Fix inceptionv3 (#1446)
This commit is contained in:
Yao Wang
Tianqi Chen
Родитель
00c87b376d
Коммит
8dbe779466
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@ -56,6 +56,8 @@ def _pooling(inputs, attrs):
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new_attrs['strides'] = attrs.get('stride', (1, 1))
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new_attrs['padding'] = attrs.get('pad', (0, 0))
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new_attrs['ceil_mode'] = (attrs.get('pooling_convention', 'valid') == 'full')
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if pool_type == 'avg':
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new_attrs['count_include_pad'] = attrs.get('count_include_pad', True)
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return _get_nnvm_op(op_name)(*inputs, **new_attrs)
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def _batch_norm(inputs, attrs):
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@ -10,6 +10,20 @@ from nnvm.testing.config import ctx_list
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def test_conv2d():
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def run_test_conv2d(sym, dtype, dshape, kshape, oshape, shape_dict, padding):
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for target, ctx in ctx_list():
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graph, lib, _ = nnvm.compiler.build(sym, target, shape_dict)
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m = graph_runtime.create(graph, lib, ctx)
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data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
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kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
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bias = tvm.nd.array(np.random.uniform(size=kshape[0]).astype(dtype))
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m.run(x=data, y_weight=kernel, y_bias=bias)
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out = m.get_output(0, tvm.nd.empty(oshape, dtype))
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c_np = topi.testing.conv2d_nchw_python(
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data.asnumpy(), kernel.asnumpy(), 1, padding)
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c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1)
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np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
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x = sym.Variable("x")
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y = sym.conv2d(x, channels=10, kernel_size=(3,3),
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name="y", padding=(1,1))
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@ -18,18 +32,17 @@ def test_conv2d():
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kshape = (10, 3, 3, 3)
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oshape = (1, 10, 18, 18)
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shape_dict = {"x": dshape}
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for target, ctx in ctx_list():
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graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
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m = graph_runtime.create(graph, lib, ctx)
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data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
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kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
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bias = tvm.nd.array(np.random.uniform(size=kshape[0]).astype(dtype))
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m.run(x=data, y_weight=kernel, y_bias=bias)
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out = m.get_output(0, tvm.nd.empty(oshape, dtype))
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c_np = topi.testing.conv2d_nchw_python(
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data.asnumpy(), kernel.asnumpy(), 1, 1)
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c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1)
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np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
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run_test_conv2d(y, dtype, dshape, kshape, oshape, shape_dict, (1,1))
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x = sym.Variable("x")
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y = sym.conv2d(x, channels=10, kernel_size=(1,3),
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name="y", padding=(0,1))
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dtype = "float32"
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dshape = (1, 3, 224, 224)
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kshape = (10, 3, 1, 3)
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oshape = (1, 10, 224, 224)
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shape_dict = {"x": dshape}
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run_test_conv2d(y, dtype, dshape, kshape, oshape, shape_dict, (0,1))
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def test_mixed_precision():
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@ -141,6 +141,14 @@ def test_forward_expand_dims():
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mx_sym = mx.sym.expand_dims(data, axis=1)
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verify_mxnet_frontend_impl(mx_sym, (2, 3, 4), (2, 1, 3, 4))
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def test_forward_pooling():
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data = mx.sym.var('data')
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mx_sym = mx.sym.Pooling(data, kernel=(3, 3), pad=(1, 1), pool_type='avg')
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verify_mxnet_frontend_impl(mx_sym, (1, 20, 8, 8), (1, 20, 8, 8))
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mx_sym = mx.sym.Pooling(data, kernel=(3, 3), pad=(1, 1), pool_type='max')
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verify_mxnet_frontend_impl(mx_sym, (1, 20, 8, 8), (1, 20, 8, 8))
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if __name__ == '__main__':
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test_forward_mlp()
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test_forward_vgg()
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@ -154,3 +162,4 @@ if __name__ == '__main__':
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test_forward_split()
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test_forward_split_squeeze()
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test_forward_expand_dims()
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test_forward_pooling()
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@ -1,4 +1,4 @@
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# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
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# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals, too-many-branches
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"""Convolution in python"""
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import numpy as np
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import scipy.signal
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@ -18,8 +18,8 @@ def conv2d_nchw_python(a_np, w_np, stride, padding):
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stride : int or a list/tuple of two ints
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Stride size, or [stride_height, stride_width]
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padding : int or str
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Padding size, or ['VALID', 'SAME']
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padding : int or str or a list/tuple of two ints
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Padding size, or ['VALID', 'SAME'], or [pad_height, pad_width]
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Returns
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-------
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@ -34,12 +34,11 @@ def conv2d_nchw_python(a_np, w_np, stride, padding):
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stride_h, stride_w = stride
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if isinstance(padding, int):
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pad_h = pad_w = padding * 2
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elif padding == 'VALID':
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pad_h = 0
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pad_w = 0
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else: # 'SAME'
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pad_h = kernel_h - 1
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pad_w = kernel_w - 1
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elif isinstance(padding, (list, tuple)):
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pad_h, pad_w = padding[0] * 2, padding[1] * 2
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else:
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pad_h = 0 if padding == 'VALID' else kernel_h - 1
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pad_w = 0 if padding == 'VALID' else kernel_w - 1
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pad_top = int(np.ceil(float(pad_h) / 2))
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pad_bottom = pad_h - pad_top
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pad_left = int(np.ceil(float(pad_w) / 2))
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@ -53,9 +52,14 @@ def conv2d_nchw_python(a_np, w_np, stride, padding):
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for n in range(batch):
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for f in range(out_channel):
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for c in range(in_channel):
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if pad_h > 0:
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if pad_h > 0 or pad_w > 0:
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apad = np.zeros((in_height + pad_h, in_width + pad_w))
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apad[pad_top:-pad_bottom, pad_left:-pad_right] = a_np[n, c]
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if pad_h == 0:
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apad[:, pad_left:-pad_right] = a_np[n, c]
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elif pad_w == 0:
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apad[pad_top:-pad_bottom, :] = a_np[n, c]
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else:
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apad[pad_top:-pad_bottom, pad_left:-pad_right] = a_np[n, c]
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else:
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apad = a_np[n, c]
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out = scipy.signal.convolve2d(
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@ -56,7 +56,7 @@ def _declaration_conv(data, kernel, stride, padding, layout, out_dtype):
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out_height = (in_height + 2 * HPAD - kernel_height) // HSTR + 1
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out_width = (in_width + 2 * WPAD - kernel_width) // WSTR + 1
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DOPAD = (HPAD != 0 and WPAD != 0)
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DOPAD = (HPAD != 0 or WPAD != 0)
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if DOPAD:
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data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
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else:
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@ -95,7 +95,7 @@ def _schedule_conv(s, data, data_pad, data_vec, kernel, kernel_vec, conv_out, ou
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sch = _get_schedule(wkl)
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HPAD, WPAD = wkl.hpad, wkl.wpad
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DOPAD = (HPAD != 0 and WPAD != 0)
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DOPAD = (HPAD != 0 or WPAD != 0)
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A, W = data, kernel_vec
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A0, A1 = data_pad, data_vec
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@ -163,7 +163,7 @@ def _declaration_conv_NCHWc(wkl, sch, data, kernel):
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out_height = (wkl.height + 2 * HPAD - wkl.hkernel) // HSTR + 1
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out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
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DOPAD = (HPAD != 0 and WPAD != 0)
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DOPAD = (HPAD != 0 or WPAD != 0)
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if DOPAD:
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data_pad = pad(data, (0, 0, HPAD, WPAD, 0), name="data_pad")
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else:
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@ -58,7 +58,7 @@ def _declaration_conv(data, kernel, stride, padding, layout, out_dtype):
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out_width = (in_width + 2 * WPAD - kernel_width) // WSTR + 1
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# pack data
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DOPAD = (HPAD != 0 and WPAD != 0)
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DOPAD = (HPAD != 0 or WPAD != 0)
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if DOPAD:
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data_pad = pad(data, (0, 0, HPAD, WPAD), name="data_pad")
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else:
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@ -108,7 +108,7 @@ def _schedule_conv(s, data, data_pad, data_vec, kernel, kernel_vec, conv_out, ou
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sch = _get_schedule(wkl)
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HPAD, WPAD = wkl.hpad, wkl.wpad
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DOPAD = (HPAD != 0 and WPAD != 0)
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DOPAD = (HPAD != 0 or WPAD != 0)
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A, W = data, kernel_vec
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A0, A1 = data_pad, data_vec
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@ -181,7 +181,7 @@ def _declaration_conv_NCHWc(wkl, sch, data, kernel):
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out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
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# pack data
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DOPAD = (HPAD != 0 and WPAD != 0)
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DOPAD = (HPAD != 0 or WPAD != 0)
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if DOPAD:
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data_pad = pad(data, (0, 0, HPAD, WPAD, 0), name="data_pad")
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else:
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