trainer add

trainer.py

@@ -0,0 +1,155 @@
+import os
+import torch
+import torch.nn as nn
+from torch import autograd
+from model.networks import Generator, LocalDis, GlobalDis
+
+
+from utils.tools import get_model_list, local_patch, spatial_discounting_mask
+from utils.logger import get_logger
+
+logger = get_logger()
+
+
+class Trainer(nn.Module):
+    def __init__(self, config):
+        super(Trainer, self).__init__()
+        self.config = config
+        self.use_cuda = self.config['cuda']
+        self.device_ids = self.config['gpu_ids']
+
+        self.netG = Generator(self.config['netG'], self.use_cuda, self.device_ids)
+        self.localD = LocalDis(self.config['netD'], self.use_cuda, self.device_ids)
+        self.globalD = GlobalDis(self.config['netD'], self.use_cuda, self.device_ids)
+
+        self.optimizer_g = torch.optim.Adam(self.netG.parameters(), lr=self.config['lr'],
+                                            betas=(self.config['beta1'], self.config['beta2']))
+        d_params = list(self.localD.parameters()) + list(self.globalD.parameters())
+        self.optimizer_d = torch.optim.Adam(d_params, lr=config['lr'],
+                                            betas=(self.config['beta1'], self.config['beta2']))
+        if self.use_cuda:
+            self.netG.to(self.device_ids[0])
+            self.localD.to(self.device_ids[0])
+            self.globalD.to(self.device_ids[0])
+
+    def forward(self, x, bboxes, masks, ground_truth, compute_loss_g=False):
+        self.train()
+        l1_loss = nn.L1Loss()
+        losses = {}
+
+        x1, x2, offset_flow = self.netG(x, masks)
+        local_patch_gt = local_patch(ground_truth, bboxes)
+        x1_inpaint = x1 * masks + x * (1. - masks)
+        x2_inpaint = x2 * masks + x * (1. - masks)
+        local_patch_x1_inpaint = local_patch(x1_inpaint, bboxes)
+        local_patch_x2_inpaint = local_patch(x2_inpaint, bboxes)
+
+        # D part
+        # wgan d loss
+        local_patch_real_pred, local_patch_fake_pred = self.dis_forward(
+            self.localD, local_patch_gt, local_patch_x2_inpaint.detach())
+        global_real_pred, global_fake_pred = self.dis_forward(
+            self.globalD, ground_truth, x2_inpaint.detach())
+        losses['wgan_d'] = torch.mean(local_patch_fake_pred - local_patch_real_pred) + \
+            torch.mean(global_fake_pred - global_real_pred) * self.config['global_wgan_loss_alpha']
+        # gradients penalty loss
+        local_penalty = self.calc_gradient_penalty(
+            self.localD, local_patch_gt, local_patch_x2_inpaint.detach())
+        global_penalty = self.calc_gradient_penalty(self.globalD, ground_truth, x2_inpaint.detach())
+        losses['wgan_gp'] = local_penalty + global_penalty
+
+        # G part
+        if compute_loss_g:
+            sd_mask = spatial_discounting_mask(self.config)
+            losses['l1'] = l1_loss(local_patch_x1_inpaint * sd_mask, local_patch_gt * sd_mask) * \
+                self.config['coarse_l1_alpha'] + \
+                l1_loss(local_patch_x2_inpaint * sd_mask, local_patch_gt * sd_mask)
+            losses['ae'] = l1_loss(x1 * (1. - masks), ground_truth * (1. - masks)) * \
+                self.config['coarse_l1_alpha'] + \
+                l1_loss(x2 * (1. - masks), ground_truth * (1. - masks))
+
+            # wgan g loss
+            local_patch_real_pred, local_patch_fake_pred = self.dis_forward(
+                self.localD, local_patch_gt, local_patch_x2_inpaint)
+            global_real_pred, global_fake_pred = self.dis_forward(
+                self.globalD, ground_truth, x2_inpaint)
+            losses['wgan_g'] = - torch.mean(local_patch_fake_pred) - \
+                torch.mean(global_fake_pred) * self.config['global_wgan_loss_alpha']
+
+        return losses, x2_inpaint, offset_flow
+
+    def dis_forward(self, netD, ground_truth, x_inpaint):
+        assert ground_truth.size() == x_inpaint.size()
+        batch_size = ground_truth.size(0)
+        batch_data = torch.cat([ground_truth, x_inpaint], dim=0)
+        batch_output = netD(batch_data)
+        real_pred, fake_pred = torch.split(batch_output, batch_size, dim=0)
+
+        return real_pred, fake_pred
+
+    # Calculate gradient penalty
+    def calc_gradient_penalty(self, netD, real_data, fake_data):
+        batch_size = real_data.size(0)
+        alpha = torch.rand(batch_size, 1, 1, 1)
+        alpha = alpha.expand_as(real_data)
+        if self.use_cuda:
+            alpha = alpha.cuda()
+
+        interpolates = alpha * real_data + (1 - alpha) * fake_data
+        interpolates = interpolates.requires_grad_().clone()
+
+        disc_interpolates = netD(interpolates)
+        grad_outputs = torch.ones(disc_interpolates.size())
+
+        if self.use_cuda:
+            grad_outputs = grad_outputs.cuda()
+
+        gradients = autograd.grad(outputs=disc_interpolates, inputs=interpolates,
+                                  grad_outputs=grad_outputs, create_graph=True,
+                                  retain_graph=True, only_inputs=True)[0]
+
+        gradients = gradients.view(batch_size, -1)
+        gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
+
+        return gradient_penalty
+
+    def inference(self, x, masks):
+        self.eval()
+        x1, x2, offset_flow = self.netG(x, masks)
+        # x1_inpaint = x1 * masks + x * (1. - masks)
+        x2_inpaint = x2 * masks + x * (1. - masks)
+
+        return x2_inpaint, offset_flow
+
+    def save_model(self, checkpoint_dir, iteration):
+        # Save generators, discriminators, and optimizers
+        gen_name = os.path.join(checkpoint_dir, 'gen_%08d.pt' % iteration)
+        dis_name = os.path.join(checkpoint_dir, 'dis_%08d.pt' % iteration)
+        opt_name = os.path.join(checkpoint_dir, 'optimizer.pt')
+        torch.save(self.netG.state_dict(), gen_name)
+        torch.save({'localD': self.localD.state_dict(),
+                    'globalD': self.globalD.state_dict()}, dis_name)
+        torch.save({'gen': self.optimizer_g.state_dict(),
+                    'dis': self.optimizer_d.state_dict()}, opt_name)
+
+    def resume(self, checkpoint_dir, iteration=0, test=False):
+        # Load generators
+        last_model_name = get_model_list(checkpoint_dir, "gen", iteration=iteration)
+        self.netG.load_state_dict(torch.load(last_model_name))
+        iteration = int(last_model_name[-11:-3])
+
+        if not test:
+            # Load discriminators
+            last_model_name = get_model_list(checkpoint_dir, "dis", iteration=iteration)
+            state_dict = torch.load(last_model_name)
+            self.localD.load_state_dict(state_dict['localD'])
+            self.globalD.load_state_dict(state_dict['globalD'])
+            # Load optimizers
+            state_dict = torch.load(os.path.join(checkpoint_dir, 'optimizer.pt'))
+            self.optimizer_d.load_state_dict(state_dict['dis'])
+            self.optimizer_g.load_state_dict(state_dict['gen'])
+
+        print("Resume from {} at iteration {}".format(checkpoint_dir, iteration))
+        logger.info("Resume from {} at iteration {}".format(checkpoint_dir, iteration))
+
+        return iteration