cuda out of memory with different crop_size

I changed cropsize in `config_base\dataset\cityscapes.pyfrom **crop_size = (512, 1024)** to **crop_size = (1024, 2048)** and got acuda out of memory` error from colab (free).

This is the traceback:

2021-06-21 13:33:03,482 - mmseg - INFO - Loaded 500 images
2021-06-21 13:33:03,483 - mmseg - INFO - Start running, host: root@c8eb715da416, work_dir: /content/drive/MyDrive/Image-Segmentation/Swin-Transformer-Semantic-Segmentation/checkpoints/swint_upernet_cityscapes_20k_1024x2048
2021-06-21 13:33:03,484 - mmseg - INFO - workflow: [('train', 1)], max: 20000 iters
Traceback (most recent call last):
File "/content/drive/MyDrive/Image-Segmentation/Swin-Transformer-Semantic-Segmentation/tools/train.py", line 163, in <module>
main()
File "/content/drive/MyDrive/Image-Segmentation/Swin-Transformer-Semantic-Segmentation/tools/train.py", line 159, in main
meta=meta)
File "/usr/local/lib/python3.7/dist-packages/mmsegmentation-0.11.0-py3.7.egg/mmseg/apis/train.py", line 116, in train_segmentor
runner.run(data_loaders, cfg.workflow)
File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/iter_based_runner.py", line 131, in run
iter_runner(iter_loaders[i], **kwargs)
File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/iter_based_runner.py", line 60, in train
outputs = self.model.train_step(data_batch, self.optimizer, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/mmcv/parallel/data_parallel.py", line 67, in train_step
return self.module.train_step(*inputs[0], **kwargs[0])
File "/usr/local/lib/python3.7/dist-packages/mmsegmentation-0.11.0-py3.7.egg/mmseg/models/segmentors/base.py", line 152, in train_step
losses = self(**data_batch)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/fp16_utils.py", line 84, in new_func
return old_func(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/mmsegmentation-0.11.0-py3.7.egg/mmseg/models/segmentors/base.py", line 122, in forward
return self.forward_train(img, img_metas, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/mmsegmentation-0.11.0-py3.7.egg/mmseg/models/segmentors/encoder_decoder.py", line 158, in forward_train
gt_semantic_seg)
File "/usr/local/lib/python3.7/dist-packages/mmsegmentation-0.11.0-py3.7.egg/mmseg/models/segmentors/encoder_decoder.py", line 102, in _decode_head_forward_train
self.train_cfg)
File "/usr/local/lib/python3.7/dist-packages/mmsegmentation-0.11.0-py3.7.egg/mmseg/models/decode_heads/decode_head.py", line 186, in forward_train
seg_logits = self.forward(inputs)
File "/usr/local/lib/python3.7/dist-packages/mmsegmentation-0.11.0-py3.7.egg/mmseg/models/decode_heads/uper_head.py", line 94, in forward
for i, lateral_conv in enumerate(self.lateral_convs)
File "/usr/local/lib/python3.7/dist-packages/mmsegmentation-0.11.0-py3.7.egg/mmseg/models/decode_heads/uper_head.py", line 94, in <listcomp>
for i, lateral_conv in enumerate(self.lateral_convs)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/conv_module.py", line 200, in forward
x = self.norm(x)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/batchnorm.py", line 136, in forward
self.weight, self.bias, bn_training, exponential_average_factor, self.eps)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py", line 2016, in batch_norm
training, momentum, eps, torch.backends.cudnn.enabled
RuntimeError: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 11.17 GiB total capacity; 10.37 GiB already allocated; 182.81 MiB free; 10.63 GiB reserved in total by PyTorch)

To prevent memory overflow I need to reduce the batch size but I cannot find which location to change the batch size parameter. Is it possible to change batch size? If not, what parameter should I change?

This is my config:


2021-06-21 13:32:59,272 - mmseg - INFO - Distributed training: False
2021-06-21 13:32:59,624 - mmseg - INFO - Config:
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=
'/content/drive/MyDrive/Image-Segmentation/Swin-Transformer-Semantic-Segmentation/checkpoints/swin_tiny_patch4_window7_224.pth',
backbone=dict(
    type='SwinTransformer',
    embed_dim=96,
    depths=[2, 2, 6, 2],
    num_heads=[3, 6, 12, 24],
    window_size=7,
    mlp_ratio=4.0,
    qkv_bias=True,
    qk_scale=None,
    drop_rate=0.0,
    attn_drop_rate=0.0,
    drop_path_rate=0.3,
    ape=False,
    patch_norm=True,
    out_indices=(0, 1, 2, 3),
    use_checkpoint=False),
decode_head=dict(
    type='UPerHead',
    in_channels=[96, 192, 384, 768],
    in_index=[0, 1, 2, 3],
    pool_scales=(1, 2, 3, 6),
    channels=512,
    dropout_ratio=0.1,
    num_classes=150,
    norm_cfg=dict(type='BN', requires_grad=True),
    align_corners=False,
    loss_decode=dict(
        type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
auxiliary_head=dict(
    type='FCNHead',
    in_channels=384,
    in_index=2,
    channels=256,
    num_convs=1,
    concat_input=False,
    dropout_ratio=0.1,
    num_classes=150,
    norm_cfg=dict(type='BN', requires_grad=True),
    align_corners=False,
    loss_decode=dict(
        type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
train_cfg=dict(),
test_cfg=dict(mode='whole'))
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (1024, 2048)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=(1024, 2048), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(
    type='Normalize',
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    to_rgb=True),
dict(type='Pad', size=(1024, 2048), pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
    type='MultiScaleFlipAug',
    img_scale=(2049, 1025),
    flip=False,
    transforms=[
        dict(type='Resize', keep_ratio=True),
        dict(type='RandomFlip'),
        dict(
            type='Normalize',
            mean=[123.675, 116.28, 103.53],
            std=[58.395, 57.12, 57.375],
            to_rgb=True),
        dict(type='ImageToTensor', keys=['img']),
        dict(type='Collect', keys=['img'])
    ])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
    type='CityscapesDataset',
    data_root='data/cityscapes/',
    img_dir='leftImg8bit/train',
    ann_dir='gtFine/train',
    pipeline=[
        dict(type='LoadImageFromFile'),
        dict(type='LoadAnnotations'),
        dict(
            type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
        dict(
            type='RandomCrop', crop_size=(1024, 2048), cat_max_ratio=0.75),
        dict(type='RandomFlip', prob=0.5),
        dict(type='PhotoMetricDistortion'),
        dict(
            type='Normalize',
            mean=[123.675, 116.28, 103.53],
            std=[58.395, 57.12, 57.375],
            to_rgb=True),
        dict(type='Pad', size=(1024, 2048), pad_val=0, seg_pad_val=255),
        dict(type='DefaultFormatBundle'),
        dict(type='Collect', keys=['img', 'gt_semantic_seg'])
    ]),
val=dict(
    type='CityscapesDataset',
    data_root='data/cityscapes/',
    img_dir='leftImg8bit/val',
    ann_dir='gtFine/val',
    pipeline=[
        dict(type='LoadImageFromFile'),
        dict(
            type='MultiScaleFlipAug',
            img_scale=(2049, 1025),
            flip=False,
            transforms=[
                dict(type='Resize', keep_ratio=True),
                dict(type='RandomFlip'),
                dict(
                    type='Normalize',
                    mean=[123.675, 116.28, 103.53],
                    std=[58.395, 57.12, 57.375],
                    to_rgb=True),
                dict(type='ImageToTensor', keys=['img']),
                dict(type='Collect', keys=['img'])
            ])
    ]),
test=dict(
    type='CityscapesDataset',
    data_root='data/cityscapes/',
    img_dir='leftImg8bit/val',
    ann_dir='gtFine/val',
    pipeline=[
        dict(type='LoadImageFromFile'),
        dict(
            type='MultiScaleFlipAug',
            img_scale=(2049, 1025),
            flip=False,
            transforms=[
                dict(type='Resize', keep_ratio=True),
                dict(type='RandomFlip'),
                dict(
                    type='Normalize',
                    mean=[123.675, 116.28, 103.53],
                    std=[58.395, 57.12, 57.375],
                    to_rgb=True),
                dict(type='ImageToTensor', keys=['img']),
                dict(type='Collect', keys=['img'])
            ])
    ]))
log_config = dict(
interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
optimizer = dict(
type='AdamW',
lr=6e-05,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
    custom_keys=dict(
        absolute_pos_embed=dict(decay_mult=0.0),
        relative_position_bias_table=dict(decay_mult=0.0),
        norm=dict(decay_mult=0.0))))
optimizer_config = dict()
lr_config = dict(
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-06,
power=1.0,
min_lr=0.0,
by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=20000)
checkpoint_config = dict(by_epoch=False, interval=2000)
evaluation = dict(interval=2000, metric='mIoU')
work_dir = '/content/drive/MyDrive/Image-Segmentation/Swin-Transformer-Semantic-Segmentation/checkpoints/swint_upernet_cityscapes_20k_1024x2048'
gpu_ids = range(0, 1)

2021-06-21 13:33:01,119 - mmseg - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: norm.weight, norm.bias, head.weight, head.bias, layers.0.blocks.1.attn_mask, layers.1.blocks.1.attn_mask, layers.2.blocks.1.attn_mask, layers.2.blocks.3.attn_mask, layers.2.blocks.5.attn_mask

missing keys in source state_dict: norm0.weight, norm0.bias, norm1.weight, norm1.bias, norm2.weight, norm2.bias, norm3.weight, norm3.bias

2021-06-21 13:33:01,126 - mmseg - INFO - EncoderDecoder( (backbone): SwinTransformer( (patch_embed): PatchEmbed( (proj): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4)) (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True) ) (pos_drop): Dropout(p=0.0, inplace=False) (layers): ModuleList( (0): BasicLayer( (blocks): ModuleList( (0): SwinTransformerBlock( (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=96, out_features=288, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=96, out_features=96, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): Identity() (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=96, out_features=384, bias=True) (act): GELU() (fc2): Linear(in_features=384, out_features=96, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): SwinTransformerBlock( (norm1): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=96, out_features=288, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=96, out_features=96, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=96, out_features=384, bias=True) (act): GELU() (fc2): Linear(in_features=384, out_features=96, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (downsample): PatchMerging( (reduction): Linear(in_features=384, out_features=192, bias=False) (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True) ) ) (1): BasicLayer( (blocks): ModuleList( (0): SwinTransformerBlock( (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=192, out_features=576, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=192, out_features=192, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=192, out_features=768, bias=True) (act): GELU() (fc2): Linear(in_features=768, out_features=192, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): SwinTransformerBlock( (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=192, out_features=576, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=192, out_features=192, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=192, out_features=768, bias=True) (act): GELU() (fc2): Linear(in_features=768, out_features=192, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (downsample): PatchMerging( (reduction): Linear(in_features=768, out_features=384, bias=False) (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) ) (2): BasicLayer( (blocks): ModuleList( (0): SwinTransformerBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=384, out_features=1536, bias=True) (act): GELU() (fc2): Linear(in_features=1536, out_features=384, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): SwinTransformerBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=384, out_features=1536, bias=True) (act): GELU() (fc2): Linear(in_features=1536, out_features=384, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (2): SwinTransformerBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=384, out_features=1536, bias=True) (act): GELU() (fc2): Linear(in_features=1536, out_features=384, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (3): SwinTransformerBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=384, out_features=1536, bias=True) (act): GELU() (fc2): Linear(in_features=1536, out_features=384, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (4): SwinTransformerBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=384, out_features=1536, bias=True) (act): GELU() (fc2): Linear(in_features=1536, out_features=384, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (5): SwinTransformerBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=384, out_features=1536, bias=True) (act): GELU() (fc2): Linear(in_features=1536, out_features=384, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (downsample): PatchMerging( (reduction): Linear(in_features=1536, out_features=768, bias=False) (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True) ) ) (3): BasicLayer( (blocks): ModuleList( (0): SwinTransformerBlock( (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=768, out_features=2304, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=768, out_features=768, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=768, out_features=3072, bias=True) (act): GELU() (fc2): Linear(in_features=3072, out_features=768, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): SwinTransformerBlock( (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (attn): WindowAttention( (qkv): Linear(in_features=768, out_features=2304, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=768, out_features=768, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (softmax): Softmax(dim=-1) ) (drop_path): DropPath() (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=768, out_features=3072, bias=True) (act): GELU() (fc2): Linear(in_features=3072, out_features=768, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) ) (norm0): LayerNorm((96,), eps=1e-05, elementwise_affine=True) (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) (decode_head): UPerHead( input_transform=multiple_select, ignore_index=255, align_corners=False (loss_decode): CrossEntropyLoss() (conv_seg): Conv2d(512, 150, kernel_size=(1, 1), stride=(1, 1)) (dropout): Dropout2d(p=0.1, inplace=False) (psp_modules): PPM( (0): Sequential( (0): AdaptiveAvgPool2d(output_size=1) (1): ConvModule( (conv): Conv2d(768, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (1): Sequential( (0): AdaptiveAvgPool2d(output_size=2) (1): ConvModule( (conv): Conv2d(768, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (2): Sequential( (0): AdaptiveAvgPool2d(output_size=3) (1): ConvModule( (conv): Conv2d(768, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (3): Sequential( (0): AdaptiveAvgPool2d(output_size=6) (1): ConvModule( (conv): Conv2d(768, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) ) (bottleneck): ConvModule( (conv): Conv2d(2816, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) (lateral_convs): ModuleList( (0): ConvModule( (conv): Conv2d(96, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU() ) (1): ConvModule( (conv): Conv2d(192, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU() ) (2): ConvModule( (conv): Conv2d(384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU() ) ) (fpn_convs): ModuleList( (0): ConvModule( (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU() ) (1): ConvModule( (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU() ) (2): ConvModule( (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU() ) ) (fpn_bottleneck): ConvModule( (conv): Conv2d(2048, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) (auxiliary_head): FCNHead( input_transform=None, ignore_index=255, align_corners=False (loss_decode): CrossEntropyLoss() (conv_seg): Conv2d(256, 150, kernel_size=(1, 1), stride=(1, 1)) (dropout): Dropout2d(p=0.1, inplace=False) (convs): Sequential( (0): ConvModule( (conv): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (activate): ReLU(inplace=True) ) ) ) )

SwinTransformer / Swin-Transformer-Semantic-Segmentation

cuda out of memory with different crop_size #28