# coding=utf-8 # Copyright 2022 Apple 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 MobileNetV2 model.""" from typing import Optional, Union import torch from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention, SemanticSegmenterOutput, ) 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 .configuration_mobilenet_v2 import MobileNetV2Config logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "MobileNetV2Config" # Base docstring _CHECKPOINT_FOR_DOC = "google/mobilenet_v2_1.0_224" _EXPECTED_OUTPUT_SHAPE = [1, 1280, 7, 7] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "google/mobilenet_v2_1.0_224" _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/mobilenet_v2_1.4_224", "google/mobilenet_v2_1.0_224", "google/mobilenet_v2_0.37_160", "google/mobilenet_v2_0.35_96", # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2 ] def _build_tf_to_pytorch_map(model, config, tf_weights=None): """ A map of modules from TF to PyTorch. """ tf_to_pt_map = {} if isinstance(model, (MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation)): backbone = model.mobilenet_v2 else: backbone = model # Use the EMA weights if available def ema(x): return x + "/ExponentialMovingAverage" if x + "/ExponentialMovingAverage" in tf_weights else x prefix = "MobilenetV2/Conv/" tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_stem.first_conv.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.first_conv.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.first_conv.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.first_conv.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.first_conv.normalization.running_var prefix = "MobilenetV2/expanded_conv/depthwise/" tf_to_pt_map[ema(prefix + "depthwise_weights")] = backbone.conv_stem.conv_3x3.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.conv_3x3.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.conv_3x3.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.conv_3x3.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.conv_3x3.normalization.running_var prefix = "MobilenetV2/expanded_conv/project/" tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_stem.reduce_1x1.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.reduce_1x1.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.reduce_1x1.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.reduce_1x1.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.reduce_1x1.normalization.running_var for i in range(16): tf_index = i + 1 pt_index = i pointer = backbone.layer[pt_index] prefix = f"MobilenetV2/expanded_conv_{tf_index}/expand/" tf_to_pt_map[ema(prefix + "weights")] = pointer.expand_1x1.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.expand_1x1.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.expand_1x1.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.expand_1x1.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.expand_1x1.normalization.running_var prefix = f"MobilenetV2/expanded_conv_{tf_index}/depthwise/" tf_to_pt_map[ema(prefix + "depthwise_weights")] = pointer.conv_3x3.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.conv_3x3.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.conv_3x3.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.conv_3x3.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.conv_3x3.normalization.running_var prefix = f"MobilenetV2/expanded_conv_{tf_index}/project/" tf_to_pt_map[ema(prefix + "weights")] = pointer.reduce_1x1.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.reduce_1x1.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.reduce_1x1.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.reduce_1x1.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.reduce_1x1.normalization.running_var prefix = "MobilenetV2/Conv_1/" tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_1x1.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_1x1.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_1x1.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_1x1.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_1x1.normalization.running_var if isinstance(model, MobileNetV2ForImageClassification): prefix = "MobilenetV2/Logits/Conv2d_1c_1x1/" tf_to_pt_map[ema(prefix + "weights")] = model.classifier.weight tf_to_pt_map[ema(prefix + "biases")] = model.classifier.bias if isinstance(model, MobileNetV2ForSemanticSegmentation): prefix = "image_pooling/" tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_pool.convolution.weight tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_pool.normalization.bias tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_pool.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = model.segmentation_head.conv_pool.normalization.running_mean tf_to_pt_map[ prefix + "BatchNorm/moving_variance" ] = model.segmentation_head.conv_pool.normalization.running_var prefix = "aspp0/" tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_aspp.convolution.weight tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_aspp.normalization.bias tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_aspp.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = model.segmentation_head.conv_aspp.normalization.running_mean tf_to_pt_map[ prefix + "BatchNorm/moving_variance" ] = model.segmentation_head.conv_aspp.normalization.running_var prefix = "concat_projection/" tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_projection.convolution.weight tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_projection.normalization.bias tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_projection.normalization.weight tf_to_pt_map[ prefix + "BatchNorm/moving_mean" ] = model.segmentation_head.conv_projection.normalization.running_mean tf_to_pt_map[ prefix + "BatchNorm/moving_variance" ] = model.segmentation_head.conv_projection.normalization.running_var prefix = "logits/semantic/" tf_to_pt_map[ema(prefix + "weights")] = model.segmentation_head.classifier.convolution.weight tf_to_pt_map[ema(prefix + "biases")] = model.segmentation_head.classifier.convolution.bias return tf_to_pt_map def load_tf_weights_in_mobilenet_v2(model, config, tf_checkpoint_path): """Load TensorFlow checkpoints in a PyTorch model.""" try: import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise # Load weights from TF model init_vars = tf.train.list_variables(tf_checkpoint_path) tf_weights = {} for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_checkpoint_path, name) tf_weights[name] = array # Build TF to PyTorch weights loading map tf_to_pt_map = _build_tf_to_pytorch_map(model, config, tf_weights) for name, pointer in tf_to_pt_map.items(): logger.info(f"Importing {name}") if name not in tf_weights: logger.info(f"{name} not in tf pre-trained weights, skipping") continue array = tf_weights[name] if "depthwise_weights" in name: logger.info("Transposing depthwise") array = np.transpose(array, (2, 3, 0, 1)) elif "weights" in name: logger.info("Transposing") if len(pointer.shape) == 2: # copying into linear layer array = array.squeeze().transpose() else: array = np.transpose(array, (3, 2, 0, 1)) if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") logger.info(f"Initialize PyTorch weight {name} {array.shape}") pointer.data = torch.from_numpy(array) tf_weights.pop(name, None) tf_weights.pop(name + "/RMSProp", None) tf_weights.pop(name + "/RMSProp_1", None) tf_weights.pop(name + "/ExponentialMovingAverage", None) tf_weights.pop(name + "/Momentum", None) logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}") return model def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int: """ Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the original TensorFlow repo. It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py """ if min_value is None: min_value = divisor new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_value < 0.9 * value: new_value += divisor return int(new_value) def apply_depth_multiplier(config: MobileNetV2Config, channels: int) -> int: return make_divisible(int(round(channels * config.depth_multiplier)), config.depth_divisible_by, config.min_depth) def apply_tf_padding(features: torch.Tensor, conv_layer: nn.Conv2d) -> torch.Tensor: """ Apply TensorFlow-style "SAME" padding to a convolution layer. See the notes at: https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2 """ in_height = int(features.shape[-2]) in_width = int(features.shape[-1]) stride_height, stride_width = conv_layer.stride kernel_height, kernel_width = conv_layer.kernel_size dilation_height, dilation_width = conv_layer.dilation if in_height % stride_height == 0: pad_along_height = max(kernel_height - stride_height, 0) else: pad_along_height = max(kernel_height - (in_height % stride_height), 0) if in_width % stride_width == 0: pad_along_width = max(kernel_width - stride_width, 0) else: pad_along_width = max(kernel_width - (in_width % stride_width), 0) pad_left = pad_along_width // 2 pad_right = pad_along_width - pad_left pad_top = pad_along_height // 2 pad_bottom = pad_along_height - pad_top padding = ( pad_left * dilation_width, pad_right * dilation_width, pad_top * dilation_height, pad_bottom * dilation_height, ) return nn.functional.pad(features, padding, "constant", 0.0) class MobileNetV2ConvLayer(nn.Module): def __init__( self, config: MobileNetV2Config, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, groups: int = 1, bias: bool = False, dilation: int = 1, use_normalization: bool = True, use_activation: Union[bool, str] = True, layer_norm_eps: Optional[float] = None, ) -> None: super().__init__() self.config = config if in_channels % groups != 0: raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.") if out_channels % groups != 0: raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.") padding = 0 if config.tf_padding else int((kernel_size - 1) / 2) * dilation self.convolution = nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode="zeros", ) if use_normalization: self.normalization = nn.BatchNorm2d( num_features=out_channels, eps=config.layer_norm_eps if layer_norm_eps is None else layer_norm_eps, momentum=0.997, affine=True, track_running_stats=True, ) else: self.normalization = None if use_activation: if isinstance(use_activation, str): self.activation = ACT2FN[use_activation] elif isinstance(config.hidden_act, str): self.activation = ACT2FN[config.hidden_act] else: self.activation = config.hidden_act else: self.activation = None def forward(self, features: torch.Tensor) -> torch.Tensor: if self.config.tf_padding: features = apply_tf_padding(features, self.convolution) features = self.convolution(features) if self.normalization is not None: features = self.normalization(features) if self.activation is not None: features = self.activation(features) return features class MobileNetV2InvertedResidual(nn.Module): def __init__( self, config: MobileNetV2Config, in_channels: int, out_channels: int, stride: int, dilation: int = 1 ) -> None: super().__init__() expanded_channels = make_divisible( int(round(in_channels * config.expand_ratio)), config.depth_divisible_by, config.min_depth ) if stride not in [1, 2]: raise ValueError(f"Invalid stride {stride}.") self.use_residual = (stride == 1) and (in_channels == out_channels) self.expand_1x1 = MobileNetV2ConvLayer( config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1 ) self.conv_3x3 = MobileNetV2ConvLayer( config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=3, stride=stride, groups=expanded_channels, dilation=dilation, ) self.reduce_1x1 = MobileNetV2ConvLayer( config, in_channels=expanded_channels, out_channels=out_channels, kernel_size=1, use_activation=False, ) def forward(self, features: torch.Tensor) -> torch.Tensor: residual = features features = self.expand_1x1(features) features = self.conv_3x3(features) features = self.reduce_1x1(features) return residual + features if self.use_residual else features class MobileNetV2Stem(nn.Module): def __init__(self, config: MobileNetV2Config, in_channels: int, expanded_channels: int, out_channels: int) -> None: super().__init__() # The very first layer is a regular 3x3 convolution with stride 2 that expands to 32 channels. # All other expansion layers use the expansion factor to compute the number of output channels. self.first_conv = MobileNetV2ConvLayer( config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=3, stride=2, ) if config.first_layer_is_expansion: self.expand_1x1 = None else: self.expand_1x1 = MobileNetV2ConvLayer( config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=1 ) self.conv_3x3 = MobileNetV2ConvLayer( config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=3, stride=1, groups=expanded_channels, ) self.reduce_1x1 = MobileNetV2ConvLayer( config, in_channels=expanded_channels, out_channels=out_channels, kernel_size=1, use_activation=False, ) def forward(self, features: torch.Tensor) -> torch.Tensor: features = self.first_conv(features) if self.expand_1x1 is not None: features = self.expand_1x1(features) features = self.conv_3x3(features) features = self.reduce_1x1(features) return features class MobileNetV2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = MobileNetV2Config load_tf_weights = load_tf_weights_in_mobilenet_v2 base_model_prefix = "mobilenet_v2" main_input_name = "pixel_values" supports_gradient_checkpointing = False def _init_weights(self, module: Union[nn.Linear, nn.Conv2d]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): 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.BatchNorm2d): module.bias.data.zero_() module.weight.data.fill_(1.0) MOBILENET_V2_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 ([`MobileNetV2Config`]): 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. """ MOBILENET_V2_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 [`MobileNetV2ImageProcessor.__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 MobileNetV2 model outputting raw hidden-states without any specific head on top.", MOBILENET_V2_START_DOCSTRING, ) class MobileNetV2Model(MobileNetV2PreTrainedModel): def __init__(self, config: MobileNetV2Config, add_pooling_layer: bool = True): super().__init__(config) self.config = config # Output channels for the projection layers channels = [16, 24, 24, 32, 32, 32, 64, 64, 64, 64, 96, 96, 96, 160, 160, 160, 320] channels = [apply_depth_multiplier(config, x) for x in channels] # Strides for the depthwise layers strides = [2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1] self.conv_stem = MobileNetV2Stem( config, in_channels=config.num_channels, expanded_channels=apply_depth_multiplier(config, 32), out_channels=channels[0], ) current_stride = 2 # first conv layer has stride 2 dilation = 1 self.layer = nn.ModuleList() for i in range(16): # Keep making the feature maps smaller or use dilated convolution? if current_stride == config.output_stride: layer_stride = 1 layer_dilation = dilation dilation *= strides[i] # larger dilation starts in next block else: layer_stride = strides[i] layer_dilation = 1 current_stride *= layer_stride self.layer.append( MobileNetV2InvertedResidual( config, in_channels=channels[i], out_channels=channels[i + 1], stride=layer_stride, dilation=layer_dilation, ) ) if config.finegrained_output and config.depth_multiplier < 1.0: output_channels = 1280 else: output_channels = apply_depth_multiplier(config, 1280) self.conv_1x1 = MobileNetV2ConvLayer( config, in_channels=channels[-1], out_channels=output_channels, kernel_size=1, ) self.pooler = nn.AdaptiveAvgPool2d((1, 1)) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def _prune_heads(self, heads_to_prune): raise NotImplementedError @add_start_docstrings_to_model_forward(MOBILENET_V2_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") hidden_states = self.conv_stem(pixel_values) all_hidden_states = () if output_hidden_states else None for i, layer_module in enumerate(self.layer): hidden_states = layer_module(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) last_hidden_state = self.conv_1x1(hidden_states) if self.pooler is not None: pooled_output = torch.flatten(self.pooler(last_hidden_state), start_dim=1) else: pooled_output = None if not return_dict: return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None) return BaseModelOutputWithPoolingAndNoAttention( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=all_hidden_states, ) @add_start_docstrings( """ MobileNetV2 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, MOBILENET_V2_START_DOCSTRING, ) class MobileNetV2ForImageClassification(MobileNetV2PreTrainedModel): def __init__(self, config: MobileNetV2Config) -> None: super().__init__(config) self.num_labels = config.num_labels self.mobilenet_v2 = MobileNetV2Model(config) last_hidden_size = self.mobilenet_v2.conv_1x1.convolution.out_channels # Classifier head self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True) self.classifier = nn.Linear(last_hidden_size, 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(MOBILENET_V2_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, 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.mobilenet_v2(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(self.dropout(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 MobileNetV2DeepLabV3Plus(nn.Module): """ The neural network from the paper "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation" https://arxiv.org/abs/1802.02611 """ def __init__(self, config: MobileNetV2Config) -> None: super().__init__() self.avg_pool = nn.AdaptiveAvgPool2d(output_size=1) self.conv_pool = MobileNetV2ConvLayer( config, in_channels=apply_depth_multiplier(config, 320), out_channels=256, kernel_size=1, stride=1, use_normalization=True, use_activation="relu", layer_norm_eps=1e-5, ) self.conv_aspp = MobileNetV2ConvLayer( config, in_channels=apply_depth_multiplier(config, 320), out_channels=256, kernel_size=1, stride=1, use_normalization=True, use_activation="relu", layer_norm_eps=1e-5, ) self.conv_projection = MobileNetV2ConvLayer( config, in_channels=512, out_channels=256, kernel_size=1, stride=1, use_normalization=True, use_activation="relu", layer_norm_eps=1e-5, ) self.dropout = nn.Dropout2d(config.classifier_dropout_prob) self.classifier = MobileNetV2ConvLayer( config, in_channels=256, out_channels=config.num_labels, kernel_size=1, use_normalization=False, use_activation=False, bias=True, ) def forward(self, features: torch.Tensor) -> torch.Tensor: spatial_size = features.shape[-2:] features_pool = self.avg_pool(features) features_pool = self.conv_pool(features_pool) features_pool = nn.functional.interpolate( features_pool, size=spatial_size, mode="bilinear", align_corners=True ) features_aspp = self.conv_aspp(features) features = torch.cat([features_pool, features_aspp], dim=1) features = self.conv_projection(features) features = self.dropout(features) features = self.classifier(features) return features @add_start_docstrings( """ MobileNetV2 model with a semantic segmentation head on top, e.g. for Pascal VOC. """, MOBILENET_V2_START_DOCSTRING, ) class MobileNetV2ForSemanticSegmentation(MobileNetV2PreTrainedModel): def __init__(self, config: MobileNetV2Config) -> None: super().__init__(config) self.num_labels = config.num_labels self.mobilenet_v2 = MobileNetV2Model(config, add_pooling_layer=False) self.segmentation_head = MobileNetV2DeepLabV3Plus(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILENET_V2_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: Examples: ```python >>> from transformers import AutoImageProcessor, MobileNetV2ForSemanticSegmentation >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513") >>> model = MobileNetV2ForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513") >>> inputs = image_processor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> # logits are of shape (batch_size, num_labels, height, width) >>> logits = outputs.logits ```""" 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 outputs = self.mobilenet_v2( pixel_values, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1] logits = self.segmentation_head(encoder_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 = nn.functional.interpolate( logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index) loss = loss_fct(upsampled_logits, labels) 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=None, )