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# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import logging
import os
import numpy as np
import torch
from core.evaluate import accuracy
from core.inference import get_final_preds
from utils.transforms import flip_back
from utils.vis import save_debug_images
logger = logging.getLogger(__name__)
def train(config, train_loader, model, criterion, optimizer, epoch,
output_dir, tb_log_dir, writer_dict):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acc = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target, target_weight, meta) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
# compute output
outputs = model(input)
target = target.cuda(non_blocking=True)
target_weight = target_weight.cuda(non_blocking=True)
if isinstance(outputs, list):
loss = criterion(outputs[0], target, target_weight)
for output in outputs[1:]:
loss += criterion(output, target, target_weight)
else:
output = outputs
loss = criterion(output, target, target_weight)
# loss = criterion(output, target, target_weight)
# compute gradient and do update step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure accuracy and record loss
losses.update(loss.item(), input.size(0))
_, avg_acc, cnt, pred = accuracy(output.detach().cpu().numpy(),
target.detach().cpu().numpy())
acc.update(avg_acc, cnt)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config.PRINT_FREQ == 0:
msg = 'Epoch: [{0}][{1}/{2}]\t' \
'Time {batch_time.val:.3f}s ({batch_time.avg:.3f}s)\t' \
'Speed {speed:.1f} samples/s\t' \
'Data {data_time.val:.3f}s ({data_time.avg:.3f}s)\t' \
'Loss {loss.val:.5f} ({loss.avg:.5f})\t' \
'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
speed=input.size(0)/batch_time.val,
data_time=data_time, loss=losses, acc=acc)
logger.info(msg)
writer = writer_dict['writer']
global_steps = writer_dict['train_global_steps']
writer.add_scalar('train_loss', losses.val, global_steps)
writer.add_scalar('train_acc', acc.val, global_steps)
writer_dict['train_global_steps'] = global_steps + 1
prefix = '{}_{}'.format(os.path.join(output_dir, 'train'), i)
save_debug_images(config, input, meta, target, pred*4, output,
prefix)
def validate(config, val_loader, val_dataset, model, criterion, output_dir,
tb_log_dir, writer_dict=None):
batch_time = AverageMeter()
losses = AverageMeter()
acc = AverageMeter()
# switch to evaluate mode
model.eval()
num_samples = len(val_dataset)
all_preds = np.zeros(
(num_samples, config.MODEL.NUM_JOINTS, 3),
dtype=np.float32
)
all_boxes = np.zeros((num_samples, 6))
image_path = []
filenames = []
imgnums = []
idx = 0
with torch.no_grad():
end = time.time()
for i, (input, target, target_weight, meta) in enumerate(val_loader):
# compute output
outputs = model(input)
if isinstance(outputs, list):
output = outputs[-1]
else:
output = outputs
if config.TEST.FLIP_TEST:
input_flipped = input.flip(3)
outputs_flipped = model(input_flipped)
if isinstance(outputs_flipped, list):
output_flipped = outputs_flipped[-1]
else:
output_flipped = outputs_flipped
output_flipped = flip_back(output_flipped.cpu().numpy(),
val_dataset.flip_pairs)
output_flipped = torch.from_numpy(output_flipped.copy()).cuda()
# feature is not aligned, shift flipped heatmap for higher accuracy
if config.TEST.SHIFT_HEATMAP:
output_flipped[:, :, :, 1:] = \
output_flipped.clone()[:, :, :, 0:-1]
output = (output + output_flipped) * 0.5
target = target.cuda(non_blocking=True)
target_weight = target_weight.cuda(non_blocking=True)
loss = criterion(output, target, target_weight)
num_images = input.size(0)
# measure accuracy and record loss
losses.update(loss.item(), num_images)
_, avg_acc, cnt, pred = accuracy(output.cpu().numpy(),
target.cpu().numpy())
acc.update(avg_acc, cnt)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
c = meta['center'].numpy()
s = meta['scale'].numpy()
score = meta['score'].numpy()
preds, maxvals = get_final_preds(
config, output.clone().cpu().numpy(), c, s)
all_preds[idx:idx + num_images, :, 0:2] = preds[:, :, 0:2]
all_preds[idx:idx + num_images, :, 2:3] = maxvals
# double check this all_boxes parts
all_boxes[idx:idx + num_images, 0:2] = c[:, 0:2]
all_boxes[idx:idx + num_images, 2:4] = s[:, 0:2]
all_boxes[idx:idx + num_images, 4] = np.prod(s*200, 1)
all_boxes[idx:idx + num_images, 5] = score
image_path.extend(meta['image'])
idx += num_images
if i % config.PRINT_FREQ == 0:
msg = 'Test: [{0}/{1}]\t' \
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' \
'Loss {loss.val:.4f} ({loss.avg:.4f})\t' \
'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time,
loss=losses, acc=acc)
logger.info(msg)
prefix = '{}_{}'.format(
os.path.join(output_dir, 'val'), i
)
save_debug_images(config, input, meta, target, pred*4, output,
prefix)
name_values, perf_indicator = val_dataset.evaluate(
config, all_preds, output_dir, all_boxes, image_path,
filenames, imgnums
)
model_name = config.MODEL.NAME
if isinstance(name_values, list):
for name_value in name_values:
_print_name_value(name_value, model_name)
else:
_print_name_value(name_values, model_name)
if writer_dict:
writer = writer_dict['writer']
global_steps = writer_dict['valid_global_steps']
writer.add_scalar(
'valid_loss',
losses.avg,
global_steps
)
writer.add_scalar(
'valid_acc',
acc.avg,
global_steps
)
if isinstance(name_values, list):
for name_value in name_values:
writer.add_scalars(
'valid',
dict(name_value),
global_steps
)
else:
writer.add_scalars(
'valid',
dict(name_values),
global_steps
)
writer_dict['valid_global_steps'] = global_steps + 1
return perf_indicator
# markdown format output
def _print_name_value(name_value, full_arch_name):
names = name_value.keys()
values = name_value.values()
num_values = len(name_value)
logger.info(
'| Arch ' +
' '.join(['| {}'.format(name) for name in names]) +
' |'
)
logger.info('|---' * (num_values+1) + '|')
if len(full_arch_name) > 15:
full_arch_name = full_arch_name[:8] + '...'
logger.info(
'| ' + full_arch_name + ' ' +
' '.join(['| {:.3f}'.format(value) for value in values]) +
' |'
)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count if self.count != 0 else 0
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