Project Name: qubvel+segmentation_models.pytorch
Language: Python
Statement Type: Arithmetic Assignment

Source Code:
def _make_stage(self, planes: int, num_blocks: int, num_se_blocks: int) -> nn.Sequential:
        """Build a stage of MobileOne model.

        :param planes: Number of output channels.
        :param num_blocks: Number of blocks in this stage.
        :param num_se_blocks: Number of SE blocks in this stage.
        :return: A stage of MobileOne model.
        """
        # Get strides for all layers
        strides = [2] + [1] * (num_blocks - 1)
        blocks = []
        for ix, stride in enumerate(strides):
            use_se = False
            if num_se_blocks > num_blocks:
                raise ValueError("Number of SE blocks cannot " "exceed number of layers.")
            if ix >= (num_blocks - num_se_blocks):
                use_se = True

            # Depthwise conv
            blocks.append(
                MobileOneBlock(
                    in_channels=self.in_planes,
                    out_channels=self.in_planes,
                    kernel_size=3,
                    stride=stride,
                    padding=1,
                    groups=self.in_planes,
                    inference_mode=self.inference_mode,
                    use_se=use_se,
                    num_conv_branches=self.num_conv_branches,
                )
            )
            # Pointwise conv
            blocks.append(
                MobileOneBlock(
                    in_channels=self.in_planes,
                    out_channels=planes,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                    groups=1,
                    inference_mode=self.inference_mode,
                    use_se=use_se,
                    num_conv_branches=self.num_conv_branches,
                )
            )
            self.in_planes = planes
            self.cur_layer_idx += 1
        return nn.Sequential(*blocks)

_make_stage(self=MobileOne(  (stage0): MobileOneBlock(    (se): Identity()    (activation): ReLU()    (rbr_conv): ModuleList(      (0): Sequential(        (conv): Conv2d(3, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)        (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      )    )    (rbr_scale): Sequential(      (conv): Conv2d(3, 48, kernel_size=(1, 1), stride=(2, 2), bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )), planes=48, num_blocks=2, num_se_blocks=0, self._backward_hooks=OrderedDict(), self._backward_pre_hooks=OrderedDict(), self._buffers=OrderedDict(), self._depth=3, self._forward_hooks=OrderedDict(), self._forward_hooks_always_called=OrderedDict(), self._forward_hooks_with_kwargs=OrderedDict(), self._forward_pre_hooks=OrderedDict(), self._forward_pre_hooks_with_kwargs=OrderedDict(), self._in_channels=3, self._is_full_backward_hook=None, self._load_state_dict_post_hooks=OrderedDict(), self._load_state_dict_pre_hooks=OrderedDict(), self._modules=OrderedDict([('stage0', MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_conv): ModuleList(    (0): Sequential(      (conv): Conv2d(3, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )  (rbr_scale): Sequential(    (conv): Conv2d(3, 48, kernel_size=(1, 1), stride=(2, 2), bias=False)    (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  )))]), self._non_persistent_buffers_set=set(), self._out_channels=(3, 48, 48, 128, 256, 1024), self._parameters=OrderedDict(), self._state_dict_hooks=OrderedDict(), self._state_dict_pre_hooks=OrderedDict(), self.cur_layer_idx=1, self.in_planes=48, self.inference_mode=False, self.num_conv_branches=4, self.training=True, self.use_se=False)

Selected Statement: strides = [2] + [1] * (num_blocks - 1)

Function Input:
self: MobileOne(  (stage0): MobileOneBlock(    (se): Identity()    (activation): ReLU()    (rbr_conv): ModuleList(      (0): Sequential(        (conv): Conv2d(3, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)        (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      )    )    (rbr_scale): Sequential(      (conv): Conv2d(3, 48, kernel_size=(1, 1), stride=(2, 2), bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  ))
planes: 48
num_blocks: 2
num_se_blocks: 0
self._backward_hooks: OrderedDict()
self._backward_pre_hooks: OrderedDict()
self._buffers: OrderedDict()
self._depth: 3
self._forward_hooks: OrderedDict()
self._forward_hooks_always_called: OrderedDict()
self._forward_hooks_with_kwargs: OrderedDict()
self._forward_pre_hooks: OrderedDict()
self._forward_pre_hooks_with_kwargs: OrderedDict()
self._in_channels: 3
self._is_full_backward_hook: None
self._load_state_dict_post_hooks: OrderedDict()
self._load_state_dict_pre_hooks: OrderedDict()
self._modules: OrderedDict([('stage0', MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_conv): ModuleList(    (0): Sequential(      (conv): Conv2d(3, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )  (rbr_scale): Sequential(    (conv): Conv2d(3, 48, kernel_size=(1, 1), stride=(2, 2), bias=False)    (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  )))])
self._non_persistent_buffers_set: set()
self._out_channels: (3, 48, 48, 128, 256, 1024)
self._parameters: OrderedDict()
self._state_dict_hooks: OrderedDict()
self._state_dict_pre_hooks: OrderedDict()
self.cur_layer_idx: 1
self.in_planes: 48
self.inference_mode: False
self.num_conv_branches: 4
self.training: True
self.use_se: False

Variable Values Before Statement:

Value After Statement Execution: [2, 1]

Variable States During Runtime (line, value):
self: 1
MobileOne(  (stage0): MobileOneBlock(    (se): Identity()    (activation): ReLU()    (rbr_conv): ModuleList(      (0): Sequential(        (conv): Conv2d(3, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)        (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      )    )    (rbr_scale): Sequential(      (conv): Conv2d(3, 48, kernel_size=(1, 1), stride=(2, 2), bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  ))
planes: 1
48
num_blocks: 1
2
num_se_blocks: 1
0
self._backward_hooks: 1
OrderedDict()
self._backward_pre_hooks: 1
OrderedDict()
self._buffers: 1
OrderedDict()
self._depth: 1
3
self._forward_hooks: 1
OrderedDict()
self._forward_hooks_always_called: 1
OrderedDict()
self._forward_hooks_with_kwargs: 1
OrderedDict()
self._forward_pre_hooks: 1
OrderedDict()
self._forward_pre_hooks_with_kwargs: 1
OrderedDict()
self._in_channels: 1
3
self._is_full_backward_hook: 1
None
self._load_state_dict_post_hooks: 1
OrderedDict()
self._load_state_dict_pre_hooks: 1
OrderedDict()
self._modules: 1
OrderedDict([('stage0', MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_conv): ModuleList(    (0): Sequential(      (conv): Conv2d(3, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )  (rbr_scale): Sequential(    (conv): Conv2d(3, 48, kernel_size=(1, 1), stride=(2, 2), bias=False)    (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  )))])
self._non_persistent_buffers_set: 1
set()
self._out_channels: 1
(3, 48, 48, 128, 256, 1024)
self._parameters: 1
OrderedDict()
self._state_dict_hooks: 1
OrderedDict()
self._state_dict_pre_hooks: 1
OrderedDict()
self.cur_layer_idx: 1
1
48
2
48
3
self.in_planes: 1
48
self.inference_mode: 1
False
self.num_conv_branches: 1
4
self.training: 1
True
self.use_se: 1
False
strides: 10
[2, 1]
blocks: 11
20
[MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_conv): ModuleList(    (0-3): 4 x Sequential(      (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=48, bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )  (rbr_scale): Sequential(    (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(2, 2), groups=48, bias=False)    (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  ))]
34
[MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_conv): ModuleList(    (0-3): 4 x Sequential(      (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=48, bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )  (rbr_scale): Sequential(    (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(2, 2), groups=48, bias=False)    (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  )), MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_skip): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  (rbr_conv): ModuleList(    (0-3): 4 x Sequential(      (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  ))]
20
[MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_conv): ModuleList(    (0-3): 4 x Sequential(      (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=48, bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )  (rbr_scale): Sequential(    (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(2, 2), groups=48, bias=False)    (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  )), MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_skip): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  (rbr_conv): ModuleList(    (0-3): 4 x Sequential(      (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )), MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_skip): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  (rbr_conv): ModuleList(    (0-3): 4 x Sequential(      (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )  (rbr_scale): Sequential(    (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), groups=48, bias=False)    (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  ))]
34
[MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_conv): ModuleList(    (0-3): 4 x Sequential(      (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=48, bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )  (rbr_scale): Sequential(    (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(2, 2), groups=48, bias=False)    (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  )), MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_skip): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  (rbr_conv): ModuleList(    (0-3): 4 x Sequential(      (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )), MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_skip): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  (rbr_conv): ModuleList(    (0-3): 4 x Sequential(      (conv): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=48, bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  )  (rbr_scale): Sequential(    (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), groups=48, bias=False)    (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  )), MobileOneBlock(  (se): Identity()  (activation): ReLU()  (rbr_skip): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)  (rbr_conv): ModuleList(    (0-3): 4 x Sequential(      (conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)      (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)    )  ))]
ix: 12
0
12
1
stride: 12
2
12
1
use_se: 13
False