AutoAndroidController/py/venv/Lib/site-packages/transformers/models/glpn/modeling_glpn.py

# coding=utf-8
# Copyright 2022 KAIST and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch GLPN model."""

import math
from typing import Optional, Union

import torch
from torch import nn

from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, DepthEstimatorOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import auto_docstring, logging
from .configuration_glpn import GLPNConfig


logger = logging.get_logger(__name__)


# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
    argument.
    """
    if drop_prob == 0.0 or not training:
        return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
    random_tensor.floor_()  # binarize
    output = input.div(keep_prob) * random_tensor
    return output


# Copied from transformers.models.segformer.modeling_segformer.SegformerDropPath
class GLPNDropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob: Optional[float] = None) -> None:
        super().__init__()
        self.drop_prob = drop_prob

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return drop_path(hidden_states, self.drop_prob, self.training)

    def extra_repr(self) -> str:
        return f"p={self.drop_prob}"


# Copied from transformers.models.segformer.modeling_segformer.SegformerOverlapPatchEmbeddings
class GLPNOverlapPatchEmbeddings(nn.Module):
    """Construct the overlapping patch embeddings."""

    def __init__(self, patch_size, stride, num_channels, hidden_size):
        super().__init__()
        self.proj = nn.Conv2d(
            num_channels,
            hidden_size,
            kernel_size=patch_size,
            stride=stride,
            padding=patch_size // 2,
        )

        self.layer_norm = nn.LayerNorm(hidden_size)

    def forward(self, pixel_values):
        embeddings = self.proj(pixel_values)
        _, _, height, width = embeddings.shape
        # (batch_size, num_channels, height, width) -> (batch_size, num_channels, height*width) -> (batch_size, height*width, num_channels)
        # this can be fed to a Transformer layer
        embeddings = embeddings.flatten(2).transpose(1, 2)
        embeddings = self.layer_norm(embeddings)
        return embeddings, height, width


# Copied from transformers.models.segformer.modeling_segformer.SegformerEfficientSelfAttention
class GLPNEfficientSelfAttention(nn.Module):
    """SegFormer's efficient self-attention mechanism. Employs the sequence reduction process introduced in the [PvT
    paper](https://huggingface.co/papers/2102.12122)."""

    def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads

        if self.hidden_size % self.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
                f"heads ({self.num_attention_heads})"
            )

        self.attention_head_size = int(self.hidden_size / self.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(self.hidden_size, self.all_head_size)
        self.key = nn.Linear(self.hidden_size, self.all_head_size)
        self.value = nn.Linear(self.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

        self.sr_ratio = sequence_reduction_ratio
        if sequence_reduction_ratio > 1:
            self.sr = nn.Conv2d(
                hidden_size, hidden_size, kernel_size=sequence_reduction_ratio, stride=sequence_reduction_ratio
            )
            self.layer_norm = nn.LayerNorm(hidden_size)

    def forward(
        self,
        hidden_states,
        height,
        width,
        output_attentions=False,
    ):
        batch_size, seq_length, _ = hidden_states.shape
        query_layer = (
            self.query(hidden_states)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )

        if self.sr_ratio > 1:
            batch_size, seq_len, num_channels = hidden_states.shape
            # Reshape to (batch_size, num_channels, height, width)
            hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
            # Apply sequence reduction
            hidden_states = self.sr(hidden_states)
            # Reshape back to (batch_size, seq_len, num_channels)
            hidden_states = hidden_states.reshape(batch_size, num_channels, -1).permute(0, 2, 1)
            hidden_states = self.layer_norm(hidden_states)

        key_layer = (
            self.key(hidden_states)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )
        value_layer = (
            self.value(hidden_states)
            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
            .transpose(1, 2)
        )

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(new_context_layer_shape)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        return outputs


# Copied from transformers.models.segformer.modeling_segformer.SegformerSelfOutput
class GLPNSelfOutput(nn.Module):
    def __init__(self, config, hidden_size):
        super().__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


# Copied from transformers.models.segformer.modeling_segformer.SegformerAttention with Segformer->GLPN
class GLPNAttention(nn.Module):
    def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio):
        super().__init__()
        self.self = GLPNEfficientSelfAttention(
            config=config,
            hidden_size=hidden_size,
            num_attention_heads=num_attention_heads,
            sequence_reduction_ratio=sequence_reduction_ratio,
        )
        self.output = GLPNSelfOutput(config, hidden_size=hidden_size)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(self, hidden_states, height, width, output_attentions=False):
        self_outputs = self.self(hidden_states, height, width, output_attentions)

        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


# Copied from transformers.models.segformer.modeling_segformer.SegformerDWConv
class GLPNDWConv(nn.Module):
    def __init__(self, dim=768):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def forward(self, hidden_states, height, width):
        batch_size, seq_len, num_channels = hidden_states.shape
        hidden_states = hidden_states.transpose(1, 2).view(batch_size, num_channels, height, width)
        hidden_states = self.dwconv(hidden_states)
        hidden_states = hidden_states.flatten(2).transpose(1, 2)

        return hidden_states


# Copied from transformers.models.segformer.modeling_segformer.SegformerMixFFN with Segformer->GLPN
class GLPNMixFFN(nn.Module):
    def __init__(self, config, in_features, hidden_features=None, out_features=None):
        super().__init__()
        out_features = out_features or in_features
        self.dense1 = nn.Linear(in_features, hidden_features)
        self.dwconv = GLPNDWConv(hidden_features)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act
        self.dense2 = nn.Linear(hidden_features, out_features)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, height, width):
        hidden_states = self.dense1(hidden_states)
        hidden_states = self.dwconv(hidden_states, height, width)
        hidden_states = self.intermediate_act_fn(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.dense2(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


# Copied from transformers.models.segformer.modeling_segformer.SegformerLayer with Segformer->GLPN
class GLPNLayer(nn.Module):
    """This corresponds to the Block class in the original implementation."""

    def __init__(self, config, hidden_size, num_attention_heads, drop_path, sequence_reduction_ratio, mlp_ratio):
        super().__init__()
        self.layer_norm_1 = nn.LayerNorm(hidden_size)
        self.attention = GLPNAttention(
            config,
            hidden_size=hidden_size,
            num_attention_heads=num_attention_heads,
            sequence_reduction_ratio=sequence_reduction_ratio,
        )
        self.drop_path = GLPNDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.layer_norm_2 = nn.LayerNorm(hidden_size)
        mlp_hidden_size = int(hidden_size * mlp_ratio)
        self.mlp = GLPNMixFFN(config, in_features=hidden_size, hidden_features=mlp_hidden_size)

    def forward(self, hidden_states, height, width, output_attentions=False):
        self_attention_outputs = self.attention(
            self.layer_norm_1(hidden_states),  # in GLPN, layernorm is applied before self-attention
            height,
            width,
            output_attentions=output_attentions,
        )

        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        # first residual connection (with stochastic depth)
        attention_output = self.drop_path(attention_output)
        hidden_states = attention_output + hidden_states

        mlp_output = self.mlp(self.layer_norm_2(hidden_states), height, width)

        # second residual connection (with stochastic depth)
        mlp_output = self.drop_path(mlp_output)
        layer_output = mlp_output + hidden_states

        outputs = (layer_output,) + outputs

        return outputs


class GLPNEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config

        # stochastic depth decay rule
        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths), device="cpu")]

        # patch embeddings
        embeddings = []
        for i in range(config.num_encoder_blocks):
            embeddings.append(
                GLPNOverlapPatchEmbeddings(
                    patch_size=config.patch_sizes[i],
                    stride=config.strides[i],
                    num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1],
                    hidden_size=config.hidden_sizes[i],
                )
            )
        self.patch_embeddings = nn.ModuleList(embeddings)

        # Transformer blocks
        blocks = []
        cur = 0
        for i in range(config.num_encoder_blocks):
            # each block consists of layers
            layers = []
            if i != 0:
                cur += config.depths[i - 1]
            for j in range(config.depths[i]):
                layers.append(
                    GLPNLayer(
                        config,
                        hidden_size=config.hidden_sizes[i],
                        num_attention_heads=config.num_attention_heads[i],
                        drop_path=dpr[cur + j],
                        sequence_reduction_ratio=config.sr_ratios[i],
                        mlp_ratio=config.mlp_ratios[i],
                    )
                )
            blocks.append(nn.ModuleList(layers))

        self.block = nn.ModuleList(blocks)

        # Layer norms
        self.layer_norm = nn.ModuleList(
            [nn.LayerNorm(config.hidden_sizes[i]) for i in range(config.num_encoder_blocks)]
        )

    def forward(
        self,
        pixel_values,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None

        batch_size = pixel_values.shape[0]

        hidden_states = pixel_values
        for idx, x in enumerate(zip(self.patch_embeddings, self.block, self.layer_norm)):
            embedding_layer, block_layer, norm_layer = x
            # first, obtain patch embeddings
            hidden_states, height, width = embedding_layer(hidden_states)
            # second, send embeddings through blocks
            for i, blk in enumerate(block_layer):
                layer_outputs = blk(hidden_states, height, width, output_attentions)
                hidden_states = layer_outputs[0]
                if output_attentions:
                    all_self_attentions = all_self_attentions + (layer_outputs[1],)
            # third, apply layer norm
            hidden_states = norm_layer(hidden_states)
            # fourth, optionally reshape back to (batch_size, num_channels, height, width)
            hidden_states = hidden_states.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous()
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


@auto_docstring
class GLPNPreTrainedModel(PreTrainedModel):
    config: GLPNConfig
    base_model_prefix = "glpn"
    main_input_name = "pixel_values"
    _no_split_modules = []

    # Copied from transformers.models.segformer.modeling_segformer.SegformerPreTrainedModel._init_weights
    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d)):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


@auto_docstring
class GLPNModel(GLPNPreTrainedModel):
    # Copied from transformers.models.segformer.modeling_segformer.SegformerModel.__init__ with Segformer->GLPN
    def __init__(self, config):
        super().__init__(config)
        self.config = config

        # hierarchical Transformer encoder
        self.encoder = GLPNEncoder(config)

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

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    @auto_docstring
    # Copied from transformers.models.segformer.modeling_segformer.SegformerModel.forward
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutput]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        encoder_outputs = self.encoder(
            pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]

        if not return_dict:
            return (sequence_output,) + encoder_outputs[1:]

        return BaseModelOutput(
            last_hidden_state=sequence_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class GLPNSelectiveFeatureFusion(nn.Module):
    """
    Selective Feature Fusion module, as explained in the [paper](https://huggingface.co/papers/2201.07436) (section 3.4). This
    module adaptively selects and integrates local and global features by attaining an attention map for each feature.
    """

    def __init__(self, in_channel=64):
        super().__init__()

        self.convolutional_layer1 = nn.Sequential(
            nn.Conv2d(in_channels=int(in_channel * 2), out_channels=in_channel, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(in_channel),
            nn.ReLU(),
        )

        self.convolutional_layer2 = nn.Sequential(
            nn.Conv2d(in_channels=in_channel, out_channels=int(in_channel / 2), kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(int(in_channel / 2)),
            nn.ReLU(),
        )

        self.convolutional_layer3 = nn.Conv2d(
            in_channels=int(in_channel / 2), out_channels=2, kernel_size=3, stride=1, padding=1
        )

        self.sigmoid = nn.Sigmoid()

    def forward(self, local_features, global_features):
        # concatenate features along the channel dimension
        features = torch.cat((local_features, global_features), dim=1)
        # pass through convolutional layers
        features = self.convolutional_layer1(features)
        features = self.convolutional_layer2(features)
        features = self.convolutional_layer3(features)
        # apply sigmoid to get two-channel attention map
        attn = self.sigmoid(features)
        # construct hybrid features by adding element-wise
        hybrid_features = local_features * attn[:, 0, :, :].unsqueeze(1) + global_features * attn[
            :, 1, :, :
        ].unsqueeze(1)

        return hybrid_features


class GLPNDecoderStage(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        should_skip = in_channels == out_channels
        self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1) if not should_skip else nn.Identity()
        self.fusion = GLPNSelectiveFeatureFusion(out_channels)
        self.upsample = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)

    def forward(self, hidden_state, residual=None):
        hidden_state = self.convolution(hidden_state)
        if residual is not None:
            hidden_state = self.fusion(hidden_state, residual)
        hidden_state = self.upsample(hidden_state)

        return hidden_state

        hidden_state = self.upsample(hidden_state)
        return hidden_state


class GLPNDecoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        # we use features from end -> start
        reserved_hidden_sizes = config.hidden_sizes[::-1]
        out_channels = config.decoder_hidden_size

        self.stages = nn.ModuleList(
            [GLPNDecoderStage(hidden_size, out_channels) for hidden_size in reserved_hidden_sizes]
        )
        # don't fuse in first stage
        self.stages[0].fusion = None

        self.final_upsample = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)

    def forward(self, hidden_states: list[torch.Tensor]) -> list[torch.Tensor]:
        stage_hidden_states = []
        stage_hidden_state = None
        for hidden_state, stage in zip(hidden_states[::-1], self.stages):
            stage_hidden_state = stage(hidden_state, stage_hidden_state)
            stage_hidden_states.append(stage_hidden_state)

        stage_hidden_states[-1] = self.final_upsample(stage_hidden_state)

        return stage_hidden_states


class SiLogLoss(nn.Module):
    r"""
    Implements the Scale-invariant log scale loss [Eigen et al., 2014](https://huggingface.co/papers/1406.2283).

    $$L=\frac{1}{n} \sum_{i} d_{i}^{2}-\frac{1}{2 n^{2}}\left(\sum_{i} d_{i}^{2}\right)$$ where $d_{i}=\log y_{i}-\log
    y_{i}^{*}$.

    """

    def __init__(self, lambd=0.5):
        super().__init__()
        self.lambd = lambd

    def forward(self, pred, target):
        valid_mask = (target > 0).detach()
        diff_log = torch.log(target[valid_mask]) - torch.log(pred[valid_mask])
        loss = torch.sqrt(torch.pow(diff_log, 2).mean() - self.lambd * torch.pow(diff_log.mean(), 2))

        return loss


class GLPNDepthEstimationHead(nn.Module):
    def __init__(self, config):
        super().__init__()

        self.config = config

        channels = config.decoder_hidden_size
        self.head = nn.Sequential(
            nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=False),
            nn.Conv2d(channels, 1, kernel_size=3, stride=1, padding=1),
        )

    def forward(self, hidden_states: list[torch.Tensor]) -> torch.Tensor:
        # use last features of the decoder
        hidden_states = hidden_states[self.config.head_in_index]

        hidden_states = self.head(hidden_states)

        predicted_depth = torch.sigmoid(hidden_states) * self.config.max_depth
        predicted_depth = predicted_depth.squeeze(dim=1)

        return predicted_depth


@auto_docstring(
    custom_intro="""
    GLPN Model transformer with a lightweight depth estimation head on top e.g. for KITTI, NYUv2.
    """
)
class GLPNForDepthEstimation(GLPNPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.glpn = GLPNModel(config)
        self.decoder = GLPNDecoder(config)
        self.head = GLPNDepthEstimationHead(config)

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

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        labels: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple[torch.Tensor], DepthEstimatorOutput]:
        r"""
        labels (`torch.FloatTensor` of shape `(batch_size, height, width)`, *optional*):
            Ground truth depth estimation maps for computing the loss.

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, GLPNForDepthEstimation
        >>> import torch
        >>> import numpy as np
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("vinvino02/glpn-kitti")
        >>> model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-kitti")

        >>> # prepare image for the model
        >>> inputs = image_processor(images=image, return_tensors="pt")

        >>> with torch.no_grad():
        ...     outputs = model(**inputs)

        >>> # interpolate to original size
        >>> post_processed_output = image_processor.post_process_depth_estimation(
        ...     outputs,
        ...     target_sizes=[(image.height, image.width)],
        ... )

        >>> # visualize the prediction
        >>> predicted_depth = post_processed_output[0]["predicted_depth"]
        >>> depth = predicted_depth * 255 / predicted_depth.max()
        >>> depth = depth.detach().cpu().numpy()
        >>> depth = Image.fromarray(depth.astype("uint8"))
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        outputs = self.glpn(
            pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=True,  # we need the intermediate hidden states
            return_dict=return_dict,
        )

        hidden_states = outputs.hidden_states if return_dict else outputs[1]

        out = self.decoder(hidden_states)
        predicted_depth = self.head(out)

        loss = None
        if labels is not None:
            loss_fct = SiLogLoss()
            loss = loss_fct(predicted_depth, labels)

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

        return DepthEstimatorOutput(
            loss=loss,
            predicted_depth=predicted_depth,
            hidden_states=outputs.hidden_states if output_hidden_states else None,
            attentions=outputs.attentions,
        )


__all__ = ["GLPNForDepthEstimation", "GLPNLayer", "GLPNModel", "GLPNPreTrainedModel"]