[3]])
sz = self.params.image_sample_size
sz = torch.Tensor([sz, sz] if isinstance(sz, int) else sz)
if self.params.get('use_image_aspect_ratio', False):
sz = self.image_sz * sz.prod().sqrt() / self.image_sz.prod().sqrt()
stride = self.params.get('feature_stride', 32)
sz = torch.round(sz / stride) * stride
self.img_sample_sz = sz
self.img_support_sz = self.img_sample_sz
# Set search area
search_area = torch.prod(self.target_sz * self.params.search_area_scale).item()
self.target_scale = math.sqrt(search_area) / self.img_sample_sz.prod().sqrt()
# Target size in base scale
self.base_target_sz = self.target_sz / self.target_scale
# Setup scale factors
if not self.params.has('scale_factors'):
self.params.scale_factors = torch.ones(1)
elif isinstance(self.params.scale_factors, (list, tuple)):
self.params.scale_factors = torch.Tensor(self.params.scale_factors)
# Setup scale bounds
self.min_scale_factor = torch.max(10 / self.base_target_sz)
self.max_scale_factor = torch.min(self.image_sz / self.base_target_sz)
# Extract and transform sample
init_backbone_feat_rgb, init_backbone_feat_d = self.generate_init_samples(im, dp)
# Initialize classifier
self.init_classifier(init_backbone_feat_rgb, init_backbone_feat_d)
# Initialize IoUNet
if self.params.get('use_iou_net', True):
self.init_iou_net(init_backbone_feat_rgb, init_backbone_feat_d)
out = {'time': time.time() - tic}
return out
def mmin(x):
return torch.min(x, dim=1, keepdim=True) #
def sgnmin(y, w):
return torch.sign(y * w) * torch.min(torch.abs(y), torch.abs(w))
#coeffs = eval_model.rbfweights.detach().data.numpy()
mx, mn = -np.inf, np.inf
cnter = 0
for i in range(len(graph_list)):
dat = data[i]
G = graph_list[i]
#dat['secondary_gram'] = torch.matmul(dat['secondary_gram'], principals)
dat['secondary_gram'] = torch.from_numpy(u[cnter:cnter + len(G), indices])
cnter += len(G)
#hks = torch.flatten(torch.matmul(dat['secondary_gram'], torch.from_numpy(coeffs))).float().detach()
hks = torch.flatten(torch.matmul(dat['secondary_gram'],
winit)).float().detach()
mx = max(float(torch.max(hks)), mx)
mn = min(float(torch.min(hks)), mn)
st = simplex_tree_constructor([list(e) for e in G.edges()])
dat['simplex_tree'] = filtration_update(st, hks.numpy())
dat['vectors'] = torch.zeros([1, 18])
#recompute persistence
pers = dat['simplex_tree'].persistence(homology_coeff_field=2)
del pers
label = torch.tensor(label).float()
print(mn, mx)
torch.set_rng_state(rng_state) #fix init state
#if fix wavelet
eval_model = ModelStatsRBF(pds, mn=mn, mx=mx, weightinit=winit)
eval_model.update = True
def _compute_coverage_loss(self, std_attn, coverage_attn):
covloss = torch.min(std_attn, coverage_attn).sum()
covloss *= self.lambda_coverage
return covloss
def initialize(self, image, state, *args, **kwargs):
# Initialize some stuff
self.frame_num = 1
if not hasattr(self.params, 'device'):
self.params.device = 'cuda' if self.params.use_gpu else 'cpu'
# Initialize features
self.initialize_features()
# Check if image is color
self.params.features.set_is_color(image.shape[2] == 3)
# Get feature specific params
self.fparams = self.params.features.get_fparams('feature_params')
self.time = 0
tic = time.time()
# Get position and size
self.pos = torch.Tensor(
[state[1] + (state[3] - 1) / 2, state[0] + (state[2] - 1) / 2])
self.target_sz = torch.Tensor([state[3], state[2]])
# Set search area
self.target_scale = 1.0
search_area = torch.prod(self.target_sz *
self.params.search_area_scale).item()
if search_area > self.params.max_image_sample_size:
self.target_scale = math.sqrt(search_area /
self.params.max_image_sample_size)
elif search_area < self.params.min_image_sample_size:
self.target_scale = math.sqrt(search_area /
self.params.min_image_sample_size)
# Check if IoUNet is used
self.use_iou_net = getattr(self.params, 'use_iou_net', True)
# Target size in base scale
self.base_target_sz = self.target_sz / self.target_scale
# Use odd square search area and set sizes
feat_max_stride = max(self.params.features.stride())
if getattr(self.params, 'search_area_shape', 'square') == 'square':
self.img_sample_sz = torch.round(
torch.sqrt(
torch.prod(self.base_target_sz *
self.params.search_area_scale))) * torch.ones(2)
elif self.params.search_area_shape == 'initrect':
self.img_sample_sz = torch.round(self.base_target_sz *
self.params.search_area_scale)
else:
raise ValueError('Unknown search area shape')
if self.params.feature_size_odd:
self.img_sample_sz += feat_max_stride - self.img_sample_sz % (
2 * feat_max_stride)
else:
self.img_sample_sz += feat_max_stride - (
self.img_sample_sz + feat_max_stride) % (2 * feat_max_stride)
# Set sizes
self.img_support_sz = self.img_sample_sz
self.feature_sz = self.params.features.size(self.img_sample_sz)
self.output_sz = self.params.score_upsample_factor * self.img_support_sz # Interpolated size of the output
self.kernel_size = self.fparams.attribute('kernel_size')
self.iou_img_sample_sz = self.img_sample_sz
# Optimization options
self.params.precond_learning_rate = self.fparams.attribute(
'learning_rate')
if self.params.CG_forgetting_rate is None or max(
self.params.precond_learning_rate) >= 1:
self.params.direction_forget_factor = 0
else:
self.params.direction_forget_factor = (
1 - max(self.params.precond_learning_rate)
)**self.params.CG_forgetting_rate
self.output_window = None
if getattr(self.params, 'window_output', False):
if getattr(self.params, 'use_clipped_window', False):
self.output_window = dcf.hann2d_clipped(
self.output_sz.long(),
self.output_sz.long() * self.params.effective_search_area /
self.params.search_area_scale,
centered=False).to(self.params.device)
else:
self.output_window = dcf.hann2d(self.output_sz.long(),
centered=False).to(
self.params.device)
# Initialize some learning things
self.init_learning()
# Convert image
im = numpy_to_torch(image)
self.im = im # For debugging only
# Setup scale bounds
self.image_sz = torch.Tensor([im.shape[2], im.shape[3]])
self.min_scale_factor = torch.max(10 / self.base_target_sz)
self.max_scale_factor = torch.min(self.image_sz / self.base_target_sz)
# Extract and transform sample
x = self.generate_init_samples(im)
# Initialize iounet
if self.use_iou_net:
self.init_iou_net()
# Initialize projection matrix
self.init_projection_matrix(x)
# Transform to get the training sample
train_x = self.preprocess_sample(x)
# Generate label function
init_y = self.init_label_function(train_x)
# Init memory
self.init_memory(train_x)
# Init optimizer and do initial optimization
self.init_optimization(train_x, init_y)
self.pos_iounet = self.pos.clone()
self.time += time.time() - tic
def _rot_loss_fn(pred, target):
loss0 = _quat_loss_fn(pred, target[:, 0])
loss1 = _quat_loss_fn(pred, target[:, 1])
return torch.min(loss0, loss1)
def backward(ctx, grad_output):
z, scales, locs, logits, pis = ctx.saved_tensors
dim = scales.size(-1)
K = logits.size(-1)
g = grad_output # l b i
g = g.unsqueeze(-2) # l b 1 i
batch_dims = locs.dim() - 2
locs_tilde = locs / scales # b j i
sigma_0 = torch.min(scales, -2, keepdim=True)[0] # b 1 i
z_shift = (z.unsqueeze(-2) - locs) / sigma_0 # l b j i
z_tilde = z.unsqueeze(-2) / scales - locs_tilde # l b j i
mu_cd = locs.unsqueeze(-2) - locs.unsqueeze(-3) # b c d i
mu_cd_norm = torch.pow(mu_cd, 2.0).sum(-1).sqrt() # b c d
mu_cd /= mu_cd_norm.unsqueeze(-1) # b c d i
diagonals = z.new_empty((K, ), dtype=torch.long)
torch.arange(K, out=diagonals)
mu_cd[..., diagonals, diagonals, :] = 0.0
mu_ll_cd = (locs.unsqueeze(-2) * mu_cd).sum(-1) # b c d
z_ll_cd = (z.unsqueeze(-2).unsqueeze(-2) * mu_cd).sum(-1) # l b c d
z_perp_cd = z.unsqueeze(-2).unsqueeze(
-2) - z_ll_cd.unsqueeze(-1) * mu_cd # l b c d i
z_perp_cd_sqr = torch.pow(z_perp_cd, 2.0).sum(-1) # l b c d
shift_indices = z.new_empty((dim, ), dtype=torch.long)
torch.arange(dim, out=shift_indices)
shift_indices = shift_indices - 1
shift_indices[0] = 0
z_shift_cumsum = torch.pow(z_shift, 2.0)
z_shift_cumsum = z_shift_cumsum.sum(-1, keepdim=True) - torch.cumsum(
z_shift_cumsum, dim=-1) # l b j i
z_tilde_cumsum = torch.cumsum(torch.pow(z_tilde, 2.0),
dim=-1) # l b j i
z_tilde_cumsum = torch.index_select(z_tilde_cumsum, -1, shift_indices)
z_tilde_cumsum[..., 0] = 0.0
r_sqr_ji = z_shift_cumsum + z_tilde_cumsum # l b j i
log_scales = torch.log(scales) # b j i
epsilons_sqr = torch.pow(z_tilde, 2.0) # l b j i
log_qs = -0.5 * epsilons_sqr - 0.5 * math.log(
2.0 * math.pi) - log_scales # l b j i
log_q_j = log_qs.sum(-1, keepdim=True) # l b j 1
q_j = torch.exp(log_q_j) # l b j 1
q_tot = (pis * q_j.squeeze(-1)).sum(-1) # l b
q_tot = q_tot.unsqueeze(-1) # l b 1
root_two = math.sqrt(2.0)
shift_log_scales = log_scales[..., shift_indices]
shift_log_scales[..., 0] = 0.0
sigma_products = torch.cumsum(shift_log_scales, dim=-1).exp() # b j i
reverse_indices = z.new_tensor(range(dim - 1, -1, -1),
dtype=torch.long)
reverse_log_sigma_0 = sigma_0.log()[..., reverse_indices] # b 1 i
sigma_0_products = torch.cumsum(reverse_log_sigma_0,
dim=-1).exp()[..., reverse_indices -
1] # b 1 i
sigma_0_products[..., -1] = 1.0
sigma_products *= sigma_0_products
logits_grad = torch.erf(z_tilde / root_two) - torch.erf(
z_shift / root_two) # l b j i
logits_grad *= torch.exp(-0.5 * r_sqr_ji) # l b j i
logits_grad = (logits_grad * g / sigma_products).sum(-1) # l b j
logits_grad = sum_leftmost(logits_grad / q_tot, -1 - batch_dims) # b j
logits_grad *= 0.5 * math.pow(2.0 * math.pi, -0.5 * (dim - 1))
logits_grad = -pis * logits_grad
logits_grad = logits_grad - logits_grad.sum(-1, keepdim=True) * pis
mu_ll_dc = torch.transpose(mu_ll_cd, -1, -2)
v_cd = torch.erf((z_ll_cd - mu_ll_cd) / root_two) - torch.erf(
(z_ll_cd + mu_ll_dc) / root_two)
v_cd *= torch.exp(-0.5 * z_perp_cd_sqr) # l b c d
mu_cd_g = (g.unsqueeze(-2) * mu_cd).sum(-1) # l b c d
v_cd *= -mu_cd_g * pis.unsqueeze(-2) * 0.5 * math.pow(
2.0 * math.pi, -0.5 * (dim - 1)) # l b c d
v_cd = pis * sum_leftmost(v_cd.sum(-1) / q_tot, -1 - batch_dims)
logits_grad += v_cd
prefactor = pis.unsqueeze(-1) * q_j * g / q_tot.unsqueeze(-1)
locs_grad = sum_leftmost(prefactor, -2 - batch_dims)
scales_grad = sum_leftmost(prefactor * z_tilde, -2 - batch_dims)
return locs_grad, scales_grad, logits_grad, None, None, None
def minimum(self, a, b):
a_ = self.as_tensor(a)
b_ = self.as_tensor(b).to(a_.dtype)
return torch.min(a_, other=b_)
def oxy(x, y):
xu = x.unsqueeze(1)
return torch.min(torch.sqrt(xu) * y, xu * torch.sqrt(y))
def forward(self, state, time_step, args, reset_flag=False):
if args.demo_type == 'uav': # SenAvo
if args.variance and args.prior_decay:
coefs = [args.variance, args.variance]
prior_decay = args.prior_decay # prior_decay:为了减小先验策略，引入减小prior_sigma的因子
else:
coefs = [0.09, 0.09]
prior_decay = 0.005
time_step = torch.Tensor([time_step])[0]
perspective = torch.atan(state[12] / state[13])
first_perspective = torch.where(
state[13] > 0, # cos朝向角度 （度数，不是pi
torch.where(state[12] > 0, perspective / np.pi * 180.0,
(perspective + 2 * np.pi) / np.pi * 180.0),
(perspective + np.pi) / np.pi * 180.0)
target = torch.atan(state[10] / state[11]) # 目标和自己的连线的角度信息
position_target = torch.where(
state[11] > 0,
torch.where(state[10] > 0, target / np.pi * 180.0,
(target + 2 * np.pi) / np.pi * 180.0),
(target + np.pi) / np.pi * 180.0)
first_target = torch.remainder( # 确定夹角 remainder(input,divisor) 返回一个新张量，包含输入input张量每个元素的除法余数，余数与除数有相同的符号。
first_perspective - position_target, 360.0)
average_direction = torch.where( # 规范化夹角 torch.sign()输入一个张量，如果是正数返回1.0，负数返回-1.0。即如果夹角大于180则直接除以180，否则取互补的角度
torch.sign(180.0 - first_target) + 1.0 > 0,
-first_target / 180.0, (360.0 - first_target) / 180.0)
variance_direction = 0.1 * average_direction + coefs[0] # 0.1
turning_free = torch.where( # argmin:返回指定维度最小的编号。state[0~9]记录的基本方向上的距离信息。最小距离编号大于5（即即将碰撞的方向为左侧）则取前者（正向加45），否则后者
torch.sign(4 - torch.argmin(state[0:9]).float()) + 1.0 > 0,
45.0 + 0.1 * average_direction,
-45.0 + 0.1 * average_direction) # 0 0.1
average_free = turning_free / 180.0 # 调整的方向
variance_free = 0.1 * average_free + coefs[0] # 0.1
average_steer = torch.where( # 最近距离是否大于碰撞距离。如果是不用转向，如果不是就调整方向
torch.sign(100 * torch.min(state[0:9]) - 15.0) + 1.0 > 0,
average_direction, average_free)
variance_steer = torch.where(
torch.sign(100 * torch.min(state[0:9]) - 15.0) + 1.0 > 0,
variance_direction, variance_free)
speed = state[14]
average_throttle = torch.clamp(2.5 - 50 * (speed / 2 + 0.5), -0.5,
0.5)
variance_throttle = 0.1 * average_throttle + coefs[1] # 0.1
decay = prior_decay * (time_step - 1) + 1
covariance = torch.cat( # 按维数0拼接
(variance_steer.unsqueeze_(0),
variance_throttle.unsqueeze_(0)), 0) * decay # 公式(25)
average = torch.cat(
(average_steer.unsqueeze_(0), average_throttle.unsqueeze_(0)),
0)
elif args.demo_type == 'uav_wrong': # Naive
if reset_flag:
self.current_direct_wrong = 'north'
self.min_distance_x = 50.0
self.min_distance_y = 50.0
if args:
coefs = args.variance * 2
prior_decay = args.prior_decay
else:
coefs = [0.09, 0.09]
prior_decay = 0.005
time_step = torch.Tensor([time_step])[0]
perspective = torch.atan(state[12] / state[13])
first_perspective = torch.where(
state[13] > 0,
torch.where(state[12] > 0, perspective / np.pi * 180.0,
(perspective + 2 * np.pi) / np.pi * 180.0),
(perspective + np.pi) / np.pi * 180.0)
target = torch.atan(state[10] / state[11])
position_target = torch.where(
state[11] > 0,
torch.where(state[10] > 0, target / np.pi * 180.0,
(target + 2 * np.pi) / np.pi * 180.0),
(target + np.pi) / np.pi * 180.0)
distance = (state[9] / 2 + 0.5) * \
(torch.sqrt(torch.Tensor([2])[0]) * 3000)
distance_y = torch.abs(distance * torch.sin(
2 * position_target / 360 * torch.Tensor([np.pi])[0]))
distance_x = torch.abs(distance * torch.cos(
2 * position_target / 360 * torch.Tensor([np.pi])[0]))
if distance_y > self.min_distance_y:
self.current_direct_wrong = 'north'
elif distance_x > self.min_distance_x:
if self.current_direct_wrong == 'north':
self.min_distance_x -= 5
self.current_direct_wrong = 'east'
else:
if self.current_direct_wrong == 'east':
self.min_distance_y -= 5
self.current_direct_wrong = 'north'
if self.current_direct_wrong == 'north':
if position_target > 0 and position_target < 180:
position_target = 90
else:
position_target = 270
else:
if position_target < 90 or position_target > 270:
position_target = 0
else:
position_target = 180
first_target = torch.remainder(first_perspective - position_target,
360.0)
average_direction = torch.where(
torch.sign(180.0 - first_target) + 1.0 > 0,
-first_target / 180.0, (360.0 - first_target) / 180.0)
variance_direction = 0.0 * average_direction + coefs[0] # 0.1
turning_free = torch.where(
torch.sign(4 - torch.argmin(state[0:9]).float()) + 1.0 > 0,
45.0 + 0 * average_direction, -45.0 + 0 * average_direction)
average_free = turning_free / 180.0
variance_free = 0.0 * average_free + coefs[0] # 0.1
average_steer = torch.where(
torch.sign(100 * torch.min(state[0:9]) - 15.0) + 1.0 > 0,
average_direction, average_free)
variance_steer = torch.where(
torch.sign(100 * torch.min(state[0:9]) - 15.0) + 1.0 > 0,
variance_direction, variance_free)
speed = state[14]
average_throttle = torch.clamp(2.5 - 50 * (speed / 2 + 0.5), -0.5,
0.5)
variance_throttle = 0.0 * average_throttle + coefs[1]
decay = prior_decay * (time_step - 1) + 1
covariance = torch.cat(
(variance_steer.unsqueeze_(0),
variance_throttle.unsqueeze_(0)), 0) * decay
average = torch.cat(
(average_steer.unsqueeze_(0), average_throttle.unsqueeze_(0)),
0)
else:
average = self.agent_ddpg.select_action(state) # 无策略则随便选
time_step = torch.Tensor([time_step])[0]
decay = args.prior_decay * (time_step - 1) + 1
covariance = torch.ones(average.shape) * 0.1 * decay
return average, covariance
def c_luckas(x, y):
xu = x.unsqueeze(1)
m = torch.ones_like(y)
return torch.min(m, xu + y)
if CUDA:
torch.cuda.synchronize()
try:
output
except NameError:
print("No detections were made")
exit()
output_recast = time.time()
#TODO: Uncomment
# im_dim_list = torch.index_select(im_dim_list, 0, output[:,0].long())
scaling_factor = torch.min(inp_dim/im_dim_list,1)[0].view(-1,1)
output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim_list[:,0].view(-1,1))/2
output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim_list[:,1].view(-1,1))/2
output[:,1:5] /= scaling_factor
for i in range(output.shape[0]):
output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim_list[0,0])
output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim_list[0,1])
class_load = time.time()
colors = pkl.load(open("pallete.dms", "rb"))
draw = time.time()
def write(x, results):
def wh_iou(wh1, wh2):
# Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
wh1 = wh1[:, None] # [N,1,2]
wh2 = wh2[None] # [1,M,2]
inter = torch.min(wh1, wh2).prod(2) # [N,M]
return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter)
