def verify_depthwise_conv2d_back_input(batch, in_channel, in_h, channel_multiplier, filter_h, stride_h, padding_h):
in_w = in_h
filter_channel = in_channel
filter_w = filter_h
stride_w = stride_h
padding_w = padding_h
out_h = np.int((in_h+2*padding_h-filter_h)/stride_h+1)
out_w = np.int((in_w+2*padding_w-filter_w)/stride_w+1)
out_channel = in_channel * channel_multiplier
ishape = [batch, in_h, in_w, in_channel]
oshape = [batch, out_h, out_w, out_channel]
# placeholder
Out_grad = tvm.placeholder(oshape, name='Out_grad')
Filter = tvm.placeholder((filter_h, filter_w, filter_channel, channel_multiplier))
# declare
In_grad = topi.nn.depthwise_conv2d_backward_input_nhwc(Filter, Out_grad, oshape, ishape,
stride=[stride_h, stride_w], padding=[padding_h, padding_w])
# schedule
schedule = schedule_depthwise_conv2d_backward_input_nhwc(In_grad)
def check_device(device):
if not tvm.module.enabled(device):
print("Skip because %s is not enabled" % device)
return
print("Running on target: %s" % device)
ctx = tvm.context(device, 0)
# build the kernel
f = tvm.build(schedule, [Filter, Out_grad, In_grad], device)
# prepare pod type for test data closure
dtype = Out_grad.dtype
out_grad_shape = get_const_tuple(Out_grad.shape)
filter_shape = get_const_tuple(Filter.shape)
# use memoize to pickle the test data for next time use
@memoize("topi.tests.test_topi_depthwise_conv2d_backward_input.nhwc")
def get_ref_data():
out_grad_np = np.random.uniform(size=out_grad_shape).astype(dtype)
filter_np = np.random.uniform(size=filter_shape).astype(dtype)
dilated_out_grad_np = topi.testing.dilate_python(out_grad_np, [1, stride_h, stride_w, 1])
# padding params in forward propagation
fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple([padding_h, padding_w], (filter_h, filter_w))
# padding params in backward propagation
bpad_top = filter_h - 1 - fpad_top
bpad_bottom = (filter_h - 1 - fpad_bottom) + (stride_h - 1)
bpad_left = filter_w - 1 - fpad_left
bpad_right = (filter_w - 1 - fpad_right) + (stride_w - 1)
padded_out_grad = np.zeros((batch, dilated_out_grad_np.shape[1]+bpad_top+bpad_bottom,
dilated_out_grad_np.shape[2]+bpad_left+bpad_right, out_channel))
padded_out_grad[:, bpad_top:dilated_out_grad_np.shape[1]+bpad_top,
bpad_left:dilated_out_grad_np.shape[2]+bpad_left, :] = dilated_out_grad_np
in_grad_np = np.zeros((batch, in_h, in_w, in_channel))
for b in range(batch):
for c in range(in_channel):
for m in range(channel_multiplier):
in_grad_np[b, :, :, c] += signal.convolve2d(padded_out_grad[b, :, :, c*channel_multiplier+m], \
filter_np[:, :, c, m], mode='valid')[0:in_h, 0:in_w]
return (out_grad_np, filter_np, in_grad_np)
(out_grad_np, filter_np, in_grad_np) = get_ref_data()
out_grad_tvm = tvm.nd.array(out_grad_np, ctx)
filter_tvm = tvm.nd.array(filter_np, ctx)
in_grad_tvm = tvm.nd.array(np.zeros(shape=ishape, dtype=dtype), ctx)
# launch the kernel
timer = f.time_evaluator(f.entry_name, ctx, number=1)
tcost = timer(filter_tvm, out_grad_tvm, in_grad_tvm).mean
np.testing.assert_allclose(in_grad_np, in_grad_tvm.asnumpy(), rtol=1e-5)
check_device("opencl")
check_device("cuda")
check_device("metal")
check_device("rocm")
def verify_depthwise_conv2d_back_input(batch, in_channel, in_h, channel_multiplier, filter_h, stride_h, padding_h):
in_w = in_h
filter_channel = in_channel
filter_w = filter_h
stride_w = stride_h
padding_w = padding_h
out_h = np.int((in_h+2*padding_h-filter_h)/stride_h+1)
out_w = np.int((in_w+2*padding_w-filter_w)/stride_w+1)
out_channel = in_channel * channel_multiplier
ishape = [batch, in_h, in_w, in_channel]
oshape = [batch, out_h, out_w, out_channel]
# placeholder
Out_grad = tvm.placeholder(oshape, name='Out_grad')
Filter = tvm.placeholder((filter_h, filter_w, filter_channel, channel_multiplier))
# declare
In_grad = topi.nn.depthwise_conv2d_backward_input_nhwc(Filter, Out_grad, oshape, ishape,
stride=[stride_h, stride_w], padding=[padding_h, padding_w])
# schedule
schedule = schedule_depthwise_conv2d_backward_input_nhwc(In_grad)
def check_device(device):
ctx = tvm.context(device, 0)
if not ctx.exist:
print("Skip because %s is not enabled" % device)
return
print("Running on target: %s" % device)
# build the kernel
f = tvm.build(schedule, [Filter, Out_grad, In_grad], device)
# prepare pod type for test data closure
dtype = Out_grad.dtype
out_grad_shape = get_const_tuple(Out_grad.shape)
filter_shape = get_const_tuple(Filter.shape)
# use memoize to pickle the test data for next time use
@memoize("topi.tests.test_topi_depthwise_conv2d_backward_input.nhwc")
def get_ref_data():
out_grad_np = np.random.uniform(size=out_grad_shape).astype(dtype)
filter_np = np.random.uniform(size=filter_shape).astype(dtype)
dilated_out_grad_np = topi.testing.dilate_python(out_grad_np, [1, stride_h, stride_w, 1])
# padding params in forward propagation
fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple([padding_h, padding_w], (filter_h, filter_w))
# padding params in backward propagation
bpad_top = filter_h - 1 - fpad_top
bpad_bottom = (filter_h - 1 - fpad_bottom) + (stride_h - 1)
bpad_left = filter_w - 1 - fpad_left
bpad_right = (filter_w - 1 - fpad_right) + (stride_w - 1)
padded_out_grad = np.zeros((batch, dilated_out_grad_np.shape[1]+bpad_top+bpad_bottom,
dilated_out_grad_np.shape[2]+bpad_left+bpad_right, out_channel))
padded_out_grad[:, bpad_top:dilated_out_grad_np.shape[1]+bpad_top,
bpad_left:dilated_out_grad_np.shape[2]+bpad_left, :] = dilated_out_grad_np
in_grad_np = np.zeros((batch, in_h, in_w, in_channel))
for b in range(batch):
for c in range(in_channel):
for m in range(channel_multiplier):
in_grad_np[b, :, :, c] += signal.convolve2d(padded_out_grad[b, :, :, c*channel_multiplier+m], \
filter_np[:, :, c, m], mode='valid')[0:in_h, 0:in_w]
return (out_grad_np, filter_np, in_grad_np)
(out_grad_np, filter_np, in_grad_np) = get_ref_data()
out_grad_tvm = tvm.nd.array(out_grad_np, ctx)
filter_tvm = tvm.nd.array(filter_np, ctx)
in_grad_tvm = tvm.nd.array(np.zeros(shape=ishape, dtype=dtype), ctx)
# launch the kernel
timer = f.time_evaluator(f.entry_name, ctx, number=1)
tcost = timer(filter_tvm, out_grad_tvm, in_grad_tvm).mean
tvm.testing.assert_allclose(in_grad_np, in_grad_tvm.asnumpy(), rtol=1e-5)
check_device("opencl")
check_device("cuda")
check_device("metal")
check_device("rocm")
check_device("vulkan")
check_device("nvptx")