segmentation_model not running

[AhmadAlkhan]

I have trained the classification model but when i start training the segmentation model i get this error:

[a.alkhan@ulhpccn11 ~]$ podman exec 3836b5a25940 python /workspace/train.py --root_dir /data/patches --model_dir /trainer --train_mode seg --train_type col --level 1 --train_epochs 10
Unet(
(encoder): EfficientNetEncoder(
(conv_stem): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNormAct2d(
32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(blocks): Sequential(
(0): Sequential(
(0): DepthwiseSeparableConv(
(conv_dw): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn1): BatchNormAct2d(
32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(8, 32, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pw): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn2): BatchNormAct2d(
16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): Identity()
)
)
(1): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
(bn2): BatchNormAct2d(
96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(96, 4, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(4, 96, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.013)
)
(1): InvertedResidual(
(conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
(bn2): BatchNormAct2d(
144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.025)
)
)
(2): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(144, 144, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=144, bias=False)
(bn2): BatchNormAct2d(
144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.038)
)
(1): InvertedResidual(
(conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)
(bn2): BatchNormAct2d(
240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.050)
)
)
(3): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False)
(bn2): BatchNormAct2d(
240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.062)
)
(1): InvertedResidual(
(conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)
(bn2): BatchNormAct2d(
480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.075)
)
(2): InvertedResidual(
(conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)
(bn2): BatchNormAct2d(
480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.088)
)
)
(4): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(480, 480, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=480, bias=False)
(bn2): BatchNormAct2d(
480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.100)
)
(1): InvertedResidual(
(conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)
(bn2): BatchNormAct2d(
672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.113)
)
(2): InvertedResidual(
(conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)
(bn2): BatchNormAct2d(
672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.125)
)
)
(5): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=672, bias=False)
(bn2): BatchNormAct2d(
672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.138)
)
(1): InvertedResidual(
(conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)
(bn2): BatchNormAct2d(
1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.150)
)
(2): InvertedResidual(
(conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)
(bn2): BatchNormAct2d(
1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.163)
)
(3): InvertedResidual(
(conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)
(bn2): BatchNormAct2d(
1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.175)
)
)
(6): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNormAct2d(
1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(conv_dw): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)
(bn2): BatchNormAct2d(
1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(se): SqueezeExcite(
(conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))
(act1): Swish()
(conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNormAct2d(
320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Identity()
)
(drop_path): DropPath(drop_prob=0.188)
)
)
)
(conv_head): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn2): BatchNormAct2d(
1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True
(drop): Identity()
(act): Swish()
)
(global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))
)
(decoder): UnetDecoder(
(center): Identity()
(blocks): ModuleList(
(0): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(432, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(1): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(296, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(2): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(152, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(3): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
(4): DecoderBlock(
(conv1): Conv2dReLU(
(0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention1): Attention(
(attention): Identity()
)
(conv2): Conv2dReLU(
(0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(attention2): Attention(
(attention): Identity()
)
)
)
)
(segmentation_head): SegmentationHead(
(0): Conv2d(16, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): Identity()
(2): Activation(
(activation): Identity()
)
)
)
[INFO] train_list: 939, valid_list: 105
[INFO] training start
Training (X / X Steps) (loss=X.X): 0[ WARN:[email protected]] global loadsave.cpp:248 findDecoder imread_('/data/patches/Col_0043/sw/level_1/pni/mask_sw/Col_0043_0000025.png'): can't open/read file: check file path/integrity]
[ WARN:[email protected]] global loadsave.cpp:248 findDecoder imread_('/data/patches/Col_0043/sw/level_1/pni/mask_sw/Col_0043_0000027.png'): can't open/read file: check file path/integrity
[ WARN:[email protected]] global loadsave.cpp:248 findDecoder imread_('/data/patches/Col_0043/sw/level_1/pni/mask_sw/Col_0043_0000008.png'): can't open/read file: check file path/integrity
Training (1 / 15 Steps) (loss=0.52428): 7%|▋ | 1/15 [00:07<01:48, 7.74s/it]
7%|▋ | 1/15 [00:07<01:48, 7.74s/it]
Traceback (most recent call last):
File "/workspace/train.py", line 114, in
main()
File "/workspace/train.py", line 110, in main
trainer.training()
File "/workspace/trainer/unet_learner.py", line 210, in training
for item in tqdm(train_iterator):
File "/opt/conda/lib/python3.7/site-packages/tqdm/std.py", line 1185, in iter
for obj in iterable:
File "/opt/conda/lib/python3.7/site-packages/tqdm/std.py", line 1185, in iter
for obj in iterable:
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 521, in next
data = self._next_data()
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1183, in _next_data
return self._process_data(data)
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
data.reraise()
File "/opt/conda/lib/python3.7/site-packages/torch/_utils.py", line 434, in reraise
raise exception
TypeError: Caught TypeError in DataLoader worker process 1.
Original Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 49, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/workspace/trainer/unet_learner.py", line 61, in getitem
augmented = self.transform(image=image, mask=mask)
File "/opt/conda/lib/python3.7/site-packages/albumentations/core/composition.py", line 195, in call
self._check_args(**data)
File "/opt/conda/lib/python3.7/site-packages/albumentations/core/composition.py", line 275, in _check_args
raise TypeError("{} must be numpy array type".format(data_name))
TypeError: mask must be numpy array type