90345f6069723476c534cdf943e962d26c810e56,python/baseline/pytorch/classify/model.py,ConvModel,_init_pool,#ConvModel#Any#,101

Before Change

        convs = []
        for i, fsz in enumerate(filtsz):
            pad = fsz//2
            conv = nn.Sequential(
                nn.Conv1d(dsz, cmotsz, fsz, padding=pad),
                pytorch_activation("relu")
            )
            convs.append(conv)
            // Add the module so its managed correctly
        self.convs = nn.ModuleList(convs)
        // Width of concat of parallel convs

After Change

        filtsz = kwargs["filtsz"]
        cmotsz = kwargs["cmotsz"]
        self.parallel_conv = ParallelConv(dsz, cmotsz, filtsz, "relu", self.pdrop)
        return self.parallel_conv.outsz

    def _pool(self, btc):
        embeddings = btc.transpose(1, 2).contiguous()
        return self.parallel_conv(embeddings)

Instances