image-match/python/py/Lib/site-packages/transformers/models/upernet/modeling_upernet.py

# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""PyTorch UperNet model. Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation."""

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ...backbone_utils import load_backbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring
from .configuration_upernet import UperNetConfig


class UperNetConvModule(nn.Module):
    """
    A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
    layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int | tuple[int, int],
        padding: int | tuple[int, int] | str = 0,
        bias: bool = False,
        dilation: int | tuple[int, int] = 1,
    ) -> None:
        super().__init__()
        self.conv = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            padding=padding,
            bias=bias,
            dilation=dilation,
        )
        self.batch_norm = nn.BatchNorm2d(out_channels)
        self.activation = nn.ReLU()

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        output = self.conv(input)
        output = self.batch_norm(output)
        output = self.activation(output)

        return output


class UperNetPyramidPoolingBlock(nn.Module):
    def __init__(self, pool_scale: int, in_channels: int, channels: int) -> None:
        super().__init__()
        self.layers = [
            nn.AdaptiveAvgPool2d(pool_scale),
            UperNetConvModule(in_channels, channels, kernel_size=1),
        ]
        for i, layer in enumerate(self.layers):
            self.add_module(str(i), layer)

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        hidden_state = input
        for layer in self.layers:
            hidden_state = layer(hidden_state)
        return hidden_state


class UperNetPyramidPoolingModule(nn.Module):
    """
    Pyramid Pooling Module (PPM) used in PSPNet.

    Args:
        pool_scales (`tuple[int]`):
            Pooling scales used in Pooling Pyramid Module.
        in_channels (`int`):
            Input channels.
        channels (`int`):
            Channels after modules, before conv_seg.
        align_corners (`bool`):
            align_corners argument of F.interpolate.
    """

    def __init__(self, pool_scales: tuple[int, ...], in_channels: int, channels: int, align_corners: bool) -> None:
        super().__init__()
        self.pool_scales = pool_scales
        self.align_corners = align_corners
        self.in_channels = in_channels
        self.channels = channels
        self.blocks = []
        for i, pool_scale in enumerate(pool_scales):
            block = UperNetPyramidPoolingBlock(pool_scale=pool_scale, in_channels=in_channels, channels=channels)
            self.blocks.append(block)
            self.add_module(str(i), block)

    def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
        ppm_outs = []
        for ppm in self.blocks:
            ppm_out = ppm(x)
            upsampled_ppm_out = nn.functional.interpolate(
                ppm_out, size=x.size()[2:], mode="bilinear", align_corners=self.align_corners
            )
            ppm_outs.append(upsampled_ppm_out)
        return ppm_outs


class UperNetHead(nn.Module):
    """
    Unified Perceptual Parsing for Scene Understanding. This head is the implementation of
    [UPerNet](https://huggingface.co/papers/1807.10221).
    """

    def __init__(self, config, in_channels):
        super().__init__()

        self.config = config
        self.pool_scales = config.pool_scales  # e.g. (1, 2, 3, 6)
        self.in_channels = in_channels
        self.channels = config.hidden_size
        self.align_corners = False
        self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1)

        # PSP Module
        self.psp_modules = UperNetPyramidPoolingModule(
            self.pool_scales,
            self.in_channels[-1],
            self.channels,
            align_corners=self.align_corners,
        )
        self.bottleneck = UperNetConvModule(
            self.in_channels[-1] + len(self.pool_scales) * self.channels,
            self.channels,
            kernel_size=3,
            padding=1,
        )
        # FPN Module
        self.lateral_convs = nn.ModuleList()
        self.fpn_convs = nn.ModuleList()
        for in_channels in self.in_channels[:-1]:  # skip the top layer
            l_conv = UperNetConvModule(in_channels, self.channels, kernel_size=1)
            fpn_conv = UperNetConvModule(self.channels, self.channels, kernel_size=3, padding=1)
            self.lateral_convs.append(l_conv)
            self.fpn_convs.append(fpn_conv)

        self.fpn_bottleneck = UperNetConvModule(
            len(self.in_channels) * self.channels,
            self.channels,
            kernel_size=3,
            padding=1,
        )

    def psp_forward(self, inputs):
        x = inputs[-1]
        psp_outs = [x]
        psp_outs.extend(self.psp_modules(x))
        psp_outs = torch.cat(psp_outs, dim=1)
        output = self.bottleneck(psp_outs)

        return output

    def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
        # build laterals
        laterals = [lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)]

        laterals.append(self.psp_forward(encoder_hidden_states))

        # build top-down path
        used_backbone_levels = len(laterals)
        for i in range(used_backbone_levels - 1, 0, -1):
            prev_shape = laterals[i - 1].shape[2:]
            laterals[i - 1] = laterals[i - 1] + nn.functional.interpolate(
                laterals[i], size=prev_shape, mode="bilinear", align_corners=self.align_corners
            )

        # build outputs
        fpn_outs = [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)]
        # append psp feature
        fpn_outs.append(laterals[-1])

        for i in range(used_backbone_levels - 1, 0, -1):
            fpn_outs[i] = nn.functional.interpolate(
                fpn_outs[i], size=fpn_outs[0].shape[2:], mode="bilinear", align_corners=self.align_corners
            )
        fpn_outs = torch.cat(fpn_outs, dim=1)
        output = self.fpn_bottleneck(fpn_outs)
        output = self.classifier(output)

        return output


class UperNetFCNHead(nn.Module):
    """
    Fully Convolution Networks for Semantic Segmentation. This head is the implementation of
    [FCNNet](https://huggingface.co/papers/1411.4038>).

    Args:
        config:
            Configuration.
        in_channels (int):
            Number of input channels.
        kernel_size (int):
            The kernel size for convs in the head. Default: 3.
        dilation (int):
            The dilation rate for convs in the head. Default: 1.
    """

    def __init__(
        self, config, in_channels, in_index: int = 2, kernel_size: int = 3, dilation: int | tuple[int, int] = 1
    ) -> None:
        super().__init__()

        self.config = config
        self.in_channels = (
            in_channels[in_index] if config.auxiliary_in_channels is None else config.auxiliary_in_channels
        )
        self.channels = config.auxiliary_channels
        self.num_convs = config.auxiliary_num_convs
        self.concat_input = config.auxiliary_concat_input
        self.in_index = in_index

        conv_padding = (kernel_size // 2) * dilation
        convs = []
        convs.append(
            UperNetConvModule(
                self.in_channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation
            )
        )
        for i in range(self.num_convs - 1):
            convs.append(
                UperNetConvModule(
                    self.channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation
                )
            )
        if self.num_convs == 0:
            self.convs = nn.Identity()
        else:
            self.convs = nn.Sequential(*convs)
        if self.concat_input:
            self.conv_cat = UperNetConvModule(
                self.in_channels + self.channels, self.channels, kernel_size=kernel_size, padding=kernel_size // 2
            )

        self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1)

    def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
        # just take the relevant feature maps
        hidden_states = encoder_hidden_states[self.in_index]
        output = self.convs(hidden_states)
        if self.concat_input:
            output = self.conv_cat(torch.cat([hidden_states, output], dim=1))
        output = self.classifier(output)
        return output


@auto_docstring
class UperNetPreTrainedModel(PreTrainedModel):
    config: UperNetConfig
    main_input_name = "pixel_values"
    input_modalities = ("image",)
    _no_split_modules = []


@auto_docstring(
    custom_intro="""
    UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.
    """
)
class UperNetForSemanticSegmentation(UperNetPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.backbone = load_backbone(config)

        # Semantic segmentation head(s)
        self.decode_head = UperNetHead(config, in_channels=self.backbone.channels)
        self.auxiliary_head = (
            UperNetFCNHead(config, in_channels=self.backbone.channels) if config.use_auxiliary_head else None
        )

        # Initialize weights and apply final processing
        self.post_init()

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.Tensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        labels: torch.Tensor | None = None,
        return_dict: bool | None = None,
        **kwargs,
    ) -> tuple | SemanticSegmenterOutput:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).

        Examples:
        ```python
        >>> from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
        >>> from PIL import Image
        >>> from huggingface_hub import hf_hub_download

        >>> image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny")
        >>> model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny")

        >>> filepath = hf_hub_download(
        ...     repo_id="hf-internal-testing/fixtures_ade20k", filename="ADE_val_00000001.jpg", repo_type="dataset"
        ... )
        >>> image = Image.open(filepath).convert("RGB")

        >>> inputs = image_processor(images=image, return_tensors="pt")

        >>> outputs = model(**inputs)

        >>> logits = outputs.logits  # shape (batch_size, num_labels, height, width)
        >>> list(logits.shape)
        [1, 150, 512, 512]
        ```"""
        if labels is not None and self.config.num_labels == 1:
            raise ValueError("The number of labels should be greater than one")

        return_dict = return_dict if return_dict is not None else self.config.return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

        outputs = self.backbone.forward_with_filtered_kwargs(
            pixel_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions
        )
        features = outputs.feature_maps

        logits = self.decode_head(features)
        logits = nn.functional.interpolate(logits, size=pixel_values.shape[2:], mode="bilinear", align_corners=False)

        auxiliary_logits = None
        if self.auxiliary_head is not None:
            auxiliary_logits = self.auxiliary_head(features)
            auxiliary_logits = nn.functional.interpolate(
                auxiliary_logits, size=pixel_values.shape[2:], mode="bilinear", align_corners=False
            )

        loss = None
        if labels is not None:
            # compute weighted loss
            loss_fct = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index)
            loss = loss_fct(logits, labels)
            if auxiliary_logits is not None:
                auxiliary_loss = loss_fct(auxiliary_logits, labels)
                loss += self.config.auxiliary_loss_weight * auxiliary_loss

        if not return_dict:
            if output_hidden_states:
                output = (logits,) + outputs[1:]
            else:
                output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SemanticSegmenterOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


__all__ = ["UperNetForSemanticSegmentation", "UperNetPreTrainedModel"]