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[FRONTEND][TENSORFLOW] Enhance with left over patches from NNVM. #2757

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merged 4 commits into from
Mar 19, 2019
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[FRONTEND][TENSORFLOW] Enhance with left over patches from NNVM. #2757

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686da01
[FRONTEND][TENSORFLOW] Enhance with left over patches from NNVM.
srkreddy1238 Mar 11, 2019
91fbd22
* review comments
srkreddy1238 Mar 12, 2019
e8769a1
* resnet fix.
srkreddy1238 Mar 12, 2019
8153f69
* review comments
srkreddy1238 Mar 12, 2019
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23 changes: 16 additions & 7 deletions nnvm/tests/python/frontend/tensorflow/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,7 +137,7 @@ def is_gpu_available():
from tensorflow.python.client import device_lib
local_device_protos = device_lib.list_local_devices()
gpu_list = [x.name for x in local_device_protos if x.device_type == 'GPU']
if len(gpu_list) < 0:
if len(gpu_list) > 0:
print("Tensorflow GPU:", gpu_list)
return True
else:
Expand Down Expand Up @@ -168,7 +168,7 @@ def _test_pooling(input_shape, **kwargs):

if is_gpu_available():
input_shape = [input_shape[ii] for ii in (0, 3, 1, 2)]
kwargs['data_layout'] = 'NCHW'
kwargs['data_format'] = 'NCHW'
_test_pooling_iteration(input_shape, **kwargs)

def test_forward_pooling():
Expand Down Expand Up @@ -225,8 +225,12 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
with tf.Graph().as_default():
in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
strides = [1] + strides + [1]
dilations = [1] + dilations + [1]
if data_format == 'NHWC':
strides = [1] + strides + [1]
dilations = [1] + dilations + [1]
else:
strides = [1, 1] + strides
dilations = [1, 1] + dilations

nn_ops.conv2d(in_data,
in_filter,
Expand Down Expand Up @@ -898,7 +902,7 @@ def test_forward_mobilenet():

#######################################################################
# ResnetV2
# ---------
# --------
def test_forward_resnetv2():
'''test resnet model'''
if is_gpu_available():
Expand All @@ -912,8 +916,13 @@ def test_forward_resnetv2():

with tf.Session() as sess:
tf_output = run_tf_graph(sess, data, 'input_tensor:0', out_node + ':0')
tvm_output = run_tvm_graph(graph_def, data, 'input_tensor', tf_output.shape, 'float32')
tvm.testing.assert_allclose(np.squeeze(tvm_output[0]), np.squeeze(tf_output[0]), rtol=1e-5, atol=1e-5)
for device in ["llvm", "cuda"]:
ctx = tvm.context(device, 0)
if not ctx.exist:
print("Skip because %s is not enabled" % device)
continue
tvm_output = run_tvm_graph(graph_def, data, 'input_tensor', len(tf_output), target=device)
tvm.testing.assert_allclose(np.squeeze(tvm_output[0]), np.squeeze(tf_output[0]), rtol=1e-5, atol=1e-5)

#######################################################################
# PTB
Expand Down
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