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kjunelee / MetaOptNet

Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)
Apache License 2.0
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Project dependencies may have API risk issues #58

Open PyDeps opened 1 year ago

PyDeps commented 1 year ago

Hi, In MetaOptNet, inappropriate dependency versioning constraints can cause risks.

Below are the dependencies and version constraints that the project is using

torch==1.0.1.post2
torchvision==0.2.2.post2
qpth==0.0.13
torchnet
tqdm

The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict. The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.

After further analysis, in this project, The version constraint of dependency tqdm can be changed to >=4.36.0,<=4.64.0.

The above modification suggestions can reduce the dependency conflicts as much as possible, and introduce the latest version as much as possible without calling Error in the projects.

The invocation of the current project includes all the following methods.

The calling methods from the tqdm 
tqdm.tqdm
The calling methods from the all methods 
self._compute_block_mask
torch.gesv
utils.check_dir
torch.save
torch.optim.SGD.zero_grad
utils.count_accuracy.item
time.time
numpy.asarray
log_prb.smoothed_one_hot.sum
torch.nn.BatchNorm2d
self.block_size.torch.zeros.cuda.long
binv
Y_support_reshaped.unsqueeze.expand.unsqueeze
self.get_iterator
batched_kronecker
b_mat.size.b_mat.new_ones.diag
n_query.n_support.tasks_per_batch.torch.ones.cuda
torch.nn.MaxPool2d
label.view.view
n_way.torch.arange.long
Y_support.reshape.transpose
self.block_size.self.block_size.self.block_size.torch.arange.view.expand.reshape
torch.nn.AvgPool2d
utils.count_accuracy
x.float
os.mkdir
nr_blocks.offsets.repeat.view
self.block_size.torch.arange.view.expand
torch.nn.functional.log_softmax
enumerate
self.bn1
b_mat.new_ones
torch.nn.ReLU
x.view.size
support.size
round
self.block_size.self.block_size.torch.arange.repeat.self.block_size.self.block_size.self.block_size.torch.arange.view.expand.reshape.torch.stack.t.cuda
pickle.load
self.block_size.width.self.block_size.height.channels.batch_size.bernoulli.sample.cuda
matrix1.reshape.unsqueeze
models.classification_heads.ClassificationHead
label.label2inds.append
torch.Tensor.Variable.cuda
self._make_layer
models.dropblock.DropBlock
self.ResNet.super.__init__
pickle._Unpickler
models.ResNet12_embedding.resnet12.cuda
torch.Size
self.bn2
self.encoder.view
torch.cat
self.block_size.torch.arange.repeat
self.block1
query.query.sum.expand_as
qp_sol.float.unsqueeze.expand.reshape
torch.stack
self.DropBlock
format
self.ClassificationHead.super.__init__
data.CIFAR_FS.CIFAR_FS
torch.Tensor.Variable.cuda.detach
n_support.n_way.tasks_per_batch.torch.zeros.cuda
data_support.reshape
n_support.n_way.tasks_per_batch.torch.ones.cuda
os.path.exists
emb_support.reshape.reshape
float
self.encoder
support_labels_one_hot.transpose.sum
one_hot
load_data
torch.autograd.Variable
get_dataset
A_i.unsqueeze.expand
numpy.mean
n_support.n_way.torch.eye.expand
self.sample_test_examples_for_base_categories
self.sample_train_and_test_examples_for_novel_categories
numpy.concatenate
dloader_val
self.block_size.width.self.block_size.height.channels.batch_size.bernoulli.sample.cuda.nonzero
matrix2.size.list.matrix1.size.list.matrix1.size.matrix2_flatten.unsqueeze.matrix1_flatten.unsqueeze.torch.bmm.reshape.permute.reshape
torch.nn.init.constant_
torchvision.transforms.Compose
matrix2.reshape
block_kernel_matrix_inter.repeat
utils.Timer
models.protonet_embedding.ProtoNetEmbedding.cuda
list
self.block_size.torch.arange.view
self.layer2
torch.zeros
models.ResNet12_embedding.resnet12
self._compute_block_mask.sum
encoded_indicies.scatter_.scatter_
Y_support.reshape.view
data_novel.buildLabelIndex.keys
computeGramMatrix
cls_head
torch.nn.functional.dropout.size
torch.sum
labels_query.reshape
utils.log
torch.autograd.Variable.detach
embedding_net
numpy.random.choice
n_way.n_support.n_support.n_way.torch.arange.long.repeat.reshape.transpose.reshape
computeGramMatrix.repeat
self.createExamplesTensorData
random.sample
numpy.unique
self.sample_base_and_novel_categories
m.weight.data.normal_
str
prototypes.prototypes.sum
utils.Timer.measure
self.layer3
n_way.n_support.tasks_per_batch.torch.ones.cuda
m.weight.data.fill_
A_i.unsqueeze.expand.unsqueeze
query.size
data.mini_imagenet.MiniImageNet
indices.view
f.write
matrix2.reshape.unsqueeze
torch.nn.Dropout
label.pred.eq.float
labels_train_transposed.sum.expand_as
query.query.sum
cls_head.reshape
n_support.n_way.n_support.n_way.tasks_per_batch.n_support.n_way.torch.eye.expand.cuda
data_query.reshape
torch.ones
torch.nn.Parameter
torch.optim.lr_scheduler.LambdaLR.step
matrix2.size.list.matrix1.size.list.matrix1.size.matrix2_flatten.unsqueeze.matrix1_flatten.unsqueeze.torch.bmm.reshape.permute
conv3x3
x_entropy.mean
len
argparse.ArgumentParser.parse_args
random.seed
self.DropBlock.super.__init__
embedding_net.state_dict
Y_support.reshape.reshape
support_labels_one_hot.reshape.reshape
torch.optim.SGD
models.protonet_embedding.ProtoNetEmbedding
torchvision.transforms.Normalize
support_labels_one_hot.reshape.transpose
n_way.n_way.tasks_per_batch.n_way.torch.eye.expand.cuda
self.ProtoNetEmbedding.super.__init__
max
torch.LongTensor
logits.torch.argmax.view.eq
set
ResNet
models.classification_heads.ClassificationHead.cuda
torchvision.transforms.ColorJitter
self.block4
train_accuracies.append
self.modules
self.block3.size
offsets.long.long
torch.nn.CrossEntropyLoss
block
qp_sol.float.unsqueeze.expand.permute
emb_query.reshape.reshape
compatibility.unsqueeze.expand.float
int
non_zero_idxs.repeat.repeat
self.BasicBlock.super.__init__
ValueError
qpth.qp.QPFunction
torch.nn.Sequential.add_module
self.block_size.torch.zeros.cuda
torch.distributions.Bernoulli.sample
utils.set_gpu
self.block.add_module
self.block3.view
numpy.array
self.layer4
data_base.buildLabelIndex.keys
torchnet.dataset.ListDataset.parallel
kernel_matrix_mask_y.kernel_matrix_mask_x.float
torch.arange
self.conv2
numpy.arange
query.dim
torch.eye
torch.nn.init.kaiming_normal_
self.block_size.self.block_size.torch.arange.repeat.self.block_size.self.block_size.self.block_size.torch.arange.view.expand.reshape.torch.stack.t
x.cuda
logits.torch.argmax.view
pickle._Unpickler.load
kernel_matrix_mask_second_term.float.float
models.R2D2_embedding.R2D2Embedding
R2D2_conv_block
self.relu
B.dim
matrix1.size
n_support.n_support.tasks_per_batch.torch.ones.cuda
range
torch.optim.SGD.step
torch.sum.transpose
val_accuracies.append
torch.optim.lr_scheduler.LambdaLR
matrix2.size
self.block
torch.nn.functional.pad
print
math.sqrt
torch.argmax
prototypes.div.div
n_way.tasks_per_batch.torch.ones.cuda
vars
tqdm.tqdm
numpy.std
prototypes.prototypes.sum.reshape.expand_as
torch.autograd.Variable.dim
offsets.long.repeat
torch.ones.detach
torch.distributions.Bernoulli
matrix2_flatten.unsqueeze.matrix1_flatten.unsqueeze.torch.bmm.reshape
n_support.n_support.tasks_per_batch.n_support.torch.eye.expand.cuda
torch.sum.view
x.float.cuda
dloader_train
os.path.join
numpy.random.seed
torch.nn.LeakyReLU
matrix1.reshape
depth.torch.Size.indices.size.torch.zeros.cuda
b_mat.size.b_mat.new_ones.diag.expand_as
n_way.torch.eye.expand
x.train
b_mat.b_mat.size.b_mat.new_ones.diag.expand_as.cuda
val_losses.append
self.R2D2Embedding.super.__init__
n_support.tasks_per_batch.support_labels.reshape.expand
f.flush
self.sampleCategories
label.pred.eq.float.mean
torch.nn.Conv2d
torch.nn.Sequential
self.block2
data.tiered_imagenet.tieredImageNet
qp_sol.float.unsqueeze
n_way.n_support.n_support.n_way.torch.arange.long.repeat.reshape.transpose
indices.size
self.sampleImageIdsFrom
qp_sol.float.unsqueeze.expand
data.FC100.FC100
self.block4.size
self.encoder.size
x.eval
torchnet.dataset.ListDataset
support_labels.view
support_labels_one_hot.reshape.view
compatibility.unsqueeze.expand.unsqueeze
PIL.Image.fromarray
n_support.torch.eye.expand
support_labels.reshape
buildLabelIndex
torchvision.transforms.RandomCrop
self.downsample
models.classification_heads.ClassificationHead.cuda.state_dict
numpy.load
argparse.ArgumentParser.add_argument
open
prototypes.prototypes.sum.reshape
models.R2D2_embedding.R2D2Embedding.cuda
B.transpose
x.view.view
n_way.n_support.n_support.n_way.torch.arange.long.repeat.reshape.transpose.reshape.repeat
support.dim
isinstance
self.head
b_mat.size
qp_sol.float.unsqueeze.expand.float
self.ConvBlock.super.__init__
self.bn3
self.transform
zip
self.avgpool
n_support.n_way.n_support.n_support.n_way.torch.arange.long.repeat.reshape.transpose.reshape.repeat.cuda
x_entropy
x.double.cuda
random.shuffle
self.sample_episode
data_loader
self._compute_block_mask.size
support.transpose
self.label2ind.keys
maxIter.QPFunction
torch.nn.DataParallel
self.layer1
torch.Tensor
ConvBlock
self.maxpool
torch.FloatTensor
Y_support_reshaped.unsqueeze.expand
argparse.ArgumentParser
torch.bmm
embedding_net.parameters
sorted
train_losses.append
x_entropy.backward
x_entropy.item
self.conv3
layers.append
B.size
numpy.sqrt
torch.nn.functional.dropout
x.double
m.bias.data.zero_
n_way.torch.arange.long.repeat
super
V.V.computeGramMatrix.detach
models.classification_heads.ClassificationHead.cuda.parameters
self.conv1
self.block4.view
compatibility.unsqueeze.expand
torch.autograd.Variable.size
torchvision.transforms.ToTensor
get_model
n_way.kernel_matrix.repeat.kernel_matrix_mask_second_term.repeat
n_support.n_way.torch.arange.long.repeat.reshape
torchvision.transforms.RandomHorizontalFlip
self.block3

@developer Could please help me check this issue? May I pull a request to fix it? Thank you very much.