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fix converter bug

This commit is contained in:
zhouwy19 2020-04-23 19:27:44 +08:00
parent c58f3fc3b7
commit b9eb3aab22
1 changed files with 14 additions and 2 deletions
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16
python/jittor/utils/pytorch_converter.py
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@ -113,6 +113,18 @@ pjmap = {
'links': {},
'extras': {},
},
'Dropout2d': {
'pytorch': {
'args': 'p=0.5, inplace=False',
},
'jittor': {
'module': 'nn',
'name': 'Dropout',
'args': 'p=0.5, is_train=False'
},
'links': {},
'extras': {},
},
'kaiming_normal_': {
'pytorch': {
'args': "tensor, a=0, mode='fan_in', nonlinearity='leaky_relu'",
@ -240,7 +252,7 @@ unsupport_ops = [
'Conv1d', 'Conv3d', 'ConvTranspose1d', 'ConvTranspose3d', 'Unfold', 'Fold',
'MaxPool1d', 'MaxPool3d', 'MaxUnpool1d', 'MaxUnpool2d', 'MaxUnpool3d', 'AvgPool1d', 'AvgPool3d', 'FractionalMaxPool2d', 'LPPool1d', 'LPPool2d', 'AdaptiveMaxPool1d', 'AdaptiveMaxPool2d', 'AdaptiveMaxPool3d', 'AdaptiveAvgPool1d', 'AdaptiveAvgPool3d',
'ReflectionPad1d', 'ReflectionPad2d', 'ReplicationPad1d', 'ReplicationPad2d', 'ReplicationPad3d', 'ZeroPad2d', 'ConstantPad1d', 'ConstantPad2d', 'ConstantPad3d', 'ELU', 'Hardshrink', 'Hardtanh', 'LogSigmoid', 'MultiheadAttention',
'PReLU', 'RReLU', 'SELU', 'CELU', 'GELU', 'Softplus', 'Softshrink', 'Softsign', 'Tanhshrink', 'Threshold', 'Softmin', 'Softmax2d', 'LogSoftmax', 'AdaptiveLogSoftmaxWithLoss', 'BatchNorm1d', 'BatchNorm3d', 'GroupNorm', 'SyncBatchNorm', 'InstanceNorm1d', 'InstanceNorm2d', 'InstanceNorm3d', 'LayerNorm', 'LocalResponseNorm', 'RNNBase', 'RNN', 'LSTM', 'GRU', 'RNNCell', 'LSTMCell', 'GRUCell', 'Transformer', 'TransformerEncoder', 'TransformerDecoder', 'TransformerEncoderLayer', 'TransformerDecoderLayer', 'Identity', 'Bilinear', 'Dropout2d', 'Dropout3d', 'AlphaDropout', 'Embedding', 'EmbeddingBag', 'CosineSimilarity', 'PairwiseDistance', 'L1Loss', 'MSELoss', 'CTCLoss', 'NLLLoss', 'PoissonNLLLoss', 'KLDivLoss', 'BCELoss', 'BCEWithLogitsLoss', 'MarginRankingLoss', 'HingeEmbeddingLoss', 'MultiLabelMarginLoss', 'SmoothL1Loss', 'SoftMarginLoss', 'MultiLabelSoftMarginLoss', 'CosineEmbeddingLoss', 'MultiMarginLoss', 'TripletMarginLoss', 'PixelShuffle', 'Upsample', 'UpsamplingNearest2d', 'UpsamplingBilinear2d', 'DataParallel', 'DistributedDataParallel', 'clip_grad_norm_', 'clip_grad_value_', 'parameters_to_vector', 'vector_to_parameters', 'BasePruningMethod', 'PruningContainer', 'Identity', 'RandomUnstructured', 'L1Unstructured', 'RandomStructured', 'LnStructured', 'CustomFromMask', 'identity', 'random_unstructured', 'l1_unstructured', 'random_structured', 'ln_structured', 'global_unstructured', 'custom_from_mask', 'remove', 'is_pruned', 'weight_norm', 'remove_weight_norm', 'spectral_norm', 'remove_spectral_norm', 'PackedSequence', 'pack_padded_sequence', 'pad_packed_sequence', 'pad_sequence', 'pack_sequence'
'PReLU', 'RReLU', 'SELU', 'CELU', 'GELU', 'Softplus', 'Softshrink', 'Softsign', 'Tanhshrink', 'Threshold', 'Softmin', 'Softmax2d', 'LogSoftmax', 'AdaptiveLogSoftmaxWithLoss', 'BatchNorm1d', 'BatchNorm3d', 'GroupNorm', 'SyncBatchNorm', 'InstanceNorm1d', 'InstanceNorm2d', 'InstanceNorm3d', 'LayerNorm', 'LocalResponseNorm', 'RNNBase', 'RNN', 'LSTM', 'GRU', 'RNNCell', 'LSTMCell', 'GRUCell', 'Transformer', 'TransformerEncoder', 'TransformerDecoder', 'TransformerEncoderLayer', 'TransformerDecoderLayer', 'Identity', 'Bilinear', 'Dropout3d', 'AlphaDropout', 'Embedding', 'EmbeddingBag', 'CosineSimilarity', 'PairwiseDistance', 'L1Loss', 'MSELoss', 'CTCLoss', 'NLLLoss', 'PoissonNLLLoss', 'KLDivLoss', 'BCELoss', 'BCEWithLogitsLoss', 'MarginRankingLoss', 'HingeEmbeddingLoss', 'MultiLabelMarginLoss', 'SmoothL1Loss', 'SoftMarginLoss', 'MultiLabelSoftMarginLoss', 'CosineEmbeddingLoss', 'MultiMarginLoss', 'TripletMarginLoss', 'PixelShuffle', 'Upsample', 'UpsamplingNearest2d', 'UpsamplingBilinear2d', 'DataParallel', 'DistributedDataParallel', 'clip_grad_norm_', 'clip_grad_value_', 'parameters_to_vector', 'vector_to_parameters', 'BasePruningMethod', 'PruningContainer', 'Identity', 'RandomUnstructured', 'L1Unstructured', 'RandomStructured', 'LnStructured', 'CustomFromMask', 'identity', 'random_unstructured', 'l1_unstructured', 'random_structured', 'ln_structured', 'global_unstructured', 'custom_from_mask', 'remove', 'is_pruned', 'weight_norm', 'remove_weight_norm', 'spectral_norm', 'remove_spectral_norm', 'PackedSequence', 'pack_padded_sequence', 'pad_packed_sequence', 'pad_sequence', 'pack_sequence'
]
support_ops = {}
@ -251,7 +263,7 @@ for key in pjmap.keys():
support_ops[key] = name
def raise_unsupport(name):
raise RuntimeError(f'{name} is not supported in Jittor yet. We will appreciate it if you provide an implementation of {a.attr} and make pull request at https://github.com/Jittor/jittor.')
raise RuntimeError(f'{name} is not supported in Jittor yet. We will appreciate it if you provide an implementation of {name} and make pull request at https://github.com/Jittor/jittor.')
def replace(a):
if hasattr(a, "attr") and a.attr in unsupport_ops: