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PaddleSeg

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PaddleSeg
未验证 提交 44b420ae 编写于 作者: W wuzewu 提交者： GitHub
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Merge pull request #356 from pennypm/master

上级 3e0ecbcb 6de94868
隐藏空白更改
内联 并排
Showing with 311 addition and 8 deletion
contrib/SpatialEmbeddings/config.py
浏览文件 @ 44b420ae
......@@ -14,6 +14,8 @@ cfg.model_path = 'SpatialEmbeddings_kitti'
# 预测结果保存路径
cfg.vis_dir = "result"
# 待预测图像输入尺寸
cfg.input_size = (384, 1248)
# sigma值
cfg.n_sigma = 2
# 中心点阈值
......
contrib/SpatialEmbeddings/infer.py
浏览文件 @ 44b420ae
......@@ -7,6 +7,7 @@ from utils.data_util import Cluster, pad_img
from PIL import Image as PILImage
import importlib
import paddle.fluid as fluid
from models import SpatialEmbeddings
args = get_arguments()
config = importlib.import_module('config')
......@@ -38,10 +39,8 @@ class TestDataSet():
def preprocess(self, img):
# 图像预处理
h, w = img.shape[:2]
h_new = (h//32 + 1 if h % 32 != 0 else h//32)*32
w_new = (w//32 + 1 if w % 32 != 0 else w//32)*32
h_new, w_new = cfg.input_size
img = np.pad(img, ((0, h_new - h), (0, w_new - w), (0, 0)), 'edge')
img = img.astype(np.float32)/255.0
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, axis=0)
......@@ -61,18 +60,33 @@ class TestDataSet():
return img_process, name_prefix, img_shape
def get_model(main_prog, startup_prog):
img_shape = [3, cfg.input_size[0], cfg.input_size[1]]
with fluid.program_guard(main_prog, startup_prog):
with fluid.unique_name.guard():
input = fluid.layers.data(name='image', shape=img_shape, dtype='float32')
output = SpatialEmbeddings(input)
return input, output
def infer():
if not os.path.exists(cfg.vis_dir):
os.makedirs(cfg.vis_dir)
startup_prog = fluid.Program()
test_prog = fluid.Program()
input, output = get_model(test_prog, startup_prog)
test_prog = test_prog.clone(for_test=True)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
# 加载预测模型
test_prog, feed_name, fetch_list = fluid.io.load_inference_model(
dirname=cfg.model_path, executor=exe, params_filename='__params__')
def if_exist(var):
return os.path.exists(os.path.join(cfg.model_path, var.name))
fluid.io.load_vars(exe, cfg.model_path, main_program=test_prog, predicate=if_exist)
#加载预测数据集
test_dataset = TestDataSet()
data_num = test_dataset.data_num
......@@ -83,9 +97,10 @@ def infer():
if image is None:
print(im_name, 'is None')
continue
# 预测
output = exe.run(program=test_prog, feed={feed_name[0]: image}, fetch_list=fetch_list)
instance_map, predictions = cluster.cluster(output[0][0], n_sigma=cfg.n_sigma, \
outputs = exe.run(program=test_prog, feed={'image': image}, fetch_list=output)
instance_map, predictions = cluster.cluster(outputs[0][0], n_sigma=cfg.n_sigma, \
min_pixel=cfg.min_pixel, threshold=cfg.threshold)
# 预测结果保存
......
contrib/SpatialEmbeddings/models.py 0 → 100644
浏览文件 @ 44b420ae
from paddle.fluid.initializer import Constant
from paddle.fluid.param_attr import ParamAttr
import paddle.fluid as fluid
def conv(input,
num_filters,
filter_size=None,
stride=1,
padding=0,
dilation=1,
act=None,
name='conv'):
return fluid.layers.conv2d(input,
filter_size=filter_size,
num_filters=num_filters,
stride=stride,
padding=padding,
dilation=dilation,
act=act,
name=name,
param_attr=name+'_weights',
bias_attr=name+'_bias')
def conv_transpose(input,
num_filters,
output_size=None,
filter_size=None,
stride=1,
padding=0,
act=None,
name='conv_transpose'):
return fluid.layers.conv2d_transpose(input,
filter_size=filter_size,
num_filters=num_filters,
stride=stride,
padding=padding,
act=act,
name=name,
param_attr=name+'_weights',
bias_attr=name+'_bias')
EPSILON=0.0010000000474974513
def bn(input, name):
bn_id = name.replace('batch_norm','')
return fluid.layers.batch_norm(input,
is_test=True,
epsilon=EPSILON,
param_attr='bn_scale'+bn_id + '_scale',
bias_attr='bn_scale'+bn_id+'_offset',
moving_mean_name=name + '_mean',
moving_variance_name=name + '_variance',
name=name)
def max_pool(input, pool_size=2, pool_stride=2, name=None):
return fluid.layers.pool2d(input,
pool_size=pool_size,
pool_stride=pool_stride,
ceil_mode=True,
pool_type='max',
exclusive=False,
name=name)
def SpatialEmbeddings(input):
conv1 = conv(input, filter_size=3, num_filters=13, stride=2, padding=1, name='conv1')
max_pool1 = fluid.layers.pool2d(input, pool_size=2, pool_stride=2, name='max_pool1')
cat1 = fluid.layers.concat([conv1, max_pool1], axis=1, name='cat1')
bn_scale1 = bn(cat1, name='batch_norm1')
relu1 = fluid.layers.relu(bn_scale1)
conv2 = conv(relu1, filter_size=3, num_filters=48, stride=2, padding=1, name='conv2')
max_pool2 = fluid.layers.pool2d(relu1, pool_size=2, pool_stride=2, name='max_pool2')
cat2 = fluid.layers.concat([conv2, max_pool2], axis=1, name='cat2')
bn_scale2 = bn(cat2, name='batch_norm2')
relu2 = fluid.layers.relu(bn_scale2)
relu3 = conv(relu2, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv3', act='relu')
conv4 = conv(relu3, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv4')
bn_scale3 = bn(conv4, name='batch_norm3')
relu4 = fluid.layers.relu(bn_scale3)
relu5 = conv(relu4, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv5', act='relu')
conv6 = conv(relu5, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv6')
bn_scale4 = bn(conv6, name='batch_norm4')
add1 = fluid.layers.elementwise_add(x=bn_scale4, y=relu2, name='add1')
relu6 = fluid.layers.relu(add1)
relu7 = conv(relu6, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv7', act='relu')
conv8 = conv(relu7, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv8')
bn_scale5 = bn(conv8, name='batch_norm5')
relu8 = fluid.layers.relu(bn_scale5)
relu9 = conv(relu8, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv9', act='relu')
conv10 = conv(relu9, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv10')
bn_scale6 = bn(conv10, name='batch_norm6')
add2 = fluid.layers.elementwise_add(x=bn_scale6, y=relu6, name='add2')
relu10 = fluid.layers.relu(add2)
relu11 = conv(relu10, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv11', act='relu')
conv12 = conv(relu11, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv12')
bn_scale7 = bn(conv12, name='batch_norm7')
relu12 = fluid.layers.relu(bn_scale7)
relu13 = conv(relu12, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv13', act='relu')
conv14 = conv(relu13, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv14')
bn_scale8 = bn(conv14, name='batch_norm8')
add3 = fluid.layers.elementwise_add(x=bn_scale8, y=relu10, name='add3')
relu14 = fluid.layers.relu(add3)
relu15 = conv(relu14, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv15', act='relu')
conv16 = conv(relu15, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv16')
bn_scale9 = bn(conv16, name='batch_norm9')
relu16 = fluid.layers.relu(bn_scale9)
relu17 = conv(relu16, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv17', act='relu')
conv18 = conv(relu17, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv18')
bn_scale10 = bn(conv18, name='batch_norm10')
add4 = fluid.layers.elementwise_add(x=bn_scale10, y=relu14, name='add4')
relu18 = fluid.layers.relu(add4)
relu19 = conv(relu18, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv19', act='relu')
conv20 = conv(relu19, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv20')
bn_scale11 = bn(conv20, name='batch_norm11')
relu20 = fluid.layers.relu(bn_scale11)
relu21 = conv(relu20, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv21', act='relu')
conv22 = conv(relu21, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv22')
bn_scale12 = bn(conv22, name='batch_norm12')
add5 = fluid.layers.elementwise_add(x=bn_scale12, y=relu18, name='add5')
relu22 = fluid.layers.relu(add5)
conv23 = conv(relu22, filter_size=3, num_filters=64, stride=2, padding=1, name='conv23')
max_pool3 = fluid.layers.pool2d(relu22, pool_size=2, pool_stride=2, name='max_pool3')
cat3 = fluid.layers.concat([conv23, max_pool3], axis=1, name='cat3')
bn_scale13 = bn(cat3, name='batch_norm13')
relu23 = fluid.layers.relu(bn_scale13)
relu24 = conv(relu23, filter_size=[3, 1], num_filters=128, padding=[1, 0], name='conv24', act='relu')
conv25 = conv(relu24, filter_size=[1, 3], num_filters=128, padding=[0, 1], name='conv25')
bn_scale14 = bn(conv25, name='batch_norm14')
relu25 = fluid.layers.relu(bn_scale14)
relu26 = conv(relu25, filter_size=[3, 1], num_filters=128, padding=[2, 0], dilation=[2, 1], name='conv26', act='relu')
conv27 = conv(relu26, filter_size=[1, 3], num_filters=128, padding=[0, 2], dilation=[1, 2], name='conv27')
bn_scale15 = bn(conv27, name='batch_norm15')
add6 = fluid.layers.elementwise_add(x=bn_scale15, y=relu23, name='add6')
relu27 = fluid.layers.relu(add6)
relu28 = conv(relu27, filter_size=[3, 1], num_filters=128, padding=[1, 0], name='conv28', act='relu')
conv29 = conv(relu28, filter_size=[1, 3], num_filters=128, padding=[0, 1], name='conv29')
bn_scale16 = bn(conv29, name='batch_norm16')
relu29 = fluid.layers.relu(bn_scale16)
relu30 = conv(relu29, filter_size=[3, 1], num_filters=128, padding=[4, 0], dilation=[4, 1], name='conv30', act='relu')
conv31 = conv(relu30, filter_size=[1, 3], num_filters=128, padding=[0, 4], dilation=[1, 4], name='conv31')
bn_scale17 = bn(conv31, name='batch_norm17')
add7 = fluid.layers.elementwise_add(x=bn_scale17, y=relu27, name='add7')
relu31 = fluid.layers.relu(add7)
relu32 = conv(relu31, filter_size=[3, 1], num_filters=128, padding=[1, 0], name='conv32', act='relu')
conv33 = conv(relu32, filter_size=[1, 3], num_filters=128, padding=[0, 1], name='conv33')
bn_scale18 = bn(conv33, name='batch_norm18')
relu33 = fluid.layers.relu(bn_scale18)
relu34 = conv(relu33, filter_size=[3, 1], num_filters=128, padding=[8, 0], dilation=[8, 1], name='conv34', act='relu')
conv35 = conv(relu34, filter_size=[1, 3], num_filters=128, padding=[0, 8], dilation=[1, 8], name='conv35')
bn_scale19 = bn(conv35, name='batch_norm19')
add8 = fluid.layers.elementwise_add(x=bn_scale19, y=relu31, name='add8')
relu35 = fluid.layers.relu(add8)
relu36 = conv(relu35, filter_size=[3, 1], num_filters=128, padding=[1, 0], name='conv36', act='relu')
conv37 = conv(relu36, filter_size=[1, 3], num_filters=128, padding=[0, 1], name='conv37')
bn_scale20 = bn(conv37, name='batch_norm20')
relu37 = fluid.layers.relu(bn_scale20)
relu38 = conv(relu37, filter_size=[3, 1], num_filters=128, padding=[16, 0], dilation=[16, 1], name='conv38', act='relu')
conv39 = conv(relu38, filter_size=[1, 3], num_filters=128, padding=[0, 16], dilation=[1, 16], name='conv39')
bn_scale21 = bn(conv39, name='batch_norm21')
add9 = fluid.layers.elementwise_add(x=bn_scale21, y=relu35, name='add9')
relu39 = fluid.layers.relu(add9)
relu40 = conv(relu39, filter_size=[3, 1], num_filters=128, padding=[1, 0], name='conv40', act='relu')
conv41 = conv(relu40, filter_size=[1, 3], num_filters=128, padding=[0, 1], name='conv41')
bn_scale22 = bn(conv41, name='batch_norm22')
relu41 = fluid.layers.relu(bn_scale22)
relu42 = conv(relu41, filter_size=[3, 1], num_filters=128, padding=[2, 0], dilation=[2, 1], name='conv42', act='relu')
conv43 = conv(relu42, filter_size=[1, 3], num_filters=128, padding=[0, 2], dilation=[1, 2], name='conv43')
bn_scale23 = bn(conv43, name='batch_norm23')
add10 = fluid.layers.elementwise_add(x=bn_scale23, y=relu39, name='add10')
relu43 = fluid.layers.relu(add10)
relu44 = conv(relu43, filter_size=[3, 1], num_filters=128, padding=[1, 0], name='conv44', act='relu')
conv45 = conv(relu44, filter_size=[1, 3], num_filters=128, padding=[0, 1], name='conv45')
bn_scale24 = bn(conv45, name='batch_norm24')
relu45 = fluid.layers.relu(bn_scale24)
relu46 = conv(relu45, filter_size=[3, 1], num_filters=128, padding=[4, 0], dilation=[4, 1], name='conv46', act='relu')
conv47 = conv(relu46, filter_size=[1, 3], num_filters=128, padding=[0, 4], dilation=[1, 4], name='conv47')
bn_scale25 = bn(conv47, name='batch_norm25')
add11 = fluid.layers.elementwise_add(x=bn_scale25, y=relu43, name='add11')
relu47 = fluid.layers.relu(add11)
relu48 = conv(relu47, filter_size=[3, 1], num_filters=128, padding=[1, 0], name='conv48', act='relu')
conv49 = conv(relu48, filter_size=[1, 3], num_filters=128, padding=[0, 1], name='conv49')
bn_scale26 = bn(conv49, name='batch_norm26')
relu49 = fluid.layers.relu(bn_scale26)
relu50 = conv(relu49, filter_size=[3, 1], num_filters=128, padding=[8, 0], dilation=[8, 1], name='conv50', act='relu')
conv51 = conv(relu50, filter_size=[1, 3], num_filters=128, padding=[0, 8], dilation=[1, 8], name='conv51')
bn_scale27 = bn(conv51, name='batch_norm27')
add12 = fluid.layers.elementwise_add(x=bn_scale27, y=relu47, name='add12')
relu51 = fluid.layers.relu(add12)
relu52 = conv(relu51, filter_size=[3, 1], num_filters=128, padding=[1, 0], name='conv52', act='relu')
conv53 = conv(relu52, filter_size=[1, 3], num_filters=128, padding=[0, 1], name='conv53')
bn_scale28 = bn(conv53, name='batch_norm28')
relu53 = fluid.layers.relu(bn_scale28)
relu54 = conv(relu53, filter_size=[3, 1], num_filters=128, padding=[16, 0], dilation=[16, 1], name='conv54', act='relu')
conv55 = conv(relu54, filter_size=[1, 3], num_filters=128, padding=[0, 16], dilation=[1, 16], name='conv55')
bn_scale29 = bn(conv55, name='batch_norm29')
add13 = fluid.layers.elementwise_add(x=bn_scale29, y=relu51, name='add13')
relu55 = fluid.layers.relu(add13)
conv_transpose1 = conv_transpose(relu55, filter_size=3, num_filters=64, stride=2, padding=1, name='conv_transpose1')
conv_transpose4 = conv_transpose(relu55, filter_size=3, num_filters=64, stride=2, padding=1, name='conv_transpose4')
bn_scale30 = bn(conv_transpose1, name='batch_norm30')
bn_scale40 = bn(conv_transpose4, name='batch_norm40')
relu56 = fluid.layers.relu(bn_scale30)
relu74 = fluid.layers.relu(bn_scale40)
relu57 = conv(relu56, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv56', act='relu')
relu75 = conv(relu74, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv72', act='relu')
conv57 = conv(relu57, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv57')
conv73 = conv(relu75, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv73')
bn_scale31 = bn(conv57, name='batch_norm31')
bn_scale41 = bn(conv73, name='batch_norm41')
relu58 = fluid.layers.relu(bn_scale31)
relu76 = fluid.layers.relu(bn_scale41)
relu59 = conv(relu58, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv58', act='relu')
relu77 = conv(relu76, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv74', act='relu')
conv59 = conv(relu59, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv59')
conv75 = conv(relu77, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv75')
bn_scale32 = bn(conv59, name='batch_norm32')
bn_scale42 = bn(conv75, name='batch_norm42')
add14 = fluid.layers.elementwise_add(x=bn_scale32, y=relu56, name='add14')
add18 = fluid.layers.elementwise_add(x=bn_scale42, y=relu74, name='add18')
relu60 = fluid.layers.relu(add14)
relu78 = fluid.layers.relu(add18)
relu61 = conv(relu60, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv60', act='relu')
relu79 = conv(relu78, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv76', act='relu')
conv61 = conv(relu61, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv61')
conv77 = conv(relu79, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv77')
bn_scale33 = bn(conv61, name='batch_norm33')
bn_scale43 = bn(conv77, name='batch_norm43')
relu62 = fluid.layers.relu(bn_scale33)
relu80 = fluid.layers.relu(bn_scale43)
relu63 = conv(relu62, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv62', act='relu')
relu81 = conv(relu80, filter_size=[3, 1], num_filters=64, padding=[1, 0], name='conv78', act='relu')
conv63 = conv(relu63, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv63')
conv79 = conv(relu81, filter_size=[1, 3], num_filters=64, padding=[0, 1], name='conv79')
bn_scale34 = bn(conv63, name='batch_norm34')
bn_scale44 = bn(conv79, name='batch_norm44')
add15 = fluid.layers.elementwise_add(x=bn_scale34, y=relu60, name='add15')
add19 = fluid.layers.elementwise_add(x=bn_scale44, y=relu78, name='add19')
relu64 = fluid.layers.relu(add15)
relu82 = fluid.layers.relu(add19)
conv_transpose2 = conv_transpose(relu64, filter_size=3, num_filters=16, stride=2, padding=1, name='conv_transpose2')
conv_transpose5 = conv_transpose(relu82, filter_size=3, num_filters=16, stride=2, padding=1, name='conv_transpose5')
bn_scale35 = bn(conv_transpose2, name='batch_norm35')
bn_scale45 = bn(conv_transpose5, name='batch_norm45')
relu65 = fluid.layers.relu(bn_scale35)
relu83 = fluid.layers.relu(bn_scale45)
relu66 = conv(relu65, filter_size=[3, 1], num_filters=16, padding=[1, 0], name='conv64', act='relu')
relu84 = conv(relu83, filter_size=[3, 1], num_filters=16, padding=[1, 0], name='conv80', act='relu')
conv65 = conv(relu66, filter_size=[1, 3], num_filters=16, padding=[0, 1], name='conv65')
conv81 = conv(relu84, filter_size=[1, 3], num_filters=16, padding=[0, 1], name='conv81')
bn_scale36 = bn(conv65, name='batch_norm36')
bn_scale46 = bn(conv81, name='batch_norm46')
relu67 = fluid.layers.relu(bn_scale36)
relu85 = fluid.layers.relu(bn_scale46)
relu68 = conv(relu67, filter_size=[3, 1], num_filters=16, padding=[1, 0], name='conv66', act='relu')
relu86 = conv(relu85, filter_size=[3, 1], num_filters=16, padding=[1, 0], name='conv82', act='relu')
conv67 = conv(relu68, filter_size=[1, 3], num_filters=16, padding=[0, 1], name='conv67')
conv83 = conv(relu86, filter_size=[1, 3], num_filters=16, padding=[0, 1], name='conv83')
bn_scale37 = bn(conv67, name='batch_norm37')
bn_scale47 = bn(conv83, name='batch_norm47')
add16 = fluid.layers.elementwise_add(x=bn_scale37, y=relu65, name='add16')
add20 = fluid.layers.elementwise_add(x=bn_scale47, y=relu83, name='add20')
relu69 = fluid.layers.relu(add16)
relu87 = fluid.layers.relu(add20)
relu70 = conv(relu69, filter_size=[3, 1], num_filters=16, padding=[1, 0], name='conv68', act='relu')
relu88 = conv(relu87, filter_size=[3, 1], num_filters=16, padding=[1, 0], name='conv84', act='relu')
conv69 = conv(relu70, filter_size=[1, 3], num_filters=16, padding=[0, 1], name='conv69')
conv85 = conv(relu88, filter_size=[1, 3], num_filters=16, padding=[0, 1], name='conv85')
bn_scale38 = bn(conv69, name='batch_norm38')
bn_scale48 = bn(conv85, name='batch_norm48')
relu71 = fluid.layers.relu(bn_scale38)
relu89 = fluid.layers.relu(bn_scale48)
relu72 = conv(relu71, filter_size=[3, 1], num_filters=16, padding=[1, 0], name='conv70', act='relu')
relu90 = conv(relu89, filter_size=[3, 1], num_filters=16, padding=[1, 0], name='conv86', act='relu')
conv71 = conv(relu72, filter_size=[1, 3], num_filters=16, padding=[0, 1], name='conv71')
conv87 = conv(relu90, filter_size=[1, 3], num_filters=16, padding=[0, 1], name='conv87')
bn_scale39 = bn(conv71, name='batch_norm39')
bn_scale49 = bn(conv87, name='batch_norm49')
add17 = fluid.layers.elementwise_add(x=bn_scale39, y=relu69, name='add17')
add21 = fluid.layers.elementwise_add(x=bn_scale49, y=relu87, name='add21')
relu73 = fluid.layers.relu(add17)
relu91 = fluid.layers.relu(add21)
conv_transpose3 = conv_transpose(relu73, filter_size=2, num_filters=4, stride=2, name='conv_transpose3')
conv_transpose6 = conv_transpose(relu91, filter_size=2, num_filters=1, stride=2, name='conv_transpose6')
cat4 = fluid.layers.concat([conv_transpose3, conv_transpose6], axis=1, name='cat4')
return cat4
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