Spaces:
Running
Running
File size: 4,412 Bytes
80ab65e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
import torch
import torch.nn as nn
from .layers import *
class FCDenseNet(nn.Module):
def __init__(self, in_channels=3, down_blocks=(5, 5, 5, 5, 5),
up_blocks=(5, 5, 5, 5, 5), bottleneck_layers=5,
growth_rate=16, out_chans_first_conv=48, n_classes=12):
super().__init__()
self.down_blocks = down_blocks
self.up_blocks = up_blocks
cur_channels_count = 0
skip_connection_channel_counts = []
## First Convolution ##
self.add_module('firstconv', nn.Conv2d(in_channels=in_channels,
out_channels=out_chans_first_conv, kernel_size=3,
stride=1, padding=1, bias=True))
cur_channels_count = out_chans_first_conv
#####################
# Downsampling path #
#####################
self.denseBlocksDown = nn.ModuleList([])
self.transDownBlocks = nn.ModuleList([])
for i in range(len(down_blocks)):
self.denseBlocksDown.append(
DenseBlock(cur_channels_count, growth_rate, down_blocks[i]))
cur_channels_count += (growth_rate*down_blocks[i])
skip_connection_channel_counts.insert(0, cur_channels_count)
self.transDownBlocks.append(TransitionDown(cur_channels_count))
#####################
# Bottleneck #
#####################
self.add_module('bottleneck', Bottleneck(cur_channels_count,
growth_rate, bottleneck_layers))
prev_block_channels = growth_rate*bottleneck_layers
cur_channels_count += prev_block_channels
#######################
# Upsampling path #
#######################
self.transUpBlocks = nn.ModuleList([])
self.denseBlocksUp = nn.ModuleList([])
for i in range(len(up_blocks)-1):
self.transUpBlocks.append(TransitionUp(
prev_block_channels, prev_block_channels))
cur_channels_count = prev_block_channels + \
skip_connection_channel_counts[i]
self.denseBlocksUp.append(DenseBlock(
cur_channels_count, growth_rate, up_blocks[i],
upsample=True))
prev_block_channels = growth_rate*up_blocks[i]
cur_channels_count += prev_block_channels
## Final DenseBlock ##
self.transUpBlocks.append(TransitionUp(
prev_block_channels, prev_block_channels))
cur_channels_count = prev_block_channels + \
skip_connection_channel_counts[-1]
self.denseBlocksUp.append(DenseBlock(
cur_channels_count, growth_rate, up_blocks[-1],
upsample=False))
cur_channels_count += growth_rate*up_blocks[-1]
## Softmax ##
self.finalConv = nn.Conv2d(in_channels=cur_channels_count,
out_channels=n_classes, kernel_size=1, stride=1,
padding=0, bias=True)
self.softmax = nn.LogSoftmax(dim=1)
def forward(self, x):
out = self.firstconv(x)
skip_connections = []
for i in range(len(self.down_blocks)):
out = self.denseBlocksDown[i](out)
skip_connections.append(out)
out = self.transDownBlocks[i](out)
out = self.bottleneck(out)
for i in range(len(self.up_blocks)):
skip = skip_connections.pop()
out = self.transUpBlocks[i](out, skip)
out = self.denseBlocksUp[i](out)
out = self.finalConv(out)
out = self.softmax(out)
return out
def FCDenseNet57(n_classes):
return FCDenseNet(
in_channels=3, down_blocks=(4, 4, 4, 4, 4),
up_blocks=(4, 4, 4, 4, 4), bottleneck_layers=4,
growth_rate=12, out_chans_first_conv=48, n_classes=n_classes)
def FCDenseNet67(n_classes):
return FCDenseNet(
in_channels=3, down_blocks=(5, 5, 5, 5, 5),
up_blocks=(5, 5, 5, 5, 5), bottleneck_layers=5,
growth_rate=16, out_chans_first_conv=48, n_classes=n_classes)
def FCDenseNet103(n_classes):
return FCDenseNet(
in_channels=3, down_blocks=(4, 5, 7, 10, 12),
up_blocks=(12, 10, 7, 5, 4), bottleneck_layers=15,
growth_rate=16, out_chans_first_conv=48, n_classes=n_classes)
|