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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import math
import einops
import torch
import apex
import torch.nn.functional as F
from megatron import get_args
from megatron.model import LayerNorm
from megatron.model.module import MegatronModule
from megatron.model.vision.utils import resize
class SetrSegmentationHead(MegatronModule):
def __init__(self, hidden_size, num_classes):
super(SetrSegmentationHead, self).__init__()
args = get_args()
self.hidden_size = hidden_size
self.num_classes = num_classes
self.img_h = args.img_h
self.img_w = args.img_w
self.patch_dim = args.patch_dim
self.layernorm = LayerNorm(hidden_size, eps=args.layernorm_epsilon)
self.conv_0 = torch.nn.Conv2d(hidden_size, hidden_size,
1, 1, bias=False)
self.norm_0 = apex.parallel.SyncBatchNorm(hidden_size)
self.conv_1 = torch.nn.Conv2d(hidden_size, num_classes, 1, 1)
def to_2D(self, x):
n, hw, c = x.shape
h = self.img_h // self.patch_dim
w = self.img_w // self.patch_dim
assert(hw == h * w)
x = x.transpose(1, 2).reshape(n, c, h, w)
return x
def forward(self, hidden_states):
# [b c h w]
hidden_states = self.layernorm(hidden_states)
hidden_states = self.to_2D(hidden_states)
hidden_states = self.conv_0(hidden_states)
hidden_states = self.norm_0(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.conv_1(hidden_states)
# [b c h w]
result = F.interpolate(hidden_states,
size=(self.img_h, self.img_w),
mode='bilinear')
return result
class MLP(torch.nn.Module):
"""
Linear Embedding
"""
def __init__(self, input_dim=2048, embed_dim=768):
super().__init__()
self.proj = torch.nn.Linear(input_dim, embed_dim)
def forward(self, x):
x = x.flatten(2).transpose(1, 2)
x = self.proj(x)
return x
class SegformerSegmentationHead(MegatronModule):
def __init__(self, feature_strides, in_channels,
embedding_dim, dropout_ratio):
super(SegformerSegmentationHead, self).__init__()
assert len(feature_strides) == len(in_channels)
assert min(feature_strides) == feature_strides[0]
args = get_args()
self.feature_strides = feature_strides
self.in_channels = in_channels
self.embedding_dim = embedding_dim
self.num_classes = args.num_classes
self.dropout_ratio = dropout_ratio
c1_in_channels, c2_in_channels, c3_in_channels, c4_in_channels = \
self.in_channels
self.linear_c4 = MLP(input_dim=c4_in_channels,
embed_dim=self.embedding_dim)
self.linear_c3 = MLP(input_dim=c3_in_channels,
embed_dim=self.embedding_dim)
self.linear_c2 = MLP(input_dim=c2_in_channels,
embed_dim=self.embedding_dim)
self.linear_c1 = MLP(input_dim=c1_in_channels,
embed_dim=self.embedding_dim)
self.conv_fuse = torch.nn.Conv2d(self.embedding_dim*4,
self.embedding_dim, 1, 1)
self.norm = apex.parallel.SyncBatchNorm(self.embedding_dim)
self.dropout = torch.nn.Dropout2d(self.dropout_ratio)
self.linear_pred = torch.nn.Conv2d(self.embedding_dim,
self.num_classes,
kernel_size=1)
def forward(self, inputs):
c1, c2, c3, c4 = inputs
############## MLP decoder on C1-C4 ###########
n, _, h, w = c4.shape
_c4 = self.linear_c4(c4).permute(0, 2, 1).reshape(n, -1, c4.shape[2], c4.shape[3])
_c4 = resize(_c4, size=c1.size()[2:], mode='bilinear', align_corners=False)
_c3 = self.linear_c3(c3).permute(0, 2, 1).reshape(n, -1, c3.shape[2], c3.shape[3])
_c3 = resize(_c3, size=c1.size()[2:], mode='bilinear', align_corners=False)
_c2 = self.linear_c2(c2).permute(0, 2, 1).reshape(n, -1, c2.shape[2], c2.shape[3])
_c2 = resize(_c2, size=c1.size()[2:], mode='bilinear', align_corners=False)
_c1 = self.linear_c1(c1).permute(0, 2, 1).reshape(n, -1, c1.shape[2], c1.shape[3])
_c = self.conv_fuse(torch.cat([_c4, _c3, _c2, _c1], dim=1))
x = self.norm(_c)
x = F.relu(x, inplace=True)
x = self.dropout(x)
x = self.linear_pred(x)
return x
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