Add wandb, make batch and image size configurable, fix some bugs, improve supervised loss function, make use of some RL stuff just implemented
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b7dbc59c25
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@ -10,6 +10,7 @@ import matplotlib.pyplot as plt
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import cv2
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import torchvision.transforms as transforms
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import wandb
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import co
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import torchext
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@ -18,32 +19,39 @@ from data import dataset
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class Worker(torchext.Worker):
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def __init__(self, args, num_workers=18, train_batch_size=4, test_batch_size=4, save_frequency=1, **kwargs):
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def __init__(self, args, num_workers=18, train_batch_size=6, test_batch_size=6, save_frequency=1, **kwargs):
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if 'batch_size' in dir(args):
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train_batch_size = args.batch_size
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test_batch_size = args.batch_size
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super().__init__(args.output_dir, args.exp_name, epochs=args.epochs, num_workers=num_workers,
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train_batch_size=train_batch_size, test_batch_size=test_batch_size,
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save_frequency=save_frequency, **kwargs)
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self.ms = args.ms
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self.pattern_path = args.pattern_path
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self.lcn_radius = args.lcn_radius
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self.dp_weight = args.dp_weight
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self.data_type = args.data_type
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self.imsizes = [(488, 648)]
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for iter in range(3):
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self.imsizes.append((int(self.imsizes[-1][0] / 2), int(self.imsizes[-1][1] / 2)))
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with open('config.json') as fp:
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config = json.load(fp)
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data_root = Path(config['DATA_ROOT'])
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self.imsizes = [tuple(map(int, config['IMSIZE'].split(',')))]
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for iter in range(3):
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self.imsizes.append((int(self.imsizes[-1][0] / 2), int(self.imsizes[-1][1] / 2)))
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self.settings_path = data_root / self.data_type / 'settings.pkl'
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sample_paths = sorted((data_root / self.data_type).glob('0*/'))
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self.train_paths = sample_paths[2 ** 10:]
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self.test_paths = sample_paths[:2 ** 8]
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# calc split
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# since we don't have a lot or RL footage, we compute it as we go
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train_size = len(sample_paths) * 0.8 // 1
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test_size = 1 - train_size
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self.train_paths = sample_paths[test_size:]
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self.test_paths = sample_paths[:train_size]
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# supervise the edge encoder with only 2**8 samples
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self.train_edge = len(self.train_paths) - 2 ** 8
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# don't just supervise the edge encoder with only 2**8 samples
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self.train_edge = len(self.train_paths)
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self.lcn_in = networks.LCN(self.lcn_radius, 0.05)
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self.disparity_loss = networks.DisparityLoss()
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@ -51,6 +59,25 @@ class Worker(torchext.Worker):
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self.sup_disp_loss = torch.nn.MSELoss()
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self.edge_loss = torch.nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([0.1]).to(self.train_device))
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# FIXME L2 Regularization, try it!!
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# l2_lambda = 0.001
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# l2_norm = sum(p.pow(2.0).sum()
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# for p in net.parameters())
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# self.sup_disp_loss = torch.nn.MSELoss() + l2_lambda * l2_norm
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# FIXME try using log of this loss, otherwise it's very large compared to others
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# self.sup_disp_loss = torch.nn.MSELoss()
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class RMSLELoss(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.mse = torch.nn.MSELoss()
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def forward(self, pred, actual):
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# FIXME rename this if log is better than sqrt
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return torch.log(self.mse(pred, actual))
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# return torch.sqrt(self.mse(torch.log(pred + 1), torch.log(actual + 1)))
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self.sup_disp_loss = RMSLELoss()
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# evaluate in the region where opencv Block Matching has valid values
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self.eval_mask = np.zeros(self.imsizes[0])
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self.eval_mask[13:self.imsizes[0][0] - 13, 140:self.imsizes[0][1] - 13] = 1
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@ -59,14 +86,13 @@ class Worker(torchext.Worker):
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self.eval_w = self.imsizes[0][1] - 13 - 140
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def get_train_set(self):
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train_set = dataset.TrackSynDataset(self.settings_path, self.train_paths, train=True, data_aug=True,
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train_set = dataset.RealWorldDataset(self.settings_path, self.train_paths, train=True, data_aug=True,
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track_length=1)
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return train_set
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def get_test_sets(self):
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test_sets = torchext.TestSets()
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test_set = dataset.TrackSynDataset(self.settings_path, self.test_paths, train=False, data_aug=True,
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test_set = dataset.RealWorldDataset(self.settings_path, self.test_paths, train=False, data_aug=True,
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track_length=1)
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test_sets.append('simple', test_set, test_frequency=1)
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@ -102,12 +128,12 @@ class Worker(torchext.Worker):
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self.data[key_std] = im_std.to(device).detach()
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def net_forward(self, net, train):
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# FIXME hier schnibbeln?
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out = net(self.data['im0'])
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return out
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def loss_forward(self, out, train):
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out, edge = out
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losses = {}
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if not (isinstance(out, tuple) or isinstance(out, list)):
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out = [out]
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if not (isinstance(edge, tuple) or isinstance(edge, list)):
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@ -118,23 +144,30 @@ class Worker(torchext.Worker):
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# apply photometric loss
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for s, l, o in zip(itertools.count(), self.losses, out):
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val, pattern_proj = l(o[0], self.data[f'im{s}'][:, 0:1, ...], self.data[f'std{s}'])
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if s == 0:
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self.pattern_proj = pattern_proj.detach()
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vals.append(val)
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# apply disparity loss
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# 1-edge as ground truth edge if inversed
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losses['photometric'] = val
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# 1-edge as ground truth edge if inverted
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if isinstance(edge, tuple):
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edge0 = 1 - torch.sigmoid(edge[0][0])
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else:
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edge0 = 1 - torch.sigmoid(edge[0])
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val = 0
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if isinstance(out[0], tuple):
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# NOTE use supervised disparity loss
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val += self.sup_disp_loss(out[0][1], self.data['disp0'])
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val += self.disparity_loss(out[0][0], edge0)
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sup_loss = self.sup_disp_loss(out[0][1], self.data['disp0'])
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val += sup_loss
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disp_loss = self.disparity_loss(out[0][0], edge0)
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val += disp_loss
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losses['GT Supervised disparity loss'] = sup_loss * self.dp_weight
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losses['OG disparity loss'] = disp_loss * self.dp_weight
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else:
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val += self.disparity_loss(out[0], edge0)
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disp_loss = self.disparity_loss(out[0], edge0)
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val += disp_loss
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losses['OG disparity loss'] = disp_loss * self.dp_weight
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if self.dp_weight > 0:
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vals.append(val * self.dp_weight)
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@ -159,15 +192,20 @@ class Worker(torchext.Worker):
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self.edge = e.detach()
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self.edge = torch.sigmoid(self.edge)
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self.edge_gt = grad.detach()
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losses['edge'] = val
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vals.append(val)
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wandb.log(losses)
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return vals
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def numpy_in_out(self, output):
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output, edge = output
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if not (isinstance(output, tuple) or isinstance(output, list)):
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output = [output]
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es = output[0][0].detach().to('cpu').numpy()
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if isinstance(output[0], tuple):
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es = output[0][0].detach().to('cpu').numpy()
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else:
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es = output[0].detach().to('cpu').numpy()
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gt = self.data['disp0'].to('cpu').numpy().astype(np.float32)
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im = self.data['im0'][:, 0:1, ...].detach().to('cpu').numpy()
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@ -250,6 +288,7 @@ class Worker(torchext.Worker):
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plt.tight_layout()
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plt.savefig(str(out_path))
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wandb.log({f'results_{"_".join(out_path.stem.split("_")[:-1])}': plt})
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plt.close(fig)
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def callback_train_post_backward(self, net, errs, output, epoch, batch_idx, masks=[]):
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@ -293,9 +332,4 @@ class Worker(torchext.Worker):
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if __name__ == '__main__':
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# FIXME Nicolas fixe idee
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# SGBM nutzen, um GT zu finden
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# bei dispnet (oder w/e) letzte paar layer 'dublizieren' (zweiten head bauen) und so mehrere Loss funktionen gleichzeitig trainieren
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# L1 + L2 und dann im selben Backwardspass optimieren
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# für das ganze forward pass anpassen
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pass
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