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Commit 6b9a094d authored by Mirzam98's avatar Mirzam98
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[fpgadataflow/fmpadding_batch]: added support for non-square images

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src/finn/custom_op/fpgadataflow/fmpadding_batch.py
+ 91
− 34
View file @ 6b9a094d
...@@ -17,9 +17,17 @@ class FMPadding_Batch(HLSCustomOp): ...@@ -17,9 +17,17 @@ class FMPadding_Batch(HLSCustomOp):
def get_nodeattr_types(self): def get_nodeattr_types(self):
my_attrs = { my_attrs = {
# spatial size of input images # spatial size of input images
"ImgDim": ("i", True, 0), "ImgDim": ("ints", True, []), # [H, W] = [Y, X]
# total padding (per dimension) to apply # total padding (per dimension) to apply
"Padding": ("i", True, 2), # NOTE: Current padding scheme that is applied tries to pad the same
# amount of zeros in front and behind the image for each dimension.
# As an example, a padding scheme such as [1, x, 3, x] is equal
# to [2, x, 2, x]
"Padding": (
"ints",
True,
[1, 1, 1, 1],
), # [H_begin, W_begin, H_end, W_end] = [Y_begin, X_begin, Y_end, X_end]
# number of channels in input image # number of channels in input image
"NumChannels": ("i", True, 0), "NumChannels": ("i", True, 0),
# SIMD Input parallelism # SIMD Input parallelism
...@@ -38,31 +46,33 @@ class FMPadding_Batch(HLSCustomOp): ...@@ -38,31 +46,33 @@ class FMPadding_Batch(HLSCustomOp):
def get_padded_odim(self): def get_padded_odim(self):
"Return the padded spatial size of the output." "Return the padded spatial size of the output."
idim_h, idim_w = self.get_nodeattr("ImgDim")
idim = self.get_nodeattr("ImgDim")
pad = self.get_nodeattr("Padding") pad = self.get_nodeattr("Padding")
return idim + pad pad_h = pad[0] + pad[2]
pad_w = pad[1] + pad[3]
odim_h = idim_h + pad_h
odim_w = idim_w + pad_w
return [odim_h, odim_w]
def get_exp_cycles(self): def get_exp_cycles(self):
odim = self.get_padded_odim() odim_h, odim_w = self.get_padded_odim()
channels = self.get_nodeattr("NumChannels") channels = self.get_nodeattr("NumChannels")
simd = self.get_nodeattr("SIMD") simd = self.get_nodeattr("SIMD")
batch_size = self.get_nodeattr("numInputVectors") batch_size = self.get_nodeattr("numInputVectors")
exp_cycles = (channels / simd) * batch_size * odim * odim exp_cycles = (channels / simd) * batch_size * odim_h * odim_w
return int(exp_cycles) return int(exp_cycles)
def get_normal_input_shape(self): def get_normal_input_shape(self):
idim = self.get_nodeattr("ImgDim") idim_h, idim_w = self.get_nodeattr("ImgDim")
num_ch = self.get_nodeattr("NumChannels") num_ch = self.get_nodeattr("NumChannels")
ishape = (1, idim_h, idim_w, num_ch)
ishape = (1, idim, idim, num_ch)
return ishape return ishape
def get_normal_output_shape(self): def get_normal_output_shape(self):
odim = self.get_padded_odim() odim_h, odim_w = self.get_padded_odim()
num_ch = self.get_nodeattr("NumChannels") num_ch = self.get_nodeattr("NumChannels")
oshape = (1, odim, odim, num_ch) oshape = (1, odim_h, odim_w, num_ch)
return oshape return oshape
def get_folded_input_shape(self): def get_folded_input_shape(self):
...@@ -148,20 +158,53 @@ class FMPadding_Batch(HLSCustomOp): ...@@ -148,20 +158,53 @@ class FMPadding_Batch(HLSCustomOp):
self.code_gen_dict["$GLOBALS$"] = ['#include "streamtools.h"'] self.code_gen_dict["$GLOBALS$"] = ['#include "streamtools.h"']
def defines(self, var): def defines(self, var):
self.code_gen_dict["$DEFINES$"] = [ idim_h, idim_w = self.get_nodeattr("ImgDim")
"""#define ImgDim1 {}\n#define OutputDim1 {}\n odim_h, odim_w = self.get_padded_odim()
#define Padding1 {}\n#define NumChannels1 {}\n pad = self.get_nodeattr("Padding")
#define PaddingStyle1 {}\n#define numReps {} pad_h = pad[0] + pad[2]
#define SIMD1 {}\n""".format( pad_w = pad[1] + pad[3]
self.get_nodeattr("ImgDim"), is_square = idim_h == idim_w
self.get_padded_odim(),
self.get_nodeattr("Padding"), if is_square:
self.get_nodeattr("NumChannels"), assert (
self.get_nodeattr("PaddingStyle"), pad_h == pad_w
self.get_nodeattr("numInputVectors"), ), "Only equal padding along the dimensions for square images is supported"
self.get_nodeattr("SIMD"), self.code_gen_dict["$DEFINES$"] = [
) """#define ImgDim1 {}\n#define OutputDim1 {}\n
] #define Padding1 {}\n#define NumChannels1 {}\n
#define SIMD1 {}\n#define PaddingStyle1 {}\n
#define numReps {}\n""".format(
idim_h,
odim_h,
pad_h,
self.get_nodeattr("NumChannels"),
self.get_nodeattr("SIMD"),
self.get_nodeattr("PaddingStyle"),
self.get_nodeattr("numInputVectors"),
)
]
else:
self.code_gen_dict["$DEFINES$"] = [
"""
#define OutputDim1_x {}\n
#define OutputDim1_y {}\n
#define Padding1_x {}\n
#define Padding1_y {}\n
#define NumChannels1 {}\n
#define SIMD1 {}\n
#define PaddingStyle1 {}\n
#define numReps {}\n
""".format(
odim_w,
odim_h,
pad_w,
pad_h,
self.get_nodeattr("NumChannels"),
self.get_nodeattr("SIMD"),
self.get_nodeattr("PaddingStyle"),
self.get_nodeattr("numInputVectors"),
)
]
def read_npy_data(self): def read_npy_data(self):
code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim") code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
...@@ -193,12 +236,26 @@ class FMPadding_Batch(HLSCustomOp): ...@@ -193,12 +236,26 @@ class FMPadding_Batch(HLSCustomOp):
def docompute(self): def docompute(self):
in_t = self.get_input_datatype().get_hls_datatype_str() in_t = self.get_input_datatype().get_hls_datatype_str()
node = self.onnx_node node = self.onnx_node
self.code_gen_dict["$DOCOMPUTE$"] = [
"""{}<ImgDim1, OutputDim1, Padding1, NumChannels1,SIMD1, idim_h, idim_w = self.get_nodeattr("ImgDim")
{}, PaddingStyle1> (in0, out, numReps);""".format( is_square = idim_h == idim_w
node.op_type, in_t
) if is_square:
] hls_call = node.op_type
self.code_gen_dict["$DOCOMPUTE$"] = [
"""{}<ImgDim1, OutputDim1, Padding1, NumChannels1,SIMD1,
{}, PaddingStyle1> (in0, out, numReps);""".format(
hls_call, in_t
)
]
else:
hls_call = "FMPadding_nonsquare_Batch"
self.code_gen_dict["$DOCOMPUTE$"] = [
"""{}<OutputDim1_x, OutputDim1_y, Padding1_x, Padding1_y, NumChannels1,
SIMD1, {}, PaddingStyle1> (in0, out, numReps);""".format(
hls_call, in_t
)
]
def dataoutstrm(self): def dataoutstrm(self):
code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim") code_gen_dir = self.get_nodeattr("code_gen_dir_cppsim")
...@@ -270,7 +327,7 @@ class FMPadding_Batch(HLSCustomOp): ...@@ -270,7 +327,7 @@ class FMPadding_Batch(HLSCustomOp):
assert ( assert (
inp.shape == exp_ishape inp.shape == exp_ishape
), """Input shape doesn't ), """Input shape doesn't
match expected shape (1, ImgDim, ImgDim, NumChannels).""" match expected shape (1, ImgDim_h, ImgDim_w, NumChannels)."""
export_idt = self.get_input_datatype() export_idt = self.get_input_datatype()
reshaped_input = inp.reshape(folded_ishape) reshaped_input = inp.reshape(folded_ishape)
...@@ -316,4 +373,4 @@ class FMPadding_Batch(HLSCustomOp): ...@@ -316,4 +373,4 @@ class FMPadding_Batch(HLSCustomOp):
assert ( assert (
context[node.output[0]].shape == exp_oshape context[node.output[0]].shape == exp_oshape
), """Output shape doesn't match expected shape ), """Output shape doesn't match expected shape
(1, OutputDim, OutputDim, NumChannels).""" (1, OutputDim_H, OutputDim_W, NumChannels)."""
tests/fpgadataflow/test_fpgadataflow_fmpadding.py
+ 37
− 18
View file @ 6b9a094d
...@@ -54,15 +54,20 @@ target_clk_ns = 10 ...@@ -54,15 +54,20 @@ target_clk_ns = 10
def make_single_fmpadding_modelwrapper(idim, padding, num_ch, simd, idt, pad_style): def make_single_fmpadding_modelwrapper(idim, padding, num_ch, simd, idt, pad_style):
pad_h = padding[0] + padding[2]
pad_w = padding[1] + padding[3]
idim_h, idim_w = idim
assert pad_style == 2, "only pad_style == 2 supported in hlslib" assert pad_style == 2, "only pad_style == 2 supported in hlslib"
assert padding > 0, "Output dim should be greater than input dim" assert pad_h > 0 or pad_w > 0, "Output dim should be greater than input dim"
odim = idim + padding odim_h = idim_h + pad_h
odim_w = idim_w + pad_w
inp = helper.make_tensor_value_info( inp = helper.make_tensor_value_info(
"inp", TensorProto.FLOAT, [1, idim, idim, num_ch] "inp", TensorProto.FLOAT, [1, idim_h, idim_w, num_ch]
) )
outp = helper.make_tensor_value_info( outp = helper.make_tensor_value_info(
"outp", TensorProto.FLOAT, [1, odim, odim, num_ch] "outp", TensorProto.FLOAT, [1, odim_h, odim_w, num_ch]
) )
FMPadding = helper.make_node( FMPadding = helper.make_node(
...@@ -94,9 +99,9 @@ def make_single_fmpadding_modelwrapper(idim, padding, num_ch, simd, idt, pad_sty ...@@ -94,9 +99,9 @@ def make_single_fmpadding_modelwrapper(idim, padding, num_ch, simd, idt, pad_sty
# input image dimension # input image dimension
@pytest.mark.parametrize("idim", [8]) @pytest.mark.parametrize("idim", [[8, 8], [10, 8]])
# number of rows and number of cols to add # number of rows and number of cols to add
@pytest.mark.parametrize("pad", [2, 3]) @pytest.mark.parametrize("pad", [[1, 1, 1, 1], [1, 1, 2, 2], [1, 3, 2, 3]])
# number of channels # number of channels
@pytest.mark.parametrize("num_ch", [2, 4]) @pytest.mark.parametrize("num_ch", [2, 4])
# Input parallelism # Input parallelism
...@@ -112,10 +117,22 @@ def make_single_fmpadding_modelwrapper(idim, padding, num_ch, simd, idt, pad_sty ...@@ -112,10 +117,22 @@ def make_single_fmpadding_modelwrapper(idim, padding, num_ch, simd, idt, pad_sty
def test_fpgadataflow_fmpadding(idim, pad, num_ch, simd, pad_style, idt, mode): def test_fpgadataflow_fmpadding(idim, pad, num_ch, simd, pad_style, idt, mode):
if num_ch % simd != 0: if num_ch % simd != 0:
pytest.skip(" num_ch % simd != 0, skipping") pytest.skip(" num_ch % simd != 0, skipping")
idim_h, idim_w = idim
pad_h = pad[0] + pad[2]
pad_w = pad[1] + pad[3]
if idim_h == idim_w and pad_h != pad_w:
pytest.skip(
"""Only equal padding along the dimensions for square images
is supported, skipping"""
)
# generate input data # generate input data
x = gen_finn_dt_tensor(idt, [1, idim, idim, num_ch]) x = gen_finn_dt_tensor(idt, [1, idim_h, idim_w, num_ch])
input_dict = {"inp": x} input_dict = {"inp": x}
odim = idim + pad odim_h = idim_h + pad_h
odim_w = idim_w + pad_w
model = make_single_fmpadding_modelwrapper(idim, pad, num_ch, simd, idt, pad_style) model = make_single_fmpadding_modelwrapper(idim, pad, num_ch, simd, idt, pad_style)
model = model.transform(InferShapes()) model = model.transform(InferShapes())
...@@ -129,24 +146,26 @@ def test_fpgadataflow_fmpadding(idim, pad, num_ch, simd, pad_style, idt, mode): ...@@ -129,24 +146,26 @@ def test_fpgadataflow_fmpadding(idim, pad, num_ch, simd, pad_style, idt, mode):
model = model.transform(HLSSynthIP()) model = model.transform(HLSSynthIP())
model = model.transform(PrepareRTLSim()) model = model.transform(PrepareRTLSim())
y_produced = oxe.execute_onnx(model, input_dict)["outp"] y_produced = oxe.execute_onnx(model, input_dict)["outp"]
expected_oshape = (1, odim, odim, num_ch) expected_oshape = (1, odim_h, odim_w, num_ch)
assert y_produced.shape == expected_oshape assert y_produced.shape == expected_oshape
# calculate reference # calculate reference
# calculate correct pad according to parameters # calculate correct pad according to parameters
if pad_style == 2: if pad_style == 2:
if pad % 2 == 0: if pad_h % 2 == 0:
pad_up = pad // 2 pad_up = pad_h // 2
pad_left = pad // 2 else:
pad_up = pad_h // 2 + 1
if pad_w % 2 == 0:
pad_left = pad_w // 2
else: else:
pad_up = pad // 2 + 1 pad_left = pad_w // 2 + 1
pad_left = pad // 2 + 1
else: else:
pad_up = pad // 2 pad_up = pad_h // 2
pad_left = pad // 2 pad_left = pad_w // 2
pad_down = pad - pad_up pad_down = pad_h - pad_up
pad_right = pad - pad_left pad_right = pad_w - pad_left
y_expected = np.pad( y_expected = np.pad(
x, ((0, 0), (pad_up, pad_down), (pad_left, pad_right), (0, 0)), "constant" x, ((0, 0), (pad_up, pad_down), (pad_left, pad_right), (0, 0)), "constant"
......
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