sandeep-krishnamurthy commented on a change in pull request #12412: Infer dtype
in SymbolBlock import from input symbol
URL: https://github.com/apache/incubator-mxnet/pull/12412#discussion_r216884637
##########
File path: tests/python/unittest/test_gluon.py
##########
@@ -336,6 +339,41 @@ def hybrid_forward(self, F, x):
net.hybridize()
assert isinstance(net(mx.nd.zeros((16, 10))), mx.nd.NDArray)
+ # Test case to verify if initializing the SymbolBlock from a model with
params
+ # other than fp32 param dtype.
+
+ # 1. Load a resnet model, cast it to fp64 and export
+ tmp = tempfile.mkdtemp()
+ tmpfile = os.path.join(tmp, 'resnet34_fp64')
+ ctx = mx.cpu(0)
+
+ net_fp32 = mx.gluon.model_zoo.vision.resnet34_v2(pretrained=True, ctx=ctx,
root=tmp)
+ net_fp32.cast('float64')
+ net_fp32.hybridize()
+ data = mx.nd.zeros((1,3,224,224), dtype='float64', ctx=ctx)
+ net_fp32.forward(data)
+ net_fp32.export(tmpfile, 0)
+
+ # 2. Load the saved model and verify if all the params are loaded
correctly.
+ # and choose one of the param to verify the type if fp64.
+ sm = mx.sym.load(tmpfile + '-symbol.json')
+ inputs = mx.sym.var('data', dtype='float64')
+ net_fp64 = mx.gluon.SymbolBlock(sm, inputs)
+ net_fp64.collect_params().load(tmpfile + '-0000.params', ctx=ctx)
+ # 3. Get a conv layer's weight parameter name. Conv layer's weight param is
+ # expected to be of dtype casted, fp64.
+ for param_name in net_fp64.params.keys():
+ if 'conv' in param_name and 'weight' in param_name:
+ break
+ assert np.dtype(net_fp64.params[param_name].dtype) == np.dtype(np.float64)
+
+ # Cast the symbol block to FP32 and try to forward a FP32 data.
Review comment:
@zhreshold - Added a test here to verify the SymbolBlock.cast functionality.
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