class Tester(object):
def __init__(self, configer):
self.crop_size = configer.get('train',
'data_transformer')['input_size']
val_trans_seq = [
x for x in configer.get('val_trans', 'trans_seq')
if 'random' not in x
]
configer.update(('val_trans', 'trans_seq'), val_trans_seq)
configer.get('val', 'data_transformer')['input_size'] = configer.get(
'test', 'data_transformer').get('input_size', None)
configer.update(('train', 'data_transformer'),
configer.get('val', 'data_transformer'))
configer.update(('val', 'batch_size'),
int(os.environ.get('batch_size', 16)))
configer.update(('test', 'batch_size'),
int(os.environ.get('batch_size', 16)))
self.save_dir = configer.get('test', 'out_dir')
self.dataset_name = configer.get('test', 'eval_set')
self.sscrop = configer.get('test', 'sscrop')
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.loss_manager = LossManager(configer)
self.module_runner = ModuleRunner(configer)
self.model_manager = ModelManager(configer)
self.seg_data_loader = DataLoader(configer)
self.seg_net = None
self.test_loader = None
self.test_size = None
self.infer_time = 0
self.infer_cnt = 0
self._init_model()
pprint.pprint(configer.params_root)
def _init_model(self):
self.seg_net = self.model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
assert self.dataset_name in ('train', 'val',
'test'), 'Cannot infer dataset name'
self.size_mode = self.configer.get(self.dataset_name,
'data_transformer')['size_mode']
if self.dataset_name != 'test':
self.test_loader = self.seg_data_loader.get_valloader(
self.dataset_name)
else:
self.test_loader = self.seg_data_loader.get_testloader(
self.dataset_name)
self.test_size = len(self.test_loader) * self.configer.get(
'val', 'batch_size')
def test(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
start_time = time.time()
image_id = 0
Log.info('save dir {}'.format(self.save_dir))
FileHelper.make_dirs(self.save_dir, is_file=False)
print('Total batches', len(self.test_loader))
for j, data_dict in enumerate(self.test_loader):
inputs = [data_dict['img']]
names = data_dict['name']
metas = data_dict['meta']
dest_dir = self.save_dir
with torch.no_grad():
offsets, logits = self.extract_offset(inputs)
print([x.shape for x in logits])
for k in range(len(inputs[0])):
image_id += 1
ori_img_size = metas[k]['ori_img_size']
border_size = metas[k]['border_size']
offset = offsets[k].squeeze().cpu().numpy()
offset = cv2.resize(
offset[:border_size[1], :border_size[0]],
tuple(ori_img_size),
interpolation=cv2.INTER_NEAREST)
print(image_id)
os.makedirs(dest_dir, exist_ok=True)
if names[k].rpartition('.')[0]:
dest_name = names[k].rpartition('.')[0] + '.mat'
else:
dest_name = names[k] + '.mat'
dest_name = os.path.join(dest_dir, dest_name)
print('Shape:', offset.shape, 'Saving to', dest_name)
data_dict = {'mat': offset}
scipy.io.savemat(dest_name, data_dict, do_compression=True)
try:
scipy.io.loadmat(dest_name)
except Exception as e:
print(e)
scipy.io.savemat(dest_name,
data_dict,
do_compression=False)
self.batch_time.update(time.time() - start_time)
start_time = time.time()
Log.info('Test Time {batch_time.sum:.3f}s'.format(
batch_time=self.batch_time))
def extract_offset(self, inputs):
if self.sscrop:
outputs = self.sscrop_test(inputs, self.crop_size)
elif self.configer.get('test', 'mode') == 'ss_test':
outputs = self.ss_test(inputs)
offsets = []
logits = []
for mask_logits, dir_logits, img in zip(*outputs[:2], inputs[0]):
h, w = img.shape[1:]
mask_logits = F.interpolate(mask_logits.unsqueeze(0),
size=(h, w),
mode='bilinear',
align_corners=True)
dir_logits = F.interpolate(dir_logits.unsqueeze(0),
size=(h, w),
mode='bilinear',
align_corners=True)
logit = torch.softmax(dir_logits, dim=1)
zero_mask = mask_logits.argmax(dim=1, keepdim=True) == 0
logits.append(mask_logits[:, 1])
offset = self._get_offset(mask_logits, dir_logits)
offsets.append(offset)
print([x.shape for x in offsets])
return offsets, logits
def _get_offset(self, mask_logits, dir_logits):
edge_mask = mask_logits[:, 1] > 0.5
dir_logits = torch.softmax(dir_logits, dim=1)
n, _, h, w = dir_logits.shape
keep_mask = edge_mask
dir_label = torch.argmax(dir_logits, dim=1).float()
offset = DTOffsetHelper.label_to_vector(dir_label)
offset = offset.permute(0, 2, 3, 1)
offset[~keep_mask, :] = 0
return offset
def _flip(self, x, dim=-1):
indices = [slice(None)] * x.dim()
indices[dim] = torch.arange(x.size(dim) - 1,
-1,
-1,
dtype=torch.long,
device=x.device)
return x[tuple(indices)]
def _flip_offset(self, x):
x = self._flip(x, dim=-1)
if len(x.shape) == 4:
return x[:, DTOffsetHelper.flipping_indices()]
else:
return x[DTOffsetHelper.flipping_indices()]
def _flip_inputs(self, inputs):
if self.size_mode == 'fix_size':
return [self._flip(x, -1) for x in inputs]
else:
return [[self._flip(x, -1) for x in xs] for xs in inputs]
def _flip_outputs(self, outputs):
funcs = [self._flip, self._flip_offset]
if self.size_mode == 'fix_size':
return [f(x) for f, x in zip(funcs, outputs)]
else:
return [[f(x) for x in xs] for f, xs in zip(funcs, outputs)]
def _tuple_sum(self, tup1, tup2, tup2_weight=1):
"""
tup1 / tup2: tuple of tensors or tuple of list of tensors
"""
if tup1 is None:
if self.size_mode == 'fix_size':
return [y * tup2_weight for y in tup2]
else:
return [[y * tup2_weight for y in ys] for ys in tup2]
else:
if self.size_mode == 'fix_size':
return [x + y * tup2_weight for x, y in zip(tup1, tup2)]
else:
return [[x + y * tup2_weight for x, y in zip(xs, ys)]
for xs, ys in zip(tup1, tup2)]
def _scale_ss_inputs(self, inputs, scale):
n, c, h, w = inputs[0].shape
size = (int(h * scale), int(w * scale))
return [
F.interpolate(inputs[0],
size=size,
mode="bilinear",
align_corners=True),
], (h, w)
def sscrop_test(self, inputs, crop_size, scale=1):
'''
Currently, sscrop_test does not support diverse_size testing
'''
scaled_inputs = inputs
img = scaled_inputs[0]
n, c, h, w = img.size(0), img.size(1), img.size(2), img.size(3)
ori_h, ori_w = h, w
full_probs = [
torch.cuda.FloatTensor(n, dim, h, w).fill_(0) for dim in (2, 8)
]
count_predictions = [
torch.cuda.FloatTensor(n, dim, h, w).fill_(0) for dim in (2, 8)
]
crop_counter = 0
height_starts = self._decide_intersection(h, crop_size[0])
width_starts = self._decide_intersection(w, crop_size[1])
for height in height_starts:
for width in width_starts:
crop_inputs = [
x[..., height:height + crop_size[0],
width:width + crop_size[1]] for x in scaled_inputs
]
prediction = self.ss_test(crop_inputs)
for j in range(2):
count_predictions[j][:, :, height:height + crop_size[0],
width:width + crop_size[1]] += 1
full_probs[j][:, :, height:height + crop_size[0],
width:width + crop_size[1]] += prediction[j]
crop_counter += 1
Log.info('predicting {:d}-th crop'.format(crop_counter))
for j in range(2):
full_probs[j] /= count_predictions[j]
full_probs[j] = F.interpolate(full_probs[j],
size=(ori_h, ori_w),
mode='bilinear',
align_corners=True)
return full_probs
def _scale_ss_outputs(self, outputs, size):
return [
F.interpolate(x, size=size, mode="bilinear", align_corners=True)
for x in outputs
]
def ss_test(self, inputs, scale=1):
if self.size_mode == 'fix_size':
scaled_inputs, orig_size = self._scale_ss_inputs(inputs, scale)
print([x.shape for x in scaled_inputs])
start = timeit.default_timer()
outputs = list(self.seg_net.forward(*scaled_inputs))
if len(outputs) == 3:
outputs = (outputs[0], outputs[2])
else:
outputs[0] = F.softmax(outputs[0], dim=1)
torch.cuda.synchronize()
end = timeit.default_timer()
return self._scale_ss_outputs(outputs, orig_size)
else:
device_ids = self.configer.get('gpu')
replicas = nn.parallel.replicate(self.seg_net.module, device_ids)
scaled_inputs, ori_sizes, outputs = [], [], []
for *i, d in zip(*inputs, device_ids):
scaled_i, ori_size_i = self._scale_ss_inputs(
[x.unsqueeze(0) for x in i], scale)
scaled_inputs.append(
[x.cuda(d, non_blocking=True) for x in scaled_i])
ori_sizes.append(ori_size_i)
scaled_outputs = nn.parallel.parallel_apply(
replicas[:len(scaled_inputs)], scaled_inputs)
for o, ori_size in zip(scaled_outputs, ori_sizes):
o = self._scale_ss_outputs(o, ori_size)
if len(o) == 3:
o = (o[0], o[2])
outputs.append([x.squeeze(0) for x in o])
outputs = list(map(list, zip(*outputs)))
return outputs
def _decide_intersection(self,
total_length,
crop_length,
crop_stride_ratio=1 / 3):
stride = int(crop_length *
crop_stride_ratio) # set the stride as the paper do
times = (total_length - crop_length) // stride + 1
cropped_starting = []
for i in range(times):
cropped_starting.append(stride * i)
if total_length - cropped_starting[-1] > crop_length:
cropped_starting.append(total_length -
crop_length) # must cover the total image
return cropped_starting
class ImageTester(object):
"""
The class for Pose Estimation. Include train, val, val & predict.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.seg_visualizer = SegVisualizer(configer)
self.loss_manager = LossManager(configer)
self.module_runner = ModuleRunner(configer)
self.model_manager = ModelManager(configer)
self.optim_scheduler = OptimScheduler(configer)
self.seg_data_loader = DataLoader(configer)
self.save_dir = self.configer.get('test', 'out_dir')
self.seg_net = None
self.test_loader = None
self.test_size = None
self.infer_time = 0
self.infer_cnt = 0
self._init_model()
def _init_model(self):
self.seg_net = self.model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
print(f"self.save_dir {self.save_dir}")
# if 'test' in self.save_dir:
# self.test_loader = self.seg_data_loader.get_testloader()
# self.test_size = len(self.test_loader) * self.configer.get('test', 'batch_size')
# print(f"self.test_size {self.test_size}")
# else:
# self.test_loader = self.seg_data_loader.get_valloader()
# self.test_size = len(self.test_loader) * self.configer.get('val', 'batch_size')
self.seg_net.eval()
def __relabel(self, label_map):
height, width = label_map.shape
label_dst = np.zeros((height, width), dtype=np.uint8)
for i in range(self.configer.get('data', 'num_classes')):
label_dst[label_map == i] = self.configer.get('data', 'label_list')[i]
label_dst = np.array(label_dst, dtype=np.uint8)
return label_dst
def test(self, img_path=None, output_dir=None, data_loader=None):
"""
Validation function during the train phase.
"""
print("test!!!")
self.seg_net.eval()
start_time = time.time()
image_id = 0
Log.info('save dir {}'.format(self.save_dir))
FileHelper.make_dirs(self.save_dir, is_file=False)
colors = get_ade_colors()
# Reader.
if img_path is not None:
input_path = img_path
else:
input_path = self.configer.get('input_image')
input_image = cv2.imread(input_path)
transform = trans.Compose([
trans.ToTensor(),
trans.Normalize(div_value=self.configer.get('normalize', 'div_value'),
mean=self.configer.get('normalize', 'mean'),
std=self.configer.get('normalize', 'std')), ])
aug_val_transform = cv2_aug_transforms.CV2AugCompose(self.configer, split='val')
pre_vis_img = None
pre_lines = None
pre_target_img = None
ori_img = input_image.copy()
h, w, _ = input_image.shape
ori_img_size = [w, h]
# print(img.shape)
input_image = aug_val_transform(input_image)
input_image = input_image[0]
h, w, _ = input_image.shape
border_size = [w, h]
input_image = transform(input_image)
# print(img)
# print(img.shape)
# inputs = data_dict['img']
# names = data_dict['name']
# metas = data_dict['meta']
# print(inputs)
with torch.no_grad():
# Forward pass.
outputs = self.ss_test([input_image])
if isinstance(outputs, torch.Tensor):
outputs = outputs.permute(0, 2, 3, 1).cpu().numpy()
n = outputs.shape[0]
else:
outputs = [output.permute(0, 2, 3, 1).cpu().numpy().squeeze() for output in outputs]
n = len(outputs)
logits = cv2.resize(outputs[0],
tuple(ori_img_size), interpolation=cv2.INTER_CUBIC)
label_img = np.asarray(np.argmax(logits, axis=-1), dtype=np.uint8)
if self.configer.exists('data', 'reduce_zero_label') and self.configer.get('data', 'reduce_zero_label'):
label_img = label_img + 1
label_img = label_img.astype(np.uint8)
if self.configer.exists('data', 'label_list'):
label_img_ = self.__relabel(label_img)
else:
label_img_ = label_img
label_img_ = Image.fromarray(label_img_, 'P')
input_name = '.'.join(os.path.basename(input_path).split('.')[:-1])
if output_dir is None:
label_path = os.path.join(self.save_dir, 'label_{}.png'.format(input_name))
else:
label_path = os.path.join(output_dir, 'label_{}.png'.format(input_name))
FileHelper.make_dirs(label_path, is_file=True)
# print(f"{label_path}")
ImageHelper.save(label_img_, label_path)
self.batch_time.update(time.time() - start_time)
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s'.format(batch_time=self.batch_time))
def offset_test(self, inputs, offset_h_maps, offset_w_maps, scale=1):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
start = timeit.default_timer()
outputs = self.seg_net.forward(inputs, offset_h_maps, offset_w_maps)
torch.cuda.synchronize()
end = timeit.default_timer()
if (self.configer.get('loss', 'loss_type') == "fs_auxce_loss") or (self.configer.get('loss', 'loss_type') == "triple_auxce_loss"):
outputs = outputs[-1]
elif self.configer.get('loss', 'loss_type') == "pyramid_auxce_loss":
outputs = outputs[1] + outputs[2] + outputs[3] + outputs[4]
outputs = F.interpolate(outputs, size=(h, w), mode='bilinear', align_corners=True)
return outputs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def ss_test(self, inputs, scale=1):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
scaled_inputs = F.interpolate(inputs, size=(int(h*scale), int(w*scale)), mode="bilinear", align_corners=True)
start = timeit.default_timer()
outputs = self.seg_net.forward(scaled_inputs)
torch.cuda.synchronize()
end = timeit.default_timer()
outputs = outputs[-1]
outputs = F.interpolate(outputs, size=(h, w), mode='bilinear', align_corners=True)
return outputs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
replicas = nn.parallel.replicate(self.seg_net.module, device_ids)
scaled_inputs, ori_size, outputs = [], [], []
for i, d in zip(inputs, device_ids):
h, w = i.size(1), i.size(2)
ori_size.append((h, w))
i = F.interpolate(i.unsqueeze(0), size=(int(h*scale), int(w*scale)), mode="bilinear", align_corners=True)
scaled_inputs.append(i.cuda(d, non_blocking=True))
scaled_outputs = nn.parallel.parallel_apply(replicas[:len(scaled_inputs)], scaled_inputs)
for i, output in enumerate(scaled_outputs):
outputs.append(F.interpolate(output[-1], size=ori_size[i], mode='bilinear', align_corners=True))
return outputs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def flip(self, x, dim):
indices = [slice(None)] * x.dim()
indices[dim] = torch.arange(x.size(dim) - 1, -1, -1,
dtype=torch.long, device=x.device)
return x[tuple(indices)]
def sscrop_test(self, inputs, crop_size, scale=1):
'''
Currently, sscrop_test does not support diverse_size testing
'''
n, c, ori_h, ori_w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
scaled_inputs = F.interpolate(inputs, size=(int(ori_h*scale), int(ori_w*scale)), mode="bilinear", align_corners=True)
n, c, h, w = scaled_inputs.size(0), scaled_inputs.size(1), scaled_inputs.size(2), scaled_inputs.size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
count_predictions = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
crop_counter = 0
height_starts = self._decide_intersection(h, crop_size[0])
width_starts = self._decide_intersection(w, crop_size[1])
for height in height_starts:
for width in width_starts:
crop_inputs = scaled_inputs[:, :, height:height+crop_size[0], width:width + crop_size[1]]
prediction = self.ss_test(crop_inputs)
count_predictions[:, :, height:height+crop_size[0], width:width + crop_size[1]] += 1
full_probs[:, :, height:height+crop_size[0], width:width + crop_size[1]] += prediction
crop_counter += 1
Log.info('predicting {:d}-th crop'.format(crop_counter))
full_probs /= count_predictions
full_probs = F.interpolate(full_probs, size=(ori_h, ori_w), mode='bilinear', align_corners=True)
return full_probs
def ms_test(self, inputs):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
if self.configer.exists('test', 'scale_weights'):
for scale, weight in zip(self.configer.get('test', 'scale_search'), self.configer.get('test', 'scale_weights')):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += weight * probs
return full_probs
else:
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
return full_probs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
full_probs = [torch.zeros(1, self.configer.get('data', 'num_classes'),
i.size(1), i.size(2)).cuda(device_ids[index], non_blocking=True)
for index, i in enumerate(inputs)]
flip_inputs = [self.flip(i, 2) for i in inputs]
if self.configer.exists('test', 'scale_weights'):
for scale, weight in zip(self.configer.get('test', 'scale_search'), self.configer.get('test', 'scale_weights')):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(flip_inputs, scale)
for i in range(len(inputs)):
full_probs[i] += weight * (probs[i] + self.flip(flip_probs[i], 3))
return full_probs
else:
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(flip_inputs, scale)
for i in range(len(inputs)):
full_probs[i] += (probs[i] + self.flip(flip_probs[i], 3))
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def ms_test_depth(self, inputs, names):
prob_list = []
scale_list = []
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
prob_list.append(probs)
scale_list.append(scale)
full_probs = self.fuse_with_depth(prob_list, scale_list, names)
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def fuse_with_depth(self, probs, scales, names):
MAX_DEPTH = 63
POWER_BASE = 0.8
if 'test' in self.save_dir:
stereo_path = "/msravcshare/dataset/cityscapes/stereo/test/"
else:
stereo_path = "/msravcshare/dataset/cityscapes/stereo/val/"
n, c, h, w = probs[0].size(0), probs[0].size(1), probs[0].size(2), probs[0].size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for index, name in enumerate(names):
stereo_map = cv2.imread(stereo_path + name + '.png', -1)
depth_map = stereo_map / 256.0
depth_map = 0.5 / depth_map
depth_map = 500 * depth_map
depth_map = np.clip(depth_map, 0, MAX_DEPTH)
depth_map = depth_map // (MAX_DEPTH // len(scales))
for prob, scale in zip(probs, scales):
scale_index = self._locate_scale_index(scale, scales)
weight_map = np.abs(depth_map - scale_index)
weight_map = np.power(POWER_BASE, weight_map)
weight_map = cv2.resize(weight_map, (w, h))
full_probs[index, :, :, :] += torch.from_numpy(np.expand_dims(weight_map, axis=0)).type(torch.cuda.FloatTensor) * prob[index, :, :, :]
return full_probs
@staticmethod
def _locate_scale_index(scale, scales):
for idx, s in enumerate(scales):
if scale == s:
return idx
return 0
def ms_test_wo_flip(self, inputs):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
full_probs += probs
return full_probs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
full_probs = [torch.zeros(1, self.configer.get('data', 'num_classes'),
i.size(1), i.size(2)).cuda(device_ids[index], non_blocking=True)
for index, i, in enumerate(inputs)]
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
for i in range(len(inputs)):
full_probs[i] += probs[i]
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def mscrop_test(self, inputs, crop_size):
'''
Currently, mscrop_test does not support diverse_size testing
'''
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
Log.info('Scale {0:.2f} prediction'.format(scale))
if scale < 1:
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
else:
probs = self.sscrop_test(inputs, crop_size, scale)
flip_probs = self.sscrop_test(self.flip(inputs, 3), crop_size, scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
return full_probs
def _decide_intersection(self, total_length, crop_length):
stride = crop_length
times = (total_length - crop_length) // stride + 1
cropped_starting = []
for i in range(times):
cropped_starting.append(stride*i)
if total_length - cropped_starting[-1] > crop_length:
cropped_starting.append(total_length - crop_length) # must cover the total image
return cropped_starting
def dense_crf_process(self, images, outputs):
'''
Reference: https://github.com/kazuto1011/deeplab-pytorch/blob/master/libs/utils/crf.py
'''
# hyperparameters of the dense crf
# baseline = 79.5
# bi_xy_std = 67, 79.1
# bi_xy_std = 20, 79.6
# bi_xy_std = 10, 79.7
# bi_xy_std = 10, iter_max = 20, v4 79.7
# bi_xy_std = 10, iter_max = 5, v5 79.7
# bi_xy_std = 5, v3 79.7
iter_max = 10
pos_w = 3
pos_xy_std = 1
bi_w = 4
bi_xy_std = 10
bi_rgb_std = 3
b = images.size(0)
mean_vector = np.expand_dims(np.expand_dims(np.transpose(np.array([102.9801, 115.9465, 122.7717])), axis=1), axis=2)
outputs = F.softmax(outputs, dim=1)
for i in range(b):
unary = outputs[i].data.cpu().numpy()
C, H, W = unary.shape
unary = dcrf_utils.unary_from_softmax(unary)
unary = np.ascontiguousarray(unary)
image = np.ascontiguousarray(images[i]) + mean_vector
image = image.astype(np.ubyte)
image = np.ascontiguousarray(image.transpose(1, 2, 0))
d = dcrf.DenseCRF2D(W, H, C)
d.setUnaryEnergy(unary)
d.addPairwiseGaussian(sxy=pos_xy_std, compat=pos_w)
d.addPairwiseBilateral(sxy=bi_xy_std, srgb=bi_rgb_std, rgbim=image, compat=bi_w)
out_crf = np.array(d.inference(iter_max))
outputs[i] = torch.from_numpy(out_crf).cuda().view(C, H, W)
return outputs
def visualize(self, label_img):
img = label_img.copy()
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img_num = 14
img[img == img_num] = 0
out_img = img.copy()
for label in HYUNDAI_POC_CATEGORIES:
red, green, blue = img[:,:,0], img[:,:,1], img[:,:,2]
mask = red == label['id']
out_img[:,:,:3][mask] = label['color']
out_img = cv2.cvtColor(out_img, cv2.COLOR_RGB2BGR)
return out_img
def get_ratio_all(self, anno_img):
total_size = 0
lines = []
for label in HYUNDAI_POC_CATEGORIES:
total = self.get_ratio(anno_img.copy(), label)
lines.append([label['name'], total])
total_size += total
for l in lines:
if total_size:
l[1] = l[1] / total_size * 100
else:
l[1] = 0
return lines
def get_ratio(self, anno_img, label):
total = 0
label_id = label['id']
if label_id == 14:
return total
label_img = (anno_img == label_id).astype(np.uint8)
# label_img = cv2.cvtColor(label_img, cv2.COLOR_BGR2GRAY)
total = np.count_nonzero(label_img)
return total
def visualize_ratio(self, ratios, video_size, ratio_w):
ratio_list = ratios.copy()
ratio_list.insert(0, ['등급','비율'])
RATIO_IMG_W = ratio_w
RATIO_IMG_H = int(video_size[0])
TEXT_MARGIN_H = 20
TEXT_MARGIN_W = 10
row_count = 14
col_count = 2
ratio_img = np.full((RATIO_IMG_H, RATIO_IMG_W, 3), 255, np.uint8)
row_h = RATIO_IMG_H / row_count
col_w = RATIO_IMG_H / row_count
center_w = RATIO_IMG_W / 2
for i in range(1, row_count):
p_y = int(i * row_h)
p_y_n = int((i+1) * row_h)
for label in HYUNDAI_POC_CATEGORIES:
if label['id'] == i:
cv2.rectangle(ratio_img, (0, p_y), (int(center_w), p_y_n), label['color'], cv2.FILLED)
for i in range(1, row_count):
p_y = int(i * row_h)
cv2.line(ratio_img, (0, p_y), (RATIO_IMG_W, p_y), (0,0,0))
cv2.line(ratio_img, (int(center_w), 0), (int(center_w), RATIO_IMG_H), (0,0,0))
for i in range(row_count):
p_y = int(i * row_h) + TEXT_MARGIN_H
p_w = int(center_w) + TEXT_MARGIN_W
ratio_img = Image.fromarray(ratio_img)
font = ImageFont.truetype("NanumGothic.ttf", 15)
draw = ImageDraw.Draw(ratio_img)
color = (0, 0, 0)
# print(ratio_list)
draw.text((0, p_y), ratio_list[i][0], font=font, fill=color)
if isinstance(ratio_list[i][1], str):
draw.text((p_w, p_y), ratio_list[i][1],font=font,fill=color)
else:
draw.text((p_w, p_y), "{:.02f}".format(ratio_list[i][1]),font=font,fill=color)
ratio_img = np.array(ratio_img)
ratio_img = cv2.cvtColor(ratio_img, cv2.COLOR_RGB2BGR)
return ratio_img
class Tester(object):
"""
The class for Pose Estimation. Include train, val, val & predict.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.seg_visualizer = SegVisualizer(configer)
self.loss_manager = LossManager(configer)
self.module_runner = ModuleRunner(configer)
self.model_manager = ModelManager(configer)
self.optim_scheduler = OptimScheduler(configer)
self.seg_data_loader = DataLoader(configer)
self.save_dir = self.configer.get('test', 'out_dir')
self.seg_net = None
self.test_loader = None
self.test_size = None
self.infer_time = 0
self.infer_cnt = 0
self._init_model()
def _init_model(self):
self.seg_net = self.model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
if 'test' in self.save_dir:
self.test_loader = self.seg_data_loader.get_testloader()
self.test_size = len(self.test_loader) * self.configer.get(
'test', 'batch_size')
else:
self.test_loader = self.seg_data_loader.get_valloader()
self.test_size = len(self.test_loader) * self.configer.get(
'val', 'batch_size')
self.seg_net.eval()
def __relabel(self, label_map):
height, width = label_map.shape
label_dst = np.zeros((height, width), dtype=np.uint8)
for i in range(self.configer.get('data', 'num_classes')):
label_dst[label_map == i] = self.configer.get(
'data', 'label_list')[i]
label_dst = np.array(label_dst, dtype=np.uint8)
return label_dst
def test(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
start_time = time.time()
image_id = 0
Log.info('save dir {}'.format(self.save_dir))
FileHelper.make_dirs(self.save_dir, is_file=False)
if self.configer.get('dataset') in ['cityscapes', 'gta5']:
colors = get_cityscapes_colors()
elif self.configer.get('dataset') == 'ade20k':
colors = get_ade_colors()
elif self.configer.get('dataset') == 'lip':
colors = get_lip_colors()
elif self.configer.get('dataset') == 'pascal_context':
colors = get_pascal_context_colors()
elif self.configer.get('dataset') == 'pascal_voc':
colors = get_pascal_voc_colors()
elif self.configer.get('dataset') == 'coco_stuff':
colors = get_cocostuff_colors()
else:
raise RuntimeError("Unsupport colors")
save_prob = False
if self.configer.get('test', 'save_prob'):
save_prob = self.configer.get('test', 'save_prob')
def softmax(X, axis=0):
max_prob = np.max(X, axis=axis, keepdims=True)
X -= max_prob
X = np.exp(X)
sum_prob = np.sum(X, axis=axis, keepdims=True)
X /= sum_prob
return X
for j, data_dict in enumerate(self.test_loader):
inputs = data_dict['img']
names = data_dict['name']
metas = data_dict['meta']
if 'val' in self.save_dir and os.environ.get('save_gt_label'):
labels = data_dict['labelmap']
with torch.no_grad():
# Forward pass.
if self.configer.exists('data',
'use_offset') and self.configer.get(
'data', 'use_offset') == 'offline':
offset_h_maps = data_dict['offsetmap_h']
offset_w_maps = data_dict['offsetmap_w']
outputs = self.offset_test(inputs, offset_h_maps,
offset_w_maps)
elif self.configer.get('test', 'mode') == 'ss_test':
outputs = self.ss_test(inputs)
elif self.configer.get('test', 'mode') == 'ms_test':
outputs = self.ms_test(inputs)
elif self.configer.get('test', 'mode') == 'ms_test_depth':
outputs = self.ms_test_depth(inputs, names)
elif self.configer.get('test', 'mode') == 'sscrop_test':
crop_size = self.configer.get('test', 'crop_size')
outputs = self.sscrop_test(inputs, crop_size)
elif self.configer.get('test', 'mode') == 'mscrop_test':
crop_size = self.configer.get('test', 'crop_size')
outputs = self.mscrop_test(inputs, crop_size)
elif self.configer.get('test', 'mode') == 'crf_ss_test':
outputs = self.ss_test(inputs)
outputs = self.dense_crf_process(inputs, outputs)
if isinstance(outputs, torch.Tensor):
outputs = outputs.permute(0, 2, 3, 1).cpu().numpy()
n = outputs.shape[0]
else:
outputs = [
output.permute(0, 2, 3, 1).cpu().numpy().squeeze()
for output in outputs
]
n = len(outputs)
for k in range(n):
image_id += 1
ori_img_size = metas[k]['ori_img_size']
border_size = metas[k]['border_size']
logits = cv2.resize(
outputs[k][:border_size[1], :border_size[0]],
tuple(ori_img_size),
interpolation=cv2.INTER_CUBIC)
# save the logits map
if self.configer.get('test', 'save_prob'):
prob_path = os.path.join(self.save_dir, "prob/",
'{}.npy'.format(names[k]))
FileHelper.make_dirs(prob_path, is_file=True)
np.save(prob_path, softmax(logits, axis=-1))
label_img = np.asarray(np.argmax(logits, axis=-1),
dtype=np.uint8)
if self.configer.exists(
'data', 'reduce_zero_label') and self.configer.get(
'data', 'reduce_zero_label'):
label_img = label_img + 1
label_img = label_img.astype(np.uint8)
if self.configer.exists('data', 'label_list'):
label_img_ = self.__relabel(label_img)
else:
label_img_ = label_img
label_img_ = Image.fromarray(label_img_, 'P')
Log.info('{:4d}/{:4d} label map generated'.format(
image_id, self.test_size))
label_path = os.path.join(self.save_dir, "label/",
'{}.png'.format(names[k]))
FileHelper.make_dirs(label_path, is_file=True)
ImageHelper.save(label_img_, label_path)
# colorize the label-map
if os.environ.get('save_gt_label'):
if self.configer.exists(
'data',
'reduce_zero_label') and self.configer.get(
'data', 'reduce_zero_label'):
label_img = labels[k] + 1
label_img = np.asarray(label_img, dtype=np.uint8)
color_img_ = Image.fromarray(label_img)
color_img_.putpalette(colors)
vis_path = os.path.join(self.save_dir, "gt_vis/",
'{}.png'.format(names[k]))
FileHelper.make_dirs(vis_path, is_file=True)
ImageHelper.save(color_img_, save_path=vis_path)
else:
color_img_ = Image.fromarray(label_img)
color_img_.putpalette(colors)
vis_path = os.path.join(self.save_dir, "vis/",
'{}.png'.format(names[k]))
FileHelper.make_dirs(vis_path, is_file=True)
ImageHelper.save(color_img_, save_path=vis_path)
self.batch_time.update(time.time() - start_time)
start_time = time.time()
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s'.format(
batch_time=self.batch_time))
def offset_test(self, inputs, offset_h_maps, offset_w_maps, scale=1):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
start = timeit.default_timer()
outputs = self.seg_net.forward(inputs, offset_h_maps,
offset_w_maps)
torch.cuda.synchronize()
end = timeit.default_timer()
if (self.configer.get('loss', 'loss_type')
== "fs_auxce_loss") or (self.configer.get(
'loss', 'loss_type') == "triple_auxce_loss"):
outputs = outputs[-1]
elif self.configer.get('loss',
'loss_type') == "pyramid_auxce_loss":
outputs = outputs[1] + outputs[2] + outputs[3] + outputs[4]
outputs = F.interpolate(outputs,
size=(h, w),
mode='bilinear',
align_corners=True)
return outputs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def ss_test(self, inputs, scale=1):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
scaled_inputs = F.interpolate(inputs,
size=(int(h * scale),
int(w * scale)),
mode="bilinear",
align_corners=True)
start = timeit.default_timer()
outputs = self.seg_net.forward(scaled_inputs)
torch.cuda.synchronize()
end = timeit.default_timer()
outputs = outputs[-1]
outputs = F.interpolate(outputs,
size=(h, w),
mode='bilinear',
align_corners=True)
return outputs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
replicas = nn.parallel.replicate(self.seg_net.module, device_ids)
scaled_inputs, ori_size, outputs = [], [], []
for i, d in zip(inputs, device_ids):
h, w = i.size(1), i.size(2)
ori_size.append((h, w))
i = F.interpolate(i.unsqueeze(0),
size=(int(h * scale), int(w * scale)),
mode="bilinear",
align_corners=True)
scaled_inputs.append(i.cuda(d, non_blocking=True))
scaled_outputs = nn.parallel.parallel_apply(
replicas[:len(scaled_inputs)], scaled_inputs)
for i, output in enumerate(scaled_outputs):
outputs.append(
F.interpolate(output[-1],
size=ori_size[i],
mode='bilinear',
align_corners=True))
return outputs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def flip(self, x, dim):
indices = [slice(None)] * x.dim()
indices[dim] = torch.arange(x.size(dim) - 1,
-1,
-1,
dtype=torch.long,
device=x.device)
return x[tuple(indices)]
def sscrop_test(self, inputs, crop_size, scale=1):
'''
Currently, sscrop_test does not support diverse_size testing
'''
n, c, ori_h, ori_w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
scaled_inputs = F.interpolate(inputs,
size=(int(ori_h * scale),
int(ori_w * scale)),
mode="bilinear",
align_corners=True)
n, c, h, w = scaled_inputs.size(0), scaled_inputs.size(
1), scaled_inputs.size(2), scaled_inputs.size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
count_predictions = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
crop_counter = 0
height_starts = self._decide_intersection(h, crop_size[0])
width_starts = self._decide_intersection(w, crop_size[1])
for height in height_starts:
for width in width_starts:
crop_inputs = scaled_inputs[:, :, height:height + crop_size[0],
width:width + crop_size[1]]
prediction = self.ss_test(crop_inputs)
count_predictions[:, :, height:height + crop_size[0],
width:width + crop_size[1]] += 1
full_probs[:, :, height:height + crop_size[0],
width:width + crop_size[1]] += prediction
crop_counter += 1
Log.info('predicting {:d}-th crop'.format(crop_counter))
full_probs /= count_predictions
full_probs = F.interpolate(full_probs,
size=(ori_h, ori_w),
mode='bilinear',
align_corners=True)
return full_probs
def ms_test(self, inputs):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
if self.configer.exists('test', 'scale_weights'):
for scale, weight in zip(
self.configer.get('test', 'scale_search'),
self.configer.get('test', 'scale_weights')):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += weight * probs
return full_probs
else:
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
return full_probs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
full_probs = [
torch.zeros(1, self.configer.get('data', 'num_classes'),
i.size(1), i.size(2)).cuda(device_ids[index],
non_blocking=True)
for index, i in enumerate(inputs)
]
flip_inputs = [self.flip(i, 2) for i in inputs]
if self.configer.exists('test', 'scale_weights'):
for scale, weight in zip(
self.configer.get('test', 'scale_search'),
self.configer.get('test', 'scale_weights')):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(flip_inputs, scale)
for i in range(len(inputs)):
full_probs[i] += weight * (probs[i] +
self.flip(flip_probs[i], 3))
return full_probs
else:
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(flip_inputs, scale)
for i in range(len(inputs)):
full_probs[i] += (probs[i] +
self.flip(flip_probs[i], 3))
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def ms_test_depth(self, inputs, names):
prob_list = []
scale_list = []
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
prob_list.append(probs)
scale_list.append(scale)
full_probs = self.fuse_with_depth(prob_list, scale_list, names)
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def fuse_with_depth(self, probs, scales, names):
MAX_DEPTH = 63
POWER_BASE = 0.8
if 'test' in self.save_dir:
stereo_path = "/msravcshare/dataset/cityscapes/stereo/test/"
else:
stereo_path = "/msravcshare/dataset/cityscapes/stereo/val/"
n, c, h, w = probs[0].size(0), probs[0].size(1), probs[0].size(
2), probs[0].size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for index, name in enumerate(names):
stereo_map = cv2.imread(stereo_path + name + '.png', -1)
depth_map = stereo_map / 256.0
depth_map = 0.5 / depth_map
depth_map = 500 * depth_map
depth_map = np.clip(depth_map, 0, MAX_DEPTH)
depth_map = depth_map // (MAX_DEPTH // len(scales))
for prob, scale in zip(probs, scales):
scale_index = self._locate_scale_index(scale, scales)
weight_map = np.abs(depth_map - scale_index)
weight_map = np.power(POWER_BASE, weight_map)
weight_map = cv2.resize(weight_map, (w, h))
full_probs[index, :, :, :] += torch.from_numpy(
np.expand_dims(weight_map, axis=0)).type(
torch.cuda.FloatTensor) * prob[index, :, :, :]
return full_probs
@staticmethod
def _locate_scale_index(scale, scales):
for idx, s in enumerate(scales):
if scale == s:
return idx
return 0
def ms_test_wo_flip(self, inputs):
if isinstance(inputs, torch.Tensor):
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
full_probs += probs
return full_probs
elif isinstance(inputs, collections.Sequence):
device_ids = self.configer.get('gpu')
full_probs = [
torch.zeros(1, self.configer.get('data', 'num_classes'),
i.size(1), i.size(2)).cuda(device_ids[index],
non_blocking=True)
for index, i, in enumerate(inputs)
]
for scale in self.configer.get('test', 'scale_search'):
probs = self.ss_test(inputs, scale)
for i in range(len(inputs)):
full_probs[i] += probs[i]
return full_probs
else:
raise RuntimeError("Unsupport data type: {}".format(type(inputs)))
def mscrop_test(self, inputs, crop_size):
'''
Currently, mscrop_test does not support diverse_size testing
'''
n, c, h, w = inputs.size(0), inputs.size(1), inputs.size(
2), inputs.size(3)
full_probs = torch.cuda.FloatTensor(
n, self.configer.get('data', 'num_classes'), h, w).fill_(0)
for scale in self.configer.get('test', 'scale_search'):
Log.info('Scale {0:.2f} prediction'.format(scale))
if scale < 1:
probs = self.ss_test(inputs, scale)
flip_probs = self.ss_test(self.flip(inputs, 3), scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
else:
probs = self.sscrop_test(inputs, crop_size, scale)
flip_probs = self.sscrop_test(self.flip(inputs, 3), crop_size,
scale)
probs = probs + self.flip(flip_probs, 3)
full_probs += probs
return full_probs
def _decide_intersection(self, total_length, crop_length):
stride = crop_length
times = (total_length - crop_length) // stride + 1
cropped_starting = []
for i in range(times):
cropped_starting.append(stride * i)
if total_length - cropped_starting[-1] > crop_length:
cropped_starting.append(total_length -
crop_length) # must cover the total image
return cropped_starting
def dense_crf_process(self, images, outputs):
'''
Reference: https://github.com/kazuto1011/deeplab-pytorch/blob/master/libs/utils/crf.py
'''
# hyperparameters of the dense crf
# baseline = 79.5
# bi_xy_std = 67, 79.1
# bi_xy_std = 20, 79.6
# bi_xy_std = 10, 79.7
# bi_xy_std = 10, iter_max = 20, v4 79.7
# bi_xy_std = 10, iter_max = 5, v5 79.7
# bi_xy_std = 5, v3 79.7
iter_max = 10
pos_w = 3
pos_xy_std = 1
bi_w = 4
bi_xy_std = 10
bi_rgb_std = 3
b = images.size(0)
mean_vector = np.expand_dims(np.expand_dims(np.transpose(
np.array([102.9801, 115.9465, 122.7717])),
axis=1),
axis=2)
outputs = F.softmax(outputs, dim=1)
for i in range(b):
unary = outputs[i].data.cpu().numpy()
C, H, W = unary.shape
unary = dcrf_utils.unary_from_softmax(unary)
unary = np.ascontiguousarray(unary)
image = np.ascontiguousarray(images[i]) + mean_vector
image = image.astype(np.ubyte)
image = np.ascontiguousarray(image.transpose(1, 2, 0))
d = dcrf.DenseCRF2D(W, H, C)
d.setUnaryEnergy(unary)
d.addPairwiseGaussian(sxy=pos_xy_std, compat=pos_w)
d.addPairwiseBilateral(sxy=bi_xy_std,
srgb=bi_rgb_std,
rgbim=image,
compat=bi_w)
out_crf = np.array(d.inference(iter_max))
outputs[i] = torch.from_numpy(out_crf).cuda().view(C, H, W)
return outputs
class Trainer(object):
"""
The class for Pose Estimation. Include train, val, val & predict.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.foward_time = AverageMeter()
self.backward_time = AverageMeter()
self.loss_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.seg_visualizer = SegVisualizer(configer)
self.loss_manager = LossManager(configer)
self.module_runner = ModuleRunner(configer)
self.model_manager = ModelManager(configer)
self.data_loader = DataLoader(configer)
self.optim_scheduler = OptimScheduler(configer)
self.data_helper = DataHelper(configer, self)
self.evaluator = get_evaluator(configer, self)
self.seg_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self.running_score = None
self._init_model()
def _init_model(self):
self.seg_net = self.model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
Log.info('Params Group Method: {}'.format(self.configer.get('optim', 'group_method')))
if self.configer.get('optim', 'group_method') == 'decay':
params_group = self.group_weight(self.seg_net)
else:
assert self.configer.get('optim', 'group_method') is None
params_group = self._get_parameters()
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(params_group)
self.train_loader = self.data_loader.get_trainloader()
self.val_loader = self.data_loader.get_valloader()
self.pixel_loss = self.loss_manager.get_seg_loss()
if is_distributed():
self.pixel_loss = self.module_runner.to_device(self.pixel_loss)
@staticmethod
def group_weight(module):
group_decay = []
group_no_decay = []
for m in module.modules():
if isinstance(m, nn.Linear):
group_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
elif isinstance(m, nn.modules.conv._ConvNd):
group_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
else:
if hasattr(m, 'weight'):
group_no_decay.append(m.weight)
if hasattr(m, 'bias'):
group_no_decay.append(m.bias)
assert len(list(module.parameters())) == len(group_decay) + len(group_no_decay)
groups = [dict(params=group_decay), dict(params=group_no_decay, weight_decay=.0)]
return groups
def _get_parameters(self):
bb_lr = []
nbb_lr = []
params_dict = dict(self.seg_net.named_parameters())
for key, value in params_dict.items():
if 'backbone' not in key:
nbb_lr.append(value)
else:
bb_lr.append(value)
params = [{'params': bb_lr, 'lr': self.configer.get('lr', 'base_lr')},
{'params': nbb_lr, 'lr': self.configer.get('lr', 'base_lr') * self.configer.get('lr', 'nbb_mult')}]
return params
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.seg_net.train()
self.pixel_loss.train()
start_time = time.time()
if "swa" in self.configer.get('lr', 'lr_policy'):
normal_max_iters = int(self.configer.get('solver', 'max_iters') * 0.75)
swa_step_max_iters = (self.configer.get('solver', 'max_iters') - normal_max_iters) // 5 + 1
if hasattr(self.train_loader.sampler, 'set_epoch'):
self.train_loader.sampler.set_epoch(self.configer.get('epoch'))
for i, data_dict in enumerate(self.train_loader):
if self.configer.get('lr', 'metric') == 'iters':
self.scheduler.step(self.configer.get('iters'))
else:
self.scheduler.step(self.configer.get('epoch'))
if self.configer.get('lr', 'is_warm'):
self.module_runner.warm_lr(
self.configer.get('iters'),
self.scheduler, self.optimizer, backbone_list=[0,]
)
(inputs, targets), batch_size = self.data_helper.prepare_data(data_dict)
self.data_time.update(time.time() - start_time)
foward_start_time = time.time()
outputs = self.seg_net(*inputs)
self.foward_time.update(time.time() - foward_start_time)
loss_start_time = time.time()
if is_distributed():
import torch.distributed as dist
def reduce_tensor(inp):
"""
Reduce the loss from all processes so that
process with rank 0 has the averaged results.
"""
world_size = get_world_size()
if world_size < 2:
return inp
with torch.no_grad():
reduced_inp = inp
dist.reduce(reduced_inp, dst=0)
return reduced_inp
loss = self.pixel_loss(outputs, targets)
backward_loss = loss
display_loss = reduce_tensor(backward_loss) / get_world_size()
else:
backward_loss = display_loss = self.pixel_loss(outputs, targets, gathered=self.configer.get('network', 'gathered'))
self.train_losses.update(display_loss.item(), batch_size)
self.loss_time.update(time.time() - loss_start_time)
backward_start_time = time.time()
self.optimizer.zero_grad()
backward_loss.backward()
self.optimizer.step()
self.backward_time.update(time.time() - backward_start_time)
# Update the vars of the train phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.configer.plus_one('iters')
# Print the log info & reset the states.
if self.configer.get('iters') % self.configer.get('solver', 'display_iter') == 0 and \
(not is_distributed() or get_rank() == 0):
Log.info('Train Epoch: {0}\tTrain Iteration: {1}\t'
'Time {batch_time.sum:.3f}s / {2}iters, ({batch_time.avg:.3f})\t'
'Forward Time {foward_time.sum:.3f}s / {2}iters, ({foward_time.avg:.3f})\t'
'Backward Time {backward_time.sum:.3f}s / {2}iters, ({backward_time.avg:.3f})\t'
'Loss Time {loss_time.sum:.3f}s / {2}iters, ({loss_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {2}iters, ({data_time.avg:3f})\n'
'Learning rate = {3}\tLoss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'.format(
self.configer.get('epoch'), self.configer.get('iters'),
self.configer.get('solver', 'display_iter'),
self.module_runner.get_lr(self.optimizer), batch_time=self.batch_time,
foward_time=self.foward_time, backward_time=self.backward_time, loss_time=self.loss_time,
data_time=self.data_time, loss=self.train_losses))
self.batch_time.reset()
self.foward_time.reset()
self.backward_time.reset()
self.loss_time.reset()
self.data_time.reset()
self.train_losses.reset()
# save checkpoints for swa
if 'swa' in self.configer.get('lr', 'lr_policy') and \
self.configer.get('iters') > normal_max_iters and \
((self.configer.get('iters') - normal_max_iters) % swa_step_max_iters == 0 or \
self.configer.get('iters') == self.configer.get('solver', 'max_iters')):
self.optimizer.update_swa()
if self.configer.get('iters') == self.configer.get('solver', 'max_iters'):
break
# Check to val the current model.
# if self.configer.get('epoch') % self.configer.get('solver', 'test_interval') == 0:
if self.configer.get('iters') % self.configer.get('solver', 'test_interval') == 0:
self.__val()
self.configer.plus_one('epoch')
def __val(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
self.pixel_loss.eval()
start_time = time.time()
replicas = self.evaluator.prepare_validaton()
data_loader = self.val_loader if data_loader is None else data_loader
for j, data_dict in enumerate(data_loader):
if j % 10 == 0:
Log.info('{} images processed\n'.format(j))
if self.configer.get('dataset') == 'lip':
(inputs, targets, inputs_rev, targets_rev), batch_size = self.data_helper.prepare_data(data_dict, want_reverse=True)
else:
(inputs, targets), batch_size = self.data_helper.prepare_data(data_dict)
with torch.no_grad():
if self.configer.get('dataset') == 'lip':
inputs = torch.cat([inputs[0], inputs_rev[0]], dim=0)
outputs = self.seg_net(inputs)
outputs_ = self.module_runner.gather(outputs)
if isinstance(outputs_, (list, tuple)):
outputs_ = outputs_[-1]
outputs = outputs_[0:int(outputs_.size(0)/2),:,:,:].clone()
outputs_rev = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),:,:,:].clone()
if outputs_rev.shape[1] == 20:
outputs_rev[:,14,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),15,:,:]
outputs_rev[:,15,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),14,:,:]
outputs_rev[:,16,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),17,:,:]
outputs_rev[:,17,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),16,:,:]
outputs_rev[:,18,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),19,:,:]
outputs_rev[:,19,:,:] = outputs_[int(outputs_.size(0)/2):int(outputs_.size(0)),18,:,:]
outputs_rev = torch.flip(outputs_rev, [3])
outputs = (outputs + outputs_rev) / 2.
self.evaluator.update_score(outputs, data_dict['meta'])
elif self.data_helper.conditions.diverse_size:
outputs = nn.parallel.parallel_apply(replicas[:len(inputs)], inputs)
for i in range(len(outputs)):
loss = self.pixel_loss(outputs[i], targets[i])
self.val_losses.update(loss.item(), 1)
outputs_i = outputs[i]
if isinstance(outputs_i, torch.Tensor):
outputs_i = [outputs_i]
self.evaluator.update_score(outputs_i, data_dict['meta'][i:i+1])
else:
outputs = self.seg_net(*inputs)
try:
loss = self.pixel_loss(
outputs, targets,
gathered=self.configer.get('network', 'gathered')
)
except AssertionError as e:
print(len(outputs), len(targets))
if not is_distributed():
outputs = self.module_runner.gather(outputs)
self.val_losses.update(loss.item(), batch_size)
self.evaluator.update_score(outputs, data_dict['meta'])
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.evaluator.update_performance()
self.configer.update(['val_loss'], self.val_losses.avg)
self.module_runner.save_net(self.seg_net, save_mode='performance')
self.module_runner.save_net(self.seg_net, save_mode='val_loss')
cudnn.benchmark = True
# Print the log info & reset the states.
if not is_distributed() or get_rank() == 0:
Log.info(
'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t'
'Loss {loss.avg:.8f}\n'.format(
batch_time=self.batch_time, loss=self.val_losses))
self.evaluator.print_scores()
self.batch_time.reset()
self.val_losses.reset()
self.evaluator.reset()
self.seg_net.train()
self.pixel_loss.train()
def train(self):
# cudnn.benchmark = True
# self.__val()
if self.configer.get('network', 'resume') is not None:
if self.configer.get('network', 'resume_val'):
self.__val(data_loader=self.data_loader.get_valloader(dataset='val'))
return
elif self.configer.get('network', 'resume_train'):
self.__val(data_loader=self.data_loader.get_valloader(dataset='train'))
return
# return
if self.configer.get('network', 'resume') is not None and self.configer.get('network', 'resume_val'):
self.__val(data_loader=self.data_loader.get_valloader(dataset='val'))
return
while self.configer.get('iters') < self.configer.get('solver', 'max_iters'):
self.__train()
# use swa to average the model
if 'swa' in self.configer.get('lr', 'lr_policy'):
self.optimizer.swap_swa_sgd()
self.optimizer.bn_update(self.train_loader, self.seg_net)
self.__val(data_loader=self.data_loader.get_valloader(dataset='val'))
def summary(self):
from lib.utils.summary import get_model_summary
import torch.nn.functional as F
self.seg_net.eval()
for j, data_dict in enumerate(self.train_loader):
print(get_model_summary(self.seg_net, data_dict['img'][0:1]))
return
class Trainer(object):
"""
The class for Pose Estimation. Include train, val, val & predict.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.running_score = RunningScore(configer)
self.seg_visualizer = SegVisualizer(configer)
self.loss_manager = LossManager(configer)
self.module_runner = ModuleRunner(configer)
self.model_manager = ModelManager(configer)
self.data_loader = DataLoader(configer)
self.optim_scheduler = OptimScheduler(configer)
self.seg_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self._init_model()
def _init_model(self):
self.seg_net = self.model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
Log.info('Params Group Method: {}'.format(
self.configer.get('optim', 'group_method')))
if self.configer.get('optim', 'group_method') == 'decay':
params_group = self.group_weight(self.seg_net)
else:
assert self.configer.get('optim', 'group_method') is None
params_group = self._get_parameters()
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(
params_group)
self.train_loader = self.data_loader.get_trainloader()
self.val_loader = self.data_loader.get_valloader()
self.pixel_loss = self.loss_manager.get_seg_loss()
@staticmethod
def group_weight(module):
group_decay = []
group_no_decay = []
for m in module.modules():
if isinstance(m, nn.Linear):
group_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
elif isinstance(m, nn.modules.conv._ConvNd):
group_decay.append(m.weight)
if m.bias is not None:
group_no_decay.append(m.bias)
else:
if hasattr(m, 'weight'):
group_no_decay.append(m.weight)
if hasattr(m, 'bias'):
group_no_decay.append(m.bias)
assert len(list(
module.parameters())) == len(group_decay) + len(group_no_decay)
groups = [
dict(params=group_decay),
dict(params=group_no_decay, weight_decay=.0)
]
return groups
def _get_parameters(self):
bb_lr = []
nbb_lr = []
params_dict = dict(self.seg_net.named_parameters())
for key, value in params_dict.items():
if 'backbone' not in key:
nbb_lr.append(value)
else:
bb_lr.append(value)
params = [{
'params': bb_lr,
'lr': self.configer.get('lr', 'base_lr')
}, {
'params':
nbb_lr,
'lr':
self.configer.get('lr', 'base_lr') *
self.configer.get('lr', 'nbb_mult')
}]
return params
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.seg_net.train()
start_time = time.time()
for i, data_dict in enumerate(self.train_loader):
if self.configer.get('lr', 'metric') == 'iters':
self.scheduler.step(self.configer.get('iters'))
else:
self.scheduler.step(self.configer.get('epoch'))
if self.configer.get('lr', 'is_warm'):
self.module_runner.warm_lr(self.configer.get('iters'),
self.scheduler,
self.optimizer,
backbone_list=[
0,
])
inputs = data_dict['img']
targets = data_dict['labelmap']
self.data_time.update(time.time() - start_time)
# Change the data type.
# inputs, targets = self.module_runner.to_device(inputs, targets)
# Forward pass.
outputs = self.seg_net(inputs)
# outputs = self.module_utilizer.gather(outputs)
# Compute the loss of the train batch & backward.
loss = self.pixel_loss(outputs,
targets,
gathered=self.configer.get(
'network', 'gathered'))
if self.configer.exists('train', 'loader') and self.configer.get(
'train', 'loader') == 'ade20k':
batch_size = self.configer.get(
'train', 'batch_size') * self.configer.get(
'train', 'batch_per_gpu')
self.train_losses.update(loss.item(), batch_size)
else:
self.train_losses.update(loss.item(), inputs.size(0))
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Update the vars of the train phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.configer.plus_one('iters')
# Print the log info & reset the states.
if self.configer.get('iters') % self.configer.get(
'solver', 'display_iter') == 0:
Log.info(
'Train Epoch: {0}\tTrain Iteration: {1}\t'
'Time {batch_time.sum:.3f}s / {2}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {2}iters, ({data_time.avg:3f})\n'
'Learning rate = {3}\tLoss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'
.format(self.configer.get('epoch'),
self.configer.get('iters'),
self.configer.get('solver', 'display_iter'),
self.module_runner.get_lr(self.optimizer),
batch_time=self.batch_time,
data_time=self.data_time,
loss=self.train_losses))
self.batch_time.reset()
self.data_time.reset()
self.train_losses.reset()
if self.configer.get('iters') == self.configer.get(
'solver', 'max_iters'):
break
# Check to val the current model.
if self.configer.get('iters') % self.configer.get(
'solver', 'test_interval') == 0:
self.__val()
self.configer.plus_one('epoch')
def __val(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
start_time = time.time()
data_loader = self.val_loader if data_loader is None else data_loader
for j, data_dict in enumerate(data_loader):
inputs = data_dict['img']
targets = data_dict['labelmap']
with torch.no_grad():
# Change the data type.
inputs, targets = self.module_runner.to_device(inputs, targets)
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the val batch.
loss = self.pixel_loss(outputs,
targets,
gathered=self.configer.get(
'network', 'gathered'))
outputs = self.module_runner.gather(outputs)
self.val_losses.update(loss.item(), inputs.size(0))
self._update_running_score(outputs[-1], data_dict['meta'])
# self.seg_running_score.update(pred.max(1)[1].cpu().numpy(), targets.cpu().numpy())
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.configer.update(['performance'],
self.running_score.get_mean_iou())
self.configer.update(['val_loss'], self.val_losses.avg)
self.module_runner.save_net(self.seg_net, save_mode='performance')
self.module_runner.save_net(self.seg_net, save_mode='val_loss')
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t'
'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time,
loss=self.val_losses))
Log.info('Mean IOU: {}\n'.format(self.running_score.get_mean_iou()))
Log.info('Pixel ACC: {}\n'.format(self.running_score.get_pixel_acc()))
self.batch_time.reset()
self.val_losses.reset()
self.running_score.reset()
self.seg_net.train()
def _update_running_score(self, pred, metas):
pred = pred.permute(0, 2, 3, 1)
for i in range(pred.size(0)):
ori_img_size = metas[i]['ori_img_size']
border_size = metas[i]['border_size']
ori_target = metas[i]['ori_target']
total_logits = cv2.resize(
pred[i, :border_size[1], :border_size[0]].cpu().numpy(),
tuple(ori_img_size),
interpolation=cv2.INTER_CUBIC)
labelmap = np.argmax(total_logits, axis=-1)
self.running_score.update(labelmap[None], ori_target[None])
def train(self):
# cudnn.benchmark = True
if self.configer.get('network',
'resume') is not None and self.configer.get(
'network', 'resume_val'):
self.__val()
while self.configer.get('iters') < self.configer.get(
'solver', 'max_iters'):
self.__train()
self.__val(data_loader=self.data_loader.get_valloader(dataset='val'))
self.__val(data_loader=self.data_loader.get_valloader(dataset='train'))
class Tester(object):
def __init__(self, configer):
self.configer = configer
self.blob_helper = BlobHelper(configer)
self.seg_visualizer = SegVisualizer(configer)
self.seg_parser = SegParser(configer)
self.seg_model_manager = ModelManager(configer)
self.seg_data_loader = DataLoader(configer)
self.module_runner = ModuleRunner(configer)
self.device = torch.device('cpu' if self.configer.get('gpu') is None else 'cuda')
self.seg_net = None
self._init_model()
def _init_model(self):
self.seg_net = self.seg_model_manager.semantic_segmentor()
self.seg_net = self.module_runner.load_net(self.seg_net)
self.seg_net.eval()
def _get_blob(self, ori_image, scale=None):
assert scale is not None
image = None
if self.configer.exists('test', 'input_size'):
image = self.blob_helper.make_input(image=ori_image,
input_size=self.configer.get('test', 'input_size'),
scale=scale)
elif self.configer.exists('test', 'min_side_length') and not self.configer.exists('test', 'max_side_length'):
image = self.blob_helper.make_input(image=ori_image,
min_side_length=self.configer.get('test', 'min_side_length'),
scale=scale)
elif not self.configer.exists('test', 'min_side_length') and self.configer.exists('test', 'max_side_length'):
image = self.blob_helper.make_input(image=ori_image,
max_side_length=self.configer.get('test', 'max_side_length'),
scale=scale)
elif self.configer.exists('test', 'min_side_length') and self.configer.exists('test', 'max_side_length'):
image = self.blob_helper.make_input(image=ori_image,
min_side_length=self.configer.get('test', 'min_side_length'),
max_side_length=self.configer.get('test', 'max_side_length'),
scale=scale)
else:
Log.error('Test setting error')
exit(1)
b, c, h, w = image.size()
border_hw = [h, w]
if self.configer.exists('test', 'fit_stride'):
stride = self.configer.get('test', 'fit_stride')
pad_w = 0 if (w % stride == 0) else stride - (w % stride) # right
pad_h = 0 if (h % stride == 0) else stride - (h % stride) # down
expand_image = torch.zeros((b, c, h + pad_h, w + pad_w)).to(image.device)
expand_image[:, :, 0:h, 0:w] = image
image = expand_image
return image, border_hw
def __test_img(self, image_path, label_path, vis_path, raw_path):
Log.info('Image Path: {}'.format(image_path))
ori_image = ImageHelper.read_image(image_path,
tool=self.configer.get('data', 'image_tool'),
mode=self.configer.get('data', 'input_mode'))
total_logits = None
if self.configer.get('test', 'mode') == 'ss_test':
total_logits = self.ss_test(ori_image)
elif self.configer.get('test', 'mode') == 'sscrop_test':
total_logits = self.sscrop_test(ori_image)
elif self.configer.get('test', 'mode') == 'ms_test':
total_logits = self.ms_test(ori_image)
elif self.configer.get('test', 'mode') == 'mscrop_test':
total_logits = self.mscrop_test(ori_image)
else:
Log.error('Invalid test mode:{}'.format(self.configer.get('test', 'mode')))
exit(1)
label_map = np.argmax(total_logits, axis=-1)
label_img = np.array(label_map, dtype=np.uint8)
ori_img_bgr = ImageHelper.get_cv2_bgr(ori_image, mode=self.configer.get('data', 'input_mode'))
image_canvas = self.seg_parser.colorize(label_img, image_canvas=ori_img_bgr)
ImageHelper.save(image_canvas, save_path=vis_path)
ImageHelper.save(ori_image, save_path=raw_path)
if self.configer.exists('data', 'label_list'):
label_img = self.__relabel(label_img)
if self.configer.exists('data', 'reduce_zero_label') and self.configer.get('data', 'reduce_zero_label'):
label_img = label_img + 1
label_img = label_img.astype(np.uint8)
label_img = Image.fromarray(label_img, 'P')
Log.info('Label Path: {}'.format(label_path))
ImageHelper.save(label_img, label_path)
def ss_test(self, ori_image):
ori_width, ori_height = ImageHelper.get_size(ori_image)
total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32)
image, border_hw = self._get_blob(ori_image, scale=1.0)
results = self._predict(image)
results = cv2.resize(results[:border_hw[0], :border_hw[1]],
(ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
return total_logits
def sscrop_test(self, ori_image):
ori_width, ori_height = ImageHelper.get_size(ori_image)
total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32)
image, border_hw = self._get_blob(ori_image, scale=1.0)
crop_size = self.configer.get('test', 'crop_size')
if image.size()[3] > crop_size[0] and image.size()[2] > crop_size[1]:
results = self._crop_predict(image, crop_size)
else:
results = self._predict(image)
results = cv2.resize(results[:border_hw[0], :border_hw[1]],
(ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
return total_logits
def mscrop_test(self, ori_image):
ori_width, ori_height = ImageHelper.get_size(ori_image)
crop_size = self.configer.get('test', 'crop_size')
total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32)
for scale in self.configer.get('test', 'scale_search'):
image, border_hw = self._get_blob(ori_image, scale=scale)
if image.size()[3] > crop_size[0] and image.size()[2] > crop_size[1]:
results = self._crop_predict(image, crop_size)
else:
results = self._predict(image)
results = cv2.resize(results[:border_hw[0], :border_hw[1]],
(ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
if self.configer.get('data', 'image_tool') == 'cv2':
mirror_image = cv2.flip(ori_image, 1)
else:
mirror_image = ori_image.transpose(Image.FLIP_LEFT_RIGHT)
image, border_hw = self._get_blob(mirror_image, scale=1.0)
if image.size()[3] > crop_size[0] and image.size()[2] > crop_size[1]:
results = self._crop_predict(image, crop_size)
else:
results = self._predict(image)
results = results[:border_hw[0], :border_hw[1]]
results = cv2.resize(results[:, ::-1], (ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
return total_logits
def ms_test(self, ori_image):
ori_width, ori_height = ImageHelper.get_size(ori_image)
total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32)
for scale in self.configer.get('test', 'scale_search'):
image, border_hw = self._get_blob(ori_image, scale=scale)
results = self._predict(image)
results = cv2.resize(results[:border_hw[0], :border_hw[1]],
(ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
if self.configer.get('data', 'image_tool') == 'cv2':
mirror_image = cv2.flip(ori_image, 1)
else:
mirror_image = ori_image.transpose(Image.FLIP_LEFT_RIGHT)
image, border_hw = self._get_blob(mirror_image, scale=scale)
results = self._predict(image)
results = results[:border_hw[0], :border_hw[1]]
results = cv2.resize(results[:, ::-1], (ori_width, ori_height), interpolation=cv2.INTER_CUBIC)
total_logits += results
return total_logits
def _crop_predict(self, image, crop_size):
height, width = image.size()[2:]
np_image = image.squeeze(0).permute(1, 2, 0).cpu().numpy()
height_starts = self._decide_intersection(height, crop_size[1])
width_starts = self._decide_intersection(width, crop_size[0])
split_crops = []
for height in height_starts:
for width in width_starts:
image_crop = np_image[height:height + crop_size[1], width:width + crop_size[0]]
split_crops.append(image_crop[np.newaxis, :])
split_crops = np.concatenate(split_crops, axis=0) # (n, crop_image_size, crop_image_size, 3)
inputs = torch.from_numpy(split_crops).permute(0, 3, 1, 2).to(self.device)
with torch.no_grad():
results = self.seg_net.forward(inputs)
results = results[-1].permute(0, 2, 3, 1).cpu().numpy()
reassemble = np.zeros((np_image.shape[0], np_image.shape[1], results.shape[-1]), np.float32)
index = 0
for height in height_starts:
for width in width_starts:
reassemble[height:height+crop_size[1], width:width+crop_size[0]] += results[index]
index += 1
return reassemble
def _decide_intersection(self, total_length, crop_length):
stride = int(crop_length * self.configer.get('test', 'crop_stride_ratio')) # set the stride as the paper do
times = (total_length - crop_length) // stride + 1
cropped_starting = []
for i in range(times):
cropped_starting.append(stride*i)
if total_length - cropped_starting[-1] > crop_length:
cropped_starting.append(total_length - crop_length) # must cover the total image
return cropped_starting
def _predict(self, inputs):
with torch.no_grad():
results = self.seg_net.forward(inputs)
results = results[-1].squeeze(0).permute(1, 2, 0).cpu().numpy()
return results
def __relabel(self, label_map):
height, width = label_map.shape
label_dst = np.zeros((height, width), dtype=np.uint8)
for i in range(self.configer.get('data', 'num_classes')):
label_dst[label_map == i] = self.configer.get('data', 'label_list')[i]
label_dst = np.array(label_dst, dtype=np.uint8)
return label_dst
def test(self):
base_dir = os.path.join(self.configer.get('project_dir'), 'results', self.configer.get('dataset'))
test_img = self.configer.get('test', 'test_img')
test_dir = self.configer.get('test', 'test_dir')
if test_img is None and test_dir is None:
Log.error('test_img & test_dir not exists.')
exit(1)
if test_img is not None and test_dir is not None:
Log.error('Either test_img or test_dir.')
exit(1)
if test_img is not None:
base_dir = os.path.join(base_dir, 'test_img')
filename = test_img.rstrip().split('/')[-1]
label_path = os.path.join(base_dir, 'label', '{}.png'.format('.'.join(filename.split('.')[:-1])))
raw_path = os.path.join(base_dir, 'raw', filename)
vis_path = os.path.join(base_dir, 'vis', '{}_vis.png'.format('.'.join(filename.split('.')[:-1])))
FileHelper.make_dirs(label_path, is_file=True)
FileHelper.make_dirs(raw_path, is_file=True)
FileHelper.make_dirs(vis_path, is_file=True)
self.__test_img(test_img, label_path, vis_path, raw_path)
else:
base_dir = os.path.join(base_dir, 'test_dir',
self.configer.get('checkpoints', 'checkpoints_name'),
self.configer.get('test', 'out_dir'))
FileHelper.make_dirs(base_dir)
for filename in FileHelper.list_dir(test_dir):
image_path = os.path.join(test_dir, filename)
label_path = os.path.join(base_dir, 'label', '{}.png'.format('.'.join(filename.split('.')[:-1])))
raw_path = os.path.join(base_dir, 'raw', filename)
vis_path = os.path.join(base_dir, 'vis', '{}_vis.png'.format('.'.join(filename.split('.')[:-1])))
FileHelper.make_dirs(label_path, is_file=True)
FileHelper.make_dirs(raw_path, is_file=True)
FileHelper.make_dirs(vis_path, is_file=True)
self.__test_img(image_path, label_path, vis_path, raw_path)
def debug(self):
base_dir = os.path.join(self.configer.get('project_dir'),
'vis/results', self.configer.get('dataset'), 'debug')
if not os.path.exists(base_dir):
os.makedirs(base_dir)
count = 0
for i, data_dict in enumerate(self.seg_data_loader.get_trainloader()):
inputs = data_dict['img']
targets = data_dict['labelmap']
for j in range(inputs.size(0)):
count = count + 1
if count > 20:
exit(1)
image_bgr = self.blob_helper.tensor2bgr(inputs[j])
label_map = targets[j].numpy()
image_canvas = self.seg_parser.colorize(label_map, image_canvas=image_bgr)
cv2.imwrite(os.path.join(base_dir, '{}_{}_vis.png'.format(i, j)), image_canvas)
cv2.imshow('main', image_canvas)
cv2.waitKey()