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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
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