[GitHub] chinakook closed issue #7517: Add Depthwise Deconvolution support?
chinakook closed issue #7517: Add Depthwise Deconvolution support? URL: https://github.com/apache/incubator-mxnet/issues/7517 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] eric-haibin-lin opened a new pull request #7638: CSRNDArray from/to scipy csr_matrix; fix rand_shape_nd
eric-haibin-lin opened a new pull request #7638: CSRNDArray from/to scipy csr_matrix; fix rand_shape_nd URL: https://github.com/apache/incubator-mxnet/pull/7638 Added preliminary/inefficient support so that user can - construct CSRNDArray from scipy csr_matrix. - convert CSRNDArray to scipy csr_matrix Also fixed `rand_shape_nd` so that it returns tuple (consistent w/ rand_shape_2d) TODO: - move copy logic to cpp backend to reduce copy/blocking. @reminisce @anirudh2290 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] im9uri commented on issue #6245: Compile lastest MXNet failed with cude 8.0
im9uri commented on issue #6245: Compile lastest MXNet failed with cude 8.0 URL: https://github.com/apache/incubator-mxnet/issues/6245#issuecomment-325259554 @xinario I replaced every instance of CUDA_R_32I in MXNet with a type that was defined in the library_type.h header file. It was probably something along the line of CUDA_R_32F. Obviously not the best workaround... but the build passed. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] qingzhouzhen closed issue #7594: pvanet Incompatible input shape, why?
qingzhouzhen closed issue #7594: pvanet Incompatible input shape, why? URL: https://github.com/apache/incubator-mxnet/issues/7594 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] jeremiedb commented on issue #7476: R-package RNN refactor
jeremiedb commented on issue #7476: R-package RNN refactor URL: https://github.com/apache/incubator-mxnet/pull/7476#issuecomment-325228128 Improve the harmonization with model.FeedForward: same fixed.params, arg.params and aux.params input arguments. Remove redundancies between model.buckets and model.train.buckets. model.buckets is as performant on single symbol model as model.Feedforward. If using BatchNorm, single symbol works fine but the still is issue if training on list of symbols. Comment out the lstm in testthat to pass CI. Integrate CPU compatible RNN construction with raw lstm and gru cells in rnn.graph. No support for masking. Still need to test efficiency of inference and potentially adapt the inference functions. Unsure if wouldn't be preferable to assume a batch.size X seq.length input dimension to interator as it's the format expectged by symnol.RNN cell. Or add a shape detector to handle it automatically as in the python API. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] atiyo opened a new issue #7637: Strange Validation and Training Losses at epoch change
atiyo opened a new issue #7637: Strange Validation and Training Losses at epoch change URL: https://github.com/apache/incubator-mxnet/issues/7637 I struggled to get some mxnet models training to a good accuracy, so I took a closer look at training and validation losses of a toy model. I noticed some strange spikes between epochs, which surprised me. I expect I'm likely doing something wrong, but I can't see what: I have tried several optimisers, with learning rates spanning several orders of magnitude. It's most plausible that I'm doing something drastically wrong, being new to mxnet. Graphic below illustrating the phenomenon, and also code to reproduce figure: ![adam_loss](https://user-images.githubusercontent.com/12828061/29753519-35b2e6e2-8b6b-11e7-8c08-14b8730efceb.png) ``` import mxnet as mx import numpy as np optimizer_choice = 'adam' learning_rate = 0.01 batch_size = 500 inputs = np.expand_dims(np.random.uniform(low=0., high=2*np.pi, size=1), axis=1) labels = np.sin(inputs) eval_inputs = np.expand_dims(np.random.uniform(low=0., high=2*np.pi, size=1), axis=1) eval_labels = np.sin(eval_inputs) data_iter = mx.io.NDArrayIter(data={'data':inputs}, label={'label':labels}, batch_size=batch_size, shuffle=True) eval_data_iter = mx.io.NDArrayIter(data={'data':eval_inputs}, label={'label':eval_labels}, batch_size=batch_size, shuffle=True) data = mx.sym.Variable('data') label = mx.sym.Variable('label') fc1 = mx.sym.FullyConnected(data=data, num_hidden=128) ac1 = mx.sym.Activation(data=fc1, act_type='relu') fc2 = mx.sym.FullyConnected(data=ac1, num_hidden=64) ac2 = mx.sym.Activation(data=fc2, act_type='relu') fc3 = mx.sym.FullyConnected(data=ac2, num_hidden=16) ac3 = mx.sym.Activation(data=fc3, act_type='relu') fc4 = mx.sym.FullyConnected(data=ac3, num_hidden=1) ac4 = mx.sym.Activation(data=fc4, act_type='tanh') loss = mx.symbol.LinearRegressionOutput(data=ac4, label=label) net = mx.module.Module(symbol=loss, data_names=['data'], label_names=['label']) train_error = [] eval_error = [] def log_error(period, log): def _callback(param): if param.nbatch % period == 0: name, value = param.eval_metric.get() log.append(value) return _callback optimizer_params={'learning_rate':learning_rate} net.fit(data_iter, optimizer=optimizer_choice, optimizer_params=optimizer_params, eval_data=eval_data_iter, eval_metric='mse', num_epoch=5, epoch_end_callback = mx.callback.do_checkpoint('test_net'), eval_batch_end_callback = log_error(1,eval_error), batch_end_callback = log_error(1,train_error) ) train_error = np.array(train_error) eval_error = np.array(eval_error) import matplotlib.pyplot as plt plt.plot(np.arange(train_error.size),train_error, label = 'Training Error') plt.plot(np.arange(eval_error.size), eval_error, label = 'Validation Error') plt.legend(loc='upper right') plt.xlabel('Batch Number') plt.ylabel('Error') plt.title('Optimizer: {}. Learning Rate: {}'.format(optimizer_choice,learning_rate)) plt.gca().set_ylim(bottom=0) plt.show() ``` ## Environment info Operating System: macOS MXNet version: 0.11.0 Python version and distribution: Python 2.7.13 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] zhanghang1989 commented on issue #7579: style transfer example
zhanghang1989 commented on issue #7579: style transfer example URL: https://github.com/apache/incubator-mxnet/pull/7579#issuecomment-325035839 I have sent a PR to web-data with the images https://github.com/dmlc/web-data/pull/26 I will update the image paths after it is merged. @szha @piiswrong Thanks! This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] chinakook commented on issue #7613: 1x1 convolution acceleration
chinakook commented on issue #7613: 1x1 convolution acceleration URL: https://github.com/apache/incubator-mxnet/pull/7613#issuecomment-325193542 @reminisce I will complete the testings. I think performance improvement and memory reduction will show up in the image detection and segmentation cases where feature maps are very big(without cudnn). You can refer to the tensorflow's optimization for the 1x1 conv, it's more simple and more clear than Caffe: [tensorflow conv forward](https://github.com/tensorflow/tensorflow/blob/a0d784bdd31b27e013a7eac58a86ba62e86db299/tensorflow/core/kernels/conv_ops_using_gemm.cc#L238) [tensorflow conv backward](https://github.com/tensorflow/tensorflow/blob/42ca99b5aae03a8122ba0db94abfe1f3f5c257dc/tensorflow/core/kernels/conv_grad_input_ops.cc#L666) This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] chinakook commented on issue #7610: error C2768 "linalg_gemm":Illegal use of explicit template arguments
chinakook commented on issue #7610: error C2768 "linalg_gemm":Illegal use of explicit template arguments URL: https://github.com/apache/incubator-mxnet/issues/7610#issuecomment-325193766 @piiswrong Thanks, It's partly solved. New issues: d:\proj\dev\mx\src\operator\linalg_impl.h(442): error C2872: 'cpu': ambiguous symbol (compiling source file D:\proj\dev\mx\src\operator\upsampling.cc) d:\proj\dev\mx\src\operator\linalg_impl.h(443): error C2872: 'cpu': ambiguous symbol (compiling source file D:\proj\dev\mx\src\operator\upsampling.cc) This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] chinakook commented on issue #7610: error C2768 "linalg_gemm":Illegal use of explicit template arguments
chinakook commented on issue #7610: error C2768 "linalg_gemm":Illegal use of explicit template arguments URL: https://github.com/apache/incubator-mxnet/issues/7610#issuecomment-325193766 It's partly solved. New issues: d:\proj\dev\mx\src\operator\linalg_impl.h(442): error C2872: 'cpu': ambiguous symbol (compiling source file D:\proj\dev\mx\src\operator\upsampling.cc) d:\proj\dev\mx\src\operator\linalg_impl.h(443): error C2872: 'cpu': ambiguous symbol (compiling source file D:\proj\dev\mx\src\operator\upsampling.cc) This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] chinakook commented on issue #7613: 1x1 convolution acceleration
chinakook commented on issue #7613: 1x1 convolution acceleration URL: https://github.com/apache/incubator-mxnet/pull/7613#issuecomment-325193542 @reminisce I will complete the testings. I think performance improvement and memory reduction will show up in the image detection and segmentation cases where feature maps are very big. You can refer to the tensorflow's optimization for the 1x1 conv, it's more simple and more clear than Caffe: [tensorflow conv forward](https://github.com/tensorflow/tensorflow/blob/a0d784bdd31b27e013a7eac58a86ba62e86db299/tensorflow/core/kernels/conv_ops_using_gemm.cc#L238) [tensorflow conv backward](https://github.com/tensorflow/tensorflow/blob/42ca99b5aae03a8122ba0db94abfe1f3f5c257dc/tensorflow/core/kernels/conv_grad_input_ops.cc#L666) This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated: [build] explicitly install JDK8 (#7574)
This is an automated email from the ASF dual-hosted git repository. liuyizhi pushed a commit to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git The following commit(s) were added to refs/heads/master by this push: new e051297 [build] explicitly install JDK8 (#7574) e051297 is described below commit e05129774e76206fe890b511c346953107b05fce Author: Nan ZhuAuthorDate: Sun Aug 27 01:12:23 2017 -0700 [build] explicitly install JDK8 (#7574) * explicitly install openjdk8 * handle earlier version of ubuntu * install software-properties-common * update -y * update commands --- docker/install/scala.sh| 10 +- docs/get_started/build_from_source.md | 8 +++- tests/ci_build/Dockerfile.ubuntu1404_cuda75_cudnn5 | 8 +++- tests/ci_build/install/ubuntu_install_scala.sh | 9 +++-- 4 files changed, 30 insertions(+), 5 deletions(-) diff --git a/docker/install/scala.sh b/docker/install/scala.sh index bb0bb9c..c1d2de6 100755 --- a/docker/install/scala.sh +++ b/docker/install/scala.sh @@ -19,7 +19,15 @@ # install libraries for mxnet's scala package on ubuntu -apt-get install -y maven default-jdk + +apt-get install -y software-properties-common +add-apt-repository -y ppa:webupd8team/java +apt-get update +echo "oracle-java8-installer shared/accepted-oracle-license-v1-1 select true" | debconf-set-selections +apt-get install -y oracle-java8-installer +apt-get install -y oracle-java8-set-default + +apt-get install -y maven wget http://downloads.lightbend.com/scala/2.11.8/scala-2.11.8.deb dpkg -i scala-2.11.8.deb diff --git a/docs/get_started/build_from_source.md b/docs/get_started/build_from_source.md index 4ff2cc0..9bf397b 100644 --- a/docs/get_started/build_from_source.md +++ b/docs/get_started/build_from_source.md @@ -367,7 +367,13 @@ Both JDK and Maven are required to build the Scala package. ```bash -sudo apt-get install -y maven default-jdk +apt-get install -y software-properties-common +add-apt-repository -y ppa:webupd8team/java +apt-get update +echo "oracle-java8-installer shared/accepted-oracle-license-v1-1 select true" | debconf-set-selections +apt-get install -y oracle-java8-installer +apt-get install -y oracle-java8-set-default +apt-get install -y maven ``` diff --git a/tests/ci_build/Dockerfile.ubuntu1404_cuda75_cudnn5 b/tests/ci_build/Dockerfile.ubuntu1404_cuda75_cudnn5 index e9810af..88fd7ce 100644 --- a/tests/ci_build/Dockerfile.ubuntu1404_cuda75_cudnn5 +++ b/tests/ci_build/Dockerfile.ubuntu1404_cuda75_cudnn5 @@ -23,7 +23,13 @@ RUN cd /usr/src/gtest && cmake CMakeLists.txt && make && cp *.a /usr/lib RUN pip install nose cpplint 'pylint==1.4.4' 'astroid==1.3.6' # MAVEN -RUN apt-get install -y maven default-jdk +RUN apt-get install -y software-properties-common +RUN add-apt-repository ppa:webupd8team/java -y +RUN apt-get update +RUN echo "oracle-java8-installer shared/accepted-oracle-license-v1-1 select true" | debconf-set-selections +RUN apt-get install -y oracle-java8-installer +RUN apt-get install -y oracle-java8-set-default +RUN apt-get install -y maven # R RUN apt-get install -y software-properties-common r-base-core libcurl4-openssl-dev libssl-dev libxml2-dev diff --git a/tests/ci_build/install/ubuntu_install_scala.sh b/tests/ci_build/install/ubuntu_install_scala.sh index 712eff9..169ece0 100755 --- a/tests/ci_build/install/ubuntu_install_scala.sh +++ b/tests/ci_build/install/ubuntu_install_scala.sh @@ -19,5 +19,10 @@ # install libraries for mxnet's scala package on ubuntu -apt-get update && apt-get install -y \ -maven default-jdk +apt-get install -y software-properties-common +add-apt-repository -y ppa:webupd8team/java +apt-get update +echo "oracle-java8-installer shared/accepted-oracle-license-v1-1 select true" | debconf-set-selections +apt-get install -y oracle-java8-installer +apt-get install -y oracle-java8-set-default +apt-get update && apt-get install -y maven -- To stop receiving notification emails like this one, please contact ['"comm...@mxnet.apache.org" '].
[GitHub] kevinthesun opened a new pull request #7636: Add script to build doc files for all versions
kevinthesun opened a new pull request #7636: Add script to build doc files for all versions URL: https://github.com/apache/incubator-mxnet/pull/7636 Script to build all versions selected. Can be used for website recovering if apache jenkins automate building system generates unexpected result. This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] piiswrong commented on a change in pull request #7635: add fashion mnist and move mnists to s3
piiswrong commented on a change in pull request #7635: add fashion mnist and move mnists to s3 URL: https://github.com/apache/incubator-mxnet/pull/7635#discussion_r135404465 ## File path: python/mxnet/gluon/data/vision.py ## @@ -102,6 +102,56 @@ def _get_data(self): self._label = label +class FashionMNIST(_DownloadedDataset): +"""A dataset of Zalando's article images consisting of fashion products, +a drop-in replacement of the original MNIST dataset from +`https://github.com/zalandoresearch/fashion-mnist`_. + +Each sample is an image (in 3D NDArray) with shape (28, 28, 1). + +Parameters +-- +root : str +Path to temp folder for storing data. +train : bool +Whether to load the training or testing set. +transform : function +A user defined callback that transforms each instance. For example:: + +transform=lambda data, label: (data.astype(np.float32)/255, label) +""" +def __init__(self, root='~/.mxnet/datasets/fashion-mnist', train=True, + transform=None): +super(FashionMNIST, self).__init__(root, train, transform) + +def _get_data(self): +if not os.path.isdir(self._root): +os.makedirs(self._root) +url = 'https://apache-mxnet.s3.amazonaws.com/gluon/dataset/fashion-mnist/' +if self._train: +data_file = download(url+'train-images-idx3-ubyte.gz', self._root, Review comment: move url into MNIST._train_data_url/_train_label_url etc and inherit MNIST This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] piiswrong closed pull request #7633: fix tests
piiswrong closed pull request #7633: fix tests URL: https://github.com/apache/incubator-mxnet/pull/7633 This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[incubator-mxnet] branch master updated: fix tests (#7633)
This is an automated email from the ASF dual-hosted git repository. jxie pushed a commit to branch master in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git The following commit(s) were added to refs/heads/master by this push: new 9aa051c fix tests (#7633) 9aa051c is described below commit 9aa051c2e87d41b4f2a61fb62728ecdf364f8997 Author: Sheng ZhaAuthorDate: Sun Aug 27 00:14:28 2017 -0700 fix tests (#7633) --- tests/python/gpu/test_operator_gpu.py | 4 ++-- tests/python/unittest/test_loss.py| 18 ++ 2 files changed, 8 insertions(+), 14 deletions(-) diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py index 11d146c..0c5771e 100644 --- a/tests/python/gpu/test_operator_gpu.py +++ b/tests/python/gpu/test_operator_gpu.py @@ -1346,11 +1346,11 @@ def test_sequence_reverse(): def test_autograd_save_memory(): -x = mx.nd.zeros((128, 1024, 1024), ctx=mx.gpu(0)) +x = mx.nd.zeros((128, 512, 512), ctx=mx.gpu(0)) x.attach_grad() with mx.autograd.record(): -for i in range(50): +for i in range(200): x = x + 1 x.wait_to_read() x.backward() diff --git a/tests/python/unittest/test_loss.py b/tests/python/unittest/test_loss.py index b864215..85875c6 100644 --- a/tests/python/unittest/test_loss.py +++ b/tests/python/unittest/test_loss.py @@ -63,7 +63,6 @@ def get_net(num_hidden): def test_ce_loss(): -mx.random.seed(1234) np.random.seed(1234) nclass = 10 N = 20 @@ -83,7 +82,6 @@ def test_ce_loss(): def test_bce_loss(): -mx.random.seed(1234) np.random.seed(1234) N = 20 data = mx.random.uniform(-1, 1, shape=(N, 20)) @@ -111,7 +109,6 @@ def test_bce_equal_ce2(): def test_kl_loss(): -mx.random.seed(1234) np.random.seed(1234) N = 20 data = mx.random.uniform(-1, 1, shape=(N, 10)) @@ -129,12 +126,11 @@ def test_kl_loss(): def test_l2_loss(): -mx.random.seed(1234) np.random.seed(1234) N = 20 data = mx.random.uniform(-1, 1, shape=(N, 10)) label = mx.random.uniform(-1, 1, shape=(N, 1)) -data_iter = mx.io.NDArrayIter(data, label, batch_size=10, label_name='label') +data_iter = mx.io.NDArrayIter(data, label, batch_size=10, label_name='label', shuffle=True) output = get_net(1) l = mx.symbol.Variable('label') Loss = gluon.loss.L2Loss() @@ -142,26 +138,25 @@ def test_l2_loss(): loss = Loss(output, l) loss = mx.sym.make_loss(loss) mod = mx.mod.Module(loss, data_names=('data',), label_names=('label',)) -mod.fit(data_iter, num_epoch=200, optimizer_params={'learning_rate': 1.}, -eval_metric=mx.metric.Loss()) +mod.fit(data_iter, num_epoch=200, optimizer_params={'learning_rate': 0.1, 'wd': 0.00045}, +initializer=mx.init.Xavier(magnitude=2), eval_metric=mx.metric.Loss()) assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 0.05 def test_l1_loss(): -mx.random.seed(1234) np.random.seed(1234) N = 20 data = mx.random.uniform(-1, 1, shape=(N, 10)) label = mx.random.uniform(-1, 1, shape=(N, 1)) -data_iter = mx.io.NDArrayIter(data, label, batch_size=10, label_name='label') +data_iter = mx.io.NDArrayIter(data, label, batch_size=10, label_name='label', shuffle=True) output = get_net(1) l = mx.symbol.Variable('label') Loss = gluon.loss.L1Loss() loss = Loss(output, l) loss = mx.sym.make_loss(loss) mod = mx.mod.Module(loss, data_names=('data',), label_names=('label',)) -mod.fit(data_iter, num_epoch=200, optimizer_params={'learning_rate': 0.1}, -initializer=mx.init.Uniform(0.5), eval_metric=mx.metric.Loss()) +mod.fit(data_iter, num_epoch=200, optimizer_params={'learning_rate': 0.01}, +initializer=mx.init.Xavier(magnitude=3), eval_metric=mx.metric.Loss()) assert mod.score(data_iter, eval_metric=mx.metric.Loss())[0][1] < 0.1 @@ -196,7 +191,6 @@ def test_ctc_loss(): def test_sample_weight_loss(): -mx.random.seed(1234) np.random.seed(1234) nclass = 10 N = 20 -- To stop receiving notification emails like this one, please contact ['"comm...@mxnet.apache.org" '].
[GitHub] qingzhouzhen commented on issue #7632: pvanet infer_shape error. Arguments
qingzhouzhen commented on issue #7632: pvanet infer_shape error. Arguments URL: https://github.com/apache/incubator-mxnet/issues/7632#issuecomment-325180013 I change the config.SCALES to 1056x640, It still went wrong with the same problem, But I can print whole layers with mx.viz.print_summary()--It demonstrates my network has no concat problem, but I change to 600*1000, it went worong, is the system fit to any config.SCALES? below layers when I use `mx.viz.print_summary(net, {"data":(1,3,1056,640),"gt_boxes": (1, 100, 5), "label": (1, 23760), "bbox_target": (1, 36, 66, 40), "bbox_weight": (1, 36, 66, 40)})`, it seems no concat problem, but `mx.viz.print_summary(net, {"data":(1,3,600,1000),"gt_boxes": (1, 100, 5), "label": (1, 20646), "bbox_target": (1, 36, 37, 62), "bbox_weight": (1, 36, 37, 62)})` does not work -- `cls_prob(SoftmaxOutput) 1000 0 cls_score custom34 cls_prob_reshape(Reshape) 64x1000 0 cls_prob bbox_pred(FullyConnected) 4000 16388096drop7 _minus75(_sub) 4000 0 bbox_pred custom34 bbox_loss_(smooth_l1) 4000 0 _minus75 _mul75(_mul)4000 0 custom34 bbox_loss_ bbox_loss(MakeLoss) 4000 0 _mul75 bbox_loss_reshape(Reshape) 64x4000 0 bbox_loss label_reshape(Reshape) 64 0 custom34 blockgrad9(BlockGrad) 64 0 label_reshape Total params: 143975542 ` . . . too much to show @reminisce This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] qingzhouzhen commented on issue #7632: pvanet infer_shape error. Arguments
qingzhouzhen commented on issue #7632: pvanet infer_shape error. Arguments URL: https://github.com/apache/incubator-mxnet/issues/7632#issuecomment-325180013 I change the config.SCALES to 1056x640, It still went wrong with the same problem, But I can print whole layers with mx.viz.print_summary(), but I change to 600*1000, it went worong, is the system fit to any config.SCALES? below layers when I use `mx.viz.print_summary(net, {"data":(1,3,1056,640),"gt_boxes": (1, 100, 5), "label": (1, 23760), "bbox_target": (1, 36, 66, 40), "bbox_weight": (1, 36, 66, 40)})`, it seems no concat problem, but `mx.viz.print_summary(net, {"data":(1,3,600,1000),"gt_boxes": (1, 100, 5), "label": (1, 20646), "bbox_target": (1, 36, 37, 62), "bbox_weight": (1, 36, 37, 62)})` does not work -- `cls_prob(SoftmaxOutput) 1000 0 cls_score custom34 cls_prob_reshape(Reshape) 64x1000 0 cls_prob bbox_pred(FullyConnected) 4000 16388096drop7 _minus75(_sub) 4000 0 bbox_pred custom34 bbox_loss_(smooth_l1) 4000 0 _minus75 _mul75(_mul)4000 0 custom34 bbox_loss_ bbox_loss(MakeLoss) 4000 0 _mul75 bbox_loss_reshape(Reshape) 64x4000 0 bbox_loss label_reshape(Reshape) 64 0 custom34 blockgrad9(BlockGrad) 64 0 label_reshape Total params: 143975542 ` . . . too much to show @reminisce This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] szha opened a new pull request #7635: add fashion mnist and move mnists to s3
szha opened a new pull request #7635: add fashion mnist and move mnists to s3 URL: https://github.com/apache/incubator-mxnet/pull/7635 @mli This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services