def _add_face(vert_set_index: int, vert_dict: dict, verts: torch.Tensor,
faces: torch.Tensor, location: list, curr_vert_num: int):
r""" Adds a face to the set of observed faces and verticies
"""
top_verts = np.array([[0, 0, 1], [1, 0, 1], [1, 1, 1], [0, 1, 1]])
bottom_verts = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]])
left_verts = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 1], [0, 1, 0]])
right_verts = np.array([[1, 0, 0], [1, 1, 0], [1, 1, 1], [1, 0, 1]])
front_verts = np.array([[0, 0, 0], [1, 0, 0], [1, 0, 1], [0, 0, 1]])
back_verts = np.array([[0, 1, 0], [0, 1, 1], [1, 1, 1], [1, 1, 0]])
vert_sets = [
top_verts, bottom_verts, left_verts, right_verts, front_verts,
back_verts
]
vert_set = vert_sets[vert_set_index]
face = np.array([0, 1, 2, 3]) + curr_vert_num
update = 4
for e, vs in enumerate(vert_set):
new_position = vs + np.array(location)
if str(new_position) in vert_dict:
index = vert_dict[str(new_position)]
face[e] = index
for idx in range(e + 1, 4):
face[idx] -= 1
update -= 1
else:
vert_dict[str(new_position)] = len(verts) + 1
verts.append(list(new_position))
faces.append(face)
curr_vert_num += update
return vert_dict, verts, faces, curr_vert_num
def _check_equation(self, checker: AnswerChecker, outputs: torch.Tensor,
batch: ProblemInstance):
"""
Verify whether the outputted equation is correct or not.
:param AnswerChecker checker: AnswerChecker instance to compute equation and check answer
:param torch.Tensor outputs:
LongTensor containing generated equations.
- If the model should generate op-tokens, Shape = [B, M, T], where B = batch size, M = beams, and T = length
- Otherwise, Shape = [B, M, T, 1+2A], where A = maximum arity.
:param batch:
:return:
"""
# Retrieve size information
batch_sz, beam_sz = outputs.shape[:2]
# Get the target field information
required_field = _unwrap_parallel(self._module).required_field
# Retrieve the target field
field = getattr(self.evalset, required_field + '_field')
# Recover string representation of gold set and generated beams
golds = field.convert_ids_to_equations(getattr(batch, required_field))
beams = [
field.convert_ids_to_equations(outputs[i]) for i in range(batch_sz)
]
outputs = []
for i in range(batch_sz):
# For each batch, retrieve information about written numbers and expected answer tuples
numbers = batch.text.number_value[i]
expected = batch.expected[i]
# Test whether the produced equation in each beam
results = [
checker.check(beam, numbers, expected) for beam in beams[i]
]
# Record outputs: (index, goldset output, generated output, correctness)
outputs.append((i, golds[i], beams[i], results))
return outputs
def fmri_prob_atlas(orig: Tensor,
recons: Tensor,
out_path: str,
rows: int,
cols: int,
bg_img: str,
mask_path: str,
display: str = 'vertical',
view_type: str = 'filled_contours',
draw_cross: bool = False,
threshold: str = 'auto',
**kwargs):
mask = nl.image.load_img(mask_path)
def save_prob_atlas(x, out_path):
img = nl.image.new_img_like(mask,
x.numpy(),
affine=mask.affine,
copy_header=True)
nlplt.plot_prob_atlas(img,
bg_img=bg_img,
view_type=view_type,
draw_cross=draw_cross,
threshold=threshold)
plt.savefig(out_path)
i = 0
n = min(rows * cols, orig.shape[0])
# Save the individual probability atlases to separate png files
for _ in range(rows):
done = False
for _ in range(cols):
if i >= n:
done = True
break
save_prob_atlas(orig[i], f'{out_path}_{i}_orig.tmp.png')
save_prob_atlas(recons[i], f'{out_path}_{i}_recons.tmp.png')
i += 1
if done:
break
# Arrange all of the png files into a grid
orig = []
recons = []
to_tensor = ToTensor()
def tmpimg_to_tensor(path):
img = to_tensor(Image.open(path)).unsqueeze(dim=0)
os.remove(path)
return img
i = 0
for _ in range(rows):
done = False
for _ in range(cols):
if i >= n:
done = True
break
orig.append(tmpimg_to_tensor(f'{out_path}_{i}_orig.tmp.png'))
recons.append(tmpimg_to_tensor(f'{out_path}_{i}_recons.tmp.png'))
i += 1
if done:
break
orig = torch.cat(orig, dim=0)
recons = torch.cat(recons, dim=0)
plot2d(orig=orig,
recons=recons,
out_path=out_path,
rows=rows,
cols=cols,
display=display,
**kwargs)
