FM / ideep

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

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

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
adam_op_test.py	3212	0644	edit dl rm
blobs_queue_db_test.py	4045	0644	edit dl rm
channel_shuffle_op_test.py	1286	0644	edit dl rm
concat_split_op_test.py	5532	0644	edit dl rm
convfusion_op_test.py	31919	0644	edit dl rm
conv_op_test.py	5666	0644	edit dl rm
conv_transpose_test.py	2595	0644	edit dl rm
copy_op_test.py	3074	0644	edit dl rm
dropout_op_test.py	2075	0644	edit dl rm
elementwise_sum_op_test.py	6317	0644	edit dl rm
expanddims_squeeze_op_test.py	4285	0644	edit dl rm
fc_op_test.py	11745	0644	edit dl rm
leaky_relu_op_test.py	2854	0644	edit dl rm
LRN_op_test.py	1195	0644	edit dl rm
moment_sgd_op_test.py	1777	0644	edit dl rm
operator_fallback_op_test.py	3450	0644	edit dl rm
order_switch_op_test.py	2299	0644	edit dl rm
pool_op_test.py	4296	0644	edit dl rm
pre_convert_test.py	4115	0644	edit dl rm
relu_op_test.py	3705	0644	edit dl rm
reshape_op_test.py	5919	0644	edit dl rm
shape_op_test.py	2631	0644	edit dl rm
sigmoid_op_test.py	775	0644	edit dl rm
softmax_op_test.py	932	0644	edit dl rm
spatial_bn_op_test.py	5237	0644	edit dl rm
test_ideep_net.py	4094	0644	edit dl rm
transform_ideep_net.py	11683	0644	edit dl rm
transpose_op_test.py	1284	0644	edit dl rm
weightedsum_op_test.py	1560	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/conv_transpose_test.py (2595B)

import unittest
import numpy as np
from hypothesis import assume, given, settings
import hypothesis.strategies as st

from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu

@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class ConvTransposeTest(hu.HypothesisTestCase):
    @given(stride=st.integers(1, 2),
           pad=st.integers(0, 3),
           kernel=st.integers(1, 5),
           adj=st.integers(0, 2),
           size=st.integers(7, 10),
           input_channels=st.integers(1, 8),
           output_channels=st.integers(1, 8),
           batch_size=st.integers(1, 3),
           use_bias=st.booleans(),
           training_mode=st.booleans(),
           compute_dX=st.booleans(),
           **mu.gcs)
    @settings(max_examples=2, timeout=100)
    def test_convolution_transpose_gradients(self, stride, pad, kernel, adj,
                                             size, input_channels,
                                             output_channels, batch_size,
                                             use_bias, training_mode,
                                             compute_dX, gc, dc):
        training = 1 if training_mode else 0
        assume(adj < stride)
        X = np.random.rand(
            batch_size, input_channels, size, size).astype(np.float32) - 0.5
        w = np.random.rand(
            input_channels, output_channels, kernel, kernel)\
            .astype(np.float32) - 0.5
        b = np.random.rand(output_channels).astype(np.float32) - 0.5
        op = core.CreateOperator(
            "ConvTranspose",
            ["X", "w", "b"] if use_bias else ["X", "w"],
            ["Y"],
            stride=stride,
            kernel=kernel,
            pad=pad,
            adj=adj,
            training_mode=training,
            no_gradient_to_input=not compute_dX,
        )

inputs = [X, w, b] if use_bias else [X, w]
        self.assertDeviceChecks(dc, op, inputs, [0], threshold=0.001)

if training_mode:
            if use_bias and compute_dX:
                # w, b, X
                outputs_to_check = [1, 2, 0]
            elif use_bias:
                # w, b
                outputs_to_check = [1, 2]
            elif compute_dX:
                # w, X
                outputs_to_check = [1, 0]
            else:
                # w
                outputs_to_check = [1]
            for i in outputs_to_check:
                self.assertGradientChecks(gc, op, inputs, i, [0])

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

import unittest import numpy as np from hypothesis import assume, given, settings import hypothesis.strategies as st from caffe2.python import core, workspace import caffe2.python.hypothesis_test_util as hu import caffe2.python.ideep_test_util as mu @unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.") class ConvTransposeTest(hu.HypothesisTestCase): @given(stride=st.integers(1, 2), pad=st.integers(0, 3), kernel=st.integers(1, 5), adj=st.integers(0, 2), size=st.integers(7, 10), input_channels=st.integers(1, 8), output_channels=st.integers(1, 8), batch_size=st.integers(1, 3), use_bias=st.booleans(), training_mode=st.booleans(), compute_dX=st.booleans(), **mu.gcs) @settings(max_examples=2, timeout=100) def test_convolution_transpose_gradients(self, stride, pad, kernel, adj, size, input_channels, output_channels, batch_size, use_bias, training_mode, compute_dX, gc, dc): training = 1 if training_mode else 0 assume(adj < stride) X = np.random.rand( batch_size, input_channels, size, size).astype(np.float32) - 0.5 w = np.random.rand( input_channels, output_channels, kernel, kernel)\ .astype(np.float32) - 0.5 b = np.random.rand(output_channels).astype(np.float32) - 0.5 op = core.CreateOperator( "ConvTranspose", ["X", "w", "b"] if use_bias else ["X", "w"], ["Y"], stride=stride, kernel=kernel, pad=pad, adj=adj, training_mode=training, no_gradient_to_input=not compute_dX, ) inputs = [X, w, b] if use_bias else [X, w] self.assertDeviceChecks(dc, op, inputs, [0], threshold=0.001) if training_mode: if use_bias and compute_dX: # w, b, X outputs_to_check = [1, 2, 0] elif use_bias: # w, b outputs_to_check = [1, 2] elif compute_dX: # w, X outputs_to_check = [1, 0] else: # w outputs_to_check = [1] for i in outputs_to_check: self.assertGradientChecks(gc, op, inputs, i, [0]) if __name__ == "__main__": unittest.main()