# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # 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. """Convert MobileNetV2 checkpoints from the tensorflow/models library.""" import argparse import json import re from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ( MobileNetV2Config, MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation, MobileNetV2ImageProcessor, load_tf_weights_in_mobilenet_v2, ) from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def get_mobilenet_v2_config(model_name): config = MobileNetV2Config(layer_norm_eps=0.001) if "quant" in model_name: raise ValueError("Quantized models are not supported.") matches = re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$", model_name) if matches: config.depth_multiplier = float(matches[1]) config.image_size = int(matches[2]) if model_name.startswith("deeplabv3_"): config.output_stride = 8 config.num_labels = 21 filename = "pascal-voc-id2label.json" else: # The TensorFlow version of MobileNetV2 predicts 1001 classes instead # of the usual 1000. The first class (index 0) is "background". config.num_labels = 1001 filename = "imagenet-1k-id2label.json" repo_id = "huggingface/label-files" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) if config.num_labels == 1001: id2label = {int(k) + 1: v for k, v in id2label.items()} id2label[0] = "background" else: id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} return config # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_movilevit_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False): """ Copy/paste/tweak model's weights to our MobileNetV2 structure. """ config = get_mobilenet_v2_config(model_name) # Load 🤗 model if model_name.startswith("deeplabv3_"): model = MobileNetV2ForSemanticSegmentation(config).eval() else: model = MobileNetV2ForImageClassification(config).eval() # Load weights from TensorFlow checkpoint load_tf_weights_in_mobilenet_v2(model, config, checkpoint_path) # Check outputs on an image, prepared by MobileNetV2ImageProcessor feature_extractor = MobileNetV2ImageProcessor( crop_size={"width": config.image_size, "height": config.image_size}, size={"shortest_edge": config.image_size + 32}, ) encoding = feature_extractor(images=prepare_img(), return_tensors="pt") outputs = model(**encoding) logits = outputs.logits if model_name.startswith("deeplabv3_"): assert logits.shape == (1, 21, 65, 65) if model_name == "deeplabv3_mobilenet_v2_1.0_513": expected_logits = torch.tensor( [ [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]], [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]], [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]], ] ) else: raise ValueError(f"Unknown model name: {model_name}") assert torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-4) else: assert logits.shape == (1, 1001) if model_name == "mobilenet_v2_1.4_224": expected_logits = torch.tensor([0.0181, -1.0015, 0.4688]) elif model_name == "mobilenet_v2_1.0_224": expected_logits = torch.tensor([0.2445, -1.1993, 0.1905]) elif model_name == "mobilenet_v2_0.75_160": expected_logits = torch.tensor([0.2482, 0.4136, 0.6669]) elif model_name == "mobilenet_v2_0.35_96": expected_logits = torch.tensor([0.1451, -0.4624, 0.7192]) else: expected_logits = None if expected_logits is not None: assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4) Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving feature extractor to {pytorch_dump_folder_path}") feature_extractor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print("Pushing to the hub...") repo_id = "google/" + model_name feature_extractor.push_to_hub(repo_id) model.push_to_hub(repo_id) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="mobilenet_v2_1.0_224", type=str, help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2__'.", ) parser.add_argument( "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)." ) parser.add_argument( "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_movilevit_checkpoint( args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub )