timm/models/nasnet.py [299:340]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type) self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1, padding=pad_type) self.comb_iter_0_left = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type) self.comb_iter_0_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type) self.comb_iter_1_left = create_pool2d('max', 3, 2, padding=pad_type) self.comb_iter_1_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type) self.comb_iter_2_left = create_pool2d('avg', 3, 2, count_include_pad=False, padding=pad_type) self.comb_iter_2_right = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type) self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type) self.comb_iter_4_left = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type) self.comb_iter_4_right = create_pool2d('max', 3, 2, padding=pad_type) def forward(self, x, x_prev): x_left = self.conv_prev_1x1(x_prev) x_right = self.conv_1x1(x) x_comb_iter_0_left = self.comb_iter_0_left(x_right) x_comb_iter_0_right = self.comb_iter_0_right(x_left) x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right x_comb_iter_1_left = self.comb_iter_1_left(x_right) x_comb_iter_1_right = self.comb_iter_1_right(x_left) x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right x_comb_iter_2_left = self.comb_iter_2_left(x_right) x_comb_iter_2_right = self.comb_iter_2_right(x_left) x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0) x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1 x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0) x_comb_iter_4_right = self.comb_iter_4_right(x_right) x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1) return x_out - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - timm/models/nasnet.py [347:388]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, 1, stride=1, padding=pad_type) self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, 1, stride=1, padding=pad_type) self.comb_iter_0_left = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type) self.comb_iter_0_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type) self.comb_iter_1_left = create_pool2d('max', 3, 2, padding=pad_type) self.comb_iter_1_right = BranchSeparables(out_chs_right, out_chs_right, 7, 2, pad_type) self.comb_iter_2_left = create_pool2d('avg', 3, 2, count_include_pad=False, padding=pad_type) self.comb_iter_2_right = BranchSeparables(out_chs_right, out_chs_right, 5, 2, pad_type) self.comb_iter_3_right = create_pool2d('avg', 3, 1, count_include_pad=False, padding=pad_type) self.comb_iter_4_left = BranchSeparables(out_chs_right, out_chs_right, 3, 1, pad_type) self.comb_iter_4_right = create_pool2d('max', 3, 2, padding=pad_type) def forward(self, x, x_prev): x_left = self.conv_prev_1x1(x_prev) x_right = self.conv_1x1(x) x_comb_iter_0_left = self.comb_iter_0_left(x_right) x_comb_iter_0_right = self.comb_iter_0_right(x_left) x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right x_comb_iter_1_left = self.comb_iter_1_left(x_right) x_comb_iter_1_right = self.comb_iter_1_right(x_left) x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right x_comb_iter_2_left = self.comb_iter_2_left(x_right) x_comb_iter_2_right = self.comb_iter_2_right(x_left) x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0) x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1 x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0) x_comb_iter_4_right = self.comb_iter_4_right(x_right) x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1) return x_out - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -