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| # Copyright (c) SenseTime Research. All rights reserved. | |
| # Copyright (c) 2019, NVIDIA Corporation. All rights reserved. | |
| # | |
| # This work is made available under the Nvidia Source Code License-NC. | |
| # To view a copy of this license, visit | |
| # https://nvlabs.github.io/stylegan2/license.html | |
| """Custom TensorFlow ops for efficient bias and activation.""" | |
| import os | |
| import numpy as np | |
| import tensorflow as tf | |
| from .. import custom_ops | |
| from ...util import EasyDict | |
| def _get_plugin(): | |
| return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu') | |
| #---------------------------------------------------------------------------- | |
| activation_funcs = { | |
| 'linear': EasyDict(func=lambda x, **_: x, def_alpha=None, def_gain=1.0, cuda_idx=1, ref='y', zero_2nd_grad=True), | |
| 'relu': EasyDict(func=lambda x, **_: tf.nn.relu(x), def_alpha=None, def_gain=np.sqrt(2), cuda_idx=2, ref='y', zero_2nd_grad=True), | |
| 'lrelu': EasyDict(func=lambda x, alpha, **_: tf.nn.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', zero_2nd_grad=True), | |
| 'tanh': EasyDict(func=lambda x, **_: tf.nn.tanh(x), def_alpha=None, def_gain=1.0, cuda_idx=4, ref='y', zero_2nd_grad=False), | |
| 'sigmoid': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x), def_alpha=None, def_gain=1.0, cuda_idx=5, ref='y', zero_2nd_grad=False), | |
| 'elu': EasyDict(func=lambda x, **_: tf.nn.elu(x), def_alpha=None, def_gain=1.0, cuda_idx=6, ref='y', zero_2nd_grad=False), | |
| 'selu': EasyDict(func=lambda x, **_: tf.nn.selu(x), def_alpha=None, def_gain=1.0, cuda_idx=7, ref='y', zero_2nd_grad=False), | |
| 'softplus': EasyDict(func=lambda x, **_: tf.nn.softplus(x), def_alpha=None, def_gain=1.0, cuda_idx=8, ref='y', zero_2nd_grad=False), | |
| 'swish': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x) * x, def_alpha=None, def_gain=np.sqrt(2), cuda_idx=9, ref='x', zero_2nd_grad=False), | |
| } | |
| #---------------------------------------------------------------------------- | |
| def fused_bias_act(x, b=None, axis=1, act='linear', alpha=None, gain=None, impl='cuda'): | |
| r"""Fused bias and activation function. | |
| Adds bias `b` to activation tensor `x`, evaluates activation function `act`, | |
| and scales the result by `gain`. Each of the steps is optional. In most cases, | |
| the fused op is considerably more efficient than performing the same calculation | |
| using standard TensorFlow ops. It supports first and second order gradients, | |
| but not third order gradients. | |
| Args: | |
| x: Input activation tensor. Can have any shape, but if `b` is defined, the | |
| dimension corresponding to `axis`, as well as the rank, must be known. | |
| b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type | |
| as `x`. The shape must be known, and it must match the dimension of `x` | |
| corresponding to `axis`. | |
| axis: The dimension in `x` corresponding to the elements of `b`. | |
| The value of `axis` is ignored if `b` is not specified. | |
| act: Name of the activation function to evaluate, or `"linear"` to disable. | |
| Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. | |
| See `activation_funcs` for a full list. `None` is not allowed. | |
| alpha: Shape parameter for the activation function, or `None` to use the default. | |
| gain: Scaling factor for the output tensor, or `None` to use default. | |
| See `activation_funcs` for the default scaling of each activation function. | |
| If unsure, consider specifying `1.0`. | |
| impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). | |
| Returns: | |
| Tensor of the same shape and datatype as `x`. | |
| """ | |
| impl_dict = { | |
| 'ref': _fused_bias_act_ref, | |
| 'cuda': _fused_bias_act_cuda, | |
| } | |
| return impl_dict[impl](x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain) | |
| #---------------------------------------------------------------------------- | |
| def _fused_bias_act_ref(x, b, axis, act, alpha, gain): | |
| """Slow reference implementation of `fused_bias_act()` using standard TensorFlow ops.""" | |
| # Validate arguments. | |
| x = tf.convert_to_tensor(x) | |
| b = tf.convert_to_tensor(b) if b is not None else tf.constant([], dtype=x.dtype) | |
| act_spec = activation_funcs[act] | |
| assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis]) | |
| assert b.shape[0] == 0 or 0 <= axis < x.shape.rank | |
| if alpha is None: | |
| alpha = act_spec.def_alpha | |
| if gain is None: | |
| gain = act_spec.def_gain | |
| # Add bias. | |
| if b.shape[0] != 0: | |
| x += tf.reshape(b, [-1 if i == axis else 1 for i in range(x.shape.rank)]) | |
| # Evaluate activation function. | |
| x = act_spec.func(x, alpha=alpha) | |
| # Scale by gain. | |
| if gain != 1: | |
| x *= gain | |
| return x | |
| #---------------------------------------------------------------------------- | |
| def _fused_bias_act_cuda(x, b, axis, act, alpha, gain): | |
| """Fast CUDA implementation of `fused_bias_act()` using custom ops.""" | |
| # Validate arguments. | |
| x = tf.convert_to_tensor(x) | |
| empty_tensor = tf.constant([], dtype=x.dtype) | |
| b = tf.convert_to_tensor(b) if b is not None else empty_tensor | |
| act_spec = activation_funcs[act] | |
| assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis]) | |
| assert b.shape[0] == 0 or 0 <= axis < x.shape.rank | |
| if alpha is None: | |
| alpha = act_spec.def_alpha | |
| if gain is None: | |
| gain = act_spec.def_gain | |
| # Special cases. | |
| if act == 'linear' and b is None and gain == 1.0: | |
| return x | |
| if act_spec.cuda_idx is None: | |
| return _fused_bias_act_ref(x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain) | |
| # CUDA kernel. | |
| cuda_kernel = _get_plugin().fused_bias_act | |
| cuda_kwargs = dict(axis=axis, act=act_spec.cuda_idx, alpha=alpha, gain=gain) | |
| # Forward pass: y = func(x, b). | |
| def func_y(x, b): | |
| y = cuda_kernel(x=x, b=b, ref=empty_tensor, grad=0, **cuda_kwargs) | |
| y.set_shape(x.shape) | |
| return y | |
| # Backward pass: dx, db = grad(dy, x, y) | |
| def grad_dx(dy, x, y): | |
| ref = {'x': x, 'y': y}[act_spec.ref] | |
| dx = cuda_kernel(x=dy, b=empty_tensor, ref=ref, grad=1, **cuda_kwargs) | |
| dx.set_shape(x.shape) | |
| return dx | |
| def grad_db(dx): | |
| if b.shape[0] == 0: | |
| return empty_tensor | |
| db = dx | |
| if axis < x.shape.rank - 1: | |
| db = tf.reduce_sum(db, list(range(axis + 1, x.shape.rank))) | |
| if axis > 0: | |
| db = tf.reduce_sum(db, list(range(axis))) | |
| db.set_shape(b.shape) | |
| return db | |
| # Second order gradients: d_dy, d_x = grad2(d_dx, d_db, x, y) | |
| def grad2_d_dy(d_dx, d_db, x, y): | |
| ref = {'x': x, 'y': y}[act_spec.ref] | |
| d_dy = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=1, **cuda_kwargs) | |
| d_dy.set_shape(x.shape) | |
| return d_dy | |
| def grad2_d_x(d_dx, d_db, x, y): | |
| ref = {'x': x, 'y': y}[act_spec.ref] | |
| d_x = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=2, **cuda_kwargs) | |
| d_x.set_shape(x.shape) | |
| return d_x | |
| # Fast version for piecewise-linear activation funcs. | |
| def func_zero_2nd_grad(x, b): | |
| y = func_y(x, b) | |
| def grad(dy): | |
| dx = grad_dx(dy, x, y) | |
| db = grad_db(dx) | |
| def grad2(d_dx, d_db): | |
| d_dy = grad2_d_dy(d_dx, d_db, x, y) | |
| return d_dy | |
| return (dx, db), grad2 | |
| return y, grad | |
| # Slow version for general activation funcs. | |
| def func_nonzero_2nd_grad(x, b): | |
| y = func_y(x, b) | |
| def grad_wrap(dy): | |
| def grad_impl(dy, x): | |
| dx = grad_dx(dy, x, y) | |
| db = grad_db(dx) | |
| def grad2(d_dx, d_db): | |
| d_dy = grad2_d_dy(d_dx, d_db, x, y) | |
| d_x = grad2_d_x(d_dx, d_db, x, y) | |
| return d_dy, d_x | |
| return (dx, db), grad2 | |
| return grad_impl(dy, x) | |
| return y, grad_wrap | |
| # Which version to use? | |
| if act_spec.zero_2nd_grad: | |
| return func_zero_2nd_grad(x, b) | |
| return func_nonzero_2nd_grad(x, b) | |
| #---------------------------------------------------------------------------- | |