code stringlengths 3 6.57k |
|---|
self.pwm.ChangeDutyCycle(self.dutycycle) |
time.sleep(Rotation.max_delay) |
cleanup(self) |
self.pwm.stop() |
time.sleep(Rotation.min_delay) |
GPIO.cleanup() |
ColorMap(object) |
__init__(self, base_color=[[0,0,1], [0,1,1], [0,1,0], [1,1,0], [1,0,0]]) |
len(self.base_color) |
__call__(self, val) |
self.to_colormap(val) |
to_colormap(self, val) |
of (R,G,B) |
math.floor(val) |
return (r,g,b) |
Trainer_PixelObjectness(Template_Trainer) |
__init__(self, args, model, optimizer, lr_policy) |
range(self.args.class_num) |
val_head.append("mean_precision_class_{}".format(i) |
range(self.args.class_num) |
val_head.append("mean_IoU_class_{}".format(i) |
self.get_argparse_arguments(self.args) |
self.tlog.mkdir("model_param") |
torch.cuda.is_available() |
torch.device('cuda:{}'.format(self.args.gpu_device_num) |
torch.device('cpu') |
torch.cuda.is_available() |
self.model.to(self.map_device) |
data_loader.get_train_loader(self.args, [(0.5, 0.5, 0.5) |
data_loader.get_val_loader(self.args, [(0.5, 0.5, 0.5) |
self._gen_cmap() |
enumerate(model.modules() |
print(idx, '->', m) |
print(args) |
print("\nsaving at {}\n".format(self.save_dir) |
_gen_cmap_voc(self, class_num=255) |
bitget(byteval, idx) |
return ((byteval & (1 << idx) |
np.zeros((class_num+1, 3) |
range(class_num+1) |
range(8) |
bitget(c, 0) |
bitget(c, 1) |
bitget(c, 2) |
np.array([r, g, b]) |
_gen_cmap(self, max_value=255) |
ColorMap() |
range(max_value+1) |
cmap.append(np.uint8(np.array(mapper(v/max_value) |
convert_to_color_map(self, img_array, color_map=None, class_num=255) |
be (width, height) |
self._gen_cmap() |
np.empty(shape=(img_array.shape[0], img_array.shape[1], 3) |
range(class_num+1) |
np.where(img_array == c) |
validate(self, count) |
torch.no_grad() |
self.model.eval() |
range(self.args.class_num) |
range(self.args.class_num) |
SegmentationMetric(self.args.class_num, map_device=self.map_device) |
self.to_tqdm(self.val_loader, desc="train val") |
enumerate(_trainval_loader) |
self.format_tensor(image, requires_grad=False, map_device=self.map_device) |
self.format_tensor(mask, requires_grad=False, map_device=self.map_device) |
self.model.inference(img) |
F.interpolate(outputs, size=[self.args.crop_size, self.args.crop_size], mode='bilinear', align_corners=False) |
F.interpolate(prob_maps, size=[self.args.crop_size, self.args.crop_size], mode='bilinear', align_corners=False) |
metric(outputs, mask) |
self.tlog.setup_output("{}_{}_batch_{}_sample".format("iter" if self.iter_wise else "epoch", count, b) |
np.ones((256,256) |
range(256) |
self.tlog.pack_output(Image.fromarray(self.convert_to_color_map(np.uint8(test_img) |
range(batch_size) |
self.tlog.pack_output(Image.fromarray(np.uint8(original_image[n].detach() |
numpy() |
np.uint8(outputs[n].squeeze(0) |
cpu() |
detach() |
numpy() |
squeeze(0) |
cpu() |
detach() |
numpy() |
self.tlog.pack_output(Image.fromarray(pred_img*255) |
self.tlog.pack_output(Image.fromarray(self.convert_to_color_map(np.uint8(prob_img[1]*255) |
np.uint8(mask[n].cpu() |
detach() |
numpy() |
self.tlog.pack_output(Image.fromarray(gt_img*255) |
self.tlog.pack_output(None, " ") |
self.tlog.pack_output(None, "validation sample", ["left: input", "center: pred cmap", "right: output mask"]) |
self.tlog.flush_output() |
metric.calc_pix_acc() |
metric.calc_mean_precision() |
metric.calc_mean_jaccard_index() |
filter(lambda n: n!=float("nan") |
range(self.args.class_num) |
precision_class.append(precision["class_{}".format(class_id) |
jaccard_class.append(jaccard_index["class_{}".format(class_id) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.