Rerange experimental
This commit is contained in:
D-X-Y
parent
d3d950d310
commit
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@ -4,8 +4,5 @@
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# This file is expected to be self-contained, expect
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# for importing from spaces to include search space.
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#####################################################
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from .drop import DropBlock2d, DropPath
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from .mlp import MLP
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from .weight_init import trunc_normal_
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from .positional_embedding import PositionalEncoder
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from .super_core import *
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@ -1,229 +0,0 @@
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""" Borrowed from https://github.com/rwightman/pytorch-image-models
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DropBlock, DropPath
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PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
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Papers:
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DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
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Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
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Code:
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DropBlock impl inspired by two Tensorflow impl that I liked:
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- https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
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- https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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def drop_block_2d(
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x,
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drop_prob: float = 0.1,
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block_size: int = 7,
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gamma_scale: float = 1.0,
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with_noise: bool = False,
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inplace: bool = False,
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batchwise: bool = False,
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):
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"""DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
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DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
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runs with success, but needs further validation and possibly optimization for lower runtime impact.
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"""
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B, C, H, W = x.shape
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total_size = W * H
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clipped_block_size = min(block_size, min(W, H))
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# seed_drop_rate, the gamma parameter
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gamma = (
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gamma_scale
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* drop_prob
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* total_size
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/ clipped_block_size ** 2
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/ ((W - block_size + 1) * (H - block_size + 1))
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)
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# Forces the block to be inside the feature map.
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w_i, h_i = torch.meshgrid(
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torch.arange(W).to(x.device), torch.arange(H).to(x.device)
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)
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valid_block = (
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(w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)
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) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
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valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
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if batchwise:
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# one mask for whole batch, quite a bit faster
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uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
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else:
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uniform_noise = torch.rand_like(x)
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block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
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block_mask = -F.max_pool2d(
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-block_mask,
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kernel_size=clipped_block_size, # block_size,
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stride=1,
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padding=clipped_block_size // 2,
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)
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if with_noise:
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normal_noise = (
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torch.randn((1, C, H, W), dtype=x.dtype, device=x.device)
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if batchwise
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else torch.randn_like(x)
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)
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if inplace:
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x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
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else:
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x = x * block_mask + normal_noise * (1 - block_mask)
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else:
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normalize_scale = (
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block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)
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).to(x.dtype)
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if inplace:
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x.mul_(block_mask * normalize_scale)
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else:
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x = x * block_mask * normalize_scale
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return x
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def drop_block_fast_2d(
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x: torch.Tensor,
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drop_prob: float = 0.1,
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block_size: int = 7,
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gamma_scale: float = 1.0,
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with_noise: bool = False,
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inplace: bool = False,
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batchwise: bool = False,
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):
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"""DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
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DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
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block mask at edges.
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"""
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B, C, H, W = x.shape
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total_size = W * H
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clipped_block_size = min(block_size, min(W, H))
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gamma = (
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gamma_scale
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* drop_prob
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* total_size
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/ clipped_block_size ** 2
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/ ((W - block_size + 1) * (H - block_size + 1))
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)
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if batchwise:
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# one mask for whole batch, quite a bit faster
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block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma
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else:
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# mask per batch element
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block_mask = torch.rand_like(x) < gamma
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block_mask = F.max_pool2d(
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block_mask.to(x.dtype),
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kernel_size=clipped_block_size,
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stride=1,
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padding=clipped_block_size // 2,
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)
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if with_noise:
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normal_noise = (
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torch.randn((1, C, H, W), dtype=x.dtype, device=x.device)
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if batchwise
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else torch.randn_like(x)
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)
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if inplace:
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x.mul_(1.0 - block_mask).add_(normal_noise * block_mask)
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else:
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x = x * (1.0 - block_mask) + normal_noise * block_mask
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else:
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block_mask = 1 - block_mask
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normalize_scale = (
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block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)
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).to(dtype=x.dtype)
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if inplace:
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x.mul_(block_mask * normalize_scale)
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else:
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x = x * block_mask * normalize_scale
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return x
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class DropBlock2d(nn.Module):
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"""DropBlock. See https://arxiv.org/pdf/1810.12890.pdf"""
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def __init__(
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self,
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drop_prob=0.1,
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block_size=7,
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gamma_scale=1.0,
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with_noise=False,
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inplace=False,
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batchwise=False,
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fast=True,
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):
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super(DropBlock2d, self).__init__()
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self.drop_prob = drop_prob
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self.gamma_scale = gamma_scale
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self.block_size = block_size
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self.with_noise = with_noise
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self.inplace = inplace
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self.batchwise = batchwise
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self.fast = fast # FIXME finish comparisons of fast vs not
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def forward(self, x):
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if not self.training or not self.drop_prob:
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return x
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if self.fast:
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return drop_block_fast_2d(
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x,
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self.drop_prob,
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self.block_size,
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self.gamma_scale,
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self.with_noise,
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self.inplace,
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self.batchwise,
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)
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else:
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return drop_block_2d(
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x,
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self.drop_prob,
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self.block_size,
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self.gamma_scale,
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self.with_noise,
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self.inplace,
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self.batchwise,
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)
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def drop_path(x, drop_prob: float = 0.0, training: bool = False):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
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the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
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changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
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'survival rate' as the argument.
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"""
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if drop_prob == 0.0 or not training:
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return x
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keep_prob = 1 - drop_prob
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shape = (x.shape[0],) + (1,) * (
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x.ndim - 1
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) # work with diff dim tensors, not just 2D ConvNets
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random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
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random_tensor.floor_() # binarize
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output = x.div(keep_prob) * random_tensor
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return output
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class DropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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@ -1,29 +0,0 @@
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import torch.nn as nn
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from typing import Optional
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class MLP(nn.Module):
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# MLP: FC -> Activation -> Drop -> FC -> Drop
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def __init__(
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self,
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in_features,
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hidden_features: Optional[int] = None,
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out_features: Optional[int] = None,
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act_layer=nn.GELU,
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drop: Optional[float] = None,
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):
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super(MLP, self).__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop or 0)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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@ -1,35 +0,0 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
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#####################################################
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import torch
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import torch.nn as nn
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import math
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class PositionalEncoder(nn.Module):
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# Attention Is All You Need: https://arxiv.org/pdf/1706.03762.pdf
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# https://github.com/pytorch/examples/blob/master/word_language_model/model.py#L65
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def __init__(self, d_model, max_seq_len, dropout=0.1):
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super(PositionalEncoder, self).__init__()
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self.d_model = d_model
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# create constant 'pe' matrix with values dependant on
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# pos and i
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pe = torch.zeros(max_seq_len, d_model)
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for pos in range(max_seq_len):
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for i in range(0, d_model):
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div = 10000 ** ((i // 2) * 2 / d_model)
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value = pos / div
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if i % 2 == 0:
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pe[pos, i] = math.sin(value)
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else:
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pe[pos, i] = math.cos(value)
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pe = pe.unsqueeze(0)
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self.dropout = nn.Dropout(p=dropout)
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self.register_buffer("pe", pe)
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def forward(self, x):
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batch, seq, fdim = x.shape[:3]
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embeddings = self.pe[:, :seq, :fdim]
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outs = self.dropout(x + embeddings)
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return outs
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@ -1,11 +1,7 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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from __future__ import division
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from __future__ import print_function
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import math
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from functools import partial
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from typing import Optional, Text
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import torch
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@ -1,11 +1,7 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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from __future__ import division
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from __future__ import print_function
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import math
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from functools import partial
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from typing import Optional, Text
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import torch
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44
xautodl/xlayers/super_rearrange.py
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44
xautodl/xlayers/super_rearrange.py
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@ -0,0 +1,44 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#############################################################
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# Borrow the idea of https://github.com/arogozhnikov/einops #
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#############################################################
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import math
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from typing import Optional, Callable
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from xautodl import spaces
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from .super_module import SuperModule
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from .super_module import IntSpaceType
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from .super_module import BoolSpaceType
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class SuperRearrange(SuperModule):
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"""Applies the rearrange operation."""
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def __init__(self, pattern, **axes_lengths):
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super(SuperRearrange, self).__init__()
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self._pattern = pattern
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self._axes_lengths = axes_lengths
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self.reset_parameters()
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@property
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def abstract_search_space(self):
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root_node = spaces.VirtualNode(id(self))
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return root_node
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def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
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raise NotImplementedError
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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raise NotImplementedError
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def extra_repr(self) -> str:
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params = repr(self._pattern)
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for axis, length in self._axes_lengths.items():
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params += ", {}={}".format(axis, length)
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return "{}({})".format(self.__class__.__name__, params)
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@ -1,12 +1,7 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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from __future__ import division
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from __future__ import print_function
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import math
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from functools import partial
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from typing import Optional, Text
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import torch
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import torch.nn as nn
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@ -1,11 +1,7 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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from __future__ import division
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from __future__ import print_function
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import math
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from functools import partial
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from typing import Optional, Callable
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import torch
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5
xautodl/xmodels/__init__.py
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5
xautodl/xmodels/__init__.py
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@ -0,0 +1,5 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
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#####################################################
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# The models in this folder is written with xlayers #
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#####################################################
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197
xautodl/xmodels/transformers.py
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197
xautodl/xmodels/transformers.py
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@ -0,0 +1,197 @@
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opyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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from __future__ import division
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from __future__ import print_function
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import math
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from functools import partial
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from typing import Optional, Text, List
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from xautodl import spaces
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from xautodl.xlayers import trunc_normal_
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from xautodl.xlayers import super_core
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__all__ = ["DefaultSearchSpace", "DEFAULT_NET_CONFIG", "get_transformer"]
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def _get_mul_specs(candidates, num):
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results = []
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for i in range(num):
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results.append(spaces.Categorical(*candidates))
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return results
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def _get_list_mul(num, multipler):
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results = []
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for i in range(1, num + 1):
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results.append(i * multipler)
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return results
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def _assert_types(x, expected_types):
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if not isinstance(x, expected_types):
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raise TypeError(
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"The type [{:}] is expected to be {:}.".format(type(x), expected_types)
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)
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DEFAULT_NET_CONFIG = None
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_default_max_depth = 5
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DefaultSearchSpace = dict(
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d_feat=6,
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embed_dim=spaces.Categorical(*_get_list_mul(8, 16)),
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num_heads=_get_mul_specs((1, 2, 4, 8), _default_max_depth),
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mlp_hidden_multipliers=_get_mul_specs((0.5, 1, 2, 4, 8), _default_max_depth),
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qkv_bias=True,
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pos_drop=0.0,
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other_drop=0.0,
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)
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class SuperTransformer(super_core.SuperModule):
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"""The super model for transformer."""
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def __init__(
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self,
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d_feat: int = 6,
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embed_dim: List[super_core.IntSpaceType] = DefaultSearchSpace["embed_dim"],
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num_heads: List[super_core.IntSpaceType] = DefaultSearchSpace["num_heads"],
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mlp_hidden_multipliers: List[super_core.IntSpaceType] = DefaultSearchSpace[
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"mlp_hidden_multipliers"
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],
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qkv_bias: bool = DefaultSearchSpace["qkv_bias"],
|
||||
pos_drop: float = DefaultSearchSpace["pos_drop"],
|
||||
other_drop: float = DefaultSearchSpace["other_drop"],
|
||||
max_seq_len: int = 65,
|
||||
):
|
||||
super(SuperTransformer, self).__init__()
|
||||
self._embed_dim = embed_dim
|
||||
self._num_heads = num_heads
|
||||
self._mlp_hidden_multipliers = mlp_hidden_multipliers
|
||||
|
||||
# the stem part
|
||||
self.input_embed = super_core.SuperAlphaEBDv1(d_feat, embed_dim)
|
||||
self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
|
||||
self.pos_embed = super_core.SuperPositionalEncoder(
|
||||
d_model=embed_dim, max_seq_len=max_seq_len, dropout=pos_drop
|
||||
)
|
||||
# build the transformer encode layers -->> check params
|
||||
_assert_types(num_heads, (tuple, list))
|
||||
_assert_types(mlp_hidden_multipliers, (tuple, list))
|
||||
assert len(num_heads) == len(mlp_hidden_multipliers), "{:} vs {:}".format(
|
||||
len(num_heads), len(mlp_hidden_multipliers)
|
||||
)
|
||||
# build the transformer encode layers -->> backbone
|
||||
layers = []
|
||||
for num_head, mlp_hidden_multiplier in zip(num_heads, mlp_hidden_multipliers):
|
||||
layer = super_core.SuperTransformerEncoderLayer(
|
||||
embed_dim,
|
||||
num_head,
|
||||
qkv_bias,
|
||||
mlp_hidden_multiplier,
|
||||
other_drop,
|
||||
)
|
||||
layers.append(layer)
|
||||
self.backbone = super_core.SuperSequential(*layers)
|
||||
|
||||
# the regression head
|
||||
self.head = super_core.SuperSequential(
|
||||
super_core.SuperLayerNorm1D(embed_dim), super_core.SuperLinear(embed_dim, 1)
|
||||
)
|
||||
trunc_normal_(self.cls_token, std=0.02)
|
||||
self.apply(self._init_weights)
|
||||
|
||||
@property
|
||||
def embed_dim(self):
|
||||
return spaces.get_max(self._embed_dim)
|
||||
|
||||
@property
|
||||
def abstract_search_space(self):
|
||||
root_node = spaces.VirtualNode(id(self))
|
||||
if not spaces.is_determined(self._embed_dim):
|
||||
root_node.append("_embed_dim", self._embed_dim.abstract(reuse_last=True))
|
||||
xdict = dict(
|
||||
input_embed=self.input_embed.abstract_search_space,
|
||||
pos_embed=self.pos_embed.abstract_search_space,
|
||||
backbone=self.backbone.abstract_search_space,
|
||||
head=self.head.abstract_search_space,
|
||||
)
|
||||
for key, space in xdict.items():
|
||||
if not spaces.is_determined(space):
|
||||
root_node.append(key, space)
|
||||
return root_node
|
||||
|
||||
def apply_candidate(self, abstract_child: spaces.VirtualNode):
|
||||
super(SuperTransformer, self).apply_candidate(abstract_child)
|
||||
xkeys = ("input_embed", "pos_embed", "backbone", "head")
|
||||
for key in xkeys:
|
||||
if key in abstract_child:
|
||||
getattr(self, key).apply_candidate(abstract_child[key])
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=0.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, super_core.SuperLinear):
|
||||
trunc_normal_(m._super_weight, std=0.02)
|
||||
if m._super_bias is not None:
|
||||
nn.init.constant_(m._super_bias, 0)
|
||||
elif isinstance(m, super_core.SuperLayerNorm1D):
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
|
||||
batch, flatten_size = input.shape
|
||||
feats = self.input_embed(input) # batch * 60 * 64
|
||||
if not spaces.is_determined(self._embed_dim):
|
||||
embed_dim = self.abstract_child["_embed_dim"].value
|
||||
else:
|
||||
embed_dim = spaces.get_determined_value(self._embed_dim)
|
||||
cls_tokens = self.cls_token.expand(batch, -1, -1)
|
||||
cls_tokens = F.interpolate(
|
||||
cls_tokens, size=(embed_dim), mode="linear", align_corners=True
|
||||
)
|
||||
feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
|
||||
feats_w_tp = self.pos_embed(feats_w_ct)
|
||||
xfeats = self.backbone(feats_w_tp)
|
||||
xfeats = xfeats[:, 0, :] # use the feature for the first token
|
||||
predicts = self.head(xfeats).squeeze(-1)
|
||||
return predicts
|
||||
|
||||
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
|
||||
batch, flatten_size = input.shape
|
||||
feats = self.input_embed(input) # batch * 60 * 64
|
||||
cls_tokens = self.cls_token.expand(batch, -1, -1)
|
||||
feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
|
||||
feats_w_tp = self.pos_embed(feats_w_ct)
|
||||
xfeats = self.backbone(feats_w_tp)
|
||||
xfeats = xfeats[:, 0, :] # use the feature for the first token
|
||||
predicts = self.head(xfeats).squeeze(-1)
|
||||
return predicts
|
||||
|
||||
|
||||
def get_transformer(config):
|
||||
if config is None:
|
||||
return SuperTransformer(6)
|
||||
if not isinstance(config, dict):
|
||||
raise ValueError("Invalid Configuration: {:}".format(config))
|
||||
name = config.get("name", "basic")
|
||||
if name == "basic":
|
||||
model = SuperTransformer(
|
||||
d_feat=config.get("d_feat"),
|
||||
embed_dim=config.get("embed_dim"),
|
||||
num_heads=config.get("num_heads"),
|
||||
mlp_hidden_multipliers=config.get("mlp_hidden_multipliers"),
|
||||
qkv_bias=config.get("qkv_bias"),
|
||||
pos_drop=config.get("pos_drop"),
|
||||
other_drop=config.get("other_drop"),
|
||||
)
|
||||
else:
|
||||
raise ValueError("Unknown model name: {:}".format(name))
|
||||
return model
|
||||
Loading…
Reference in New Issue
Block a user