def get_feature_vectors()

in improved_diffusion/unet.py [0:0]

• 19 lines of code
• 4 McCabe index (conditional complexity)

    def get_feature_vectors(self, x, timesteps, y=None):
        """
        Apply the model and return all of the intermediate tensors.

        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :param y: an [N] Tensor of labels, if class-conditional.
        :return: a dict with the following keys:
                 - 'down': a list of hidden state tensors from downsampling.
                 - 'middle': the tensor of the output of the lowest-resolution
                             block in the model.
                 - 'up': a list of hidden state tensors from upsampling.
        """
        hs = []
        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
        if self.num_classes is not None:
            assert y.shape == (x.shape[0],)
            emb = emb + self.label_emb(y)
        result = dict(down=[], up=[])
        h = x.type(self.inner_dtype)
        for module in self.input_blocks:
            h = module(h, emb)
            hs.append(h)
            result["down"].append(h.type(x.dtype))
        h = self.middle_block(h, emb)
        result["middle"] = h.type(x.dtype)
        for module in self.output_blocks:
            cat_in = th.cat([h, hs.pop()], dim=1)
            h = module(cat_in, emb)
            result["up"].append(h.type(x.dtype))
        return result