I'm creating a document in a Rmarkdown file and knitting to HTML for file submission. Generating seeded samples using the sample function provide different results in the console and the knitted file.
I am using R Studio version 1.0.153 and R 3.6.0
edit: I have updated R Studio to version 1.2.1335 and am still having this issue
set.seed(1)
rnorm(1)
sample(1:10, 1)
in the console and knitted file, the value for rnorm(1) is the same, however, in the console, I see that I have sampled 6 in the console, and 7 in the knitted document
R 3.6.0 changed the sampling method. With the new method (default or Rejection) I get 7. With the old one I get 6:
set.seed(1)
rnorm(1)
#> [1] -0.6264538
sample(1:10, 1)
#> [1] 7
set.seed(1, sample.kind = "Rounding")
#> Warning in set.seed(1, sample.kind = "Rounding"): non-uniform 'Rounding'
#> sampler used
rnorm(1)
#> [1] -0.6264538
sample(1:10, 1)
#> [1] 6
Created on 2019-05-23 by the reprex package (v0.2.1)
So it seems you have somehow set sample.kind = "Rounding" in the console. You can check this from the output of RNGkind().
Related
Update #1 (original question and details below):
As per the suggestion of #MatthijsHollemans below I've tried to run this by removing dynamic_axes from the initial create_onnx step below. This removed both:
Description of image feature 'input_image' has missing or non-positive width 0.
and
Input 'input_image' of layer '63' not found in any of the outputs of the preceeding layers.
Unfortunately this opens up two sub-questions:
I still want to have a functional ONNX model. Is there a more appropriate way to make H and W dynamic? Or should I be saving two versions of the ONNX model, one without dynamic_axes for the CoreML conversion, and one with for use as a valid ONNX model?
Although this solves the compilation error in xcode (specified below) it introduces the following runtime issues:
Finalizing CVPixelBuffer 0x282f4c5a0 while lock count is 1.
[espresso] [Espresso::handle_ex_plan] exception=Invalid X-dimension 1/480 status=-7
[coreml] Error binding image input buffer input_image: -7
[coreml] Failure in bindInputsAndOutputs.
I am calling this the same way I was calling the fixed size model, which does still work fine. The image dimensions are 640 x 480.
As specified below the model should accept any image between 64x64 and higher.
For flexible shape models, do I need to provide an input differently in xcode?
Original Question (parts still relevant)
I have been slowly working on converting a style transfer model from pytorch > onnx > coreml. One of the issues that has been a struggle is flexible/dynamic input + output shape.
This method (besides i/o renaming) has worked well on iOS 12 & 13 when using a static input shape.
I am using the following code to do the onnx > coreml conversion:
def create_coreml(name):
mlmodel = convert(
model="onnx/" + name + ".onnx",
preprocessing_args={'is_bgr': True},
deprocessing_args={'is_bgr': True},
image_input_names=['input_image'],
image_output_names=['stylized_image'],
minimum_ios_deployment_target='13'
)
spec = mlmodel.get_spec()
img_size_ranges = flexible_shape_utils.NeuralNetworkImageSizeRange()
img_size_ranges.add_height_range((64, -1))
img_size_ranges.add_width_range((64, -1))
flexible_shape_utils.update_image_size_range(
spec,
feature_name='input_image',
size_range=img_size_ranges)
flexible_shape_utils.update_image_size_range(
spec,
feature_name='stylized_image',
size_range=img_size_ranges)
mlmodel = coremltools.models.MLModel(spec)
mlmodel.save("mlmodel/" + name + ".mlmodel")
Although the conversion 'succeeds' there are a couple of warnings (spaces added for readability):
Translation to CoreML spec completed. Now compiling the CoreML model.
/usr/local/lib/python3.7/site-packages/coremltools/models/model.py:111:
RuntimeWarning: You will not be able to run predict() on this Core ML model. Underlying exception message was:
Error compiling model:
"Error reading protobuf spec. validator error: Description of image feature 'input_image' has missing or non-positive width 0.".
RuntimeWarning)
Model Compilation done.
/usr/local/lib/python3.7/site-packages/coremltools/models/model.py:111:
RuntimeWarning: You will not be able to run predict() on this Core ML model. Underlying exception message was:
Error compiling model:
"compiler error: Input 'input_image' of layer '63' not found in any of the outputs of the preceeding layers.
".
RuntimeWarning)
If I ignore these warnings and try to compile the model for latest targets (13.0) I get the following error in xcode:
coremlc: Error: compiler error: Input 'input_image' of layer '63' not found in any of the outputs of the preceeding layers.
Here is what the problematic area appears to look like in netron:
My main question is how can I get these two warnings out of the way?
Happy to provide any other details.
Thanks for any advice!
Below is my pytorch > onnx conversion:
def create_onnx(name):
prior = torch.load("pth/" + name + ".pth")
model = transformer.TransformerNetwork()
model.load_state_dict(prior)
dummy_input = torch.zeros(1, 3, 64, 64) # I wasn't sure what I would set the H W to here?
torch.onnx.export(model, dummy_input, "onnx/" + name + ".onnx",
verbose=True,
opset_version=10,
input_names=["input_image"], # These are being renamed from garbled originals.
output_names=["stylized_image"], # ^
dynamic_axes={'input_image':
{2: 'height', 3: 'width'},
'stylized_image':
{2: 'height', 3: 'width'}}
)
onnx.save_model(original_model, "onnx/" + name + ".onnx")
I'm training a LSTM and I'm defining parameters and regression layer. I get the error in the title with this code:
lstm_cells = [
tf.contrib.rnn.LSTMCell(num_units=num_nodes[li],
state_is_tuple=True,
initializer= tf.contrib.layers.xavier_initializer()
)
for li in range(n_layers)]
drop_lstm_cells = [tf.contrib.rnn.DropoutWrapper(
lstm, input_keep_prob=1.0,output_keep_prob=1.0-dropout, state_keep_prob=1.0-dropout
) for lstm in lstm_cells]
drop_multi_cell = tf.contrib.rnn.MultiRNNCell(drop_lstm_cells)
multi_cell = tf.contrib.rnn.MultiRNNCell(lstm_cells)
w = tf.get_variable('w',shape=[num_nodes[-1], 1], initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable('b',initializer=tf.random_uniform([1],-0.1,0.1))
I'm using tensorflow2 and I have already read the https://www.tensorflow.org/guide/migrate guide and I think almost everything on the net.
But I'm not able to solve it.
How can I do it?
This error occurs because the contrib module has been removed from version 2 of tensorflow. There are two solutions to this problem:
You can delete the current package and install one of the Series 1 versions.
You can use this command, which is also compatible with the version two package: Use tf.compat.v1.nn.rnn_cell.LSTMCell instead of tf.contrib.rnn.LSTMCell and use tf.initializers.GlorotUniform () instead of tf.contrib.layers.xavier_initializer () in other command which include rnn you can use tf.compat.v1.nn.rnn_cell.
tf.contrib.rnn.LSTMCell -> tf.compat.v1.nn.rnn_cell.LSTMCell or tf.keras.layers.LSTMCell
tf.contrib.rnn.DropoutWrapper -> tf.compat.v1.nn.rnn_cell.DropoutWrapper or tf.keras.layers.DropOut
tf.contrib.rnn.MultiRNNCell -> tf.compat.v1.nn.rnn_cell.MultiRNNCell or tf.keras.layers.RNN
tf.contrib has moved out of TF starting TF 2.0 alpha.
Take a look at these tf 2.0 release notes https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0-alpha0
You can upgrade your TF 1.x code to TF 2.x using the tf_upgrade_v2 script
https://www.tensorflow.org/alpha/guide/upgrade
I'm implementing some RL in PyTorch and had to write my own mse_loss function (which I found on Stackoverflow ;) ).
The loss function is:
def mse_loss(input_, target_):
return torch.sum(
(input_ - target_) * (input_ - target_)) / input_.data.nelement()
Now, in my training loop, the first input is something like:
tensor([-1.7610e+10]), tensor([-6.5097e+10])
With this input I'll get the error:
Unable to get repr for <class 'torch.Tensor'>
Computing a = (input_ - target_) works fine, while b = a * a respectively b = torch.pow(a, 2) will fail with the error metioned above.
Does anyone know a fix for this?
Thanks a lot!
Update:
I just tried using torch.nn.functional.mse_loss which will result in the same error..
I had the same error,when I use the below code
criterion = torch.nn.CrossEntropyLoss().cuda()
output=output.cuda()
target=target.cuda()
loss=criterion(output, target)
but I finally found my wrong：output is like tensor([[0.5746,0.4254]]) and target is like tensor([2]),the number 2 is out of indice of output
when I not use GPU,this error message is:
RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed. at /opt/conda/conda-bld/pytorch-nightly_1547458468907/work/aten/src/THNN/generic/ClassNLLCriterion.c:93
Are you using a GPU ?
I had simillar problem (but I was using gather operations), and when I moved my tensors to CPU I could get a proper error message. I fixed the error, switched back to GPU and it was alright.
Maybe pytorch has trouble outputing the correct error when it comes from inside the GPU.
I am trying to learn linearK estimates on a small linnet object from the CRC spatstat book (chapter 17) and when I use the linearK function, spatstat throws an error. I have documented the process in the comments in the r code below. The error is as below.
Error in seq.default(from = 0, to = right, length.out = npos + 1L) : 'to' cannot be NA, NaN or infinite
I do not understand how to resolve this. I am following this process:
# I have data of points for each data of the week
# d1 is district 1 of the city.
# I did the step below otherwise it was giving me tbl class
d1_data=lapply(split(d1, d1$openDatefactor),as.data.frame)
# I previously create a linnet and divided it into districts of the city
d1_linnet = districts_linnet[["d1"]]
# I create point pattern for each day
d1_ppp = lapply(d1_data, function(x) as.ppp(x, W=Window(d1_linnet)))
plot(d1_ppp[[1]], which.marks="type")
# I am then converting the point pattern to a point pattern on linear network
d1_lpp <- as.lpp(d1_ppp[[1]], L=d1_linnet, W=Window(d1_linnet))
d1_lpp
Point pattern on linear network
3 points
15 columns of marks: ‘status’, ‘number_of_’, ‘zip’, ‘ward’,
‘police_dis’, ‘community_’, ‘type’, ‘days’, ‘NAME’,
‘DISTRICT’, ‘openDatefactor’, ‘OpenDate’, ‘coseDatefactor’,
‘closeDate’ and ‘instance’
Linear network with 4286 vertices and 6183 lines
Enclosing window: polygonal boundary
enclosing rectangle: [441140.9, 448217.7] x [4640080, 4652557] units
# the errors start from plotting this lpp object
plot(d1_lpp)
"show.all" is not a graphical parameter
Show Traceback
Error in plot.window(...) : need finite 'xlim' values
coords(d1_lpp)
x y seg tp
441649.2 4649853 5426 0.5774863
445716.9 4648692 5250 0.5435492
444724.6 4646320 677 0.9189631
3 rows
And then consequently, I also get error on linearK(d1_lpp)
Error in seq.default(from = 0, to = right, length.out = npos + 1L) : 'to' cannot be NA, NaN or infinite
I feel lpp object has the problem, but I find it hard to interpret the errors and how to resolve them. Could someone please guide me?
Thanks
I can confirm there is a bug in plot.lpp when trying to plot the marked point pattern on the linear network. That will hopefully be fixed soon. You can plot the unmarked point pattern using
plot(unmark(d1_lpp))
I cannot reproduce the problem with linearK. Which version of spatstat are you running? In the development version on my laptop spatstat_1.51-0.073 everything works. There has been changes to this code recently, so it is likely that this will be solved by updating to development version (see https://github.com/spatstat/spatstat).
I am using sVM-light for binary classification.and I am using SVM in the learning mode.
I have my train.dat file ready.but when i run this command ,instead of creating file model ,it writes somethings in terminal:
my command:
./svm_learn example1/train.dat example1/model
output:
Scanning examples...done
Reading examples into memory...Feature numbers must be larger or equal to 1!!!
: Success
LINE: -1 0:1.0 6:1.0 16:1.0 18:1.0 28:1.0 29:1.0 31:1.0 48:1.0 58:1.0 73:1.0 82:1.0 93:1.0 95:1.0 106:1.0 108:1.0 118:1.0 121:1.0 122:1.0151:1.0 164:1.0 167:1.0 169:1.0 170:1.0 179:1.0 190:1.0 193:1.0 220:1.0 237:1.0250:1.0 252:1.0 267:1.0 268:1.0 269:1.0 278:1.0 283:1.0 291:1.0 300:1.0 305:1.0320:1.0 332:1.0 336:1.0 342:1.0 345:1.0 348:1.0 349:1.0 350:1.0 368:1.0 370:1.0384:1.0 390:1.0 394:1.0 395:1.0 396:1.0 397:1.0 400:1.0 401:1.0 408:1.0 416:1.0427:1.0 433:1.0 435:1.0 438:1.0 441:1.0 446:1.0 456:1.0 471:1.0 485:1.0 510:1.0523:1.0 525:1.0 526:1.0 532:1.0 540:1.0 553:1.0 567:1.0 568:1.0 581:1.0 583:1.0604:1.0 611:1.0 615:1.0 616:1.0 618:1.0 623:1.0 624:1.0 626:1.0 651:1.0 659:1.0677:1.0 678:1.0 683:1.0 690:1.0 694:1.0 699:1.0 713:1.0 714:1.0 720:1.0 722:1.0731:1.0 738:1.0 755:1.0 761:1.0 763:1.0 768:1.0 776:1.0 782:1.0 792:1.0 817:1.0823:1.0 827:1.0 833:1.0 834:1.0 838:1.0 842:1.0 848:1.0 851:1.0 863:1.0 867:1.0890:1.0 900:1.0 903:1.0 923:1.0 935:1.0 942:1.0 946:1.0 947:1.0 949:1.0 956:1.0962:1.0 965:1.0 968:1.0 983:1.0 986:1.0 987:1.0 990:1.0 998:1.0 1007:1.0 1014:1.0 1019:1.0 1022:1.0 1024:1.0 1029:1.0 1030:1.01032:1.0 1047:1.0 1054:1.0 1063:1.0 1069:1.0 1076:1.0 1085:1.0 1093:1.0 1098:1.0 1108:1.0 1109:1.01116:1.0 1120:1.0 1133:1.0 1134:1.0 1135:1.0 1138:1.0 1139:1.0 1144:1.0 1146:1.0 1148:1.0 1149:1.01161:1.0 1165:1.0 1169:1.0 1170:1.0 1177:1.0 1187:1.0 1194:1.0 1212:1.0 1214:1.0 1239:1.0 1243:1.01251:1.0 1257:1.0 1274:1.0 1278:1.0 1292:1.0 1297:1.0 1304:1.0 1319:1.0 1324:1.0 1325:1.0 1353:1.01357:1.0 1366:1.0 1374:1.0 1379:1.0 1392:1.0 1394:1.0 1407:1.0 1412:1.0 1414:1.0 1419:1.0 1433:1.01435:1.0 1437:1.0 1453:1.0 1463:1.0 1464:1.0 1469:1.0 1477:1.0 1481:1.0 1487:1.0 1506:1.0 1514:1.01519:1.0 1526:1.0 1536:1.0 1549:1.0 1551:1.0 1553:1.0 1561:1.0 1569:1.0 1578:1.0 1603:1.0 1610:1.01615:1.0 1617:1.0 1625:1.0 1638:1.0 1646:1.0 1663:1.0 1666:1.0 1672:1.0 1681:1.0 1690:1.0 1697:1.01699:1.0 1706:1.0 1708:1.0 1717:1.0 1719:1.0 1732:1.0 1737:1.0 1756:1.0 1766:1.0 1771:1.0 1789:1.01804:1.0 1805:1.0 1808:1.0 1814:1.0 1815:1.0 1820:1.0 1824:1.0 1832:1.0 1841:1.0 1844:1.0 1852:1.01861:1.0 1875:1.0 1899:1.0 1902:1.0 1904:1.0 1905:1.0 1917:1.0 1918:1.0 1919:1.0 1921:1.0 1926:1.01934:1.0 1937:1.0 1942:1.0 1956:1.0 1965:1.0 1966:1.0 1970:1.0 1971:1.0 1980:1.0 1995:1.0 2000:1.02009:1.0 2010:1.0 2012:1.0 2015:1.0 2018:1.0 2022:1.0 2047:1.0 2076:1.0 2082:1.0 2095:1.0 2108:1.02114:1.0 2123:1.0 2130:1.0 2133:1.0 2141:1.0 2142:1.0 2143:1.0 2148:1.0 2157:1.0 2160:1.0 2162:1.02170:1.0 2195:1.0 2199:1.0 2201:1.0 2202:1.0 2205:1.0 2211:1.0 2218:1.0
I dont know what to do.
p.s.when i make my train.dat very shorter ,everything works fine!!!
Thank you
From what I could interpret from the log, your training set has an issue.
The first few characters of the training row that has issue are
-1 0:1.0 6:1.0
The issue is not with the size but with feature indexing. You are starting your feature index at 0 (0:1) whereas svmlight requires that all feature index be equal or greater than 1.
Change the indexing to start at 1 and it should work fine.