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models.py
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import math
import torch
import torch.nn as nn
from torch.nn import init
BATCHNORM_TRACK_RUNNING_STATS = False
BATCHNORM_MOVING_AVERAGE_DECAY = 0.9997
class BNorm_init(nn.BatchNorm2d):
def reset_parameters(self):
init.uniform_(self.weight, 0, 1)
init.zeros_(self.bias)
class Conv2d_init(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode="zeros"):
super(Conv2d_init, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode)
def reset_parameters(self):
init.xavier_normal_(self.weight)
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
def _conv_block(in_chanels, out_chanels, kernel_size, padding):
return nn.Sequential(Conv2d_init(in_channels=in_chanels, out_channels=out_chanels,
kernel_size=kernel_size, padding=padding, bias=False),
FeatureNorm(num_features=out_chanels, eps=0.001),
nn.ReLU())
class FeatureNorm(nn.Module):
def __init__(self, num_features, feature_index=1, rank=4, reduce_dims=(2, 3), eps=0.001, include_bias=True):
super(FeatureNorm, self).__init__()
self.shape = [1] * rank
self.shape[feature_index] = num_features
self.reduce_dims = reduce_dims
self.scale = nn.Parameter(torch.ones(self.shape, requires_grad=True, dtype=torch.float))
self.bias = nn.Parameter(torch.zeros(self.shape, requires_grad=True, dtype=torch.float)) if include_bias else nn.Parameter(
torch.zeros(self.shape, requires_grad=False, dtype=torch.float))
self.eps = eps
def forward(self, features):
f_std = torch.std(features, dim=self.reduce_dims, keepdim=True)
f_mean = torch.mean(features, dim=self.reduce_dims, keepdim=True)
return self.scale * ((features - f_mean) / (f_std + self.eps).sqrt()) + self.bias
class SegDecNet(nn.Module):
def __init__(self, device, input_width, input_height, input_channels):
super(SegDecNet, self).__init__()
if input_width % 8 != 0 or input_height % 8 != 0:
raise Exception(f"Input size must be divisible by 8! width={input_width}, height={input_height}")
self.input_width = input_width
self.input_height = input_height
self.input_channels = input_channels
self.volume = nn.Sequential(_conv_block(self.input_channels, 32, 5, 2),
_conv_block(32, 32, 5, 2),
nn.MaxPool2d(2),
_conv_block(32, 64, 5, 2),
_conv_block(64, 64, 5, 2),
_conv_block(64, 64, 5, 2),
nn.MaxPool2d(2),
_conv_block(64, 64, 5, 2),
_conv_block(64, 64, 5, 2),
_conv_block(64, 64, 5, 2),
_conv_block(64, 64, 5, 2),
nn.MaxPool2d(2),
_conv_block(64, 1024, 15, 7))
self.seg_mask = nn.Sequential(
Conv2d_init(in_channels=1024, out_channels=1, kernel_size=1, padding=0, bias=False),
FeatureNorm(num_features=1, eps=0.001, include_bias=False))
self.extractor = nn.Sequential(nn.MaxPool2d(kernel_size=2),
_conv_block(in_chanels=1025, out_chanels=8, kernel_size=5, padding=2),
nn.MaxPool2d(kernel_size=2),
_conv_block(in_chanels=8, out_chanels=16, kernel_size=5, padding=2),
nn.MaxPool2d(kernel_size=2),
_conv_block(in_chanels=16, out_chanels=32, kernel_size=5, padding=2))
self.global_max_pool_feat = nn.MaxPool2d(kernel_size=32)
self.global_avg_pool_feat = nn.AvgPool2d(kernel_size=32)
self.global_max_pool_seg = nn.MaxPool2d(kernel_size=(self.input_height / 8, self.input_width / 8))
self.global_avg_pool_seg = nn.AvgPool2d(kernel_size=(self.input_height / 8, self.input_width / 8))
self.fc = nn.Linear(in_features=66, out_features=1)
# with torch.no_grad():
self.volume_lr_multiplier_layer = GradientMultiplyLayer().apply
self.glob_max_lr_multiplier_layer = GradientMultiplyLayer().apply
self.glob_avg_lr_multiplier_layer = GradientMultiplyLayer().apply
self.device = device
# @torch.jit.script_method
def set_gradient_multipliers(self, multiplier):
self.volume_lr_multiplier_mask = (torch.ones((1,)) * multiplier).to(self.device)
self.glob_max_lr_multiplier_mask = (torch.ones((1,)) * multiplier).to(self.device)
self.glob_avg_lr_multiplier_mask = (torch.ones((1,)) * multiplier).to(self.device)
# @torch.jit.script_method
def forward(self, input):
volume = self.volume(input)
seg_mask = self.seg_mask(volume)
cat = torch.cat([volume, seg_mask], dim=1)
cat = self.volume_lr_multiplier_layer(cat, self.volume_lr_multiplier_mask)
features = self.extractor(cat)
global_max_feat = torch.max(torch.max(features, dim=-1, keepdim=True)[0], dim=-2, keepdim=True)[0]
global_avg_feat = torch.mean(features, dim=(-1, -2), keepdim=True)
global_max_seg = torch.max(torch.max(seg_mask, dim=-1, keepdim=True)[0], dim=-2, keepdim=True)[0]
global_avg_seg = torch.mean(seg_mask, dim=(-1, -2), keepdim=True)
global_max_feat = global_max_feat.reshape(global_max_feat.size(0), -1)
global_avg_feat = global_avg_feat.reshape(global_avg_feat.size(0), -1)
global_max_seg = global_max_seg.reshape(global_max_seg.size(0), -1)
global_max_seg = self.glob_max_lr_multiplier_layer(global_max_seg, self.glob_max_lr_multiplier_mask)
global_avg_seg = global_avg_seg.reshape(global_avg_seg.size(0), -1)
global_avg_seg = self.glob_avg_lr_multiplier_layer(global_avg_seg, self.glob_avg_lr_multiplier_mask)
fc_in = torch.cat([global_max_feat, global_avg_feat, global_max_seg, global_avg_seg], dim=1)
fc_in = fc_in.reshape(fc_in.size(0), -1)
prediction = self.fc(fc_in)
return prediction, seg_mask
class GradientMultiplyLayer(torch.autograd.Function):
@staticmethod
def forward(ctx, input, mask_bw):
ctx.save_for_backward(mask_bw)
return input
@staticmethod
def backward(ctx, grad_output):
mask_bw, = ctx.saved_tensors
return grad_output.mul(mask_bw), None
@staticmethod
def symbolic(g: torch._C.Graph, input: torch._C.Value, mask_bw) -> torch._C.Value:
# return g.op("Clip", input, g.op("Constant", value_t=torch.tensor(0, dtype=torch.float)))
# ret = g.op("Constant", input) # , mask_bw)
return input # ret