pad_l = valid.paddingAmounts.borderAmounts[1].startEdgeSize
pad_b = valid.paddingAmounts.borderAmounts[0].endEdgeSize
pad_r = valid.paddingAmounts.borderAmounts[1].endEdgeSize
- inexpr = _op.nn.pad(data=inexpr, pad_width=((0, 0),
- (0, 0),
- (pad_t, pad_b),
- (pad_l, pad_r)))
+ if not all(v == 0 for v in (pad_t, pad_l, pad_b, pad_r)):
+ inexpr = _op.nn.pad(data=inexpr, pad_width=((0, 0),
+ (0, 0),
+ (pad_t, pad_b),
+ (pad_l, pad_r)))
elif op.WhichOneof('ConvolutionPaddingType') == 'same':
assert op.same.asymmetryMode == 0, "Only support BOTTOM_RIGHT_HEAVY mode, " \
"which is used by tf/caffe and so on"
pad_l = valid.paddingAmounts.borderAmounts[1].startEdgeSize
pad_b = valid.paddingAmounts.borderAmounts[0].endEdgeSize
pad_r = valid.paddingAmounts.borderAmounts[1].endEdgeSize
- params['padding'] = [pad_t, pad_l, pad_b, pad_r]
+ if not all(v == 0 for v in (pad_t, pad_l, pad_b, pad_r)):
+ params['padding'] = [pad_t, pad_l, pad_b, pad_r]
elif op.WhichOneof('PoolingPaddingType') == 'includeLastPixel':
# I don't know if this is correct
valid = op.includeLastPixel
dst = 'cat.png'
real_dst = download_testdata(url, dst, module='data')
img = Image.open(real_dst).resize((224, 224))
- img = np.transpose(img, (2, 0, 1))[np.newaxis, :]
+ # CoreML's standard model image format is BGR
+ img_bgr = np.array(img)[:, :, ::-1]
+ img = np.transpose(img_bgr, (2, 0, 1))[np.newaxis, :]
return np.asarray(img)
\ No newline at end of file