Paddle1.8-Pytorch-API对照表
环境：paddlepaddle1.8
|
Pytorch的API名称 |
Paddle的API名称 |
|
torch.abs |
fluid.layers.abs(x, name=None) |
|
torch.acos |
fluid.layers.acos(x, name=None) |
|
torch.add |
fluid.layers.elementwise_add(x, y, axis=-1, act=None, name=None) |
|
torch.allclose |
fluid.layers.allclose |
|
torch.arange |
fluid.layers.range(start, end, step, dtype) |
|
torch.argmax |
fluid.layers.argmax |
|
torch.argmin |
fluid.layers.argmin |
|
torch.argsort |
fluid.layers.argsort(input, axis=-1, descending=False, name=None) |
|
torch.as_strided |
fluid.layers.strided_slice(input, axes, starts, ends, strides): |
|
torch.asin |
fluid.layers.asin(x, name=None) |
|
torch.atan |
fluid.layers.atan(x, name=None) |
|
torch.cat |
fluid.layers.concat(input, axis=0, name=None) |
|
torch.ceil |
fluid.layers.ceil |
|
torch.chunk |
fluid.layers.unstack(x, axis=0, num=None) |
|
torch.cos |
fluid.layers.cos |
|
torch.cumsum |
fluid.layers.cumsum(x, axis=None, exclusive=None, reverse=None) |
|
torch.diag |
fluid.layer.diag(diagonal) |
|
torch.diag_embed |
fluid.layer.diag_embed |
|
torch.div |
fluid.layers.elementwise_div(x, y, axis=-1, act=None, name=None) |
|
torch.eq |
fluid.layers.equal(x, y, cond=None) |
|
torch.equal |
fluid.layers.elementwise_equal(x, y, name=None) |
|
torch.equal |
fluid.layers.equal(x, y, cond=None) |
|
torch.erf |
fluid.layers.erf(x) |
|
torch.exp |
fluid.layers.exp(x, name=None) |
|
torch.eye |
fluid.layers.eye(num_rows, num_columns=None, batch_shape=None, dtype='float32') |
|
torch.flatten |
fluid.layers.flatten |
|
torch.flip |
fluid.layers.flip |
|
torch.floor |
fluid.layers.floor(x, name=None) |
|
torch.fmod |
fluid.layers.elementwise_mod(x, y, axis=-1, act=None, name=None) |
|
torch.from_numpy |
fluid.dygraph.to_variable(value, block=None, name=None) |
|
torch.full |
fluid.layers.full(shape, fill_value, out=None, dtype=None, device=None, stop_gradient=True, name=None) |
|
torch.full_like |
fluid.layers.full_like |
|
torch.gather |
fluid.layers.gather(input, axis, index, out=None, sparse_grad=False, name=None) |
|
torch.ge |
fluid.layers.greater_equal |
|
torch.gt |
fluid.layers.greater_than |
|
torch.le |
fluid.layers.less_equal |
|
torch.linspace |
fluid.layers.linspace(start, stop, num, dtype) |
|
torch.log |
fluid.layers.log(x, name=None) |
|
torch.logical_not |
fluid.layers.logical_not(x, out=None, name=None) |
|
torch.logical_xor |
fluid.layers.logical_xor(x, y, out=None, name=None) |
|
torch.lt |
fluid.layers.less_than |
|
torch.masked_select |
fluid.layers.masked_select |
|
torch.matmul |
fluid.layers.matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None) |
|
torch.max |
fluid.layers.max(input, axis=None, keepdim=False, out=None, name=None) -> (Tensor, LongTensor) |
|
torch.mean |
fluid.layers.reduce_mean(input, dim=None, keep_dim=False, name=None) |
|
torch.min |
fluid.layers.min |
|
torch.mm |
fluid.layers.mm |
|
torch.mul |
fluid.layers.mul |
|
torch.mv |
fluid.layers.matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None) |
|
torch.ne |
fluid.layers.not_equal |
|
torch.nn.AdaptiveAvgPool2d |
fluid.layers.adaptive_pool2d |
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torch.nn.AdaptiveAvgPool3d |
fluid.layers.adaptive_pool3d |
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torch.nn.AdaptiveMaxPool2d |
fluid.layers.adaptive_pool2d |
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torch.nn.AdaptiveMaxPool3d |
fluid.layers.adaptive_pool3d |
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torch.nn.add_module |
fluid.dygraph.Layer.add_sublayer(name, sublayer) |
|
torch.nn.append |
fluid.dygraph.LayerList.append(sublayer) |
|
torch.nn.append |
fluid.dygraph.ParameterList.append(parameter) |
|
torch.nn.AvgPool2d |
fluid.layers.pool2d(pool_type为'avg') |
|
torch.nn.AvgPool3d |
fluid.layers.pool3d(pool_type为'avg') |
|
torch.nn.BatchNorm1d |
fluid.layers.batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', in_place=False, name=None, moving_mean_name=None, moving_variance_name=None, do_model_average_for_mean_and_var=False, use_global_stats=False) |
|
torch.nn.BatchNorm2d |
fluid.layers.batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', in_place=False, name=None, moving_mean_name=None, moving_variance_name=None, do_model_average_for_mean_and_var=False, use_global_stats=False) |
|
torch.nn.BatchNorm3d |
fluid.layers.batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', in_place=False, name=None, moving_mean_name=None, moving_variance_name=None, do_model_average_for_mean_and_var=False, use_global_stats=False) |
|
torch.nn.Bilinear |
fluid.layers.bilinear_tensor_product(x, y, size, act=None, name=None, param_attr=None, bias_attr=None) |
|
torch.nn.ConstantPad1d |
fluid.layers.pad |
|
torch.nn.ConstantPad2d |
fluid.layers.pad / fluid.layers.pad2d |
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torch.nn.ConstantPad3d |
fluid.layers.pad |
|
torch.nn.Conv2d |
fluid.dygraph.Conv2D |
|
torch.nn.Conv3d |
fluid.dygraph.Conv3D |
|
torch.nn.ConvTranspose2d |
fluid.layers.conv2d_transpose |
|
torch.nn.ConvTranspose3d |
fluid.layers.conv3d_transpose |
|
torch.nn.double |
fluid.layers.cast(x, dtype) |
|
torch.nn.Dropout |
fluid.layers.dropout(x, dropout_prob, is_test=False, seed=None, name=None, dropout_implementation='downgrade_in_infer') |
|
torch.nn.ELU |
fluid.layers.elu(x, alpha=1.0, name=None) |
|
torch.nn.Embedding |
fluid.dygraph.Embedding |
|
torch.nn.eval |
fluid.dygraph.Layer.eval() |
|
torch.nn.float |
fluid.layers.cast(x, dtype) |
|
torch.nn.Fold |
fluid.layers.unfold |
|
torch.nn.functional.adaptive_avg_pool2d |
fluid.layers.adaptive_pool2d |
|
torch.nn.functional.adaptive_avg_pool3d |
fluid.layers.adaptive_pool3d |
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torch.nn.functional.adaptive_max_pool2d |
fluid.layers.adaptive_pool2d |
|
torch.nn.functional.adaptive_max_pool3d |
fluid.layers.adaptive_pool3d |
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torch.nn.functional.affine_grid |
fluid.layers.affine_grid |
|
torch.nn.functional.avg_pool2d |
fluid.layers.pool2d(input, pool_size=-1, pool_type="max", pool_stride=1, pool_padding=0, global_pooling=False, use_cudnn=True, ceil_mode=False, name=None, exclusive=True, data_format="NCHW")设置pool_type="avg" |
|
torch.nn.functional.avg_pool3d |
fluid.layers.pool3d(input, pool_size=-1, pool_type="max", pool_stride=1, pool_padding=0, global_pooling=False, use_cudnn=True, ceil_mode=False, name=None, exclusive=True, data_format="NCDHW")设置pool_type="avg" |
|
torch.nn.functional.cross_entropy |
fluid.layers.softmax_with_cross_entropy |
|
torch.nn.functional.ctc_loss |
fluid.layers.warpctc |
|
torch.nn.functional.elu |
fluid.layers.elu |
|
torch.nn.functional.embedding |
fluid.embedding |
|
torch.nn.functional.embedding_bag |
fluid.contrib.fused_embedding_seq_pool |
|
torch.nn.functional.glu |
fluid.nets.glu(input, dim=-1) |
|
torch.nn.functional.grid_sample |
fluid.layers.grid_sampler |
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torch.nn.functional.hardshrink |
fluid.layers.hard_shrink |
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torch.nn.functional.interpolate |
fluid.layers.interpolate |
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torch.nn.functional.kl_div |
fluid.layers.kldiv_loss |
|
torch.nn.functional.leaky_relu |
fluid.layers.leaky_relu |
|
torch.nn.functional.logsigmoid |
fluid.layers.logsigmoid |
|
torch.nn.functional.margin_ranking_loss |
fluid.layers.margin_rank_loss(label, left, right, margin=0.1, name=None) |
|
torch.nn.functional.max_pool2d |
fluid.layers.pool2d(input, pool_size=-1, pool_type="max", pool_stride=1, pool_padding=0, global_pooling=False, use_cudnn=True, ceil_mode=False, name=None, exclusive=True, data_format="NCHW")设置pool_type="max" |
|
torch.nn.functional.max_pool3d |
fluid.layers.pool3d(input, pool_size=-1, pool_type="max", pool_stride=1, pool_padding=0, global_pooling=False, use_cudnn=True, ceil_mode=False, name=None, exclusive=True, data_format="NCDHW")设置pool_type="max" |
|
torch.nn.functional.mse_loss |
fluid.layers.mse_loss(input, label) |
|
torch.nn.functional.one_hot |
fluid.one_hot |
|
torch.nn.functional.pad |
fluid.layers.pad(x, paddings, pad_value=0.0, name=None) |
|
torch.nn.functional.pixel_shuffle |
fluid.layers.pixel_shuffle(x, upscale_factor) |
|
torch.nn.functional.prelu |
fluid.layers.prelu(x, mode, param_attr=None, name=None) |
|
torch.nn.functional.relu |
fluid.layers.relu(x, name=None) |
|
torch.nn.functional.relu6 |
fluid.layers.relu6 |
|
torch.nn.functional.selu |
fluid.layers.selu |
|
torch.nn.functional.sigmoid |
fluid.layers.sigmoid |
|
torch.nn.functional.smooth_l1_loss |
fluid.layers.smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None) |
|
torch.nn.functional.softmax |
fluid.layers.softmax |
|
torch.nn.functional.softplus |
fluid.layers.softplus |
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torch.nn.functional.softshrink |
fluid.layers.softshrink |
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torch.nn.functional.softsign |
fluid.layers.softsign |
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torch.nn.functional.tanh |
fluid.layers.tanh |
|
torch.nn.functional.tanhshrink |
fluid.layers.tanh_shrink |
|
torch.nn.functional.threshold |
fluid.layers.thresholded_relu |
|
torch.nn.functional.unfold |
fluid.layers.unfold(x, kernel_size, strides=1, paddings=0, dilation=1, name=None) |
|
torch.nn.GroupNorm |
fluid.layers.group_norm(input, groups, epsilon=1e-05, param_attr=None, bias_attr=None, act=None, data_layout='NCHW', name=None) |
|
torch.nn.GRU |
fluid.layers.dynamic_gru |
|
torch.nn.GRUCell |
fluid.layers.GRUCell |
|
torch.nn.half |
fluid.layers.cast(x, dtype) |
|
torch.nn.Hardshrink |
fluid.layers.hard_shrink(x, threshold=None) |
|
torch.nn.init.constant_ |
fluid.initializer.ConstantInitializer(value=0.0, force_cpu=False) |
|
torch.nn.init.eye_ |
fluid.layers.eye(num_rows, num_columns=None, batch_shape=None, dtype='float32') |
|
torch.nn.init.kaiming_normal_ |
fluid.initializer.MSRAInitializer(uniform=True, fan_in=None, seed=0) |
|
torch.nn.init.kaiming_uniform_ |
fluid.initializer.MSRAInitializer(uniform=True, fan_in=None, seed=0) |
|
torch.nn.init.normal_ |
fluid.initializer.NormalInitializer(loc=0.0, scale=1.0, seed=0) |
|
torch.nn.init.ones_ |
fluid.layers.ones(shape, dtype, force_cpu=False) |
|
torch.nn.init.uniform_ |
fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0) |
|
torch.nn.init.xavier_normal_ |
fluid.initializer.XavierInitializer(uniform=True, fan_in=None, fan_out=None, seed=0 |
|
torch.nn.init.xavier_uniform_ |
fluid.initializer.XavierInitializer(uniform=True, fan_in=None, fan_out=None, seed=0 |
|
torch.nn.init.zeros_ |
fluid.layers.zeros(shape, dtype, force_cpu=False) |
|
torch.nn.InstanceNorm1d |
fluid.layers.instance_norm |
|
torch.nn.InstanceNorm2d |
fluid.layers.instance_norm |
|
torch.nn.InstanceNorm3d |
fluid.layers.instance_norm |
|
torch.nn.LayerNorm |
fluid.layers.layer_norm(input, scale=True, shift=True, begin_norm_axis=1, epsilon=1e-05, param_attr=None, bias_attr=None, act=None, name=None) |
|
torch.nn.LeakyReLU |
fluid.layers.leaky_relu(x, alpha=0.02, name=None) |
|
torch.nn.Linear |
fluid.dygraph.Linear(input_dim, output_dim, param_attr=None, bias_attr=None, act=None, dtype='float32') |
|
torch.nn.load_state_dict |
fluid.dygraph.Layer.set_dict(stat_dict, include_sublayers=True) |
|
torch.nn.LogSigmoid |
fluid.layers.logsigmoid(x, name=None) |
|
torch.nn.LSTM |
fluid.layers.lstm |
|
torch.nn.LSTMCell |
fluid.layers.LSTMCell |
|
torch.nn.MarginRankingLoss |
fluid.layers.margin_rank_loss(label, left, right, margin=0.1, name=None) |
|
torch.nn.MaxPool2d |
fluid.layers.pool2d(pool_type为'max') |
|
torch.nn.MaxPool3d |
fluid.layers.pool3d(pool_type为'max') |
|
torch.nn.Module |
fluid.dygraph.Layer() |
|
torch.nn.modules |
fluid.dygraph.Layer.sublayers(include_sublayers=True) |
|
torch.nn.MSELoss |
fluid.dygraph.MSELoss(input, label) |
|
torch.nn.MultiheadAttention |
fluid.nets.scaled_dot_product_attention |
|
torch.nn.named_modules |
fluid.dygraph.Layer.named_sublayers(prefix='',include_sublayers=True,include_self=False,layers_set=None) |
|
torch.nn.named_parameters |
fluid.dygraph.Layer.named_parameters(prefix='', include_sublayers=True) |
|
torch.nn.ParameterList |
fluid.dygraph.ParameterList |
|
torch.nn.parameters |
fluid.dygraph.Layer.parameters(include_sublayers=True) |
|
torch.nn.PixelShuffle |
fluid.layers.pixel_shuffle |
|
torch.nn.PReLU |
fluid.layers.prelu |
|
torch.nn.ReflectionPad2d |
fluid.layers.pad2d |
|
torch.nn.register_parameter |
fluid.dygraph.Layer.add_parameter(name, parameter) |
|
torch.nn.ReLU |
fluid.layers.relu(x, name=None) |
|
torch.nn.ReLU6 |
fluid.layers.relu6 |
|
torch.nn.ReplicationPad2d |
fluid.layers.pad2d |
|
torch.nn.requires_grad_ |
fluid.Variable().stop_gradient |
|
torch.nn.RNN |
fluid.layers.rnn |
|
torch.nn.RNNCell |
fluid.layers.RNNCell |
|
torch.nn.SELU |
fluid.layers.selu |
|
torch.nn.Sequential |
fluid.dygraph.Sequential(*layers) |
|
torch.nn.Sigmoid |
fluid.layers.sigmoid(x, name=None) |
|
torch.nn.SmoothL1Loss |
fluid.layers.smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None) |
|
torch.nn.Softmax |
fluid.layers.softmax(input, use_cudnn=False, name=None, axis=-1) |
|
torch.nn.Softplus |
fluid.layers.softplus |
|
torch.nn.Softshrink |
fluid.layers.softshrink(x, alpha=None) |
|
torch.nn.Softsign |
fluid.layers.softsign(x, name=None) |
|
torch.nn.state_dict |
fluid.dygraph.Layer.state_dict |
|
torch.nn.Tanh |
fluid.layers.tanh(x, name=None) |
|
torch.nn.Tanhshrink |
fluid.layers.tanh_shrin |
|
torch.nn.Threshold |
fluid.layers.thresholded_relu(x, threshold=None) |
|
torch.nn.train |
fluid.dygraph.Layer.train() |
|
torch.nn.type |
fluid.layers.cast(x, dtype) |
|
torch.nn.UpsamplingBilinear2d |
fluid.layers.resize_bilinear |
|
torch.nn.UpsamplingNearest2d |
fluid.layers.resize_nearest |
|
torch.nn.zero_grad |
fluid.dygraph.Layer.clear_gradients() |
|
torch.nn.ZeroPad2d |
fluid.layers.pad / fluid.layers.pad2d |
|
torch.norm |
fluid.layers.l2_normalize |
|
torch.normal |
fluid.layers.gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32') |
|
torch.numel |
fluid.layers.size(input) |
|
torch.ones |
fluid.layers.ones |
|
torch.ones_like |
fluid.layers.ones_like |
|
torch.pow |
fluid.layers.pow |
|
torch.prod |
fluid.layers.reduce_prod(input, dim=None, keep_dim=False, name=None) |
|
torch.rand_like |
fluid.layers.gaussian_random_batch_size_like(input, shape, input_dim_idx=0, output_dim_idx=0, mean=0.0, std=1.0, seed=0, dtype='float32')[ |
|
torch.randint |
fluid.layers.randint(low, high=None, shape=None, out=None, dtype=None, device=None, stop_gradient=False, seed=0, name=None) |
|
torch.randn |
fluid.layers.randn |
|
torch.randn_like |
fluid.layers.gaussian_random_batch_size_like |
|
torch.randperm |
fluid.layers.randperm |
|
torch.range |
fluid.layers.range |
|
torch.reciprocal |
fluid.layers.reciprocal(x, name=None) |
|
torch.reshape |
fluid.layers.reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None) |
|
torch.roll |
fluid.layers.roll |
|
torch.round |
fluid.layers.round(x, name=None) |
|
torch.rsqrt |
fluid.layers.rsqrt(x, name=None) |
|
torch.seed |
fluid.Program.random_seed |
|
torch.set_num_interop_threads |
fluid.ExecutionStrategy.num_threads |
|
torch.set_num_threads |
fluid.cpu_places(device_count=None) |
|
torch.sigmoid |
fluid.layers.sigmoid(x, name=None) |
|
torch.sign |
fluid.layers.sign(x) |
|
torch.sin |
fluid.layers.sin(x, name=None) |
|
torch.split |
fluid.layers.split(input, num_or_sections, dim=-1, name=None) |
|
torch.sqrt |
fluid.layers.squeeze(input, axes, name=None) |
|
torch.squeeze |
fluid.layers.squeeze(input, axes, name=None) |
|
torch.stack |
fluid.layers.stack(x, axis=0) |
|
torch.std_mean |
fluid.layers.mean |
|
torch.sum |
fluid.layers.sum(x) |
|
torch.sum |
fluid.layers.elementwise_add(x, y, axis=-1, act=None, name=None) |
|
torch.tan |
fluid.layers.tanh(x, name=None) |
|
torch.tanh |
fluid.layers.tanh(x, name=None) |
|
torch.Tensor.abs |
fluid.layers.abs(x, name=None) |
|
torch.Tensor.abs_ |
fluid.layers.abs(x, name=None) |
|
torch.Tensor.acos |
fluid.layers.acos(x, name=None) |
|
torch.Tensor.acos_ |
fluid.layers.acos(x, name=None) |
|
torch.Tensor.add_ |
fluid.layers.elementwise_add(x, y, axis=-1, act=None, name=None) |
|
torch.Tensor.asin_ |
fluid.layers.asin(x, name=None) |
|
torch.Tensor.atan_ |
fluid.layers.atan(x, name=None) |
|
torch.Tensor.backward |
fluid.gradients(targets, inputs, target_gradients=None, no_grad_set=None)paddle 目标变量和起始 |
|
torch.Tensor.bfloat16 |
fluid.layers.cast(x, float16) |
|
torch.Tensor.bmm |
fluid.layers.matmul |
|
torch.Tensor.bool |
fluid.layers.cast(x, bool) |
|
torch.Tensor.byte |
fluid.layers.cast(x, uint8) |
|
torch.Tensor.ceil |
fluid.layers.ceil |
|
torch.Tensor.clamp |
fluid.layers.clip |
|
torch.Tensor.clamp_ |
fluid.layers.clip(x, min, max, name=None) |
|
torch.Tensor.clone |
fluid.layers.assign(input, output=None) |
|
torch.Tensor.cos |
fluid.layers.cos |
|
torch.Tensor.cumsum |
fluid.layers.cumsum |
|
torch.Tensor.dequantize |
fluid.layers.dequantize() |
|
torch.Tensor.diag |
fluid.layers.diag |
|
torch.Tensor.div |
fluid.layers.elementwise_div(x, y, axis=-1, act=None, name=None) |
|
torch.Tensor.eq |
fluid.layers.equal |
|
torch.Tensor.eq_ |
fluid.layers.equal(x, y, cond=None) |
|
torch.Tensor.exp |
fluid.layers.exp(x, name=None) |
|
torch.Tensor.exp_ |
fluid.layers.exp(x, name=None) |
|
torch.Tensor.expand |
fluid.layers.expand(x, expand_times, name=None) |
|
torch.Tensor.expand_as |
fluid.layers.expand_as(x, target_tensor, name=None) |
|
torch.Tensor.fill_diagonal_ |
fluid.layers.diag(diagonal) |
|
torch.Tensor.flatten |
fluid.layers.flatten |
|
torch.Tensor.flip |
fluid.layers.reverse |
|
torch.Tensor.floor |
fluid.layers.floor |
|
torch.Tensor.floor_ |
fluid.layers.floor(x, name=None) |
|
torch.Tensor.fmod_ |
fluid.layers.elementwise_mod(x, y, axis=-1, act=None, name=None) |
|
torch.Tensor.gather |
fluid.layers.gather(input, index, overwrite=True) |
|
torch.Tensor.ge |
fluid.layers.greater_equal(x, y, cond=None) |
|
torch.Tensor.ge_ |
fluid.layers.greater_equal(x, y, cond=None) |
|
torch.Tensor.gt |
fluid.layers.greater_than(x, y, cond=None) |
|
torch.Tensor.gt_ |
fluid.layers.greater_than(x, y, cond=None) |
|
torch.Tensor.hardshrink |
fluid.layers.hard_shrink(x, threshold=None) |
|
torch.Tensor.index_select |
fluid.layers.multiplex(inputs, index) |
|
torch.Tensor.int |
fluid.layers.cast |
|
torch.Tensor.le |
fluid.layers.less_equal(x, y, cond=None) |
|
torch.Tensor.le_ |
fluid.layers.less_equal(x, y, cond=None) |
|
torch.Tensor.log |
fluid.layers.log(x, name=None) |
|
torch.Tensor.log_ |
fluid.layers.log(x, name=None) |
|
torch.Tensor.logical_not |
fluid.layers.logical_not(x, out=None, name=None) |
|
torch.Tensor.logical_not_ |
fluid.layers.logical_not(x, out=None, name=None) |
|
torch.Tensor.logical_xor |
fluid.layers.logical_xor(x, y, out=None, name=None) |
|
torch.Tensor.logical_xor_ |
fluid.layers.logical_xor(x, y, out=None, name=None) |
|
torch.Tensor.lt |
fluid.layers.less_than(x, y, cond=None) |
|
torch.Tensor.lt_ |
fluid.layers.less_than(x, y, force_cpu=None, cond=None) |
|
torch.Tensor.masked_select |
fluid.layers.masked_select(input, mask) |
|
torch.Tensor.matmul |
fluid.layers.matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None) |
|
torch.Tensor.matrix_power |
fluid.layers.pow(x, factor=1.0, name=None) |
|
torch.Tensor.max |
fluid.layers.reduce_max(input, dim=None, keep_dim=False, name=None) |
|
torch.Tensor.mean |
fluid.layers.mean(x, name=None) |
|
torch.Tensor.min |
fluid.layers.reduce_min(input, dim=None, keep_dim=False, name=None) |
|
torch.Tensor.mul |
fluid.layers.mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None) |
|
torch.Tensor.mul_ |
fluid.layers.mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None) |
|
torch.Tensor.multinomial |
fluid.layers.sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32') |
|
torch.Tensor.mv |
fluid.layers.mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None) |
|
torch.Tensor.narrow_copy |
fluid.layers.slice(input, axes, starts, ends) |
|
torch.Tensor.ndimension |
fluid.layers.rank(input) |
|
torch.Tensor.ne_ |
fluid.layers.not_equal(x, y, cond=None) |
|
torch.Tensor.normal_ |
fluid.layers.Normal(loc, scale) |
|
torch.Tensor.numel |
fluid.layers.size(input) |
|
torch.Tensor.permute |
fluid.layers.transpose(x, perm, name=None) |
|
torch.Tensor.pow |
fluid.layers.pow(x, factor=1.0, name=None) |
|
torch.Tensor.pow_ |
fluid.layers.pow(x, factor=1.0, name=None) |
|
torch.Tensor.prod |
fluid.layers.reduce_prod(input, dim=None, keep_dim=False, name=None)[ |
|
torch.Tensor.put_ |
fluid.layers.scatter(input, index, updates, name=None, overwrite=True)[源代码] |
|
torch.Tensor.random_ |
fluid.layers.uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0) |
|
torch.Tensor.reciprocal |
fluid.layers.reciprocal(x, name=None) |
|
torch.Tensor.reciprocal_ |
fluid.layers.reciprocal(x, name=None) |
|
torch.Tensor.remainder_ |
fluid.layers.elementwise_mod(x, y, axis=-1, act=None, name=None) |
|
torch.Tensor.requires_grad |
fluid.Variable.stop_gradient(属性) |
|
torch.Tensor.requires_grad_ |
fluid.Variable.stop_gradient(属性) |
|
torch.Tensor.reshape |
fluid.layers.reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None) |
|
torch.Tensor.reshape_as |
fluid.layers.reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None) |
|
torch.Tensor.resize_as_ |
fluid.layers.reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None) |
|
torch.Tensor.round |
fluid.layers.round(x, name=None) |
|
torch.Tensor.round_ |
fluid.layers.round(x, name=None) |
|
torch.Tensor.rsqrt |
fluid.layers.rsqrt(x, name=None) |
|
torch.Tensor.rsqrt_ |
fluid.layers.rsqrt(x, name=None) |
|
torch.Tensor.scatter |
fluid.layers.scatter(input, index, updates, name=None, overwrite=True) |
|
torch.Tensor.scatter_ |
fluid.layers.scatter(input, index, updates, name=None, overwrite=True) |
|
torch.Tensor.scatter_add |
fluid.layers.scatter(input, index, updates, name=None, overwrite=True) |
|
torch.Tensor.scatter_add_ |
fluid.layers.scatter(input, index, updates, name=None, overwrite=True) |
|
torch.Tensor.select |
fluid.layers.slice |
|
torch.Tensor.short |
fluid.layers.cast(x, dtype) |
|
torch.Tensor.sigmoid |
fluid.layers.sigmoid(x, name=None) |
|
torch.Tensor.sigmoid_ |
fluid.layers.sigmoid(x, name=None) |
|
torch.Tensor.sign |
fluid.layers.sign(x) |
|
torch.Tensor.sign_ |
fluid.layers.sign(x) |
|
torch.Tensor.sin |
fluid.layers.sin(x, name=None) |
|
torch.Tensor.sin_ |
fluid.layers.sin(x, name=None) |
|
torch.Tensor.split |
fluid.layers.split(input, num_or_sections, dim=-1, name=None) |
|
torch.Tensor.sqrt |
fluid.layers.sqrt(x, name=None) |
|
torch.Tensor.sqrt_ |
fluid.layers.sqrt(x, name=None) |
|
torch.Tensor.squeeze |
fluid.layers.squeeze(input, axes, name=None) |
|
torch.Tensor.squeeze_ |
fluid.layers.squeeze(input, axes, name=None) |
|
torch.Tensor.sub |
fluid.layers.elementwise_sub(x, y, axis=-1, act=None, name=None) |
|
torch.Tensor.sub_ |
fluid.layers.elementwise_sub(x, y, axis=-1, act=None, name=None) |
|
torch.Tensor.sum |
fluid.layers.sum(x) |
|
torch.Tensor.t_ |
fluid.layers.transpose(x, perm, name=None) |
|
torch.Tensor.tanh |
fluid.layers.tanh(x, name=None) |
|
torch.Tensor.tanh_ |
fluid.layers.tanh(x, name=None) |
|
torch.Tensor.to |
fluid.layers.cast(x, dtype) |
|
torch.Tensor.topk |
fluid.layers.topk(input, k, name=None) |
|
torch.Tensor.transpose |
fluid.layers.transpose(x, perm, name=None) |
|
torch.Tensor.transpose_ |
fluid.layers.transpose(x, perm, name=None) |
|
torch.Tensor.trunc |
fluid.layers.floor(x, name=None) |
|
torch.Tensor.unbind |
fluid.layers.unstack(x, axis=0, num=None) |
|
torch.Tensor.unique |
fluid.layers.unique |
|
torch.Tensor.unsqueeze |
fluid.layers.unsqueeze |
|
torch.Tensor.unsqueeze_ |
fluid.layers.unsqueeze(input, axes, name=None) |
|
torch.Tensor.view |
fluid.layers.reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None) |
|
torch.Tensor.where |
fluid.layers.where |
|
torch.Tensor.zero_ |
fluid.layers.zeros(shape, dtype, force_cpu=False) |
|
torch.topk |
fluid.layers.topk(input, k, name=None) |
|
torch.transpose |
fluid.layers.transpose(x, perm, name=None) |
|
torch.tril |
fluid.layers.tril |
|
torch.triu |
fluid.layers.triu |
|
torch.unique |
fluid.layers.unique(x, dtype='int32') |
|
torch.unsqueeze |
fluid.layers.unsqueeze(input, axes, name=None) |
|
torch.var_mean |
fluid.layers.mean |
|
torch.where |
fluid.layers.where(condition) |
|
torch.zeros |
fluid.layers.zeros(shape, dtype, force_cpu=False) |
|
torch.zeros_like |
fluid.layers.zeros_like(x, out=None) |
|
torch.utils.data.dataloader.default_collate |
fluid.io.default_collate_fn |
|
torch.optim.lr_scheduler.StepLR |
fluid.dygraph.StepDecay |
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