Echo/fcb_photo_review
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

更换文档检测模型

Browse Source
This commit is contained in:
liuyebo
2024-08-27 14:42:45 +08:00
parent aea6f19951
commit 1514e09c40
2072 changed files with 254336 additions and 4967 deletions
Download Patch File Download Diff File Expand all files Collapse all files

720
paddle_detection/ppdet/modeling/backbones/focalnet.py Normal file
View File

@@ -0,0 +1,720 @@
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is based on https://github.com/microsoft/FocalNet/blob/main/classification/focalnet.py
"""
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from ppdet.modeling.shape_spec import ShapeSpec
from ppdet.core.workspace import register, serializable
from .transformer_utils import DropPath, Identity
from .transformer_utils import add_parameter, to_2tuple
from .transformer_utils import ones_, zeros_, trunc_normal_
from .swin_transformer import Mlp
__all__ = ['FocalNet']
MODEL_cfg = {
'focalnet_T_224_1k_srf': dict(
embed_dim=96,
depths=[2, 2, 6, 2],
focal_levels=[2, 2, 2, 2],
focal_windows=[3, 3, 3, 3],
drop_path_rate=0.2,
use_conv_embed=False,
use_postln=False,
use_postln_in_modulation=False,
use_layerscale=False,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_tiny_srf_pretrained.pdparams',
),
'focalnet_S_224_1k_srf': dict(
embed_dim=96,
depths=[2, 2, 18, 2],
focal_levels=[2, 2, 2, 2],
focal_windows=[3, 3, 3, 3],
drop_path_rate=0.3,
use_conv_embed=False,
use_postln=False,
use_postln_in_modulation=False,
use_layerscale=False,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_small_srf_pretrained.pdparams',
),
'focalnet_B_224_1k_srf': dict(
embed_dim=128,
depths=[2, 2, 18, 2],
focal_levels=[2, 2, 2, 2],
focal_windows=[3, 3, 3, 3],
drop_path_rate=0.5,
use_conv_embed=False,
use_postln=False,
use_postln_in_modulation=False,
use_layerscale=False,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_base_srf_pretrained.pdparams',
),
'focalnet_T_224_1k_lrf': dict(
embed_dim=96,
depths=[2, 2, 6, 2],
focal_levels=[3, 3, 3, 3],
focal_windows=[3, 3, 3, 3],
drop_path_rate=0.2,
use_conv_embed=False,
use_postln=False,
use_postln_in_modulation=False,
use_layerscale=False,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_tiny_lrf_pretrained.pdparams',
),
'focalnet_S_224_1k_lrf': dict(
embed_dim=96,
depths=[2, 2, 18, 2],
focal_levels=[3, 3, 3, 3],
focal_windows=[3, 3, 3, 3],
drop_path_rate=0.3,
use_conv_embed=False,
use_postln=False,
use_postln_in_modulation=False,
use_layerscale=False,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_small_lrf_pretrained.pdparams',
),
'focalnet_B_224_1k_lrf': dict(
embed_dim=128,
depths=[2, 2, 18, 2],
focal_levels=[3, 3, 3, 3],
focal_windows=[3, 3, 3, 3],
drop_path_rate=0.5,
use_conv_embed=False,
use_postln=False,
use_postln_in_modulation=False,
use_layerscale=False,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_base_lrf_pretrained.pdparams',
),
'focalnet_L_384_22k_fl3': dict(
embed_dim=192,
depths=[2, 2, 18, 2],
focal_levels=[3, 3, 3, 3],
focal_windows=[5, 5, 5, 5],
drop_path_rate=0.5,
use_conv_embed=True,
use_postln=True,
use_postln_in_modulation=False,
use_layerscale=True,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_large_lrf_384_pretrained.pdparams',
),
'focalnet_L_384_22k_fl4': dict(
embed_dim=192,
depths=[2, 2, 18, 2],
focal_levels=[4, 4, 4, 4],
focal_windows=[3, 3, 3, 3],
drop_path_rate=0.5,
use_conv_embed=True,
use_postln=True,
use_postln_in_modulation=False,
use_layerscale=True,
normalize_modulator=True, #
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_large_lrf_384_fl4_pretrained.pdparams',
),
'focalnet_XL_384_22k_fl3': dict(
embed_dim=256,
depths=[2, 2, 18, 2],
focal_levels=[3, 3, 3, 3],
focal_windows=[5, 5, 5, 5],
drop_path_rate=0.5,
use_conv_embed=True,
use_postln=True,
use_postln_in_modulation=False,
use_layerscale=True,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_xlarge_lrf_384_pretrained.pdparams',
),
'focalnet_XL_384_22k_fl4': dict(
embed_dim=256,
depths=[2, 2, 18, 2],
focal_levels=[4, 4, 4, 4],
focal_windows=[3, 3, 3, 3],
drop_path_rate=0.5,
use_conv_embed=True,
use_postln=True,
use_postln_in_modulation=False,
use_layerscale=True,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_xlarge_lrf_384_fl4_pretrained.pdparams',
),
'focalnet_H_224_22k_fl3': dict(
embed_dim=352,
depths=[2, 2, 18, 2],
focal_levels=[3, 3, 3, 3],
focal_windows=[3, 3, 3, 3],
drop_path_rate=0.5,
use_conv_embed=True,
use_postln=True,
use_postln_in_modulation=True, #
use_layerscale=True,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_huge_lrf_224_pretrained.pdparams',
),
'focalnet_H_224_22k_fl4': dict(
embed_dim=352,
depths=[2, 2, 18, 2],
focal_levels=[4, 4, 4, 4],
focal_windows=[3, 3, 3, 3],
drop_path_rate=0.5,
use_conv_embed=True,
use_postln=True,
use_postln_in_modulation=True, #
use_layerscale=True,
normalize_modulator=False,
pretrained='https://bj.bcebos.com/v1/paddledet/models/pretrained/focalnet_huge_lrf_224_fl4_pretrained.pdparams',
),
}
class FocalModulation(nn.Layer):
"""
Args:
dim (int): Number of input channels.
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
focal_level (int): Number of focal levels
focal_window (int): Focal window size at focal level 1
focal_factor (int): Step to increase the focal window. Default: 2
use_postln_in_modulation (bool): Whether use post-modulation layernorm
normalize_modulator (bool): Whether use normalize in modulator
"""
def __init__(self,
dim,
proj_drop=0.,
focal_level=2,
focal_window=7,
focal_factor=2,
use_postln_in_modulation=False,
normalize_modulator=False):
super().__init__()
self.dim = dim
# specific args for focalv3
self.focal_level = focal_level
self.focal_window = focal_window
self.focal_factor = focal_factor
self.use_postln_in_modulation = use_postln_in_modulation
self.normalize_modulator = normalize_modulator
self.f = nn.Linear(
dim, 2 * dim + (self.focal_level + 1), bias_attr=True)
self.h = nn.Conv2D(
dim,
dim,
kernel_size=1,
stride=1,
padding=0,
groups=1,
bias_attr=True)
self.act = nn.GELU()
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.focal_layers = nn.LayerList()
if self.use_postln_in_modulation:
self.ln = nn.LayerNorm(dim)
for k in range(self.focal_level):
kernel_size = self.focal_factor * k + self.focal_window
self.focal_layers.append(
nn.Sequential(
nn.Conv2D(
dim,
dim,
kernel_size=kernel_size,
stride=1,
groups=dim,
padding=kernel_size // 2,
bias_attr=False),
nn.GELU()))
def forward(self, x):
""" Forward function.
Args:
x: input features with shape of (B, H, W, C)
"""
_, _, _, C = x.shape
x = self.f(x)
x = x.transpose([0, 3, 1, 2])
q, ctx, gates = paddle.split(x, (C, C, self.focal_level + 1), 1)
ctx_all = 0
for l in range(self.focal_level):
ctx = self.focal_layers[l](ctx)
ctx_all = ctx_all + ctx * gates[:, l:l + 1]
ctx_global = self.act(ctx.mean(2, keepdim=True).mean(3, keepdim=True))
ctx_all = ctx_all + ctx_global * gates[:, self.focal_level:]
if self.normalize_modulator:
ctx_all = ctx_all / (self.focal_level + 1)
x_out = q * self.h(ctx_all)
x_out = x_out.transpose([0, 2, 3, 1])
if self.use_postln_in_modulation:
x_out = self.ln(x_out)
x_out = self.proj(x_out)
x_out = self.proj_drop(x_out)
return x_out
class FocalModulationBlock(nn.Layer):
""" Focal Modulation Block.
Args:
dim (int): Number of input channels.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
drop (float, optional): Dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Layer, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Layer, optional): Normalization layer. Default: nn.LayerNorm
focal_level (int): number of focal levels
focal_window (int): focal kernel size at level 1
use_postln (bool): Whether use layernorm after modulation. Default: False.
use_postln_in_modulation (bool): Whether use post-modulation layernorm. Default: False.
normalize_modulator (bool): Whether use normalize in modulator
use_layerscale (bool): Whether use layerscale proposed in CaiT. Default: False
layerscale_value (float): Value for layer scale. Default: 1e-4
"""
def __init__(self,
dim,
mlp_ratio=4.,
drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
focal_level=2,
focal_window=9,
use_postln=False,
use_postln_in_modulation=False,
normalize_modulator=False,
use_layerscale=False,
layerscale_value=1e-4):
super().__init__()
self.dim = dim
self.mlp_ratio = mlp_ratio
self.focal_window = focal_window
self.focal_level = focal_level
self.use_postln = use_postln
self.use_layerscale = use_layerscale
self.norm1 = norm_layer(dim)
self.modulation = FocalModulation(
dim,
proj_drop=drop,
focal_level=self.focal_level,
focal_window=self.focal_window,
use_postln_in_modulation=use_postln_in_modulation,
normalize_modulator=normalize_modulator)
self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop)
self.H = None
self.W = None
self.gamma_1 = 1.0
self.gamma_2 = 1.0
if self.use_layerscale:
self.gamma_1 = add_parameter(self,
layerscale_value * paddle.ones([dim]))
self.gamma_2 = add_parameter(self,
layerscale_value * paddle.ones([dim]))
def forward(self, x):
"""
Args:
x: Input feature, tensor size (B, H*W, C).
"""
B, L, C = x.shape
H, W = self.H, self.W
assert L == H * W, "input feature has wrong size"
shortcut = x
if not self.use_postln:
x = self.norm1(x)
x = x.reshape([-1, H, W, C])
# FM
x = self.modulation(x).reshape([-1, H * W, C])
if self.use_postln:
x = self.norm1(x)
# FFN
x = shortcut + self.drop_path(self.gamma_1 * x)
if self.use_postln:
x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x)))
else:
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class BasicLayer(nn.Layer):
""" A basic focal modulation layer for one stage.
Args:
dim (int): Number of feature channels
depth (int): Depths of this stage.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
drop (float, optional): Dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Layer, optional): Normalization layer. Default: nn.LayerNorm
downsample (nn.Layer | None, optional): Downsample layer at the end of the layer. Default: None
focal_level (int): Number of focal levels
focal_window (int): Focal window size at focal level 1
use_conv_embed (bool): Whether use overlapped convolution for patch embedding
use_layerscale (bool): Whether use layerscale proposed in CaiT. Default: False
layerscale_value (float): Value of layerscale
use_postln (bool): Whether use layernorm after modulation. Default: False.
use_postln_in_modulation (bool): Whether use post-modulation layernorm. Default: False.
normalize_modulator (bool): Whether use normalize in modulator
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
"""
def __init__(self,
dim,
depth,
mlp_ratio=4.,
drop=0.,
drop_path=0.,
norm_layer=nn.LayerNorm,
downsample=None,
focal_level=2,
focal_window=9,
use_conv_embed=False,
use_layerscale=False,
layerscale_value=1e-4,
use_postln=False,
use_postln_in_modulation=False,
normalize_modulator=False,
use_checkpoint=False):
super().__init__()
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.LayerList([
FocalModulationBlock(
dim=dim,
mlp_ratio=mlp_ratio,
drop=drop,
drop_path=drop_path[i]
if isinstance(drop_path, np.ndarray) else drop_path,
act_layer=nn.GELU,
norm_layer=norm_layer,
focal_level=focal_level,
focal_window=focal_window,
use_postln=use_postln,
use_postln_in_modulation=use_postln_in_modulation,
normalize_modulator=normalize_modulator,
use_layerscale=use_layerscale,
layerscale_value=layerscale_value) for i in range(depth)
])
# patch merging layer
if downsample is not None:
self.downsample = downsample(
patch_size=2,
in_chans=dim,
embed_dim=2 * dim,
use_conv_embed=use_conv_embed,
norm_layer=norm_layer,
is_stem=False)
else:
self.downsample = None
def forward(self, x, H, W):
"""
Args:
x: Input feature, tensor size (B, H*W, C).
"""
for blk in self.blocks:
blk.H, blk.W = H, W
x = blk(x)
if self.downsample is not None:
x_reshaped = x.transpose([0, 2, 1]).reshape(
[x.shape[0], x.shape[-1], H, W])
x_down = self.downsample(x_reshaped)
x_down = x_down.flatten(2).transpose([0, 2, 1])
Wh, Ww = (H + 1) // 2, (W + 1) // 2
return x, H, W, x_down, Wh, Ww
else:
return x, H, W, x, H, W
class PatchEmbed(nn.Layer):
""" Image to Patch Embedding
Args:
patch_size (int): Patch token size. Default: 4.
in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Layer, optional): Normalization layer. Default: None
use_conv_embed (bool): Whether use overlapped convolution for patch embedding. Default: False
is_stem (bool): Is the stem block or not.
"""
def __init__(self,
patch_size=4,
in_chans=3,
embed_dim=96,
norm_layer=None,
use_conv_embed=False,
is_stem=False):
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.in_chans = in_chans
self.embed_dim = embed_dim
if use_conv_embed:
# if we choose to use conv embedding, then we treat the stem and non-stem differently
if is_stem:
kernel_size = 7
padding = 2
stride = 4
else:
kernel_size = 3
padding = 1
stride = 2
self.proj = nn.Conv2D(
in_chans,
embed_dim,
kernel_size=kernel_size,
stride=stride,
padding=padding)
else:
self.proj = nn.Conv2D(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = None
def forward(self, x):
_, _, H, W = x.shape
if W % self.patch_size[1] != 0:
# for 3D tensor: [pad_left, pad_right]
# for 4D tensor: [pad_left, pad_right, pad_top, pad_bottom]
x = F.pad(x, [0, self.patch_size[1] - W % self.patch_size[1], 0, 0])
W += W % self.patch_size[1]
if H % self.patch_size[0] != 0:
x = F.pad(x, [0, 0, 0, self.patch_size[0] - H % self.patch_size[0]])
H += H % self.patch_size[0]
x = self.proj(x)
if self.norm is not None:
_, _, Wh, Ww = x.shape
x = x.flatten(2).transpose([0, 2, 1])
x = self.norm(x)
x = x.transpose([0, 2, 1]).reshape([-1, self.embed_dim, Wh, Ww])
return x
@register
@serializable
class FocalNet(nn.Layer):
""" FocalNet backbone
Args:
arch (str): Architecture of FocalNet
out_indices (Sequence[int]): Output from which stages.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters.
patch_size (int | tuple(int)): Patch size. Default: 4.
in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
depths (tuple[int]): Depths of each FocalNet Transformer stage.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
drop_rate (float): Dropout rate.
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
norm_layer (nn.Layer): Normalization layer. Default: nn.LayerNorm.
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
focal_levels (Sequence[int]): Number of focal levels at four stages
focal_windows (Sequence[int]): Focal window sizes at first focal level at four stages
use_conv_embed (bool): Whether use overlapped convolution for patch embedding
use_layerscale (bool): Whether use layerscale proposed in CaiT. Default: False
layerscale_value (float): Value of layerscale
use_postln (bool): Whether use layernorm after modulation. Default: False.
use_postln_in_modulation (bool): Whether use post-modulation layernorm. Default: False.
normalize_modulator (bool): Whether use normalize in modulator
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
"""
def __init__(
self,
arch='focalnet_T_224_1k_srf',
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
patch_size=4,
in_chans=3,
embed_dim=96,
depths=[2, 2, 6, 2],
mlp_ratio=4.,
drop_rate=0.,
drop_path_rate=0.2, # 0.5 better for large+ models
norm_layer=nn.LayerNorm,
patch_norm=True,
focal_levels=[2, 2, 2, 2],
focal_windows=[3, 3, 3, 3],
use_conv_embed=False,
use_layerscale=False,
layerscale_value=1e-4,
use_postln=False,
use_postln_in_modulation=False,
normalize_modulator=False,
use_checkpoint=False,
pretrained=None):
super(FocalNet, self).__init__()
assert arch in MODEL_cfg.keys(), "Unsupported arch: {}".format(arch)
embed_dim = MODEL_cfg[arch]['embed_dim']
depths = MODEL_cfg[arch]['depths']
drop_path_rate = MODEL_cfg[arch]['drop_path_rate']
focal_levels = MODEL_cfg[arch]['focal_levels']
focal_windows = MODEL_cfg[arch]['focal_windows']
use_conv_embed = MODEL_cfg[arch]['use_conv_embed']
use_layerscale = MODEL_cfg[arch]['use_layerscale']
use_postln = MODEL_cfg[arch]['use_postln']
use_postln_in_modulation = MODEL_cfg[arch]['use_postln_in_modulation']
normalize_modulator = MODEL_cfg[arch]['normalize_modulator']
if pretrained is None:
pretrained = MODEL_cfg[arch]['pretrained']
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.num_layers = len(depths)
self.patch_norm = patch_norm
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None,
use_conv_embed=use_conv_embed,
is_stem=True)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth decay rule
dpr = np.linspace(0, drop_path_rate, sum(depths))
# build layers
self.layers = nn.LayerList()
for i_layer in range(self.num_layers):
layer = BasicLayer(
dim=int(embed_dim * 2**i_layer),
depth=depths[i_layer],
mlp_ratio=mlp_ratio,
drop=drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
downsample=PatchEmbed
if (i_layer < self.num_layers - 1) else None,
focal_level=focal_levels[i_layer],
focal_window=focal_windows[i_layer],
use_conv_embed=use_conv_embed,
use_layerscale=use_layerscale,
layerscale_value=layerscale_value,
use_postln=use_postln,
use_postln_in_modulation=use_postln_in_modulation,
normalize_modulator=normalize_modulator,
use_checkpoint=use_checkpoint)
self.layers.append(layer)
num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
self.num_features = num_features
# add a norm layer for each output
for i_layer in out_indices:
layer = norm_layer(num_features[i_layer])
layer_name = f'norm{i_layer}'
self.add_sublayer(layer_name, layer)
self.apply(self._init_weights)
self._freeze_stages()
if pretrained:
if 'http' in pretrained: #URL
path = paddle.utils.download.get_weights_path_from_url(
pretrained)
else: #model in local path
path = pretrained
self.set_state_dict(paddle.load(path))
def _freeze_stages(self):
if self.frozen_stages >= 0:
self.patch_embed.eval()
for param in self.patch_embed.parameters():
param.stop_gradient = True
if self.frozen_stages >= 2:
self.pos_drop.eval()
for i in range(0, self.frozen_stages - 1):
m = self.layers[i]
m.eval()
for param in m.parameters():
param.stop_gradient = True
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
zeros_(m.bias)
elif isinstance(m, nn.LayerNorm):
zeros_(m.bias)
ones_(m.weight)
def forward(self, x):
x = self.patch_embed(x['image'])
B, _, Wh, Ww = x.shape
x = x.flatten(2).transpose([0, 2, 1])
x = self.pos_drop(x)
outs = []
for i in range(self.num_layers):
layer = self.layers[i]
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
if i in self.out_indices:
norm_layer = getattr(self, f'norm{i}')
x_out = norm_layer(x_out)
out = x_out.reshape([-1, H, W, self.num_features[i]]).transpose(
(0, 3, 1, 2))
outs.append(out)
return outs
@property
def out_shape(self):
out_strides = [4, 8, 16, 32]
return [
ShapeSpec(
channels=self.num_features[i], stride=out_strides[i])
for i in self.out_indices
]