inference/network.py

import torch
import torch.nn as nn


class SignWithSigmoidGrad(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x):
        result = (x > 0).float()
        sigmoid_result = torch.sigmoid(x)
        ctx.save_for_backward(sigmoid_result)
        return result

    @staticmethod
    def backward(ctx, grad_result):
        (sigmoid_result,) = ctx.saved_tensors
        if ctx.needs_input_grad[0]:
            grad_input = grad_result * sigmoid_result * (1 - sigmoid_result)
        else:
            grad_input = None
        return grad_input


class Painter(nn.Module):

    def __init__(self, param_per_stroke, total_strokes, hidden_dim, n_heads=8, n_enc_layers=3, n_dec_layers=3):
        super().__init__()
        self.enc_img = nn.Sequential(
            nn.ReflectionPad2d(1),
            nn.Conv2d(3, 32, 3, 1),
            nn.BatchNorm2d(32),
            nn.ReLU(True),
            nn.ReflectionPad2d(1),
            nn.Conv2d(32, 64, 3, 2),
            nn.BatchNorm2d(64),
            nn.ReLU(True),
            nn.ReflectionPad2d(1),
            nn.Conv2d(64, 128, 3, 2),
            nn.BatchNorm2d(128),
            nn.ReLU(True))
        self.enc_canvas = nn.Sequential(
            nn.ReflectionPad2d(1),
            nn.Conv2d(3, 32, 3, 1),
            nn.BatchNorm2d(32),
            nn.ReLU(True),
            nn.ReflectionPad2d(1),
            nn.Conv2d(32, 64, 3, 2),
            nn.BatchNorm2d(64),
            nn.ReLU(True),
            nn.ReflectionPad2d(1),
            nn.Conv2d(64, 128, 3, 2),
            nn.BatchNorm2d(128),
            nn.ReLU(True))
        self.conv = nn.Conv2d(128 * 2, hidden_dim, 1)
        self.transformer = nn.Transformer(hidden_dim, n_heads, n_enc_layers, n_dec_layers)
        self.linear_param = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(True),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(True),
            nn.Linear(hidden_dim, param_per_stroke))
        self.linear_decider = nn.Linear(hidden_dim, 1)
        self.query_pos = nn.Parameter(torch.rand(total_strokes, hidden_dim))
        self.row_embed = nn.Parameter(torch.rand(8, hidden_dim // 2))
        self.col_embed = nn.Parameter(torch.rand(8, hidden_dim // 2))

    def forward(self, img, canvas):
        b, _, H, W = img.shape
        img_feat = self.enc_img(img)
        canvas_feat = self.enc_canvas(canvas)
        h, w = img_feat.shape[-2:]
        feat = torch.cat([img_feat, canvas_feat], dim=1)
        feat_conv = self.conv(feat)

        pos_embed = torch.cat([
            self.col_embed[:w].unsqueeze(0).contiguous().repeat(h, 1, 1),
            self.row_embed[:h].unsqueeze(1).contiguous().repeat(1, w, 1),
        ], dim=-1).flatten(0, 1).unsqueeze(1)
        hidden_state = self.transformer(pos_embed + feat_conv.flatten(2).permute(2, 0, 1).contiguous(),
                                        self.query_pos.unsqueeze(1).contiguous().repeat(1, b, 1))
        hidden_state = hidden_state.permute(1, 0, 2).contiguous()
        param = self.linear_param(hidden_state)
        decision = self.linear_decider(hidden_state)
        return param, decision