# 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 MobileNetV1 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 from ...modeling_utils import PreTrainedModel from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_mobilenet_v1 import MobileNetV1Config logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "MobileNetV1Config" # Base docstring _CHECKPOINT_FOR_DOC = "google/mobilenet_v1_1.0_224" _EXPECTED_OUTPUT_SHAPE = [1, 1024, 7, 7] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "google/mobilenet_v1_1.0_224" _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/mobilenet_v1_1.0_224", "google/mobilenet_v1_0.75_192", # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1 ] 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, MobileNetV1ForImageClassification): backbone = model.mobilenet_v1 else: backbone = model prefix = "MobilenetV1/Conv2d_0/" tf_to_pt_map[prefix + "weights"] = backbone.conv_stem.convolution.weight tf_to_pt_map[prefix + "BatchNorm/beta"] = backbone.conv_stem.normalization.bias tf_to_pt_map[prefix + "BatchNorm/gamma"] = backbone.conv_stem.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.normalization.running_var for i in range(13): tf_index = i + 1 pt_index = i * 2 pointer = backbone.layer[pt_index] prefix = f"MobilenetV1/Conv2d_{tf_index}_depthwise/" tf_to_pt_map[prefix + "depthwise_weights"] = pointer.convolution.weight tf_to_pt_map[prefix + "BatchNorm/beta"] = pointer.normalization.bias tf_to_pt_map[prefix + "BatchNorm/gamma"] = pointer.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.normalization.running_var pointer = backbone.layer[pt_index + 1] prefix = f"MobilenetV1/Conv2d_{tf_index}_pointwise/" tf_to_pt_map[prefix + "weights"] = pointer.convolution.weight tf_to_pt_map[prefix + "BatchNorm/beta"] = pointer.normalization.bias tf_to_pt_map[prefix + "BatchNorm/gamma"] = pointer.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.normalization.running_var if isinstance(model, MobileNetV1ForImageClassification): prefix = "MobilenetV1/Logits/Conv2d_1c_1x1/" tf_to_pt_map[prefix + "weights"] = model.classifier.weight tf_to_pt_map[prefix + "biases"] = model.classifier.bias return tf_to_pt_map def load_tf_weights_in_mobilenet_v1(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) logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}") return model 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, in_width = features.shape[-2:] stride_height, stride_width = conv_layer.stride kernel_height, kernel_width = conv_layer.kernel_size 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, pad_right, pad_top, pad_bottom) return nn.functional.pad(features, padding, "constant", 0.0) class MobileNetV1ConvLayer(nn.Module): def __init__( self, config: MobileNetV1Config, in_channels: int, out_channels: int, kernel_size: int, stride: Optional[int] = 1, groups: Optional[int] = 1, bias: bool = False, use_normalization: Optional[bool] = True, use_activation: Optional[bool or str] = True, ) -> 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) self.convolution = nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, bias=bias, padding_mode="zeros", ) if use_normalization: self.normalization = nn.BatchNorm2d( num_features=out_channels, eps=config.layer_norm_eps, momentum=0.9997, 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 MobileNetV1PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = MobileNetV1Config load_tf_weights = load_tf_weights_in_mobilenet_v1 base_model_prefix = "mobilenet_v1" 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_V1_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 ([`MobileNetV1Config`]): 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_V1_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 [`MobileNetV1ImageProcessor.__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 MobileNetV1 model outputting raw hidden-states without any specific head on top.", MOBILENET_V1_START_DOCSTRING, ) class MobileNetV1Model(MobileNetV1PreTrainedModel): def __init__(self, config: MobileNetV1Config, add_pooling_layer: bool = True): super().__init__(config) self.config = config depth = 32 out_channels = max(int(depth * config.depth_multiplier), config.min_depth) self.conv_stem = MobileNetV1ConvLayer( config, in_channels=config.num_channels, out_channels=out_channels, kernel_size=3, stride=2, ) strides = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1] self.layer = nn.ModuleList() for i in range(13): in_channels = out_channels if strides[i] == 2 or i == 0: depth *= 2 out_channels = max(int(depth * config.depth_multiplier), config.min_depth) self.layer.append( MobileNetV1ConvLayer( config, in_channels=in_channels, out_channels=in_channels, kernel_size=3, stride=strides[i], groups=in_channels, ) ) self.layer.append( MobileNetV1ConvLayer( config, in_channels=in_channels, out_channels=out_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_V1_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 = 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( """ MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, MOBILENET_V1_START_DOCSTRING, ) class MobileNetV1ForImageClassification(MobileNetV1PreTrainedModel): def __init__(self, config: MobileNetV1Config) -> None: super().__init__(config) self.num_labels = config.num_labels self.mobilenet_v1 = MobileNetV1Model(config) last_hidden_size = self.mobilenet_v1.layer[-1].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_V1_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.mobilenet_v1(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, )