make the netadapter `__str__` in the same order as `forward`.

[wyli]

Okay, my confusion was with how the layers are printed when you print the full NetAdapter instance. It shows the pool layer first, which made me think this was the first layer to receive the input tensor, even though the pool layer is called after model.features() in model.forward().

import monai
import torchvision as tv
import torch

r3d = tv.models.video.r3d_18()
model = monai.networks.nets.NetAdapter(r3d)

The order of calls is features -> pool [optional] -> flatten [optional] -> fc. But this is how it looks:

print(model)

NetAdapter(
  (pool): AvgPool2d(kernel_size=7, stride=1, padding=0)
  (features): Sequential(
    (0): BasicStem(
      (0): Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2), padding=(1, 3, 3), bias=False)
      (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
    )
    (1): Sequential(
      (0): BasicBlock(
        (conv1): Sequential(
          (0): Conv3DSimple(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (conv2): Sequential(
          (0): Conv3DSimple(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (relu): ReLU(inplace=True)
      )
      (1): BasicBlock(
        (conv1): Sequential(
          (0): Conv3DSimple(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (conv2): Sequential(
          (0): Conv3DSimple(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (relu): ReLU(inplace=True)
      )
    )
    (2): Sequential(
      (0): BasicBlock(
        (conv1): Sequential(
          (0): Conv3DSimple(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (conv2): Sequential(
          (0): Conv3DSimple(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False)
          (1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): BasicBlock(
        (conv1): Sequential(
          (0): Conv3DSimple(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (conv2): Sequential(
          (0): Conv3DSimple(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (relu): ReLU(inplace=True)
      )
    )
    (3): Sequential(
      (0): BasicBlock(
        (conv1): Sequential(
          (0): Conv3DSimple(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (conv2): Sequential(
          (0): Conv3DSimple(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False)
          (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): BasicBlock(
        (conv1): Sequential(
          (0): Conv3DSimple(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (conv2): Sequential(
          (0): Conv3DSimple(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (relu): ReLU(inplace=True)
      )
    )
    (4): Sequential(
      (0): BasicBlock(
        (conv1): Sequential(
          (0): Conv3DSimple(256, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (conv2): Sequential(
          (0): Conv3DSimple(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False)
          (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): BasicBlock(
        (conv1): Sequential(
          (0): Conv3DSimple(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU(inplace=True)
        )
        (conv2): Sequential(
          (0): Conv3DSimple(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
          (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (relu): ReLU(inplace=True)
      )
    )
    (5): AdaptiveAvgPool3d(output_size=(1, 1, 1))
  )
  (fc): Linear(in_features=512, out_features=1, bias=True)
)

I guess it would be nice if the __str__ was in the same order as forward.

Originally posted by @mattwarkentin in #3210 (reply in thread)