"""
Modified from https://github.com/whai362/PVT/blob/v2/classification/pvt.py
"""
import numpy as np
from functools import partial
import oneflow as flow
import oneflow.nn as nn
import oneflow.nn.functional as F
from flowvision.layers import trunc_normal_, DropPath
from .utils import load_state_dict_from_url
from .registry import ModelCreator
from .helpers import to_2tuple
model_urls = {
"pvt_tiny": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/PVT/pvt_tiny.zip",
"pvt_small": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/PVT/pvt_small.zip",
"pvt_medium": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/PVT/pvt_medium.zip",
"pvt_large": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/PVT/pvt_large.zip",
"pvt_huge_v2": None,
}
class Mlp(nn.Module):
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.Module):
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
qk_scale=None,
attn_drop=0.0,
proj_drop=0.0,
sr_ratio=1,
):
super().__init__()
assert (
dim % num_heads == 0
), f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.sr_ratio = sr_ratio
if sr_ratio > 1:
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
self.norm = nn.LayerNorm(dim)
def forward(self, x, H, W):
B, N, C = x.shape
q = (
self.q(x)
.reshape(B, N, self.num_heads, C // self.num_heads)
.permute(0, 2, 1, 3)
)
if self.sr_ratio > 1:
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
x_ = self.norm(x_)
kv = (
self.kv(x_)
.reshape(B, -1, 2, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
else:
kv = (
self.kv(x)
.reshape(B, -1, 2, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
k, v = kv[0], kv[1]
attn = flow.matmul(q, k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (flow.matmul(attn, v)).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4,
qkv_bias=False,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
sr_ratio=1,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop,
sr_ratio=sr_ratio,
)
# 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 > 0.0 else nn.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,
)
def forward(self, x, H, W):
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
self.num_patches = self.H * self.W
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
)
self.norm = nn.LayerNorm(embed_dim)
def forward(self, x):
B, C, H, W = x.shape
x = self.proj(x).flatten(2).transpose(1, 2)
x = self.norm(x)
H, W = H // self.patch_size[0], W // self.patch_size[1]
return x, (H, W)
class PyramidVisionTransformer(nn.Module):
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
embed_dims=[64, 128, 256, 512],
num_heads=[1, 2, 4, 8],
mlp_ratios=[4, 4, 4, 4],
qkv_bias=False,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.0,
norm_layer=nn.LayerNorm,
depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1],
num_stages=4,
):
super().__init__()
self.num_classes = num_classes
self.depths = depths
self.num_stages = num_stages
# stochastic depth
dpr = [
x.item() for x in flow.linspace(0, drop_path_rate, sum(depths))
] # stochastic depth decay rule
cur = 0
for i in range(num_stages):
patch_embed = PatchEmbed(
img_size=img_size if i == 0 else img_size // (2 ** (i + 1)),
patch_size=patch_size if i == 0 else 2,
in_chans=in_chans if i == 0 else embed_dims[i - 1],
embed_dim=embed_dims[i],
)
num_patches = (
patch_embed.num_patches
if i != num_stages - 1
else patch_embed.num_patches + 1
)
pos_embed = nn.Parameter(flow.zeros(1, num_patches, embed_dims[i]))
pos_drop = nn.Dropout(p=drop_rate)
block = nn.ModuleList(
[
Block(
dim=embed_dims[i],
num_heads=num_heads[i],
mlp_ratio=mlp_ratios[i],
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[cur + j],
norm_layer=norm_layer,
sr_ratio=sr_ratios[i],
)
for j in range(depths[i])
]
)
cur += depths[i]
setattr(self, f"patch_embed{i + 1}", patch_embed)
setattr(self, f"pos_embed{i + 1}", pos_embed)
setattr(self, f"pos_drop{i + 1}", pos_drop)
setattr(self, f"block{i + 1}", block)
self.norm = norm_layer(embed_dims[3])
# cls_token
self.cls_token = nn.Parameter(flow.zeros(1, 1, embed_dims[3]))
# classification head
self.head = (
nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
)
# init weights
for i in range(num_stages):
pos_embed = getattr(self, f"pos_embed{i + 1}")
trunc_normal_(pos_embed, std=0.02)
trunc_normal_(self.cls_token, std=0.02)
self.apply(self._init_weights)
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, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=""):
self.num_classes = num_classes
self.head = (
nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
)
def _get_pos_embed(self, pos_embed, patch_embed, H, W):
if H * W == self.patch_embed1.num_patches:
return pos_embed
else:
return (
F.interpolate(
pos_embed.reshape(1, patch_embed.H, patch_embed.W, -1).permute(
0, 3, 1, 2
),
size=(H, W),
mode="bilinear",
)
.reshape(1, -1, H * W)
.permute(0, 2, 1)
)
def forward_features(self, x):
B = x.shape[0]
for i in range(self.num_stages):
patch_embed = getattr(self, f"patch_embed{i + 1}")
pos_embed = getattr(self, f"pos_embed{i + 1}")
pos_drop = getattr(self, f"pos_drop{i + 1}")
block = getattr(self, f"block{i + 1}")
x, (H, W) = patch_embed(x)
if i == self.num_stages - 1:
cls_tokens = self.cls_token.expand(B, -1, -1)
x = flow.cat((cls_tokens, x), dim=1)
pos_embed_ = self._get_pos_embed(pos_embed[:, 1:], patch_embed, H, W)
pos_embed = flow.cat((pos_embed[:, 0:1], pos_embed_), dim=1)
else:
pos_embed = self._get_pos_embed(pos_embed, patch_embed, H, W)
x = pos_drop(x + pos_embed)
for blk in block:
x = blk(x, H, W)
if i != self.num_stages - 1:
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
x = self.norm(x)
return x[:, 0]
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def _create_pvt(arch, pretrained=False, progress=True, **model_kwargs):
model = PyramidVisionTransformer(**model_kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
model.load_state_dict(state_dict)
return model
[docs]@ModelCreator.register_model
def pvt_tiny(pretrained=False, progress=True, **kwargs):
"""
Constructs the PVT-tiny model.
.. note::
PVT-tiny model from `"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions" <https://arxiv.org/pdf/2102.12122.pdf>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> pvt_tiny = flowvision.models.pvt_tiny(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=224,
patch_size=4,
embed_dims=(64, 128, 320, 512),
depths=(2, 2, 2, 2),
num_heads=(1, 2, 5, 8),
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
mlp_ratios=(8, 8, 4, 4),
sr_ratios=(8, 4, 2, 1),
drop_path_rate=0.1,
**kwargs,
)
return _create_pvt(
"pvt_tiny", pretrained=pretrained, progress=progress, **model_kwargs,
)
[docs]@ModelCreator.register_model
def pvt_small(pretrained=False, progress=True, **kwargs):
"""
Constructs the PVT-small model.
.. note::
PVT-small model from `"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions" <https://arxiv.org/pdf/2102.12122.pdf>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> pvt_small = flowvision.models.pvt_small(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=224,
patch_size=4,
embed_dims=(64, 128, 320, 512),
depths=(3, 4, 6, 3),
num_heads=(1, 2, 5, 8),
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
mlp_ratios=(8, 8, 4, 4),
sr_ratios=(8, 4, 2, 1),
drop_path_rate=0.1,
**kwargs,
)
return _create_pvt(
"pvt_small", pretrained=pretrained, progress=progress, **model_kwargs,
)
[docs]@ModelCreator.register_model
def pvt_medium(pretrained=False, progress=True, **kwargs):
"""
Constructs the PVT-medium model.
.. note::
PVT-medium model from `"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions" <https://arxiv.org/pdf/2102.12122.pdf>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> pvt_medium = flowvision.models.pvt_medium(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=224,
patch_size=4,
embed_dims=(64, 128, 320, 512),
depths=(3, 4, 18, 3),
num_heads=(1, 2, 5, 8),
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
mlp_ratios=(8, 8, 4, 4),
sr_ratios=(8, 4, 2, 1),
drop_path_rate=0.1,
**kwargs,
)
return _create_pvt(
"pvt_medium", pretrained=pretrained, progress=progress, **model_kwargs,
)
[docs]@ModelCreator.register_model
def pvt_large(pretrained=False, progress=True, **kwargs):
"""
Constructs the PVT-large model.
.. note::
PVT-large model from `"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions" <https://arxiv.org/pdf/2102.12122.pdf>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> pvt_large = flowvision.models.pvt_large(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=224,
patch_size=4,
embed_dims=(64, 128, 320, 512),
depths=(3, 8, 27, 3),
num_heads=(1, 2, 5, 8),
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
mlp_ratios=(8, 8, 4, 4),
sr_ratios=(8, 4, 2, 1),
drop_path_rate=0.1,
**kwargs,
)
return _create_pvt(
"pvt_large", pretrained=pretrained, progress=progress, **model_kwargs,
)