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SwinTransformer / Swin-Transformer-Object-Detection

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.
https://arxiv.org/abs/2103.14030
Apache License 2.0
1.79k stars 378 forks source link

convergence problem on cityscapes #63

Closed ngfuong closed 3 years ago

ngfuong commented 3 years ago

I trained cityscapes_detection for 2 epochs already but loss is always around 1.3-1.4

What should I check to ensure loss going down? Thank you!

This is my config:

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=40000)
checkpoint_config = dict(by_epoch=False, interval=1000)
evaluation = dict(interval=1000, metric='mIoU')
work_dir = '/content/drive/MyDrive/Image-Segmentation/Swin-Transformer-Semantic-Segmentation/checkpoints/swin_base_upernet_cityscapes_40k'
gpu_ids = range(0, 1)

2021-06-29 19:39:06,810 - mmseg - INFO - EncoderDecoder(
  (backbone): SwinTransformer(
    (patch_embed): PatchEmbed(
      (proj): Conv2d(3, 128, kernel_size=(4, 4), stride=(4, 4))
      (norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
    )
    (pos_drop): Dropout(p=0.0, inplace=False)
    (layers): ModuleList(
      (0): BasicLayer(
        (blocks): ModuleList(
          (0): SwinTransformerBlock(
            (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=128, out_features=384, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=128, out_features=128, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): Identity()
            (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=128, out_features=512, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=512, out_features=128, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (1): SwinTransformerBlock(
            (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=128, out_features=384, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=128, out_features=128, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=128, out_features=512, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=512, out_features=128, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
        (downsample): PatchMerging(
          (reduction): Linear(in_features=512, out_features=256, bias=False)
          (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
        )
      )
      (1): BasicLayer(
        (blocks): ModuleList(
          (0): SwinTransformerBlock(
            (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=256, out_features=768, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=256, out_features=256, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=256, out_features=1024, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=1024, out_features=256, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (1): SwinTransformerBlock(
            (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=256, out_features=768, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=256, out_features=256, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=256, out_features=1024, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=1024, out_features=256, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
        (downsample): PatchMerging(
          (reduction): Linear(in_features=1024, out_features=512, bias=False)
          (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        )
      )
      (2): BasicLayer(
        (blocks): ModuleList(
          (0): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (1): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (2): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (3): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (4): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (5): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (6): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (7): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (8): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (9): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (10): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (11): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (12): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (13): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (14): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (15): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (16): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (17): SwinTransformerBlock(
            (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=512, out_features=1536, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=512, out_features=512, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=512, out_features=2048, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=2048, out_features=512, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
        (downsample): PatchMerging(
          (reduction): Linear(in_features=2048, out_features=1024, bias=False)
          (norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)
        )
      )
      (3): BasicLayer(
        (blocks): ModuleList(
          (0): SwinTransformerBlock(
            (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=1024, out_features=3072, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=1024, out_features=1024, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
          (1): SwinTransformerBlock(
            (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            (attn): WindowAttention(
              (qkv): Linear(in_features=1024, out_features=3072, bias=True)
              (attn_drop): Dropout(p=0.0, inplace=False)
              (proj): Linear(in_features=1024, out_features=1024, bias=True)
              (proj_drop): Dropout(p=0.0, inplace=False)
              (softmax): Softmax(dim=-1)
            )
            (drop_path): DropPath()
            (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            (mlp): Mlp(
              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
              (act): GELU()
              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
              (drop): Dropout(p=0.0, inplace=False)
            )
          )
        )
      )
    )
    (norm0): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
    (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
    (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
    (norm3): LayerNorm((1024,), 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, 19, 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(1024, 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(1024, 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(1024, 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(1024, 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(3072, 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(128, 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(256, 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(512, 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, 19, kernel_size=(1, 1), stride=(1, 1))
    (dropout): Dropout2d(p=0.1, inplace=False)
    (convs): Sequential(
      (0): ConvModule(
        (conv): Conv2d(512, 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)
      )
    )
  )
)
Starboy-at-earth commented 3 years ago

How do you solve the problem?

ngfuong commented 3 years ago

How do you solve the problem?

The problem was mainly because I was using 1 GPU, so I reduce the learning rate by 8 times (compared to 8 GPUs in the original config). I also modified some other parameters. You can check my fork for the configuration!