wehub-resource-sync
1337 lines
54 KiB
Python
1337 lines
54 KiB
Python
# coding=utf-8
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2022 Intel Labs, OpenMMLab and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Paddle DPT (Dense Prediction Transformers) model.
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This implementation is heavily inspired by OpenMMLab's implementation, found here:
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https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/decode_heads/dpt_head.py.
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"""
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import collections.abc
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import math
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from dataclasses import dataclass
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from functools import partial
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from typing import List, Optional, Tuple, Union
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddle.distributed.fleet.utils import recompute
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from paddle.nn import CrossEntropyLoss
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from ...utils.initializer import normal_, ones_, zeros_
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from ..activations import ACT2FN
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from ..bit.configuration import BitConfig
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from ..bit.modeling import BitBackbone
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from ..model_outputs import (
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BaseModelOutput,
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DepthEstimatorOutput,
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ModelOutput,
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SemanticSegmenterOutput,
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)
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from ..model_utils import PretrainedModel
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from .configuration import DPTConfig
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__all__ = [
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"DPTPretrainedModel",
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"DPTModel",
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"DPTForDepthEstimation",
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"DPTForSemanticSegmentation",
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]
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@dataclass
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class BaseModelOutputWithIntermediateActivations(ModelOutput):
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"""
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Base class for model's outputs that also contains intermediate activations that can be used at later stages. Useful
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in the context of Vision models.:
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Args:
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last_hidden_state (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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intermediate_activations (`tuple(paddle.Tensor)`, *optional*):
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Intermediate activations that can be used to compute hidden states of the model at various layers.
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"""
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last_hidden_states: paddle.Tensor = None
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intermediate_activations: Optional[Tuple[paddle.Tensor]] = None
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@dataclass
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class BaseModelOutputWithPoolingAndIntermediateActivations(ModelOutput):
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"""
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Base class for model's outputs that also contains a pooling of the last hidden states as well as intermediate
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activations that can be used by the model at later stages.
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Args:
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last_hidden_state (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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pooler_output (`paddle.Tensor` of shape `(batch_size, hidden_size)`):
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Last layer hidden-state of the first token of the sequence (classification token) after further processing
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through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
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the classification token after processing through a linear layer and a tanh activation function. The linear
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layer weights are trained from the next sentence prediction (classification) objective during pretraining.
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hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `paddle.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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intermediate_activations (`tuple(paddle.Tensor)`, *optional*):
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Intermediate activations that can be used to compute hidden states of the model at various layers.
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"""
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last_hidden_state: paddle.Tensor = None
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pooler_output: paddle.Tensor = None
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hidden_states: Optional[Tuple[paddle.Tensor]] = None
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attentions: Optional[Tuple[paddle.Tensor]] = None
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intermediate_activations: Optional[Tuple[paddle.Tensor]] = None
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class DPTViTHybridEmbeddings(nn.Layer):
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"""
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This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
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`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
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Transformer.
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"""
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def __init__(self, config, feature_size=None):
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super().__init__()
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image_size, patch_size = config.image_size, config.patch_size
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num_channels, hidden_size = config.num_channels, config.hidden_size
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image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
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patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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if isinstance(config.backbone_config, BitConfig):
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self.backbone = BitBackbone(config.backbone_config)
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else:
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raise NotImplementedError
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feature_dim = self.backbone.channels[-1]
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if len(config.backbone_config.out_features) != 3:
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raise ValueError(
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f"Expected backbone to have 3 output features, got {len(config.backbone_config.out_features)}"
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)
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self.residual_feature_map_index = [0, 1] # Always take the output of the first and second backbone stage
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if feature_size is None:
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feat_map_shape = config.backbone_featmap_shape
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feature_size = feat_map_shape[-2:]
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feature_dim = feat_map_shape[1]
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else:
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feature_size = (
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feature_size if isinstance(feature_size, collections.abc.Iterable) else (feature_size, feature_size)
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)
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feature_dim = self.backbone.channels[-1]
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self.image_size = image_size
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self.patch_size = patch_size[0]
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self.num_channels = num_channels
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self.projection = nn.Conv2D(feature_dim, hidden_size, kernel_size=1)
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self.cls_token = self.create_parameter(
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[1, 1, config.hidden_size],
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dtype=paddle.get_default_dtype(),
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default_initializer=nn.initializer.Constant(0.0),
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)
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self.position_embeddings = self.create_parameter(
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[1, num_patches + 1, config.hidden_size],
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dtype=paddle.get_default_dtype(),
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default_initializer=nn.initializer.Constant(0.0),
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)
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def _resize_pos_embed(self, posemb, grid_size_height, grid_size_width, start_index=1):
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posemb_tok = posemb[:, :start_index]
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posemb_grid = posemb[0, start_index:]
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old_grid_size = int(math.sqrt(len(posemb_grid)))
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posemb_grid = posemb_grid.reshape([1, old_grid_size, old_grid_size, -1]).transpose([0, 3, 1, 2])
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posemb_grid = F.interpolate(posemb_grid, size=(grid_size_height, grid_size_width), mode="bilinear")
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posemb_grid = posemb_grid.transpose([0, 2, 3, 1]).reshape([1, grid_size_height * grid_size_width, -1])
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posemb = paddle.concat([posemb_tok, posemb_grid], axis=1)
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return posemb
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def forward(
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self, pixel_values: paddle.Tensor, interpolate_pos_encoding: bool = False, return_dict: bool = False
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) -> paddle.Tensor:
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batch_size, num_channels, height, width = pixel_values.shape
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if num_channels != self.num_channels:
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raise ValueError(
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"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
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)
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if not interpolate_pos_encoding:
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if height != self.image_size[0] or width != self.image_size[1]:
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raise ValueError(
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f"Input image size ({height}*{width}) doesn't match model"
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f" ({self.image_size[0]}*{self.image_size[1]})."
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)
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position_embeddings = self._resize_pos_embed(
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self.position_embeddings, height // self.patch_size, width // self.patch_size
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)
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backbone_output = self.backbone(pixel_values)
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features = backbone_output.feature_maps[-1]
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# Retrieve also the intermediate activations to use them at later stages
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output_hidden_states = [backbone_output.feature_maps[index] for index in self.residual_feature_map_index]
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embeddings = self.projection(features).flatten(2).transpose([0, 2, 1])
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cls_tokens = self.cls_token.expand([batch_size, -1, -1])
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embeddings = paddle.concat((cls_tokens, embeddings), axis=1)
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# add positional encoding to each token
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embeddings = embeddings + position_embeddings
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if not return_dict:
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return (embeddings, output_hidden_states)
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# Return hidden states and intermediate activations
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return BaseModelOutputWithIntermediateActivations(
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last_hidden_states=embeddings,
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intermediate_activations=output_hidden_states,
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)
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class DPTViTEmbeddings(nn.Layer):
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"""
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Construct the CLS token, position and patch embeddings.
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"""
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def __init__(self, config):
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super().__init__()
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self.cls_token = self.create_parameter(
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[1, 1, config.hidden_size],
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dtype=paddle.get_default_dtype(),
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default_initializer=nn.initializer.Constant(0.0),
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)
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self.patch_embeddings = DPTViTPatchEmbeddings(config)
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num_patches = self.patch_embeddings.num_patches
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self.position_embeddings = self.create_parameter(
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[1, num_patches + 1, config.hidden_size],
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dtype=paddle.get_default_dtype(),
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default_initializer=nn.initializer.Constant(0.0),
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)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.config = config
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def _resize_pos_embed(self, posemb, grid_size_height, grid_size_width, start_index=1):
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posemb_tok = posemb[:, :start_index]
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posemb_grid = posemb[0, start_index:]
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old_grid_size = int(math.sqrt(len(posemb_grid)))
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posemb_grid = posemb_grid.reshape([1, old_grid_size, old_grid_size, -1]).transpose([0, 3, 1, 2])
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posemb_grid = F.interpolate(posemb_grid, size=(grid_size_height, grid_size_width), mode="bilinear")
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posemb_grid = posemb_grid.transpose([0, 2, 3, 1]).reshape([1, grid_size_height * grid_size_width, -1])
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posemb = paddle.concat([posemb_tok, posemb_grid], axis=1)
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return posemb
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def forward(self, pixel_values, return_dict=False):
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batch_size, num_channels, height, width = pixel_values.shape
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# possibly interpolate position encodings to handle varying image sizes
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patch_size = self.config.patch_size
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position_embeddings = self._resize_pos_embed(
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self.position_embeddings, height // patch_size, width // patch_size
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)
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embeddings = self.patch_embeddings(pixel_values)
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batch_size = embeddings.shape[0]
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# add the [CLS] token to the embedded patch tokens
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cls_tokens = self.cls_token.expand([batch_size, -1, -1])
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embeddings = paddle.concat((cls_tokens, embeddings), axis=1)
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# add positional encoding to each token
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embeddings = embeddings + position_embeddings
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embeddings = self.dropout(embeddings)
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if not return_dict:
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return (embeddings,)
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return BaseModelOutputWithIntermediateActivations(last_hidden_states=embeddings)
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class DPTViTPatchEmbeddings(nn.Layer):
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"""
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Image to Patch Embedding.
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"""
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def __init__(self, config):
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super().__init__()
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image_size, patch_size = config.image_size, config.patch_size
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num_channels, hidden_size = config.num_channels, config.hidden_size
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image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
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patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_patches = num_patches
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self.projection = nn.Conv2D(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
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def forward(self, pixel_values):
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batch_size, num_channels, height, width = pixel_values.shape
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if num_channels != self.num_channels:
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raise ValueError(
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"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
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)
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embeddings = self.projection(pixel_values).flatten(2).transpose([0, 2, 1])
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return embeddings
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class DPTViTSelfAttention(nn.Layer):
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def __init__(self, config: DPTConfig) -> None:
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
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f"heads {config.num_attention_heads}."
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.scale = math.sqrt(self.attention_head_size)
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self.query = nn.Linear(config.hidden_size, self.all_head_size, bias_attr=config.qkv_bias)
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self.key = nn.Linear(config.hidden_size, self.all_head_size, bias_attr=config.qkv_bias)
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self.value = nn.Linear(config.hidden_size, self.all_head_size, bias_attr=config.qkv_bias)
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self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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def transpose_for_scores(self, x: paddle.Tensor) -> paddle.Tensor:
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new_x_shape = x.shape[:-1] + [self.num_attention_heads, self.attention_head_size]
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x = x.reshape(new_x_shape)
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return x.transpose([0, 2, 1, 3])
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def forward(
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self, hidden_states, output_attentions: bool = False
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) -> Union[Tuple[paddle.Tensor, paddle.Tensor], Tuple[paddle.Tensor]]:
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mixed_query_layer = self.query(hidden_states)
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(mixed_query_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = paddle.matmul(query_layer, key_layer, transpose_y=True)
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attention_scores = attention_scores / self.scale
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# Normalize the attention scores to probabilities.
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attention_probs = F.softmax(attention_scores, axis=-1)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.dropout(attention_probs)
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context_layer = paddle.matmul(attention_probs, value_layer)
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context_layer = context_layer.transpose([0, 2, 1, 3])
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new_context_layer_shape = context_layer.shape[:-2] + [
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self.all_head_size,
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]
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context_layer = context_layer.reshape(new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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return outputs
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class DPTViTSelfOutput(nn.Layer):
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"""
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The residual connection is defined in DPTLayer instead of here (as is the case with other models), due to the
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layernorm applied before each block.
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"""
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def __init__(self, config: DPTConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: paddle.Tensor, input_tensor: paddle.Tensor) -> paddle.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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class DPTViTAttention(nn.Layer):
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def __init__(self, config: DPTConfig) -> None:
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super().__init__()
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self.attention = DPTViTSelfAttention(config)
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self.output = DPTViTSelfOutput(config)
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def forward(
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self,
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hidden_states: paddle.Tensor,
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output_attentions: bool = False,
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) -> Union[Tuple[paddle.Tensor, paddle.Tensor], Tuple[paddle.Tensor]]:
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self_outputs = self.attention(hidden_states, output_attentions)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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class DPTViTIntermediate(nn.Layer):
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def __init__(self, config: DPTConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states: paddle.Tensor) -> paddle.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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class DPTViTOutput(nn.Layer):
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def __init__(self, config: DPTConfig) -> None:
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super().__init__()
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self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: paddle.Tensor, input_tensor: paddle.Tensor) -> paddle.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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hidden_states = hidden_states + input_tensor
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return hidden_states
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class DPTViTLayer(nn.Layer):
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"""This corresponds to the Block class in the timm implementation."""
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def __init__(self, config: DPTConfig) -> None:
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super().__init__()
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self.chunk_size_feed_forward = config.chunk_size_feed_forward
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self.seq_len_dim = 1
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self.attention = DPTViTAttention(config)
|
|
self.intermediate = DPTViTIntermediate(config)
|
|
self.output = DPTViTOutput(config)
|
|
self.layernorm_before = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
|
|
self.layernorm_after = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: paddle.Tensor,
|
|
output_attentions: bool = False,
|
|
) -> Union[Tuple[paddle.Tensor, paddle.Tensor], Tuple[paddle.Tensor]]:
|
|
self_attention_outputs = self.attention(
|
|
self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention
|
|
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
|
|
hidden_states = attention_output + hidden_states
|
|
|
|
# in ViT, layernorm is also applied after self-attention
|
|
layer_output = self.layernorm_after(hidden_states)
|
|
layer_output = self.intermediate(layer_output)
|
|
|
|
# second residual connection is done here
|
|
layer_output = self.output(layer_output, hidden_states)
|
|
|
|
outputs = (layer_output,) + outputs
|
|
|
|
return outputs
|
|
|
|
|
|
class DPTViTEncoder(nn.Layer):
|
|
def __init__(self, config: DPTConfig) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.LayerList([DPTViTLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: paddle.Tensor,
|
|
output_attentions: bool = False,
|
|
output_hidden_states: bool = False,
|
|
return_dict: bool = True,
|
|
) -> Union[tuple, BaseModelOutput]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attentions = () if output_attentions else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
return module(*inputs, output_attentions)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = recompute(
|
|
create_custom_forward(layer_module),
|
|
hidden_states,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(hidden_states, output_attentions)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
class DPTReassembleStage(nn.Layer):
|
|
"""
|
|
This class reassembles the hidden states of the backbone into image-like feature representations at various
|
|
resolutions.
|
|
This happens in 3 stages:
|
|
1. Map the N + 1 tokens to a set of N tokens, by taking into account the readout ([CLS]) token according to
|
|
`config.readout_type`.
|
|
2. Project the channel dimension of the hidden states according to `config.neck_hidden_sizes`.
|
|
3. Resizing the spatial dimensions (height, width).
|
|
Args:
|
|
config (`[DPTConfig]`):
|
|
Model configuration class defining the model architecture.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
self.layers = nn.LayerList()
|
|
if config.is_hybrid:
|
|
self._init_reassemble_dpt_hybrid(config)
|
|
else:
|
|
self._init_reassemble_dpt(config)
|
|
|
|
self.neck_ignore_stages = config.neck_ignore_stages
|
|
|
|
def _init_reassemble_dpt_hybrid(self, config):
|
|
r""" "
|
|
For DPT-Hybrid the first 2 reassemble layers are set to `nn.Identity()`, please check the official
|
|
implementation: https://github.com/isl-org/DPT/blob/f43ef9e08d70a752195028a51be5e1aff227b913/dpt/vit.py#L438
|
|
for more details.
|
|
"""
|
|
for i, factor in zip(range(len(config.neck_hidden_sizes)), config.reassemble_factors):
|
|
if i <= 1:
|
|
self.layers.append(nn.Identity())
|
|
elif i > 1:
|
|
self.layers.append(DPTReassembleLayer(config, channels=config.neck_hidden_sizes[i], factor=factor))
|
|
|
|
if config.readout_type != "project":
|
|
raise ValueError(f"Readout type {config.readout_type} is not supported for DPT-Hybrid.")
|
|
|
|
# When using DPT-Hybrid the readout type is set to "project". The sanity check is done on the config file
|
|
self.readout_projects = nn.LayerList()
|
|
for i in range(len(config.neck_hidden_sizes)):
|
|
if i <= 1:
|
|
self.readout_projects.append(nn.Sequential(nn.Identity()))
|
|
elif i > 1:
|
|
self.readout_projects.append(
|
|
nn.Sequential(nn.Linear(2 * config.hidden_size, config.hidden_size), ACT2FN[config.hidden_act])
|
|
)
|
|
|
|
def _init_reassemble_dpt(self, config):
|
|
for i, factor in zip(range(len(config.neck_hidden_sizes)), config.reassemble_factors):
|
|
self.layers.append(DPTReassembleLayer(config, channels=config.neck_hidden_sizes[i], factor=factor))
|
|
|
|
if config.readout_type == "project":
|
|
self.readout_projects = nn.LayerList()
|
|
for _ in range(len(config.neck_hidden_sizes)):
|
|
self.readout_projects.append(
|
|
nn.Sequential(nn.Linear(2 * config.hidden_size, config.hidden_size), ACT2FN[config.hidden_act])
|
|
)
|
|
|
|
def forward(self, hidden_states: List[paddle.Tensor]) -> List[paddle.Tensor]:
|
|
"""
|
|
Args:
|
|
hidden_states (`List[paddle.Tensor]`, each of shape `(batch_size, sequence_length + 1, hidden_size)`):
|
|
List of hidden states from the backbone.
|
|
"""
|
|
out = []
|
|
|
|
for i, hidden_state in enumerate(hidden_states):
|
|
if i not in self.neck_ignore_stages:
|
|
# reshape to (B, C, H, W)
|
|
hidden_state, cls_token = hidden_state[:, 1:], hidden_state[:, 0]
|
|
batch_size, sequence_length, num_channels = hidden_state.shape
|
|
size = int(math.sqrt(sequence_length))
|
|
hidden_state = hidden_state.reshape([batch_size, size, size, num_channels])
|
|
hidden_state = hidden_state.transpose([0, 3, 1, 2])
|
|
|
|
feature_shape = hidden_state.shape
|
|
if self.config.readout_type == "project":
|
|
# reshape to (B, H*W, C)
|
|
hidden_state = hidden_state.flatten(2).transpose([0, 2, 1])
|
|
readout = cls_token.unsqueeze(1).expand_as(hidden_state)
|
|
# concatenate the readout token to the hidden states and project
|
|
hidden_state = self.readout_projects[i](paddle.concat((hidden_state, readout), axis=-1))
|
|
# reshape back to (B, C, H, W)
|
|
hidden_state = hidden_state.transpose([0, 2, 1]).reshape(feature_shape)
|
|
elif self.config.readout_type == "add":
|
|
hidden_state = hidden_state.flatten(2) + cls_token.unsqueeze(-1)
|
|
hidden_state = hidden_state.reshape(feature_shape)
|
|
hidden_state = self.layers[i](hidden_state)
|
|
out.append(hidden_state)
|
|
|
|
return out
|
|
|
|
|
|
class DPTReassembleLayer(nn.Layer):
|
|
def __init__(self, config, channels, factor):
|
|
super().__init__()
|
|
# projection
|
|
self.projection = nn.Conv2D(in_channels=config.hidden_size, out_channels=channels, kernel_size=1)
|
|
|
|
# up/down sampling depending on factor
|
|
if factor > 1:
|
|
self.resize = nn.Conv2DTranspose(channels, channels, kernel_size=factor, stride=factor, padding=0)
|
|
elif factor == 1:
|
|
self.resize = nn.Identity()
|
|
elif factor < 1:
|
|
# so should downsample
|
|
self.resize = nn.Conv2D(channels, channels, kernel_size=3, stride=int(1 / factor), padding=1)
|
|
|
|
def forward(self, hidden_state):
|
|
hidden_state = self.projection(hidden_state)
|
|
hidden_state = self.resize(hidden_state)
|
|
return hidden_state
|
|
|
|
|
|
class DPTFeatureFusionStage(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.layers = nn.LayerList()
|
|
for _ in range(len(config.neck_hidden_sizes)):
|
|
self.layers.append(DPTFeatureFusionLayer(config))
|
|
|
|
def forward(self, hidden_states):
|
|
# reversing the hidden_states, we start from the last
|
|
hidden_states = hidden_states[::-1]
|
|
|
|
fused_hidden_states = []
|
|
# first layer only uses the last hidden_state
|
|
fused_hidden_state = self.layers[0](hidden_states[0])
|
|
fused_hidden_states.append(fused_hidden_state)
|
|
# looping from the last layer to the second
|
|
for hidden_state, layer in zip(hidden_states[1:], self.layers[1:]):
|
|
fused_hidden_state = layer(fused_hidden_state, hidden_state)
|
|
fused_hidden_states.append(fused_hidden_state)
|
|
|
|
return fused_hidden_states
|
|
|
|
|
|
class DPTPreActResidualLayer(nn.Layer):
|
|
"""
|
|
ResidualConvUnit, pre-activate residual unit.
|
|
Args:
|
|
config (`[DPTConfig]`):
|
|
Model configuration class defining the model architecture.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.use_batch_norm = config.use_batch_norm_in_fusion_residual
|
|
self.activation1 = ACT2FN["relu"]
|
|
self.convolution1 = nn.Conv2D(
|
|
config.fusion_hidden_size,
|
|
config.fusion_hidden_size,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
bias_attr=not self.use_batch_norm,
|
|
)
|
|
|
|
self.activation2 = ACT2FN["relu"]
|
|
self.convolution2 = nn.Conv2D(
|
|
config.fusion_hidden_size,
|
|
config.fusion_hidden_size,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
bias_attr=not self.use_batch_norm,
|
|
)
|
|
|
|
if self.use_batch_norm:
|
|
self.batch_norm1 = nn.BatchNorm2D(config.fusion_hidden_size)
|
|
self.batch_norm2 = nn.BatchNorm2D(config.fusion_hidden_size)
|
|
|
|
def forward(self, hidden_state: paddle.Tensor) -> paddle.Tensor:
|
|
residual = hidden_state
|
|
hidden_state = self.activation1(hidden_state)
|
|
|
|
hidden_state = self.convolution1(hidden_state)
|
|
|
|
if self.use_batch_norm:
|
|
hidden_state = self.batch_norm1(hidden_state)
|
|
|
|
hidden_state = self.activation2(hidden_state)
|
|
hidden_state = self.convolution2(hidden_state)
|
|
|
|
if self.use_batch_norm:
|
|
hidden_state = self.batch_norm2(hidden_state)
|
|
|
|
return hidden_state + residual
|
|
|
|
|
|
class DPTFeatureFusionLayer(nn.Layer):
|
|
"""Feature fusion layer, merges feature maps from different stages.
|
|
Args:
|
|
config (`[DPTConfig]`):
|
|
Model configuration class defining the model architecture.
|
|
align_corners (`bool`, *optional*, defaults to `True`):
|
|
The align_corner setting for bilinear upsample.
|
|
"""
|
|
|
|
def __init__(self, config, align_corners=True):
|
|
super().__init__()
|
|
|
|
self.align_corners = align_corners
|
|
|
|
self.projection = nn.Conv2D(config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=1)
|
|
|
|
self.residual_layer1 = DPTPreActResidualLayer(config)
|
|
self.residual_layer2 = DPTPreActResidualLayer(config)
|
|
|
|
def forward(self, hidden_state, residual=None):
|
|
if residual is not None:
|
|
if hidden_state.shape != residual.shape:
|
|
residual = F.interpolate(
|
|
residual, size=(hidden_state.shape[2], hidden_state.shape[3]), mode="bilinear", align_corners=False
|
|
)
|
|
hidden_state = hidden_state + self.residual_layer1(residual)
|
|
|
|
hidden_state = self.residual_layer2(hidden_state)
|
|
hidden_state = F.interpolate(hidden_state, scale_factor=2, mode="bilinear", align_corners=self.align_corners)
|
|
hidden_state = self.projection(hidden_state)
|
|
|
|
return hidden_state
|
|
|
|
|
|
class DPTPretrainedModel(PretrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = DPTConfig
|
|
base_model_prefix = "dpt"
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
if isinstance(module, (nn.Linear, nn.Conv2D, nn.Conv2DTranspose)):
|
|
# Slightly different from the TF version which uses truncated_normal for initialization
|
|
# cf https://github.com/pytorch/pytorch/pull/5617
|
|
normal_(module.weight, mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
zeros_(module.bias)
|
|
elif isinstance(module, nn.LayerNorm):
|
|
zeros_(module.bias)
|
|
ones_(module.weight)
|
|
|
|
def _set_gradient_checkpointing(self, module, value=False):
|
|
if isinstance(module, DPTViTEncoder):
|
|
module.gradient_checkpointing = value
|
|
|
|
def gradient_checkpointing_enable(self):
|
|
"""
|
|
Activates gradient checkpointing for the current model.
|
|
|
|
Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
|
|
activations".
|
|
"""
|
|
if not self.supports_gradient_checkpointing:
|
|
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
|
|
self.apply(partial(self._set_gradient_checkpointing, value=True))
|
|
|
|
def gradient_checkpointing_disable(self):
|
|
"""
|
|
Deactivates gradient checkpointing for the current model.
|
|
|
|
Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
|
|
activations".
|
|
"""
|
|
if self.supports_gradient_checkpointing:
|
|
self.apply(partial(self._set_gradient_checkpointing, value=False))
|
|
|
|
|
|
class DPTModel(DPTPretrainedModel):
|
|
"""
|
|
The bare DPT Model transformer outputting raw hidden-states without any specific head on top.
|
|
|
|
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
|
|
Refer to the superclass documentation for the generic methods.
|
|
|
|
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
|
|
/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
|
|
and refer to the Paddle documentation for all matter related to general usage and behavior.
|
|
|
|
Args:
|
|
config (:class:`DPTConfig`):
|
|
An instance of DPTConfig used to construct DPTModel.
|
|
add_pooling_layer (`bool`, *optional*, defaults to True):
|
|
Whether to add a pooler layer.
|
|
"""
|
|
|
|
def __init__(self, config, add_pooling_layer=True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
# vit encoder
|
|
if config.is_hybrid:
|
|
self.embeddings = DPTViTHybridEmbeddings(config)
|
|
else:
|
|
self.embeddings = DPTViTEmbeddings(config)
|
|
self.encoder = DPTViTEncoder(config)
|
|
|
|
self.layernorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
|
|
self.pooler = DPTViTPooler(config) if add_pooling_layer else None
|
|
|
|
def get_input_embeddings(self):
|
|
if self.config.is_hybrid:
|
|
return self.embeddings
|
|
else:
|
|
return self.embeddings.patch_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
pixel_values: paddle.Tensor,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPoolingAndIntermediateActivations]:
|
|
"""
|
|
The DPTModel forward method, overrides the `__call__()` special method.
|
|
|
|
Args:
|
|
pixel_values (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`DPTImageProcessor`]. See [`DPTImageProcessor.__call__`]
|
|
for details.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`BaseModelOutputWithPoolingAndIntermediateActivations` object. If `False`, the output
|
|
will be a tuple of tensors. Defaults to `None`.
|
|
|
|
Returns:
|
|
An instance of :class:`BaseModelOutputWithPoolingAndIntermediateActivations` if
|
|
`return_dict=True`. Otherwise it returns a tuple of tensors corresponding
|
|
to ordered and not None (depending on the input arguments) fields of
|
|
:class:`BaseModelOutputWithPoolingAndIntermediateActivations`.
|
|
|
|
"""
|
|
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
|
|
|
|
embedding_output = self.embeddings(pixel_values, return_dict=return_dict)
|
|
|
|
embedding_last_hidden_states = embedding_output[0] if not return_dict else embedding_output.last_hidden_states
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_last_hidden_states,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = encoder_outputs[0]
|
|
sequence_output = self.layernorm(sequence_output)
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
|
|
return head_outputs + encoder_outputs[1:] + embedding_output[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndIntermediateActivations(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
intermediate_activations=embedding_output.intermediate_activations,
|
|
)
|
|
|
|
|
|
class DPTViTPooler(nn.Layer):
|
|
def __init__(self, config: DPTConfig):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = nn.Tanh()
|
|
|
|
def forward(self, hidden_states):
|
|
# We "pool" the model by simply taking the hidden state corresponding
|
|
# to the first token.
|
|
first_token_tensor = hidden_states[:, 0]
|
|
pooled_output = self.dense(first_token_tensor)
|
|
pooled_output = self.activation(pooled_output)
|
|
return pooled_output
|
|
|
|
|
|
class DPTNeck(nn.Layer):
|
|
"""
|
|
DPTNeck. A neck is a module that is normally used between the backbone and the head. It takes a list of tensors as
|
|
input and produces another list of tensors as output. For DPT, it includes 2 stages:
|
|
* DPTReassembleStage
|
|
* DPTFeatureFusionStage.
|
|
Args:
|
|
config (dict): config dict.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
# postprocessing
|
|
self.reassemble_stage = DPTReassembleStage(config)
|
|
self.convs = nn.LayerList()
|
|
for channel in config.neck_hidden_sizes:
|
|
self.convs.append(nn.Conv2D(channel, config.fusion_hidden_size, kernel_size=3, padding=1, bias_attr=False))
|
|
|
|
# fusion
|
|
self.fusion_stage = DPTFeatureFusionStage(config)
|
|
|
|
def forward(self, hidden_states: List[paddle.Tensor]) -> List[paddle.Tensor]:
|
|
if not isinstance(hidden_states, list):
|
|
raise ValueError("hidden_states should be a list of tensors")
|
|
|
|
if len(hidden_states) != len(self.config.neck_hidden_sizes):
|
|
raise ValueError("The number of hidden states should be equal to the number of neck hidden sizes.")
|
|
|
|
# postprocess hidden states
|
|
features = self.reassemble_stage(hidden_states)
|
|
|
|
features = [self.convs[i](feature) for i, feature in enumerate(features)]
|
|
|
|
# fusion blocks
|
|
output = self.fusion_stage(features)
|
|
|
|
return output
|
|
|
|
|
|
class DPTDepthEstimationHead(nn.Layer):
|
|
"""
|
|
Output head head consisting of 3 convolutional layers. It progressively halves the feature dimension and upsamples
|
|
the predictions to the input resolution after the first convolutional layer (details can be found in the paper's
|
|
supplementary material).
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
|
|
features = config.fusion_hidden_size
|
|
self.head = nn.Sequential(
|
|
nn.Conv2D(features, features // 2, kernel_size=3, stride=1, padding=1),
|
|
nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True),
|
|
nn.Conv2D(features // 2, 32, kernel_size=3, stride=1, padding=1),
|
|
ACT2FN["relu"],
|
|
nn.Conv2D(32, 1, kernel_size=1, stride=1, padding=0),
|
|
ACT2FN["relu"],
|
|
)
|
|
|
|
def forward(self, hidden_states: List[paddle.Tensor]) -> paddle.Tensor:
|
|
# use last features
|
|
hidden_states = hidden_states[self.config.head_in_index]
|
|
|
|
predicted_depth = self.head(hidden_states)
|
|
|
|
predicted_depth = predicted_depth.squeeze(axis=1)
|
|
|
|
return predicted_depth
|
|
|
|
|
|
class DPTForDepthEstimation(DPTPretrainedModel):
|
|
"""
|
|
DPT Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2.
|
|
|
|
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
|
|
Refer to the superclass documentation for the generic methods.
|
|
|
|
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
|
|
/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
|
|
and refer to the Paddle documentation for all matter related to general usage and behavior.
|
|
|
|
Args:
|
|
config (:class:`DPTConfig`):
|
|
An instance of DPTConfig used to construct DPTForDepthEstimation.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.dpt = DPTModel(config, add_pooling_layer=False)
|
|
|
|
# Neck
|
|
self.neck = DPTNeck(config)
|
|
|
|
# Depth estimation head
|
|
self.head = DPTDepthEstimationHead(config)
|
|
|
|
def forward(
|
|
self,
|
|
pixel_values: paddle.Tensor,
|
|
labels: Optional[paddle.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[paddle.Tensor], DepthEstimatorOutput]:
|
|
r"""
|
|
Args:
|
|
pixel_values (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`DPTImageProcessor`]. See [`DPTImageProcessor.__call__`]
|
|
for details.
|
|
labels (`paddle.Tensor` of shape `(batch_size, height, width)`, *optional*):
|
|
Ground truth depth estimation maps for computing the loss.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`DepthEstimatorOutput` object. If `False`, the output
|
|
will be a tuple of tensors. Defaults to `None`.
|
|
|
|
Returns:
|
|
An instance of :class:`DepthEstimatorOutput` if
|
|
`return_dict=True`. Otherwise it returns a tuple of tensors corresponding
|
|
to ordered and not None (depending on the input arguments) fields of
|
|
:class:`DepthEstimatorOutput`.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from paddlenlp.transformers import DPTImageProcessor, DPTForDepthEstimation
|
|
>>> import paddle
|
|
>>> import paddle.nn.functional as F
|
|
>>> 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 = DPTImageProcessor.from_pretrained("Intel/dpt-large")
|
|
>>> model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
|
|
|
|
>>> # prepare image for the model
|
|
>>> inputs = image_processor(images=image, return_tensors="pd")
|
|
|
|
>>> with paddle.no_grad():
|
|
... outputs = model(**inputs)
|
|
... predicted_depth = outputs.predicted_depth
|
|
|
|
>>> # interpolate to original size
|
|
>>> prediction = F.interpolate(
|
|
... predicted_depth.unsqueeze(1),
|
|
... size=image.size[::-1],
|
|
... mode="bicubic",
|
|
... align_corners=False,
|
|
... )
|
|
|
|
>>> # visualize the prediction
|
|
>>> output = prediction.squeeze().cpu().numpy()
|
|
>>> formatted = (output * 255 / np.max(output)).astype("uint8")
|
|
>>> depth = Image.fromarray(formatted)
|
|
```"""
|
|
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.dpt(
|
|
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]
|
|
|
|
# only keep certain features based on config.backbone_out_indices
|
|
# note that the hidden_states also include the initial embeddings
|
|
if not self.config.is_hybrid:
|
|
hidden_states = [
|
|
feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices
|
|
]
|
|
else:
|
|
backbone_hidden_states = list(outputs.intermediate_activations) if return_dict else list(outputs[-1])
|
|
backbone_hidden_states.extend(
|
|
feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices[2:]
|
|
)
|
|
|
|
hidden_states = backbone_hidden_states
|
|
|
|
hidden_states = self.neck(hidden_states)
|
|
|
|
predicted_depth = self.head(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
raise NotImplementedError("Training is not implemented yet")
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
class DPTSemanticSegmentationHead(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
|
|
features = config.fusion_hidden_size
|
|
self.head = nn.Sequential(
|
|
nn.Conv2D(features, features, kernel_size=3, padding=1, bias_attr=False),
|
|
nn.BatchNorm2D(features),
|
|
ACT2FN["relu"],
|
|
nn.Dropout(config.semantic_classifier_dropout),
|
|
nn.Conv2D(features, config.num_labels, kernel_size=1),
|
|
nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True),
|
|
)
|
|
|
|
def forward(self, hidden_states: List[paddle.Tensor]) -> paddle.Tensor:
|
|
# use last features
|
|
hidden_states = hidden_states[self.config.head_in_index]
|
|
|
|
logits = self.head(hidden_states)
|
|
|
|
return logits
|
|
|
|
|
|
class DPTAuxiliaryHead(nn.Layer):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
|
|
features = config.fusion_hidden_size
|
|
self.head = nn.Sequential(
|
|
nn.Conv2D(features, features, kernel_size=3, padding=1, bias_attr=False),
|
|
nn.BatchNorm2D(features),
|
|
ACT2FN["relu"],
|
|
nn.Dropout(0.1, False),
|
|
nn.Conv2D(features, config.num_labels, kernel_size=1),
|
|
)
|
|
|
|
def forward(self, hidden_states):
|
|
logits = self.head(hidden_states)
|
|
|
|
return logits
|
|
|
|
|
|
class DPTForSemanticSegmentation(DPTPretrainedModel):
|
|
"""
|
|
DPT Model with a semantic segmentation head on top e.g. for ADE20k, CityScapes.
|
|
|
|
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
|
|
Refer to the superclass documentation for the generic methods.
|
|
|
|
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
|
|
/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
|
|
and refer to the Paddle documentation for all matter related to general usage and behavior.
|
|
|
|
Args:
|
|
config (:class:`DPTConfig`):
|
|
An instance of DPTConfig used to construct DPTForSemanticSegmentation.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
self.dpt = DPTModel(config, add_pooling_layer=False)
|
|
|
|
# Neck
|
|
self.neck = DPTNeck(config)
|
|
|
|
# Segmentation head(s)
|
|
self.head = DPTSemanticSegmentationHead(config)
|
|
self.auxiliary_head = DPTAuxiliaryHead(config) if config.use_auxiliary_head else None
|
|
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[paddle.Tensor] = None,
|
|
labels: Optional[paddle.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple[paddle.Tensor], SemanticSegmenterOutput]:
|
|
r"""
|
|
Args:
|
|
pixel_values (`paddle.Tensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`DPTImageProcessor`]. See [`DPTImageProcessor.__call__`]
|
|
for details.
|
|
labels (`paddle.Tensor` 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).
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`SemanticSegmenterOutput` object. If `False`, the output
|
|
will be a tuple of tensors. Defaults to `None`.
|
|
|
|
Returns:
|
|
An instance of :class:`SemanticSegmenterOutput` if
|
|
`return_dict=True`. Otherwise it returns a tuple of tensors corresponding
|
|
to ordered and not None (depending on the input arguments) fields of
|
|
:class:`SemanticSegmenterOutput`.
|
|
|
|
Examples:
|
|
```python
|
|
>>> from paddlenlp.transformers import DPTImageProcessor, DPTForSemanticSegmentation
|
|
>>> 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 = DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
|
|
>>> model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade")
|
|
|
|
>>> inputs = image_processor(images=image, return_tensors="pd")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> logits = outputs.logits
|
|
```"""
|
|
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.dpt(
|
|
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]
|
|
|
|
# only keep certain features based on config.backbone_out_indices
|
|
# note that the hidden_states also include the initial embeddings
|
|
if not self.config.is_hybrid:
|
|
hidden_states = [
|
|
feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices
|
|
]
|
|
else:
|
|
backbone_hidden_states = list(outputs.intermediate_activations) if return_dict else list(outputs[-1])
|
|
backbone_hidden_states.extend(
|
|
feature for idx, feature in enumerate(hidden_states[1:]) if idx in self.config.backbone_out_indices[2:]
|
|
)
|
|
|
|
hidden_states = backbone_hidden_states
|
|
|
|
hidden_states = self.neck(hidden_states)
|
|
|
|
logits = self.head(hidden_states)
|
|
|
|
auxiliary_logits = None
|
|
if self.auxiliary_head is not None:
|
|
auxiliary_logits = self.auxiliary_head(hidden_states[-1])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.num_labels == 1:
|
|
raise ValueError("The number of labels should be greater than one")
|
|
else:
|
|
# upsample logits to the images' original size
|
|
upsampled_logits = F.interpolate(logits, size=labels.shape[-2:], mode="bilinear", align_corners=False)
|
|
if auxiliary_logits is not None:
|
|
upsampled_auxiliary_logits = F.interpolate(
|
|
auxiliary_logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
|
|
)
|
|
# compute weighted loss
|
|
loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
|
|
# upsampled_logits and upsampled_auxiliary_logits 's shape [b, num_labels, h, w] -> [b, h, w, num_labels]
|
|
main_loss = loss_fct(upsampled_logits.transpose([0, 2, 3, 1]), labels)
|
|
auxiliary_loss = loss_fct(upsampled_auxiliary_logits.transpose([0, 2, 3, 1]), labels)
|
|
loss = main_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 if output_hidden_states else None,
|
|
attentions=outputs.attentions,
|
|
)
|