76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import math
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import torch.nn as nn
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from scepter.modules.model.backbone.video.bricks.stems.base_3d_stem import \
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Base3DStem
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from scepter.modules.model.registry import STEMS
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from scepter.modules.utils.config import dict_to_yaml
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@STEMS.register_class()
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class R2Plus1DStem(Base3DStem):
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para_dict = {}
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para_dict.update(Base3DStem.para_dict)
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def __init__(self, cfg, logger=None):
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super(R2Plus1DStem, self).__init__(cfg, logger=logger)
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def _construct(self):
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mid_dim = int(
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math.floor(
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(self.kernel_size[0] * self.kernel_size[1] *
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self.kernel_size[2] * self.dim_in * self.num_filters) /
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(self.kernel_size[1] * self.kernel_size[2] * self.dim_in +
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self.kernel_size[0] * self.num_filters)))
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self.a1 = nn.Conv3d(in_channels=self.dim_in,
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out_channels=mid_dim,
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kernel_size=(1, self.kernel_size[1],
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self.kernel_size[2]),
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stride=(1, self.stride[1], self.stride[2]),
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padding=(0, self.kernel_size[1] // 2,
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self.kernel_size[2] // 2),
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bias=False)
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self.a1_bn = nn.BatchNorm3d(mid_dim, **self.bn_params)
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self.a1_relu = nn.ReLU(inplace=True)
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self.a2 = nn.Conv3d(in_channels=mid_dim,
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out_channels=self.num_filters,
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kernel_size=(self.kernel_size[0], 1, 1),
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stride=(self.stride[0], 1, 1),
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padding=(self.kernel_size[0] // 2, 0, 0),
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bias=False)
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self.a2_bn = nn.BatchNorm3d(self.num_filters, **self.bn_params)
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self.a2_relu = nn.ReLU(inplace=True)
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def forward(self, x):
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x = self.a1(x)
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x = self.a1_bn(x)
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x = self.a1_relu(x)
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x = self.a2(x)
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x = self.a2_bn(x)
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x = self.a2_relu(x)
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return x
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@staticmethod
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def get_config_template():
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'''
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{ "ENV" :
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{ "description" : "",
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"A" : {
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"value": 1.0,
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"description": ""
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}
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}
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}
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:return:
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'''
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return dict_to_yaml('STEM',
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__class__.__name__,
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R2Plus1DStem.para_dict,
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set_name=True)
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