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e5461d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | # common.py
import copy
import numpy as np
import scipy.ndimage as ndimage
import torch
import torch.nn.functional as F
from torch import nn
class Preprocessing(torch.nn.Module):
def __init__(self, input_dims, output_dim):
super(Preprocessing, self).__init__()
self.input_dims = input_dims
self.output_dim = output_dim
self.preprocessing_modules = torch.nn.ModuleList()
for _ in input_dims:
module = MeanMapper(output_dim)
self.preprocessing_modules.append(module)
def forward(self, features):
_features = []
for module, feature in zip(self.preprocessing_modules, features):
_features.append(module(feature))
return torch.stack(_features, dim=1)
class MeanMapper(torch.nn.Module):
def __init__(self, preprocessing_dim):
super(MeanMapper, self).__init__()
self.preprocessing_dim = preprocessing_dim
def forward(self, features):
features = features.reshape(len(features), 1, -1)
return F.adaptive_avg_pool1d(features, self.preprocessing_dim).squeeze(1)
class Aggregator(torch.nn.Module):
def __init__(self, target_dim):
super(Aggregator, self).__init__()
self.target_dim = target_dim
def forward(self, features):
"""Returns reshaped and average pooled features."""
features = features.reshape(len(features), 1, -1)
features = F.adaptive_avg_pool1d(features, self.target_dim)
return features.reshape(len(features), -1)
class RescaleSegmentor:
def __init__(self, device, target_size=288):
self.device = device
self.target_size = target_size
self.smoothing = 4
def convert_to_segmentation(self, patch_scores):
with torch.no_grad():
if isinstance(patch_scores, np.ndarray):
patch_scores = torch.from_numpy(patch_scores)
_scores = patch_scores.to(self.device)
_scores = _scores.unsqueeze(1)
_scores = F.interpolate(
_scores, size=self.target_size, mode="bilinear", align_corners=False
)
_scores = _scores.squeeze(1)
patch_scores = _scores.cpu().numpy()
return [ndimage.gaussian_filter(patch_score, sigma=self.smoothing) for patch_score in patch_scores]
class NetworkFeatureAggregator(torch.nn.Module):
"""Efficient extraction of network features."""
def __init__(self, backbone, layers_to_extract_from, device, train_backbone=False):
super(NetworkFeatureAggregator, self).__init__()
"""Extraction of network features.
Runs a network only to the last layer of the list of layers where
network features should be extracted from.
Args:
backbone: torchvision.model
layers_to_extract_from: [list of str]
"""
self.layers_to_extract_from = layers_to_extract_from
self.backbone = backbone
self.device = device
self.train_backbone = train_backbone
if not hasattr(backbone, "hook_handles"):
self.backbone.hook_handles = []
for handle in self.backbone.hook_handles:
handle.remove()
self.outputs = {}
for extract_layer in layers_to_extract_from:
self.register_hook(extract_layer)
self.to(self.device)
def forward(self, images, eval=True):
self.outputs.clear()
if self.train_backbone and not eval:
self.backbone.train()
self.backbone(images)
else:
self.backbone.eval()
with torch.no_grad():
self.backbone(images)
return self.outputs
def feature_dimensions(self, input_shape):
"""Computes the feature dimensions for all layers given input_shape."""
_input = torch.ones([1] + list(input_shape)).to(self.device)
_output = self(_input)
return [_output[layer].shape[1] for layer in self.layers_to_extract_from]
def register_hook(self, layer_name):
module = self.find_module(self.backbone, layer_name)
if module is not None:
forward_hook = ForwardHook(self.outputs, layer_name, self.layers_to_extract_from[-1])
if isinstance(module, torch.nn.Sequential):
hook = module[-1].register_forward_hook(forward_hook)
else:
hook = module.register_forward_hook(forward_hook)
self.backbone.hook_handles.append(hook)
else:
raise ValueError(f"Module {layer_name} not found in the model")
def find_module(self, model, module_name):
for name, module in model.named_modules():
if name == module_name:
return module
elif '.' in module_name:
father, child = module_name.split('.', 1)
if name == father:
return self.find_module(module, child)
return None
class ForwardHook:
def __init__(self, hook_dict, layer_name: str, last_layer_to_extract: str):
self.hook_dict = hook_dict
self.layer_name = layer_name
self.raise_exception_to_break = copy.deepcopy(
layer_name == last_layer_to_extract
)
def __call__(self, module, input, output):
self.hook_dict[self.layer_name] = output
return None
class LastLayerToExtractReachedException(Exception):
pass
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