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from ..torch_core import * |
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from ..basic_data import * |
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from ..basic_train import * |
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from .image import * |
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from ..train import Interpretation |
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from textwrap import wrap |
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__all__ = ['SegmentationInterpretation', 'ObjectDetectionInterpretation'] |
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class SegmentationInterpretation(Interpretation): |
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"Interpretation methods for segmenatation models." |
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def __init__(self, learn:Learner, preds:Tensor, y_true:Tensor, losses:Tensor, |
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ds_type:DatasetType=DatasetType.Valid): |
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super(SegmentationInterpretation, self).__init__(learn,preds,y_true,losses,ds_type) |
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self.pred_class = self.preds.argmax(dim=1) |
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self.c2i = {c:i for i,c in enumerate(self.data.classes)} |
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self.i2c = {i:c for c,i in self.c2i.items()} |
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def top_losses(self, sizes:Tuple, k:int=None, largest=True): |
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"Reduce flatten loss to give a single loss value for each image" |
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losses = self.losses.view(-1, np.prod(sizes)).mean(-1) |
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return losses.topk(ifnone(k, len(losses)), largest=largest) |
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def _interp_show(self, ims:ImageSegment, classes:Collection=None, sz:int=20, cmap='tab20', |
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title_suffix:str=None): |
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"Show ImageSegment with color mapping labels" |
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fig,axes=plt.subplots(1,2,figsize=(sz,sz)) |
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np_im = to_np(ims.data).copy() |
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if classes is not None: |
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class_idxs = [self.c2i[c] for c in classes] |
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mask = np.max(np.stack([np_im==i for i in class_idxs]),axis=0) |
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np_im = (np_im*mask).astype(np.float) |
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np_im[np.where(mask==0)] = np.nan |
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im=axes[0].imshow(np_im[0], cmap=cmap) |
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np_im_labels = list(np.unique(np_im[~np.isnan(np_im)])) |
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c = len(np_im_labels); n = math.ceil(np.sqrt(c)) |
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label_im = np.array(np_im_labels + [np.nan]*(n**2-c)).reshape(n,n) |
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axes[1].imshow(label_im, cmap=cmap) |
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for i,l in enumerate([self.i2c[l] for l in np_im_labels]): |
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div,mod=divmod(i,n) |
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l = "\n".join(wrap(l,10)) if len(l) > 10 else l |
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axes[1].text(mod, div, f"{l}", ha='center', color='white', fontdict={'size':sz}) |
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if title_suffix: |
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axes[0].set_title(f"{title_suffix}_imsegment") |
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axes[1].set_title(f"{title_suffix}_labels") |
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def show_xyz(self, i, classes:list=None, sz=10): |
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'show (image, true and pred) from self.ds with color mappings, optionally only plot' |
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x,y = self.ds[i] |
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self.ds.show_xys([x],[y], figsize=(sz/2,sz/2)) |
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self._interp_show(ImageSegment(self.y_true[i]), classes, sz=sz, title_suffix='true') |
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self._interp_show(ImageSegment(self.pred_class[i][None,:]), classes, sz=sz, title_suffix='pred') |
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def _generate_confusion(self): |
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"Average and Per Image Confusion: intersection of pixels given a true label, true label sums to 1" |
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single_img_confusion = [] |
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mean_confusion = [] |
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n = self.pred_class.shape[0] |
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for c_j in range(self.data.c): |
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true_binary = self.y_true.squeeze(1) == c_j |
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total_true = true_binary.view(n,-1).sum(dim=1).float() |
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for c_i in range(self.data.c): |
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pred_binary = self.pred_class == c_i |
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total_intersect = (true_binary*pred_binary).view(n,-1).sum(dim=1).float() |
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p_given_t = (total_intersect / (total_true)) |
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p_given_t_mean = p_given_t[~torch.isnan(p_given_t)].mean() |
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single_img_confusion.append(p_given_t) |
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mean_confusion.append(p_given_t_mean) |
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self.single_img_cm = to_np(torch.stack(single_img_confusion).permute(1,0).view(-1, self.data.c, self.data.c)) |
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self.mean_cm = to_np(torch.tensor(mean_confusion).view(self.data.c, self.data.c)) |
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return self.mean_cm, self.single_img_cm |
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def _plot_intersect_cm(self, cm, title="Intersection with Predict given True"): |
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"Plot confusion matrices: self.mean_cm or self.single_img_cm generated by `_generate_confusion`" |
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from IPython.display import display, HTML |
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fig,ax=plt.subplots(1,1,figsize=(10,10)) |
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im=ax.imshow(cm, cmap="Blues") |
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ax.set_xlabel("Predicted") |
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ax.set_ylabel("True") |
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ax.set_title(f"{title}") |
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ax.set_xticks(range(self.data.c)) |
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ax.set_yticks(range(self.data.c)) |
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ax.set_xticklabels(self.data.classes, rotation='vertical') |
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ax.set_yticklabels(self.data.classes) |
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fig.colorbar(im) |
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df = (pd.DataFrame([self.data.classes, cm.diagonal()], index=['label', 'score']) |
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.T.sort_values('score', ascending=False)) |
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with pd.option_context('display.max_colwidth', -1): |
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display(HTML(df.to_html(index=False))) |
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return df |
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class ObjectDetectionInterpretation(Interpretation): |
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"Interpretation methods for classification models." |
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def __init__(self, learn:Learner, preds:Tensor, y_true:Tensor, losses:Tensor, ds_type:DatasetType=DatasetType.Valid): |
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raise NotImplementedError |
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super(ObjectDetectionInterpretation, self).__init__(learn,preds,y_true,losses,ds_type) |
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