File size: 7,465 Bytes
18dd6ad |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
# 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 zoedepth.data.preprocess import get_black_border
from .base_trainer import BaseTrainer
from torchvision import transforms
from PIL import Image
import numpy as np
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.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
"""
images, depths_gt = batch['image'].to(
self.device), batch['depth'].to(self.device)
dataset = batch['dataset'][0]
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']
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])
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 % int(self.config.log_images_every * self.iters_per_epoch)) == 0:
# -99 is treated as invalid depth in the log_images function and is colored grey.
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'])
if self.config.get("log_rel", False):
self.log_images(
scalar_field={"RelPred": output["relative_depth"][0]}, prefix="TrainRel")
self.scaler.update()
self.optimizer.zero_grad()
return losses
@torch.no_grad()
def eval_infer(self, x):
with amp.autocast(enabled=self.config.use_amp):
m = self.model.module if self.config.multigpu else self.model
pred_depths = m(x)['metric_depth']
return pred_depths
@torch.no_grad()
def crop_aware_infer(self, x):
# if we are not avoiding the black border, we can just use the normal inference
if not self.config.get("avoid_boundary", False):
return self.eval_infer(x)
# otherwise, we need to crop the image to avoid the black border
# For now, this may be a bit slow due to converting to numpy and back
# We assume no normalization is done on the input image
# get the black border
assert x.shape[0] == 1, "Only batch size 1 is supported for now"
x_pil = transforms.ToPILImage()(x[0].cpu())
x_np = np.array(x_pil, dtype=np.uint8)
black_border_params = get_black_border(x_np)
top, bottom, left, right = black_border_params.top, black_border_params.bottom, black_border_params.left, black_border_params.right
x_np_cropped = x_np[top:bottom, left:right, :]
x_cropped = transforms.ToTensor()(Image.fromarray(x_np_cropped))
# run inference on the cropped image
pred_depths_cropped = self.eval_infer(x_cropped.unsqueeze(0).to(self.device))
# resize the prediction to x_np_cropped's size
pred_depths_cropped = nn.functional.interpolate(
pred_depths_cropped, size=(x_np_cropped.shape[0], x_np_cropped.shape[1]), mode="bilinear", align_corners=False)
# pad the prediction back to the original size
pred_depths = torch.zeros((1, 1, x_np.shape[0], x_np.shape[1]), device=pred_depths_cropped.device, dtype=pred_depths_cropped.dtype)
pred_depths[:, :, top:bottom, left:right] = pred_depths_cropped
return pred_depths
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]
mask = batch["mask"].to(self.device)
if 'has_valid_depth' in batch:
if not batch['has_valid_depth']:
return None, None
depths_gt = depths_gt.squeeze().unsqueeze(0).unsqueeze(0)
mask = mask.squeeze().unsqueeze(0).unsqueeze(0)
if dataset == 'nyu':
pred_depths = self.crop_aware_infer(images)
else:
pred_depths = self.eval_infer(images)
pred_depths = pred_depths.squeeze().unsqueeze(0).unsqueeze(0)
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
|