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# coding=utf-8 | |
# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" PyTorch ConvNext model.""" | |
from typing import Optional, Tuple, Union | |
import torch | |
import torch.utils.checkpoint | |
from torch import nn | |
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss | |
from ...activations import ACT2FN | |
from ...modeling_outputs import ( | |
BackboneOutput, | |
BaseModelOutputWithNoAttention, | |
BaseModelOutputWithPoolingAndNoAttention, | |
ImageClassifierOutputWithNoAttention, | |
) | |
from ...modeling_utils import PreTrainedModel | |
from ...utils import ( | |
add_code_sample_docstrings, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
logging, | |
replace_return_docstrings, | |
) | |
from ...utils.backbone_utils import BackboneMixin | |
from .configuration_convnext import ConvNextConfig | |
logger = logging.get_logger(__name__) | |
# General docstring | |
_CONFIG_FOR_DOC = "ConvNextConfig" | |
# Base docstring | |
_CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224" | |
_EXPECTED_OUTPUT_SHAPE = [1, 768, 7, 7] | |
# Image classification docstring | |
_IMAGE_CLASS_CHECKPOINT = "facebook/convnext-tiny-224" | |
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" | |
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"facebook/convnext-tiny-224", | |
# See all ConvNext models at https://huggingface.co/models?filter=convnext | |
] | |
# Copied from transformers.models.beit.modeling_beit.drop_path | |
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: | |
""" | |
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). | |
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, | |
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... | |
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the | |
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the | |
argument. | |
""" | |
if drop_prob == 0.0 or not training: | |
return input | |
keep_prob = 1 - drop_prob | |
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets | |
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) | |
random_tensor.floor_() # binarize | |
output = input.div(keep_prob) * random_tensor | |
return output | |
# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->ConvNext | |
class ConvNextDropPath(nn.Module): | |
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" | |
def __init__(self, drop_prob: Optional[float] = None) -> None: | |
super().__init__() | |
self.drop_prob = drop_prob | |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: | |
return drop_path(hidden_states, self.drop_prob, self.training) | |
def extra_repr(self) -> str: | |
return "p={}".format(self.drop_prob) | |
class ConvNextLayerNorm(nn.Module): | |
r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. | |
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, | |
width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). | |
""" | |
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): | |
super().__init__() | |
self.weight = nn.Parameter(torch.ones(normalized_shape)) | |
self.bias = nn.Parameter(torch.zeros(normalized_shape)) | |
self.eps = eps | |
self.data_format = data_format | |
if self.data_format not in ["channels_last", "channels_first"]: | |
raise NotImplementedError(f"Unsupported data format: {self.data_format}") | |
self.normalized_shape = (normalized_shape,) | |
def forward(self, x: torch.Tensor) -> torch.Tensor: | |
if self.data_format == "channels_last": | |
x = torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) | |
elif self.data_format == "channels_first": | |
input_dtype = x.dtype | |
x = x.float() | |
u = x.mean(1, keepdim=True) | |
s = (x - u).pow(2).mean(1, keepdim=True) | |
x = (x - u) / torch.sqrt(s + self.eps) | |
x = x.to(dtype=input_dtype) | |
x = self.weight[:, None, None] * x + self.bias[:, None, None] | |
return x | |
class ConvNextEmbeddings(nn.Module): | |
"""This class is comparable to (and inspired by) the SwinEmbeddings class | |
found in src/transformers/models/swin/modeling_swin.py. | |
""" | |
def __init__(self, config): | |
super().__init__() | |
self.patch_embeddings = nn.Conv2d( | |
config.num_channels, config.hidden_sizes[0], kernel_size=config.patch_size, stride=config.patch_size | |
) | |
self.layernorm = ConvNextLayerNorm(config.hidden_sizes[0], eps=1e-6, data_format="channels_first") | |
self.num_channels = config.num_channels | |
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: | |
num_channels = pixel_values.shape[1] | |
if num_channels != self.num_channels: | |
raise ValueError( | |
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." | |
) | |
embeddings = self.patch_embeddings(pixel_values) | |
embeddings = self.layernorm(embeddings) | |
return embeddings | |
class ConvNextLayer(nn.Module): | |
"""This corresponds to the `Block` class in the original implementation. | |
There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C, | |
H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back | |
The authors used (2) as they find it slightly faster in PyTorch. | |
Args: | |
config ([`ConvNextConfig`]): Model configuration class. | |
dim (`int`): Number of input channels. | |
drop_path (`float`): Stochastic depth rate. Default: 0.0. | |
""" | |
def __init__(self, config, dim, drop_path=0): | |
super().__init__() | |
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv | |
self.layernorm = ConvNextLayerNorm(dim, eps=1e-6) | |
self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers | |
self.act = ACT2FN[config.hidden_act] | |
self.pwconv2 = nn.Linear(4 * dim, dim) | |
self.layer_scale_parameter = ( | |
nn.Parameter(config.layer_scale_init_value * torch.ones((dim)), requires_grad=True) | |
if config.layer_scale_init_value > 0 | |
else None | |
) | |
self.drop_path = ConvNextDropPath(drop_path) if drop_path > 0.0 else nn.Identity() | |
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: | |
input = hidden_states | |
x = self.dwconv(hidden_states) | |
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) | |
x = self.layernorm(x) | |
x = self.pwconv1(x) | |
x = self.act(x) | |
x = self.pwconv2(x) | |
if self.layer_scale_parameter is not None: | |
x = self.layer_scale_parameter * x | |
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) | |
x = input + self.drop_path(x) | |
return x | |
class ConvNextStage(nn.Module): | |
"""ConvNeXT stage, consisting of an optional downsampling layer + multiple residual blocks. | |
Args: | |
config ([`ConvNextConfig`]): Model configuration class. | |
in_channels (`int`): Number of input channels. | |
out_channels (`int`): Number of output channels. | |
depth (`int`): Number of residual blocks. | |
drop_path_rates(`List[float]`): Stochastic depth rates for each layer. | |
""" | |
def __init__(self, config, in_channels, out_channels, kernel_size=2, stride=2, depth=2, drop_path_rates=None): | |
super().__init__() | |
if in_channels != out_channels or stride > 1: | |
self.downsampling_layer = nn.Sequential( | |
ConvNextLayerNorm(in_channels, eps=1e-6, data_format="channels_first"), | |
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride), | |
) | |
else: | |
self.downsampling_layer = nn.Identity() | |
drop_path_rates = drop_path_rates or [0.0] * depth | |
self.layers = nn.Sequential( | |
*[ConvNextLayer(config, dim=out_channels, drop_path=drop_path_rates[j]) for j in range(depth)] | |
) | |
def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor: | |
hidden_states = self.downsampling_layer(hidden_states) | |
hidden_states = self.layers(hidden_states) | |
return hidden_states | |
class ConvNextEncoder(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
self.stages = nn.ModuleList() | |
drop_path_rates = [ | |
x.tolist() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths)).split(config.depths) | |
] | |
prev_chs = config.hidden_sizes[0] | |
for i in range(config.num_stages): | |
out_chs = config.hidden_sizes[i] | |
stage = ConvNextStage( | |
config, | |
in_channels=prev_chs, | |
out_channels=out_chs, | |
stride=2 if i > 0 else 1, | |
depth=config.depths[i], | |
drop_path_rates=drop_path_rates[i], | |
) | |
self.stages.append(stage) | |
prev_chs = out_chs | |
def forward( | |
self, | |
hidden_states: torch.FloatTensor, | |
output_hidden_states: Optional[bool] = False, | |
return_dict: Optional[bool] = True, | |
) -> Union[Tuple, BaseModelOutputWithNoAttention]: | |
all_hidden_states = () if output_hidden_states else None | |
for i, layer_module in enumerate(self.stages): | |
if output_hidden_states: | |
all_hidden_states = all_hidden_states + (hidden_states,) | |
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, | |
) | |
class ConvNextPreTrainedModel(PreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = ConvNextConfig | |
base_model_prefix = "convnext" | |
main_input_name = "pixel_values" | |
supports_gradient_checkpointing = True | |
def _init_weights(self, module): | |
"""Initialize the weights""" | |
if isinstance(module, (nn.Linear, nn.Conv2d)): | |
# Slightly different from the TF version which uses truncated_normal for initialization | |
# cf https://github.com/pytorch/pytorch/pull/5617 | |
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) | |
if module.bias is not None: | |
module.bias.data.zero_() | |
elif isinstance(module, nn.LayerNorm): | |
module.bias.data.zero_() | |
module.weight.data.fill_(1.0) | |
def _set_gradient_checkpointing(self, module, value=False): | |
if isinstance(module, ConvNextEncoder): | |
module.gradient_checkpointing = value | |
CONVNEXT_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 ([`ConvNextConfig`]): 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. | |
""" | |
CONVNEXT_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 | |
[`ConvNextImageProcessor.__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. | |
""" | |
class ConvNextModel(ConvNextPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.config = config | |
self.embeddings = ConvNextEmbeddings(config) | |
self.encoder = ConvNextEncoder(config) | |
# final layernorm layer | |
self.layernorm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def forward( | |
self, | |
pixel_values: torch.FloatTensor = 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.embeddings(pixel_values) | |
encoder_outputs = self.encoder( | |
embedding_output, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
) | |
last_hidden_state = encoder_outputs[0] | |
# global average pooling, (N, C, H, W) -> (N, C) | |
pooled_output = self.layernorm(last_hidden_state.mean([-2, -1])) | |
if not return_dict: | |
return (last_hidden_state, pooled_output) + encoder_outputs[1:] | |
return BaseModelOutputWithPoolingAndNoAttention( | |
last_hidden_state=last_hidden_state, | |
pooler_output=pooled_output, | |
hidden_states=encoder_outputs.hidden_states, | |
) | |
class ConvNextForImageClassification(ConvNextPreTrainedModel): | |
def __init__(self, config): | |
super().__init__(config) | |
self.num_labels = config.num_labels | |
self.convnext = ConvNextModel(config) | |
# Classifier head | |
self.classifier = ( | |
nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity() | |
) | |
# Initialize weights and apply final processing | |
self.post_init() | |
def forward( | |
self, | |
pixel_values: torch.FloatTensor = None, | |
labels: Optional[torch.LongTensor] = None, | |
output_hidden_states: Optional[bool] = 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.convnext(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, | |
) | |
class ConvNextBackbone(ConvNextPreTrainedModel, BackboneMixin): | |
def __init__(self, config): | |
super().__init__(config) | |
super()._init_backbone(config) | |
self.embeddings = ConvNextEmbeddings(config) | |
self.encoder = ConvNextEncoder(config) | |
self.num_features = [config.hidden_sizes[0]] + config.hidden_sizes | |
# Add layer norms to hidden states of out_features | |
hidden_states_norms = {} | |
for stage, num_channels in zip(self._out_features, self.channels): | |
hidden_states_norms[stage] = ConvNextLayerNorm(num_channels, data_format="channels_first") | |
self.hidden_states_norms = nn.ModuleDict(hidden_states_norms) | |
# initialize weights and apply final processing | |
self.post_init() | |
def forward( | |
self, | |
pixel_values: torch.Tensor, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
) -> BackboneOutput: | |
""" | |
Returns: | |
Examples: | |
```python | |
>>> from transformers import AutoImageProcessor, AutoBackbone | |
>>> import torch | |
>>> from PIL import Image | |
>>> import requests | |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
>>> image = Image.open(requests.get(url, stream=True).raw) | |
>>> processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224") | |
>>> model = AutoBackbone.from_pretrained("facebook/convnext-tiny-224") | |
>>> inputs = processor(image, return_tensors="pt") | |
>>> outputs = model(**inputs) | |
```""" | |
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 | |
) | |
embedding_output = self.embeddings(pixel_values) | |
outputs = self.encoder( | |
embedding_output, | |
output_hidden_states=True, | |
return_dict=True, | |
) | |
hidden_states = outputs.hidden_states | |
feature_maps = () | |
# we skip the stem | |
for idx, (stage, hidden_state) in enumerate(zip(self.stage_names[1:], hidden_states[1:])): | |
if stage in self.out_features: | |
hidden_state = self.hidden_states_norms[stage](hidden_state) | |
feature_maps += (hidden_state,) | |
if not return_dict: | |
output = (feature_maps,) | |
if output_hidden_states: | |
output += (outputs.hidden_states,) | |
return output | |
return BackboneOutput( | |
feature_maps=feature_maps, | |
hidden_states=outputs.hidden_states if output_hidden_states else None, | |
attentions=None, | |
) | |