diff --git a/timm/models/layers/drop.py b/timm/models/layers/drop.py index 00bed078..5f2008c0 100644 --- a/timm/models/layers/drop.py +++ b/timm/models/layers/drop.py @@ -22,44 +22,89 @@ import math def drop_block_2d( - x, drop_prob: float = 0.1, training: bool = False, block_size: int = 7, - gamma_scale: float = 1.0, drop_with_noise: bool = False): + x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0, + with_noise: bool = False, inplace: bool = False, batchwise: bool = False): """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf DropBlock with an experimental gaussian noise option. This layer has been tested on a few training runs with success, but needs further validation and possibly optimization for lower runtime impact. - """ - if drop_prob == 0. or not training: - return x - _, _, height, width = x.shape - total_size = width * height - clipped_block_size = min(block_size, min(width, height)) + B, C, H, W = x.shape + total_size = W * H + clipped_block_size = min(block_size, min(W, H)) # seed_drop_rate, the gamma parameter - seed_drop_rate = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / ( - (width - block_size + 1) * - (height - block_size + 1)) + gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / ( + (W - block_size + 1) * (H - block_size + 1)) # Forces the block to be inside the feature map. - w_i, h_i = torch.meshgrid(torch.arange(width).to(x.device), torch.arange(height).to(x.device)) - valid_block = ((w_i >= clipped_block_size // 2) & (w_i < width - (clipped_block_size - 1) // 2)) & \ - ((h_i >= clipped_block_size // 2) & (h_i < height - (clipped_block_size - 1) // 2)) - valid_block = torch.reshape(valid_block, (1, 1, height, width)).float() - - uniform_noise = torch.rand_like(x, dtype=torch.float32) - block_mask = ((2 - seed_drop_rate - valid_block + uniform_noise) >= 1).float() + w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device)) + valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \ + ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2)) + valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype) + + if batchwise: + # one mask for whole batch, quite a bit faster + uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) + else: + uniform_noise = torch.rand_like(x) + block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype) block_mask = -F.max_pool2d( -block_mask, - kernel_size=clipped_block_size, # block_size, ??? + kernel_size=clipped_block_size, # block_size, stride=1, padding=clipped_block_size // 2) - if drop_with_noise: - normal_noise = torch.randn_like(x) - x = x * block_mask + normal_noise * (1 - block_mask) + if with_noise: + normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x) + if inplace: + x.mul_(block_mask).add_(normal_noise * (1 - block_mask)) + else: + x = x * block_mask + normal_noise * (1 - block_mask) + else: + normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype) + if inplace: + x.mul_(block_mask * normalize_scale) + else: + x = x * block_mask * normalize_scale + return x + + +def drop_block_fast_2d( + x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7, + gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False): + """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf + + DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid + block mask at edges. + """ + B, C, H, W = x.shape + total_size = W * H + clipped_block_size = min(block_size, min(W, H)) + gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / ( + (W - block_size + 1) * (H - block_size + 1)) + + if batchwise: + # one mask for whole batch, quite a bit faster + block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma + else: + # mask per batch element + block_mask = torch.rand_like(x) < gamma + block_mask = F.max_pool2d( + block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2) + + if with_noise: + normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x) + if inplace: + x.mul_(1. - block_mask).add_(normal_noise * block_mask) + else: + x = x * (1. - block_mask) + normal_noise * block_mask else: - normalize_scale = block_mask.numel() / (torch.sum(block_mask) + 1e-7) - x = x * block_mask * normalize_scale + block_mask = 1 - block_mask + normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(dtype=x.dtype) + if inplace: + x.mul_(block_mask * normalize_scale) + else: + x = x * block_mask * normalize_scale return x @@ -70,15 +115,28 @@ class DropBlock2d(nn.Module): drop_prob=0.1, block_size=7, gamma_scale=1.0, - with_noise=False): + with_noise=False, + inplace=False, + batchwise=False, + fast=True): super(DropBlock2d, self).__init__() self.drop_prob = drop_prob self.gamma_scale = gamma_scale self.block_size = block_size self.with_noise = with_noise + self.inplace = inplace + self.batchwise = batchwise + self.fast = fast # FIXME finish comparisons of fast vs not def forward(self, x): - return drop_block_2d(x, self.drop_prob, self.training, self.block_size, self.gamma_scale, self.with_noise) + if not self.training or not self.drop_prob: + return x + if self.fast: + return drop_block_fast_2d( + x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise) + else: + return drop_block_2d( + x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise) def drop_path(x, drop_prob: float = 0., training: bool = False): diff --git a/timm/models/layers/split_attn.py b/timm/models/layers/split_attn.py index 383c4583..023ab6af 100644 --- a/timm/models/layers/split_attn.py +++ b/timm/models/layers/split_attn.py @@ -31,25 +31,24 @@ class RadixSoftmax(nn.Module): class SplitAttnConv2d(nn.Module): """Split-Attention Conv2d """ - def __init__(self, in_channels, channels, kernel_size, stride=1, padding=0, + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=False, radix=2, reduction_factor=4, act_layer=nn.ReLU, norm_layer=None, drop_block=None, **kwargs): super(SplitAttnConv2d, self).__init__() self.radix = radix - self.cardinality = groups - self.channels = channels - mid_chs = channels * radix + self.drop_block = drop_block + mid_chs = out_channels * radix attn_chs = max(in_channels * radix // reduction_factor, 32) + self.conv = nn.Conv2d( in_channels, mid_chs, kernel_size, stride, padding, dilation, groups=groups * radix, bias=bias, **kwargs) self.bn0 = norm_layer(mid_chs) if norm_layer is not None else None self.act0 = act_layer(inplace=True) - self.fc1 = nn.Conv2d(channels, attn_chs, 1, groups=self.cardinality) + self.fc1 = nn.Conv2d(out_channels, attn_chs, 1, groups=groups) self.bn1 = norm_layer(attn_chs) if norm_layer is not None else None self.act1 = act_layer(inplace=True) - self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=self.cardinality) - self.drop_block = drop_block + self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=groups) self.rsoftmax = RadixSoftmax(radix, groups) def forward(self, x): @@ -63,7 +62,7 @@ class SplitAttnConv2d(nn.Module): B, RC, H, W = x.shape if self.radix > 1: x = x.reshape((B, self.radix, RC // self.radix, H, W)) - x_gap = torch.sum(x, dim=1) + x_gap = x.sum(dim=1) else: x_gap = x x_gap = F.adaptive_avg_pool2d(x_gap, 1) diff --git a/timm/models/resnest.py b/timm/models/resnest.py index 849543ba..33b051ef 100644 --- a/timm/models/resnest.py +++ b/timm/models/resnest.py @@ -76,10 +76,10 @@ class ResNestBottleneck(nn.Module): else: avd_stride = 0 self.radix = radix + self.drop_block = drop_block self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False) self.bn1 = norm_layer(group_width) - self.drop_block1 = drop_block if drop_block is not None else None self.act1 = act_layer(inplace=True) self.avd_first = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and avd_first else None @@ -88,20 +88,17 @@ class ResNestBottleneck(nn.Module): group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation, dilation=first_dilation, groups=cardinality, radix=radix, norm_layer=norm_layer, drop_block=drop_block) self.bn2 = None # FIXME revisit, here to satisfy current torchscript fussyness - self.drop_block2 = None self.act2 = None else: self.conv2 = nn.Conv2d( group_width, group_width, kernel_size=3, stride=stride, padding=first_dilation, dilation=first_dilation, groups=cardinality, bias=False) self.bn2 = norm_layer(group_width) - self.drop_block2 = drop_block if drop_block is not None else None self.act2 = act_layer(inplace=True) self.avd_last = nn.AvgPool2d(3, avd_stride, padding=1) if avd_stride > 0 and not avd_first else None self.conv3 = nn.Conv2d(group_width, planes * 4, kernel_size=1, bias=False) self.bn3 = norm_layer(planes*4) - self.drop_block3 = drop_block if drop_block is not None else None self.act3 = act_layer(inplace=True) self.downsample = downsample @@ -113,8 +110,8 @@ class ResNestBottleneck(nn.Module): out = self.conv1(x) out = self.bn1(out) - if self.drop_block1 is not None: - out = self.drop_block1(out) + if self.drop_block is not None: + out = self.drop_block(out) out = self.act1(out) if self.avd_first is not None: @@ -123,8 +120,8 @@ class ResNestBottleneck(nn.Module): out = self.conv2(out) if self.bn2 is not None: out = self.bn2(out) - if self.drop_block2 is not None: - out = self.drop_block2(out) + if self.drop_block is not None: + out = self.drop_block(out) out = self.act2(out) if self.avd_last is not None: @@ -132,8 +129,8 @@ class ResNestBottleneck(nn.Module): out = self.conv3(out) out = self.bn3(out) - if self.drop_block3 is not None: - out = self.drop_block3(out) + if self.drop_block is not None: + out = self.drop_block(out) if self.downsample is not None: residual = self.downsample(x)