def predict(self, chips, windows, tmp_dir):
"""Return predictions for a chip using model.
Args:
chips: [[height, width, channels], ...] numpy array of chips
windows: List of boxes that are the windows aligned with the chips.
Return:
Labels object containing predictions
"""
self.load_model(tmp_dir)
# (batch_size, h, w, nchannels) --> (batch_size, nchannels, h, w)
chips = torch.Tensor(chips).permute((0, 3, 1, 2)) / 255.
chips = chips.to(self.device)
model = self.inf_learner.model.eval()
preds = model(chips).detach().cpu()
labels = ChipClassificationLabels()
for class_probs, window in zip(preds, windows):
# Add 1 to class_id since they start at 1.
class_id = int(class_probs.argmax() + 1)
labels.set_cell(window, class_id, class_probs)
return labels
def predict(self, chips, windows, tmp_dir):
"""Return predictions for a batch of chips.
Args:
chips: (numpy.ndarray) of shape (n, height, width, nb_channels)
containing a batch of chips
windows: (List<Box>) windows that are aligned with the chips which
are aligned with the chips.
Return:
(ChipClassificationLabels) containing predictions
"""
self.load_model(tmp_dir)
# (batch_size, h, w, nchannels) --> (batch_size, nchannels, h, w)
chips = torch.Tensor(chips).permute((0, 3, 1, 2)) / 255.
chips = chips.to(self.device)
model = self.model.eval()
with torch.no_grad():
out = model(chips).cpu()
labels = ChipClassificationLabels()
for class_probs, window in zip(out, windows):
# Add 1 to class_id since they start at 1.
class_id = int(class_probs.argmax() + 1)
labels.set_cell(window, class_id, class_probs.numpy())
return labels
def make_labels(self, class_ids):
"""Make 2x2 grid label store.
Args:
class_ids: 2x2 array of class_ids to use
"""
cell_size = 200
y_cells = 2
x_cells = 2
labels = ChipClassificationLabels()
for yind in range(y_cells):
for xind in range(x_cells):
ymin = yind * cell_size
xmin = xind * cell_size
ymax = ymin + cell_size
xmax = xmin + cell_size
window = Box(ymin, xmin, ymax, xmax)
class_id = class_ids[yind][xind]
new_labels = ChipClassificationLabels()
new_labels.set_cell(window, class_id)
labels.extend(new_labels)
return labels
def empty_labels(self):
return ChipClassificationLabels()