Comments (17)
Looks like it's working! Basically a few changes need to be made:
- As @bharatsingh430 mentioned, copy the content of multi_proposal.cu to multi_proposal.cc
- remove cuda dependencies such as #include <mshadow/cuda/reduce.cuh>
- make necessary changes to create CPU tensor rather than GPU tensor (replace all
Tensor<gpu
withTensor<cpu
) - replace
cudaMemcpy
withmemcpy
I put the file (multi_proposal.cc) in a public gist so people can refer to it as needed:
https://gist.github.com/xiaoyongzhu/4ae8d9df580c84e33157ff8d68f9ce89
from sniper.
if you write the proposal layer in cpu (not sure if we wrote it, may be its there), it should work
from sniper.
@bharatsingh430 sorry you mean the (soft) NMS part? Or the RPN part?
from sniper.
RPN part
from sniper.
ok, i checked, that layer has CPU support, it should work
from sniper.
Cool - will try it out and let you know the result!
from sniper.
@bharatsingh430 Thanks for the response, unfortunately I tried to run the code on CPU but there's no detection at all for the demo image in the repository (I tried to print out the predictions for the CPU inference and GPU inference, and they look very different). Here's what I've done:
Basically I've re-compiled the SNIPER-mxnet code with the following changes:
- Since the deformable_im2col operator is not available on CPU, I need to follow the issue mentioned in the Deformable-RCNN repository (msracver/Deformable-ConvNets#124), and replace three files: src/operator/contrib/deformable_psroi_pooling.cc, src/operator/contrib/nn/deformable_im2col.h, src/operator/contrib/psroi_pooling.cc
- I've done this for Deformable-RCNN repository with the above file changes in order to run Deformable-RCNN using CPU, and the result looks good - so I think this proves that the above three pieces of code is bug free (BTW when I was doing this, I was using mxnet from the master branch, which should be newer than the forked one in SNIPER-mxnet repo)
- I've also modified src/operator/contrib/nn/deformable_im2col.cuh file, since during compilation there's a name conflict. Basically I've renamed
get_gradient_weight
toget_gradient_weight_cuda
to avoid the name conflict. - in the configuration files, I changed
fp16: true
tofalse
, since otherwise it won't go thru. - other than the above changes, the only other change I made was in the
demo.py
file to switch between CPU/GPU:
if gpu_only:
context = [mx.gpu(int(config.gpus[0]))]
else:
context = [mx.cpu()]
- another interesting phenomenon is that when I was trying to compile Deformable-RCNN code in CPU, I didn't encounter the
get_gradient_weight
name conflict. Although I am doing the almost same changes for SNIPER-mxnet, I encounteredget_gradient_weight
name conflict. Not sure if this is related.
Since I am still new to mxnet, I am not sure what are the correct ways to debug this? Also, any insights regarding this?
I've also attached all the changes I've made in the zip file below in case you are interested in. Any pointers will be appreciated!
from sniper.
can you try to compare the conv5 dumps for CPU and GPU versions, if they are not the same then probably something is not correct in the deformable convolution layers. If it is the same, then I can check the proposal layer once more, and probably the deformable roi pooling layers.
from sniper.
Thanks @bharatsingh430 , however I am not sure how should I dump the value for a certain layer during forward pass (sorry still new to mxnet)?
I've tried to dump the output after the forward pass for both the cpu and the gpu version, in case there's some interesting finding... As you can see, basically for the cpu version, it classifies everything as background (with a very high confidence of 1.0...)
from sniper.
I've tried a few methods to print out the output for resnet conv5 layer (the layer's name is cat4_output
I think). However there are a few output format issue I met in mxnet binding step which I am lost. (For example it gave me errors like this: ValueError: You created Module with Module(..., data_names=['d', 'a', 't', 'a']) but input with name 'd' is not found in symbol.list_arguments(). Did you mean one of: data).
Anyway, I have compiled the SNIPER-mxnet cpu version and put it here. Just a simple pip install should be fine:
https://chestxray.blob.core.windows.net/chestxraytutorial/mxnet-1.2.0-cp27-cp27mu-linux_x86_64.whl
If @bharatsingh430 you can help to dump the output of the cpu version (or give me some guidance) I will appreciate it!
from sniper.
you need to change the symbol, mx.sym.Group add the layer's output you want to dump and then you can access it in the output which the network generates...
from sniper.
Thanks @bharatsingh430 , and I've output the two layers (relut and relu1) for both CPU and GPU, and they are the same. So I assume there might be something wrong with either the MultiProposal
layer, or the DeformablePSROIPooling
layer. Basically I did the following:
# in get_symbol_rcnn, line 248,249
relut = self.resnetc5(conv_feat, deform=True)
relu1 = mx.symbol.Concat(*[conv_feat, relut], name='cat4')
....
# line 368
group = mx.sym.Group([rois, cls_prob, bbox_pred, im_ids, relut, relu1])
And the result is as below (I only take the first a few lines since the dump files are around 500~MB.
CPU:
<NDArray 1x2048x88x125 @cpu(0)>, 'cat4_output':
[[[[-0.199 0.493 0.557 0.630 0.259 0.152 0.491 0.544 0.402
0.501 0.020 0.358 0.934 1.098 0.734 0.652 0.519 0.430
0.085 0.117 0.010 -0.238 0.201 0.361 0.354 0.875 0.492
0.445 0.315 0.215 0.373 0.379 0.078 0.077 -0.045 0.110
0.253 0.402 0.515 0.230 0.273 0.667 0.465 0.757 0.510
0.785 0.645 0.430 0.328 -0.303 0.087 0.194 0.410 0.480
0.327 0.187 0.047 0.398 0.490 0.513 0.377 0.410 0.414
0.197 -0.087 -0.067 -0.040 -0.206 -0.323 -0.123 -0.180 0.133
0.351 0.714 0.478 0.056 -0.090 -0.163 -0.205 -0.013 0.186
0.094 0.083 -0.223 0.091 0.334 0.247 0.314 0.028 0.284
0.182 0.303 0.332 -0.238 -0.146 0.118 0.109 0.275 0.252
0.115 0.482 0.252 0.274 0.255 0.034 0.115 0.231 0.204
-0.139 0.137 0.065 0.150 0.198 0.338 0.247 0.109 0.002
-0.003 0.345 0.215 0.333 0.107 -0.223 0.412 -0.013]
[-0.085 0.624 0.563 0.426 0.202 0.136 0.365 0.177 0.040
0.298 0.475 0.432 0.714 0.652 0.562 0.556 0.356 0.217
0.104 0.059 -0.077 -0.135 -0.208 0.072 0.172 0.553 0.384
0.497 0.345 -0.057 0.250 0.631 0.167 -0.225 -0.118 0.068
-0.047 0.387 0.621 0.581 0.435 0.271 0.139 0.640 0.764
0.631 0.558 0.412 -0.087 -0.562 -0.167 -0.042 0.343 0.410
0.224 0.216 0.139 0.338 0.217 0.159 0.121 0.471 0.455
0.124 -0.168 0.006 -0.270 -0.612 -0.306 -0.349 -0.282 -0.028
0.151 0.239 0.310 -0.110 -0.146 -0.045 -0.544 -0.135 0.389
0.393 0.351 0.113 0.246 0.369 0.220 0.004 -0.190 -0.049
-0.178 -0.113 0.152 -0.223 0.082 -0.079 0.032 0.020 0.258
0.096 0.211 0.181 0.133 0.177 -0.130 -0.079 0.304 0.103
0.107 0.385 0.495 0.288 0.342 0.308 0.084 -0.182 -0.127
0.123 0.425 0.180 0.331 0.350 -0.095 0.745 0.073]
[-0.079 0.404 0.186 0.333 -0.033 -0.114 0.300 -0.067 0.035
0.087 0.544 0.347 0.457 0.338 0.222 0.510 0.445 0.546
0.120 0.172 0.203 0.049 -0.225 0.095 -0.021 0.207 0.055
-0.083 -0.185 -0.519 -0.257 0.511 -0.147 -0.401 0.181 0.076
-0.395 -0.328 0.295 0.563 0.447 0.307 -0.016 0.261 0.658
<NDArray 1x300x81 @cpu(0)>, '_plus32_output':
[[[[ 0.097 0.144 0.145 0.051 -0.140 -0.175 -0.100 -0.086 -0.021
-0.096 -0.308 0.024 -0.059 -0.148 -0.102 -0.097 -0.091 0.058
0.099 0.065 -0.036 -0.208 -0.178 -0.130 -0.116 0.072 -0.063
-0.142 -0.113 -0.077 -0.078 -0.087 -0.147 -0.154 -0.018 -0.009
-0.014 -0.052 -0.086 -0.074 0.064 0.049 -0.045 -0.006 -0.006
0.146 0.257 0.110 0.086 0.305 0.239 0.047 0.145 0.333
0.195 0.193 0.264 0.291 0.264 0.076 0.035 0.022 0.200
0.232 0.147 0.127 0.210 0.246 0.204 0.121 0.150 -0.015
0.114 0.067 0.132 0.147 0.215 0.152 0.242 0.092 0.259
0.433 0.406 0.237 0.405 0.480 0.377 0.485 0.563 0.420
0.450 0.464 0.448 0.412 0.551 0.612 0.392 0.282 0.253
0.297 0.146 0.142 0.413 0.337 0.338 0.403 0.407 0.429
0.449 0.435 0.192 0.376 0.340 0.487 0.507 0.356 0.206
-0.031 0.026 0.014 0.212 -0.037 -0.036 -0.160 -0.160]
[-0.166 0.185 0.072 -0.073 0.053 0.115 0.122 0.070 0.177
0.066 -0.017 0.103 0.130 0.219 0.228 0.142 0.001 0.038
0.154 0.261 0.324 0.174 0.071 0.022 0.106 0.075 0.141
0.028 0.036 0.138 0.064 0.064 0.150 0.077 0.156 0.139
0.179 0.039 0.110 0.010 0.005 -0.092 0.033 -0.002 -0.165
0.037 0.073 0.218 0.237 0.182 0.177 0.118 0.166 0.184
0.232 0.335 0.301 0.317 0.170 0.105 0.179 0.208 0.504
0.333 0.429 0.470 0.312 0.399 0.225 0.183 0.263 0.209
0.137 0.200 0.205 0.250 0.271 0.235 0.351 0.399 0.379
0.438 0.409 0.439 0.424 0.460 0.487 0.576 0.468 0.510
0.420 0.487 0.664 0.695 0.429 0.330 0.441 0.597 0.345
0.435 0.424 0.353 0.298 0.387 0.348 0.445 0.597 0.610
0.679 0.586 0.481 0.535 0.492 0.435 0.445 0.349 0.261
0.373 0.341 0.404 0.579 0.314 0.269 0.157 0.126]
[ 0.001 0.063 0.013 0.045 -0.003 -0.012 0.102 0.140 0.163
0.109 -0.081 -0.109 -0.146 0.165 0.049 -0.019 -0.056 -0.065
-0.043 0.158 0.376 0.277 0.308 0.252 0.042 -0.051 0.124
0.129 -0.016 0.038 -0.084 0.030 0.263 0.164 0.284 0.159
0.221 0.016 0.120 -0.099 0.120 0.260 0.211 0.120 -0.014
-0.065 0.122 0.202 0.274 0.118 0.108 0.010 0.143 0.232
0.287 0.340 0.356 0.395 0.306 0.249 0.120 0.208 0.360
0.140 0.378 0.435 0.508 0.388 0.228 0.232 0.116 0.268
0.298 0.217 0.169 0.093 0.086 0.196 0.316 0.376 0.402
0.521 0.495 0.426 0.414 0.503 0.379 0.320 0.249 0.109
0.229 0.161 0.277 0.447 0.519 0.382 0.149 0.324 0.376
0.439 0.337 0.256 0.307 0.227 0.238 0.340 0.531 0.452
0.465 0.408 0.312 0.273 0.352 0.235 0.100 0.160 0.224
0.333 0.211 0.260 0.201 0.389 0.457 0.276 0.298]
[-0.068 0.148 0.094 0.063 0.048 0.095 0.109 0.126 0.180
0.016 0.032 0.030 0.020 0.241 0.167 0.181 0.015 0.115
0.225 0.243 0.327 0.343 0.323 0.316 0.139 0.027 0.033
-0.030 -0.063 -0.043 -0.002 0.007 0.190 0.169 0.180 0.116
0.117 0.211 0.086 0.097 0.088 0.187 0.175 0.117 0.064
0.036 0.212 0.134 -0.045 -0.201 0.033 0.197 0.280 0.177
0.136 0.353 0.316 0.294 0.336 0.244 0.177 0.104 0.203
0.177 0.310 0.453 0.515 0.408 0.240 0.181 0.198 0.216
0.112 0.109 0.111 0.116 0.144 0.248 0.218 0.271 0.307
0.427 0.450 0.401 0.405 0.367 0.302 0.424 0.459 0.359
0.238 0.328 0.518 0.510 0.423 0.372 0.323 0.513 0.378
0.250 0.168 0.381 0.384 0.365 0.214 0.269 0.254 0.261
0.344 0.333 0.264 0.228 0.346 0.270 0.257 0.278 0.231
0.242 0.306 0.331 0.382 0.439 0.582 0.377 0.300]
[-0.166 0.031 -0.018 -0.040 0.170 0.077 0.069 0.077 0.022
0.072 -0.009 0.079 0.204 0.219 0.315 0.284 0.229 0.232
0.233 0.299 0.254 0.408 0.308 0.176 0.013 0.087 0.006
-0.032 -0.138 -0.014 0.024 0.050 0.244 0.131 0.130 -0.005
-0.080 0.039 -0.139 0.057 0.230 0.253 0.451 0.225 0.347
0.250 0.357 0.250 -0.050 -0.046 0.080 0.373 0.441 0.343
0.286 0.335 0.415 0.476 0.536 0.292 0.085 0.254 0.307
0.328 0.350 0.602 0.524 0.481 0.291 0.242 0.176 0.200
GPU output
<NDArray 1x2048x88x125 @gpu(0)>, 'cat4_output':
[[[[-0.199 0.493 0.557 0.630 0.259 0.152 0.491 0.544 0.402
0.501 0.020 0.358 0.934 1.098 0.734 0.652 0.519 0.430
0.085 0.117 0.010 -0.238 0.201 0.361 0.354 0.875 0.492
0.445 0.315 0.215 0.373 0.379 0.078 0.077 -0.045 0.110
0.253 0.402 0.515 0.230 0.273 0.667 0.465 0.757 0.510
0.785 0.645 0.430 0.328 -0.303 0.087 0.194 0.410 0.480
0.327 0.187 0.047 0.398 0.490 0.513 0.377 0.410 0.414
0.197 -0.087 -0.067 -0.040 -0.206 -0.323 -0.123 -0.180 0.133
0.351 0.714 0.478 0.056 -0.090 -0.163 -0.205 -0.013 0.186
0.094 0.083 -0.223 0.091 0.334 0.247 0.314 0.028 0.284
0.182 0.303 0.332 -0.238 -0.146 0.118 0.109 0.275 0.252
0.115 0.482 0.252 0.274 0.255 0.034 0.115 0.231 0.204
-0.139 0.137 0.065 0.150 0.198 0.338 0.247 0.109 0.002
-0.003 0.345 0.215 0.333 0.107 -0.223 0.412 -0.013]
[-0.085 0.624 0.563 0.426 0.202 0.136 0.365 0.177 0.040
0.298 0.475 0.432 0.714 0.652 0.562 0.556 0.356 0.217
0.104 0.059 -0.077 -0.135 -0.208 0.072 0.172 0.553 0.384
0.497 0.345 -0.057 0.250 0.631 0.167 -0.225 -0.118 0.068
-0.047 0.387 0.621 0.581 0.435 0.271 0.139 0.640 0.764
0.631 0.558 0.412 -0.087 -0.562 -0.167 -0.042 0.343 0.410
0.224 0.216 0.139 0.338 0.217 0.159 0.121 0.471 0.455
0.124 -0.168 0.006 -0.270 -0.612 -0.306 -0.349 -0.282 -0.028
0.151 0.239 0.310 -0.110 -0.146 -0.045 -0.544 -0.135 0.389
0.393 0.351 0.113 0.246 0.369 0.220 0.004 -0.190 -0.049
-0.178 -0.113 0.152 -0.223 0.082 -0.079 0.032 0.020 0.258
0.096 0.211 0.181 0.133 0.177 -0.130 -0.079 0.304 0.103
0.107 0.385 0.495 0.288 0.342 0.308 0.084 -0.182 -0.127
0.123 0.425 0.180 0.331 0.350 -0.095 0.745 0.073]
[-0.079 0.404 0.186 0.333 -0.033 -0.114 0.300 -0.067 0.035
0.087 0.544 0.347 0.457 0.338 0.222 0.510 0.445 0.546
0.120 0.172 0.203 0.049 -0.225 0.095 -0.021 0.207 0.055
-0.083 -0.185 -0.519 -0.257 0.511 -0.147 -0.401 0.181 0.076
-0.395 -0.328 0.295 0.563 0.447 0.307 -0.016 0.261 0.658
<NDArray 1x300x81 @gpu(0)>, '_plus32_output':
[[[[ 0.097 0.144 0.145 0.051 -0.140 -0.175 -0.100 -0.086 -0.021
-0.096 -0.308 0.024 -0.059 -0.148 -0.102 -0.097 -0.091 0.058
0.099 0.065 -0.036 -0.208 -0.178 -0.130 -0.116 0.072 -0.063
-0.142 -0.113 -0.077 -0.078 -0.087 -0.147 -0.154 -0.018 -0.009
-0.014 -0.052 -0.086 -0.074 0.064 0.049 -0.045 -0.006 -0.006
0.146 0.257 0.110 0.086 0.305 0.239 0.047 0.145 0.333
0.195 0.193 0.264 0.291 0.264 0.076 0.035 0.022 0.200
0.232 0.147 0.127 0.210 0.246 0.204 0.121 0.150 -0.015
0.114 0.067 0.132 0.147 0.215 0.152 0.242 0.092 0.259
0.433 0.406 0.237 0.405 0.480 0.377 0.485 0.563 0.420
0.450 0.464 0.448 0.412 0.551 0.612 0.392 0.282 0.253
0.297 0.146 0.142 0.413 0.337 0.338 0.403 0.407 0.430
0.449 0.435 0.192 0.376 0.340 0.487 0.507 0.356 0.206
-0.031 0.026 0.014 0.212 -0.037 -0.036 -0.160 -0.160]
[-0.166 0.185 0.072 -0.073 0.053 0.115 0.122 0.070 0.177
0.066 -0.017 0.103 0.130 0.219 0.228 0.142 0.001 0.038
0.154 0.261 0.324 0.174 0.071 0.022 0.106 0.075 0.141
0.028 0.036 0.138 0.064 0.064 0.150 0.077 0.156 0.139
0.179 0.039 0.110 0.010 0.005 -0.092 0.033 -0.002 -0.165
0.037 0.073 0.218 0.237 0.182 0.177 0.118 0.166 0.184
0.232 0.335 0.301 0.317 0.170 0.105 0.179 0.208 0.504
0.333 0.429 0.470 0.312 0.399 0.225 0.183 0.263 0.209
0.137 0.200 0.205 0.250 0.271 0.235 0.351 0.399 0.379
0.438 0.409 0.439 0.424 0.460 0.487 0.576 0.468 0.510
0.420 0.487 0.664 0.695 0.429 0.330 0.441 0.597 0.345
0.435 0.424 0.353 0.298 0.387 0.348 0.445 0.597 0.610
0.679 0.586 0.481 0.535 0.492 0.435 0.445 0.349 0.261
0.373 0.341 0.404 0.579 0.314 0.269 0.157 0.126]
[ 0.001 0.063 0.013 0.045 -0.003 -0.012 0.102 0.140 0.163
0.109 -0.081 -0.109 -0.146 0.165 0.049 -0.019 -0.056 -0.065
-0.043 0.158 0.376 0.277 0.308 0.252 0.042 -0.051 0.124
0.129 -0.016 0.038 -0.084 0.030 0.263 0.164 0.284 0.159
0.221 0.016 0.120 -0.099 0.120 0.260 0.211 0.120 -0.014
from sniper.
thanks, if you can check the output of the proposal layer and the features after the pooling as well, that will give a clear idea which one is failing. If its the proposal layer, I can check it and get back to you
from sniper.
BTW - I've replaced the deformable_psroi_pooling.cc
file in order to make it run on CPU (as I mentioned above). I've also tested this file with some other repository so I think it is fine. Since you guys didn't change this file, so I think the problem might be in the MultiProposal
layer. Will check and let you know soon!
from sniper.
OK here's more data. We can see that the input of the MultiProposal
layer is the same (denoted as rpn_cls_prob_reshape_output
below), but the output of MultiProposal
layer is different (denoted as rois_output
. The output of DeformablePSROIPooling
layer is also different, but I think this is because the input of DeformablePSROIPooling
layer is already different.
CPU output
<NDArray 300x5 @cpu(0)>, 'rpn_cls_prob_reshape_output':
[[[[ 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.997
0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000]
[ 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000]
[ 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
<NDArray 1 @cpu(0)>, 'rois_output':
[[ 0.000 280.119 204.702 467.711 399.569]
[ 0.000 157.547 188.911 342.748 397.438]
[ 0.000 185.453 198.982 387.434 400.531]
[ 0.000 229.094 208.812 428.486 396.312]
[ 0.000 307.040 195.348 501.990 393.009]
[ 0.000 341.803 201.006 548.583 394.455]
[ 0.000 396.528 191.727 591.324 387.371]
[ 0.000 1156.895 681.321 1201.000 743.583]
[ 0.000 38.503 178.544 207.934 410.373]
[ 0.000 79.362 180.744 250.460 407.440]
[ 0.000 108.472 186.929 290.305 401.470]
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[ 0.000 993.391 674.792 1182.917 749.072]
[ 0.000 128.083 215.154 321.601 413.789]
[ 0.000 173.980 700.681 478.930 799.000]
[ 0.000 168.017 231.889 373.320 417.049]
[ 0.000 203.315 231.655 416.539 412.567]
[ 0.000 5.146 172.476 170.600 426.306]
[ 0.000 772.012 576.201 889.586 730.370]
[ 0.000 1175.192 169.566 1201.000 424.347]
[ 0.000 1156.714 158.541 1201.000 427.482]
[ 0.000 1131.555 164.173 1201.000 418.241]
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[ 0.000 452.740 218.070 667.618 406.802]
[ 0.000 426.838 204.409 637.058 395.885]
[ 0.000 99.064 151.861 267.066 384.954]
[ 0.000 836.372 556.619 940.184 737.266]
[ 0.000 251.144 181.788 444.279 375.302]
[ 0.000 1105.779 689.475 1201.000 756.210]
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<NDArray 1 @gpu(0)>, 'rois_output':
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from sniper.
ok, for inference there is no CUDA code written other than the memcpy. I can make the changes in some time, but if you want to get this working soon, you just need to get rid of the memcpy and paste all the content from the cu file to the cc file, i am talking about https://github.com/mahyarnajibi/SNIPER-mxnet/blob/master/src/operator/multi_proposal.cc and https://github.com/mahyarnajibi/SNIPER-mxnet/blob/master/src/operator/multi_proposal.cu files.
from sniper.
@bharatsingh430 Cool - I'll probably try to implement it myself, and will let you know how it goes!
Thanks for the help!
from sniper.
Related Issues (20)
- The process of main_train.py never ends
- PyYAML yaml.load(input) Deprecation
- KeyError: 'conv_new_2_weight' HOT 1
- I have got lost in INSTALLATION HOT 1
- detection score is low HOT 3
- errors in INSTALLATION
- Changes required to run demo.py to detect all classes in openimages
- ImportError: lib/nms/gpu_nms.so: undefined symbol: __cudaPopCallConfiguration HOT 1
- Sniper HOT 1
- I got lost in the Installation. What is your configuration environment? such as the version of gcc, python, cuda, etc. HOT 2
- AssertionError: number of boxes matrix must match number of images. HOT 1
- COCO_train2017_rpn.pkl
- broken link to imagenet pretrained checkpoint HOT 1
- How to understand the resolution of pretrained dataset refered in snip paper?
- "soft" is missing in cvpr3k branch
- cannot download the pretrained model nor sniper model HOT 1
- error: ‘PyThreadState {aka struct _ts}’ has no member named ‘exc_type’; did you mean ‘curexc_type’? *type = tstate->exc_type;
- would the focus mask branch downgrade performance of original detector in single-scale training and inference
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from sniper.