Visualizing the net-structure when training the net
Jacoobr opened this issue · 1 comments
Hi @AlexMa011 , Thanks for your great work. Now when i use "make_dot" function that from graphviz package to visulize the net-structure, i got a weird result.
(1.) Firstly, i add two lines code in the tarin.py script as follow:
r = net(x,x1,x2,x3)
g = make_dot(r, params=dict(net.named_parameters()))
g.render('graph',view=False)
result = r.contiguous().view(-1, 28*28+3)
When i run the script train.py, the program just stopped at this line code:g = make_dot(r, params=dict(net.named_parameters()))` , then i killed the thread by "Ctrl+C" shortcut. I can see
a dot format file called "graph" , there is the content of "graph":
digraph { graph [size="12,12"] node [align=left fontsize=12 height=0.2 ranksep=0.1 shape=box style=filled] 140010753187920 [label=GatherBackward] 140010753109776 -> 140010753187920 140010753109776 [label=ViewBackward] 140010753242896 -> 140010753109776 140010753242896 [label=ThAddmmBackward] 140010753243024 -> 140010753242896 140010753243024 [label=ExpandBackward] 140010753190480 -> 140010753243024 140010753190480 [label=BroadcastBackward] 140010753243280 -> 140010753190480 140010753243280 [label="module.model1.0.weight (64, 3, 3, 3)" fillcolor=lightblue] 140010753243152 -> 140010753190480 140010753243152 [label="module.model1.0.bias (64)" fillcolor=lightblue] 140010753243344 -> 140010753190480 140010753243344 [label="module.model1.1.weight (64)" fillcolor=lightblue] 140010753243408 -> 140010753190480 140010753243408 [label="module.model1.1.bias (64)" fillcolor=lightblue] 140010753243472 -> 140010753190480 140010753243472 [label="module.model1.3.weight (64, 64, 3, 3)" fillcolor=lightblue] 140010753243536 -> 140010753190480 140010753243536 [label="module.model1.3.bias (64)" fillcolor=lightblue] 140010753243600 -> 140010753190480 140010753243600 [label="module.model1.4.weight (64)" fillcolor=lightblue] 140010753243664 -> 140010753190480 140010753243664 [label="module.model1.4.bias (64)" fillcolor=lightblue] 140010753243728 -> 140010753190480 140010753243728 [label="module.model1.7.weight (128, 64, 3, 3)" fillcolor=lightblue] 140010753243792 -> 140010753190480 140010753243792 [label="module.model1.7.bias (128)" fillcolor=lightblue] 140010753243856 -> 140010753190480 140010753243856 [label="module.model1.8.weight (128)" fillcolor=lightblue] 140010753243920 -> 140010753190480 140010753243920 [label="module.model1.8.bias (128)" fillcolor=lightblue] 140010753243984 -> 140010753190480 140010753243984 [label="module.model1.10.weight (128, 128, 3, 3)" fillcolor=lightblue] 140010753244048 -> 140010753190480 140010753244048 [label="module.model1.10.bias (128)" fillcolor=lightblue] 140010753244112 -> 140010753190480 140010753244112 [label="module.model1.11.weight (128)" fillcolor=lightblue] 140010753244176 -> 140010753190480 140010753244176 [label="module.model1.11.bias (128)" fillcolor=lightblue] 140010753244240 -> 140010753190480 140010753244240 [label="module.model2.0.weight (256, 128, 3, 3)" fillcolor=lightblue] 140010753244304 -> 140010753190480 140010753244304 [label="module.model2.0.bias (256)" fillcolor=lightblue] 140010753244368 -> 140010753190480 140010753244368 [label="module.model2.1.weight (256)" fillcolor=lightblue] 140010753244432 -> 140010753190480 140010753244432 [label="module.model2.1.bias (256)" fillcolor=lightblue] 140010753244496 -> 140010753190480 140010753244496 [label="module.model2.3.weight (256, 256, 3, 3)" fillcolor=lightblue] 140010753244560 -> 140010753190480 140010753244560 [label="module.model2.3.bias (256)" fillcolor=lightblue] 140010753244624 -> 140010753190480 140010753244624 [label="module.model2.4.weight (256)" fillcolor=lightblue] 140010753244688 -> 140010753190480 140010753244688 [label="module.model2.4.bias (256)" fillcolor=lightblue] 140010753244752 -> 140010753190480 140010753244752 [label="module.model2.6.weight (256, 256, 3, 3)" fillcolor=lightblue] 140010753244816 -> 140010753190480 140010753244816 [label="module.model2.6.bias (256)" fillcolor=lightblue] 140010753244880 -> 140010753190480 140010753244880 [label="module.model2.7.weight (256)" fillcolor=lightblue] 140010753244944 -> 140010753190480 140010753244944 [label="module.model2.7.bias (256)" fillcolor=lightblue] 140010753245008 -> 140010753190480 140010753245008 [label="module.model3.0.weight (512, 256, 3, 3)" fillcolor=lightblue] 140010753245072 -> 140010753190480 140010753245072 [label="module.model3.0.bias (512)" fillcolor=lightblue] 140010753245136 -> 140010753190480 140010753245136 [label="module.model3.1.weight (512)" fillcolor=lightblue] 140010753286224 -> 140010753190480 140010753286224 [label="module.model3.1.bias (512)" fillcolor=lightblue] 140010753286288 -> 140010753190480 140010753286288 [label="module.model3.3.weight (512, 512, 3, 3)" fillcolor=lightblue] 140010753286352 -> 140010753190480 140010753286352 [label="module.model3.3.bias (512)" fillcolor=lightblue] 140010753286416 -> 140010753190480 140010753286416 [label="module.model3.4.weight (512)" fillcolor=lightblue] 140010753286480 -> 140010753190480 140010753286480 [label="module.model3.4.bias (512)" fillcolor=lightblue] 140010753286544 -> 140010753190480 140010753286544 [label="module.model3.6.weight (512, 512, 3, 3)" fillcolor=lightblue] 140010753286608 -> 140010753190480 140010753286608 [label="module.model3.6.bias (512)" fillcolor=lightblue] 140010753286672 -> 140010753190480 140010753286672 [label="module.model3.7.weight (512)" fillcolor=lightblue] 140010753286736 -> 140010753190480 140010753286736 [label="module.model3.7.bias (512)" fillcolor=lightblue] 140010753286800 -> 140010753190480 140010753286800 [label="module.model4.1.weight (512, 512, 3, 3)" fillcolor=lightblue] 140010753286864 -> 140010753190480 140010753286864 [label="module.model4.1.bias (512)" fillcolor=lightblue] 140010753286928 -> 140010753190480 140010753286928 [label="module.model4.2.weight (512)" fillcolor=lightblue] 140010753286992 -> 140010753190480 140010753286992 [label="module.model4.2.bias (512)" fillcolor=lightblue] 140010753287056 -> 140010753190480 140010753287056 [label="module.model4.4.weight (512, 512, 3, 3)" fillcolor=lightblue] 140010753287120 -> 140010753190480 140010753287120 [label="module.model4.4.bias (512)" fillcolor=lightblue] 140010753287184 -> 140010753190480 140010753287184 [label="module.model4.5.weight (512)" fillcolor=lightblue] 140010753287248 -> 140010753190480 140010753287248 [label="module.model4.5.bias (512)" fillcolor=lightblue] 140010753287312 -> 140010753190480 140010753287312 [label="module.model4.7.weight (512, 512, 3, 3)" fillcolor=lightblue] 140010753287376 -> 140010753190480 140010753287376 [label="module.model4.7.bias (512)" fillcolor=lightblue] 140010753287440 -> 140010753190480 140010753287440 [label="module.model4.8.weight (512)" fillcolor=lightblue] 140010753287504 -> 140010753190480 140010753287504 [label="module.model4.8.bias (512)" fillcolor=lightblue] 140010753287568 -> 140010753190480 140010753287568 [label="module.convlayer1.0.weight (128, 128, 3, 3)" fillcolor=lightblue] 140010753287632 -> 140010753190480 140010753287632 [label="module.convlayer1.0.bias (128)" fillcolor=lightblue] 140010753287696 -> 140010753190480 140010753287696 [label="module.convlayer1.2.weight (128)" fillcolor=lightblue] 140010753287760 -> 140010753190480 140010753287760 [label="module.convlayer1.2.bias (128)" fillcolor=lightblue] 140010753287824 -> 140010753190480 140010753287824 [label="module.convlayer2.0.weight (128, 256, 3, 3)" fillcolor=lightblue] 140010753287888 -> 140010753190480 140010753287888 [label="module.convlayer2.0.bias (128)" fillcolor=lightblue] 140010753287952 -> 140010753190480 140010753287952 [label="module.convlayer2.2.weight (128)" fillcolor=lightblue] 140010753288016 -> 140010753190480 140010753288016 [label="module.convlayer2.2.bias (128)" fillcolor=lightblue] 140010753288080 -> 140010753190480 140010753288080 [label="module.convlayer3.0.weight (128, 512, 3, 3)" fillcolor=lightblue] 140010753288144 -> 140010753190480 140010753288144 [label="module.convlayer3.0.bias (128)" fillcolor=lightblue] 140010753288208 -> 140010753190480 140010753288208 [label="module.convlayer3.2.weight (128)" fillcolor=lightblue] 140010753288272 -> 140010753190480 140010753288272 [label="module.convlayer3.2.bias (128)" fillcolor=lightblue] 140010753288336 -> 140010753190480 140010753288336 [label="module.convlayer4.0.weight (128, 512, 3, 3)" fillcolor=lightblue] 140010753288400 -> 140010753190480 140010753288400 [label="module.convlayer4.0.bias (128)" fillcolor=lightblue] 140010753288464 -> 140010753190480 140010753288464 [label="module.convlayer4.2.weight (128)" fillcolor=lightblue] 140010753288528 -> 140010753190480 140010753288528 [label="module.convlayer4.2.bias (128)" fillcolor=lightblue] 140010753288592 -> 140010753190480 140010753288592 [label="module.convlayer5.0.weight (128, 512, 3, 3)" fillcolor=lightblue] 140010753288656 -> 140010753190480 140010753288656 [label="module.convlayer5.0.bias (128)" fillcolor=lightblue] 140010753288720 -> 140010753190480 140010753288720 [label="module.convlayer5.2.weight (128)" fillcolor=lightblue] 140010753288784 -> 140010753190480 140010753288784 [label="module.convlayer5.2.bias (128)" fillcolor=lightblue] 140010753288848 -> 140010753190480 140010753288848 [label="module.linear2.weight (787, 1568)" fillcolor=lightblue] 140010753288912 -> 140010753190480 140010753288912 [label="module.linear2.bias (787)" fillcolor=lightblue] 140010753288976 -> 140010753190480 140010753288976 [label="module.lstmlayer.weight_ih_l0 (6272, 7846)" fillcolor=lightblue] 140010753289040 -> 140010753190480 140010753289040 [label="module.lstmlayer.weight_hh_l0 (6272, 1568)" fillcolor=lightblue] 140010753289104 -> 140010753190480 140010753289104 [label="module.lstmlayer.bias_ih_l0 (6272)" fillcolor=lightblue] 140010753289168 -> 140010753190480 140010753289168 [label="module.lstmlayer.bias_hh_l0 (6272)" fillcolor=lightblue] 140010753289232 -> 140010753190480 ...... }
Finally, I use the command dot -Tpdf graph -o graph_.pdf to convert the dot format to pdf for easily visual.
I got a weird result:
I don't know what's wrong with my code and hope for your appreciative help or your suggestions for visulizing the net structure of polygon-rnn. Thank U again~.
Sorry to reply late. I am kind of busy recently. I think the model of polygon-rnn is quite easy. However, it has the complicated forward process, thus it may be difficult to use some tools to draw the graph. I will try to test the method you mentioned, but I'm afraid it won't work.