AnyTouch/probe_engine.py at main · GeWu-Lab/AnyTouch · GitHub

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
import math
import sys
from typing import Iterable

import torch

import util.misc as misc
import util.lr_sched as lr_sched

from timm.utils import accuracy
from sklearn.metrics import accuracy_score

def train_one_epoch(model: torch.nn.Module,
                    data_loader, optimizer: torch.optim.Optimizer,
                    device: torch.device, epoch: int, loss_scaler,
                    log_writer=None,
                    args=None):
    model.train(True)
    metric_logger = misc.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    header = 'Epoch: [{}]'.format(epoch)
    print_freq = 20

    accum_iter = args.accum_iter

    if args.dataset in ['material', 'obj2', 'obj1', 'objreal']:
        loss_func = torch.nn.CrossEntropyLoss()
    else:
        loss_func = torch.nn.BCEWithLogitsLoss()

    optimizer.zero_grad()

    if log_writer is not None:
        print('log_dir: {}'.format(log_writer.log_dir))

    for data_iter_step, (samples, sensors, labels) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):

        # we use a per iteration (instead of per epoch) lr scheduler
        if data_iter_step % accum_iter == 0:
            lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)

        samples = samples.to(device, non_blocking=True)
        sensors = sensors.to(device, non_blocking=True).int()
        labels = labels.to(device, non_blocking=True)

        if args.use_universal:
            sensors = torch.ones_like(sensors) * -1
            sensors = sensors.int()

        with torch.cuda.amp.autocast():
            out = model(samples, sensor_type = sensors)
            if args.dataset in ['rough', 'hard', 'feel']:
                out = out.squeeze(1)
                labels = labels.float()
            loss = loss_func(out, labels)

        loss_value = loss.item()

        if not math.isfinite(loss_value):
            print("Loss is {}, stopping training".format(loss_value))
            sys.exit(1)

        loss /= accum_iter
        loss_scaler(loss, optimizer, parameters=model.parameters(),
                    update_grad=(data_iter_step + 1) % accum_iter == 0)
        if (data_iter_step + 1) % accum_iter == 0:
            optimizer.zero_grad()

        torch.cuda.synchronize()

        metric_logger.update(loss=loss_value)

        lr = optimizer.param_groups[0]["lr"]
        metric_logger.update(lr=lr)

        loss_value_reduce = misc.all_reduce_mean(loss_value)
        if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
            """ We use epoch_1000x as the x-axis in tensorboard.
            This calibrates different curves when batch size changes.
            """
            epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
            log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x)
            log_writer.add_scalar('lr', lr, epoch_1000x)


    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger)
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}

@torch.no_grad()
def evaluate(data_loader, model, device, args):
    if args.dataset in ['material', 'obj2', 'obj1', 'objreal']:
        criterion = torch.nn.CrossEntropyLoss()
    else:
        criterion = torch.nn.BCEWithLogitsLoss()
        sigmoid = torch.nn.Sigmoid()

    metric_logger = misc.MetricLogger(delimiter="  ")
    header = 'Test:'

    # switch to evaluation mode
    model.eval()

    for batch in metric_logger.log_every(data_loader, 40, header):
        images = batch[0]
        sensors = batch[1]
        target = batch[-1]
        images = images.to(device, non_blocking=True)
        sensors = sensors.to(device, non_blocking=True).int()
        target = target.to(device, non_blocking=True)

        if args.use_universal:
            sensors = torch.ones_like(sensors) * -1
            sensors = sensors.int()
        # compute output
        with torch.cuda.amp.autocast():
            output = model(images, sensor_type = sensors)
            if args.dataset in ['rough', 'hard', 'feel']:
                output = output.squeeze(1)
                target = target.float()
            loss = criterion(output, target)

        if args.dataset in ['material', 'obj2', 'obj1', 'objreal']:
            acc1, acc5 = accuracy(output, target, topk=(1,5))
        else:
            output = sigmoid(output)
            predictions = (output > 0.5).float()
            correct_predictions = (predictions == target).sum().item()
            acc1 = correct_predictions / target.size(0) * 100.0

        batch_size = images.shape[0]
        metric_logger.update(loss=loss.item())
        if args.dataset in ['material', 'obj2', 'obj1', 'objreal']:
            metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
        else:
            metric_logger.meters['acc1'].update(acc1, n=batch_size)
    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print('* Acc@1 {top1.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1, losses=metric_logger.loss))

    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}