| ''' |
| @Created by chaofengc (chaofenghust@gmail.com) |
| |
| @Modified by yangxy (yangtao9009@gmail.com) |
| ''' |
|
|
| from blocks import * |
| import torch |
| from torch import nn |
| import numpy as np |
|
|
| def define_P(in_size=512, out_size=512, min_feat_size=32, relu_type='LeakyReLU', isTrain=False, weight_path=None): |
| net = ParseNet(in_size, out_size, min_feat_size, 64, 19, norm_type='bn', relu_type=relu_type, ch_range=[32, 256]) |
| if not isTrain: |
| net.eval() |
| if weight_path is not None: |
| net.load_state_dict(torch.load(weight_path)) |
| return net |
|
|
|
|
| class ParseNet(nn.Module): |
| def __init__(self, |
| in_size=128, |
| out_size=128, |
| min_feat_size=32, |
| base_ch=64, |
| parsing_ch=19, |
| res_depth=10, |
| relu_type='prelu', |
| norm_type='bn', |
| ch_range=[32, 512], |
| ): |
| super().__init__() |
| self.res_depth = res_depth |
| act_args = {'norm_type': norm_type, 'relu_type': relu_type} |
| min_ch, max_ch = ch_range |
|
|
| ch_clip = lambda x: max(min_ch, min(x, max_ch)) |
| min_feat_size = min(in_size, min_feat_size) |
|
|
| down_steps = int(np.log2(in_size//min_feat_size)) |
| up_steps = int(np.log2(out_size//min_feat_size)) |
|
|
| |
| self.encoder = [] |
| self.encoder.append(ConvLayer(3, base_ch, 3, 1)) |
| head_ch = base_ch |
| for i in range(down_steps): |
| cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2) |
| self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args)) |
| head_ch = head_ch * 2 |
|
|
| self.body = [] |
| for i in range(res_depth): |
| self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args)) |
|
|
| self.decoder = [] |
| for i in range(up_steps): |
| cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2) |
| self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args)) |
| head_ch = head_ch // 2 |
|
|
| self.encoder = nn.Sequential(*self.encoder) |
| self.body = nn.Sequential(*self.body) |
| self.decoder = nn.Sequential(*self.decoder) |
| self.out_img_conv = ConvLayer(ch_clip(head_ch), 3) |
| self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch) |
|
|
| def forward(self, x): |
| feat = self.encoder(x) |
| x = feat + self.body(feat) |
| x = self.decoder(x) |
| out_img = self.out_img_conv(x) |
| out_mask = self.out_mask_conv(x) |
| return out_mask, out_img |
|
|
|
|
|
|
