# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from collections.abc import Callable
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import Constant
from ...utils import load_ckpt
from ..registry import BACKBONES
from ..weight_init import trunc_normal_
__all__ = ['VisionTransformer']
zeros_ = Constant(value=0.)
ones_ = Constant(value=1.)
def to_2tuple(x):
return tuple([x] * 2)
def drop_path(x, drop_prob=0., training=False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
# issuecomment-532968956 ...
See discussion: https://github.com/tensorflow/tpu/issues/494
"""
if drop_prob == 0. or not training:
return x
keep_prob = paddle.to_tensor(1 - drop_prob, dtype=x.dtype)
shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
random_tensor = keep_prob + paddle.rand(shape).astype(x.dtype)
random_tensor = paddle.floor(random_tensor) # binarize
output = x.divide(keep_prob) * random_tensor
return output
class DropPath(nn.Layer):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
class Identity(nn.Layer):
def __init__(self):
super(Identity, self).__init__()
def forward(self, input):
return input
class Mlp(nn.Layer):
def __init__(self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Layer):
def __init__(self,
dim,
num_heads=8,
qkv_bias=False,
qk_scale=None,
attn_drop=0.0,
proj_drop=0.0):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.attn_drop = nn.Dropout(attn_drop)
def forward(self, x):
N, C = x.shape[1:]
qkv = self.qkv(x).reshape(
(-1, N, 3, self.num_heads, C // self.num_heads)).transpose(
(2, 0, 3, 1, 4))
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q.matmul(k.transpose((0, 1, 3, 2)))) * self.scale
attn = nn.functional.softmax(attn, axis=-1)
attn = self.attn_drop(attn)
x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((-1, N, C))
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Layer):
def __init__(self,
dim,
num_heads,
mlp_ratio=4.0,
qkv_bias=False,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.1,
act_layer=nn.GELU,
norm_layer='nn.LayerNorm',
epsilon=1e-5,
attention_type='divided_space_time'):
super().__init__()
self.attention_type = attention_type
if isinstance(norm_layer, str):
self.norm1 = eval(norm_layer)(dim, epsilon=epsilon)
elif isinstance(norm_layer, Callable):
self.norm1 = norm_layer(dim, epsilon=epsilon)
else:
raise TypeError(
"The norm_layer must be str or paddle.nn.layer.Layer class")
self.attn = Attention(dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop)
# Temporal Attention Parameters
if self.attention_type == 'divided_space_time':
if isinstance(norm_layer, str):
self.temporal_norm1 = eval(norm_layer)(dim, epsilon=epsilon)
elif isinstance(norm_layer, Callable):
self.temporal_norm1 = norm_layer(dim, epsilon=epsilon)
else:
raise TypeError(
"The norm_layer must be str or paddle.nn.layer.Layer class")
self.temporal_attn = Attention(dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop)
self.temporal_fc = nn.Linear(dim, dim)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
if isinstance(norm_layer, str):
self.norm2 = eval(norm_layer)(dim, epsilon=epsilon)
elif isinstance(norm_layer, Callable):
self.norm2 = norm_layer(dim, epsilon=epsilon)
else:
raise TypeError(
"The norm_layer must be str or paddle.nn.layer.Layer class")
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop)
def forward(self, x, B, T, W):
num_spatial_tokens = (x.shape[1] - 1) // T
H = num_spatial_tokens // W
if self.attention_type in ['space_only', 'joint_space_time']:
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
elif self.attention_type == 'divided_space_time':
########## Temporal ##########
xt = x[:, 1:, :]
_, _, _, _t, _m = B, H, W, T, xt.shape[-1]
xt = xt.reshape([-1, _t, _m])
res_temporal = self.drop_path(
self.temporal_attn(self.temporal_norm1(xt)))
_, _h, _w, _t, _m = B, H, W, T, res_temporal.shape[-1]
res_temporal = res_temporal.reshape([-1, _h * _w * _t, _m])
res_temporal = self.temporal_fc(res_temporal)
xt = x[:, 1:, :] + res_temporal
########## Spatial ##########
init_cls_token = x[:, 0, :].unsqueeze(1)
cls_token = init_cls_token.tile((1, T, 1))
_b, _t, _m = cls_token.shape
cls_token = cls_token.reshape([-1, _m]).unsqueeze(1)
xs = xt
_, _h, _w, _t, _m = B, H, W, T, xs.shape[-1]
xs = xs.reshape([-1, _h, _w, _t, _m]).transpose(
(0, 3, 1, 2, 4)).reshape([-1, _h * _w, _m])
xs = paddle.concat((cls_token, xs), axis=1)
res_spatial = self.drop_path(self.attn(self.norm1(xs)))
# Taking care of CLS token
cls_token = res_spatial[:, 0, :]
_, _t, _m = B, T, cls_token.shape[-1]
cls_token = cls_token.reshape([-1, _t, _m])
# averaging for every frame
cls_token = paddle.mean(cls_token, axis=1, keepdim=True)
res_spatial = res_spatial[:, 1:, :]
_, _t, _h, _w, _m = B, T, H, W, res_spatial.shape[-1]
res_spatial = res_spatial.reshape([-1, _t, _h, _w, _m]).transpose(
(0, 2, 3, 1, 4)).reshape([-1, _h * _w * _t, _m])
res = res_spatial
x = xt
x = paddle.concat((init_cls_token, x), axis=1) + paddle.concat(
(cls_token, res), axis=1)
# Mlp
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
else:
raise NotImplementedError
class PatchEmbed(nn.Layer):
""" Image to Patch Embedding
"""
def __init__(self,
img_size=224,
patch_size=16,
in_channels=3,
embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] //
patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2D(in_channels,
embed_dim,
kernel_size=patch_size,
stride=patch_size)
def forward(self, x):
B, C, T, H, W = x.shape
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = x.transpose((0, 2, 1, 3, 4))
x = x.reshape([-1, C, H, W])
x = self.proj(x)
W = x.shape[-1]
x = x.flatten(2).transpose((0, 2, 1))
return x, T, W
@BACKBONES.register()
class VisionTransformer(nn.Layer):
""" Vision Transformer with support for patch input
"""
def __init__(self,
pretrained=None,
img_size=224,
patch_size=16,
in_channels=3,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=False,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.1,
norm_layer='nn.LayerNorm',
epsilon=1e-5,
num_seg=8,
attention_type='divided_space_time',
**args):
super().__init__()
self.pretrained = pretrained
self.num_seg = num_seg
self.attention_type = attention_type
self.num_features = self.embed_dim = embed_dim
self.patch_embed = PatchEmbed(img_size=img_size,
patch_size=patch_size,
in_channels=in_channels,
embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
# Positional Embeddings
self.cls_token = self.create_parameter(shape=(1, 1, embed_dim),
default_initializer=zeros_)
self.pos_embed = self.create_parameter(shape=(1, num_patches + 1,
embed_dim),
default_initializer=zeros_)
self.pos_drop = nn.Dropout(p=drop_rate)
if self.attention_type != 'space_only':
self.time_embed = self.create_parameter(shape=(1, num_seg,
embed_dim),
default_initializer=zeros_)
self.time_drop = nn.Dropout(p=drop_rate)
self.add_parameter("pos_embed", self.pos_embed)
self.add_parameter("cls_token", self.cls_token)
dpr = np.linspace(0, drop_path_rate, depth)
self.blocks = nn.LayerList([
Block(dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
epsilon=epsilon,
attention_type=self.attention_type) for i in range(depth)
])
self.norm = eval(norm_layer)(embed_dim, epsilon=epsilon)
def init_weights(self):
"""First init model's weight"""
trunc_normal_(self.pos_embed, std=0.02)
trunc_normal_(self.cls_token, std=0.02)
self.apply(self._init_fn)
if self.attention_type == 'divided_space_time':
i = 0
for m in self.blocks.sublayers(include_self=True):
m_str = str(m)
if 'Block' in m_str:
if i > 0:
zeros_(m.temporal_fc.weight)
zeros_(m.temporal_fc.bias)
i += 1
"""Second, if provide pretrained ckpt, load it"""
if isinstance(
self.pretrained, str
) and self.pretrained.strip() != "": # load pretrained weights
load_ckpt(self,
self.pretrained,
num_patches=self.patch_embed.num_patches,
num_seg=self.num_seg,
attention_type=self.attention_type)
def _init_fn(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight)
if m.bias is not None:
zeros_(m.bias)
elif isinstance(m, nn.LayerNorm):
ones_(m.weight)
zeros_(m.bias)
def forward_features(self, x):
# B = x.shape[0]
B = paddle.shape(x)[0]
x, T, W = self.patch_embed(x) # [BT,nH*nW,F]
cls_tokens = self.cls_token.expand((B * T, -1, -1)) # [1,1,F]->[BT,1,F]
x = paddle.concat((cls_tokens, x), axis=1)
pos_interp = (x.shape[1] != self.pos_embed.shape[1])
if pos_interp:
pos_embed = self.pos_embed
cls_pos_embed = pos_embed[0, 0, :].unsqueeze(0).unsqueeze(1)
other_pos_embed = pos_embed[0, 1:, :].unsqueeze(0).transpose(
(0, 2, 1))
P = int(other_pos_embed.shape[2]**0.5)
H = x.shape[1] // W
other_pos_embed = other_pos_embed.reshape([1, x.shape[2], P, P])
new_pos_embed = F.interpolate(other_pos_embed,
size=(H, W),
mode='nearest')
new_pos_embed = new_pos_embed.flatten(2)
new_pos_embed = new_pos_embed.transpose((0, 2, 1))
new_pos_embed = paddle.concat((cls_pos_embed, new_pos_embed),
axis=1)
x = x + new_pos_embed
else:
x = x + self.pos_embed
x = self.pos_drop(x)
# Time Embeddings
if self.attention_type != 'space_only':
cls_tokens = x[:B, 0, :].unsqueeze(1) if B > 0 else x.split(
T)[0].index_select(paddle.to_tensor([0]), axis=1)
x = x[:, 1:]
_, _n, _m = x.shape
_t = T
x = x.reshape([-1, _t, _n, _m]).transpose(
(0, 2, 1, 3)).reshape([-1, _t, _m])
# Resizing time embeddings in case they don't match
time_interp = (T != self.time_embed.shape[1])
if time_interp: # T' != T
time_embed = self.time_embed.transpose((0, 2, 1)).unsqueeze(0)
new_time_embed = F.interpolate(time_embed,
size=(T, x.shape[-1]),
mode='nearest').squeeze(0)
new_time_embed = new_time_embed.transpose((0, 2, 1))
x = x + new_time_embed
else:
x = x + self.time_embed
x = self.time_drop(x)
_, _t, _m = x.shape
x = x.reshape([-1, W * W * T, _m])
x = paddle.concat((cls_tokens, x), axis=1)
# Attention blocks
for blk in self.blocks:
x = blk(x, B, T, W)
# Predictions for space-only baseline
if self.attention_type == 'space_only':
_, _n, _m = x.shape
_t = T
x = x.reshape([-1, _t, _n, _m])
x = paddle.mean(x, 1) # averaging predictions for every frame
x = self.norm(x)
return x[:, 0] # [B, embed_dim]
def forward(self, x):
x = self.forward_features(x)
return x