1031 lines
37 KiB
Python
1031 lines
37 KiB
Python
# coding=utf-8
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# Copyright 2023 Apple Inc. 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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#
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# Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE
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""" PyTorch MobileViTV2 model."""
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...modeling_outputs import (
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BaseModelOutputWithNoAttention,
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BaseModelOutputWithPoolingAndNoAttention,
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ImageClassifierOutputWithNoAttention,
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SemanticSegmenterOutput,
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)
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from .configuration_mobilevitv2 import MobileViTV2Config
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "MobileViTV2Config"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "apple/mobilevitv2-1.0-imagenet1k-256"
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_EXPECTED_OUTPUT_SHAPE = [1, 512, 8, 8]
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# Image classification docstring
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_IMAGE_CLASS_CHECKPOINT = "apple/mobilevitv2-1.0-imagenet1k-256"
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_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
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from ..deprecated._archive_maps import MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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# Copied from transformers.models.mobilevit.modeling_mobilevit.make_divisible
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def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
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"""
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Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
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original TensorFlow repo. It can be seen here:
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https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
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"""
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if min_value is None:
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min_value = divisor
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new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_value < 0.9 * value:
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new_value += divisor
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return int(new_value)
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def clip(value: float, min_val: float = float("-inf"), max_val: float = float("inf")) -> float:
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return max(min_val, min(max_val, value))
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# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTConvLayer with MobileViT->MobileViTV2
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class MobileViTV2ConvLayer(nn.Module):
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def __init__(
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self,
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config: MobileViTV2Config,
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in_channels: int,
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out_channels: int,
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kernel_size: int,
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stride: int = 1,
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groups: int = 1,
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bias: bool = False,
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dilation: int = 1,
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use_normalization: bool = True,
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use_activation: Union[bool, str] = True,
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) -> None:
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super().__init__()
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padding = int((kernel_size - 1) / 2) * dilation
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if in_channels % groups != 0:
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raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
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if out_channels % groups != 0:
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raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")
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self.convolution = nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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bias=bias,
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padding_mode="zeros",
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)
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if use_normalization:
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self.normalization = nn.BatchNorm2d(
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num_features=out_channels,
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eps=1e-5,
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momentum=0.1,
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affine=True,
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track_running_stats=True,
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)
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else:
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self.normalization = None
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if use_activation:
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if isinstance(use_activation, str):
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self.activation = ACT2FN[use_activation]
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elif isinstance(config.hidden_act, str):
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self.activation = ACT2FN[config.hidden_act]
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else:
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self.activation = config.hidden_act
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else:
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self.activation = None
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def forward(self, features: torch.Tensor) -> torch.Tensor:
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features = self.convolution(features)
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if self.normalization is not None:
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features = self.normalization(features)
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if self.activation is not None:
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features = self.activation(features)
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return features
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# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTInvertedResidual with MobileViT->MobileViTV2
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class MobileViTV2InvertedResidual(nn.Module):
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"""
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Inverted residual block (MobileNetv2): https://arxiv.org/abs/1801.04381
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"""
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def __init__(
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self, config: MobileViTV2Config, in_channels: int, out_channels: int, stride: int, dilation: int = 1
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) -> None:
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super().__init__()
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expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8)
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if stride not in [1, 2]:
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raise ValueError(f"Invalid stride {stride}.")
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self.use_residual = (stride == 1) and (in_channels == out_channels)
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self.expand_1x1 = MobileViTV2ConvLayer(
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config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1
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)
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self.conv_3x3 = MobileViTV2ConvLayer(
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config,
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in_channels=expanded_channels,
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out_channels=expanded_channels,
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kernel_size=3,
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stride=stride,
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groups=expanded_channels,
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dilation=dilation,
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)
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self.reduce_1x1 = MobileViTV2ConvLayer(
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config,
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in_channels=expanded_channels,
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out_channels=out_channels,
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kernel_size=1,
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use_activation=False,
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)
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def forward(self, features: torch.Tensor) -> torch.Tensor:
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residual = features
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features = self.expand_1x1(features)
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features = self.conv_3x3(features)
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features = self.reduce_1x1(features)
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return residual + features if self.use_residual else features
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# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTMobileNetLayer with MobileViT->MobileViTV2
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class MobileViTV2MobileNetLayer(nn.Module):
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def __init__(
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self, config: MobileViTV2Config, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1
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) -> None:
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super().__init__()
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self.layer = nn.ModuleList()
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for i in range(num_stages):
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layer = MobileViTV2InvertedResidual(
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config,
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in_channels=in_channels,
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out_channels=out_channels,
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stride=stride if i == 0 else 1,
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)
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self.layer.append(layer)
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in_channels = out_channels
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def forward(self, features: torch.Tensor) -> torch.Tensor:
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for layer_module in self.layer:
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features = layer_module(features)
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return features
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class MobileViTV2LinearSelfAttention(nn.Module):
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"""
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This layer applies a self-attention with linear complexity, as described in MobileViTV2 paper:
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https://arxiv.org/abs/2206.02680
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Args:
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config (`MobileVitv2Config`):
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Model configuration object
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embed_dim (`int`):
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`input_channels` from an expected input of size :math:`(batch_size, input_channels, height, width)`
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"""
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def __init__(self, config: MobileViTV2Config, embed_dim: int) -> None:
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super().__init__()
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self.qkv_proj = MobileViTV2ConvLayer(
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config=config,
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in_channels=embed_dim,
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out_channels=1 + (2 * embed_dim),
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bias=True,
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kernel_size=1,
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use_normalization=False,
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use_activation=False,
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)
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self.attn_dropout = nn.Dropout(p=config.attn_dropout)
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self.out_proj = MobileViTV2ConvLayer(
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config=config,
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in_channels=embed_dim,
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out_channels=embed_dim,
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bias=True,
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kernel_size=1,
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use_normalization=False,
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use_activation=False,
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)
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self.embed_dim = embed_dim
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# (batch_size, embed_dim, num_pixels_in_patch, num_patches) --> (batch_size, 1+2*embed_dim, num_pixels_in_patch, num_patches)
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qkv = self.qkv_proj(hidden_states)
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# Project hidden_states into query, key and value
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# Query --> [batch_size, 1, num_pixels_in_patch, num_patches]
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# value, key --> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
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query, key, value = torch.split(qkv, split_size_or_sections=[1, self.embed_dim, self.embed_dim], dim=1)
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# apply softmax along num_patches dimension
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context_scores = torch.nn.functional.softmax(query, dim=-1)
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context_scores = self.attn_dropout(context_scores)
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# Compute context vector
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# [batch_size, embed_dim, num_pixels_in_patch, num_patches] x [batch_size, 1, num_pixels_in_patch, num_patches] -> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
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context_vector = key * context_scores
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# [batch_size, embed_dim, num_pixels_in_patch, num_patches] --> [batch_size, embed_dim, num_pixels_in_patch, 1]
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context_vector = torch.sum(context_vector, dim=-1, keepdim=True)
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# combine context vector with values
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# [batch_size, embed_dim, num_pixels_in_patch, num_patches] * [batch_size, embed_dim, num_pixels_in_patch, 1] --> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
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out = torch.nn.functional.relu(value) * context_vector.expand_as(value)
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out = self.out_proj(out)
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return out
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class MobileViTV2FFN(nn.Module):
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def __init__(
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self,
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config: MobileViTV2Config,
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embed_dim: int,
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ffn_latent_dim: int,
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ffn_dropout: float = 0.0,
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) -> None:
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super().__init__()
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self.conv1 = MobileViTV2ConvLayer(
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config=config,
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in_channels=embed_dim,
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out_channels=ffn_latent_dim,
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kernel_size=1,
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stride=1,
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bias=True,
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use_normalization=False,
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use_activation=True,
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)
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self.dropout1 = nn.Dropout(ffn_dropout)
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self.conv2 = MobileViTV2ConvLayer(
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config=config,
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in_channels=ffn_latent_dim,
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out_channels=embed_dim,
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kernel_size=1,
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stride=1,
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bias=True,
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use_normalization=False,
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use_activation=False,
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)
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self.dropout2 = nn.Dropout(ffn_dropout)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.conv1(hidden_states)
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hidden_states = self.dropout1(hidden_states)
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hidden_states = self.conv2(hidden_states)
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hidden_states = self.dropout2(hidden_states)
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return hidden_states
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class MobileViTV2TransformerLayer(nn.Module):
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def __init__(
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self,
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config: MobileViTV2Config,
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embed_dim: int,
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ffn_latent_dim: int,
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dropout: float = 0.0,
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) -> None:
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super().__init__()
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self.layernorm_before = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=config.layer_norm_eps)
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self.attention = MobileViTV2LinearSelfAttention(config, embed_dim)
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self.dropout1 = nn.Dropout(p=dropout)
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self.layernorm_after = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=config.layer_norm_eps)
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self.ffn = MobileViTV2FFN(config, embed_dim, ffn_latent_dim, config.ffn_dropout)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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layernorm_1_out = self.layernorm_before(hidden_states)
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attention_output = self.attention(layernorm_1_out)
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hidden_states = attention_output + hidden_states
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layer_output = self.layernorm_after(hidden_states)
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layer_output = self.ffn(layer_output)
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layer_output = layer_output + hidden_states
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return layer_output
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class MobileViTV2Transformer(nn.Module):
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def __init__(self, config: MobileViTV2Config, n_layers: int, d_model: int) -> None:
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super().__init__()
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ffn_multiplier = config.ffn_multiplier
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ffn_dims = [ffn_multiplier * d_model] * n_layers
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# ensure that dims are multiple of 16
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ffn_dims = [int((d // 16) * 16) for d in ffn_dims]
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self.layer = nn.ModuleList()
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for block_idx in range(n_layers):
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transformer_layer = MobileViTV2TransformerLayer(
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config, embed_dim=d_model, ffn_latent_dim=ffn_dims[block_idx]
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)
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self.layer.append(transformer_layer)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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for layer_module in self.layer:
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hidden_states = layer_module(hidden_states)
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return hidden_states
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class MobileViTV2Layer(nn.Module):
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"""
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MobileViTV2 layer: https://arxiv.org/abs/2206.02680
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"""
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def __init__(
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self,
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config: MobileViTV2Config,
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in_channels: int,
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out_channels: int,
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attn_unit_dim: int,
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n_attn_blocks: int = 2,
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dilation: int = 1,
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stride: int = 2,
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) -> None:
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super().__init__()
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self.patch_width = config.patch_size
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self.patch_height = config.patch_size
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cnn_out_dim = attn_unit_dim
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if stride == 2:
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self.downsampling_layer = MobileViTV2InvertedResidual(
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config,
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in_channels=in_channels,
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out_channels=out_channels,
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stride=stride if dilation == 1 else 1,
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dilation=dilation // 2 if dilation > 1 else 1,
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)
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in_channels = out_channels
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else:
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self.downsampling_layer = None
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# Local representations
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self.conv_kxk = MobileViTV2ConvLayer(
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config,
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=config.conv_kernel_size,
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groups=in_channels,
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)
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self.conv_1x1 = MobileViTV2ConvLayer(
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config,
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in_channels=in_channels,
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out_channels=cnn_out_dim,
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kernel_size=1,
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use_normalization=False,
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use_activation=False,
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)
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# Global representations
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self.transformer = MobileViTV2Transformer(config, d_model=attn_unit_dim, n_layers=n_attn_blocks)
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# self.layernorm = MobileViTV2LayerNorm2D(attn_unit_dim, eps=config.layer_norm_eps)
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self.layernorm = nn.GroupNorm(num_groups=1, num_channels=attn_unit_dim, eps=config.layer_norm_eps)
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# Fusion
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self.conv_projection = MobileViTV2ConvLayer(
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config,
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in_channels=cnn_out_dim,
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out_channels=in_channels,
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kernel_size=1,
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use_normalization=True,
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use_activation=False,
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)
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def unfolding(self, feature_map: torch.Tensor) -> Tuple[torch.Tensor, Tuple[int, int]]:
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batch_size, in_channels, img_height, img_width = feature_map.shape
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patches = nn.functional.unfold(
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feature_map,
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kernel_size=(self.patch_height, self.patch_width),
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stride=(self.patch_height, self.patch_width),
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)
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patches = patches.reshape(batch_size, in_channels, self.patch_height * self.patch_width, -1)
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return patches, (img_height, img_width)
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def folding(self, patches: torch.Tensor, output_size: Tuple[int, int]) -> torch.Tensor:
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batch_size, in_dim, patch_size, n_patches = patches.shape
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patches = patches.reshape(batch_size, in_dim * patch_size, n_patches)
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feature_map = nn.functional.fold(
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patches,
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output_size=output_size,
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kernel_size=(self.patch_height, self.patch_width),
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stride=(self.patch_height, self.patch_width),
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)
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return feature_map
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def forward(self, features: torch.Tensor) -> torch.Tensor:
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# reduce spatial dimensions if needed
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if self.downsampling_layer:
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features = self.downsampling_layer(features)
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# local representation
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features = self.conv_kxk(features)
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features = self.conv_1x1(features)
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# convert feature map to patches
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patches, output_size = self.unfolding(features)
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# learn global representations
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patches = self.transformer(patches)
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patches = self.layernorm(patches)
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# convert patches back to feature maps
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# [batch_size, patch_height, patch_width, input_dim] --> [batch_size, input_dim, patch_height, patch_width]
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features = self.folding(patches, output_size)
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features = self.conv_projection(features)
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return features
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class MobileViTV2Encoder(nn.Module):
|
|
def __init__(self, config: MobileViTV2Config) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
self.layer = nn.ModuleList()
|
|
self.gradient_checkpointing = False
|
|
|
|
# segmentation architectures like DeepLab and PSPNet modify the strides
|
|
# of the classification backbones
|
|
dilate_layer_4 = dilate_layer_5 = False
|
|
if config.output_stride == 8:
|
|
dilate_layer_4 = True
|
|
dilate_layer_5 = True
|
|
elif config.output_stride == 16:
|
|
dilate_layer_5 = True
|
|
|
|
dilation = 1
|
|
|
|
layer_0_dim = make_divisible(
|
|
clip(value=32 * config.width_multiplier, min_val=16, max_val=64), divisor=8, min_value=16
|
|
)
|
|
|
|
layer_1_dim = make_divisible(64 * config.width_multiplier, divisor=16)
|
|
layer_2_dim = make_divisible(128 * config.width_multiplier, divisor=8)
|
|
layer_3_dim = make_divisible(256 * config.width_multiplier, divisor=8)
|
|
layer_4_dim = make_divisible(384 * config.width_multiplier, divisor=8)
|
|
layer_5_dim = make_divisible(512 * config.width_multiplier, divisor=8)
|
|
|
|
layer_1 = MobileViTV2MobileNetLayer(
|
|
config,
|
|
in_channels=layer_0_dim,
|
|
out_channels=layer_1_dim,
|
|
stride=1,
|
|
num_stages=1,
|
|
)
|
|
self.layer.append(layer_1)
|
|
|
|
layer_2 = MobileViTV2MobileNetLayer(
|
|
config,
|
|
in_channels=layer_1_dim,
|
|
out_channels=layer_2_dim,
|
|
stride=2,
|
|
num_stages=2,
|
|
)
|
|
self.layer.append(layer_2)
|
|
|
|
layer_3 = MobileViTV2Layer(
|
|
config,
|
|
in_channels=layer_2_dim,
|
|
out_channels=layer_3_dim,
|
|
attn_unit_dim=make_divisible(config.base_attn_unit_dims[0] * config.width_multiplier, divisor=8),
|
|
n_attn_blocks=config.n_attn_blocks[0],
|
|
)
|
|
self.layer.append(layer_3)
|
|
|
|
if dilate_layer_4:
|
|
dilation *= 2
|
|
|
|
layer_4 = MobileViTV2Layer(
|
|
config,
|
|
in_channels=layer_3_dim,
|
|
out_channels=layer_4_dim,
|
|
attn_unit_dim=make_divisible(config.base_attn_unit_dims[1] * config.width_multiplier, divisor=8),
|
|
n_attn_blocks=config.n_attn_blocks[1],
|
|
dilation=dilation,
|
|
)
|
|
self.layer.append(layer_4)
|
|
|
|
if dilate_layer_5:
|
|
dilation *= 2
|
|
|
|
layer_5 = MobileViTV2Layer(
|
|
config,
|
|
in_channels=layer_4_dim,
|
|
out_channels=layer_5_dim,
|
|
attn_unit_dim=make_divisible(config.base_attn_unit_dims[2] * config.width_multiplier, divisor=8),
|
|
n_attn_blocks=config.n_attn_blocks[2],
|
|
dilation=dilation,
|
|
)
|
|
self.layer.append(layer_5)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
output_hidden_states: bool = False,
|
|
return_dict: bool = True,
|
|
) -> Union[tuple, BaseModelOutputWithNoAttention]:
|
|
all_hidden_states = () if output_hidden_states else None
|
|
|
|
for i, layer_module in enumerate(self.layer):
|
|
if self.gradient_checkpointing and self.training:
|
|
hidden_states = self._gradient_checkpointing_func(
|
|
layer_module.__call__,
|
|
hidden_states,
|
|
)
|
|
else:
|
|
hidden_states = layer_module(hidden_states)
|
|
|
|
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] if v is not None)
|
|
|
|
return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
|
|
|
|
|
|
# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTPreTrainedModel with MobileViT->MobileViTV2,mobilevit->mobilevitv2
|
|
class MobileViTV2PreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = MobileViTV2Config
|
|
base_model_prefix = "mobilevitv2"
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
|
|
"""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.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
|
|
|
|
MOBILEVITV2_START_DOCSTRING = r"""
|
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
|
|
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
|
|
behavior.
|
|
|
|
Parameters:
|
|
config ([`MobileViTV2Config`]): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
MOBILEVITV2_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
|
|
[`MobileViTImageProcessor.__call__`] for details.
|
|
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 or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare MobileViTV2 model outputting raw hidden-states without any specific head on top.",
|
|
MOBILEVITV2_START_DOCSTRING,
|
|
)
|
|
class MobileViTV2Model(MobileViTV2PreTrainedModel):
|
|
def __init__(self, config: MobileViTV2Config, expand_output: bool = True):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.expand_output = expand_output
|
|
|
|
layer_0_dim = make_divisible(
|
|
clip(value=32 * config.width_multiplier, min_val=16, max_val=64), divisor=8, min_value=16
|
|
)
|
|
|
|
self.conv_stem = MobileViTV2ConvLayer(
|
|
config,
|
|
in_channels=config.num_channels,
|
|
out_channels=layer_0_dim,
|
|
kernel_size=3,
|
|
stride=2,
|
|
use_normalization=True,
|
|
use_activation=True,
|
|
)
|
|
self.encoder = MobileViTV2Encoder(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_index, heads in heads_to_prune.items():
|
|
mobilevitv2_layer = self.encoder.layer[layer_index]
|
|
if isinstance(mobilevitv2_layer, MobileViTV2Layer):
|
|
for transformer_layer in mobilevitv2_layer.transformer.layer:
|
|
transformer_layer.attention.prune_heads(heads)
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEVITV2_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPoolingAndNoAttention,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
modality="vision",
|
|
expected_output=_EXPECTED_OUTPUT_SHAPE,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
|
|
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
|
|
|
|
if pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
embedding_output = self.conv_stem(pixel_values)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
if self.expand_output:
|
|
last_hidden_state = encoder_outputs[0]
|
|
|
|
# global average pooling: (batch_size, channels, height, width) -> (batch_size, channels)
|
|
pooled_output = torch.mean(last_hidden_state, dim=[-2, -1], keepdim=False)
|
|
else:
|
|
last_hidden_state = encoder_outputs[0]
|
|
pooled_output = None
|
|
|
|
if not return_dict:
|
|
output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,)
|
|
return output + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndNoAttention(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
MobileViTV2 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
|
|
ImageNet.
|
|
""",
|
|
MOBILEVITV2_START_DOCSTRING,
|
|
)
|
|
class MobileViTV2ForImageClassification(MobileViTV2PreTrainedModel):
|
|
def __init__(self, config: MobileViTV2Config) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.mobilevitv2 = MobileViTV2Model(config)
|
|
|
|
out_channels = make_divisible(512 * config.width_multiplier, divisor=8) # layer 5 output dimension
|
|
# Classifier head
|
|
self.classifier = (
|
|
nn.Linear(in_features=out_channels, out_features=config.num_labels)
|
|
if config.num_labels > 0
|
|
else nn.Identity()
|
|
)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEVITV2_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_IMAGE_CLASS_CHECKPOINT,
|
|
output_type=ImageClassifierOutputWithNoAttention,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.mobilevitv2(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
|
|
|
|
pooled_output = outputs.pooler_output if return_dict else outputs[1]
|
|
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return ImageClassifierOutputWithNoAttention(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
)
|
|
|
|
|
|
# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTASPPPooling with MobileViT->MobileViTV2
|
|
class MobileViTV2ASPPPooling(nn.Module):
|
|
def __init__(self, config: MobileViTV2Config, in_channels: int, out_channels: int) -> None:
|
|
super().__init__()
|
|
|
|
self.global_pool = nn.AdaptiveAvgPool2d(output_size=1)
|
|
|
|
self.conv_1x1 = MobileViTV2ConvLayer(
|
|
config,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
use_normalization=True,
|
|
use_activation="relu",
|
|
)
|
|
|
|
def forward(self, features: torch.Tensor) -> torch.Tensor:
|
|
spatial_size = features.shape[-2:]
|
|
features = self.global_pool(features)
|
|
features = self.conv_1x1(features)
|
|
features = nn.functional.interpolate(features, size=spatial_size, mode="bilinear", align_corners=False)
|
|
return features
|
|
|
|
|
|
class MobileViTV2ASPP(nn.Module):
|
|
"""
|
|
ASPP module defined in DeepLab papers: https://arxiv.org/abs/1606.00915, https://arxiv.org/abs/1706.05587
|
|
"""
|
|
|
|
def __init__(self, config: MobileViTV2Config) -> None:
|
|
super().__init__()
|
|
|
|
encoder_out_channels = make_divisible(512 * config.width_multiplier, divisor=8) # layer 5 output dimension
|
|
in_channels = encoder_out_channels
|
|
out_channels = config.aspp_out_channels
|
|
|
|
if len(config.atrous_rates) != 3:
|
|
raise ValueError("Expected 3 values for atrous_rates")
|
|
|
|
self.convs = nn.ModuleList()
|
|
|
|
in_projection = MobileViTV2ConvLayer(
|
|
config,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=1,
|
|
use_activation="relu",
|
|
)
|
|
self.convs.append(in_projection)
|
|
|
|
self.convs.extend(
|
|
[
|
|
MobileViTV2ConvLayer(
|
|
config,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=3,
|
|
dilation=rate,
|
|
use_activation="relu",
|
|
)
|
|
for rate in config.atrous_rates
|
|
]
|
|
)
|
|
|
|
pool_layer = MobileViTV2ASPPPooling(config, in_channels, out_channels)
|
|
self.convs.append(pool_layer)
|
|
|
|
self.project = MobileViTV2ConvLayer(
|
|
config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu"
|
|
)
|
|
|
|
self.dropout = nn.Dropout(p=config.aspp_dropout_prob)
|
|
|
|
def forward(self, features: torch.Tensor) -> torch.Tensor:
|
|
pyramid = []
|
|
for conv in self.convs:
|
|
pyramid.append(conv(features))
|
|
pyramid = torch.cat(pyramid, dim=1)
|
|
|
|
pooled_features = self.project(pyramid)
|
|
pooled_features = self.dropout(pooled_features)
|
|
return pooled_features
|
|
|
|
|
|
# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTDeepLabV3 with MobileViT->MobileViTV2
|
|
class MobileViTV2DeepLabV3(nn.Module):
|
|
"""
|
|
DeepLabv3 architecture: https://arxiv.org/abs/1706.05587
|
|
"""
|
|
|
|
def __init__(self, config: MobileViTV2Config) -> None:
|
|
super().__init__()
|
|
self.aspp = MobileViTV2ASPP(config)
|
|
|
|
self.dropout = nn.Dropout2d(config.classifier_dropout_prob)
|
|
|
|
self.classifier = MobileViTV2ConvLayer(
|
|
config,
|
|
in_channels=config.aspp_out_channels,
|
|
out_channels=config.num_labels,
|
|
kernel_size=1,
|
|
use_normalization=False,
|
|
use_activation=False,
|
|
bias=True,
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
features = self.aspp(hidden_states[-1])
|
|
features = self.dropout(features)
|
|
features = self.classifier(features)
|
|
return features
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
MobileViTV2 model with a semantic segmentation head on top, e.g. for Pascal VOC.
|
|
""",
|
|
MOBILEVITV2_START_DOCSTRING,
|
|
)
|
|
class MobileViTV2ForSemanticSegmentation(MobileViTV2PreTrainedModel):
|
|
def __init__(self, config: MobileViTV2Config) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.mobilevitv2 = MobileViTV2Model(config, expand_output=False)
|
|
self.segmentation_head = MobileViTV2DeepLabV3(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(MOBILEVITV2_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, SemanticSegmenterOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
|
|
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
|
|
|
|
Returns:
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Examples:
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```python
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>>> import requests
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>>> import torch
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>>> from PIL import Image
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>>> from transformers import AutoImageProcessor, MobileViTV2ForSemanticSegmentation
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>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> image_processor = AutoImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
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>>> model = MobileViTV2ForSemanticSegmentation.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
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>>> inputs = image_processor(images=image, return_tensors="pt")
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>>> with torch.no_grad():
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... outputs = model(**inputs)
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>>> # logits are of shape (batch_size, num_labels, height, width)
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>>> logits = outputs.logits
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```"""
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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outputs = self.mobilevitv2(
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pixel_values,
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output_hidden_states=True, # we need the intermediate hidden states
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return_dict=return_dict,
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)
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encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
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logits = self.segmentation_head(encoder_hidden_states)
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loss = None
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if labels is not None:
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if self.config.num_labels == 1:
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raise ValueError("The number of labels should be greater than one")
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else:
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# upsample logits to the images' original size
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upsampled_logits = nn.functional.interpolate(
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logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
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)
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loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
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loss = loss_fct(upsampled_logits, labels)
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if not return_dict:
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if output_hidden_states:
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output = (logits,) + outputs[1:]
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else:
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output = (logits,) + outputs[2:]
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return ((loss,) + output) if loss is not None else output
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return SemanticSegmenterOutput(
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loss=loss,
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logits=logits,
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hidden_states=outputs.hidden_states if output_hidden_states else None,
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attentions=None,
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)
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