Docs for saliency, Credits section in Readme, gradcam ignored for unknown model types
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Shital Shah
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@ -117,6 +117,10 @@ We would love your contributions, feedback, questions, and feature requests! Ple
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Join the TensorWatch group on [Facebook](https://www.facebook.com/groups/378075159472803/) to stay up to date or ask any questions.
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## Credits
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TensorWatch utilizes several open source libraries for many of its features. These includes: [hiddenlayer](https://github.com/waleedka/hiddenlayer), [torchstat](https://github.com/Swall0w/torchstat), [Visual-Attribution](https://github.com/yulongwang12/visual-attribution), [pyzmq](https://github.com/zeromq/pyzmq), [receptivefield](https://github.com/fornaxai/receptivefield), [nbformat](https://github.com/jupyter/nbformat). Please see `install_requires` section in [setup.py](setup.py) for upto date list.
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## License
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This project is released under the MIT License. Please review the [License file](LICENSE.txt) for more details.
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Различия файлов скрыты, потому что одна или несколько строк слишком длинны
2
setup.py
2
setup.py
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@ -23,6 +23,6 @@ setuptools.setup(
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"Operating System :: OS Independent",
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),
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install_requires=[
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'matplotlib', 'numpy', 'pyzmq', 'plotly', 'torchstat', 'receptivefield', 'ipywidgets', 'sklearn'
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'matplotlib', 'numpy', 'pyzmq', 'plotly', 'torchstat', 'receptivefield', 'ipywidgets', 'sklearn', 'nbformat'
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]
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)
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@ -3,7 +3,7 @@
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from torchvision import transforms
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from . import pytorch_utils
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import json
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import json, os
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def get_image_transform():
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transf = transforms.Compose([ #TODO: cache these transforms?
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@ -21,6 +21,9 @@ def get_normalize_transform():
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return transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])
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def image2batch(image):
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return pytorch_utils.image2batch(image, image_transform=get_image_transform())
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def predict(model, images, image_transform=None, device=None):
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logits = pytorch_utils.batch_predict(model, images,
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input_transform=image_transform or get_image_transform(), device=device)
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@ -39,15 +42,18 @@ def probabilities2classes(probs, topk=5):
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top_probs = probs.topk(topk)
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# return (probability, class_id, class_label, class_code)
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return tuple((p,c, labels.index2label_text(c), labels.index2label_code(c)) \
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for p, c in zip(top_probs[0][0].detach().numpy(), top_probs[1][0].detach().numpy()))
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for p, c in zip(top_probs[0][0].data.cpu().numpy(), top_probs[1][0].data.cpu().numpy()))
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class ImagenetLabels:
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def __init__(self, json_path='imagenet_class_index.json'):
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def __init__(self, json_path=None):
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self._idx2label = []
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self._idx2cls = []
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self._cls2label = {}
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self._cls2idx = {}
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with open(json_path, "r") as read_file:
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json_path = json_path or os.path.join(os.path.dirname(__file__), 'imagenet_class_index.json')
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with open(os.path.abspath(json_path), "r") as read_file:
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class_json = json.load(read_file)
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self._idx2label = [class_json[str(k)][1] for k in range(len(class_json))]
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self._idx2cls = [class_json[str(k)][0] for k in range(len(class_json))]
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@ -21,17 +21,20 @@ def tensors2batch(tensors, preprocess_transform=None):
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def int2tensor(val):
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return torch.LongTensor([val])
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def image2batch(image, image_transform=None):
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if image_transform:
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input_x = image_transform(image)
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else: # if no transforms supplied then just convert PIL image to tensor
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input_x = transforms.ToTensor()(image)
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input_x = input_x.unsqueeze(0) #convert to batch of 1
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return input_x
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def image_class2tensor(image_path, class_index=None, image_convert_mode=None,
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image_transform=None):
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raw_input = image_utils.open_image(os.path.abspath(image_path), convert_mode=image_convert_mode)
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if image_transform:
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input_x = image_transform(raw_input)
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else:
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input_x = transforms.ToTensor()(raw_input)
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input_x = input_x.unsqueeze(0) #convert to batch of 1
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image_pil = image_utils.open_image(os.path.abspath(image_path), convert_mode=image_convert_mode)
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input_x = image2batch(image_pil, image_transform)
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target_class = int2tensor(class_index) if class_index is not None else None
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return raw_input, input_x, target_class
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return image_pil, input_x, target_class
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def batch_predict(model, inputs, input_transform=None, device=None):
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if input_transform:
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@ -3,6 +3,9 @@ from .backprop import VanillaGradExplainer
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def _get_layer(model, key_list):
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if key_list is None:
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return None
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a = model
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for key in key_list:
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a = a._modules[key]
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@ -27,12 +30,13 @@ class GradCAMExplainer(VanillaGradExplainer):
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def backward_hook(m, grad_i, grad_o):
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self.intermediate_grad.append(grad_o[0].data.clone())
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if self.use_inp:
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self.target_layer.register_forward_hook(forward_hook_input)
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else:
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self.target_layer.register_forward_hook(forward_hook_output)
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if self.target_layer is not None:
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if self.use_inp:
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self.target_layer.register_forward_hook(forward_hook_input)
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else:
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self.target_layer.register_forward_hook(forward_hook_output)
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self.target_layer.register_backward_hook(backward_hook)
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self.target_layer.register_backward_hook(backward_hook)
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def _reset_intermediate_lists(self):
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self.intermediate_act = []
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@ -43,14 +47,16 @@ class GradCAMExplainer(VanillaGradExplainer):
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_ = super(GradCAMExplainer, self)._backprop(inp, ind)
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grad = self.intermediate_grad[0]
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act = self.intermediate_act[0]
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if len(self.intermediate_grad):
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grad = self.intermediate_grad[0]
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act = self.intermediate_act[0]
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weights = grad.sum(-1).sum(-1).unsqueeze(-1).unsqueeze(-1)
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cam = weights * act
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cam = cam.sum(1).unsqueeze(1)
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weights = grad.sum(-1).sum(-1).unsqueeze(-1).unsqueeze(-1)
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cam = weights * act
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cam = cam.sum(1).unsqueeze(1)
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cam = torch.clamp(cam, min=0)
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return cam
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cam = torch.clamp(cam, min=0)
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return cam
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else:
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return None
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@ -52,7 +52,7 @@ def _get_layer_path(model):
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return ['avgpool'] #layer4
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elif model.__class__.__name__ == 'Inception3':
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return ['Mixed_7c', 'branch_pool'] # ['conv2d_94'], 'mixed10'
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else: #unknown network
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else: #TODO: guess layer for other networks?
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return None
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def get_saliency(model, raw_input, input, label, method='integrate_grad', layer_path=None):
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@ -75,11 +75,14 @@ def get_saliency(model, raw_input, input, label, method='integrate_grad', layer_
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exp = _get_explainer(method, model, layer_path)
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saliency = exp.explain(input, label, raw_input)
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saliency = saliency.abs().sum(dim=1)[0].squeeze()
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saliency -= saliency.min()
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saliency /= (saliency.max() + 1e-20)
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if saliency is not None:
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saliency = saliency.abs().sum(dim=1)[0].squeeze()
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saliency -= saliency.min()
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saliency /= (saliency.max() + 1e-20)
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return saliency.detach().cpu().numpy()
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return saliency.detach().cpu().numpy()
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else:
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return None
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def get_image_saliency_results(model, raw_image, input, label,
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methods=['lime_imagenet', 'gradcam', 'smooth_grad',
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@ -88,15 +91,17 @@ def get_image_saliency_results(model, raw_image, input, label,
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results = []
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for method in methods:
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sal = get_saliency(model, raw_image, input, label, method=method)
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results.append(ImageSaliencyResult(raw_image, sal, method))
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if sal is not None:
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results.append(ImageSaliencyResult(raw_image, sal, method))
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return results
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def get_image_saliency_plot(image_saliency_results, cols = 2, figsize = None):
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def get_image_saliency_plot(image_saliency_results, cols:int=2, figsize:tuple=None):
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import matplotlib.pyplot as plt # delayed import due to matplotlib threading issue
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rows = math.ceil(len(image_saliency_results) / cols)
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figsize=figsize or (8, 3 * rows)
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figure = plt.figure(figsize=figsize) #figsize=(8, 3)
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figure = plt.figure(figsize=figsize)
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for i, r in enumerate(image_saliency_results):
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ax = figure.add_subplot(rows, cols, i+1)
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@ -106,7 +111,8 @@ def get_image_saliency_plot(image_saliency_results, cols = 2, figsize = None):
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#upsampler = nn.Upsample(size=(raw_image.height, raw_image.width), mode='bilinear')
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saliency_upsampled = skimage.transform.resize(r.saliency,
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(r.raw_image.height, r.raw_image.width))
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(r.raw_image.height, r.raw_image.width),
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mode='reflect')
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image_utils.show_image(r.raw_image, img2=saliency_upsampled,
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alpha2=r.saliency_alpha, cmap2=r.saliency_cmap, ax=ax)
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@ -47,7 +47,7 @@ class WatcherClient(WatcherBase):
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utils.debug_log("WatcherClient is closed", verbosity=1)
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super(WatcherClient, self).close()
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def devices_or_default(self, devices:Sequence[str])->Sequence[str]: # overriden
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def devices_or_default(self, devices:Sequence[str])->Sequence[str]: # overridden
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# TODO: this method is duplicated in Watcher and WatcherClient
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# make sure TCP port is attached to tcp device
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@ -5,7 +5,7 @@
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<ProjectGuid>9a7fe67e-93f0-42b5-b58f-77320fc639e4</ProjectGuid>
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<ProjectHome>
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</ProjectHome>
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<StartupFile>simple_log\sum_lazy.py</StartupFile>
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<StartupFile>post_train\saliency.py</StartupFile>
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<SearchPath>
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</SearchPath>
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<WorkingDirectory>.</WorkingDirectory>
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