def draw_polyline_nice_corners(cr,
points,
arrow_degrees,
arrow_len,
arrow_start=False,
arrow_end=False):
ex, ey = points[-1]
prev = points[0]
if arrow_start:
cr.move_to(prev[0], prev[1])
draw_arrow(cr, utils.make_vector(points[1], points[0]), arrow_degrees,
arrow_len)
cr.move_to(prev[0], prev[1])
for i in xrange(1, len(points) - 1):
a = utils.make_vector(points[i - 1], points[i])
b = utils.make_vector(points[i], points[i + 1])
la = utils.vector_len(a)
lb = utils.vector_len(b)
if la < 0.01 or lb < 0.01:
continue
v = utils.vector_mul_scalar(utils.normalize_vector(a), min(la, 20.0))
w = utils.vector_mul_scalar(utils.normalize_vector(b), min(lb, 20.0))
t = utils.vector_diff(points[i], v)
cr.line_to(t[0], t[1])
cr.rel_curve_to(v[0], v[1], v[0], v[1], v[0] + w[0], v[1] + w[1])
cr.line_to(ex, ey)
cr.stroke()
if arrow_end:
cr.move_to(ex, ey)
draw_arrow(cr, utils.make_vector(points[-2], points[-1]),
arrow_degrees, arrow_len)
def draw_polyline_nice_corners(cr, points, arrow_degrees, arrow_len, arrow_start = False, arrow_end = False):
ex, ey = points[-1]
prev = points[0]
if arrow_start:
cr.move_to(prev[0],prev[1])
draw_arrow(cr, utils.make_vector(points[1], points[0]), arrow_degrees, arrow_len)
cr.move_to(prev[0],prev[1])
for i in xrange(1, len(points) - 1):
a = utils.make_vector(points[i-1], points[i])
b = utils.make_vector(points[i], points[i + 1])
la = utils.vector_len(a)
lb = utils.vector_len(b)
if la < 0.01 or lb < 0.01:
continue
v = utils.vector_mul_scalar(utils.normalize_vector(a), min(la, 20.0))
w = utils.vector_mul_scalar(utils.normalize_vector(b), min(lb, 20.0))
t = utils.vector_diff(points[i], v)
cr.line_to(t[0], t[1])
cr.rel_curve_to(v[0], v[1], v[0], v[1], v[0] + w[0], v[1] + w[1])
cr.line_to(ex,ey)
cr.stroke()
if arrow_end:
cr.move_to(ex, ey)
draw_arrow(cr, utils.make_vector(points[-2], points[-1]), arrow_degrees, arrow_len)
def on_mouse_move(self, event, position):
if self.scroll_point:
change = utils.make_vector(position, self.scroll_point)
self.canvas.set_viewport(
utils.vector_add(self.canvas.get_viewport(), change))
return
if self.mass_selection_origin:
self.mass_selection_cursor = position
self.mouseover_items = self.get_items_in_mass_selection()
self.set_highlight()
return
if self.drag_items:
change = utils.make_vector(self.drag_mouse_origin, position)
for i, item in enumerate(self.drag_items):
new_position = utils.vector_add(self.drag_items_origin[i],
change)
new_position = utils.snap_to_grid(new_position, self.grid_size)
item.set_position(new_position)
self.canvas.redraw()
return
item = self.get_item_at_position(position)
if item:
self.canvas.set_cursor(get_cursor(item.action))
if self.selection:
self.mouseover_items = item.get_group()
self.set_highlight()
elif self.mouseover_items:
self.canvas.set_cursor(None)
self.mouseover_items = []
self.set_highlight()
else:
self.canvas.set_cursor(None)
def test(model, data):
acc, total = 0, 0
for d in data:
q = d[0]
#print('q', ' '.join(q))
labels = d[1]
cands = d[2]
# preprare answer labels
label_indices = [cands.index(l) for l in labels if l in cands]
# build data
q = make_vector([q], w2i, len(q))
cands = [label_to_ans_text[c] for c in cands] # id to text
max_cand_len = min(args.max_sent_len, max([len(c) for c in cands]))
cands = make_vector(cands, w2i, max_cand_len)
# predict
scores = [model(q, c.unsqueeze(0)).data[0] for c in cands]
pred_idx = np.argmax(scores)
if pred_idx in label_indices:
#print('correct')
acc += 1
#else:
#print('wrong')
total += 1
print('Test Acc:', 100 * acc / total, '%')
def on_mouse_move(self, event, position):
if self.scroll_point:
change = utils.make_vector(position, self.scroll_point)
self.canvas.set_viewport(
utils.vector_add(self.canvas.get_viewport(), change))
return
if self.drag_items:
change = utils.make_vector(self.drag_mouse_origin, position)
for i, item in enumerate(self.drag_items):
new_position = utils.vector_add(self.drag_items_origin[i], change)
new_position = utils.snap_to_grid(new_position, self.grid_size)
item.set_position(new_position)
self.canvas.redraw()
return
item = self.get_item_at_position(position)
if item:
self.canvas.set_cursor(get_cursor(item.action))
if self.selection:
self.mouseover_items = item.get_group()
self.set_highlight()
elif self.mouseover_items:
self.canvas.set_cursor(None)
self.mouseover_items = []
self.set_highlight()
else:
self.canvas.set_cursor(None)
def on_mouse_move(self, event, position):
if self.resize_item:
change = utils.make_vector(self.initial_mouse, position)
change = utils.snap_to_grid(change, self.grid_size)
new_size = utils.vector_add(self.initial_size, change)
new_size = utils.vector_at_least(new_size, 0, 0)
self.resize_item.size = new_size
self.canvas.redraw()
else:
NetEditCanvasConfig.on_mouse_move(self, event, position)
def on_mouse_move(self, event, position):
if self.resize_item:
change = utils.make_vector(self.initial_mouse, position)
change = utils.snap_to_grid(change, self.grid_size)
new_size = utils.vector_add(self.initial_size, change)
new_size = utils.vector_at_least(new_size, 0, 0)
self.resize_item.size = new_size
self.canvas.redraw()
else:
NetEditCanvasConfig.on_mouse_move(self, event, position)
def set_position(self, position):
points = self.multiline.get_points()
if position is None:
self.point_index = (len(points) - 1) / 2
self.line_param = 0.5
self.offset = (0.0, 0.0)
else:
self.point_index, self.line_param = \
utils.nearest_point_of_multiline(points, position)
self.offset = utils.make_vector(
self.compute_point_on_multiline(points), position)
def set_position(self, position):
points = self.multiline.get_points()
if position is None:
self.point_index = (len(points) - 1) / 2
self.line_param = 0.5
self.offset = (0.0, 0.0)
else:
self.point_index, self.line_param = \
utils.nearest_point_of_multiline(points, position)
self.offset = utils.make_vector(self.compute_point_on_multiline(points),
position)
def train(model, data, test_data, optimizer, n_epochs=4, batch_size=256):
for epoch in range(n_epochs):
model.train()
print('epoch', epoch)
random.shuffle(data) # TODO use idxies
losses = []
for i, d in enumerate(tqdm(data)):
q, pos, negs = d[0], d[1], d[2]
vec_q = make_vector([q], w2i, len(q))
vec_pos = make_vector([pos], w2i, len(pos))
pos_sim = model(vec_q, vec_pos)
for _ in range(50):
neg = random.choice(negs)
vec_neg = make_vector([neg], w2i, len(neg))
neg_sim = model(vec_q, vec_neg)
loss = loss_fn(pos_sim, neg_sim)
if loss.data[0] != 0:
losses.append(loss)
break
if len(losses) == batch_size or i == len(data) - 1:
loss = torch.mean(torch.stack(losses, 0).squeeze(), 0)
#print(loss.data[0])
#print(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses = []
filename = '{}/Epoch-{}.model'.format('./checkpoints', epoch)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'loss': loss,
},
filename=filename)
test(model, test_data)
def get_size(self):
return utils.make_vector(self.point1.get_position(), self.point2.get_position())
def get_size(self):
return utils.make_vector(self.point1.get_position(),
self.point2.get_position())
