| from fastai.vision.all import * |
| import cv2 |
| import gradio as gr |
| import glob |
|
|
| class Hook(): |
| def hook_func(self, m, i, o): self.stored = o.detach().clone() |
|
|
| learn = load_learner("resnet152_fit_one_cycle_freeze_91acc.pkl", cpu=True) |
|
|
| path = "train_val_cropped" |
| dblock = DataBlock(blocks = (ImageBlock, CategoryBlock), |
| get_items = get_image_files, |
| splitter = GrandparentSplitter(train_name="train", |
| valid_name="valid"), |
| get_y=parent_label, |
| item_tfms=RandomResizedCrop(128, min_scale=0.7), |
| batch_tfms=[*aug_transforms(), |
| Normalize.from_stats(*imagenet_stats)]) |
| dls_augmented = dblock.dataloaders(path, shuffle=True) |
|
|
| class Hook(): |
| def hook_func(self, m, i, o): self.stored = o.detach().clone() |
|
|
| def gradcam(img_create): |
|
|
| pred,idx,probs=learn.predict(img_create) |
| |
| x,= first(dls_augmented.test_dl([img_create])) |
| hook_output = Hook() |
| hook = learn.model[0].register_forward_hook(hook_output.hook_func) |
| with torch.no_grad(): output = learn.model.eval()(x) |
| act = hook_output.stored[0] |
| hook.remove() |
|
|
| input_size=act.shape[0] |
| out_size=learn.model[1][-1].in_features |
| kernel_size=act.shape[1] |
| a=act |
| new_act=tensor(np.zeros((out_size,kernel_size,kernel_size))) |
| sum=tensor(np.zeros((1,kernel_size,kernel_size))) |
| for i in range(0,input_size,4): |
| sum=tensor(np.zeros((1,kernel_size,kernel_size))) |
| for j in range(i,i+4): |
| sum=sum+act[j,:,:] |
| new_act[int(i/4),:,:]=sum/4 |
| cam_map = torch.einsum('ck,kij->cij', learn.model[1][-1].weight, new_act) |
|
|
| x_dec = TensorImage(dls_augmented.train.decode((x,))[0][0]) |
| |
| fig,ax = plt.subplots() |
| x_dec.show(ctx=ax) |
| ax.imshow(cam_map[1].detach().cpu(), alpha=0.6, extent=(0,128,128,0), interpolation='bilinear', cmap='magma'); |
| plt.tight_layout() |
| fig.savefig("gcam.jpg", dpi=199) |
| im=cv2.imread("gcam.jpg",1) |
| im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) |
| |
|
|
| return im, dict(zip(categories, map(float, probs))) |
|
|
| def classify_img(img): |
| pred,idx,probs=learn.predict(img) |
| return dict(zip(categories, map(float, probs))) |
|
|
| categories = ('arbanasi', 'filibe', 'gjirokoster', 'iskodra', 'kula', 'kuzguncuk', 'larissa_ampelakia', 'mardin', 'ohrid', 'pristina', 'safranbolu', 'selanik', 'sozopol_suzebolu', 'tiran', 'varna') |
| |
| |
| |
|
|
| image=gr.inputs.Image(shape=(128,128)) |
| label=gr.outputs.Label() |
| |
| |
| |
|
|
| examples=["filibe-1-1.jpg", |
| "ohrid-3-1.jpg", |
| "varna-1-1.jpg"] |
|
|
|
|
| demo = gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples) |
|
|
| demo.launch(inline=False) |
