# 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" 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 _no_split_modules = [] 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 if labels is not None and self.config.num_labels == 1: raise ValueError("The number of labels should be greater than one") 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: # 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, ) __all__ = [ "MobileNetV2ForImageClassification", "MobileNetV2ForSemanticSegmentation", "MobileNetV2Model", "MobileNetV2PreTrainedModel", "load_tf_weights_in_mobilenet_v2", ]