源码地址点击此处
https://github.com/xieenze/Trans2Seg.git
然后你需要修改Trans2Seg-master/segmentron/solver/loss.py
文件,删除或注释第九行的from ..models.pointrend import point_sample
,同时需要删除或注释掉391-414行的内容,删除438-440行的内容,最终你的Trans2Seg-master/segmentron/solver/loss.py
具有如下内容
"""Custom losses."""
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from .lovasz_losses import lovasz_softmax
from ..data.dataloader import datasets
from ..config import cfg
__all__ = ['get_segmentation_loss']
class TranslabLoss(nn.CrossEntropyLoss):
def __init__(self, aux=True, aux_weight=0.2, ignore_index=-1, **kwargs):
super(TranslabLoss, self).__init__(ignore_index=ignore_index)
self.aux = aux
self.aux_weight = aux_weight
def _aux_forward(self, *inputs, **kwargs):
*preds, target = tuple(inputs)
loss = super(TranslabLoss, self).forward(preds[0], target)
for i in range(1, len(preds)):
aux_loss = super(TranslabLoss, self).forward(preds[i], target)
loss += self.aux_weight * aux_loss
return loss
def _multiple_forward(self, *inputs):
*preds, target = tuple(inputs)
loss = super(TranslabLoss, self).forward(preds[0], target)
for i in range(1, len(preds)):
loss += super(TranslabLoss, self).forward(preds[i], target)
return loss
def forward(self, *inputs, **kwargs):
preds, target = tuple(inputs)
inputs = tuple(list(preds) + [target])
loss = dict(loss=super(TranslabLoss, self).forward(*inputs))
return loss
class MixSoftmaxCrossEntropyLoss(nn.CrossEntropyLoss):
def __init__(self, aux=True, aux_weight=0.2, ignore_index=-1, **kwargs):
super(MixSoftmaxCrossEntropyLoss, self).__init__(ignore_index=ignore_index)
self.aux = aux
self.aux_weight = aux_weight
def _aux_forward(self, *inputs, **kwargs):
*preds, target = tuple(inputs)
loss = super(MixSoftmaxCrossEntropyLoss, self).forward(preds[0], target)
for i in range(1, len(preds)):
aux_loss = super(MixSoftmaxCrossEntropyLoss, self).forward(preds[i], target)
loss += self.aux_weight * aux_loss
return loss
def _multiple_forward(self, *inputs):
*preds, target = tuple(inputs)
loss = super(MixSoftmaxCrossEntropyLoss, self).forward(preds[0], target)
for i in range(1, len(preds)):
loss += super(MixSoftmaxCrossEntropyLoss, self).forward(preds[i], target)
return loss
def forward(self, *inputs, **kwargs):
preds, target = tuple(inputs)
inputs = tuple(list(preds) + [target])
if self.aux:
return dict(loss=self._aux_forward(*inputs))
elif len(preds) > 1:
return dict(loss=self._multiple_forward(*inputs))
else:
return dict(loss=super(MixSoftmaxCrossEntropyLoss, self).forward(*inputs))
class ICNetLoss(nn.CrossEntropyLoss):
"""Cross Entropy Loss for ICNet"""
def __init__(self, aux_weight=0.4, ignore_index=-1, **kwargs):
super(ICNetLoss, self).__init__(ignore_index=ignore_index)
self.aux_weight = aux_weight
def forward(self, *inputs):
preds, target = tuple(inputs)
inputs = tuple(list(preds) + [target])
pred, pred_sub4, pred_sub8, pred_sub16, target = tuple(inputs)
# [batch, W, H] -> [batch, 1, W, H]
target = target.unsqueeze(1).float()
target_sub4 = F.interpolate(target, pred_sub4.size()[2:], mode='bilinear', align_corners=True).squeeze(1).long()
target_sub8 = F.interpolate(target, pred_sub8.size()[2:], mode='bilinear', align_corners=True).squeeze(1).long()
target_sub16 = F.interpolate(target, pred_sub16.size()[2:], mode='bilinear', align_corners=True).squeeze(
1).long()
loss1 = super(ICNetLoss, self).forward(pred_sub4, target_sub4)
loss2 = super(ICNetLoss, self).forward(pred_sub8, target_sub8)
loss3 = super(ICNetLoss, self).forward(pred_sub16, target_sub16)
return dict(loss=loss1 + loss2 * self.aux_weight + loss3 * self.aux_weight)
class OhemCrossEntropy2d(nn.Module):
def __init__(self, ignore_index=-1, thresh=0.7, min_kept=100000, use_weight=True, **kwargs):
super(OhemCrossEntropy2d, self).__init__()
self.ignore_index = ignore_index
self.thresh = float(thresh)
self.min_kept = int(min_kept)
if use_weight:
weight = torch.FloatTensor([0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754,
1.0489, 0.8786, 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955,
1.0865, 1.1529, 1.0507])
self.criterion = torch.nn.CrossEntropyLoss(weight=weight, ignore_index=ignore_index)
else:
self.criterion = torch.nn.CrossEntropyLoss(ignore_index=ignore_index)
def forward(self, pred, target):
n, c, h, w = pred.size()
target = target.view(-1)
valid_mask = target.ne(self.ignore_index)
target = target * valid_mask.long()
num_valid = valid_mask.sum()
prob = F.softmax(pred, dim=1)
prob = prob.transpose(0, 1).reshape(c, -1)
if self.min_kept > num_valid:
print("Lables: {}".format(num_valid))
elif num_valid > 0:
# prob = prob.masked_fill_(1 - valid_mask, 1)
prob = prob.masked_fill_(~valid_mask, 1)
mask_prob = prob[target, torch.arange(len(target), dtype=torch.long)]
threshold = self.thresh
if self.min_kept > 0:
index = mask_prob.argsort()
threshold_index = index[min(len(index), self.min_kept) - 1]
if mask_prob[threshold_index] > self.thresh:
threshold = mask_prob[threshold_index]
kept_mask = mask_prob.le(threshold)
valid_mask = valid_mask * kept_mask
target = target * kept_mask.long()
target = target.masked_fill_(~valid_mask, self.ignore_index)
target = target.view(n, h, w)
return self.criterion(pred, target)
class EncNetLoss(nn.CrossEntropyLoss):
"""2D Cross Entropy Loss with SE Loss"""
def __init__(self, aux=False, aux_weight=0.4, weight=None, ignore_index=-1, **kwargs):
super(EncNetLoss, self).__init__(weight, None, ignore_index)
self.se_loss = cfg.MODEL.ENCNET.SE_LOSS
self.se_weight = cfg.MODEL.ENCNET.SE_WEIGHT
self.nclass = datasets[cfg.DATASET.NAME].NUM_CLASS
self.aux = aux
self.aux_weight = aux_weight
self.bceloss = nn.BCELoss(weight)
def forward(self, *inputs):
preds, target = tuple(inputs)
inputs = tuple(list(preds) + [target])
if not self.se_loss and not self.aux:
return super(EncNetLoss, self).forward(*inputs)
elif not self.se_loss:
pred1, pred2, target = tuple(inputs)
loss1 = super(EncNetLoss, self).forward(pred1, target)
loss2 = super(EncNetLoss, self).forward(pred2, target)
return dict(loss=loss1 + self.aux_weight * loss2)
elif not self.aux:
pred, se_pred, target = tuple(inputs)
se_target = self._get_batch_label_vector(target, nclass=self.nclass).type_as(pred)
loss1 = super(EncNetLoss, self).forward(pred, target)
loss2 = self.bceloss(torch.sigmoid(se_pred), se_target)
return dict(loss=loss1 + self.se_weight * loss2)
else:
pred1, se_pred, pred2, target = tuple(inputs)
se_target = self._get_batch_label_vector(target, nclass=self.nclass).type_as(pred1)
loss1 = super(EncNetLoss, self).forward(pred1, target)
loss2 = super(EncNetLoss, self).forward(pred2, target)
loss3 = self.bceloss(torch.sigmoid(se_pred), se_target)
return dict(loss=loss1 + self.aux_weight * loss2 + self.se_weight * loss3)
@staticmethod
def _get_batch_label_vector(target, nclass):
# target is a 3D Variable BxHxW, output is 2D BxnClass
batch = target.size(0)
tvect = Variable(torch.zeros(batch, nclass))
for i in range(batch):
hist = torch.histc(target[i].cpu().data.float(),
bins=nclass, min=0,
max=nclass - 1)
vect = hist > 0
tvect[i] = vect
return tvect
class MixSoftmaxCrossEntropyOHEMLoss(OhemCrossEntropy2d):
def __init__(self, aux=False, aux_weight=0.4, weight=None, ignore_index=-1, **kwargs):
super(MixSoftmaxCrossEntropyOHEMLoss, self).__init__(ignore_index=ignore_index)
self.aux = aux
self.aux_weight = aux_weight
self.bceloss = nn.BCELoss(weight)
def _aux_forward(self, *inputs, **kwargs):
*preds, target = tuple(inputs)
loss = super(MixSoftmaxCrossEntropyOHEMLoss, self).forward(preds[0], target)
for i in range(1, len(preds)):
aux_loss = super(MixSoftmaxCrossEntropyOHEMLoss, self).forward(preds[i], target)
loss += self.aux_weight * aux_loss
return loss
def forward(self, *inputs):
preds, target = tuple(inputs)
inputs = tuple(list(preds) + [target])
if self.aux:
return dict(loss=self._aux_forward(*inputs))
else:
return dict(loss=super(MixSoftmaxCrossEntropyOHEMLoss, self).forward(*inputs))
class LovaszSoftmax(nn.Module):
def __init__(self, aux=True, aux_weight=0.2, ignore_index=-1, **kwargs):
super(LovaszSoftmax, self).__init__()
self.aux = aux
self.aux_weight = aux_weight
self.ignore_index = ignore_index
def _aux_forward(self, *inputs, **kwargs):
*preds, target = tuple(inputs)
loss = lovasz_softmax(F.softmax(preds[0], dim=1), target, ignore=self.ignore_index)
for i in range(1, len(preds)):
aux_loss = lovasz_softmax(F.softmax(preds[i], dim=1), target, ignore=self.ignore_index)
loss += self.aux_weight * aux_loss
return loss
def _multiple_forward(self, *inputs):
*preds, target = tuple(inputs)
loss = super(MixSoftmaxCrossEntropyLoss, self).forward(preds[0], target)
for i in range(1, len(preds)):
loss += super(MixSoftmaxCrossEntropyLoss, self).forward(preds[i], target)
return loss
def forward(self, *inputs, **kwargs):
preds, target = tuple(inputs)
inputs = tuple(list(preds) + [target])
if self.aux:
return dict(loss=self._aux_forward(*inputs))
elif len(preds) > 1:
return dict(loss=self._multiple_forward(*inputs))
else:
return dict(loss=super(MixSoftmaxCrossEntropyLoss, self).forward(*inputs))
class FocalLoss(nn.Module):
def __init__(self, alpha=0.5, gamma=2, weight=None, aux=True, aux_weight=0.2, ignore_index=-1,
size_average=True):
super().__init__()
self.alpha = alpha
self.gamma = gamma
self.weight = weight
self.ignore_index = ignore_index
self.aux = aux
self.aux_weight = aux_weight
self.size_average = size_average
self.ce_fn = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index)
def _aux_forward(self, *inputs, **kwargs):
*preds, target = tuple(inputs)
loss = self._base_forward(preds[0], target)
for i in range(1, len(preds)):
aux_loss = self._base_forward(preds[i], target)
loss += self.aux_weight * aux_loss
return loss
def _base_forward(self, output, target):
if output.dim() > 2:
output = output.contiguous().view(output.size(0), output.size(1), -1)
output = output.transpose(1, 2)
output = output.contiguous().view(-1, output.size(2)).squeeze()
if target.dim() == 4:
target = target.contiguous().view(target.size(0), target.size(1), -1)
target = target.transpose(1, 2)
target = target.contiguous().view(-1, target.size(2)).squeeze()
elif target.dim() == 3:
target = target.view(-1)
else:
target = target.view(-1, 1)
logpt = self.ce_fn(output, target)
pt = torch.exp(-logpt)
loss = ((1 - pt) ** self.gamma) * self.alpha * logpt
if self.size_average:
return loss.mean()
else:
return loss.sum()
def forward(self, *inputs, **kwargs):
preds, target = tuple(inputs)
inputs = tuple(list(preds) + [target])
return dict(loss=self._aux_forward(*inputs))
class BinaryDiceLoss(nn.Module):
"""Dice loss of binary class
Args:
smooth: A float number to smooth loss, and avoid NaN error, default: 1
p: Denominator value: \sum{x^p} + \sum{y^p}, default: 2
predict: A tensor of shape [N, *]
target: A tensor of shape same with predict
reduction: Reduction method to apply, return mean over batch if 'mean',
return sum if 'sum', return a tensor of shape [N,] if 'none'
Returns:
Loss tensor according to arg reduction
Raise:
Exception if unexpected reduction
"""
def __init__(self, smooth=1, p=2, reduction='mean'):
super(BinaryDiceLoss, self).__init__()
self.smooth = smooth
self.p = p
self.reduction = reduction
def forward(self, predict, target, valid_mask):
assert predict.shape[0] == target.shape[0], "predict & target batch size don't match"
predict = predict.contiguous().view(predict.shape[0], -1)
target = target.contiguous().view(target.shape[0], -1)
valid_mask = valid_mask.contiguous().view(valid_mask.shape[0], -1)
num = torch.sum(torch.mul(predict, target) * valid_mask, dim=1) * 2 + self.smooth
den = torch.sum((predict.pow(self.p) + target.pow(self.p)) * valid_mask, dim=1) + self.smooth
loss = 1 - num / den
if self.reduction == 'mean':
return loss.mean()
elif self.reduction == 'sum':
return loss.sum()
elif self.reduction == 'none':
return loss
else:
raise Exception('Unexpected reduction {}'.format(self.reduction))
class DiceLoss(nn.Module):
"""Dice loss, need one hot encode input"""
def __init__(self, weight=None, aux=True, aux_weight=0.4, ignore_index=-1, **kwargs):
super(DiceLoss, self).__init__()
self.kwargs = kwargs
self.weight = weight
self.ignore_index = ignore_index
self.aux = aux
self.aux_weight = aux_weight
def _base_forward(self, predict, target, valid_mask):
dice = BinaryDiceLoss(**self.kwargs)
total_loss = 0
predict = F.softmax(predict, dim=1)
for i in range(target.shape[-1]):
if i != self.ignore_index:
dice_loss = dice(predict[:, i], target[..., i], valid_mask)
if self.weight is not None:
assert self.weight.shape[0] == target.shape[1], \
'Expect weight shape [{}], get[{}]'.format(target.shape[1], self.weight.shape[0])
dice_loss *= self.weights[i]
total_loss += dice_loss
return total_loss / target.shape[-1]
def _aux_forward(self, *inputs, **kwargs):
*preds, target = tuple(inputs)
valid_mask = (target != self.ignore_index).long()
target_one_hot = F.one_hot(torch.clamp_min(target, 0))
loss = self._base_forward(preds[0], target_one_hot, valid_mask)
for i in range(1, len(preds)):
aux_loss = self._base_forward(preds[i], target_one_hot, valid_mask)
loss += self.aux_weight * aux_loss
return loss
def forward(self, *inputs):
preds, target = tuple(inputs)
inputs = tuple(list(preds) + [target])
return dict(loss=self._aux_forward(*inputs))
def get_segmentation_loss(model, use_ohem=False, **kwargs):
if use_ohem:
return MixSoftmaxCrossEntropyOHEMLoss(**kwargs)
elif cfg.SOLVER.LOSS_NAME == 'lovasz':
logging.info('Use lovasz loss!')
return LovaszSoftmax(**kwargs)
elif cfg.SOLVER.LOSS_NAME == 'focal':
logging.info('Use focal loss!')
return FocalLoss(**kwargs)
elif cfg.SOLVER.LOSS_NAME == 'dice':
logging.info('Use dice loss!')
return DiceLoss(**kwargs)
elif cfg.SOLVER.LOSS_NAME == 'binary_dice':
logging.info('Use binary_dice loss!')
return BinaryDiceLoss(**kwargs)
model = model.lower()
if model == 'icnet':
return ICNetLoss(**kwargs)
elif model == 'encnet':
return EncNetLoss(**kwargs)
elif model == 'translab':
return TranslabLoss(**kwargs)
else:
return MixSoftmaxCrossEntropyLoss(**kwargs)
数据集的目录结构如下,注意到此目录结构在datasets/transparent
下
Trans10K_v2
├── test
│ ├── images
│ └── masks_12
├── train
│ ├── images
│ └── masks_12
└── validation
├── images
└── masks_12
谷歌云盘下载: Google Drive.
百度云盘下载Baidu Drive. code: oqms
git clone https://github.com/NVIDIA/apex
cd apex
python setup.py install
cd XXX/Trans2Seg
pip install thop
pip install -U pyyaml
python setup.py develop --user
如果你是单卡,可用如下方式运行
python tools/train.py --config-file configs/trans10kv2/trans2seg/trans2seg_medium.yaml
2021-07-05 05:56:52,829 Segmentron INFO: Using 1 GPUs
2021-07-05 05:56:52,830 Segmentron INFO: Namespace(config_file='configs/trans10kv2/trans2seg/trans2seg_medium.yaml', device='cuda', distributed=False, input_img='tools/demo_vis.png', local_rank=0, log_iter=10, no_cuda=False, num_gpus=1, opts=[], resume=None, skip_val=False, test=False, val_epoch=1, vis=False)
2021-07-05 05:56:52,830 Segmentron INFO: {
"SEED": 1024,
"TIME_STAMP": "2021-07-05-05-56",
"ROOT_PATH": "/content/Trans2Seg/Trans2Seg",
"PHASE": "train",
"DATASET": {
"NAME": "transparent11",
"MEAN": [
0.485,
0.456,
0.406
],
"STD": [
0.229,
0.224,
0.225
],
"IGNORE_INDEX": -1,
"WORKERS": 8,
"MODE": "testval"
},
"AUG": {
"MIRROR": true,
"BLUR_PROB": 0.0,
"BLUR_RADIUS": 0.0,
"COLOR_JITTER": null,
"CROP": false
},
"TRAIN": {
"EPOCHS": 50,
"BATCH_SIZE": 4,
"CROP_SIZE": [
512,
512
],
"BASE_SIZE": 512,
"MODEL_SAVE_DIR": "workdirs/trans10kv2/trans2seg_small",
"LOG_SAVE_DIR": "workdirs/",
"PRETRAINED_MODEL_PATH": "",
"BACKBONE_PRETRAINED": true,
"BACKBONE_PRETRAINED_PATH": "",
"RESUME_MODEL_PATH": "",
"SYNC_BATCH_NORM": true,
"SNAPSHOT_EPOCH": 1,
"APEX": false
},
"SOLVER": {
"LR": 0.0001,
"OPTIMIZER": "adam",
"EPSILON": 1e-08,
"MOMENTUM": 0.9,
"WEIGHT_DECAY": 0.0001,
"DECODER_LR_FACTOR": 10.0,
"LR_SCHEDULER": "poly",
"POLY": {
"POWER": 0.9
},
"STEP": {
"GAMMA": 0.1,
"DECAY_EPOCH": [
10,
20
]
},
"WARMUP": {
"EPOCHS": 0.0,
"FACTOR": 0.3333333333333333,
"METHOD": "linear"
},
"OHEM": false,
"AUX": false,
"AUX_WEIGHT": 0.4,
"LOSS_NAME": ""
},
"TEST": {
"TEST_MODEL_PATH": "",
"BATCH_SIZE": 16,
"CROP_SIZE": [
512,
512
],
"SCALES": [
1.0
],
"FLIP": false
},
"VISUAL": {
"OUTPUT_DIR": "../runs/visual/"
},
"MODEL": {
"MODEL_NAME": "Trans2Seg",
"BACKBONE": "resnet50c",
"BACKBONE_SCALE": 1.0,
"MULTI_LOSS_WEIGHT": [
1.0
],
"DEFAULT_GROUP_NUMBER": 32,
"DEFAULT_EPSILON": 1e-05,
"BN_TYPE": "BN",
"BN_EPS_FOR_ENCODER": null,
"BN_EPS_FOR_DECODER": null,
"OUTPUT_STRIDE": 16,
"BN_MOMENTUM": null,
"DANET": {
"MULTI_DILATION": null,
"MULTI_GRID": false
},
"DEEPLABV3_PLUS": {
"USE_ASPP": true,
"ENABLE_DECODER": true,
"ASPP_WITH_SEP_CONV": true,
"DECODER_USE_SEP_CONV": true
},
"OCNet": {
"OC_ARCH": "base"
},
"ENCNET": {
"SE_LOSS": true,
"SE_WEIGHT": 0.2,
"LATERAL": true
},
"CCNET": {
"RECURRENCE": 2
},
"CGNET": {
"STAGE2_BLOCK_NUM": 3,
"STAGE3_BLOCK_NUM": 21
},
"POINTREND": {
"BASEMODEL": "DeepLabV3_Plus"
},
"HRNET": {
"PRETRAINED_LAYERS": [
"*"
],
"STEM_INPLANES": 64,
"FINAL_CONV_KERNEL": 1,
"WITH_HEAD": true,
"STAGE1": {
"NUM_MODULES": 1,
"NUM_BRANCHES": 1,
"NUM_BLOCKS": [
1
],
"NUM_CHANNELS": [
32
],
"BLOCK": "BOTTLENECK",
"FUSE_METHOD": "SUM"
},
"STAGE2": {
"NUM_MODULES": 1,
"NUM_BRANCHES": 2,
"NUM_BLOCKS": [
4,
4
],
"NUM_CHANNELS": [
32,
64
],
"BLOCK": "BASIC",
"FUSE_METHOD": "SUM"
},
"STAGE3": {
"NUM_MODULES": 1,
"NUM_BRANCHES": 3,
"NUM_BLOCKS": [
4,
4,
4
],
"NUM_CHANNELS": [
32,
64,
128
],
"BLOCK": "BASIC",
"FUSE_METHOD": "SUM"
},
"STAGE4": {
"NUM_MODULES": 1,
"NUM_BRANCHES": 4,
"NUM_BLOCKS": [
4,
4,
4,
4
],
"NUM_CHANNELS": [
32,
64,
128,
256
],
"BLOCK": "BASIC",
"FUSE_METHOD": "SUM"
}
},
"TRANS2Seg": {
"embed_dim": 256,
"depth": 4,
"num_heads": 8,
"mlp_ratio": 3.0,
"hid_dim": 64
}
}
}
2021-07-05 05:56:52,871 Segmentron INFO: Found 5000 images in the folder /content/Trans2Seg/Trans2Seg/datasets/transparent/Trans10K_cls12
2021-07-05 05:56:52,880 Segmentron INFO: Found 1000 images in the folder /content/Trans2Seg/Trans2Seg/datasets/transparent/Trans10K_cls12
2021-07-05 05:56:52,914 Segmentron INFO: Found 4428 images in the folder /content/Trans2Seg/Trans2Seg/datasets/transparent/Trans10K_cls12
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
cpuset_checked))
2021-07-05 05:56:53,368 Segmentron INFO: load backbone pretrained model from url..
Downloading: "https://github.com/LikeLy-Journey/SegmenTron/releases/download/v0.1.0/resnet50-25c4b509.pth" to /root/.cache/torch/hub/checkpoints/resnet50-25c4b509.pth
100% 98.2M/98.2M [00:01<00:00, 97.6MB/s]
2021-07-05 05:56:54,723 Segmentron INFO:
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)
return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
2021-07-05 05:57:09,046 Segmentron INFO: Trans2Seg flops: 49.034G input shape is [3, 512, 512], params: 56.215M
2021-07-05 05:57:09,046 Segmentron INFO: Not use SyncBatchNorm!
2021-07-05 05:57:09,048 Segmentron INFO: Start training, Total Epochs: 50 = Total Iterations 62500
2021-07-05 05:57:19,654 Segmentron INFO: Epoch: 1/50 || Iters: 10/1250 || Lr: 0.000100 || Loss: 1.6398 || Cost Time: 0:00:10 || Estimated Time: 18:24:17
2021-07-05 05:57:24,757 Segmentron INFO: Epoch: 1/50 || Iters: 20/1250 || Lr: 0.000100 || Loss: 1.3416 || Cost Time: 0:00:15 || Estimated Time: 13:37:45
2021-07-05 05:57:29,885 Segmentron INFO: Epoch: 1/50 || Iters: 30/1250 || Lr: 0.000100 || Loss: 0.6450 || Cost Time: 0:00:20 || Estimated Time: 12:03:02
2021-07-05 05:57:35,063 Segmentron INFO: Epoch: 1/50 || Iters: 40/1250 || Lr: 0.000100 || Loss: 0.8031 || Cost Time: 0:00:26 || Estimated Time: 11:17:00
2021-07-05 05:57:40,265 Segmentron INFO: Epoch: 1/50 || Iters: 50/1250 || Lr: 0.000100 || Loss: 0.7714 || Cost Time: 0:00:31 || Estimated Time: 10:49:46
2021-07-05 05:57:45,509 Segmentron INFO: Epoch: 1/50 || Iters: 60/1250 || Lr: 0.000100 || Loss: 0.8005 || Cost Time: 0:00:36 || Estimated Time: 10:32:19
2021-07-05 05:57:50,793 Segmentron INFO: Epoch: 1/50 || Iters: 70/1250 || Lr: 0.000100 || Loss: 1.8231 || Cost Time: 0:00:41 || Estimated Time: 10:20:26
2021-07-05 05:57:56,126 Segmentron INFO: Epoch: 1/50 || Iters: 80/1250 || Lr: 0.000100 || Loss: 1.7568 || Cost Time: 0:00:47 || Estimated Time: 10:12:09
2021-07-05 05:58:01,511 Segmentron INFO: Epoch: 1/50 || Iters: 90/1250 || Lr: 0.000100 || Loss: 0.6441 || Cost Time: 0:00:52 || Estimated Time: 10:06:17
2021-07-05 05:58:06,946 Segmentron INFO: Epoch: 1/50 || Iters: 100/1250 || Lr: 0.000100 || Loss: 0.6335 || Cost Time: 0:00:57 || Estimated Time: 10:02:05
2021-07-05 05:58:12,436 Segmentron INFO: Epoch: 1/50 || Iters: 110/1250 || Lr: 0.000100 || Loss: 1.1686 || Cost Time: 0:01:03 || Estimated Time: 9:59:10
2021-07-05 05:58:17,987 Segmentron INFO: Epoch: 1/50 || Iters: 120/1250 || Lr: 0.000100 || Loss: 1.0597 || Cost Time: 0:01:08 || Estimated Time: 9:57:15
2021-07-05 05:58:23,625 Segmentron INFO: Epoch: 1/50 || Iters: 130/1250 || Lr: 0.000100 || Loss: 0.6590 || Cost Time: 0:01:14 || Estimated Time: 9:56:17
2021-07-05 05:58:29,321 Segmentron INFO: Epoch: 1/50 || Iters: 140/1250 || Lr: 0.000100 || Loss: 1.2513 || Cost Time: 0:01:20 || Estimated Time: 9:55:53
2021-07-05 05:58:35,029 Segmentron INFO: Epoch: 1/50 || Iters: 150/1250 || Lr: 0.000100 || Loss: 0.9338 || Cost Time: 0:01:25 || Estimated Time: 9:55:37
2021-07-05 05:58:40,683 Segmentron INFO: Epoch: 1/50 || Iters: 160/1250 || Lr: 0.000100 || Loss: 0.4313 || Cost Time: 0:01:31 || Estimated Time: 9:55:01
2021-07-05 05:58:46,268 Segmentron INFO: Epoch: 1/50 || Iters: 170/1250 || Lr: 0.000100 || Loss: 0.5899 || Cost Time: 0:01:37 || Estimated Time: 9:54:03
2021-07-05 05:58:51,802 Segmentron INFO: Epoch: 1/50 || Iters: 180/1250 || Lr: 0.000100 || Loss: 0.9178 || Cost Time: 0:01:42 || Estimated Time: 9:52:54
2021-07-05 05:58:57,312 Segmentron INFO: Epoch: 1/50 || Iters: 190/1250 || Lr: 0.000100 || Loss: 0.7366 || Cost Time: 0:01:48 || Estimated Time: 9:51:43
2021-07-05 05:59:02,790 Segmentron INFO: Epoch: 1/50 || Iters: 200/1250 || Lr: 0.000100 || Loss: 0.6236 || Cost Time: 0:01:53 || Estimated Time: 9:50:29
2021-07-05 05:59:08,267 Segmentron INFO: Epoch: 1/50 || Iters: 210/1250 || Lr: 0.000100 || Loss: 1.0859 || Cost Time: 0:01:59 || Estimated Time: 9:49:21
2021-07-05 05:59:13,726 Segmentron INFO: Epoch: 1/50 || Iters: 220/1250 || Lr: 0.000100 || Loss: 1.0176 || Cost Time: 0:02:04 || Estimated Time: 9:48:14
2021-07-05 05:59:19,187 Segmentron INFO: Epoch: 1/50 || Iters: 230/1250 || Lr: 0.000100 || Loss: 0.9893 || Cost Time: 0:02:10 || Estimated Time: 9:47:12
2021-07-05 05:59:24,645 Segmentron INFO: Epoch: 1/50 || Iters: 240/1250 || Lr: 0.000100 || Loss: 0.8127 || Cost Time: 0:02:15 || Estimated Time: 9:46:15
2021-07-05 05:59:30,120 Segmentron INFO: Epoch: 1/50 || Iters: 250/1250 || Lr: 0.000100 || Loss: 1.3774 || Cost Time: 0:02:21 || Estimated Time: 9:45:25
2021-07-05 05:59:35,597 Segmentron INFO: Epoch: 1/50 || Iters: 260/1250 || Lr: 0.000100 || Loss: 0.4150 || Cost Time: 0:02:26 || Estimated Time: 9:44:40
2021-07-05 05:59:41,077 Segmentron INFO: Epoch: 1/50 || Iters: 270/1250 || Lr: 0.000100 || Loss: 0.6798 || Cost Time: 0:02:32 || Estimated Time: 9:43:59
2021-07-05 05:59:46,586 Segmentron INFO: Epoch: 1/50 || Iters: 280/1250 || Lr: 0.000100 || Loss: 0.3734 || Cost Time: 0:02:37 || Estimated Time: 9:43:26
2021-07-05 05:59:52,103 Segmentron INFO: Epoch: 1/50 || Iters: 290/1250 || Lr: 0.000100 || Loss: 0.7941 || Cost Time: 0:02:43 || Estimated Time: 9:42:56
2021-07-05 05:59:57,639 Segmentron INFO: Epoch: 1/50 || Iters: 300/1250 || Lr: 0.000100 || Loss: 0.5892 || Cost Time: 0:02:48 || Estimated Time: 9:42:33
2021-07-05 06:00:03,177 Segmentron INFO: Epoch: 1/50 || Iters: 310/1250 || Lr: 0.000100 || Loss: 0.5118 || Cost Time: 0:02:54 || Estimated Time: 9:42:11
2021-07-05 06:00:08,715 Segmentron INFO: Epoch: 1/50 || Iters: 320/1250 || Lr: 0.000100 || Loss: 0.6326 || Cost Time: 0:02:59 || Estimated Time: 9:41:50
2021-07-05 06:00:14,250 Segmentron INFO: Epoch: 1/50 || Iters: 330/1250 || Lr: 0.000100 || Loss: 0.6859 || Cost Time: 0:03:05 || Estimated Time: 9:41:30
2021-07-05 06:00:19,776 Segmentron INFO: Epoch: 1/50 || Iters: 340/1250 || Lr: 0.000100 || Loss: 0.9854 || Cost Time: 0:03:10 || Estimated Time: 9:41:08
2021-07-05 06:00:25,292 Segmentron INFO: Epoch: 1/50 || Iters: 350/1250 || Lr: 0.000099 || Loss: 0.5796 || Cost Time: 0:03:16 || Estimated Time: 9:40:46
2021-07-05 06:00:30,809 Segmentron INFO: Epoch: 1/50 || Iters: 360/1250 || Lr: 0.000099 || Loss: 0.3313 || Cost Time: 0:03:21 || Estimated Time: 9:40:25
2021-07-05 06:00:36,319 Segmentron INFO: Epoch: 1/50 || Iters: 370/1250 || Lr: 0.000099 || Loss: 0.7653 || Cost Time: 0:03:27 || Estimated Time: 9:40:04
2021-07-05 06:00:41,804 Segmentron INFO: Epoch: 1/50 || Iters: 380/1250 || Lr: 0.000099 || Loss: 0.5316 || Cost Time: 0:03:32 || Estimated Time: 9:39:39
2021-07-05 06:00:47,282 Segmentron INFO: Epoch: 1/50 || Iters: 390/1250 || Lr: 0.000099 || Loss: 0.5340 || Cost Time: 0:03:38 || Estimated Time: 9:39:14
2021-07-05 06:00:52,771 Segmentron INFO: Epoch: 1/50 || Iters: 400/1250 || Lr: 0.000099 || Loss: 0.7623 || Cost Time: 0:03:43 || Estimated Time: 9:38:52
2021-07-05 06:00:58,262 Segmentron INFO: Epoch: 1/50 || Iters: 410/1250 || Lr: 0.000099 || Loss: 0.5127 || Cost Time: 0:03:49 || Estimated Time: 9:38:31
2021-07-05 06:01:03,751 Segmentron INFO: Epoch: 1/50 || Iters: 420/1250 || Lr: 0.000099 || Loss: 0.5150 || Cost Time: 0:03:54 || Estimated Time: 9:38:10
2021-07-05 06:01:09,257 Segmentron INFO: Epoch: 1/50 || Iters: 430/1250 || Lr: 0.000099 || Loss: 0.9970 || Cost Time: 0:04:00 || Estimated Time: 9:37:53
2021-07-05 06:01:14,772 Segmentron INFO: Epoch: 1/50 || Iters: 440/1250 || Lr: 0.000099 || Loss: 0.6717 || Cost Time: 0:04:05 || Estimated Time: 9:37:37
2021-07-05 06:01:20,293 Segmentron INFO: Epoch: 1/50 || Iters: 450/1250 || Lr: 0.000099 || Loss: 0.9943 || Cost Time: 0:04:11 || Estimated Time: 9:37:23
2021-07-05 06:01:25,819 Segmentron INFO: Epoch: 1/50 || Iters: 460/1250 || Lr: 0.000099 || Loss: 0.5162 || Cost Time: 0:04:16 || Estimated Time: 9:37:09
2021-07-05 06:01:31,355 Segmentron INFO: Epoch: 1/50 || Iters: 470/1250 || Lr: 0.000099 || Loss: 0.3666 || Cost Time: 0:04:22 || Estimated Time: 9:36:58
2021-07-05 06:01:36,888 Segmentron INFO: Epoch: 1/50 || Iters: 480/1250 || Lr: 0.000099 || Loss: 0.7810 || Cost Time: 0:04:27 || Estimated Time: 9:36:46
2021-07-05 06:01:42,427 Segmentron INFO: Epoch: 1/50 || Iters: 490/1250 || Lr: 0.000099 || Loss: 0.9375 || Cost Time: 0:04:33 || Estimated Time: 9:36:35
2021-07-05 06:01:47,957 Segmentron INFO: Epoch: 1/50 || Iters: 500/1250 || Lr: 0.000099 || Loss: 0.7715 || Cost Time: 0:04:38 || Estimated Time: 9:36:24
2021-07-05 06:01:53,492 Segmentron INFO: Epoch: 1/50 || Iters: 510/1250 || Lr: 0.000099 || Loss: 0.7611 || Cost Time: 0:04:44 || Estimated Time: 9:36:13
2021-07-05 06:01:59,015 Segmentron INFO: Epoch: 1/50 || Iters: 520/1250 || Lr: 0.000099 || Loss: 1.3777 || Cost Time: 0:04:49 || Estimated Time: 9:36:01
2021-07-05 06:02:04,528 Segmentron INFO: Epoch: 1/50 || Iters: 530/1250 || Lr: 0.000099 || Loss: 0.4394 || Cost Time: 0:04:55 || Estimated Time: 9:35:48
2021-07-05 06:02:10,029 Segmentron INFO: Epoch: 1/50 || Iters: 540/1250 || Lr: 0.000099 || Loss: 0.4215 || Cost Time: 0:05:00 || Estimated Time: 9:35:34
2021-07-05 06:02:15,548 Segmentron INFO: Epoch: 1/50 || Iters: 550/1250 || Lr: 0.000099 || Loss: 0.2931 || Cost Time: 0:05:06 || Estimated Time: 9:35:22
2021-07-05 06:02:21,062 Segmentron INFO: Epoch: 1/50 || Iters: 560/1250 || Lr: 0.000099 || Loss: 0.4534 || Cost Time: 0:05:12 || Estimated Time: 9:35:10
2021-07-05 06:02:26,563 Segmentron INFO: Epoch: 1/50 || Iters: 570/1250 || Lr: 0.000099 || Loss: 0.7274 || Cost Time: 0:05:17 || Estimated Time: 9:34:57
2021-07-05 06:02:32,071 Segmentron INFO: Epoch: 1/50 || Iters: 580/1250 || Lr: 0.000099 || Loss: 0.2845 || Cost Time: 0:05:23 || Estimated Time: 9:34:45
2021-07-05 06:02:37,582 Segmentron INFO: Epoch: 1/50 || Iters: 590/1250 || Lr: 0.000099 || Loss: 0.4167 || Cost Time: 0:05:28 || Estimated Time: 9:34:33
2021-07-05 06:02:43,087 Segmentron INFO: Epoch: 1/50 || Iters: 600/1250 || Lr: 0.000099 || Loss: 0.7590 || Cost Time: 0:05:34 || Estimated Time: 9:34:21
2021-07-05 06:02:48,595 Segmentron INFO: Epoch: 1/50 || Iters: 610/1250 || Lr: 0.000099 || Loss: 0.5357 || Cost Time: 0:05:39 || Estimated Time: 9:34:09
2021-07-05 06:02:54,104 Segmentron INFO: Epoch: 1/50 || Iters: 620/1250 || Lr: 0.000099 || Loss: 0.4961 || Cost Time: 0:05:45 || Estimated Time: 9:33:58
2021-07-05 06:02:59,607 Segmentron INFO: Epoch: 1/50 || Iters: 630/1250 || Lr: 0.000099 || Loss: 0.5018 || Cost Time: 0:05:50 || Estimated Time: 9:33:46
2021-07-05 06:03:05,094 Segmentron INFO: Epoch: 1/50 || Iters: 640/1250 || Lr: 0.000099 || Loss: 1.0672 || Cost Time: 0:05:56 || Estimated Time: 9:33:33
2021-07-05 06:03:10,593 Segmentron INFO: Epoch: 1/50 || Iters: 650/1250 || Lr: 0.000099 || Loss: 0.2307 || Cost Time: 0:06:01 || Estimated Time: 9:33:22
2021-07-05 06:03:16,084 Segmentron INFO: Epoch: 1/50 || Iters: 660/1250 || Lr: 0.000099 || Loss: 0.8019 || Cost Time: 0:06:07 || Estimated Time: 9:33:09
2021-07-05 06:03:21,586 Segmentron INFO: Epoch: 1/50 || Iters: 670/1250 || Lr: 0.000099 || Loss: 0.7613 || Cost Time: 0:06:12 || Estimated Time: 9:32:58
2021-07-05 06:03:27,086 Segmentron INFO: Epoch: 1/50 || Iters: 680/1250 || Lr: 0.000099 || Loss: 0.6910 || Cost Time: 0:06:18 || Estimated Time: 9:32:47
2021-07-05 06:03:32,570 Segmentron INFO: Epoch: 1/50 || Iters: 690/1250 || Lr: 0.000099 || Loss: 0.5595 || Cost Time: 0:06:23 || Estimated Time: 9:32:35
2021-07-05 06:03:38,067 Segmentron INFO: Epoch: 1/50 || Iters: 700/1250 || Lr: 0.000099 || Loss: 1.1118 || Cost Time: 0:06:29 || Estimated Time: 9:32:24
2021-07-05 06:03:43,560 Segmentron INFO: Epoch: 1/50 || Iters: 710/1250 || Lr: 0.000099 || Loss: 0.4333 || Cost Time: 0:06:34 || Estimated Time: 9:32:13
2021-07-05 06:03:49,057 Segmentron INFO: Epoch: 1/50 || Iters: 720/1250 || Lr: 0.000099 || Loss: 0.3692 || Cost Time: 0:06:40 || Estimated Time: 9:32:02
2021-07-05 06:03:54,561 Segmentron INFO: Epoch: 1/50 || Iters: 730/1250 || Lr: 0.000099 || Loss: 0.8774 || Cost Time: 0:06:45 || Estimated Time: 9:31:52
2021-07-05 06:04:00,045 Segmentron INFO: Epoch: 1/50 || Iters: 740/1250 || Lr: 0.000099 || Loss: 0.5601 || Cost Time: 0:06:50 || Estimated Time: 9:31:41
2021-07-05 06:04:05,534 Segmentron INFO: Epoch: 1/50 || Iters: 750/1250 || Lr: 0.000099 || Loss: 0.3583 || Cost Time: 0:06:56 || Estimated Time: 9:31:30
2021-07-05 06:04:11,036 Segmentron INFO: Epoch: 1/50 || Iters: 760/1250 || Lr: 0.000099 || Loss: 0.6755 || Cost Time: 0:07:01 || Estimated Time: 9:31:20
2021-07-05 06:04:16,538 Segmentron INFO: Epoch: 1/50 || Iters: 770/1250 || Lr: 0.000099 || Loss: 0.7194 || Cost Time: 0:07:07 || Estimated Time: 9:31:11
2021-07-05 06:04:22,030 Segmentron INFO: Epoch: 1/50 || Iters: 780/1250 || Lr: 0.000099 || Loss: 0.8702 || Cost Time: 0:07:12 || Estimated Time: 9:31:00
2021-07-05 06:04:27,525 Segmentron INFO: Epoch: 1/50 || Iters: 790/1250 || Lr: 0.000099 || Loss: 0.6927 || Cost Time: 0:07:18 || Estimated Time: 9:30:50
2021-07-05 06:04:33,026 Segmentron INFO: Epoch: 1/50 || Iters: 800/1250 || Lr: 0.000099 || Loss: 0.3912 || Cost Time: 0:07:23 || Estimated Time: 9:30:41
2021-07-05 06:04:38,529 Segmentron INFO: Epoch: 1/50 || Iters: 810/1250 || Lr: 0.000099 || Loss: 0.7517 || Cost Time: 0:07:29 || Estimated Time: 9:30:32
2021-07-05 06:04:44,022 Segmentron INFO: Epoch: 1/50 || Iters: 820/1250 || Lr: 0.000099 || Loss: 0.6233 || Cost Time: 0:07:34 || Estimated Time: 9:30:22
2021-07-05 06:04:49,524 Segmentron INFO: Epoch: 1/50 || Iters: 830/1250 || Lr: 0.000099 || Loss: 0.4822 || Cost Time: 0:07:40 || Estimated Time: 9:30:13
2021-07-05 06:04:55,015 Segmentron INFO: Epoch: 1/50 || Iters: 840/1250 || Lr: 0.000099 || Loss: 0.3702 || Cost Time: 0:07:45 || Estimated Time: 9:30:03
2021-07-05 06:05:00,513 Segmentron INFO: Epoch: 1/50 || Iters: 850/1250 || Lr: 0.000099 || Loss: 0.4319 || Cost Time: 0:07:51 || Estimated Time: 9:29:54
2021-07-05 06:05:06,013 Segmentron INFO: Epoch: 1/50 || Iters: 860/1250 || Lr: 0.000099 || Loss: 0.4764 || Cost Time: 0:07:56 || Estimated Time: 9:29:45
2021-07-05 06:05:11,509 Segmentron INFO: Epoch: 1/50 || Iters: 870/1250 || Lr: 0.000099 || Loss: 1.0134 || Cost Time: 0:08:02 || Estimated Time: 9:29:36