FM / operator

/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/

/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
activation_ops_test.py	9691	0644	edit dl rm
adadelta_test.py	7932	0644	edit dl rm
adagrad_test.py	7586	0644	edit dl rm
adagrad_test_helper.py	5181	0644	edit dl rm
adam_test.py	21559	0644	edit dl rm
affine_channel_op_test.py	3784	0644	edit dl rm
alias_with_name_test.py	938	0644	edit dl rm
apmeter_test.py	2738	0644	edit dl rm
arg_ops_test.py	1917	0644	edit dl rm
assert_test.py	797	0644	edit dl rm
async_net_barrier_test.py	946	0644	edit dl rm
atomic_ops_test.py	4104	0644	edit dl rm
basic_rnn_test.py	4720	0644	edit dl rm
batch_box_cox_test.py	5080	0644	edit dl rm
batch_bucketize_op_test.py	3730	0644	edit dl rm
batch_moments_op_test.py	2795	0644	edit dl rm
batch_sparse_to_dense_op_test.py	4199	0644	edit dl rm
bbox_transform_test.py	12258	0644	edit dl rm
bisect_percentile_op_test.py	6227	0644	edit dl rm
blobs_queue_db_test.py	3240	0644	edit dl rm
boolean_mask_test.py	16389	0644	edit dl rm
boolean_unmask_test.py	1711	0644	edit dl rm
box_with_nms_limit_op_test.py	8750	0644	edit dl rm
bucketize_op_test.py	930	0644	edit dl rm
cast_op_test.py	1600	0644	edit dl rm
ceil_op_test.py	888	0644	edit dl rm
channel_backprop_stats_op_test.py	2131	0644	edit dl rm
channel_shuffle_test.py	1794	0644	edit dl rm
channel_stats_op_test.py	2639	0644	edit dl rm
checkpoint_test.py	1500	0644	edit dl rm
clip_op_test.py	1984	0644	edit dl rm
clip_tensor_op_test.py	2076	0644	edit dl rm
collect_and_distribute_fpn_rpn_proposals_op_test.py	11269	0644	edit dl rm
concat_op_cost_test.py	2858	0644	edit dl rm
concat_split_op_test.py	7266	0644	edit dl rm
conditional_test.py	995	0644	edit dl rm
conftest.py	1446	0644	edit dl rm
conv_test.py	32473	0644	edit dl rm
conv_transpose_test.py	15945	0644	edit dl rm
copy_ops_test.py	7374	0644	edit dl rm
copy_rows_to_tensor_op_test.py	2526	0644	edit dl rm
cosine_embedding_criterion_op_test.py	1953	0644	edit dl rm
counter_ops_test.py	3348	0644	edit dl rm
crf_test.py	5315	0644	edit dl rm
cross_entropy_ops_test.py	10085	0644	edit dl rm
ctc_beam_search_decoder_op_test.py	5197	0644	edit dl rm
ctc_greedy_decoder_op_test.py	4743	0644	edit dl rm
cudnn_recurrent_test.py	5817	0644	edit dl rm
dataset_ops_test.py	23847	0644	edit dl rm
data_couple_op_test.py	858	0644	edit dl rm
decay_adagrad_test.py	2694	0644	edit dl rm
deform_conv_test.py	19276	0644	edit dl rm
dense_vector_to_id_list_op_test.py	2044	0644	edit dl rm
depthwise_3x3_conv_test.py	1863	0644	edit dl rm
detectron_keypoints.py	7973	0644	edit dl rm
distance_op_test.py	4351	0644	edit dl rm
dropout_op_test.py	2971	0644	edit dl rm
duplicate_operands_test.py	734	0644	edit dl rm
elementwise_linear_op_test.py	1382	0644	edit dl rm
elementwise_logical_ops_test.py	4617	0644	edit dl rm
elementwise_ops_test.py	33354	0644	edit dl rm
elementwise_op_broadcast_test.py	17466	0644	edit dl rm
emptysample_ops_test.py	1977	0644	edit dl rm
enforce_finite_op_test.py	1286	0644	edit dl rm
ensure_clipped_test.py	1505	0644	edit dl rm
ensure_cpu_output_op_test.py	1244	0644	edit dl rm
erf_op_test.py	749	0644	edit dl rm
expand_op_test.py	2109	0644	edit dl rm
fc_operator_test.py	3720	0644	edit dl rm
feature_maps_ops_test.py	21492	0644	edit dl rm
filler_ops_test.py	8476	0644	edit dl rm
find_op_test.py	1316	0644	edit dl rm
flatten_op_test.py	922	0644	edit dl rm
flexible_top_k_test.py	2609	0644	edit dl rm
floor_op_test.py	894	0644	edit dl rm
fused_nbit_rowwise_conversion_ops_test.py	14077	0644	edit dl rm
fused_nbit_rowwise_test_helper.py	2693	0644	edit dl rm
gather_ops_test.py	9216	0644	edit dl rm
gather_ranges_op_test.py	9125	0644	edit dl rm
given_tensor_byte_string_to_uint8_fill_op_test.py	1392	0644	edit dl rm
given_tensor_fill_op_test.py	1503	0644	edit dl rm
glu_op_test.py	1212	0644	edit dl rm
group_conv_test.py	2870	0644	edit dl rm
group_norm_op_test.py	5252	0644	edit dl rm
gru_test.py	12932	0644	edit dl rm
heatmap_max_keypoint_op_test.py	4770	0644	edit dl rm
histogram_test.py	3097	0644	edit dl rm
hsm_test.py	9456	0644	edit dl rm
hyperbolic_ops_test.py	1472	0644	edit dl rm
im2col_col2im_test.py	4311	0644	edit dl rm
image_input_op_test.py	17345	0644	edit dl rm
index_hash_ops_test.py	2885	0644	edit dl rm
index_ops_test.py	4597	0644	edit dl rm
instance_norm_test.py	9917	0644	edit dl rm
integral_image_ops_test.py	3419	0644	edit dl rm
jsd_ops_test.py	1044	0644	edit dl rm
key_split_ops_test.py	1289	0644	edit dl rm
lars_test.py	1354	0644	edit dl rm
layer_norm_op_test.py	14983	0644	edit dl rm
leaky_relu_test.py	5639	0644	edit dl rm
learning_rate_adaption_op_test.py	2837	0644	edit dl rm
learning_rate_op_test.py	8652	0644	edit dl rm
lengths_pad_op_test.py	1625	0644	edit dl rm
lengths_reducer_fused_nbit_rowwise_ops_test.py	15495	0644	edit dl rm
lengths_tile_op_test.py	1332	0644	edit dl rm
lengths_top_k_ops_test.py	2371	0644	edit dl rm
length_split_op_test.py	4868	0644	edit dl rm
listwise_l2r_operator_test.py	8740	0644	edit dl rm
load_save_test.py	33241	0644	edit dl rm
locally_connected_op_test.py	7761	0644	edit dl rm
loss_ops_test.py	902	0644	edit dl rm
lpnorm_op_test.py	2725	0644	edit dl rm
map_ops_test.py	2249	0644	edit dl rm
margin_ranking_criterion_op_test.py	1816	0644	edit dl rm
math_ops_test.py	1603	0644	edit dl rm
matmul_op_test.py	10096	0644	edit dl rm
mean_op_test.py	1469	0644	edit dl rm
merge_id_lists_op_test.py	2989	0644	edit dl rm
mkl_conv_op_test.py	1547	0644	edit dl rm
mkl_packed_fc_op_test.py	2647	0644	edit dl rm
mod_op_test.py	1459	0644	edit dl rm
moments_op_test.py	1722	0644	edit dl rm
momentum_sgd_test.py	6480	0644	edit dl rm
mpi_test.py	8154	0644	edit dl rm
mul_gradient_benchmark.py	1509	0644	edit dl rm
negate_gradient_op_test.py	1518	0644	edit dl rm
ngram_ops_test.py	2327	0644	edit dl rm
normalize_op_test.py	1679	0644	edit dl rm
numpy_tile_op_test.py	1924	0644	edit dl rm
one_hot_ops_test.py	7478	0644	edit dl rm
onnx_while_test.py	3070	0644	edit dl rm
order_switch_test.py	1306	0644	edit dl rm
pack_ops_test.py	12634	0644	edit dl rm
pack_rnn_sequence_op_test.py	2891	0644	edit dl rm
pad_test.py	1377	0644	edit dl rm
partition_ops_test.py	6838	0644	edit dl rm
percentile_op_test.py	4427	0644	edit dl rm
piecewise_linear_transform_test.py	6187	0644	edit dl rm
pooling_test.py	16508	0644	edit dl rm
prepend_dim_test.py	1505	0644	edit dl rm
python_op_test.py	1312	0644	edit dl rm
quantile_test.py	3276	0644	edit dl rm
rand_quantization_op_speed_test.py	3128	0644	edit dl rm
rank_loss_operator_test.py	5752	0644	edit dl rm
rebatching_queue_test.py	9047	0644	edit dl rm
record_queue_test.py	3125	0644	edit dl rm
recurrent_network_test.py	14048	0644	edit dl rm
recurrent_net_executor_test.py	10922	0644	edit dl rm
reduce_ops_test.py	17341	0644	edit dl rm
reduction_ops_test.py	4664	0644	edit dl rm
reshape_ops_test.py	8211	0644	edit dl rm
resize_op_test.py	9417	0644	edit dl rm
rmac_regions_op_test.py	3178	0644	edit dl rm
rms_norm_op_test.py	1325	0644	edit dl rm
rnn_cell_test.py	59707	0644	edit dl rm
roi_align_rotated_op_test.py	7567	0644	edit dl rm
rowwise_counter_test.py	2205	0644	edit dl rm
scale_op_test.py	2177	0644	edit dl rm
segment_ops_test.py	25745	0644	edit dl rm
self_binning_histogram_test.py	12915	0644	edit dl rm
selu_op_test.py	3232	0644	edit dl rm
sequence_ops_test.py	16000	0644	edit dl rm
shape_inference_test.py	25708	0644	edit dl rm
sinusoid_position_encoding_op_test.py	2308	0644	edit dl rm
softmax_ops_test.py	23685	0644	edit dl rm
softplus_op_test.py	516	0644	edit dl rm
sparse_dropout_with_replacement_op_test.py	2885	0644	edit dl rm
sparse_gradient_checker_test.py	1294	0644	edit dl rm
sparse_itemwise_dropout_with_replacement_op_test.py	2913	0644	edit dl rm
sparse_lengths_sum_benchmark.py	4159	0644	edit dl rm
sparse_lp_regularizer_test.py	2553	0644	edit dl rm
sparse_normalize_test.py	3136	0644	edit dl rm
sparse_ops_test.py	3469	0644	edit dl rm
sparse_to_dense_mask_op_test.py	3693	0644	edit dl rm
spatial_bn_op_test.py	20182	0644	edit dl rm
specialized_segment_ops_test.py	11775	0644	edit dl rm
split_op_cost_test.py	8645	0644	edit dl rm
square_root_divide_op_test.py	2179	0644	edit dl rm
stats_ops_test.py	1789	0644	edit dl rm
stats_put_ops_test.py	6596	0644	edit dl rm
storm_test.py	6507	0644	edit dl rm
string_ops_test.py	4154	0644	edit dl rm
text_file_reader_test.py	2517	0644	edit dl rm
thresholded_relu_op_test.py	2323	0644	edit dl rm
tile_op_test.py	3887	0644	edit dl rm
top_k_test.py	9113	0644	edit dl rm
torch_integration_test.py	39941	0644	edit dl rm
transpose_op_test.py	2722	0644	edit dl rm
trigonometric_op_test.py	1715	0644	edit dl rm
unique_ops_test.py	2255	0644	edit dl rm
unique_uniform_fill_op_test.py	1335	0644	edit dl rm
unsafe_coalesce_test.py	2940	0644	edit dl rm
upsample_op_test.py	7308	0644	edit dl rm
utility_ops_test.py	15054	0644	edit dl rm
video_input_op_test.py	10503	0644	edit dl rm
weighted_multi_sample_test.py	1997	0644	edit dl rm
weighted_sample_test.py	2739	0644	edit dl rm
weighted_sum_test.py	3052	0644	edit dl rm
weight_scale_test.py	2057	0644	edit dl rm
wngrad_test.py	8279	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/shape_inference_test.py (25708B)

import numpy as np
import unittest

from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, test_util, model_helper, brew, build

@unittest.skipIf(build.CAFFE2_NO_OPERATOR_SCHEMA,
                 'Built with CAFFE2_NO_OPERATOR_SCHEMA')
class TestShapeInference(test_util.TestCase):

def testShapeInferenceSimpleFC(self):
        m = model_helper.ModelHelper(name="test_model")

brew.fc(m, "data", "fc1", dim_in=96, dim_out=32)
        brew.fc(m, "fc1", "fc2", dim_in=32, dim_out=55)

for b in [0, 64]:
            (shapes, types) = workspace.InferShapesAndTypes(
                [m.param_init_net, m.net],
                {'data': [b, 96]}
            )

self.assertEquals(shapes['data'], [b, 96])
            self.assertEquals(shapes['fc1_w'], [32, 96])
            self.assertEquals(shapes['fc1_b'], [32])
            self.assertEquals(shapes['fc1'], [b, 32])
            self.assertEquals(shapes['fc2_w'], [55, 32])
            self.assertEquals(shapes['fc2_b'], [55])
            self.assertEquals(shapes['fc2'], [b, 55])

def testFCAxis2(self):
        model = model_helper.ModelHelper(name="test_model")
        model.net.FC(["x", "w", "b"], ["y"], axis=2)
        workspace.FeedBlob("x", np.random.rand(4, 20, 36).astype(np.float32))
        workspace.FeedBlob("w", np.random.rand(36, 36).astype(np.float32))
        workspace.FeedBlob("b", np.random.rand(36,).astype(np.float32))
        self.InferTensorRunAndCompare(model)

def testFCTransposed(self):
        model = model_helper.ModelHelper(name="test_model")
        model.net.FCTransposed(["x", "wt", "b"], ["y"])
        workspace.FeedBlob("x", np.random.rand(20, 36).astype(np.float32))
        workspace.FeedBlob("wt", np.random.rand(36, 48).astype(np.float32))
        workspace.FeedBlob("b", np.random.rand(48,).astype(np.float32))
        self.InferTensorRunAndCompare(model)

def testShapeInferenceSlice(self):
        model = model_helper.ModelHelper(name="test_model")
        model.net.Slice(["x"], ["y"], starts=[0, 0, 0, 0], ends=[-1, -1, -3, -1])
        workspace.FeedBlob("x", np.random.rand(64, 1, 255, 384).astype(np.float32))

slice_starts = np.array([0, 0, 0, 0]).astype(np.int32)
        slice_ends = np.array([-1, -1, -3, -1]).astype(np.int32)
        slice_starts = model.net.GivenTensorIntFill(
            [], shape=[4], values=slice_starts)
        slice_ends = model.net.GivenTensorIntFill(
            [], shape=[4], values=slice_ends)
        model.net.Slice(["x2", slice_starts, slice_ends], ["y2"])
        workspace.FeedBlob("x2", np.random.rand(64, 1, 255, 384).astype(np.float32))

self.InferTensorRunAndCompare(model, ["y2"])

def testShapeInferenceDistances(self):
        model = model_helper.ModelHelper(name="test_model")
        model.net.L1Distance(["x1", "y1"], "dl1_D1")
        model.net.SquaredL2Distance(["x1", "y1"], "dl2_D1")
        model.net.CosineSimilarity(["x1", "y1"], "dcos_D1")
        model.net.DotProduct(["x1", "y1"], "ddot_D1")
        model.net.DotProductWithPadding(["x1", "y1"], "ddotpad_D1")

model.net.L1Distance(["x2", "y2"], "dl1_D2")
        model.net.SquaredL2Distance(["x2", "y2"], "dl2_D2")
        model.net.CosineSimilarity(["x2", "y2"], "dcos_D2")
        model.net.DotProduct(["x2", "y2"], "ddot_D2")
        model.net.DotProductWithPadding(["x2", "z2"], "ddotpad_D2")

workspace.FeedBlob("x1", np.random.rand(10).astype(np.float32))
        workspace.FeedBlob("y1", np.random.rand(10).astype(np.float32))

workspace.FeedBlob("x2", np.random.rand(10, 5).astype(np.float32))
        workspace.FeedBlob("y2", np.random.rand(10, 5).astype(np.float32))
        workspace.FeedBlob("z2", np.random.rand(10, 4).astype(np.float32))
        self.InferTensorRunAndCompare(model)

def testShapeInferenceReduceBackFrontX(self):
        model = model_helper.ModelHelper(name="test_model")
        model.net.ReduceBackSum(["x"], ["x_back_sum"])
        model.net.ReduceBackMean(["x"], ["x_back_mean"])
        model.net.ReduceBackMax(["x"], ["x_back_max"])
        model.net.ReduceFrontSum(["x"], ["x_front_sum"])
        model.net.ReduceFrontMean(["x"], ["x_front_mean"])
        model.net.ReduceFrontMax(["x"], ["x_front_max"])

workspace.FeedBlob("x", np.random.rand(10, 12, 18).astype(np.float32))
        self.InferTensorRunAndCompare(model)

def testGather(self):
        model = model_helper.ModelHelper(name="test_model")
        model.net.Gather(["X", "idx"], "Y")
        workspace.FeedBlob("X", np.random.rand(100, 4, 5).astype(np.float32))
        workspace.FeedBlob("idx", np.array([[3, 18], [99, 4], [2, 5]]).astype(np.int32))
        self.InferTensorRunAndCompare(model)

def testShapeInferenceConvNet(self):
        model = model_helper.ModelHelper(name="convtest")
        model.NHWC2NCHW("data", "data_nchw")
        brew.conv(model, "data_nchw", 'conv1', 3, 64,
                   weight_init=("MSRAFill", {}), kernel=7,
                   stride=2, pad=3, no_bias=0)
        brew.spatial_bn(model, 'conv1', 'conv1_spatbn_relu', 64, epsilon=1e-3, is_test=False)
        brew.relu(model, 'conv1_spatbn_relu', 'conv1_spatbn_relu')
        brew.max_pool(model, 'conv1_spatbn_relu', 'pool1', kernel=3, stride=2)
        brew.fc(model, 'pool1', 'fc', dim_in=(64 * 56 * 56), dim_out=100)
        brew.dropout(model, 'fc', 'fc_drop', is_test=False)
        model.Sigmoid('fc_drop', 'fc_sigm')
        brew.softmax(model, 'fc_sigm', 'softmax')
        model.LabelCrossEntropy(['softmax', 'label'], 'xent')
        loss = model.AveragedLoss('xent', 'loss')

model.AddGradientOperators([loss])

LR = model.param_init_net.ConstantFill(
            [], 'LR', shape=[1], value=0.1
        )

for param in model.GetParams():
            param_grad = model.param_to_grad[param]
            param_momentum = model.param_init_net.ConstantFill(
                [param], param + '_momentum', value=0.0
            )
            model.net.MomentumSGDUpdate(
                [param_grad, param_momentum, LR, param],
                [param_grad, param_momentum, param],
            )

workspace.FeedBlob(
            "data",
            np.random.rand(16, 227, 227, 3).astype(np.float32),
        )
        workspace.FeedBlob(
            "label",
            (100 * np.random.rand(16)).astype(np.int32),
        )
        workspace.FeedBlob(
            "label",
            (100 * np.random.rand(16)).astype(np.int32),
        )
        # Then do automatic comparison test: run the next once to
        # initialize everything
        self.InferTensorRunAndCompare(model)

def testShapeInferenceTranspose(self):
        model = model_helper.ModelHelper(name="test_model")

workspace.FeedBlob(
            "tensor",
            np.random.rand(4, 2, 3, 3, 5).astype(np.float32)
        )

# Testing with axes undefined
        brew.transpose(
            model,
            ["tensor"],
            "transpose",
        )
        self.InferTensorRunAndCompare(model)

# Testing with axes defined
        brew.transpose(
            model,
            ["tensor"],
            "transpose",
            axes=np.random.permutation(5)
        )

return self.InferTensorRunAndCompare(model)

def testShapeInferencePad(self):
        model = model_helper.ModelHelper(name="padtest")
        model.PadImage("data", 'padded', pad_t=100, pad_l=37, pad_b=28,
                       pad_r=20, mode="constant", order="NCHW")

workspace.FeedBlob(
            "data",
            np.random.rand(16, 3, 228, 228).astype(np.float32),
        )

self.InferTensorRunAndCompare(model)

def testShapeInferenceTwoClass(self):
        model = model_helper.ModelHelper(name="twoclass")
        model.MakeTwoClass("v", "v2")
        workspace.FeedBlob("v", np.random.rand(32).astype(np.float32))
        self.InferTensorRunAndCompare(model)

def testShapeInferencePadZero(self):
        model = model_helper.ModelHelper(name="padtest")
        model.PadImage("data", 'padded', pad=0, mode="constant",
                       order="NCHW")

workspace.FeedBlob(
            "data",
            np.random.rand(16, 3, 228, 228).astype(np.float32),
        )

self.InferTensorRunAndCompare(model)

def testShapeInferenceMatMul(self):
        model = model_helper.ModelHelper(name="test_model")

model.MatMul(["x", "y"], "MatMul")

workspace.FeedBlob("x", np.random.rand(10, 5).astype(np.float32))
        workspace.FeedBlob("y", np.random.rand(5, 10).astype(np.float32))

self.InferTensorRunAndCompare(model)

def testShapeInferenceSoftmaxWithLoss(self):
        model = model_helper.ModelHelper(name="test_model")

model.SoftmaxWithLoss(
            ["logits", "labels"],
            ["softmax", "loss"],
        )

# 2D Shape of [batch_size, num_classes]
        workspace.FeedBlob(
            "logits",
            np.random.rand(4, 3).astype(np.float32),
        )

# Shape of size batch_size with all values [0, num_classes)
        workspace.FeedBlob(
            "labels",
            np.random.randint(low=0, high=3, size=(4, 1)).astype(np.int32),
        )
        self.InferTensorRunAndCompare(model)

# Testing with 1D labels arg
        workspace.FeedBlob(
            "logits",
            np.random.rand(4, 3).astype(np.float32),
        )

workspace.FeedBlob(
            "labels",
            np.random.randint(low=0, high=3, size=4).astype(np.int32),
        )
        self.InferTensorRunAndCompare(model)

# Testing with weight_tensor
        model.SoftmaxWithLoss(
            ["logits", "labels", "weight_tensor"],
            ["softmax", "loss"],
        )

workspace.FeedBlob(
            "logits",
            np.random.rand(4, 3).astype(np.float32),
        )

workspace.FeedBlob(
            "labels",
            np.random.randint(low=0, high=3, size=4).astype(np.int32),
        )

workspace.FeedBlob(
            "weight_tensor",
            np.random.rand(4).astype(np.float32),
        )
        self.InferTensorRunAndCompare(model)

# Test spatial model
        model = model_helper.ModelHelper(name="test_model")
        workspace.FeedBlob(
            "img",
            np.random.rand(32, 19, 33, 28).astype(np.float32)
        )
        workspace.FeedBlob(
            "img_labels",
            (np.random.rand(32, 33, 28) * 19).astype(np.int32)
        )
        model.SpatialSoftmaxWithLoss(
            ["img", "img_labels"],
            ["softmax_img", "loss"],
        )
        self.InferTensorRunAndCompare(model)

def testShapeInferenceIm2Col(self):
        # Test with NCHW
        model = model_helper.ModelHelper(name="test_model")
        model.Im2Col("X", "Y", pad=1, kernel=4, dilation=2, stride=2,
                     order="NCHW")

workspace.FeedBlob(
            "X",
            np.random.rand(16, 3, 228, 228).astype(np.float32),
        )

self.InferTensorRunAndCompare(model)

# Test with NHWC
        model = model_helper.ModelHelper(name="test_model")
        model.Im2Col("X", "Y", pad=1, kernel=4, dilation=2, stride=2,
                     order="NHWC")

workspace.FeedBlob(
            "X",
            np.random.rand(16, 228, 228, 3).astype(np.float32),
        )

self.InferTensorRunAndCompare(model)

# Test with different width and height
        model = model_helper.ModelHelper(name="test_model")
        model.Im2Col("X", "Y", pad=1, kernel_h=8, kernel_w=4,
                     dilation=2, stride=2)

workspace.FeedBlob(
            "X",
            np.random.rand(16, 3, 228, 114).astype(np.float32),
        )

self.InferTensorRunAndCompare(model)

def testShapeInferenceTile(self):
        m = model_helper.ModelHelper(name="test_model")

workspace.FeedBlob(
            "tensor",
            np.random.rand(4, 2, 3, 3, 5).astype(np.float32)
        )

# Testing with axes undefined
        for i in range(0, 4):
            m.net.Tile(
                "tensor", "tiled_tensor_{}".format(i), tiles=5, axis=i)
        self.InferTensorRunAndCompare(m)

def testShapeInferenceFlatten(self):
        model = model_helper.ModelHelper(name="test_model")
        model.FlattenToVec("X", "FlatVec")
        model.FlattenToVec("empty", "EmptyFlatVec")
        workspace.FeedBlob("X", np.random.rand(17, 5, 13).astype(np.float32))
        workspace.FeedBlob("empty", np.random.rand(0, 2, 3).astype(np.float32))

self.InferTensorRunAndCompare(model)

# test Flatten with default axis (=1)
        model = model_helper.ModelHelper(name="test_model")
        model.Flatten("X", "Flat")
        model.Flatten("empty", "EmptyFlat")
        workspace.FeedBlob("X", np.random.rand(17, 5, 13).astype(np.float32))
        workspace.FeedBlob("empty", np.random.rand(0, 2, 3).astype(np.float32))

self.InferTensorRunAndCompare(model)

# test Flatten with axis
        model = model_helper.ModelHelper(name="test_model")
        x = np.random.randn(17, 5, 13)
        for axis in range(x.ndim + 1):
            model.Flatten("x", "Flat", axis=axis)
            workspace.FeedBlob("x", x)
            self.InferTensorRunAndCompare(model)

empty = np.random.randn(0, 5, 13)
        for axis in range(empty.ndim + 1):
            model.Flatten("empty", "Flat", axis=axis)
            workspace.FeedBlob("empty", empty)
            self.InferTensorRunAndCompare(model)

def testShapeInferenceReshape(self):
        model = model_helper.ModelHelper(name="test_model")
        model.Reshape("X", ["Reshaped", "Old_Shape"], shape=[8, 0, -1, 2])
        workspace.FeedBlob("X", np.random.rand(4, 26, 32).astype(np.float32))

self.InferTensorRunAndCompare(model)

def testShapeInferenceUnique(self):
        for n in [0, 1]:
            model = model_helper.ModelHelper(name="test_model")
            model.Unique("X", ["Y"])
            model.Unique("X", ["Z", "remap"])
            workspace.FeedBlob("X", np.random.rand(n).astype(np.int64))
            self.InferTensorRunAndCompare(model)

def testLengthsSum(self):
        model = model_helper.ModelHelper(name="test_model")
        model.LengthsSum(["X", "length"], ["sum"])
        workspace.FeedBlob("X", np.random.rand(6, 32).astype(np.float32))
        workspace.FeedBlob("length", np.array([1, 2, 3], dtype=np.int32))

self.InferTensorRunAndCompare(model)

def testLengthsPad(self):
        model = model_helper.ModelHelper(name="test_model")
        model.LengthsPad(
            ["X", "length"],
            ["X_padded"],
            target_length=10,
            padding_value=-1.0,
        )
        workspace.FeedBlob("X", np.random.rand(6, 32).astype(np.float32))
        workspace.FeedBlob("length", np.array([1, 2, 3], dtype=np.int32))

self.InferTensorRunAndCompare(model)

def testConcat(self):
        net = core.Net("concat")

def testConcatInt32(self):
        net = core.Net("concat")

net.Concat(["A", "B"], ["C", "splits"], axis=1)
        net.Concat(["C", "D"], ["E", "splitsE"], order="NCHW")
        net.Concat(["E", "F"], ["G", "splitsG"], add_axis=1, order="NHWC")
        (shapes, types) = workspace.InferShapesAndTypes(
            [net],
            blob_dimensions={
                'A': [10, 12, 9, 10],
                'B': [10, 9, 9, 10],
                'D': [10, 2, 9, 10],
                'F': [10, 23, 9, 10]
            },
            blob_types={
                'A': core.DataType.INT32,
                'B': core.DataType.INT32,
                'D': core.DataType.INT32,
                'F': core.DataType.INT32,
            }
        )
        self.assertEqual(shapes['C'], [10, 21, 9, 10])
        self.assertEqual(shapes['splits'], [2])
        self.assertEqual(shapes['E'], [10, 23, 9, 10])
        self.assertEqual(shapes['G'], [10, 23, 9, 2, 10])
        self.assertEqual(types['C'], core.DataType.INT32)
        self.assertEqual(types['splits'], core.DataType.INT32)
        self.assertEqual(types['E'], core.DataType.INT32)
        self.assertEqual(types['G'], core.DataType.INT32)

def testSqueeze(self):
        net = core.Net("sq")
        net.Squeeze(["data"], ["data_squeezed"], dims=[3, 1])
        (shapes, types) = workspace.InferShapesAndTypes(
            [net],
            {'data': [64, 1, 96, 1, 4]}
        )
        self.assertEqual(shapes['data_squeezed'], [64, 96, 4])

def testCast(self):
        model = model_helper.ModelHelper(name="test_model")

types = [
            ('bool', np.bool, caffe2_pb2.TensorProto.BOOL),
            #('byte', None, caffe2_pb2.TensorProto.BYTE),
            ('int8', np.int8, caffe2_pb2.TensorProto.INT8),
            ('uint8', np.uint8, caffe2_pb2.TensorProto.UINT8),
            ('int16', np.int16, caffe2_pb2.TensorProto.INT16),
            ('uint16', np.uint16, caffe2_pb2.TensorProto.UINT16),
            #('float16', np.float16, caffe2_pb2.TensorProto.FLOAT16),
            ('int32', np.int32, caffe2_pb2.TensorProto.INT32),
            ('float', np.float32, caffe2_pb2.TensorProto.FLOAT),
            ('int64', np.int64, caffe2_pb2.TensorProto.INT64),
            ('double', np.float64, caffe2_pb2.TensorProto.DOUBLE),
            #('string', None, caffe2_pb2.TensorProto.STRING),
        ]

for (xstr, xnp, _) in types:
            xname = 'X%s' % xstr
            workspace.FeedBlob(xname, np.random.rand(1).astype(xnp))
            for (ystr, _, yc2) in types:
                yname = 'Y%s_to_%s' % (xstr, ystr)
                model.Cast(xname, yname, to=yc2)

self.InferTensorRunAndCompare(model)

def testShapeInferenceRoiPool(self):
        for is_test in [True, False]:
            model = model_helper.ModelHelper(name="test_model")
            outputs = ['Y'] if is_test else ['Y', 'argmaxes']
            model.net.RoIPool(
                ['X', 'R'], outputs, pooled_h=4, pooled_w=5, is_test=is_test)
            workspace.FeedBlob(
                "X",
                np.random.rand(100, 3, 4, 5).astype(np.float32))
            workspace.FeedBlob(
                "R",
                np.random.rand(2, 5).astype(np.float32))
            self.InferTensorRunAndCompare(model)

def testShapeInferencePow(self):
        model = model_helper.ModelHelper(name="powtest")
        model.Pow("x", 'y', exponent=-1.0)
        workspace.FeedBlob('x', np.random.rand(1, 2, 3, 4).astype(np.float32))
        self.InferTensorRunAndCompare(model)

def testInt8Conversion(self):
        model = model_helper.ModelHelper(name="fp32_int8_conversion_test")
        model.FloatToFused8BitRowwiseQuantized('x', 'x_8bit')
        model.Fused8BitRowwiseQuantizedToFloat('x_8bit', 'x_recovered')
        workspace.FeedBlob('x', np.random.rand(100, 150).astype(np.float32))
        self.InferTensorRunAndCompare(model)
        x = workspace.FetchBlob('x')
        x_recovered = workspace.FetchBlob('x_recovered')
        # TODO: find a tighter bound
        assert(np.allclose(x, x_recovered, atol=1e-2))

model = model_helper.ModelHelper(name="fp32_int8_conversion_test")
        model.FloatToFused8BitRowwiseQuantizedHalfScaleBias('x', 'x_8bit')
        model.Fused8BitRowwiseQuantizedHalfScaleBiasToFloat('x_8bit', 'x_recovered')
        workspace.FeedBlob('x', np.random.rand(100, 150).astype(np.float32))
        self.InferTensorRunAndCompare(model)
        x = workspace.FetchBlob('x')
        x_recovered = workspace.FetchBlob('x_recovered')
        # TODO: find a tighter bound
        assert(np.allclose(x, x_recovered, atol=1e-2))

def testHalfInt8Conversion(self):
        model = model_helper.ModelHelper(name="fp16_int8_conversion_test")
        model.HalfFloatToFused8BitRowwiseQuantized('x', 'x_8bit')
        model.Fused8BitRowwiseQuantizedToHalfFloat('x_8bit', 'x_recovered')
        workspace.FeedBlob('x', np.random.rand(100, 150).astype(np.float16))
        self.InferTensorRunAndCompare(model)
        x = workspace.FetchBlob('x')
        x_recovered = workspace.FetchBlob('x_recovered')
        # TODO: find a tighter bound
        assert(np.allclose(x, x_recovered, atol=1e-2))

model = model_helper.ModelHelper(name="fp16_int8_conversion_test")
        model.HalfFloatToFused8BitRowwiseQuantizedHalfScaleBias('x', 'x_8bit')
        model.Fused8BitRowwiseQuantizedHalfScaleBiasToHalfFloat('x_8bit', 'x_recovered')
        workspace.FeedBlob('x', np.random.rand(100, 150).astype(np.float16))
        self.InferTensorRunAndCompare(model)
        x = workspace.FetchBlob('x')
        x_recovered = workspace.FetchBlob('x_recovered')
        # TODO: find a tighter bound
        assert(np.allclose(x, x_recovered, atol=1e-2))

def testLearningRateOp(self):
        net = core.Net("lr_test")
        iteration = net.ConstantFill(
            [],
            "iteration",
            shape=[1],
            value=0,
            dtype=core.DataType.INT64,
        )
        lr = net.LearningRate(
            [iteration],
            net.NextScopedBlob("weight_decay"),
            base_lr=0.5,
            policy="constantWarmup",
            multiplier=0.0,
            num_iter=0,
        )
        (shapes, types) = workspace.InferShapesAndTypes(
            [net],
        )
        self.assertEqual(shapes['weight_decay'], [1])

def testShapeOp(self):
        model = model_helper.ModelHelper(name="shape_op_test")
        model.Shape('x', 'y')
        workspace.FeedBlob('x', np.random.rand(100, 150).astype(np.float32))
        self.InferTensorRunAndCompare(model)

def InferTensorRunAndCompare(self, model, expected_uninferred_blobs=None):
        '''
        Runs shape inference, and then the model to check
        that the inferred shapes agree with the actual ones

'expected_uninferred_blobs' is the list of blobs for which type and
        shape cannot be inferred.
        '''
        (shapes, types) = workspace.InferShapesAndTypes(
            [model.param_init_net, model.net],
        )

# .. Create net
        workspace.RunNetOnce(model.param_init_net)
        workspace.CreateNet(model.net, True)
        workspace.RunNet(model.Proto().name)

# ... and then check the shapes mismatch
        correct_shapes = {}
        correct_types = {}
        for b in workspace.Blobs():
            arr = workspace.FetchBlob(b)
            correct_shapes[b] = arr.shape
            if type(arr) is np.ndarray:
                if arr.dtype == np.dtype('float32'):
                    correct_types[b] = caffe2_pb2.TensorProto.FLOAT
                elif arr.dtype == np.dtype('int32'):
                    correct_types[b] = caffe2_pb2.TensorProto.INT32
                # BYTE
                # STRING
                elif arr.dtype == np.dtype('bool'):
                    correct_types[b] = caffe2_pb2.TensorProto.BOOL
                elif arr.dtype == np.dtype('uint8'):
                    correct_types[b] = caffe2_pb2.TensorProto.UINT8
                elif arr.dtype == np.dtype('int8'):
                    correct_types[b] = caffe2_pb2.TensorProto.INT8
                elif arr.dtype == np.dtype('uint16'):
                    correct_types[b] = caffe2_pb2.TensorProto.UINT16
                elif arr.dtype == np.dtype('int16'):
                    correct_types[b] = caffe2_pb2.TensorProto.INT16
                elif arr.dtype == np.dtype('int64'):
                    correct_types[b] = caffe2_pb2.TensorProto.INT64
                elif arr.dtype == np.dtype('float16'):
                    correct_types[b] = caffe2_pb2.TensorProto.FLOAT16
                elif arr.dtype == np.dtype('float64'):
                    correct_types[b] = caffe2_pb2.TensorProto.DOUBLE
                else:
                    correct_types[b] = "unknown {}".format(arr.dtype)
            else:
                correct_types[b] = str(type(arr))

if expected_uninferred_blobs is None:
            expected_uninferred_blobs = []
        for b in correct_shapes:
            # skip blobs for which shape couldn't be inferred
            if b in expected_uninferred_blobs:
                continue
            self.assertTrue(
                np.array_equal(
                    np.array(shapes[b]).astype(np.int32),
                    np.array(correct_shapes[b]).astype(np.int32)
                ),
                "Shape {} mismatch: {} vs. correct {}".format(
                    b, shapes[b], correct_shapes[b]
                )
            )
            self.assertFalse(
                b not in types and b in correct_types,
                "Type for {} not defined".format(b),
            )
            self.assertEqual(
                types[b],
                correct_types[b],
                "Type {} mismatch: {} vs. {}".format(
                    b, types[b], correct_types[b],
                )
            )

if __name__ == "__main__":
    unittest.main()

cmd: