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great work I would like to be enlightned more about this part if you allow, because I couldnt get to understand its role in prediction part. Thanks in advance: for i in range(7): if i == 0: # reverse first dimension mymat = predict(img[::-1,:,:], model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(mymat[0][0][0], mymat[3][12][13]) print("Case 1",img.shape, mymat.shape) elif i == 1: # reverse second dimension temp = predict(img[:,::-1,:], model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(temp[0][0][0], temp[3][12][13]) print("Case 2", temp.shape, mymat.shape) mymat = np.mean( np.array([ temp[:,::-1,:], mymat ]), axis=0 ) elif i == 2: # transpose(interchange) first and second dimensions temp = predict(img.transpose([1,0,2]), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(temp[0][0][0], temp[3][12][13]) print("Case 3", temp.shape, mymat.shape) mymat = np.mean( np.array([ temp.transpose(0,2,1), mymat ]), axis=0 ) elif i == 3: temp = predict(np.rot90(img, 1), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES) #print(temp.transpose([2,0,1])[0][0][0], temp.transpose([2,0,1])[3][12][13]) print("Case 4", temp.shape, mymat.shape) mymat = np.mean( np.array([ np.rot90(temp, -1).transpose([2,0,1]), mymat ]), axis=0 ) elif i == 4: temp = predict(np.rot90(img,2), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES) #print(temp.transpose([2,0,1])[0][0][0], temp.transpose([2,0,1])[3][12][13]) print("Case 5", temp.shape, mymat.shape) mymat = np.mean( np.array([ np.rot90(temp,-2).transpose([2,0,1]), mymat ]), axis=0 ) elif i == 5: temp = predict(np.rot90(img,3), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES) #print(temp.transpose([2,0,1])[0][0][0], temp.transpose([2,0,1])[3][12][13]) print("Case 6", temp.shape, mymat.shape) mymat = np.mean( np.array([ np.rot90(temp, -3).transpose(2,0,1), mymat ]), axis=0 ) else: temp = predict(img, model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(temp[0][0][0], temp[3][12][13]) print("Case 7", temp.shape, mymat.shape) mymat = np.mean( np.array([ temp, mymat ]), axis=0 )
for i in range(7): if i == 0: # reverse first dimension mymat = predict(img[::-1,:,:], model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(mymat[0][0][0], mymat[3][12][13]) print("Case 1",img.shape, mymat.shape) elif i == 1: # reverse second dimension temp = predict(img[:,::-1,:], model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(temp[0][0][0], temp[3][12][13]) print("Case 2", temp.shape, mymat.shape) mymat = np.mean( np.array([ temp[:,::-1,:], mymat ]), axis=0 ) elif i == 2: # transpose(interchange) first and second dimensions temp = predict(img.transpose([1,0,2]), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(temp[0][0][0], temp[3][12][13]) print("Case 3", temp.shape, mymat.shape) mymat = np.mean( np.array([ temp.transpose(0,2,1), mymat ]), axis=0 ) elif i == 3: temp = predict(np.rot90(img, 1), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES) #print(temp.transpose([2,0,1])[0][0][0], temp.transpose([2,0,1])[3][12][13]) print("Case 4", temp.shape, mymat.shape) mymat = np.mean( np.array([ np.rot90(temp, -1).transpose([2,0,1]), mymat ]), axis=0 ) elif i == 4: temp = predict(np.rot90(img,2), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES) #print(temp.transpose([2,0,1])[0][0][0], temp.transpose([2,0,1])[3][12][13]) print("Case 5", temp.shape, mymat.shape) mymat = np.mean( np.array([ np.rot90(temp,-2).transpose([2,0,1]), mymat ]), axis=0 ) elif i == 5: temp = predict(np.rot90(img,3), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES) #print(temp.transpose([2,0,1])[0][0][0], temp.transpose([2,0,1])[3][12][13]) print("Case 6", temp.shape, mymat.shape) mymat = np.mean( np.array([ np.rot90(temp, -3).transpose(2,0,1), mymat ]), axis=0 ) else: temp = predict(img, model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(temp[0][0][0], temp[3][12][13]) print("Case 7", temp.shape, mymat.shape) mymat = np.mean( np.array([ temp, mymat ]), axis=0 )
The text was updated successfully, but these errors were encountered:
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great work
I would like to be enlightned more about this part if you allow, because I couldnt get to understand its role in prediction part.
Thanks in advance:
for i in range(7): if i == 0: # reverse first dimension mymat = predict(img[::-1,:,:], model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(mymat[0][0][0], mymat[3][12][13]) print("Case 1",img.shape, mymat.shape) elif i == 1: # reverse second dimension temp = predict(img[:,::-1,:], model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(temp[0][0][0], temp[3][12][13]) print("Case 2", temp.shape, mymat.shape) mymat = np.mean( np.array([ temp[:,::-1,:], mymat ]), axis=0 ) elif i == 2: # transpose(interchange) first and second dimensions temp = predict(img.transpose([1,0,2]), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(temp[0][0][0], temp[3][12][13]) print("Case 3", temp.shape, mymat.shape) mymat = np.mean( np.array([ temp.transpose(0,2,1), mymat ]), axis=0 ) elif i == 3: temp = predict(np.rot90(img, 1), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES) #print(temp.transpose([2,0,1])[0][0][0], temp.transpose([2,0,1])[3][12][13]) print("Case 4", temp.shape, mymat.shape) mymat = np.mean( np.array([ np.rot90(temp, -1).transpose([2,0,1]), mymat ]), axis=0 ) elif i == 4: temp = predict(np.rot90(img,2), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES) #print(temp.transpose([2,0,1])[0][0][0], temp.transpose([2,0,1])[3][12][13]) print("Case 5", temp.shape, mymat.shape) mymat = np.mean( np.array([ np.rot90(temp,-2).transpose([2,0,1]), mymat ]), axis=0 ) elif i == 5: temp = predict(np.rot90(img,3), model, patch_sz=PATCH_SZ, n_classes=N_CLASSES) #print(temp.transpose([2,0,1])[0][0][0], temp.transpose([2,0,1])[3][12][13]) print("Case 6", temp.shape, mymat.shape) mymat = np.mean( np.array([ np.rot90(temp, -3).transpose(2,0,1), mymat ]), axis=0 ) else: temp = predict(img, model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1]) #print(temp[0][0][0], temp[3][12][13]) print("Case 7", temp.shape, mymat.shape) mymat = np.mean( np.array([ temp, mymat ]), axis=0 )The text was updated successfully, but these errors were encountered: