[GitHub] [incubator-mxnet] DongfeiJi commented on issue #18643: ndarray.contrib.boolean_mask can not be hybridize
DongfeiJi commented on issue #18643: URL: https://github.com/apache/incubator-mxnet/issues/18643#issuecomment-652163234 > Hi @DongfeiJi , > It works for me on MXNet 2.0. > Note that `boolean_mask` doesn't work when mask are all `zero/false`, since the traditional operator doesn't support zero-size array. > > ```python > import mxnet as mx > from mxnet import gluon > from mxnet.gluon.loss import Loss, _apply_weighting > > class NewTripletLoss(Loss): > def __init__(self, batch_size_per_gpu, margin=1, weight=None, batch_axis=0, **kwargs): > super(NewTripletLoss, self).__init__(weight, batch_axis, **kwargs) > self.batch_size_per_gpu = batch_size_per_gpu > self.margin = margin > def hybrid_forward(self, F, embeddings, labels, sample_weight=None): > N = self.batch_size_per_gpu > # get distance > xx = F.power(embeddings, 2).sum(1, keepdims=True).tile((1, self.batch_size_per_gpu)) > dist = F.broadcast_add(xx, xx.transpose()) > dist = F.broadcast_sub(dist, 2 * F.dot(embeddings, embeddings.transpose())) > dist = F.clip(dist, 1e-12, 1e12) > # get mask > labels = F.cast(labels, dtype='float32') > labels = labels.expand_dims(1).tile((1, self.batch_size_per_gpu)) > is_pos = F.broadcast_equal(labels, labels.transpose()) > is_neg = F.broadcast_not_equal(labels, labels.transpose()) > # hard example mining > dist_mat = dist.reshape((self.batch_size_per_gpu * self.batch_size_per_gpu,)) > pos_mask = is_pos.reshape((self.batch_size_per_gpu * self.batch_size_per_gpu,)) > dist_ap = F.contrib.boolean_mask(dist_mat, pos_mask).reshape((self.batch_size_per_gpu, -1)) > #dist_ap = F.broadcast_mul(dist_mat, pos_mask).reshape((self.batch_size_per_gpu, -1)) > dist_ap = F.max(dist_ap, axis=1) > neg_mask = is_neg.reshape((self.batch_size_per_gpu * self.batch_size_per_gpu,)) > dist_an = F.contrib.boolean_mask(dist_mat, neg_mask).reshape((self.batch_size_per_gpu, -1)) > #dist_an = F.broadcast_mul(dist_mat, neg_mask).reshape((self.batch_size_per_gpu, -1)) > dist_an = F.min(dist_an, axis=1) > # add margin > margin = F.full(shape=(self.batch_size_per_gpu, 1), val=self.margin) > loss = F.broadcast_add(F.broadcast_sub(dist_ap, dist_an), margin) > loss = F.maximum(loss, F.zeros_like(loss)) > # apply weight > loss = _apply_weighting(F, loss, self._weight, sample_weight) > return F.mean(loss, axis=self._batch_axis, exclude=True) > > block = NewTripletLoss(2) > block.hybridize() > embeddings = mx.nd.array([[1.0, 0.0, 1.0], [1.0, 1.0, 0.0]]).reshape((2,3)) > embeddings.attach_grad() > labels = mx.nd.array([0, 1]).reshape((2, )) > with mx.autograd.record(): > out = block(embeddings, labels) > out.sum().backward() > print(out) > mx.nd.waitall() > ``` u can review this, i upload the example code [https://github.com/DongfeiJi/chineseocr_lite/blob/master/jdf.py](url) This is an automated message from the Apache Git Service. To respond to the message, please log on to 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
[GitHub] [incubator-mxnet] DongfeiJi commented on issue #18643: ndarray.contrib.boolean_mask can not be hybridize
DongfeiJi commented on issue #18643: URL: https://github.com/apache/incubator-mxnet/issues/18643#issuecomment-652161754 > Hi @DongfeiJi , > It works for me on MXNet 2.0. > Note that `boolean_mask` doesn't work when mask are all `zero/false`, since the traditional operator doesn't support zero-size array. > > ```python > import mxnet as mx > from mxnet import gluon > from mxnet.gluon.loss import Loss, _apply_weighting > > class NewTripletLoss(Loss): > def __init__(self, batch_size_per_gpu, margin=1, weight=None, batch_axis=0, **kwargs): > super(NewTripletLoss, self).__init__(weight, batch_axis, **kwargs) > self.batch_size_per_gpu = batch_size_per_gpu > self.margin = margin > def hybrid_forward(self, F, embeddings, labels, sample_weight=None): > N = self.batch_size_per_gpu > # get distance > xx = F.power(embeddings, 2).sum(1, keepdims=True).tile((1, self.batch_size_per_gpu)) > dist = F.broadcast_add(xx, xx.transpose()) > dist = F.broadcast_sub(dist, 2 * F.dot(embeddings, embeddings.transpose())) > dist = F.clip(dist, 1e-12, 1e12) > # get mask > labels = F.cast(labels, dtype='float32') > labels = labels.expand_dims(1).tile((1, self.batch_size_per_gpu)) > is_pos = F.broadcast_equal(labels, labels.transpose()) > is_neg = F.broadcast_not_equal(labels, labels.transpose()) > # hard example mining > dist_mat = dist.reshape((self.batch_size_per_gpu * self.batch_size_per_gpu,)) > pos_mask = is_pos.reshape((self.batch_size_per_gpu * self.batch_size_per_gpu,)) > dist_ap = F.contrib.boolean_mask(dist_mat, pos_mask).reshape((self.batch_size_per_gpu, -1)) > #dist_ap = F.broadcast_mul(dist_mat, pos_mask).reshape((self.batch_size_per_gpu, -1)) > dist_ap = F.max(dist_ap, axis=1) > neg_mask = is_neg.reshape((self.batch_size_per_gpu * self.batch_size_per_gpu,)) > dist_an = F.contrib.boolean_mask(dist_mat, neg_mask).reshape((self.batch_size_per_gpu, -1)) > #dist_an = F.broadcast_mul(dist_mat, neg_mask).reshape((self.batch_size_per_gpu, -1)) > dist_an = F.min(dist_an, axis=1) > # add margin > margin = F.full(shape=(self.batch_size_per_gpu, 1), val=self.margin) > loss = F.broadcast_add(F.broadcast_sub(dist_ap, dist_an), margin) > loss = F.maximum(loss, F.zeros_like(loss)) > # apply weight > loss = _apply_weighting(F, loss, self._weight, sample_weight) > return F.mean(loss, axis=self._batch_axis, exclude=True) > > block = NewTripletLoss(2) > block.hybridize() > embeddings = mx.nd.array([[1.0, 0.0, 1.0], [1.0, 1.0, 0.0]]).reshape((2,3)) > embeddings.attach_grad() > labels = mx.nd.array([0, 1]).reshape((2, )) > with mx.autograd.record(): > out = block(embeddings, labels) > out.sum().backward() > print(out) > mx.nd.waitall() > ``` Thank you again for your reply. It is OK to hybridize directly. However, if the initialization of the model is delayed, there will be problems. You can run my code. I report an error because I use gluon's trainer, and the mxnet version is 1.5. PS: This is an example of my code, for simplify, i do not use gluon.trainer. If use nd, it is ok, when hybridize, it does not work. `import mxnet from mxnet import nd from mxnet.gluon import nn from mxnet.gluon.loss import Loss, _apply_weighting class MyBlock(nn.HybridBlock): def __init__(self, **kwargs): super(MyBlock, self).__init__(**kwargs) self.conv = nn.Conv2D(channels=2048, kernel_size=1, strides=1, padding=0, use_bias=False) self.pool = nn.GlobalAvgPool2D() self.flatten = nn.Flatten() def hybrid_forward(self, F, x): x = self.conv(x) x = self.pool(x) x = self.flatten(x) return x class NewTripletLoss(Loss): def __init__(self, batch_size_per_gpu, margin=1, weight=None, batch_axis=0, **kwargs): super(NewTripletLoss, self).__init__(weight, batch_axis, **kwargs) self.batch_size_per_gpu = batch_size_per_gpu self.margin = margin def hybrid_forward(self, F, embeddings, labels, sample_weight=None): N = self.batch_size_per_gpu # get distance xx = F.power(embeddings, 2).sum(1, keepdims=True).tile((1, self.batch_size_per_gpu)) dist = F.broadcast_add(xx, xx.transpose()) dist = F.broadcast_sub(dist, 2 * F.dot(embeddings, embeddings.transpose())) dist = F.clip(dist, 1e-12, 1e12).sqrt() print(dist) # get mask
[GitHub] [incubator-mxnet] DongfeiJi commented on issue #18643: ndarray.contrib.boolean_mask can not be hybridize
DongfeiJi commented on issue #18643: URL: https://github.com/apache/incubator-mxnet/issues/18643#issuecomment-651723106 This problem will occur when this operation is used in loss This is an automated message from the Apache Git Service. To respond to the message, please log on to 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
[GitHub] [incubator-mxnet] DongfeiJi commented on issue #18643: ndarray.contrib.boolean_mask can not be hybridize
DongfeiJi commented on issue #18643: URL: https://github.com/apache/incubator-mxnet/issues/18643#issuecomment-651722037 > provide a minimal reproduce examp This is an automated message from the Apache Git Service. To respond to the message, please log on to 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
[GitHub] [incubator-mxnet] DongfeiJi commented on issue #18643: ndarray.contrib.boolean_mask can not be hybridize
DongfeiJi commented on issue #18643: URL: https://github.com/apache/incubator-mxnet/issues/18643#issuecomment-651721010 > Could you provide a minimal reproduce example? > > In MXNet unittest, The test of hybridized `boolean_mask` works. > https://github.com/apache/incubator-mxnet/blob/master/tests/python/unittest/test_dynamic_shape.py#L39 --- Thank you for your reply. Yes, I read the code before. But in my scenario, I used it in the loss function，when i use broadcast_mul it can hybridize, use boolean_mask it does not work. `class NewTripletLoss(Loss): def __init__(self, batch_size_per_gpu, margin=1, weight=None, batch_axis=0, **kwargs): super(NewTripletLoss, self).__init__(weight, batch_axis, **kwargs) self.batch_size_per_gpu = batch_size_per_gpu self.margin = margin def hybrid_forward(self, F, embeddings, labels, sample_weight=None): N = self.batch_size_per_gpu # get distance xx = F.power(embeddings, 2).sum(1, keepdims=True).tile((1, self.batch_size_per_gpu)) dist = F.broadcast_add(xx, xx.transpose()) dist = F.broadcast_sub(dist, 2 * F.dot(embeddings, embeddings.transpose())) dist = F.clip(dist, 1e-12, 1e12) # get mask labels = F.cast(labels, dtype='float32') labels = labels.expand_dims(1).tile((1, self.batch_size_per_gpu)) is_pos = F.broadcast_equal(labels, labels.transpose()) is_neg = F.broadcast_not_equal(labels, labels.transpose()) # hard example mining dist_mat = dist.reshape((self.batch_size_per_gpu * self.batch_size_per_gpu,)) pos_mask = is_pos.reshape((self.batch_size_per_gpu * self.batch_size_per_gpu,)) dist_ap = F.contrib.boolean_mask(dist_mat, pos_mask).reshape((self.batch_size_per_gpu, -1)) # dist_ap = F.broadcast_mul(dist_mat, pos_mask).reshape((self.batch_size_per_gpu, -1)) dist_ap = F.max(dist_ap, axis=1) neg_mask = is_neg.reshape((self.batch_size_per_gpu * self.batch_size_per_gpu,)) dist_an = F.contrib.boolean_mask(dist_mat, neg_mask).reshape((self.batch_size_per_gpu, -1)) # dist_an = F.broadcast_mul(dist_mat, neg_mask).reshape((self.batch_size_per_gpu, -1)) dist_an = F.min(dist_an, axis=1) # add margin margin = F.full(shape=(self.batch_size_per_gpu, 1), val=self.margin) loss = F.broadcast_add(F.broadcast_sub(dist_ap, dist_an), margin) loss = F.maximum(loss, F.zeros_like(loss)) # apply weight loss = _apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss, axis=self._batch_axis, exclude=True)` This is an automated message from the Apache Git Service. To respond to the message, please log on to 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
