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EAN_16f.py
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"""
An example combining `Temporal Shift Module` with `ResNet`. This implementation
is based on `Temporal Segment Networks`, which merges temporal dimension into
batch, i.e. inputs [N*T, C, H, W]. Here we show the case with residual connections
and zero padding with 8 frames as input.
"""
import sys
sys.path.append("/data/tianyuan/AST/checkpoints/EAB_SOITr/ablation_res50/SOTA_8frame_lite")
import torch.nn as nn
import torch.nn.functional as F
import torch as tr
# from tsm_util import tsm
import torch.utils.model_zoo as model_zoo
# from spatial_correlation_sampler import SpatialCorrelationSampler
from EAB_rfs555 import *
from einops import rearrange, reduce, repeat
from einops.layers.torch import Rearrange
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
def conv1x1x1(in_planes, out_planes, stride=1):
"""1x1x1 convolution"""
return nn.Conv3d(in_planes, out_planes, kernel_size=1, stride=stride, bias=True)
class BasicBlock2(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, num_segments,stride=1, downsample=None, remainder=0):
super(BasicBlock2, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.remainder =remainder
self.num_segments = num_segments
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, num_segments, stride=1, downsample=None, remainder=0):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.remainder= remainder
self.num_segments = num_segments
def d2_to_d3(self,x):
bt,c,h,w = x.size()
t = 16
b = bt//t
return x.reshape(b,t,c,h,w).transpose(1,2)
def d3_to_d2(self,x):
b,c,t,h,w = x.size()
return x.transpose(1,2).reshape(b*t,c,h,w)
def forward(self, x):
identity = x
# out = tsm(x, self.num_segments, 'zero')
out = x
out = self.conv1(out)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ShiftModule(nn.Module):
def __init__(self, input_channels, n_segment=8,n_div=8, mode='shift'):
super(ShiftModule, self).__init__()
self.input_channels = input_channels
self.n_segment = n_segment
self.fold_div = n_div
self.fold = self.input_channels // self.fold_div
self.conv = nn.Conv1d(self.fold_div*self.fold, self.fold_div*self.fold,
kernel_size=3, padding=1, groups=self.fold_div*self.fold,
bias=False)
if mode == 'shift':
self.conv.weight.requires_grad = True
self.conv.weight.data.zero_()
self.conv.weight.data[:self.fold, 0, 2] = 1 # shift left
self.conv.weight.data[self.fold: 2 * self.fold, 0, 0] = 1 # shift right
if 2*self.fold < self.input_channels:
self.conv.weight.data[2 * self.fold:, 0, 1] = 1 # fixed
elif mode == 'fixed':
self.conv.weight.requires_grad = True
self.conv.weight.data.zero_()
self.conv.weight.data[:, 0, 1] = 1 # fixed
elif mode == 'norm':
self.conv.weight.requires_grad = True
def forward(self, x):
nt, c, h, w = x.size()
n_batch = nt // self.n_segment
x = x.view(n_batch, self.n_segment, c, h, w)
x = x.permute(0, 3, 4, 2, 1) # (n_batch, h, w, c, n_segment)
x = x.contiguous().view(n_batch*h*w, c, self.n_segment)
x = self.conv(x) # (n_batch*h*w, c, n_segment)
x = x.view(n_batch, h, w, c, self.n_segment)
x = x.permute(0, 4, 3, 1, 2) # (n_batch, n_segment, c, h, w)
x = x.contiguous().view(nt, c, h, w)
return x
class MEModule(nn.Module):
""" Motion exciation module
:param reduction=16
:param n_segment=8/16
"""
def __init__(self, channel, reduction=16, n_segment=16):
super(MEModule, self).__init__()
self.channel = channel
self.reduction = reduction
self.n_segment = n_segment
self.conv1 = nn.Conv2d(
in_channels=self.channel,
out_channels=self.channel//self.reduction,
kernel_size=1,
bias=False)
self.bn1 = nn.BatchNorm2d(num_features=self.channel//self.reduction)
self.conv2 = nn.Conv2d(
in_channels=self.channel//self.reduction,
out_channels=self.channel//self.reduction,
kernel_size=3,
padding=1,
groups=channel//self.reduction,
bias=False)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.sigmoid = nn.Sigmoid()
self.pad = (0, 0, 0, 0, 0, 0, 0, 1)
self.conv3 = nn.Conv2d(
in_channels=self.channel//self.reduction,
out_channels=self.channel,
kernel_size=1,
bias=False)
self.bn3 = nn.BatchNorm2d(num_features=self.channel)
self.identity = nn.Identity()
def forward(self, x):
nt, c, h, w = x.size()
bottleneck = self.conv1(x) # nt, c//r, h, w
bottleneck = self.bn1(bottleneck) # nt, c//r, h, w
# t feature
reshape_bottleneck = bottleneck.view((-1, self.n_segment) + bottleneck.size()[1:]) # n, t, c//r, h, w
t_fea, __ = reshape_bottleneck.split([self.n_segment-1, 1], dim=1) # n, t-1, c//r, h, w
# apply transformation conv to t+1 feature
conv_bottleneck = self.conv2(bottleneck) # nt, c//r, h, w
# reshape fea: n, t, c//r, h, w
reshape_conv_bottleneck = conv_bottleneck.view((-1, self.n_segment) + conv_bottleneck.size()[1:])
__, tPlusone_fea = reshape_conv_bottleneck.split([1, self.n_segment-1], dim=1) # n, t-1, c//r, h, w
# motion fea = t+1_fea - t_fea
# pad the last timestamp
diff_fea = tPlusone_fea - t_fea # n, t-1, c//r, h, w
# pad = (0,0,0,0,0,0,0,1)
diff_fea_pluszero = F.pad(diff_fea, self.pad, mode="constant", value=0) # n, t, c//r, h, w
diff_fea_pluszero = diff_fea_pluszero.view((-1,) + diff_fea_pluszero.size()[2:]) #nt, c//r, h, w
y = self.avg_pool(diff_fea_pluszero) # nt, c//r, 1, 1
y = self.conv3(y) # nt, c, 1, 1
y = self.bn3(y) # nt, c, 1, 1
y = self.sigmoid(y) # nt, c, 1, 1
y = y - 0.5
output = x + x * y.expand_as(x)
return output
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes,num_segments, stride=1, downsample=None, remainder=0):
super(Bottleneck, self).__init__()
self.inplanes = inplanes
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.remainder= remainder
self.num_segments = num_segments
# self.t_conv = ShiftModule(planes, n_segment=8, n_div=2, mode='fixed')
self.t_conv = ShiftModule(planes, n_segment=self.num_segments, n_div=8, mode='shift')
self.me = MEModule(planes, reduction=16, n_segment=16)
def forward(self, x):
identity = x
out = x
out = self.conv1(out)
out = self.bn1(out)
out = self.relu(out)
out = self.me(out)
# if self.inplanes>64:
# out = self.t_conv(out)
out = self.t_conv(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
from SOI_Tr import SparseTransformer
# from non_local_sparse import NLBlockND
class ResNet(nn.Module):
def __init__(self, block, block2, layers, num_segments, flow_estimation, num_classes=1000, zero_init_residual=False):
super(ResNet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.relu = nn.ReLU(inplace=True)
self.sigmoid = nn.Sigmoid()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.softmax = nn.Softmax(dim=1)
self.num_segments = num_segments
self.layer1 = self._make_layer(block, 64, layers[0], num_segments=num_segments)
self.layer2 = self._make_layer(block, 128, layers[1], num_segments=num_segments, stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], num_segments=num_segments, stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], num_segments=num_segments, stride=2)
self.relation1 = STCeptionLayer_max(64,4)
self.relation2 = STCeptionLayer_max(64*block.expansion,4)
self.relation3 = STCeptionLayer_max(128*block.expansion,4)
self.relation4 = STCeptionLayer_max(256*block.expansion,4)
self.sparse_transformer = SparseTransformer(2048,16,4,4)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
self.x_down = 2
self.p_num=7
self.p1 = self.p2 = 112//self.p_num
self.patch_rgb_dim = self.p1 * self.p2 *3
self.patch_dim = 256
self.stm_proj1 = nn.Sequential(
nn.Conv3d(self.patch_rgb_dim,self.patch_dim,1,1,0,bias=False),
nn.BatchNorm3d(self.patch_dim),
nn.ReLU(inplace=True),
nn.Conv3d(self.patch_dim,self.patch_dim,1,1,0,bias=False),
)
self.stm_st_model1 = nn.Sequential(
nn.Conv3d(self.patch_dim,self.patch_dim,3,1,1,bias=False,groups = 16),
nn.BatchNorm3d(self.patch_dim),
nn.ReLU(inplace=True),
nn.Conv3d(self.patch_dim,self.patch_dim,1,1,0,bias=False),
nn.BatchNorm3d(self.patch_dim),
)
self.stm_st_model2 = nn.Sequential(
nn.Conv3d(self.patch_dim,self.patch_dim,3,1,1,bias=False,groups =16),
nn.BatchNorm3d(self.patch_dim),
)
self.stack_diff_motion = nn.Sequential(
nn.Conv2d(12,24,3,2,1,bias=False),
nn.ReLU(inplace=True),
nn.BatchNorm2d(24),
nn.Conv2d(24,64,3,1,1,bias=False,groups=4),
nn.ReLU(inplace=True),
nn.BatchNorm2d(64),
nn.Conv2d(64,64,1,1,0),
nn.Conv2d(64,64,3,1,1,bias=False,groups=16),
)
self.proj_back = nn.Sequential(
nn.Conv3d(self.patch_dim,self.patch_dim,1,1,0,bias=False),
nn.BatchNorm3d(self.patch_dim),
nn.ReLU(inplace=True),
nn.Conv3d(self.patch_dim,8* 8 *(16),1,1,0,bias=False),
)
self.proj_back[0].weight.data = self.proj_back[0].weight.data*0.01
for n,m in self.named_modules():
if "expand_c1" in n:
continue
elif "Tr_BN" in n:
continue
elif "t_conv" in n:
continue
elif "proj_back" in n:
continue
elif isinstance(m, nn.Conv1d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm3d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# self.register_parameter("fusion_weight",nn.Parameter(torch.zeros(1)))
# torch.nn.init.normal(self.fusion_weight)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, num_segments, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, num_segments, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
remainder =int( i % 3)
layers.append(block(self.inplanes, planes, num_segments, remainder=remainder))
return nn.Sequential(*layers)
def d2_to_d3(self,x,t=16):
bt,c,h,w = x.size()
b = bt//t
return x.reshape(b,t,c,h,w).transpose(1,2)
def d3_to_d2(self,x):
b,c,t,h,w = x.size()
return x.transpose(1,2).reshape(b*t,c,h,w)
def forward(self, x, temperature=0):
x_t = self.d2_to_d3(x,t=80) ### b c 40 h w
b,c,l,h,w = x_t.size()
x_flatten = x_t.reshape(b,c,16,5,h,w)
x = x_flatten[:,:,:,2] ## mid frames
x_short_clip = x_flatten.permute(0,2,1,3,4,5).reshape(b*16,c,5,h,w)
# x_short_clip_down_sample = F.upsample(x_short_clip,scale_factor= (1,0.5,0.5),mode='trilinear')
x_short_clip_down_sample = F.upsample(x_short_clip,scale_factor= (1,0.5,0.5))
x_short_clip_down_sample = x_short_clip_down_sample[:,:,1:5] - x_short_clip_down_sample[:,:,0:4]
x_short_clip_stack = x_short_clip_down_sample.reshape(b*16,12,112,112)
x_short_clip_stack = self.stack_diff_motion(x_short_clip_stack)
x_short_clip_stack = x_short_clip_stack.reshape(b,16,64,56,56).transpose(1,2)
X_patches = rearrange(x_short_clip_down_sample,"b c t (h p1) (w p2) -> b (p1 p2 c) t h w",p1 = self.p1,p2 = self.p2)
x_features = self.stm_proj1(X_patches)
x_features = x_features+self.stm_st_model1(x_features)
x_features = x_features+self.stm_st_model2(x_features)
x_patches = self.proj_back(x_features)
X_patches = rearrange(x_patches,"b (p1 p2 c) t h w -> b c t (h p1) (w p2)",c = 16,p1 = 8,p2 = 8)
# print(X_patches.size())
# exit()
x_res = X_patches.reshape(b,16,4*16,56,56).transpose(1,2)
x = self.d3_to_d2(x)
input = x
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.d2_to_d3(x)
# w = 0.75
x =0.6*x+ 0.2* x_res + 0.2*x_short_clip_stack
x,_,matrix1 = self.relation1(x)
x = self.d3_to_d2(x)
x = self.layer1(x)
x = self.d2_to_d3(x)
x,_,matrix2 = self.relation2(x)
x = self.d3_to_d2(x)
x = self.layer2(x)
x = self.d2_to_d3(x)
x,_,matrix3 = self.relation3(x)
x = self.d3_to_d2(x)
x = self.layer3(x)
x = self.d2_to_d3(x)
x,_,matrix4 = self.relation4(x)
x = self.d3_to_d2(x)
x = self.layer4(x)
x_d3 = self.d2_to_d3(x)
# feature_from_transformer = x_d3.mean(-1).mean(-1).mean(-1)
feature_from_transformer,x_score = self.sparse_transformer(x_d3)
x_out = feature_from_transformer
# print(feature_from_transformer.size(),x.size(),"fuck")
x = self.fc(x_out)
return x,None
# return x, {
# 'matrix1':matrix1,
# 'matrix2':matrix2,
# 'matrix3':matrix3,
# 'matrix4':matrix4,
# 'input':self.d2_to_d3(input)
# }
def resnet18(pretrained=True, shift='TSM',num_segments = 8, flow_estimation=0,alpha=0,beta=0, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if (shift =='TSM'):
model = ResNet(BasicBlock, BasicBlock, [2, 2, 2, 2], num_segments=num_segments , flow_estimation=flow_estimation, **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet18'])
new_state_dict = model.state_dict()
for k, v in pretrained_dict.items():
if (k in new_state_dict):
new_state_dict.update({k:v})
# print ("%s layer has pretrained weights" % k)
model.load_state_dict(new_state_dict)
return model
def resnet34(pretrained=False, shift='TSM',num_segments = 8, flow_estimation=0,**kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if (shift =='TSM'):
model = ResNet(BasicBlock, BasicBlock, [3, 4, 6, 3],num_segments=num_segments , flow_estimation=flow_estimation, **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet34'])
new_state_dict = model.state_dict()
for k, v in pretrained_dict.items():
if (k in new_state_dict):
new_state_dict.update({k:v})
# print ("%s layer has pretrained weights" % k)
model.load_state_dict(new_state_dict)
return model
def resnet50(pretrained=True, shift='TSM',num_segments = 16, flow_estimation=0,alpha=0,beta=0, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if (shift =='TSM'):
model = ResNet(Bottleneck, Bottleneck, [3, 4, 6, 3],num_segments=num_segments , flow_estimation=flow_estimation, **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet50'])
new_state_dict = model.state_dict()
for k, v in pretrained_dict.items():
if (k in new_state_dict):
new_state_dict.update({k:v})
# print ("%s layer has pretrained weights" % k)
model.load_state_dict(new_state_dict)
return model
def resnet101(pretrained=False, shift='TSM',num_segments = 8, flow_estimation=0, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if (shift =='TSM'):
model = ResNet(Bottleneck, Bottleneck, [3, 4, 23, 3],num_segments=num_segments , flow_estimation=flow_estimation, **kwargs)
if pretrained:
pretrained_dict = model_zoo.load_url(model_urls['resnet101'])
new_state_dict = model.state_dict()
for k, v in pretrained_dict.items():
if (k in new_state_dict):
new_state_dict.update({k:v})
# print ("%s layer has pretrained weights" % k)
model.load_state_dict(new_state_dict)
return model