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/dports/misc/py-onnx/onnx-1.10.2/onnx/backend/test/case/node/
H A Daveragepool.py144 x_shape = np.shape(x)
147 out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
149 y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'AVG')
166 x_shape = np.shape(x)
169 out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
188 x_shape = np.shape(x)
212 x_shape = np.shape(x)
242 x_shape = np.shape(x)
272 x_shape = np.shape(x)
303 x_shape = np.shape(x)
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H A Dmaxpool.py209 x_shape = np.shape(x)
212 out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
214 y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'MAX')
231 x_shape = np.shape(x)
234 out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
253 x_shape = np.shape(x)
256 out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
277 x_shape = np.shape(x)
307 x_shape = np.shape(x)
337 x_shape = np.shape(x)
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H A Dpool_op_common.py45 x_shape, # type: Sequence[int] argument
53 spatial_size = len(x_shape) - 2
54 y = np.zeros([x_shape[0], x_shape[1]] + list(out_shape))
56 for shape in itertools.product(range(x_shape[0]), range(x_shape[1]), *[range(int(
57 …(x_shape[i + 2] + pad_shape[i] - kernel_shape[i]) / strides_shape[i] + 1)) for i in range(spatial_…
/dports/misc/glow/glow-f24d960e3cc80db95ac0bc17b1900dbf60ca044a/thirdparty/onnx/onnx/backend/test/case/node/
H A Daveragepool.py142 x_shape = np.shape(x)
145 out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
147 y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'AVG')
164 x_shape = np.shape(x)
167 out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
186 x_shape = np.shape(x)
210 x_shape = np.shape(x)
240 x_shape = np.shape(x)
270 x_shape = np.shape(x)
301 x_shape = np.shape(x)
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H A Dmaxpool.py177 x_shape = np.shape(x)
180 out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
182 y = pool(padded, x_shape, kernel_shape, strides, out_shape, [0], 'MAX')
199 x_shape = np.shape(x)
202 out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
221 x_shape = np.shape(x)
224 out_shape = get_output_shape('VALID', x_shape[2:], kernel_shape, strides)
245 x_shape = np.shape(x)
275 x_shape = np.shape(x)
305 x_shape = np.shape(x)
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H A Dpool_op_common.py43 x_shape, # type: Sequence[int] argument
51 spatial_size = len(x_shape) - 2
52 y = np.zeros([x_shape[0], x_shape[1]] + list(out_shape))
54 for shape in itertools.product(range(x_shape[0]), range(x_shape[1]), *[range(int(
55 …(x_shape[i + 2] + pad_shape[i] - kernel_shape[i]) / strides_shape[i] + 1)) for i in range(spatial_…
/dports/graphics/tesseract/tesseract-5.0.0/src/lstm/
H A Dreversed.cpp34 StaticShape x_shape(input_shape); in OutputShape() local
35 x_shape.set_width(input_shape.height()); in OutputShape()
36 x_shape.set_height(input_shape.width()); in OutputShape()
37 x_shape = stack_[0]->OutputShape(x_shape); in OutputShape()
38 x_shape.SetShape(x_shape.batch(), x_shape.width(), x_shape.height(), x_shape.depth()); in OutputShape()
39 return x_shape; in OutputShape()
/dports/misc/tvm/incubator-tvm-0.6.1/python/tvm/contrib/
H A Dcudnn.py182 x_shape, argument
216 assert len(x_shape) == 4
227 x_shape[0].value,
228 x_shape[1].value,
229 x_shape[2].value,
230 x_shape[3].value,
246 x_shape, argument
289 x_shape[0].value,
290 x_shape[1].value,
291 x_shape[2].value,
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/dports/misc/py-tvm/incubator-tvm-0.6.1/python/tvm/contrib/
H A Dcudnn.py182 x_shape, argument
216 assert len(x_shape) == 4
227 x_shape[0].value,
228 x_shape[1].value,
229 x_shape[2].value,
230 x_shape[3].value,
246 x_shape, argument
289 x_shape[0].value,
290 x_shape[1].value,
291 x_shape[2].value,
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/python/tvm/contrib/
H A Dcudnn.py148 def _prepare_global_func_params(dims, pad, stride, dilation, x_shape=None, w_shape=None): argument
150 if x_shape:
151 assert isinstance(x_shape, list)
152 assert len(x_shape) == full_dims
173 xshape = np.array(x_shape, dtype=np.int32) if x_shape else None
174 wshape = np.array(w_shape, dtype=np.int32) if x_shape else None
212 dims = len(x_shape)
216 dims - 2, pad, stride, dilation, x_shape, w_shape
242 x_shape, argument
281 dims = len(x_shape)
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/dports/science/py-chainer/chainer-7.8.0/chainer/functions/activation/
H A Dmaxout.py70 x_shape = x.shape
71 if x_shape[axis] % pool_size != 0:
74 x_shape[axis], pool_size)
78 shape = (x_shape[:axis] +
79 (x_shape[axis] // pool_size, pool_size) +
80 x_shape[axis + 1:])
/dports/misc/py-onnx-tf/onnx-tf-1.6.0/onnx_tf/handlers/backend/
H A Dupsample.py23 x_shape = x.get_shape().as_list()
24 if len(x_shape) != 4:
35 x_shape = x.get_shape().as_list()
38 new_height = np.floor(x_shape[2] * scales[2])
39 new_weight = np.floor(x_shape[3] * scales[3])
58 x_shape = x.get_shape().as_list()
68 h_w_shape = x_shape[2:]
H A Dresize.py48 x_shape = x.get_shape().as_list()
49 if len(x_shape) != 4:
124 x_shape = tf_shape(x)
136 h_w_shape = tf.where(x_in_NCHW_format, x_shape[2:], x_shape[1:3])
165 x_shape = tf_shape(x)
197 h_w_shape = tf.where(x_in_NCHW_format, x_shape[2:], x_shape[1:3])
202 sizes.set_shape(x_shape.shape)
227 box_indices = tf.cast(tf.range(0, x_shape[0]), dtype=tf.int32)
/dports/science/py-chainer/chainer-7.8.0/chainerx_cc/chainerx/cuda/
H A Dcuda_conv_test.cc48 Shape x_shape{batch_size, in_channels}; in TEST() local
49 std::copy(in_dims.begin(), in_dims.end(), std::back_inserter(x_shape)); in TEST()
54 …Array x = testing::BuildArray(x_shape).WithLinearData<float>(-x_shape.GetTotalSize() / 2.0f, 1.0f)… in TEST()
94 Shape x_shape{batch_size, in_channels}; in TEST() local
95 std::copy(in_dims.begin(), in_dims.end(), std::back_inserter(x_shape)); in TEST()
100 …Array x = testing::BuildArray(x_shape).WithLinearData<float>(-x_shape.GetTotalSize() / 2.0f, 1.0f)… in TEST()
139 Shape x_shape{batch_size, in_channels}; in TEST() local
140 std::copy(in_dims.begin(), in_dims.end(), std::back_inserter(x_shape)); in TEST()
144 …Array x = testing::BuildArray(x_shape).WithLinearData<float>(-x_shape.GetTotalSize() / 2.0f, 1.0f)… in TEST()
/dports/misc/tvm/incubator-tvm-0.6.1/tests/python/relay/
H A Dtest_op_grad_level2.py27 def verify_max_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode): argument
28 x = relay.var("x", relay.TensorType(x_shape, "float32"))
36 data = np.random.rand(*x_shape).astype("float32")
55 def verify_avg_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode, count_include_pad): argument
56 x = relay.var("x", relay.TensorType(x_shape, "float32"))
64 data = np.random.rand(*x_shape).astype("float32")
84 def verify_global_avg_pool2d_grad(x_shape): argument
85 x = relay.var("x", relay.TensorType(x_shape, "float32"))
92 data = np.random.rand(*x_shape).astype("float32")
95 ref_grad = topi.testing.pool_grad_nchw(data, out_grad, pool_size=(x_shape[2], x_shape[3]),
H A Dtest_pass_combine_parallel_conv2d.py61 x = relay.var("x", shape=x_shape)
62 in_c = x_shape[1]
106 def check(x_shape, channels1, channels2): argument
107 x = relay.var("x", shape=x_shape)
108 in_c = x_shape[1]
146 def check(x_shape, channels1, channels2): argument
147 x = relay.var("x", shape=x_shape)
148 in_c = x_shape[1]
184 def check(x_shape, repeat): argument
185 x = relay.var("x", shape=x_shape)
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/dports/misc/py-tvm/incubator-tvm-0.6.1/tests/python/relay/
H A Dtest_op_grad_level2.py27 def verify_max_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode): argument
28 x = relay.var("x", relay.TensorType(x_shape, "float32"))
36 data = np.random.rand(*x_shape).astype("float32")
55 def verify_avg_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode, count_include_pad): argument
56 x = relay.var("x", relay.TensorType(x_shape, "float32"))
64 data = np.random.rand(*x_shape).astype("float32")
84 def verify_global_avg_pool2d_grad(x_shape): argument
85 x = relay.var("x", relay.TensorType(x_shape, "float32"))
92 data = np.random.rand(*x_shape).astype("float32")
95 ref_grad = topi.testing.pool_grad_nchw(data, out_grad, pool_size=(x_shape[2], x_shape[3]),
H A Dtest_pass_combine_parallel_conv2d.py61 x = relay.var("x", shape=x_shape)
62 in_c = x_shape[1]
106 def check(x_shape, channels1, channels2): argument
107 x = relay.var("x", shape=x_shape)
108 in_c = x_shape[1]
146 def check(x_shape, channels1, channels2): argument
147 x = relay.var("x", shape=x_shape)
148 in_c = x_shape[1]
184 def check(x_shape, repeat): argument
185 x = relay.var("x", shape=x_shape)
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/dports/science/py-chainer/chainer-7.8.0/chainer/functions/math/
H A Dbias.py40 x_shape = x.shape
43 assert x_shape[axis:axis + len(y_shape)] == y_shape
45 [1] * (len(x_shape) - axis - len(y_shape)))
47 y2 = broadcast.broadcast_to(y1, x_shape)
H A Dscale.py40 x_shape = x.shape
43 assert x_shape[axis:axis + len(y_shape)] == y_shape
45 [1] * (len(x_shape) - axis - len(y_shape)))
47 y2 = broadcast.broadcast_to(y1, x_shape)
/dports/misc/mnn/MNN-1.2.0/test/plugin/
H A DPluginTest.cpp42 auto x_shape = x->getInfo(); in _PluginMatMul() local
44 MNN_CHECK(x_shape->dim.size() == 2, "2-D shape is required."); in _PluginMatMul()
47 int M = x_shape->dim[0]; in _PluginMatMul()
48 int K = x_shape->dim[1]; in _PluginMatMul()
51 M = x_shape->dim[1]; in _PluginMatMul()
52 K = x_shape->dim[0]; in _PluginMatMul()
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/tvm/tests/python/relay/
H A Dtest_op_grad_level2.py29 def verify_max_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode): argument
30 x = relay.var("x", relay.TensorType(x_shape, "float32"))
39 data = np.random.rand(*x_shape).astype("float32")
69 def verify_avg_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode, count_include_pad): argument
70 x = relay.var("x", relay.TensorType(x_shape, "float32"))
84 data = np.random.rand(*x_shape).astype("float32")
124 def verify_global_avg_pool2d_grad(x_shape): argument
125 x = relay.var("x", relay.TensorType(x_shape, "float32"))
132 data = np.random.rand(*x_shape).astype("float32")
138 pool_size=(x_shape[2], x_shape[3]),
H A Dtest_pass_combine_parallel_conv2d.py73 x = relay.var("x", shape=x_shape)
74 in_c = x_shape[1]
122 def check(x_shape, channels1, channels2): argument
123 x = relay.var("x", shape=x_shape)
124 in_c = x_shape[1]
166 def check(x_shape, channels1, channels2): argument
167 x = relay.var("x", shape=x_shape)
168 in_c = x_shape[1]
207 def check(x_shape, repeat): argument
208 x = relay.var("x", shape=x_shape)
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/dports/science/py-scipy/scipy-1.7.1/scipy/interpolate/
H A Dpolyint.py77 x, x_shape = self._prepare_x(x)
79 return self._finish_y(y, x_shape)
90 x_shape = x.shape
91 return x.ravel(), x_shape
93 def _finish_y(self, y, x_shape): argument
96 if self._y_axis != 0 and x_shape != ():
97 nx = len(x_shape)
176 x, x_shape = self._prepare_x(x)
181 nx = len(x_shape)
215 x, x_shape = self._prepare_x(x)
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/dports/science/py-chainer/chainer-7.8.0/chainer/functions/normalization/
H A Ddecorrelated_batch_normalization.py48 def _calc_axis_and_m(x_shape, batch_size): argument
50 spatial_ndim = len(x_shape) - 2
53 m *= x_shape[i]
85 x_shape = x.shape
86 b, c = x_shape[:2]
89 spatial_axis, m = _calc_axis_and_m(x_shape, b)
111 y = y_hat.reshape((c, b) + x_shape[2:]).transpose(
222 x_shape = x.shape
223 b, c = x_shape[:2]
226 spatial_axis, m = _calc_axis_and_m(x_shape, b)
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