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/gru_test.py (12932B)

from caffe2.python import workspace, core, scope, gru_cell
from caffe2.python.model_helper import ModelHelper
from caffe2.python.rnn.rnn_cell_test_util import sigmoid, tanh, _prepare_rnn
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from caffe2.proto import caffe2_pb2

from functools import partial
from hypothesis import given
from hypothesis import settings as ht_settings
import hypothesis.strategies as st
import numpy as np
import unittest

def gru_unit(*args, **kwargs):
    '''
    Implements one GRU unit, for one time step

Shapes:
    hidden_t_prev.shape     = (1, N, D)
    gates_out_t.shape       = (1, N, G)
    seq_lenths.shape        = (N,)
    '''

drop_states = kwargs.get('drop_states', False)
    sequence_lengths = kwargs.get('sequence_lengths', True)

if sequence_lengths:
        hidden_t_prev, gates_out_t, seq_lengths, timestep = args
    else:
        hidden_t_prev, gates_out_t, timestep = args

N = hidden_t_prev.shape[1]
    D = hidden_t_prev.shape[2]
    G = gates_out_t.shape[2]
    t = (timestep * np.ones(shape=(N, D))).astype(np.int32)
    assert t.shape == (N, D)
    assert G == 3 * D
    # Calculate reset, update, and output gates separately
    # because output gate depends on reset gate.
    gates_out_t = gates_out_t.reshape(N, 3, D)
    reset_gate_t = gates_out_t[:, 0, :].reshape(N, D)
    update_gate_t = gates_out_t[:, 1, :].reshape(N, D)
    output_gate_t = gates_out_t[:, 2, :].reshape(N, D)

# Calculate gate outputs.
    reset_gate_t = sigmoid(reset_gate_t)
    update_gate_t = sigmoid(update_gate_t)
    output_gate_t = tanh(output_gate_t)

if sequence_lengths:
        seq_lengths = (np.ones(shape=(N, D)) *
                       seq_lengths.reshape(N, 1)).astype(np.int32)
        assert seq_lengths.shape == (N, D)
        valid = (t < seq_lengths).astype(np.int32)
    else:
        valid = np.ones(shape=(N, D))
    assert valid.shape == (N, D)
    hidden_t = update_gate_t * hidden_t_prev + \
        (1 - update_gate_t) * output_gate_t
    hidden_t = hidden_t * valid + hidden_t_prev * \
        (1 - valid) * (1 - drop_states)
    hidden_t = hidden_t.reshape(1, N, D)

return (hidden_t, )

def gru_reference(input, hidden_input,
                  reset_gate_w, reset_gate_b,
                  update_gate_w, update_gate_b,
                  output_gate_w, output_gate_b,
                  seq_lengths, drop_states=False,
                  linear_before_reset=False):
    D = hidden_input.shape[hidden_input.ndim - 1]
    T = input.shape[0]
    N = input.shape[1]
    G = input.shape[2]
    print("Dimensions: T= ", T, " N= ", N, " G= ", G, " D= ", D)
    hidden = np.zeros(shape=(T + 1, N, D))
    hidden[0, :, :] = hidden_input

for t in range(T):
        input_t = input[t].reshape(1, N, G)
        hidden_t_prev = hidden[t].reshape(1, N, D)

# Split input contributions for three gates.
        input_t = input_t.reshape(N, 3, D)
        input_reset = input_t[:, 0, :].reshape(N, D)
        input_update = input_t[:, 1, :].reshape(N, D)
        input_output = input_t[:, 2, :].reshape(N, D)

reset_gate = np.dot(hidden_t_prev, reset_gate_w.T) + reset_gate_b
        reset_gate = reset_gate + input_reset

update_gate = np.dot(hidden_t_prev, update_gate_w.T) + update_gate_b
        update_gate = update_gate + input_update

if linear_before_reset:
            with_linear = np.dot(
                hidden_t_prev, output_gate_w.T) + output_gate_b
            output_gate = sigmoid(reset_gate) * with_linear
        else:
            with_reset = hidden_t_prev * sigmoid(reset_gate)
            output_gate = np.dot(with_reset, output_gate_w.T) + output_gate_b
        output_gate = output_gate + input_output

gates_out_t = np.concatenate(
            (reset_gate, update_gate, output_gate),
            axis=2,
        )
        print(reset_gate, update_gate, output_gate, gates_out_t, sep="\n")

(hidden_t, ) = gru_unit(
            hidden_t_prev,
            gates_out_t,
            seq_lengths,
            t,
            drop_states=drop_states
        )
        hidden[t + 1] = hidden_t

return (
        hidden[1:],
        hidden[-1].reshape(1, N, D),
    )

def gru_unit_op_input():
    '''
    Create input tensor where each dimension is from 1 to 4, ndim=3 and
    last dimension size is a factor of 3

hidden_t_prev.shape     = (1, N, D)
    '''
    dims_ = st.tuples(
        st.integers(min_value=1, max_value=1),  # 1, one timestep
        st.integers(min_value=1, max_value=4),  # n
        st.integers(min_value=1, max_value=4),  # d
    )

def create_input(dims):
        dims = list(dims)
        dims[2] *= 3
        return hu.arrays(dims)

return dims_.flatmap(create_input)

def gru_input():
    '''
    Create input tensor where each dimension is from 1 to 4, ndim=3 and
    last dimension size is a factor of 3
    '''
    dims_ = st.tuples(
        st.integers(min_value=1, max_value=4),  # t
        st.integers(min_value=1, max_value=4),  # n
        st.integers(min_value=1, max_value=4),  # d
    )

def create_input(dims):
        dims = list(dims)
        dims[2] *= 3
        return hu.arrays(dims)

return dims_.flatmap(create_input)

def _prepare_gru_unit_op(gc, n, d, outputs_with_grads,
                         forward_only=False, drop_states=False,
                         sequence_lengths=False,
                         two_d_initial_states=None):
    print("Dims: (n,d) = ({},{})".format(n, d))

def generate_input_state(n, d):
        if two_d_initial_states:
            return np.random.randn(n, d).astype(np.float32)
        else:
            return np.random.randn(1, n, d).astype(np.float32)

model = ModelHelper(name='external')

with scope.NameScope("test_name_scope"):
        if sequence_lengths:
            hidden_t_prev, gates_t, seq_lengths, timestep = \
                model.net.AddScopedExternalInputs(
                    "hidden_t_prev",
                    "gates_t",
                    'seq_lengths',
                    "timestep",
                )
        else:
            hidden_t_prev, gates_t, timestep = \
                model.net.AddScopedExternalInputs(
                    "hidden_t_prev",
                    "gates_t",
                    "timestep",
                )
        workspace.FeedBlob(
            hidden_t_prev,
            generate_input_state(n, d).astype(np.float32),
            device_option=gc
        )
        workspace.FeedBlob(
            gates_t,
            generate_input_state(n, 3 * d).astype(np.float32),
            device_option=gc
        )

if sequence_lengths:
            inputs = [hidden_t_prev, gates_t, seq_lengths, timestep]
        else:
            inputs = [hidden_t_prev, gates_t, timestep]

hidden_t = model.net.GRUUnit(
            inputs,
            ['hidden_t'],
            forget_bias=0.0,
            drop_states=drop_states,
            sequence_lengths=sequence_lengths,
        )
        model.net.AddExternalOutputs(hidden_t)
        workspace.RunNetOnce(model.param_init_net)

if sequence_lengths:
            # 10 is used as a magic number to simulate some reasonable timestep
            # and generate some reasonable seq. lengths
            workspace.FeedBlob(
                seq_lengths,
                np.random.randint(1, 10, size=(n,)).astype(np.int32),
                device_option=gc
            )

workspace.FeedBlob(
            timestep,
            np.random.randint(1, 10, size=(1,)).astype(np.int32),
            device_option=core.DeviceOption(caffe2_pb2.CPU),
        )
        print("Feed {}".format(timestep))

return hidden_t, model.net

class GRUCellTest(serial.SerializedTestCase):

# Test just for GRUUnitOp
    @serial.given(
        seed=st.integers(0, 2**32 - 1),
        input_tensor=gru_unit_op_input(),
        fwd_only=st.booleans(),
        drop_states=st.booleans(),
        sequence_lengths=st.booleans(),
        **hu.gcs
    )
    def test_gru_unit_op(self, seed, input_tensor, fwd_only,
                         drop_states, sequence_lengths, gc, dc):
        np.random.seed(seed)
        outputs_with_grads = [0]
        ref = gru_unit
        ref = partial(ref)

t, n, d = input_tensor.shape
        assert d % 3 == 0
        d = d // 3
        ref = partial(ref, drop_states=drop_states,
                      sequence_lengths=sequence_lengths)

with core.DeviceScope(gc):
            net = _prepare_gru_unit_op(gc, n, d,
                                       outputs_with_grads=outputs_with_grads,
                                       forward_only=fwd_only,
                                       drop_states=drop_states,
                                       sequence_lengths=sequence_lengths)[1]
        # here we don't provide a real input for the net but just for one of
        # its ops (RecurrentNetworkOp). So have to hardcode this name
        workspace.FeedBlob("test_name_scope/external/recurrent/i2h",
                           input_tensor,
                           device_option=gc)
        print(str(net.Proto()))
        op = net._net.op[-1]
        inputs = [workspace.FetchBlob(name) for name in op.input]

self.assertReferenceChecks(
            gc,
            op,
            inputs,
            ref,
            input_device_options={"test_name_scope/timestep": hu.cpu_do},
            outputs_to_check=[0],
        )

# Checking for hidden_prev and gates gradients
        if not fwd_only:
            for param in range(2):
                print("Check param {}".format(param))
                self.assertGradientChecks(
                    device_option=gc,
                    op=op,
                    inputs=inputs,
                    outputs_to_check=param,
                    outputs_with_grads=outputs_with_grads,
                    threshold=0.0001,
                    stepsize=0.005,
                    input_device_options={
                        "test_name_scope/timestep": hu.cpu_do},
                )

@given(
        seed=st.integers(0, 2**32 - 1),
        input_tensor=gru_input(),
        fwd_only=st.booleans(),
        drop_states=st.booleans(),
        linear_before_reset=st.booleans(),
        **hu.gcs
    )
    @ht_settings(max_examples=20, deadline=None)
    def test_gru_main(self, seed, **kwargs):
        np.random.seed(seed)
        for outputs_with_grads in [[0], [1], [0, 1]]:
            self.gru_base(gru_cell.GRU, gru_reference,
                          outputs_with_grads=outputs_with_grads,
                          **kwargs)

def gru_base(self, create_rnn, ref, outputs_with_grads,
                 input_tensor, fwd_only, drop_states, linear_before_reset, gc, dc):

print("GRU test parameters: ", locals())
        t, n, d = input_tensor.shape
        assert d % 3 == 0
        d = d // 3
        ref = partial(ref,
                      drop_states=drop_states,
                      linear_before_reset=linear_before_reset)
        with core.DeviceScope(gc):
            net = _prepare_rnn(
                t, n, d, create_rnn,
                outputs_with_grads=outputs_with_grads,
                memory_optim=False,
                forget_bias=0.0,
                forward_only=fwd_only,
                drop_states=drop_states,
                linear_before_reset=linear_before_reset,
                num_states=1,
            )[1]
        # here we don't provide a real input for the net but just for one of
        # its ops (RecurrentNetworkOp). So have to hardcode this name
        workspace.FeedBlob("test_name_scope/external/recurrent/i2h",
                           input_tensor,
                           device_option=gc)
        op = net._net.op[-1]
        inputs = [workspace.FetchBlob(name) for name in op.input]

self.assertReferenceChecks(
            gc,
            op,
            inputs,
            ref,
            input_device_options={"test_name_scope/timestep": hu.cpu_do},
            outputs_to_check=list(range(2)),
        )

# Checking for input, gates_t_w and gates_t_b gradients
        if not fwd_only:
            for param in range(2):
                print("Check param {}".format(param))
                self.assertGradientChecks(
                    device_option=gc,
                    op=op,
                    inputs=inputs,
                    outputs_to_check=param,
                    outputs_with_grads=outputs_with_grads,
                    threshold=0.001,
                    stepsize=0.005,
                    input_device_options={
                        "test_name_scope/timestep": hu.cpu_do},
                )

if __name__ == "__main__":
    workspace.GlobalInit([
        'caffe2',
        '--caffe2_log_level=0',
    ])
    unittest.main()

cmd: