test my images error

[lfdeep]

How to test my images?

[EchoAmor]

@lfdeep have u solved this problem? I want to test my own pictures too .

[lfdeep]

I write inference.py according to author's test code. But when visualize, the author code is a bit wrong, and now I am debugging the code in the visualization section.

@lfdeep have u solved this problem? I want to test my own pictures too .

[EchoAmor]

will u upload your code when you solve this problem ? I am not so good at coding now, I would very appreciate it if u can share ! Thanks very much ! @lfdeep

[lfdeep]

will u upload your code when you solve this problem ? I am not so good at coding now, I would very appreciate it if u can share ! Thanks very much ! @lfdeep

Yes,Can you give me your contact information?

[EchoAmor]

@lfdeep yeah my email is echocw@mail.sim.ac.cn, thanks very much!

[dipanjan06]

@lfdeep , I am also trying to test on my image. It would be helpful if you share your inference code at dipanjan06@gmail.com

[lfdeep]

@lfdeep , I am also trying to test on my image. It would be helpful if you share your inference code at dipanjan06@gmail.com

hello，I will sort out the inference and visualization code and send it to you.

[gaussiangit]

@lfdeep Hey man can you share link to your inference code, I am trying one myself facing some difficulties.

[ShiweiJin]

@lfdeep , I met with the same problem here. Could you please share your inference and visualization code? It would be really appreciated. My email is fishswayking@gmail.com. Thank you in advance.

[lfdeep]

@lfdeep Hey man can you share link to your inference code, I am trying one myself facing some difficulties.

Can you give me your email?

[gaussiangit]

@lfdeep iamgaussian@hotmail.com Thanks

[mumuyanyan]

@lfdeep muyanshuimuyanshui@163.com Thank you very much

[phongnhhn92]

@lfdeep phongnhhn92@gmail.com Can you please send me the inference code as well ? Thanks a lot.

[manueldiduch]

Hi @lfdeep, Could you send me your inference code, please? Thank you so much. manuel.diduchi@gmail.com

[gaussiangit]

@lfdeep any update ? thanks

[6216]

@lfdeep hi，I met the same problem, could you share the code with me, please? Thanks very much ！jiaqiwang_jq@163.com

[AI-liu]

import os import torch import torch.nn as nn import argparse import cv2 import numpy as np

from upsnet.config.config import * from upsnet.config.parse_args import parse_args

from upsnet.models import *

from PIL import Image, ImageDraw

def get_pallete():

pallete_raw = np.zeros((256, 3)).astype('uint8')
pallete = np.zeros((256, 3)).astype('uint8')

pallete_raw[5, :] =  [111,  74,   0]
pallete_raw[6, :] =  [ 81,   0,  81]
pallete_raw[7, :] =  [128,  64, 128]
pallete_raw[8, :] =  [244,  35, 232]
pallete_raw[9, :] =  [250, 170, 160]
pallete_raw[10, :] = [230, 150, 140]
pallete_raw[11, :] = [ 70,  70,  70]
pallete_raw[12, :] = [102, 102, 156]
pallete_raw[13, :] = [190, 153, 153]
pallete_raw[14, :] = [180, 165, 180]
pallete_raw[15, :] = [150, 100, 100]
pallete_raw[16, :] = [150, 120,  90]
pallete_raw[17, :] = [153, 153, 153]
pallete_raw[18, :] = [153, 153, 153]
pallete_raw[19, :] = [250, 170,  30]
pallete_raw[20, :] = [220, 220,   0]
pallete_raw[21, :] = [107, 142,  35]
pallete_raw[22, :] = [152, 251, 152]
pallete_raw[23, :] = [ 70, 130, 180]
pallete_raw[24, :] = [220,  20,  60]
pallete_raw[25, :] = [255,   0,   0]
pallete_raw[26, :] = [  0,   0, 142]
pallete_raw[27, :] = [  0,   0,  70]
pallete_raw[28, :] = [  0,  60, 100]
pallete_raw[29, :] = [  0,   0,  90]
pallete_raw[30, :] = [  0,   0, 110]
pallete_raw[31, :] = [  0,  80, 100]
pallete_raw[32, :] = [  0,   0, 230]
pallete_raw[33, :] = [119,  11,  32]

train2regular = [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33]

for i in range(len(train2regular)):
    pallete[i, :] = pallete_raw[train2regular[i], :]

pallete = pallete.reshape(-1)

# return pallete_raw
return pallete

parser = argparse.ArgumentParser() args, rest = parser.parse_known_args() args.cfg = "/home/cicv/UPSNet-master/upsnet/experiments/upsnet_resnet50_cityscapes_16gpu.yaml" args.weight_path = "/home/cicv/UPSNet-master/model/upsnet_resnet_50_cityscapes_12000.pth" args.eval_only = "Ture" update_config(args.cfg)

test_model = eval("resnet_50_upsnet")().cuda() test_model.load_state_dict(torch.load(args.weight_path))

print(test_model)

for p in test_model.parameters(): p.requires_grad = False

test_model.eval()

im = cv2.imread("lindau_000000_000019_leftImg8bit.png") im_resize = cv2.resize(im,(2048,1024),interpolation=cv2.INTER_CUBIC) im_resize = im_resize.transpose(2, 0, 1)

im_tensor = torch.from_numpy(im_resize) im_tensor =torch.unsqueeze(im_tensor,0).type(torch.FloatTensor).cuda() print(im_tensor.shape) # torch.Size([1, 3, 1024, 2048])

test_fake_numpy_data = np.random.rand(1,3) data = {'data': im_tensor , 'im_info' : test_fake_numpy_data} print(data['im_info']) output = test_model(data)

print(output)

print(output['fcn_outputs'])

pallete = get_pallete() segmentation_result = np.uint8(np.squeeze(np.copy(output['fcn_outputs']))) segmentation_result = Image.fromarray(segmentation_result) segmentation_result.putpalette(pallete) segmentation_result = segmentation_result.resize((im.shape[1],im.shape[0])) segmentation_result.save("hello_result.png")

[AI-liu]

you can change to your image. im = cv2.imread("lindau_000000_000019_leftImg8bit.png")

im = cv2.imread("your_imgae_XXXX.png")

------------------ 原始邮件 ------------------ 发件人: "lfdeep"notifications@github.com; 发送时间: 2019年7月19日(星期五) 上午10:11 收件人: "uber-research/UPSNet"UPSNet@noreply.github.com; 抄送: "136758759"136758759@qq.com;"Comment"comment@noreply.github.com; 主题: Re: [uber-research/UPSNet] test my images error (#56)

import os import torch import torch.nn as nn import argparse import cv2 import numpy as np

from upsnet.config.config import * from upsnet.config.parse_args import parse_args

from upsnet.models import *

from PIL import Image, ImageDraw

def get_pallete(): pallete_raw = np.zeros((256, 3)).astype('uint8') pallete = np.zeros((256, 3)).astype('uint8') pallete_raw[5, :] = [111, 74, 0] pallete_raw[6, :] = [ 81, 0, 81] pallete_raw[7, :] = [128, 64, 128] pallete_raw[8, :] = [244, 35, 232] pallete_raw[9, :] = [250, 170, 160] pallete_raw[10, :] = [230, 150, 140] pallete_raw[11, :] = [ 70, 70, 70] pallete_raw[12, :] = [102, 102, 156] pallete_raw[13, :] = [190, 153, 153] pallete_raw[14, :] = [180, 165, 180] pallete_raw[15, :] = [150, 100, 100] pallete_raw[16, :] = [150, 120, 90] pallete_raw[17, :] = [153, 153, 153] pallete_raw[18, :] = [153, 153, 153] pallete_raw[19, :] = [250, 170, 30] pallete_raw[20, :] = [220, 220, 0] pallete_raw[21, :] = [107, 142, 35] pallete_raw[22, :] = [152, 251, 152] pallete_raw[23, :] = [ 70, 130, 180] pallete_raw[24, :] = [220, 20, 60] pallete_raw[25, :] = [255, 0, 0] pallete_raw[26, :] = [ 0, 0, 142] pallete_raw[27, :] = [ 0, 0, 70] pallete_raw[28, :] = [ 0, 60, 100] pallete_raw[29, :] = [ 0, 0, 90] pallete_raw[30, :] = [ 0, 0, 110] pallete_raw[31, :] = [ 0, 80, 100] pallete_raw[32, :] = [ 0, 0, 230] pallete_raw[33, :] = [119, 11, 32] train2regular = [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33] for i in range(len(train2regular)): pallete[i, :] = pallete_raw[train2regular[i], :] pallete = pallete.reshape(-1) # return pallete_raw return pallete
parser = argparse.ArgumentParser() args, rest = parser.parse_known_args() args.cfg = "/home/cicv/UPSNet-master/upsnet/experiments/upsnet_resnet50_cityscapes_16gpu.yaml" args.weight_path = "/home/cicv/UPSNet-master/model/upsnet_resnet_50_cityscapes_12000.pth" args.eval_only = "Ture" update_config(args.cfg)

test_model = eval("resnet_50_upsnet")().cuda() test_model.load_state_dict(torch.load(args.weight_path))

print(test_model)

for p in test_model.parameters(): p.requires_grad = False

test_model.eval()

im = cv2.imread("lindau_000000_000019_leftImg8bit.png") im_resize = cv2.resize(im,(2048,1024),interpolation=cv2.INTER_CUBIC) im_resize = im_resize.transpose(2, 0, 1)

im_tensor = torch.from_numpy(im_resize) im_tensor =torch.unsqueeze(im_tensor,0).type(torch.FloatTensor).cuda() print(im_tensor.shape) # torch.Size([1, 3, 1024, 2048])

test_fake_numpy_data = np.random.rand(1,3) data = {'data': im_tensor , 'im_info' : test_fake_numpy_data} print(data['im_info']) output = test_model(data)

print(output)

print(output['fcn_outputs'])

pallete = get_pallete() segmentation_result = np.uint8(np.squeeze(np.copy(output['fcn_outputs']))) segmentation_result = Image.fromarray(segmentation_result) segmentation_result.putpalette(pallete) segmentation_result = segmentation_result.resize((im.shape[1],im.shape[0])) segmentation_result.save("hello_result.png")

this only test cityscaspes model!

— You are receiving this because you commented. Reply to this email directly, view it on GitHub, or mute the thread.

[lfdeep]

you can change to your image. im = cv2.imread("lindau_000000_000019_leftImg8bit.png") im = cv2.imread("your_imgae_XXXX.png") … ------------------ 原始邮件 ------------------ 发件人: "lfdeep"notifications@github.com; 发送时间: 2019年7月19日(星期五) 上午10:11 收件人: "uber-research/UPSNet"UPSNet@noreply.github.com; 抄送: "136758759"136758759@qq.com;"Comment"comment@noreply.github.com; 主题: Re: [uber-research/UPSNet] test my images error (#56) import os import torch import torch.nn as nn import argparse import cv2 import numpy as np from upsnet.config.config import from upsnet.config.parse_args import parse_args from upsnet.models import from PIL import Image, ImageDraw def get_pallete(): pallete_raw = np.zeros((256, 3)).astype('uint8') pallete = np.zeros((256, 3)).astype('uint8') pallete_raw[5, :] = [111, 74, 0] pallete_raw[6, :] = [ 81, 0, 81] pallete_raw[7, :] = [128, 64, 128] pallete_raw[8, :] = [244, 35, 232] pallete_raw[9, :] = [250, 170, 160] pallete_raw[10, :] = [230, 150, 140] pallete_raw[11, :] = [ 70, 70, 70] pallete_raw[12, :] = [102, 102, 156] pallete_raw[13, :] = [190, 153, 153] pallete_raw[14, :] = [180, 165, 180] pallete_raw[15, :] = [150, 100, 100] pallete_raw[16, :] = [150, 120, 90] pallete_raw[17, :] = [153, 153, 153] pallete_raw[18, :] = [153, 153, 153] pallete_raw[19, :] = [250, 170, 30] pallete_raw[20, :] = [220, 220, 0] pallete_raw[21, :] = [107, 142, 35] pallete_raw[22, :] = [152, 251, 152] pallete_raw[23, :] = [ 70, 130, 180] pallete_raw[24, :] = [220, 20, 60] pallete_raw[25, :] = [255, 0, 0] pallete_raw[26, :] = [ 0, 0, 142] pallete_raw[27, :] = [ 0, 0, 70] pallete_raw[28, :] = [ 0, 60, 100] pallete_raw[29, :] = [ 0, 0, 90] pallete_raw[30, :] = [ 0, 0, 110] pallete_raw[31, :] = [ 0, 80, 100] pallete_raw[32, :] = [ 0, 0, 230] pallete_raw[33, :] = [119, 11, 32] train2regular = [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33] for i in range(len(train2regular)): pallete[i, :] = pallete_raw[train2regular[i], :] pallete = pallete.reshape(-1) # return pallete_raw return pallete parser = argparse.ArgumentParser() args, rest = parser.parse_known_args() args.cfg = "/home/cicv/UPSNet-master/upsnet/experiments/upsnet_resnet50_cityscapes_16gpu.yaml" args.weight_path = "/home/cicv/UPSNet-master/model/upsnet_resnet_50_cityscapes_12000.pth" args.eval_only = "Ture" update_config(args.cfg) test_model = eval("resnet_50_upsnet")().cuda() test_model.load_state_dict(torch.load(args.weight_path)) #print(test_model) for p in test_model.parameters(): p.requires_grad = False test_model.eval() im = cv2.imread("lindau_000000_000019_leftImg8bit.png") im_resize = cv2.resize(im,(2048,1024),interpolation=cv2.INTER_CUBIC) im_resize = im_resize.transpose(2, 0, 1) im_tensor = torch.from_numpy(im_resize) im_tensor =torch.unsqueeze(im_tensor,0).type(torch.FloatTensor).cuda() print(im_tensor.shape) # torch.Size([1, 3, 1024, 2048]) test_fake_numpy_data = np.random.rand(1,3) data = {'data': im_tensor , 'im_info' : test_fake_numpy_data} print(data['im_info']) output = test_model(data) #print(output) print(output['fcn_outputs']) pallete = get_pallete() segmentation_result = np.uint8(np.squeeze(np.copy(output['fcn_outputs']))) segmentation_result = Image.fromarray(segmentation_result) segmentation_result.putpalette(pallete) segmentation_result = segmentation_result.resize((im.shape[1],im.shape[0])) segmentation_result.save("hello_result.png") this only test cityscaspes model! — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or mute the thread.

thanks! this code only test cityscapes model! Classes of cityscapes is less than coco. i have soved this problem and can test coco model.

[6216]

thanks for your code.Thanks so much.

	王佳琪

邮箱：jiaqiwang_jq@163.com |

签名由 网易邮箱大师 定制

On 07/19/2019 18:21, lfdeep wrote:

you can change to your image. im = cv2.imread("lindau_000000_000019_leftImg8bit.png") im = cv2.imread("your_imgae_XXXX.png") … ------------------ 原始邮件 ------------------ 发件人: "lfdeep"notifications@github.com; 发送时间: 2019年7月19日(星期五) 上午10:11 收件人: "uber-research/UPSNet"UPSNet@noreply.github.com; 抄送: "136758759"136758759@qq.com;"Comment"comment@noreply.github.com; 主题: Re: [uber-research/UPSNet] test my images error (#56) import os import torch import torch.nn as nn import argparse import cv2 import numpy as np from upsnet.config.config import from upsnet.config.parse_args import parse_args from upsnet.models import from PIL import Image, ImageDraw def get_pallete(): pallete_raw = np.zeros((256, 3)).astype('uint8') pallete = np.zeros((256, 3)).astype('uint8') pallete_raw[5, :] = [111, 74, 0] pallete_raw[6, :] = [ 81, 0, 81] pallete_raw[7, :] = [128, 64, 128] pallete_raw[8, :] = [244, 35, 232] pallete_raw[9, :] = [250, 170, 160] pallete_raw[10, :] = [230, 150, 140] pallete_raw[11, :] = [ 70, 70, 70] pallete_raw[12, :] = [102, 102, 156] pallete_raw[13, :] = [190, 153, 153] pallete_raw[14, :] = [180, 165, 180] pallete_raw[15, :] = [150, 100, 100] pallete_raw[16, :] = [150, 120, 90] pallete_raw[17, :] = [153, 153, 153] pallete_raw[18, :] = [153, 153, 153] pallete_raw[19, :] = [250, 170, 30] pallete_raw[20, :] = [220, 220, 0] pallete_raw[21, :] = [107, 142, 35] pallete_raw[22, :] = [152, 251, 152] pallete_raw[23, :] = [ 70, 130, 180] pallete_raw[24, :] = [220, 20, 60] pallete_raw[25, :] = [255, 0, 0] pallete_raw[26, :] = [ 0, 0, 142] pallete_raw[27, :] = [ 0, 0, 70] pallete_raw[28, :] = [ 0, 60, 100] pallete_raw[29, :] = [ 0, 0, 90] pallete_raw[30, :] = [ 0, 0, 110] pallete_raw[31, :] = [ 0, 80, 100] pallete_raw[32, :] = [ 0, 0, 230] pallete_raw[33, :] = [119, 11, 32] train2regular = [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33] for i in range(len(train2regular)): pallete[i, :] = pallete_raw[train2regular[i], :] pallete = pallete.reshape(-1) # return pallete_raw return pallete parser = argparse.ArgumentParser() args, rest = parser.parse_known_args() args.cfg = "/home/cicv/UPSNet-master/upsnet/experiments/upsnet_resnet50_cityscapes_16gpu.yaml" args.weight_path = "/home/cicv/UPSNet-master/model/upsnet_resnet_50_cityscapes_12000.pth" args.eval_only = "Ture" update_config(args.cfg) test_model = eval("resnet_50_upsnet")().cuda() test_model.load_state_dict(torch.load(args.weight_path)) #print(test_model) for p in test_model.parameters(): p.requires_grad = False test_model.eval() im = cv2.imread("lindau_000000_000019_leftImg8bit.png") im_resize = cv2.resize(im,(2048,1024),interpolation=cv2.INTER_CUBIC) im_resize = im_resize.transpose(2, 0, 1) im_tensor = torch.from_numpy(im_resize) im_tensor =torch.unsqueeze(im_tensor,0).type(torch.FloatTensor).cuda() print(im_tensor.shape) # torch.Size([1, 3, 1024, 2048]) test_fake_numpy_data = np.random.rand(1,3) data = {'data': im_tensor , 'im_info' : test_fake_numpy_data} print(data['im_info']) output = test_model(data) #print(output) print(output['fcn_outputs']) pallete = get_pallete() segmentation_result = np.uint8(np.squeeze(np.copy(output['fcn_outputs']))) segmentation_result = Image.fromarray(segmentation_result) segmentation_result.putpalette(pallete) segmentation_result = segmentation_result.resize((im.shape[1],im.shape[0])) segmentation_result.save("hello_result.png") this only test cityscaspes model! — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or mute the thread.

thanks! this code only test cityscapes model! Classes of cityscapes is less than coco. i have soved this problem and can test coco model.

— You are receiving this because you commented. Reply to this email directly, view it on GitHub, or mute the thread.

[jshi31]

@lfdeep , I am also trying to test on my image. It would be helpful if you share your inference code at jingshi0521@gmail.com

[SunNYNO1]

@lfdeep , i am facing the same problem, could you share your inference code for coco dataset for me? thank you very much. my email : sunnyno231@gmail.com

[lfdeep]

sunnyno231@gmail.com

Already sent

[discretecoder]

@lfdeep could you send me as well ? my email id is discreetcoder@gmail.com thanks

[lfdeep]

@lfdeep could you send me as well ? my email id is discreetcoder@gmail.com thanks

Already sent

[SunNYNO1]

sunnyno231@gmail.com

Already sent

Sorry, I didn't receive your email, could you send me again? please send to this email：1246680611@qq.com, thank you very much.

[WindAndCloud]

@lfdeep could you send me as well ? my email id is 1271595926@qq.com thank you very much ~-~

[Scharduldeshpande]

@lfdeep thanks, could you send me as well? my email id is shardul.p.deshpande@gmail.com

[ywher]

@lfdeep could you send me as well? my email id is 958118196@qq.com. thanks a lot!

[squashking]

@lfdeep could you send me a copy of your code for panoptic segmentation as well? litianyou1988@gmail.com Thanks a lot !

[squashking]

import os import torch import torch.nn as nn import argparse import cv2 import numpy as np

from upsnet.config.config import * from upsnet.config.parse_args import parse_args

from upsnet.models import *

from PIL import Image, ImageDraw

def get_pallete():

pallete_raw = np.zeros((256, 3)).astype('uint8')
pallete = np.zeros((256, 3)).astype('uint8')

pallete_raw[5, :] =  [111,  74,   0]
pallete_raw[6, :] =  [ 81,   0,  81]
pallete_raw[7, :] =  [128,  64, 128]
pallete_raw[8, :] =  [244,  35, 232]
pallete_raw[9, :] =  [250, 170, 160]
pallete_raw[10, :] = [230, 150, 140]
pallete_raw[11, :] = [ 70,  70,  70]
pallete_raw[12, :] = [102, 102, 156]
pallete_raw[13, :] = [190, 153, 153]
pallete_raw[14, :] = [180, 165, 180]
pallete_raw[15, :] = [150, 100, 100]
pallete_raw[16, :] = [150, 120,  90]
pallete_raw[17, :] = [153, 153, 153]
pallete_raw[18, :] = [153, 153, 153]
pallete_raw[19, :] = [250, 170,  30]
pallete_raw[20, :] = [220, 220,   0]
pallete_raw[21, :] = [107, 142,  35]
pallete_raw[22, :] = [152, 251, 152]
pallete_raw[23, :] = [ 70, 130, 180]
pallete_raw[24, :] = [220,  20,  60]
pallete_raw[25, :] = [255,   0,   0]
pallete_raw[26, :] = [  0,   0, 142]
pallete_raw[27, :] = [  0,   0,  70]
pallete_raw[28, :] = [  0,  60, 100]
pallete_raw[29, :] = [  0,   0,  90]
pallete_raw[30, :] = [  0,   0, 110]
pallete_raw[31, :] = [  0,  80, 100]
pallete_raw[32, :] = [  0,   0, 230]
pallete_raw[33, :] = [119,  11,  32]

train2regular = [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33]

for i in range(len(train2regular)):
    pallete[i, :] = pallete_raw[train2regular[i], :]

pallete = pallete.reshape(-1)

# return pallete_raw
return pallete

parser = argparse.ArgumentParser() args, rest = parser.parse_known_args() args.cfg = "/home/cicv/UPSNet-master/upsnet/experiments/upsnet_resnet50_cityscapes_16gpu.yaml" args.weight_path = "/home/cicv/UPSNet-master/model/upsnet_resnet_50_cityscapes_12000.pth" args.eval_only = "Ture" update_config(args.cfg)

test_model = eval("resnet_50_upsnet")().cuda() test_model.load_state_dict(torch.load(args.weight_path))

print(test_model)

for p in test_model.parameters(): p.requires_grad = False

test_model.eval()

im = cv2.imread("lindau_000000_000019_leftImg8bit.png") im_resize = cv2.resize(im,(2048,1024),interpolation=cv2.INTER_CUBIC) im_resize = im_resize.transpose(2, 0, 1)

im_tensor = torch.from_numpy(im_resize) im_tensor =torch.unsqueeze(im_tensor,0).type(torch.FloatTensor).cuda() print(im_tensor.shape) # torch.Size([1, 3, 1024, 2048])

test_fake_numpy_data = np.random.rand(1,3) data = {'data': im_tensor , 'im_info' : test_fake_numpy_data} print(data['im_info']) output = test_model(data)

print(output)

print(output['fcn_outputs'])

pallete = get_pallete() segmentation_result = np.uint8(np.squeeze(np.copy(output['fcn_outputs']))) segmentation_result = Image.fromarray(segmentation_result) segmentation_result.putpalette(pallete) segmentation_result = segmentation_result.resize((im.shape[1],im.shape[0])) segmentation_result.save("hello_result.png")

@AI-liu I have tested your code but it seems not for panoptic segmentation. Attached is the result I got for an outdoor image. I have also tried replace output['fcn_outputs'] with output['panoptic_outputs'], but the result is the same. Do you have code for panoptic segmentation? Thanks a lot!

[sunnyheart008205]

@lfdeep Could you please send your test code for coco model? Thanks very much. My email address is sxj_njust@163.com

[choodly]

@lfdeep Could you send me your inference code for coco dataset? My email is qdly0406@163.com . Thank you very much.

[t-mm-dh]

@lfdeep Could you send me your inference code for coco dataset? My email is 857391372@qq.com . Thank you very much.

[Skywalker666666]

@lfdeep can you send me your demo for coco dataset? very interested in how to test single image for coco chinayzleo@126.com

[advancenOO]

@lfdeep Could you please send me your code for cityscapes? My email is aidaqian126@163.com Thanks a lot.

[radarjonathan]

Could someone post the code for the coco dataset? Would help a lot!!

[gedaye11]

I write inference.py according to author's test code. But when visualize, the author code is a bit wrong, and now I am debugging the code in the visualization section.

@lfdeep have u solved this problem? I want to test my own pictures too .

Hi @lfdeep Could you please send me your visualization code for COCO? My email is 909915571@qq.com.