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Merge pull request #672 from ufal/img_feat
Extracting image features
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,10 +1,42 @@ | ||
| from typing import List | ||
| from typing import List, Callable, Iterable | ||
| import os | ||
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| from typeguard import check_argument_types | ||
| import numpy as np | ||
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| def numpy_reader(files: List[str]): | ||
| def single_tensor(files: List[str]) -> np.ndarray: | ||
| """Load a single tensor from a numpy file.""" | ||
| check_argument_types() | ||
| if len(files) == 1: | ||
| return np.load(files[0]) | ||
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| return np.concatenate([np.load(f) for f in files], axis=0) | ||
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| def from_file_list(prefix: str, | ||
| default_tensor_name: str = "arr_0") -> Callable: | ||
| """Load a list of numpy arrays from a list of .npz numpy files. | ||
| Args: | ||
| prefix: A common prefix for the files in the list. | ||
| default_tensor_name: Key of the tensors to load from the npz files. | ||
| Returns: | ||
| A generator function that yields the loaded arryas. | ||
| """ | ||
| check_argument_types() | ||
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| def load(files: List[str]) -> Iterable[np.ndarray]: | ||
| for list_file in files: | ||
| with open(list_file, encoding="utf-8") as f_list: | ||
| for line in f_list: | ||
| path = os.path.join(prefix, line.rstrip()) | ||
| with np.load(path) as npz: | ||
| yield npz[default_tensor_name] | ||
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| return load | ||
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| # pylint: disable=invalid-name | ||
| numpy_file_list_reader = from_file_list(prefix="") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,117 @@ | ||
| #!/usr/bin/env python3 | ||
| """Extract imagenet features from given images. | ||
| The script reads a list of pahts to images (specified by path prefix and list | ||
| of relative paths), process the images using an imagenet network and extract a | ||
| given convolutional map from the image. The maps are saved as numpy tensors in | ||
| files with a different prefix and the same relative path from this prefix | ||
| ending with .npz. | ||
| """ | ||
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| import argparse | ||
| import os | ||
| import sys | ||
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| import numpy as np | ||
| import tensorflow as tf | ||
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| from neuralmonkey.dataset import Dataset | ||
| from neuralmonkey.encoders.imagenet_encoder import ImageNet | ||
| from neuralmonkey.logging import log | ||
| from neuralmonkey.readers.image_reader import single_image_for_imagenet | ||
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| SUPPORTED_NETWORKS = [ | ||
| "vgg_16", "vgg_19", "resnet_v2_50", "resnet_v2_101", "resnet_v2_152"] | ||
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| def main(): | ||
| parser = argparse.ArgumentParser(description=__doc__) | ||
| parser.add_argument("--net", type=str, choices=SUPPORTED_NETWORKS, | ||
| help="Type of imagenet network.") | ||
| parser.add_argument("--input-prefix", type=str, default="", | ||
| help="Prefix of the image path.") | ||
| parser.add_argument("--output-prefix", type=str, default="", | ||
| help="Prefix of the path to the output numpy files.") | ||
| parser.add_argument("--slim-models", type=str, required=True, | ||
| help="Path to SLIM models in cloned tensorflow/models " | ||
| "repository") | ||
| parser.add_argument("--model-checkpoint", type=str, required=True, | ||
| help="Path to the ImageNet model checkpoint.") | ||
| parser.add_argument("--conv-map", type=str, required=True, | ||
| help="Name of the convolutional map that is.") | ||
| parser.add_argument("--images", type=str, | ||
| help="File with paths to images or stdin by default.") | ||
| parser.add_argument("--batch-size", type=int, default=128) | ||
| args = parser.parse_args() | ||
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| if not os.path.exists(args.input_prefix): | ||
| raise ValueError("Directory {} does not exist.".format( | ||
| args.input_prefix)) | ||
| if not os.path.exists(args.output_prefix): | ||
| raise ValueError("Directory {} does not exist.".format( | ||
| args.output_prefix)) | ||
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| if args.net.startswith("vgg_"): | ||
| img_size = 224 | ||
| vgg_normalization = True | ||
| zero_one_normalization = False | ||
| elif args.net.startswith("resnet_v2"): | ||
| img_size = 229 | ||
| vgg_normalization = False | ||
| zero_one_normalization = True | ||
| else: | ||
| raise ValueError("Unspported network: {}.".format(args._net)) | ||
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| log("Creating graph for the ImageNet network.") | ||
| imagenet = ImageNet( | ||
| name="imagenet", data_id="images", network_type=args.net, | ||
| slim_models_path=args.slim_models, load_checkpoint=args.model_checkpoint, | ||
| spatial_layer=args.conv_map) | ||
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| log("Creating TensorFlow session.") | ||
| session = tf.Session() | ||
| session.run(tf.global_variables_initializer()) | ||
| log("Loading ImageNet model variables.") | ||
| imagenet.load(session) | ||
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| if args.images is None: | ||
| log("No input file provided, reading paths from stdin.") | ||
| source = sys.stdin | ||
| else: | ||
| source = open(args.images) | ||
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| images = [] | ||
| image_paths = [] | ||
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| def process_images(): | ||
| dataset = Dataset("dataset", {"images": np.array(images)}, {}) | ||
| feed_dict = imagenet.feed_dict(dataset) | ||
| feature_maps = session.run(imagenet.spatial_states, feed_dict=feed_dict) | ||
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| for features, rel_path in zip(feature_maps, image_paths): | ||
| npz_path = os.path.join(args.output_prefix, rel_path + ".npz") | ||
| os.makedirs(os.path.dirname(npz_path), exist_ok=True) | ||
| np.savez(npz_path, features) | ||
| print(npz_path) | ||
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| for img in source: | ||
| img_path = os.path.join(args.input_prefix, img.rstrip()) | ||
| images.append(single_image_for_imagenet( | ||
| img_path, img_size, img_size, vgg_normalization, | ||
| zero_one_normalization)) | ||
| image_paths.append(img.rstrip()) | ||
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| if len(images) >= args.batch_size: | ||
| process_images() | ||
| images = [] | ||
| image_paths = [] | ||
| process_images() | ||
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| if args.images is not None: | ||
| source.close() | ||
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| if __name__ == "__main__": | ||
| main() |