import migraphx
p = migraphx.parse_onnx("conv_relu_maxpool_test.onnx")
print(p)
print("Compiling ...")
p.compile(migraphx.get_target("gpu"), offload_copy=False)
print(p)
params = {}
for key, value in p.get_parameter_shapes().items():
print("Parameter {} -> {}".format(key, value))
params[key] = migraphx.to_gpu(migraphx.generate_argument(value))
r = migraphx.from_gpu(p.run(params))
print(r)
filename = token[0]
label = token[1]
img_pil = Image.open(imagedir + "/" + filename)
if img_pil.mode != 'RGB':
img_pil2 = Image.new("RGB", img_pil.size)
img_pil2.paste(img_pil)
img_pil = img_pil2
img_tensor = preprocess(img_pil)
img_tensor.unsqueeze_(0)
img_variable = Variable(img_tensor)
image = img_variable.numpy()
try:
tmp_result = migraphx.to_gpu(migraphx.argument(image))
params['0'] = tmp_result
result = np.array(migraphx.from_gpu(model.run(params)), copy=False)
maxval = result.argmax()
maxlist = result.argsort()
if int(label) == maxlist[0][999]:
ilabel = 'first'
elif int(label) == maxlist[0][998]:
ilabel = 'second'
elif int(label) == maxlist[0][997]:
ilabel = 'third'
elif int(label) == maxlist[0][996]:
ilabel = 'fourth'
elif int(label) == maxlist[0][995]:
ilabel = 'fifth'
else:
ilabel = 'missing'
print filename, ilabel, int(label), maxlist[0][999], maxlist[0][
def run(p):
params = {}
for key, value in p.get_parameter_shapes().items():
params[key] = migraphx.to_gpu(migraphx.generate_argument(value))
return migraphx.from_gpu(p.run(params))
