diff options
author | Jeremy Johnson <jeremy.johnson@arm.com> | 2022-04-26 15:47:21 +0100 |
---|---|---|
committer | Jeremy Johnson <jeremy.johnson@arm.com> | 2022-04-28 17:47:03 +0100 |
commit | 4a6fb9bdb4188b53516f7df0fbaa3695cce76319 (patch) | |
tree | 37f854bc24699c4c885323cc3299d06d0995d255 /verif/generator | |
parent | 9a66abbd1da6547fd2cba1512d2f07fd1525de4d (diff) | |
download | reference_model-4a6fb9bdb4188b53516f7df0fbaa3695cce76319.tar.gz |
Update tensor ops ERROR_IF criteria
Update to ref model to check ERROR_IF criteria for pooling
and convolution ops to match specification
Update to tosa_verif_build_tests to produce valid test ranges and
new ERROR_IF tests
Plus update pooling ops big kernel to 9 (from 6) for better testing
coverage and set dilation to 1 and add out_pad bottom & right for
transpose_conv2d to match specification
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: Ic5759872d40ae8d3f3d07043d9a0f2fa0244d72e
Diffstat (limited to 'verif/generator')
-rw-r--r-- | verif/generator/tosa_arg_gen.py | 113 | ||||
-rw-r--r-- | verif/generator/tosa_error_if.py | 196 | ||||
-rw-r--r-- | verif/generator/tosa_test_gen.py | 114 |
3 files changed, 324 insertions, 99 deletions
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py index e3492cd..f63a7df 100644 --- a/verif/generator/tosa_arg_gen.py +++ b/verif/generator/tosa_arg_gen.py @@ -1031,7 +1031,9 @@ class TosaArgGen: # Can't use stride=0, as it is used to derive output shape, as a divisor s_vals = [testGen.rng.choice(range(-5, 0))] else: - s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] + # Stride must be greater than 1 to force non-integer error + startStride = 1 if error_name != ErrorIf.PoolingOutputShapeNonInteger else 2 + s_vals = [x for x in range(startStride, testGen.args.max_conv_stride + 1)] strides = {x for x in itertools.product(*([s_vals] * k_rank))} if error_name == ErrorIf.DilationSmallerOne: d_vals = [testGen.rng.choice(range(-5, 1))] @@ -1055,7 +1057,7 @@ class TosaArgGen: # There are too many parameter combinations, so generate them sparsely, # very sparse for negative tests - sparsity_factor = 2 if error_name else 100 + sparsity_factor = 2 if error_name else 120 sparsity = len(paddings) * len(strides) * len(dilations) // sparsity_factor + 1 # If there are only a small number of tests, just select them all if sparsity < 13: @@ -1084,16 +1086,37 @@ class TosaArgGen: and (ifm_shape[2] + p[2] + p[3]) > d[1] and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > d[2])) ): - arg_list.append( - ( - "st{}_pad{}_dilat{}".format( - "".join([str(x) for x in s]), - "".join([str(x) for x in p]), - "".join([str(x) for x in d]), - ), - [s, p, d], + remainders = [] + for index in range(k_rank): + pad_offset = index * 2 + remainders.append( + ( + ifm_shape[index + 1] + - 1 + + p[pad_offset] + + p[pad_offset + 1] + - (k[index] - 1) * d[index] + ) + % s[index] + ) + if ( + # the parameters must produce integer exact output + error_name != ErrorIf.ConvOutputShapeNonInteger + and max(remainders) == 0 + ) or ( + error_name == ErrorIf.ConvOutputShapeNonInteger + and max(remainders) > 0 + ): + arg_list.append( + ( + "st{}_pad{}_dilat{}".format( + "".join([str(x) for x in s]), + "".join([str(x) for x in p]), + "".join([str(x) for x in d]), + ), + [s, p, d], + ) ) - ) n += 1 return arg_list @@ -1116,17 +1139,16 @@ class TosaArgGen: p_vals = [testGen.rng.choice(range(-5, 0))] else: p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] - paddings = {x for x in itertools.product(*([p_vals] * 2))} + paddings = {x for x in itertools.product(*([p_vals] * 4))} if error_name == ErrorIf.StrideSmallerOne: # Can't use stride=0, as it is used to derive output shape, as a divisor s_vals = [testGen.rng.choice(range(-5, 0))] else: s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] strides = {x for x in itertools.product(*([s_vals] * 2))} - if error_name == ErrorIf.DilationSmallerOne: - d_vals = [testGen.rng.choice(range(-5, 1))] - else: - d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] + # Dilation is not supported by the specification for transpose conv2d + # TODO: Remove this completely when schema has been updated + d_vals = [1] dilations = {x for x in itertools.product(*([d_vals] * 2))} if not error_name: @@ -1134,16 +1156,14 @@ class TosaArgGen: if max(ifm_shape) < 64: bigPadding = 9 paddings.update( - {x for x in itertools.product(*([[0, bigPadding]] * 2))} + {x for x in itertools.product(*([[0, bigPadding]] * 4))} ) bigStride = 8 strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) - bigDilation = 7 - dilations.update({x for x in itertools.product(*([[1, bigDilation]] * 2))}) # There are too many parameter combinations, so generate them sparsely, # very sparse for negative tests - sparsity_factor = 2 if error_name else 100 + sparsity_factor = 2 if error_name else 10 sparsity = len(paddings) * len(strides) * len(dilations) // sparsity_factor + 1 # If there are only a small number of tests, just select them all if sparsity < 13: @@ -1159,18 +1179,8 @@ class TosaArgGen: for d in sorted(list(dilations)): if n % sparsity == 0: # Determine the output shape - oh = ( - ifm_shape[1] - - filter_shape[1] - - (filter_shape[1] - 1) * (d[0] - 1) - + 2 * p[0] - ) // s[0] + 1 - ow = ( - ifm_shape[2] - - filter_shape[2] - - (filter_shape[2] - 1) * (d[1] - 1) - + 2 * p[1] - ) // s[1] + 1 + oh = (ifm_shape[1] - 1) * s[0] - p[0] - p[1] + filter_shape[1] + ow = (ifm_shape[2] - 1) * s[1] - p[2] - p[3] + filter_shape[2] os = [ifm_shape[0], oh, ow, filter_shape[0]] arg_list.append( ( @@ -1231,7 +1241,9 @@ class TosaArgGen: # Generate comprehensive argument lists p_vals = [x for x in range(0, testGen.args.max_pooling_padding + 1)] paddings = {x for x in itertools.product(*([p_vals] * 4))} - s_vals = [x for x in range(1, testGen.args.max_pooling_stride + 1)] + # Stride must be greater than 1 to force non-integer error + startStride = 1 if error_name != ErrorIf.PoolingOutputShapeNonInteger else 2 + s_vals = [x for x in range(startStride, testGen.args.max_pooling_stride + 1)] strides = {x for x in itertools.product(*([s_vals] * 2))} k_vals = [x for x in range(2, testGen.args.max_pooling_kernel + 1)] kernels = {x for x in itertools.product(*([k_vals] * 2))} @@ -1239,8 +1251,10 @@ class TosaArgGen: if testGen.args.oversize: # add some oversize argument values bigStride = 7 - strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) - bigKernel = 6 + strides.update( + {x for x in itertools.product(*([[startStride, bigStride]] * 2))} + ) + bigKernel = 9 kernels.update({x for x in itertools.product(*([[2, bigKernel]] * 2))}) if max(shape) < 64: # padding must be less than the kernel size @@ -1289,16 +1303,27 @@ class TosaArgGen: and (shape[1] + p[0] + p[1]) > k[0] and (shape[2] + p[2] + p[3]) > k[1] ): - arg_list.append( - ( - "st{}_kern{}_pad{}".format( - "".join([str(x) for x in s]), - "".join([str(x) for x in k]), - "".join([str(x) for x in p]), - ), - [s, p, k], + remainder_h = (shape[1] + p[0] + p[1] - k[0]) % s[0] + remainder_w = (shape[2] + p[2] + p[3] - k[1]) % s[1] + if ( + # the parameters must produce integer exact output + error_name != ErrorIf.PoolingOutputShapeNonInteger + and remainder_h == 0 + and remainder_w == 0 + ) or ( + error_name == ErrorIf.PoolingOutputShapeNonInteger + and (remainder_h != 0 or remainder_w != 0) + ): + arg_list.append( + ( + "st{}_kern{}_pad{}".format( + "".join([str(x) for x in s]), + "".join([str(x) for x in k]), + "".join([str(x) for x in p]), + ), + [s, p, k], + ) ) - ) n += 1 return arg_list diff --git a/verif/generator/tosa_error_if.py b/verif/generator/tosa_error_if.py index caf63e3..e7e758f 100644 --- a/verif/generator/tosa_error_if.py +++ b/verif/generator/tosa_error_if.py @@ -42,6 +42,9 @@ class ErrorIf(object): PadSmallerZero = "PadSmallerZero" PadLargerEqualKernel = "PadLargerEqualKernel" PoolingOutputShapeMismatch = "PoolingOutputShapeMismatch" + PoolingOutputShapeNonInteger = "PoolingOutputShapeNonInteger" + ConvOutputShapeMismatch = "ConvOutputShapeMismatch" + ConvOutputShapeNonInteger = "ConvOutputShapeNonInteger" ScaleNotTrue = "ScaleNotTrue" ScaleTrue = "ScaleTrue" TensorSizeInputOutputMismatch = "TensorSizeInputOutputMismatch" @@ -1226,6 +1229,20 @@ class TosaErrorValidator: return info_dict @staticmethod + def checkPoolingParams(kernel, stride, pad): + return ( + min(kernel) >= 1 + and min(stride) >= 1 + and min(pad) >= 0 + and not ( + pad[0] >= kernel[0] + or pad[1] >= kernel[0] + or pad[2] >= kernel[1] + or pad[3] >= kernel[1] + ) + ) + + @staticmethod def evPoolingOutputShapeMismatch(check=False, **kwargs): error_name = ErrorIf.PoolingOutputShapeMismatch param_reqs = {"rank": None, "dtype": None, "shape": None} @@ -1252,25 +1269,11 @@ class TosaErrorValidator: # calculate correct height, width dimensions if stride_x != 0 and stride_y != 0: - y_correct = ( - IH + pad_top + pad_bottom + stride_y - kernel_y - ) // stride_y - x_correct = ( - IW + pad_left + pad_right + stride_x - kernel_x - ) // stride_x + y_correct = ((IH + pad_top + pad_bottom - kernel_y) // stride_y) + 1 + x_correct = ((IW + pad_left + pad_right - kernel_x) // stride_x) + 1 # ensure parameters are valid - params_valid = ( - min(kernel) >= 1 - and min(stride) >= 1 - and min(pad) >= 0 - and not ( - pad[0] >= kernel[0] - or pad[1] >= kernel[0] - or pad[2] >= kernel[1] - or pad[3] >= kernel[1] - ) - ) + params_valid = TosaErrorValidator.checkPoolingParams(kernel, stride, pad) if params_valid and (OH != y_correct or OW != x_correct): error_result = True @@ -1284,6 +1287,165 @@ class TosaErrorValidator: return info_dict @staticmethod + def evPoolingOutputShapeNonInteger(check=False, **kwargs): + error_name = ErrorIf.PoolingOutputShapeNonInteger + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Parameters do not yield exact integer output dimensions" + + if check: + pad = kwargs["pad"] + pad_top, pad_bottom, pad_left, pad_right = pad[0], pad[1], pad[2], pad[3] + + kernel = kwargs["kernel"] + kernel_y, kernel_x = kernel[0], kernel[1] + + input_shape = kwargs["input_shape"] + IH, IW = input_shape[1], input_shape[2] + + stride = kwargs["stride"] + stride_y, stride_x = stride[0], stride[1] + + # calculate remainder of height, width dimensions + if stride_x != 0 and stride_y != 0: + y_remainder = (IH + pad_top + pad_bottom - kernel_y) % stride_y + x_remainder = (IW + pad_left + pad_right - kernel_x) % stride_x + + # ensure parameters are valid + params_valid = TosaErrorValidator.checkPoolingParams(kernel, stride, pad) + if params_valid and (y_remainder != 0 or x_remainder != 0): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs, + } + return info_dict + + @staticmethod + def checkConvParams(weight_shape, stride, pad, dilation): + return ( + # Check kernel sizes + min(weight_shape[1:-1]) >= 1 + and min(stride) >= 1 + and min(pad) >= 0 + and (dilation is None or min(dilation) >= 1) + ) + + @staticmethod + def evConvOutputShapeMismatch(check=False, **kwargs): + error_name = ErrorIf.ConvOutputShapeMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = ( + "Mismatch between output shape provided and expected output shape" + ) + + if check: + op = kwargs["op"] + pad = kwargs["pad"] + weight_shape = kwargs["weight_shape"] + input_shape = kwargs["input_shape"] + output_shape = kwargs["output_shape"] + dilation = kwargs["dilation"] if op["op"] != Op.TRANSPOSE_CONV2D else None + stride = kwargs["stride"] + + kernel_offset = 0 if op["op"] == Op.DEPTHWISE_CONV2D else 1 + + # calculate correct dimensions + dims_correct = [] + if min(stride) > 0: + for index in range(len(stride)): + pad_offset = index * 2 + if op["op"] == Op.TRANSPOSE_CONV2D: + dims_correct.append( + (input_shape[index + 1] - 1) * stride[index] + - pad[pad_offset] + - pad[pad_offset + 1] + + weight_shape[index + kernel_offset] + ) + else: + dims_correct.append( + ( + input_shape[index + 1] + - 1 + + pad[pad_offset] + + pad[pad_offset + 1] + - (weight_shape[index + kernel_offset] - 1) + * dilation[index] + ) + // stride[index] + + 1 + ) + + # ensure parameters are valid + params_valid = TosaErrorValidator.checkConvParams( + weight_shape, stride, pad, dilation + ) + + if params_valid and output_shape[1:-1] != dims_correct: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs, + } + return info_dict + + @staticmethod + def evConvOutputShapeNonInteger(check=False, **kwargs): + error_name = ErrorIf.ConvOutputShapeNonInteger + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Parameters do not yield exact integer output dimensions" + + if check: + op = kwargs["op"] + pad = kwargs["pad"] + weight_shape = kwargs["weight_shape"] + input_shape = kwargs["input_shape"] + dilation = kwargs["dilation"] + stride = kwargs["stride"] + + kernel_offset = 0 if op["op"] == Op.DEPTHWISE_CONV2D else 1 + + # calculate correct height, width dimensions + remainders = [] + if min(stride) > 0: + for index in range(len(stride)): + pad_offset = index * 2 + remainders.append( + ( + input_shape[index + 1] + - 1 + + pad[pad_offset] + + pad[pad_offset + 1] + - (weight_shape[index + kernel_offset] - 1) + * dilation[index] + ) + % stride[index] + ) + + # ensure parameters are valid + params_valid = TosaErrorValidator.checkConvParams( + weight_shape, stride, pad, dilation + ) + if params_valid and max(remainders) > 0: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs, + } + return info_dict + + @staticmethod def evArgmaxOutputShapeMismatch(check=False, **kwargs): error_name = ErrorIf.ArgmaxOutputShapeMismatch param_reqs = {"rank": [2, 4], "dtype": None, "shape": None} diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py index 38365d0..7c2b9de 100644 --- a/verif/generator/tosa_test_gen.py +++ b/verif/generator/tosa_test_gen.py @@ -630,6 +630,8 @@ class TosaTestGen: stride=strides, dilation=dilations, input_shape=ifm.shape, + weight_shape=filter.shape, + output_shape=result_tens.shape, ): return None @@ -692,6 +694,8 @@ class TosaTestGen: stride=strides, dilation=dilations, input_shape=ifm.shape, + weight_shape=filter.shape, + output_shape=result_tens.shape, ): return None @@ -715,7 +719,7 @@ class TosaTestGen: error_name=None, qinfo=None, ): - assert len(outpad) == 2 + assert len(outpad) == 4 result_tens = OutputShaper.transposeConv2DOp( self.ser, self.rng, ifm, output_shape, error_name ) @@ -753,8 +757,9 @@ class TosaTestGen: output_list=output_list, pad=outpad, stride=stride, - dilation=dilation, input_shape=ifm.shape, + weight_shape=filter.shape, + output_shape=result_tens.shape, ): return None @@ -816,6 +821,8 @@ class TosaTestGen: stride=strides, dilation=dilations, input_shape=ifm.shape, + weight_shape=filter.shape, + output_shape=result_tens.shape, ): return None @@ -2393,6 +2400,7 @@ class TosaTestGen: TosaErrorValidator.evOutputZeroPointNotZero, TosaErrorValidator.evPadLargerEqualKernel, TosaErrorValidator.evPoolingOutputShapeMismatch, + TosaErrorValidator.evPoolingOutputShapeNonInteger, ), }, # Templated operator. Filled in by createDynamicOpLists @@ -2420,6 +2428,8 @@ class TosaTestGen: TosaErrorValidator.evStrideSmallerOne, TosaErrorValidator.evDilationSmallerOne, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evConvOutputShapeMismatch, + TosaErrorValidator.evConvOutputShapeNonInteger, ), "template": True, }, @@ -2448,6 +2458,8 @@ class TosaTestGen: TosaErrorValidator.evStrideSmallerOne, TosaErrorValidator.evDilationSmallerOne, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evConvOutputShapeMismatch, + TosaErrorValidator.evConvOutputShapeNonInteger, ), "template": True, }, @@ -2477,6 +2489,8 @@ class TosaTestGen: TosaErrorValidator.evStrideSmallerOne, TosaErrorValidator.evDilationSmallerOne, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evConvOutputShapeMismatch, + TosaErrorValidator.evConvOutputShapeNonInteger, ), "template": True, }, @@ -2546,6 +2560,7 @@ class TosaTestGen: TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evPadLargerEqualKernel, TosaErrorValidator.evPoolingOutputShapeMismatch, + TosaErrorValidator.evPoolingOutputShapeNonInteger, ), }, # Templated operator. Filled in by createDynamicOpLists @@ -2574,8 +2589,8 @@ class TosaTestGen: TosaErrorValidator.evWeightZeroPointNotZero, TosaErrorValidator.evPadSmallerZero, TosaErrorValidator.evStrideSmallerOne, - TosaErrorValidator.evDilationSmallerOne, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evConvOutputShapeMismatch, ), "template": True, }, @@ -3887,30 +3902,30 @@ class OutputShaper: # Filter: OHWI # OFM: NHWC - if len(padding) == 2: - # Expand padding to 4 parameters in the case of transpose_conv2d - # From H,W to T,B,L,R - padding = [padding[0], padding[0], padding[1], padding[1]] - h = ( ifm.shape[1] - - filter.shape[1] - - (filter.shape[1] - 1) * (dilations[0] - 1) + - 1 + padding[0] + padding[1] + - (filter.shape[1] - 1) * dilations[0] ) // strides[0] + 1 w = ( ifm.shape[2] - - filter.shape[2] - - (filter.shape[2] - 1) * (dilations[1] - 1) + - 1 + padding[2] + padding[3] + - (filter.shape[2] - 1) * dilations[1] ) // strides[1] + 1 - # Avoid illegal dimensions, which can be generated in error_if tests - h = max(h, 1) - w = max(w, 1) + if error_name == ErrorIf.ConvOutputShapeMismatch: + choices = [1, 2, 3] + change = rng.choice(choices) + # increment in multiples of stride to not hit non-integer error case + if change in [1, 3]: + h = h + (rng.choice(choices) * strides[0]) + if change in [2, 3]: + w = w + (rng.choice(choices) * strides[1]) ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] @@ -3941,32 +3956,38 @@ class OutputShaper: d = ( ifm.shape[1] - - filter.shape[1] - - (filter.shape[1] - 1) * (dilations[0] - 1) + - 1 + padding[0] + padding[1] + - (filter.shape[1] - 1) * dilations[0] ) // strides[0] + 1 h = ( ifm.shape[2] - - filter.shape[2] - - (filter.shape[2] - 1) * (dilations[1] - 1) + - 1 + padding[2] + padding[3] + - (filter.shape[2] - 1) * dilations[1] ) // strides[1] + 1 w = ( ifm.shape[3] - - filter.shape[3] - - (filter.shape[3] - 1) * (dilations[2] - 1) + - 1 + padding[4] + padding[5] + - (filter.shape[3] - 1) * dilations[2] ) // strides[2] + 1 - # Avoid illegal dimensions, which can be generated in error_if tests - d = max(d, 1) - h = max(h, 1) - w = max(w, 1) + if error_name == ErrorIf.ConvOutputShapeMismatch: + choices = [1, 2, 3, 4] + change = rng.choice(choices) + # increment in multiples of stride to not hit non-integer error case + if change in [1, 4]: + d = d + (rng.choice(choices) * strides[0]) + if change in [2, 4]: + h = h + (rng.choice(choices) * strides[1]) + if change in [3, 4]: + w = w + (rng.choice(choices) * strides[2]) ofm_shape = [ifm.shape[0], d, h, w, filter.shape[0]] @@ -3995,25 +4016,31 @@ class OutputShaper: # IFM: NHWC # Filter: HWCM # OFM: NHW C*M + h = ( ifm.shape[1] - - filter.shape[0] - - (filter.shape[0] - 1) * (dilations[0] - 1) + - 1 + padding[0] + padding[1] + - (filter.shape[0] - 1) * dilations[0] ) // strides[0] + 1 w = ( ifm.shape[2] - - filter.shape[1] - - (filter.shape[1] - 1) * (dilations[1] - 1) + - 1 + padding[2] + padding[3] + - (filter.shape[1] - 1) * dilations[1] ) // strides[1] + 1 - # Avoid illegal dimensions, which can be generated in error_if tests - h = max(h, 1) - w = max(w, 1) + if error_name == ErrorIf.ConvOutputShapeMismatch: + choices = [1, 2, 3] + change = rng.choice(choices) + # increment in multiples of stride to not hit non-integer error case + if change in [1, 3]: + h = h + (rng.choice(choices) * strides[0]) + if change in [2, 3]: + w = w + (rng.choice(choices) * strides[1]) ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] @@ -4043,14 +4070,17 @@ class OutputShaper: h = 1 w = 1 else: - h = (ifm.shape[1] + pad[0] + pad[1] + stride[0] - kernel[0]) // stride[0] - w = (ifm.shape[2] + pad[2] + pad[3] + stride[1] - kernel[1]) // stride[1] + h = (ifm.shape[1] + pad[0] + pad[1] - kernel[0]) // stride[0] + 1 + w = (ifm.shape[2] + pad[2] + pad[3] - kernel[1]) // stride[1] + 1 if error_name == ErrorIf.PoolingOutputShapeMismatch: - choices = [1, 2, 3, 4, 5] - h = h + rng.choice(choices) - w = w + rng.choice(choices) - + choices = [1, 2, 3] + change = rng.choice(choices) + # increment in multiples of stride to not hit non-integer error case + if change in [1, 3]: + h = h + (rng.choice(choices) * stride[0]) + if change in [2, 3]: + w = w + (rng.choice(choices) * stride[1]) ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] if error_name == ErrorIf.WrongOutputType: @@ -4468,6 +4498,14 @@ class OutputShaper: @staticmethod def transposeConv2DOp(ser, rng, ifm, output_shape, error_name=None): + if error_name == ErrorIf.ConvOutputShapeMismatch: + choices = [1, 2, 3] + change = rng.choice(choices) + if change in [1, 3]: + output_shape[1] = output_shape[1] + rng.choice(choices) + if change in [2, 3]: + output_shape[2] = output_shape[2] + rng.choice(choices) + if ifm.dtype == DType.INT8: out_dtype = DType.INT32 elif ifm.dtype == DType.INT16: |