File size: 6,068 Bytes
24f9881
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
# MIT License

# Copyright (c) 2022 Intelligent Systems Lab Org

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

# File author: Shariq Farooq Bhat

import torch
import torch.cuda.amp as amp
import torch.nn as nn

from zoedepth.trainers.loss import GradL1Loss, SILogLoss
from zoedepth.utils.config import DATASETS_CONFIG
from zoedepth.utils.misc import compute_metrics

from .base_trainer import BaseTrainer


class Trainer(BaseTrainer):
    def __init__(self, config, model, train_loader, test_loader=None, device=None):
        super().__init__(config, model, train_loader,
                         test_loader=test_loader, device=device)
        self.device = device
        self.silog_loss = SILogLoss()
        self.grad_loss = GradL1Loss()
        self.domain_classifier_loss = nn.CrossEntropyLoss()

        self.scaler = amp.GradScaler(enabled=self.config.use_amp)

    def train_on_batch(self, batch, train_step):
        """
        Expects a batch of images and depth as input
        batch["image"].shape : batch_size, c, h, w
        batch["depth"].shape : batch_size, 1, h, w

        Assumes all images in a batch are from the same dataset
        """

        images, depths_gt = batch['image'].to(
            self.device), batch['depth'].to(self.device)
        # batch['dataset'] is a tensor strings all valued either 'nyu' or 'kitti'. labels nyu -> 0, kitti -> 1
        dataset = batch['dataset'][0]
        # Convert to 0s or 1s
        domain_labels = torch.Tensor([dataset == 'kitti' for _ in range(
            images.size(0))]).to(torch.long).to(self.device)

        # m = self.model.module if self.config.multigpu else self.model

        b, c, h, w = images.size()
        mask = batch["mask"].to(self.device).to(torch.bool)

        losses = {}

        with amp.autocast(enabled=self.config.use_amp):
            output = self.model(images)
            pred_depths = output['metric_depth']
            domain_logits = output['domain_logits']

            l_si, pred = self.silog_loss(
                pred_depths, depths_gt, mask=mask, interpolate=True, return_interpolated=True)
            loss = self.config.w_si * l_si
            losses[self.silog_loss.name] = l_si

            if self.config.w_grad > 0:
                l_grad = self.grad_loss(pred, depths_gt, mask=mask)
                loss = loss + self.config.w_grad * l_grad
                losses[self.grad_loss.name] = l_grad
            else:
                l_grad = torch.Tensor([0])

            if self.config.w_domain > 0:
                l_domain = self.domain_classifier_loss(
                    domain_logits, domain_labels)
                loss = loss + self.config.w_domain * l_domain
                losses["DomainLoss"] = l_domain
            else:
                l_domain = torch.Tensor([0.])

        self.scaler.scale(loss).backward()

        if self.config.clip_grad > 0:
            self.scaler.unscale_(self.optimizer)
            nn.utils.clip_grad_norm_(
                self.model.parameters(), self.config.clip_grad)

        self.scaler.step(self.optimizer)

        if self.should_log and self.step > 1 and (self.step % int(self.config.log_images_every * self.iters_per_epoch)) == 0:
            depths_gt[torch.logical_not(mask)] = -99
            self.log_images(rgb={"Input": images[0, ...]}, depth={"GT": depths_gt[0], "PredictedMono": pred[0]}, prefix="Train",
                            min_depth=DATASETS_CONFIG[dataset]['min_depth'], max_depth=DATASETS_CONFIG[dataset]['max_depth'])

        self.scaler.update()
        self.optimizer.zero_grad(set_to_none=True)

        return losses

    def validate_on_batch(self, batch, val_step):
        images = batch['image'].to(self.device)
        depths_gt = batch['depth'].to(self.device)
        dataset = batch['dataset'][0]
        if 'has_valid_depth' in batch:
            if not batch['has_valid_depth']:
                return None, None

        depths_gt = depths_gt.squeeze().unsqueeze(0).unsqueeze(0)
        with amp.autocast(enabled=self.config.use_amp):
            m = self.model.module if self.config.multigpu else self.model
            pred_depths = m(images)["metric_depth"]
        pred_depths = pred_depths.squeeze().unsqueeze(0).unsqueeze(0)

        mask = torch.logical_and(
            depths_gt > self.config.min_depth, depths_gt < self.config.max_depth)
        with amp.autocast(enabled=self.config.use_amp):
            l_depth = self.silog_loss(
                pred_depths, depths_gt, mask=mask.to(torch.bool), interpolate=True)

        metrics = compute_metrics(depths_gt, pred_depths, **self.config)
        losses = {f"{self.silog_loss.name}": l_depth.item()}

        if val_step == 1 and self.should_log:
            depths_gt[torch.logical_not(mask)] = -99
            self.log_images(rgb={"Input": images[0]}, depth={"GT": depths_gt[0], "PredictedMono": pred_depths[0]}, prefix="Test",
                            min_depth=DATASETS_CONFIG[dataset]['min_depth'], max_depth=DATASETS_CONFIG[dataset]['max_depth'])

        return metrics, losses