From 534072d826b304e70a4cf4ebe61ab8407cd0b3d8 Mon Sep 17 00:00:00 2001
From: Yury Nahshan <yury.nahshan@intel.com>
Date: Tue, 10 Sep 2019 18:02:00 +0300
Subject: [PATCH] ACIQ clipping in post-training quantization (#173)
ACIQ clipping method, as described in:
Post training 4-bit quantization of convolutional networks for rapid-deployment
(Ron Banner , Yury Nahshan, Daniel Soudry)
(NeurIPS 2019)
https://arxiv.org/abs/1810.05723
Co-authored-by: Yury Nahshan <yury.nahshan@intel.com>
Co-authored-by: Lev Zlotnik <lev.zlotnik@intel.com>
---
distiller/data_loggers/collector.py | 52 +-
distiller/quantization/q_utils.py | 78 +
distiller/quantization/range_linear.py | 87 +-
.../stats/resnet18_quant_stats.yaml | 1674 ++++---
.../stats/resnet50_quant_stats.yaml | 4243 +++++++++--------
tests/test_quant_utils.py | 4 +
6 files changed, 3408 insertions(+), 2730 deletions(-)
diff --git a/distiller/data_loggers/collector.py b/distiller/data_loggers/collector.py
index dca2419..612f7d3 100755
--- a/distiller/data_loggers/collector.py
+++ b/distiller/data_loggers/collector.py
@@ -314,7 +314,7 @@ class _QuantStatsRecord(object):
records = OrderedDict()
records['min'] = float_info.max
records['max'] = -float_info.max
- for stat_name in ['avg_min', 'avg_max', 'mean', 'std']:
+ for stat_name in ['avg_min', 'avg_max', 'mean', 'std', 'b']:
records[stat_name] = 0
records['shape'] = ''
return records
@@ -389,6 +389,7 @@ class QuantCalibrationStatsCollector(ActivationStatsCollector):
self.batch_idx = 0
self.inplace_runtime_check = inplace_runtime_check
+ self.collecting_laplace = False
if disable_inplace_attrs:
if not inplace_attr_names:
@@ -398,6 +399,38 @@ class QuantCalibrationStatsCollector(ActivationStatsCollector):
if hasattr(m, n):
setattr(m, n, False)
+ def _check_required_stats(self):
+ """
+ Check whether the required statistics were collected to allow collecting laplace distribution stats.
+ """
+ for name, module in self.model.named_modules():
+ if distiller.has_children(module) or isinstance(module, torch.nn.Identity):
+ continue
+ if not hasattr(module, 'quant_stats'):
+ raise RuntimeError('Collection of Laplace distribution statistics is '
+ 'only allowed after collection of stats has started.')
+ for i, input_stats_record in enumerate(module.quant_stats.inputs):
+ if 'mean' not in input_stats_record:
+ raise RuntimeError('The required stats for input[%d] in module "%s" were not collected. '
+ 'Please collect the required statistics using `collector.start()` and evaluating'
+ ' the model for enough batches.' % (i, name))
+ if 'mean' not in module.quant_stats.output:
+ raise RuntimeError('The required stats for the output in module "%s" were not collected. '
+ 'Please collect the required statistics using `collector.start()` and evaluating'
+ ' the model for enough batches.' % name)
+
+ def start_laplace(self):
+ self._check_required_stats()
+ self.collecting_laplace = True
+ # reset batch_idx for all leaf modules
+ for module in self.model.modules():
+ if distiller.has_children(module) or isinstance(module, torch.nn.Identity):
+ continue
+ module.batch_idx = 0
+
+ def stop_laplace(self):
+ self.collecting_laplace = False
+
def _activation_stats_cb(self, module, inputs, output):
def update_mean(old_mean, new_val):
return old_mean + (new_val - old_mean) / module.batch_idx
@@ -412,10 +445,20 @@ class QuantCalibrationStatsCollector(ActivationStatsCollector):
M += mean_diffs.sum()
return sqrt((M / (total_values_so_far + numel - 1)).item())
+ def update_b(values, old_b, mean):
+ """
+ Updates the 'b' parameter of Laplace Distribution.
+ """
+ current_b = (values - mean).abs().mean().item()
+ return old_b + (current_b - old_b) / module.batch_idx
+
def update_record(record, tensor):
if not tensor.is_contiguous():
tensor = tensor.contiguous()
act = tensor.view(tensor.size(0), -1)
+ if self.collecting_laplace:
+ record['b'] = update_b(act, record['b'], record['mean'])
+ return
# In the general case, the average min/max that we're collecting are averages over the per-sample
# min/max values. That is - we first calculate the min/max for each sample in the batch, then average
@@ -690,7 +733,14 @@ def collect_quant_stats(model, test_fn, save_dir=None, classes=None, inplace_run
disable_inplace_attrs=disable_inplace_attrs,
inplace_attr_names=inplace_attr_names)
with collector_context(quant_stats_collector):
+ msglogger.info('Pass 1: Collecting min, max, avg_min, avg_max, mean, std')
test_fn(model=model)
+ # Collect Laplace distribution stats:
+ msglogger.info('Pass 2: Collecting b parameter')
+ quant_stats_collector.start_laplace()
+ test_fn(model=model)
+ quant_stats_collector.stop_laplace()
+
msglogger.info('Stats collection complete')
if save_dir is not None:
save_path = os.path.join(save_dir, 'acts_quantization_stats.yaml')
diff --git a/distiller/quantization/q_utils.py b/distiller/quantization/q_utils.py
index 25f8f7c..91d665a 100644
--- a/distiller/quantization/q_utils.py
+++ b/distiller/quantization/q_utils.py
@@ -14,6 +14,7 @@
# limitations under the License.
#
+from enum import Enum
import torch
@@ -175,6 +176,83 @@ def approx_scale_as_mult_and_shift(fp32_scale, mult_bits, limit=False):
return multiplier / (2 ** shift_bits)
+class AciqClipper(object):
+ """
+ Implemented according to https://arxiv.org/pdf/1810.05723.pdf
+ """
+ alpha_laplace = {0: 1.05, 1: 1.86, 2: 2.83, 3: 3.89, 4: 5.03, 5: 6.2, 6: 7.41, 7: 8.64, 8: 9.89}
+ alpha_laplace_positive = {0: 1.86, 1: 2.83, 2: 3.89, 3: 5.02, 4: 6.2, 5: 7.41, 6: 8.64, 7: 9.89, 8: 11.16}
+ alpha_gauss = {1: 1.24, 2: 1.71, 3: 2.15, 4: 2.55, 5: 2.93, 6: 3.28, 7: 3.61, 8: 3.92}
+ alpha_gauss_positive = {1: 1.71, 2: 2.15, 3: 2.55, 4: 2.93, 5: 3.28, 6: 3.61, 7: 3.92, 8: 4.2}
+
+ class AciqClippingType(Enum):
+ Laplace = 1
+ Gauss = 2
+
+ @staticmethod
+ def get_alpha_laplace(t, across_dim=None, num_bits=8, half_range=False):
+ if isinstance(t, torch.Tensor):
+ # Mean of means across dims is equivalent to global mean
+ b = torch.mean(torch.abs(t - t.mean()))
+ elif isinstance(t, dict):
+ # t is Quant Calibration activation stats dict.
+ b = t['b']
+ else:
+ raise TypeError("Only torch.Tensors or quantization calibration activation stats dicts are acceptable.")
+ return b * (AciqClipper.alpha_laplace_positive[num_bits] if half_range
+ else AciqClipper.alpha_laplace[num_bits])
+
+ @staticmethod
+ def get_alpha_gauss(t, across_dim=None, num_bits=8, half_range=False):
+ if isinstance(t, torch.Tensor):
+ # Mean of means across dims is equivalent to global mean
+ std = torch.std(t)
+ elif isinstance(t, dict):
+ # t is Quant Calibration activation stats dict.
+ std = t['std']
+ else:
+ raise TypeError("Only torch.Tensors or quantization calibration activation stats dicts are acceptable.")
+ return std * (AciqClipper.alpha_gauss_positive[num_bits] if half_range
+ else AciqClipper.alpha_gauss[num_bits])
+
+
+class AciqSymmetricClipper(AciqClipper):
+ def __init__(self, num_bits, clip_type=AciqClipper.AciqClippingType.Laplace):
+ self.num_bits = num_bits
+ self.clip_type = clip_type
+
+ def __call__(self, t, across_dim=None):
+ if self.clip_type == AciqClipper.AciqClippingType.Laplace:
+ alpha = AciqClipper.get_alpha_laplace(t, across_dim, self.num_bits)
+ else:
+ alpha = AciqClipper.get_alpha_gauss(t, across_dim, self.num_bits)
+ if isinstance(t, dict):
+ mean = torch.tensor(t['mean'])
+ else:
+ mean = t.mean()
+ return torch.abs(mean) + alpha
+
+
+class AciqAsymmetricClipper(AciqClipper):
+ def __init__(self, num_bits, clip_type=AciqClipper.AciqClippingType.Laplace):
+ self.num_bits = num_bits
+ self.clip_type = clip_type
+
+ def __call__(self, t, across_dim=None, half_range=False):
+ if isinstance(t, dict):
+ mean, min_val = torch.tensor(t['mean']), torch.tensor(t['avg_min'])
+ else:
+ mean = t.mean()
+ min_val = get_tensor_min_max(t, across_dim)[0].mean()
+ if self.clip_type == AciqClipper.AciqClippingType.Laplace:
+ alpha = AciqClipper.get_alpha_laplace(t, across_dim, self.num_bits, half_range=half_range)
+ else:
+ alpha = AciqClipper.get_alpha_gauss(t, across_dim, self.num_bits, half_range=half_range)
+ min_val = torch.max(min_val, mean - alpha)
+
+ return min_val, min_val + 2 * alpha
+
+
def get_quantized_range(num_bits, signed=True):
if signed:
n = 2 ** (num_bits - 1)
diff --git a/distiller/quantization/range_linear.py b/distiller/quantization/range_linear.py
index 29a639b..e161f7f 100644
--- a/distiller/quantization/range_linear.py
+++ b/distiller/quantization/range_linear.py
@@ -52,6 +52,10 @@ class ClipMode(Enum):
# Clip value calculated as mean of tensor + N standard deviations. N should be specified separately
N_STD = 2
+ # ACIQ Clipping Modes -
+ GAUSS = 3
+ LAPLACE = 4
+
def _verify_enum_value(val, enum_cls):
cls_name = enum_cls.__name__
@@ -75,20 +79,24 @@ def verify_clip_mode(mode):
return _verify_enum_value(mode, ClipMode)
-def _get_saturation_fn(quant_mode, clip_mode, num_stds):
+def _get_saturation_fn(quant_mode, clip_mode, num_stds, num_bits=None):
if quant_mode == LinearQuantMode.SYMMETRIC:
fns = {ClipMode.NONE: get_tensor_max_abs,
ClipMode.AVG: get_tensor_avg_max_abs,
- ClipMode.N_STD: partial(get_tensor_mean_n_stds_max_abs, n_stds=num_stds)}
+ ClipMode.N_STD: partial(get_tensor_mean_n_stds_max_abs, n_stds=num_stds),
+ ClipMode.GAUSS: AciqSymmetricClipper(num_bits, AciqClipper.AciqClippingType.Gauss),
+ ClipMode.LAPLACE: AciqSymmetricClipper(num_bits, AciqClipper.AciqClippingType.Laplace)}
else: # Asymmetric mode
fns = {ClipMode.NONE: get_tensor_min_max,
ClipMode.AVG: get_tensor_avg_min_max,
- ClipMode.N_STD: partial(get_tensor_mean_n_stds_min_max, n_stds=num_stds)}
+ ClipMode.N_STD: partial(get_tensor_mean_n_stds_min_max, n_stds=num_stds),
+ ClipMode.GAUSS: AciqAsymmetricClipper(num_bits, AciqClipper.AciqClippingType.Gauss),
+ ClipMode.LAPLACE: AciqAsymmetricClipper(num_bits, AciqClipper.AciqClippingType.Laplace)}
return fns[clip_mode]
def _get_quant_params_from_tensor(tensor, num_bits, mode, clip=ClipMode.NONE, per_channel=False, num_stds=None,
- scale_approx_mult_bits=None):
+ half_range=False, scale_approx_mult_bits=None):
if per_channel and tensor.dim() not in [2, 4]:
raise ValueError('Per channel quantization possible only with 2D or 4D tensors (linear or conv layer weights)')
@@ -97,14 +105,18 @@ def _get_quant_params_from_tensor(tensor, num_bits, mode, clip=ClipMode.NONE, pe
raise ValueError('N_STD clipping not supported with per-channel quantization')
if num_stds is None:
raise ValueError('Clip mode set top N_STD but \'num_stds\' parameter not provided')
+ if half_range and clip not in [ClipMode.GAUSS, ClipMode.LAPLACE]:
+ warnings.warn("Using clip_half_range without ACIQ clip modes (GAUSS or LAPACE) will have no"
+ " effect.")
dim = 0 if clip == ClipMode.AVG or per_channel else None
- sat_fn = _get_saturation_fn(mode, clip, num_stds)
+ sat_fn = _get_saturation_fn(mode, clip, num_stds, num_bits)
if mode == LinearQuantMode.SYMMETRIC:
sat_val = sat_fn(tensor, dim)
scale, zp = symmetric_linear_quantization_params(num_bits, sat_val)
else: # Asymmetric mode
- sat_min, sat_max = sat_fn(tensor, dim)
+ sat_min, sat_max = sat_fn(tensor, dim) if clip not in [ClipMode.GAUSS, ClipMode.LAPLACE] \
+ else sat_fn(tensor, dim, half_range=half_range)
signed = mode == LinearQuantMode.ASYMMETRIC_SIGNED
scale, zp = asymmetric_linear_quantization_params(num_bits, sat_min, sat_max, signed=signed)
@@ -120,13 +132,17 @@ def _get_quant_params_from_tensor(tensor, num_bits, mode, clip=ClipMode.NONE, pe
return scale, zp
-def _get_quant_params_from_stats_dict(stats, num_bits, mode, clip=ClipMode.NONE, num_stds=None,
+def _get_quant_params_from_stats_dict(stats, num_bits, mode, clip=ClipMode.NONE, num_stds=None, half_range=False,
scale_approx_mult_bits=None):
if clip == ClipMode.N_STD:
if num_stds is None:
raise ValueError('Clip mode set to N_STD but \'num_stds\' parameter not provided')
if num_stds <= 0:
raise ValueError('n_stds must be > 0, got {}'.format(num_stds))
+ if half_range and clip not in [ClipMode.GAUSS, ClipMode.LAPLACE]:
+ warnings.warn("Using clip_half_range without ACIQ clip modes (GAUSS or LAPACE) will have no"
+ " effect.")
+
prefix = 'avg_' if clip == ClipMode.AVG else ''
sat_min = torch.tensor(float(stats[prefix + 'min']))
sat_max = torch.tensor(float(stats[prefix + 'max']))
@@ -135,6 +151,13 @@ def _get_quant_params_from_stats_dict(stats, num_bits, mode, clip=ClipMode.NONE,
std = torch.tensor(float(stats['std']))
sat_min = torch.max(sat_min, mean - num_stds * std)
sat_max = torch.min(sat_max, mean + num_stds * std)
+ elif clip in (ClipMode.LAPLACE, ClipMode.GAUSS):
+ clip = AciqClipper.AciqClippingType.Laplace if clip == ClipMode.LAPLACE else AciqClipper.AciqClippingType.Gauss
+ if mode == LinearQuantMode.SYMMETRIC:
+ sat_min, sat_max = AciqSymmetricClipper(num_bits, clip)(stats)
+ else:
+ sat_min, sat_max = AciqAsymmetricClipper(num_bits, clip)(stats, half_range=half_range)
+
if mode == LinearQuantMode.SYMMETRIC:
scale, zp = symmetric_linear_quantization_params(num_bits, torch.max(sat_min.abs_(), sat_max.abs_()))
else:
@@ -157,7 +180,8 @@ def add_post_train_quant_args(argparser):
'asym_s': LinearQuantMode.ASYMMETRIC_SIGNED,
'asym_u': LinearQuantMode.ASYMMETRIC_UNSIGNED}
- str_to_clip_mode_map = {'none': ClipMode.NONE, 'avg': ClipMode.AVG, 'n_std': ClipMode.N_STD}
+ str_to_clip_mode_map = {'none': ClipMode.NONE, 'avg': ClipMode.AVG, 'n_std': ClipMode.N_STD,
+ 'gauss': ClipMode.GAUSS, 'laplace': ClipMode.LAPLACE}
def from_dict(d, val_str):
try:
@@ -220,6 +244,8 @@ class RangeLinearQuantWrapper(nn.Module):
parameters. Dict should be in the format exported by distiller.data_loggers.QuantCalibrationStatsCollector.
If None then parameters are calculated dynamically.
clip_n_stds (float): When clip_acts == ClipMode.N_STD, this is the number of standard deviations to use
+ clip_half_range (bool): use half range clipping.
+ NOTE - this only works with ACIQ clip modes i.e. GAUSS and LAPLACE
scale_approx_mult_bits (int): If not None, scale factors will be approximated using an integer multiplication
followed by a bit-wise shift. This eliminates floating-point scale factors, replacing them with integer
calculations.
@@ -227,7 +253,8 @@ class RangeLinearQuantWrapper(nn.Module):
"""
def __init__(self, wrapped_module, num_bits_acts, num_bits_accum=32, mode=LinearQuantMode.SYMMETRIC,
- clip_acts=ClipMode.NONE, activation_stats=None, clip_n_stds=None, scale_approx_mult_bits=None):
+ clip_acts=ClipMode.NONE, activation_stats=None, clip_n_stds=None, clip_half_range=False,
+ scale_approx_mult_bits=None):
super(RangeLinearQuantWrapper, self).__init__()
self.wrapped_module = wrapped_module
@@ -236,6 +263,7 @@ class RangeLinearQuantWrapper(nn.Module):
self.mode = mode
self.clip_acts = clip_acts
self.clip_n_stds = clip_n_stds
+ self.clip_half_range = clip_half_range
self.scale_approx_mult_bits = scale_approx_mult_bits
# Controls whether output is de-quantized at end of forward op. Meant as a debug / test flag only
@@ -253,14 +281,16 @@ class RangeLinearQuantWrapper(nn.Module):
self.num_inputs = 0
for idx, stats in activation_stats['inputs'].items():
self.num_inputs += 1
- scale, zp = _get_quant_params_from_stats_dict(stats, num_bits_acts, mode, clip_acts, clip_n_stds,
+ scale, zp = _get_quant_params_from_stats_dict(stats, num_bits_acts,
+ mode, clip_acts, clip_n_stds, clip_half_range,
scale_approx_mult_bits)
prefix = 'in_{0}_'.format(idx)
self.register_buffer(prefix + 'scale', scale)
self.register_buffer(prefix + 'zero_point', zp)
- scale, zp = _get_quant_params_from_stats_dict(activation_stats['output'], num_bits_acts, mode, clip_acts,
- clip_n_stds, scale_approx_mult_bits)
+ scale, zp = _get_quant_params_from_stats_dict(activation_stats['output'], num_bits_acts, mode,
+ clip_acts, clip_n_stds, clip_half_range,
+ scale_approx_mult_bits)
self.register_buffer('output_scale', scale)
self.register_buffer('output_zero_point', zp)
else:
@@ -415,14 +445,15 @@ class RangeLinearQuantParamLayerWrapper(RangeLinearQuantWrapper):
output channel
activation_stats (dict): See RangeLinearQuantWrapper
clip_n_stds (int): See RangeLinearQuantWrapper
+ clip_half_range (bool) : See RangeLinearQuantWrapper
scale_approx_mult_bits (int): See RangeLinearQuantWrapper
"""
def __init__(self, wrapped_module, num_bits_acts, num_bits_params, num_bits_accum=32,
mode=LinearQuantMode.SYMMETRIC, clip_acts=ClipMode.NONE, per_channel_wts=False, activation_stats=None,
- clip_n_stds=None, scale_approx_mult_bits=None):
+ clip_n_stds=None, clip_half_range=False, scale_approx_mult_bits=None):
super(RangeLinearQuantParamLayerWrapper, self).__init__(wrapped_module, num_bits_acts, num_bits_accum, mode,
clip_acts, activation_stats, clip_n_stds,
- scale_approx_mult_bits)
+ clip_half_range, scale_approx_mult_bits)
if not isinstance(wrapped_module, (nn.Conv2d, nn.Conv3d, nn.Linear)):
raise ValueError(self.__class__.__name__ + ' can wrap only Conv2D, Conv3D and Linear modules')
@@ -595,10 +626,10 @@ class RangeLinearQuantMatmulWrapper(RangeLinearQuantWrapper):
"""
def __init__(self, wrapped_module, num_bits_acts, num_bits_accum=32,
mode=LinearQuantMode.SYMMETRIC, clip_acts=ClipMode.NONE, activation_stats=None,
- clip_n_stds=None, scale_approx_mult_bits=None):
+ clip_n_stds=None, clip_half_range=False, scale_approx_mult_bits=None):
super(RangeLinearQuantMatmulWrapper, self).__init__(wrapped_module, num_bits_acts, num_bits_accum, mode,
- clip_acts, activation_stats, clip_n_stds,
- scale_approx_mult_bits)
+ clip_acts, activation_stats, clip_n_stds, clip_half_range,
+ scale_approx_mult_bits)
if not isinstance(wrapped_module, (distiller.modules.Matmul, distiller.modules.BatchMatmul)):
raise ValueError(self.__class__.__name__ + ' can wrap only Matmul modules')
@@ -664,7 +695,7 @@ class NoStatsError(NotImplementedError):
class RangeLinearQuantConcatWrapper(RangeLinearQuantWrapper):
def __init__(self, wrapped_module, num_bits_acts, mode=LinearQuantMode.SYMMETRIC, clip_acts=ClipMode.NONE,
- activation_stats=None, clip_n_stds=None, scale_approx_mult_bits=None):
+ activation_stats=None, clip_n_stds=None, clip_half_range=False, scale_approx_mult_bits=None):
if not isinstance(wrapped_module, distiller.modules.Concat):
raise ValueError(self.__class__.__name__ + ' can only wrap distiller.modules.Concat modules')
@@ -674,7 +705,7 @@ class RangeLinearQuantConcatWrapper(RangeLinearQuantWrapper):
super(RangeLinearQuantConcatWrapper, self).__init__(wrapped_module, num_bits_acts, mode=mode,
clip_acts=clip_acts, activation_stats=activation_stats,
- clip_n_stds=clip_n_stds,
+ clip_n_stds=clip_n_stds, clip_half_range=clip_half_range,
scale_approx_mult_bits=scale_approx_mult_bits)
if self.preset_act_stats:
@@ -714,7 +745,7 @@ class RangeLinearQuantConcatWrapper(RangeLinearQuantWrapper):
class RangeLinearQuantEltwiseAddWrapper(RangeLinearQuantWrapper):
def __init__(self, wrapped_module, num_bits_acts, mode=LinearQuantMode.SYMMETRIC, clip_acts=ClipMode.NONE,
- activation_stats=None, clip_n_stds=None, scale_approx_mult_bits=None):
+ activation_stats=None, clip_n_stds=None, clip_half_range=False, scale_approx_mult_bits=None):
if not isinstance(wrapped_module, distiller.modules.EltwiseAdd):
raise ValueError(self.__class__.__name__ + ' can only wrap distiller.modules.EltwiseAdd modules')
@@ -725,6 +756,7 @@ class RangeLinearQuantEltwiseAddWrapper(RangeLinearQuantWrapper):
super(RangeLinearQuantEltwiseAddWrapper, self).__init__(wrapped_module, num_bits_acts, mode=mode,
clip_acts=clip_acts, activation_stats=activation_stats,
clip_n_stds=clip_n_stds,
+ clip_half_range=clip_half_range,
scale_approx_mult_bits=scale_approx_mult_bits)
if self.preset_act_stats:
@@ -767,7 +799,7 @@ class RangeLinearQuantEltwiseAddWrapper(RangeLinearQuantWrapper):
class RangeLinearQuantEltwiseMultWrapper(RangeLinearQuantWrapper):
def __init__(self, wrapped_module, num_bits_acts, mode=LinearQuantMode.SYMMETRIC, clip_acts=ClipMode.NONE,
- activation_stats=None, clip_n_stds=None, scale_approx_mult_bits=None):
+ activation_stats=None, clip_n_stds=None, clip_half_range=False, scale_approx_mult_bits=None):
if not isinstance(wrapped_module, distiller.modules.EltwiseMult):
raise ValueError(self.__class__.__name__ + ' can only wrap distiller.modules.EltwiseMult modules')
@@ -778,6 +810,7 @@ class RangeLinearQuantEltwiseMultWrapper(RangeLinearQuantWrapper):
super(RangeLinearQuantEltwiseMultWrapper, self).__init__(wrapped_module, num_bits_acts, mode=mode,
clip_acts=clip_acts, activation_stats=activation_stats,
clip_n_stds=clip_n_stds,
+ clip_half_range=clip_half_range,
scale_approx_mult_bits=scale_approx_mult_bits)
if self.preset_act_stats:
@@ -937,21 +970,24 @@ class PostTrainLinearQuantizer(Quantizer):
def replace_param_layer(module, name, qbits_map, per_channel_wts=per_channel_wts,
mode=mode, fp16=fp16, scale_approx_mult_bits=scale_approx_mult_bits,
- clip_acts=clip_acts, clip_n_stds=clip_n_stds):
+ clip_acts=clip_acts, clip_half_range=False, clip_n_stds=clip_n_stds):
if fp16:
return FP16Wrapper(module)
+
+ # TODO: Try auto-detecting when clip_half_range is needed
+ # instead of having the user pass it as a parameter (same for replace_non_param_layer)
norm_name = distiller.utils.normalize_module_name(name)
clip_acts = verify_clip_mode(clip_acts)
return RangeLinearQuantParamLayerWrapper(module, qbits_map[name].acts, qbits_map[name].wts,
num_bits_accum=self.bits_accum, mode=mode, clip_acts=clip_acts,
per_channel_wts=per_channel_wts,
activation_stats=self.model_activation_stats.get(norm_name, None),
- clip_n_stds=clip_n_stds,
+ clip_n_stds=clip_n_stds, clip_half_range=clip_half_range,
scale_approx_mult_bits=scale_approx_mult_bits)
def replace_non_param_layer(wrapper_type, module, name, qbits_map, fp16=fp16,
scale_approx_mult_bits=scale_approx_mult_bits,
- clip_acts=clip_acts, clip_n_stds=clip_n_stds):
+ clip_acts=clip_acts, clip_n_stds=clip_n_stds, clip_half_range=False):
if fp16:
return FP16Wrapper(module)
norm_name = distiller.utils.normalize_module_name(name)
@@ -959,7 +995,8 @@ class PostTrainLinearQuantizer(Quantizer):
try:
return wrapper_type(module, qbits_map[name].acts, mode=mode, clip_acts=clip_acts,
activation_stats=self.model_activation_stats.get(norm_name, None),
- clip_n_stds=clip_n_stds, scale_approx_mult_bits=scale_approx_mult_bits)
+ clip_n_stds=clip_n_stds, clip_half_range=clip_half_range,
+ scale_approx_mult_bits=scale_approx_mult_bits)
except NoStatsError:
msglogger.warning('WARNING: {0} - quantization of {1} without stats not supported. '
'Keeping the original FP32 module'.format(name, module.__class__.__name__))
diff --git a/examples/quantization/post_train_quant/stats/resnet18_quant_stats.yaml b/examples/quantization/post_train_quant/stats/resnet18_quant_stats.yaml
index 1b9814d..5513beb 100644
--- a/examples/quantization/post_train_quant/stats/resnet18_quant_stats.yaml
+++ b/examples/quantization/post_train_quant/stats/resnet18_quant_stats.yaml
@@ -1,7 +1,7 @@
# ResNet-18 activation statistics for post-training quantization
# Generated by running:
#
-# python compress_classifier.py -a resnet18 -p 10 -j 22 <path_to_imagenet_dataset> --pretrained --qe-calibration 0.05
+# python compress_classifier.py -a resnet18 -p 10 -j 22 <path_to_imagenet_dataset> --pretrained --qe-calibration 0.05 --seed 0
#
# Notes:
# * Stats collection is triggered by the '--qe-calibration' flag. It will run evaluation on the specified portion of
@@ -15,1286 +15,1430 @@ conv1:
0:
min: -2.1179039478302
max: 2.640000104904175
- avg_min: -2.0155762195587155
- avg_max: 2.48675365447998
- mean: -0.05581759512424469
- std: 1.181673563348384
+ avg_min: -2.0179028272628785
+ avg_max: 2.4849105119705195
+ mean: -0.02644771572668106
+ std: 1.1904664382080992
+ b: 1.0037795305252075
shape: (256, 3, 224, 224)
output:
- min: -35.76463317871094
- max: 35.28437423706055
- avg_min: -22.443804359436033
- avg_max: 22.809664916992187
- mean: -1.8954842971652397e-05
- std: 1.7586114042828802
+ min: -37.17167663574219
+ max: 36.08745574951172
+ avg_min: -22.434925651550294
+ avg_max: 22.787699317932127
+ mean: -0.0001754729266394861
+ std: 1.7580939173603671
+ b: 1.0786008000373841
shape: (256, 64, 112, 112)
bn1:
inputs:
0:
- min: -35.76463317871094
- max: 35.28437423706055
- avg_min: -22.443804359436033
- avg_max: 22.809664916992187
- mean: -1.8954842971652397e-05
- std: 1.7586114042828802
+ min: -37.17167663574219
+ max: 36.08745574951172
+ avg_min: -22.434925651550294
+ avg_max: 22.787699317932127
+ mean: -0.0001754729266394861
+ std: 1.7580939173603671
+ b: 1.0786008000373841
shape: (256, 64, 112, 112)
output:
- min: -6.65138578414917
- max: 7.338484287261963
- avg_min: -2.8493101835250854
- avg_max: 3.349868631362915
- mean: 0.18185057491064072
- std: 0.41268930715153396
+ min: -6.58004093170166
+ max: 7.357358932495117
+ avg_min: -2.821167016029358
+ avg_max: 3.334167599678039
+ mean: 0.18160294145345687
+ std: 0.4129777229999846
+ b: 0.28660632371902467
shape: (256, 64, 112, 112)
relu:
inputs:
0:
- min: -6.65138578414917
- max: 7.338484287261963
- avg_min: -2.8493101835250854
- avg_max: 3.349868631362915
- mean: 0.18185057491064072
- std: 0.41268930715153396
+ min: -6.58004093170166
+ max: 7.357358932495117
+ avg_min: -2.821167016029358
+ avg_max: 3.334167599678039
+ mean: 0.18160294145345687
+ std: 0.4129777229999846
+ b: 0.28660632371902467
shape: (256, 64, 112, 112)
output:
min: 0.0
- max: 7.338484287261963
+ max: 7.357358932495117
avg_min: 0.0
- avg_max: 3.349868631362915
- mean: 0.2598995238542557
- std: 0.27431192907072754
+ avg_max: 3.334167599678039
+ mean: 0.25995961129665374
+ std: 0.2736657824588164
+ b: 0.21144414991140364
shape: (256, 64, 112, 112)
maxpool:
inputs:
0:
min: 0.0
- max: 7.338484287261963
+ max: 7.357358932495117
avg_min: 0.0
- avg_max: 3.349868631362915
- mean: 0.2598995238542557
- std: 0.27431192907072754
+ avg_max: 3.334167599678039
+ mean: 0.25995961129665374
+ std: 0.2736657824588164
+ b: 0.21144414991140364
shape: (256, 64, 112, 112)
output:
min: 0.0
- max: 7.338484287261963
+ max: 7.357358932495117
avg_min: 0.0
- avg_max: 3.349868631362915
- mean: 0.37854534685611724
- std: 0.34987264981490734
+ avg_max: 3.334167599678039
+ mean: 0.37670861780643466
+ std: 0.34820184864463305
+ b: 0.2668348789215088
shape: (256, 64, 56, 56)
layer1.0.conv1:
inputs:
0:
min: 0.0
- max: 7.338484287261963
+ max: 7.357358932495117
avg_min: 0.0
- avg_max: 3.349868631362915
- mean: 0.37854534685611724
- std: 0.34987264981490734
+ avg_max: 3.334167599678039
+ mean: 0.37670861780643466
+ std: 0.34820184864463305
+ b: 0.2668348789215088
shape: (256, 64, 56, 56)
output:
- min: -17.075458526611328
- max: 9.973828315734863
- avg_min: -10.725397968292237
- avg_max: 4.599550724029541
- mean: -0.9369088828563691
- std: 1.1919106838283573
+ min: -16.87494659423828
+ max: 10.887310028076172
+ avg_min: -10.703684616088868
+ avg_max: 4.577920770645141
+ mean: -0.9323187172412872
+ std: 1.1861670190309386
+ b: 0.8818115055561065
shape: (256, 64, 56, 56)
layer1.0.bn1:
inputs:
0:
- min: -17.075458526611328
- max: 9.973828315734863
- avg_min: -10.725397968292237
- avg_max: 4.599550724029541
- mean: -0.9369088828563691
- std: 1.1919106838283573
+ min: -16.87494659423828
+ max: 10.887310028076172
+ avg_min: -10.703684616088868
+ avg_max: 4.577920770645141
+ mean: -0.9323187172412872
+ std: 1.1861670190309386
+ b: 0.8818115055561065
shape: (256, 64, 56, 56)
output:
- min: -7.84696102142334
- max: 4.932901382446289
- avg_min: -4.371330738067627
- avg_max: 1.7983517408370973
- mean: -0.050233672186732296
- std: 0.4317025532895933
+ min: -7.912492752075195
+ max: 4.465096473693848
+ avg_min: -4.370650768280029
+ avg_max: 1.781515896320343
+ mean: -0.04791574366390705
+ std: 0.42959841758460565
+ b: 0.30843749046325686
shape: (256, 64, 56, 56)
layer1.0.relu1:
inputs:
0:
- min: -7.84696102142334
- max: 4.932901382446289
- avg_min: -4.371330738067627
- avg_max: 1.7983517408370973
- mean: -0.050233672186732296
- std: 0.4317025532895933
+ min: -7.912492752075195
+ max: 4.465096473693848
+ avg_min: -4.370650768280029
+ avg_max: 1.781515896320343
+ mean: -0.04791574366390705
+ std: 0.42959841758460565
+ b: 0.30843749046325686
shape: (256, 64, 56, 56)
output:
min: 0.0
- max: 4.932901382446289
+ max: 4.465096473693848
avg_min: 0.0
- avg_max: 1.7983517408370973
- mean: 0.12365709394216537
- std: 0.15240964320349404
+ avg_max: 1.781515896320343
+ mean: 0.12415257766842841
+ std: 0.15184144817035658
+ b: 0.12101016268134118
shape: (256, 64, 56, 56)
layer1.0.conv2:
inputs:
0:
min: 0.0
- max: 4.932901382446289
+ max: 4.465096473693848
avg_min: 0.0
- avg_max: 1.7983517408370973
- mean: 0.12365709394216537
- std: 0.15240964320349404
+ avg_max: 1.781515896320343
+ mean: 0.12415257766842841
+ std: 0.15184144817035658
+ b: 0.12101016268134118
shape: (256, 64, 56, 56)
output:
- min: -7.605445861816406
- max: 4.186079502105713
- avg_min: -2.843373680114746
- avg_max: 1.8348817229270935
- mean: -0.07289719507098198
- std: 0.3931055464123539
+ min: -7.069628715515137
+ max: 3.4729065895080566
+ avg_min: -2.847720456123352
+ avg_max: 1.832327115535736
+ mean: -0.07194625288248062
+ std: 0.39378847887850943
+ b: 0.29262713491916664
shape: (256, 64, 56, 56)
layer1.0.bn2:
inputs:
0:
- min: -7.605445861816406
- max: 4.186079502105713
- avg_min: -2.843373680114746
- avg_max: 1.8348817229270935
- mean: -0.07289719507098198
- std: 0.3931055464123539
+ min: -7.069628715515137
+ max: 3.4729065895080566
+ avg_min: -2.847720456123352
+ avg_max: 1.832327115535736
+ mean: -0.07194625288248062
+ std: 0.39378847887850943
+ b: 0.29262713491916664
shape: (256, 64, 56, 56)
output:
- min: -7.029930591583252
- max: 6.096140384674072
- avg_min: -3.364328002929687
- avg_max: 2.671983480453491
- mean: -0.007625545584596693
- std: 0.4165292712020149
+ min: -6.498610496520996
+ max: 5.046974182128906
+ avg_min: -3.3557324409484863
+ avg_max: 2.6549140930175783
+ mean: -0.006219549803063273
+ std: 0.41592445628222435
+ b: 0.30928740203380584
shape: (256, 64, 56, 56)
layer1.0.relu2:
inputs:
0:
- min: -6.755189895629883
- max: 7.848092555999756
- avg_min: -3.2848753213882445
- avg_max: 3.888275837898254
- mean: 0.37091979682445525
- std: 0.5165421680711509
+ min: -6.361315727233887
+ max: 7.920192718505859
+ avg_min: -3.27264986038208
+ avg_max: 3.8547550201416017
+ mean: 0.3704890608787537
+ std: 0.5144624319661206
+ b: 0.3796554893255234
shape: (256, 64, 56, 56)
output:
min: 0.0
- max: 7.848092555999756
+ max: 7.920192718505859
avg_min: 0.0
- avg_max: 3.888275837898254
- mean: 0.4402212053537369
- std: 0.40145754203197104
+ avg_max: 3.8547550201416017
+ mean: 0.4392666757106781
+ std: 0.39984883193751414
+ b: 0.31421991586685183
shape: (256, 64, 56, 56)
layer1.0.add:
inputs:
0:
- min: -7.029930591583252
- max: 6.096140384674072
- avg_min: -3.364328002929687
- avg_max: 2.671983480453491
- mean: -0.007625545584596693
- std: 0.4165292712020149
+ min: -6.498610496520996
+ max: 5.046974182128906
+ avg_min: -3.3557324409484863
+ avg_max: 2.6549140930175783
+ mean: -0.006219549803063273
+ std: 0.41592445628222435
+ b: 0.30928740203380584
shape: (256, 64, 56, 56)
1:
min: 0.0
- max: 7.338484287261963
+ max: 7.357358932495117
avg_min: 0.0
- avg_max: 3.349868631362915
- mean: 0.37854534685611724
- std: 0.34987264981490734
+ avg_max: 3.334167599678039
+ mean: 0.37670861780643466
+ std: 0.34820184864463305
+ b: 0.2668348789215088
shape: (256, 64, 56, 56)
output:
- min: -6.755189895629883
- max: 7.848092555999756
- avg_min: -3.2848753213882445
- avg_max: 3.888275837898254
- mean: 0.37091979682445525
- std: 0.5165421680711509
+ min: -6.361315727233887
+ max: 7.920192718505859
+ avg_min: -3.27264986038208
+ avg_max: 3.8547550201416017
+ mean: 0.3704890608787537
+ std: 0.5144624319661206
+ b: 0.3796554893255234
shape: (256, 64, 56, 56)
layer1.1.conv1:
inputs:
0:
min: 0.0
- max: 7.848092555999756
+ max: 7.920192718505859
avg_min: 0.0
- avg_max: 3.888275837898254
- mean: 0.4402212053537369
- std: 0.40145754203197104
+ avg_max: 3.8547550201416017
+ mean: 0.4392666757106781
+ std: 0.39984883193751414
+ b: 0.31421991586685183
shape: (256, 64, 56, 56)
output:
- min: -12.157726287841797
- max: 11.766583442687988
- avg_min: -7.595281553268432
- avg_max: 5.0373931407928465
- mean: -0.6426207780838012
- std: 1.1092499675944172
+ min: -11.491942405700684
+ max: 11.20591926574707
+ avg_min: -7.546071910858155
+ avg_max: 5.024500560760498
+ mean: -0.6395838379859924
+ std: 1.105064830518023
+ b: 0.8696278989315033
shape: (256, 64, 56, 56)
layer1.1.bn1:
inputs:
0:
- min: -12.157726287841797
- max: 11.766583442687988
- avg_min: -7.595281553268432
- avg_max: 5.0373931407928465
- mean: -0.6426207780838012
- std: 1.1092499675944172
+ min: -11.491942405700684
+ max: 11.20591926574707
+ avg_min: -7.546071910858155
+ avg_max: 5.024500560760498
+ mean: -0.6395838379859924
+ std: 1.105064830518023
+ b: 0.8696278989315033
shape: (256, 64, 56, 56)
output:
- min: -6.2613606452941895
- max: 4.365638732910156
- avg_min: -2.9217535495758056
- avg_max: 2.205676364898682
- mean: -0.09596083089709283
- std: 0.3880057257286341
+ min: -5.977788925170898
+ max: 4.373744964599609
+ avg_min: -2.9008329391479495
+ avg_max: 2.194632339477539
+ mean: -0.09455725699663163
+ std: 0.3864656062954132
+ b: 0.2955675601959229
shape: (256, 64, 56, 56)
layer1.1.relu1:
inputs:
0:
- min: -6.2613606452941895
- max: 4.365638732910156
- avg_min: -2.9217535495758056
- avg_max: 2.205676364898682
- mean: -0.09596083089709283
- std: 0.3880057257286341
+ min: -5.977788925170898
+ max: 4.373744964599609
+ avg_min: -2.9008329391479495
+ avg_max: 2.194632339477539
+ mean: -0.09455725699663163
+ std: 0.3864656062954132
+ b: 0.2955675601959229
shape: (256, 64, 56, 56)
output:
min: 0.0
- max: 4.365638732910156
+ max: 4.373744964599609
avg_min: 0.0
- avg_max: 2.205676364898682
- mean: 0.09787589758634567
- std: 0.15355313597491865
+ avg_max: 2.194632339477539
+ mean: 0.09786231890320779
+ std: 0.1530014232794471
+ b: 0.11548511683940887
shape: (256, 64, 56, 56)
layer1.1.conv2:
inputs:
0:
min: 0.0
- max: 4.365638732910156
+ max: 4.373744964599609
avg_min: 0.0
- avg_max: 2.205676364898682
- mean: 0.09787589758634567
- std: 0.15355313597491865
+ avg_max: 2.194632339477539
+ mean: 0.09786231890320779
+ std: 0.1530014232794471
+ b: 0.11548511683940887
shape: (256, 64, 56, 56)
output:
- min: -5.982235431671143
- max: 3.2972500324249268
- avg_min: -2.2474620580673217
- avg_max: 1.9966080427169801
- mean: -0.05330531485378742
- std: 0.31662548381935623
+ min: -5.538784027099609
+ max: 3.5764734745025635
+ avg_min: -2.2407279253005985
+ avg_max: 1.994869089126587
+ mean: -0.05254508629441261
+ std: 0.31610883462752315
+ b: 0.23407733142375947
shape: (256, 64, 56, 56)
layer1.1.bn2:
inputs:
0:
- min: -5.982235431671143
- max: 3.2972500324249268
- avg_min: -2.2474620580673217
- avg_max: 1.9966080427169801
- mean: -0.05330531485378742
- std: 0.31662548381935623
+ min: -5.538784027099609
+ max: 3.5764734745025635
+ avg_min: -2.2407279253005985
+ avg_max: 1.994869089126587
+ mean: -0.05254508629441261
+ std: 0.31610883462752315
+ b: 0.23407733142375947
shape: (256, 64, 56, 56)
output:
- min: -12.66700553894043
- max: 6.743299961090088
- avg_min: -4.81916880607605
- avg_max: 3.31194531917572
- mean: -0.03622294031083584
- std: 0.4647229141768063
+ min: -10.714021682739258
+ max: 7.266103267669678
+ avg_min: -4.802960872650147
+ avg_max: 3.3009435892105103
+ mean: -0.03512122202664614
+ std: 0.4628194601345676
+ b: 0.3304642677307129
shape: (256, 64, 56, 56)
layer1.1.relu2:
inputs:
0:
- min: -12.66700553894043
- max: 9.340755462646484
- avg_min: -4.6399578094482425
- avg_max: 5.094357872009278
- mean: 0.4039983034133911
- std: 0.6460796599137519
+ min: -10.714021682739258
+ max: 8.999561309814453
+ avg_min: -4.612377023696899
+ avg_max: 5.063360500335693
+ mean: 0.40414546132087703
+ std: 0.6436291942389393
+ b: 0.4835752308368682
shape: (256, 64, 56, 56)
output:
min: 0.0
- max: 9.340755462646484
+ max: 8.999561309814453
avg_min: 0.0
- avg_max: 5.094357872009278
- mean: 0.500373387336731
- std: 0.4991277746127741
+ avg_max: 5.063360500335693
+ mean: 0.4997546374797821
+ std: 0.4977076703010359
+ b: 0.3939255177974701
shape: (256, 64, 56, 56)
layer1.1.add:
inputs:
0:
- min: -12.66700553894043
- max: 6.743299961090088
- avg_min: -4.81916880607605
- avg_max: 3.31194531917572
- mean: -0.03622294031083584
- std: 0.4647229141768063
+ min: -10.714021682739258
+ max: 7.266103267669678
+ avg_min: -4.802960872650147
+ avg_max: 3.3009435892105103
+ mean: -0.03512122202664614
+ std: 0.4628194601345676
+ b: 0.3304642677307129
shape: (256, 64, 56, 56)
1:
min: 0.0
- max: 7.848092555999756
+ max: 7.920192718505859
avg_min: 0.0
- avg_max: 3.888275837898254
- mean: 0.4402212053537369
- std: 0.40145754203197104
+ avg_max: 3.8547550201416017
+ mean: 0.4392666757106781
+ std: 0.39984883193751414
+ b: 0.31421991586685183
shape: (256, 64, 56, 56)
output:
- min: -12.66700553894043
- max: 9.340755462646484
- avg_min: -4.6399578094482425
- avg_max: 5.094357872009278
- mean: 0.4039983034133911
- std: 0.6460796599137519
+ min: -10.714021682739258
+ max: 8.999561309814453
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shape: (256, 64, 56, 56)
layer2.0.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 64, 56, 56)
output:
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- mean: -0.30288831889629364
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shape: (256, 128, 28, 28)
layer2.0.bn1:
inputs:
0:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.0.relu1:
inputs:
0:
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shape: (256, 128, 28, 28)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.0.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.0.bn2:
inputs:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.0.relu2:
inputs:
0:
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shape: (256, 128, 28, 28)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.0.downsample.0:
inputs:
0:
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avg_min: 0.0
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shape: (256, 64, 56, 56)
output:
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shape: (256, 128, 28, 28)
layer2.0.downsample.1:
inputs:
0:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.0.add:
inputs:
0:
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shape: (256, 128, 28, 28)
1:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.1.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.1.bn1:
inputs:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.1.relu1:
inputs:
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shape: (256, 128, 28, 28)
output:
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.1.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.1.bn2:
inputs:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.1.relu2:
inputs:
0:
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shape: (256, 128, 28, 28)
output:
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.1.add:
inputs:
0:
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shape: (256, 128, 28, 28)
1:
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer3.0.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 256, 14, 14)
layer3.0.bn1:
inputs:
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.0.relu1:
inputs:
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.0.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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- avg_max: 1.984291195869446
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- std: 0.3721248356476338
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shape: (256, 256, 14, 14)
layer3.0.bn2:
inputs:
0:
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- mean: -0.12965155243873594
- std: 0.3721248356476338
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.0.relu2:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.0.downsample.0:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 256, 14, 14)
layer3.0.downsample.1:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.0.add:
inputs:
0:
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shape: (256, 256, 14, 14)
1:
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.1.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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- avg_max: 2.7561988115310667
- mean: -0.41444424390792844
- std: 0.5287273763772131
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shape: (256, 256, 14, 14)
layer3.1.bn1:
inputs:
0:
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- mean: -0.41444424390792844
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.1.relu1:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.1.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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- avg_max: 1.1405210494995117
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shape: (256, 256, 14, 14)
layer3.1.bn2:
inputs:
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.1.relu2:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.1.add:
inputs:
0:
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shape: (256, 256, 14, 14)
1:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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- avg_max: 3.2279526948928834
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shape: (256, 256, 14, 14)
layer4.0.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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shape: (256, 512, 7, 7)
layer4.0.bn1:
inputs:
0:
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shape: (256, 512, 7, 7)
output:
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shape: (256, 512, 7, 7)
layer4.0.relu1:
inputs:
0:
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shape: (256, 512, 7, 7)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 7, 7)
layer4.0.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 7, 7)
output:
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shape: (256, 512, 7, 7)
layer4.0.bn2:
inputs:
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shape: (256, 512, 7, 7)
output:
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shape: (256, 512, 7, 7)
layer4.0.relu2:
inputs:
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+ b: 0.4232411742210388
shape: (256, 512, 7, 7)
output:
min: 0.0
- max: 8.46895980834961
+ max: 8.567574501037598
avg_min: 0.0
- avg_max: 3.330011248588562
- mean: 0.06851729899644853
- std: 0.2003228502381722
+ avg_max: 3.315259170532226
+ mean: 0.06856812238693237
+ std: 0.20035843135120962
+ b: 0.11173125430941581
shape: (256, 512, 7, 7)
layer4.0.downsample.0:
inputs:
0:
min: 0.0
- max: 8.119378089904785
+ max: 9.872881889343262
avg_min: 0.0
- avg_max: 3.2279526948928834
- mean: 0.12238837555050848
- std: 0.2129835573395784
+ avg_max: 3.2831672906875613
+ mean: 0.12256589457392691
+ std: 0.2133638127835183
+ b: 0.15300007164478302
shape: (256, 256, 14, 14)
output:
- min: -1.9270222187042236
+ min: -2.0376739501953125
max: 2.3532819747924805
- avg_min: -0.9864736735820769
- avg_max: 1.140747332572937
- mean: -0.04585805423557758
- std: 0.16336331221817668
+ avg_min: -0.9992474794387818
+ avg_max: 1.1578348278999329
+ mean: -0.04574307352304459
+ std: 0.1642893687178303
+ b: 0.12143977582454682
shape: (256, 512, 7, 7)
layer4.0.downsample.1:
inputs:
0:
- min: -1.9270222187042236
+ min: -2.0376739501953125
max: 2.3532819747924805
- avg_min: -0.9864736735820769
- avg_max: 1.140747332572937
- mean: -0.04585805423557758
- std: 0.16336331221817668
+ avg_min: -0.9992474794387818
+ avg_max: 1.1578348278999329
+ mean: -0.04574307352304459
+ std: 0.1642893687178303
+ b: 0.12143977582454682
shape: (256, 512, 7, 7)
output:
- min: -3.5343539714813232
+ min: -3.7014033794403076
max: 4.346354007720947
- avg_min: -1.905341112613678
- avg_max: 1.7950459957122802
- mean: -0.19914873987436293
- std: 0.27221953386376296
+ avg_min: -1.9194710731506348
+ avg_max: 1.8273812174797057
+ mean: -0.19890957176685334
+ std: 0.2736153024520793
+ b: 0.20122731775045397
shape: (256, 512, 7, 7)
layer4.0.add:
inputs:
0:
- min: -5.792877674102783
- max: 7.841123104095459
- avg_min: -2.789129066467285
- avg_max: 2.627374529838562
- mean: -0.2150831580162048
- std: 0.45363315501783613
+ min: -5.783980846405029
+ max: 6.245330333709717
+ avg_min: -2.800101137161255
+ avg_max: 2.623097443580628
+ mean: -0.21531924307346342
+ std: 0.453539395756688
+ b: 0.34523969888687134
shape: (256, 512, 7, 7)
1:
- min: -3.5343539714813232
+ min: -3.7014033794403076
max: 4.346354007720947
- avg_min: -1.905341112613678
- avg_max: 1.7950459957122802
- mean: -0.19914873987436293
- std: 0.27221953386376296
+ avg_min: -1.9194710731506348
+ avg_max: 1.8273812174797057
+ mean: -0.19890957176685334
+ std: 0.2736153024520793
+ b: 0.20122731775045397
shape: (256, 512, 7, 7)
output:
- min: -6.891504764556885
- max: 8.46895980834961
- avg_min: -3.20503408908844
- avg_max: 3.330011248588562
- mean: -0.41423189938068394
- std: 0.5510562453718115
+ min: -6.930498123168945
+ max: 8.567574501037598
+ avg_min: -3.1913995504379273
+ avg_max: 3.315259170532226
+ mean: -0.4142288058996201
+ std: 0.5505862330408511
+ b: 0.4232411742210388
shape: (256, 512, 7, 7)
layer4.1.conv1:
inputs:
0:
min: 0.0
- max: 8.46895980834961
+ max: 8.567574501037598
avg_min: 0.0
- avg_max: 3.330011248588562
- mean: 0.06851729899644853
- std: 0.2003228502381722
+ avg_max: 3.315259170532226
+ mean: 0.06856812238693237
+ std: 0.20035843135120962
+ b: 0.11173125430941581
shape: (256, 512, 7, 7)
output:
min: -5.210203170776367
- max: 3.5200774669647217
- avg_min: -2.7268222093582155
- avg_max: 2.039318561553955
- mean: -0.6206760883331299
- std: 0.41510913930456855
+ max: 3.741835594177246
+ avg_min: -2.727949595451355
+ avg_max: 2.022892165184021
+ mean: -0.6183191597461701
+ std: 0.41441253046239257
+ b: 0.3147238880395889
shape: (256, 512, 7, 7)
layer4.1.bn1:
inputs:
0:
min: -5.210203170776367
- max: 3.5200774669647217
- avg_min: -2.7268222093582155
- avg_max: 2.039318561553955
- mean: -0.6206760883331299
- std: 0.41510913930456855
+ max: 3.741835594177246
+ avg_min: -2.727949595451355
+ avg_max: 2.022892165184021
+ mean: -0.6183191597461701
+ std: 0.41441253046239257
+ b: 0.3147238880395889
shape: (256, 512, 7, 7)
output:
min: -4.808124542236328
- max: 4.104608535766602
- avg_min: -2.1864486694335934
- avg_max: 1.4413557529449463
- mean: -0.2617767214775086
- std: 0.32745514342278503
+ max: 4.1657257080078125
+ avg_min: -2.1843703031539916
+ avg_max: 1.4314042687416078
+ mean: -0.25993406772613525
+ std: 0.32693443700689745
+ b: 0.25053864270448684
shape: (256, 512, 7, 7)
layer4.1.relu1:
inputs:
0:
min: -4.808124542236328
- max: 4.104608535766602
- avg_min: -2.1864486694335934
- avg_max: 1.4413557529449463
- mean: -0.2617767214775086
- std: 0.32745514342278503
+ max: 4.1657257080078125
+ avg_min: -2.1843703031539916
+ avg_max: 1.4314042687416078
+ mean: -0.25993406772613525
+ std: 0.32693443700689745
+ b: 0.25053864270448684
shape: (256, 512, 7, 7)
output:
min: 0.0
- max: 4.104608535766602
+ max: 4.1657257080078125
avg_min: 0.0
- avg_max: 1.4413557529449463
- mean: 0.030183648318052293
- std: 0.0890167884469397
+ avg_max: 1.4314042687416078
+ mean: 0.030445635877549646
+ std: 0.08933091998969779
+ b: 0.049756981804966925
shape: (256, 512, 7, 7)
layer4.1.conv2:
inputs:
0:
min: 0.0
- max: 4.104608535766602
+ max: 4.1657257080078125
avg_min: 0.0
- avg_max: 1.4413557529449463
- mean: 0.030183648318052293
- std: 0.0890167884469397
+ avg_max: 1.4314042687416078
+ mean: 0.030445635877549646
+ std: 0.08933091998969779
+ b: 0.049756981804966925
shape: (256, 512, 7, 7)
output:
- min: -1.083118200302124
- max: 2.9936976432800293
- avg_min: -0.5540908932685852
- avg_max: 0.8956171870231628
- mean: -0.027465152740478515
- std: 0.11994073669763537
+ min: -1.1300753355026245
+ max: 4.191241264343262
+ avg_min: -0.5563786149024963
+ avg_max: 0.9013789415359497
+ mean: -0.027124672755599023
+ std: 0.1204256230341855
+ b: 0.08786393702030182
shape: (256, 512, 7, 7)
layer4.1.bn2:
inputs:
0:
- min: -1.083118200302124
- max: 2.9936976432800293
- avg_min: -0.5540908932685852
- avg_max: 0.8956171870231628
- mean: -0.027465152740478515
- std: 0.11994073669763537
+ min: -1.1300753355026245
+ max: 4.191241264343262
+ avg_min: -0.5563786149024963
+ avg_max: 0.9013789415359497
+ mean: -0.027124672755599023
+ std: 0.1204256230341855
+ b: 0.08786393702030182
shape: (256, 512, 7, 7)
output:
- min: -17.7739200592041
- max: 49.293739318847656
- avg_min: -8.384725379943847
- avg_max: 15.652357387542724
- mean: 0.3391309440135956
- std: 1.9559910002542347
+ min: -19.004796981811523
+ max: 68.5389175415039
+ avg_min: -8.409132289886475
+ avg_max: 15.7423565864563
+ mean: 0.34465126097202303
+ std: 1.9625875514236744
+ b: 1.4261910796165467
shape: (256, 512, 7, 7)
layer4.1.relu2:
inputs:
0:
- min: -17.7739200592041
- max: 53.34544372558594
- avg_min: -8.348113536834717
- avg_max: 16.909835433959962
- mean: 0.4076482564210892
- std: 2.0075340226308156
+ min: -19.004796981811523
+ max: 73.84921264648438
+ avg_min: -8.37563362121582
+ avg_max: 17.005590820312502
+ mean: 0.41321938037872313
+ std: 2.0140747496097013
+ b: 1.4604507446289061
shape: (256, 512, 7, 7)
output:
min: 0.0
- max: 53.34544372558594
+ max: 73.84921264648438
avg_min: 0.0
- avg_max: 16.909835433959962
- mean: 0.9270834982395172
- std: 1.5093241017607464
+ avg_max: 17.005590820312502
+ mean: 0.9329111337661743
+ std: 1.5150135025832752
+ b: 1.068066442012787
shape: (256, 512, 7, 7)
layer4.1.add:
inputs:
0:
- min: -17.7739200592041
- max: 49.293739318847656
- avg_min: -8.384725379943847
- avg_max: 15.652357387542724
- mean: 0.3391309440135956
- std: 1.9559910002542347
+ min: -19.004796981811523
+ max: 68.5389175415039
+ avg_min: -8.409132289886475
+ avg_max: 15.7423565864563
+ mean: 0.34465126097202303
+ std: 1.9625875514236744
+ b: 1.4261910796165467
shape: (256, 512, 7, 7)
1:
min: 0.0
- max: 8.46895980834961
+ max: 8.567574501037598
avg_min: 0.0
- avg_max: 3.330011248588562
- mean: 0.06851729899644853
- std: 0.2003228502381722
+ avg_max: 3.315259170532226
+ mean: 0.06856812238693237
+ std: 0.20035843135120962
+ b: 0.11173125430941581
shape: (256, 512, 7, 7)
output:
- min: -17.7739200592041
- max: 53.34544372558594
- avg_min: -8.348113536834717
- avg_max: 16.909835433959962
- mean: 0.4076482564210892
- std: 2.0075340226308156
+ min: -19.004796981811523
+ max: 73.84921264648438
+ avg_min: -8.37563362121582
+ avg_max: 17.005590820312502
+ mean: 0.41321938037872313
+ std: 2.0140747496097013
+ b: 1.4604507446289061
shape: (256, 512, 7, 7)
avgpool:
inputs:
0:
min: 0.0
- max: 53.34544372558594
+ max: 73.84921264648438
avg_min: 0.0
- avg_max: 16.909835433959962
- mean: 0.9270834982395172
- std: 1.5093241017607464
+ avg_max: 17.005590820312502
+ mean: 0.9329111337661743
+ std: 1.5150135025832752
+ b: 1.068066442012787
shape: (256, 512, 7, 7)
output:
min: 0.0
- max: 17.51531410217285
- avg_min: 0.00017440698211430573
- avg_max: 6.207659435272217
- mean: 0.9270835041999818
- std: 0.9303102411121627
+ max: 23.313302993774414
+ avg_min: 0.000172000216844026
+ avg_max: 6.189624261856079
+ mean: 0.9329111397266387
+ std: 0.9293646051355287
+ b: 0.68059321641922
shape: (256, 512, 1, 1)
fc:
inputs:
0:
min: 0.0
- max: 17.51531410217285
- avg_min: 0.00017440698211430573
- avg_max: 6.207659435272217
- mean: 0.9270835041999818
- std: 0.9303102411121627
+ max: 23.313302993774414
+ avg_min: 0.000172000216844026
+ avg_max: 6.189624261856079
+ mean: 0.9329111397266387
+ std: 0.9293646051355287
+ b: 0.68059321641922
shape: (256, 512)
output:
- min: -12.437793731689453
- max: 40.4469108581543
- avg_min: -6.941068887710571
- avg_max: 16.39320945739746
- mean: 2.7895814673684066e-05
- std: 2.8083362700072496
+ min: -12.27712631225586
+ max: 37.946144104003906
+ avg_min: -6.967176866531372
+ avg_max: 16.258214187622066
+ mean: 2.813304145092843e-05
+ std: 2.8061255378564596
+ b: 2.0943870067596433
shape: (256, 1000)
diff --git a/examples/quantization/post_train_quant/stats/resnet50_quant_stats.yaml b/examples/quantization/post_train_quant/stats/resnet50_quant_stats.yaml
index e59ae7b..5a0a5c0 100644
--- a/examples/quantization/post_train_quant/stats/resnet50_quant_stats.yaml
+++ b/examples/quantization/post_train_quant/stats/resnet50_quant_stats.yaml
@@ -1,7 +1,7 @@
# ResNet-50 activation statistics for post-training quantization
# Generated by running:
#
-# python compress_classifier.py -a resnet18 -p 10 -j 22 <path_to_imagenet_dataset> --pretrained --qe-calibration 0.05
+# python compress_classifier.py -a resnet18 -p 10 -j 22 <path_to_imagenet_dataset> --pretrained --qe-calibration 0.05 --seed 0
#
# Notes:
# * Stats collection is triggered by the '--qe-calibration' flag. It will run evaluation on the specified portion of
@@ -9,185 +9,206 @@
# * Once completed, a file named 'quantization_stats.yaml' is dumped in the log directory
# * In this example we collect stats for the pre-trained ResNet-18 model from torchvision. If you trained the FP32
# model yourself, you'll need to load the checkpoint using the '--resume' flag.
+
conv1:
inputs:
0:
min: -2.1179039478302
max: 2.640000104904175
- avg_min: -2.0194480657577514
- avg_max: 2.4895985126495357
- mean: -0.04953605402261019
- std: 1.1911271065308935
+ avg_min: -2.014737010002136
+ avg_max: 2.4884546041488647
+ mean: -0.033971998607739806
+ std: 1.201734408022518
+ b: 1.010804271697998
shape: (256, 3, 224, 224)
output:
- min: -29.021743774414062
- max: 27.613426208496094
- avg_min: -17.249443244934085
- avg_max: 17.603945541381833
- mean: 0.0037155995145440102
- std: 1.6641648668493714
+ min: -29.77520751953125
+ max: 27.7259464263916
+ avg_min: -17.287668418884277
+ avg_max: 17.605259132385253
+ mean: 0.003127608919749036
+ std: 1.6707808577493286
+ b: 0.9622739136219025
shape: (256, 64, 112, 112)
bn1:
inputs:
0:
- min: -29.021743774414062
- max: 27.613426208496094
- avg_min: -17.249443244934085
- avg_max: 17.603945541381833
- mean: 0.0037155995145440102
- std: 1.6641648668493714
+ min: -29.77520751953125
+ max: 27.7259464263916
+ avg_min: -17.287668418884277
+ avg_max: 17.605259132385253
+ mean: 0.003127608919749036
+ std: 1.6707808577493286
+ b: 0.9622739136219025
shape: (256, 64, 112, 112)
output:
- min: -8.01382064819336
- max: 9.588595390319824
- avg_min: -2.8591087579727175
- avg_max: 3.2792069911956787
- mean: 0.2111692249774933
- std: 0.39293307299795177
+ min: -6.802585124969482
+ max: 8.709970474243164
+ avg_min: -2.875292038917541
+ avg_max: 3.292344069480896
+ mean: 0.21110961586236954
+ std: 0.39392244937427096
+ b: 0.2580239772796631
shape: (256, 64, 112, 112)
relu:
inputs:
0:
- min: -8.01382064819336
- max: 9.588595390319824
- avg_min: -2.8591087579727175
- avg_max: 3.2792069911956787
- mean: 0.2111692249774933
- std: 0.39293307299795177
+ min: -6.802585124969482
+ max: 8.709970474243164
+ avg_min: -2.875292038917541
+ avg_max: 3.292344069480896
+ mean: 0.21110961586236954
+ std: 0.39392244937427096
+ b: 0.2580239772796631
shape: (256, 64, 112, 112)
output:
min: 0.0
- max: 9.588595390319824
+ max: 8.709970474243164
avg_min: 0.0
- avg_max: 3.2792069911956787
- mean: 0.27234929502010347
- std: 0.2906493627939896
+ avg_max: 3.292344069480896
+ mean: 0.27247288525104524
+ std: 0.29089813933159364
+ b: 0.2078553184866905
shape: (256, 64, 112, 112)
maxpool:
inputs:
0:
min: 0.0
- max: 9.588595390319824
+ max: 8.709970474243164
avg_min: 0.0
- avg_max: 3.2792069911956787
- mean: 0.27234929502010347
- std: 0.2906493627939896
+ avg_max: 3.292344069480896
+ mean: 0.27247288525104524
+ std: 0.29089813933159364
+ b: 0.2078553184866905
shape: (256, 64, 112, 112)
output:
min: 0.0
- max: 9.588595390319824
+ max: 8.709970474243164
avg_min: 0.0
- avg_max: 3.2792069911956787
- mean: 0.41020182669162747
- std: 0.34590466330561787
+ avg_max: 3.292344069480896
+ mean: 0.40832236111164094
+ std: 0.3473619222413323
+ b: 0.2558086723089218
shape: (256, 64, 56, 56)
layer1.0.conv1:
inputs:
0:
min: 0.0
- max: 9.588595390319824
+ max: 8.709970474243164
avg_min: 0.0
- avg_max: 3.2792069911956787
- mean: 0.41020182669162747
- std: 0.34590466330561787
+ avg_max: 3.292344069480896
+ mean: 0.40832236111164094
+ std: 0.3473619222413323
+ b: 0.2558086723089218
shape: (256, 64, 56, 56)
output:
- min: -6.134335994720459
- max: 5.119430065155029
- avg_min: -2.87510347366333
- avg_max: 3.2264750003814697
- mean: -0.16020772010087964
- std: 0.4356245989366653
+ min: -6.828606605529785
+ max: 5.236318111419678
+ avg_min: -2.8953623533248902
+ avg_max: 3.21773624420166
+ mean: -0.15903166681528091
+ std: 0.4356199981478919
+ b: 0.2817686200141907
shape: (256, 64, 56, 56)
layer1.0.bn1:
inputs:
0:
- min: -6.134335994720459
- max: 5.119430065155029
- avg_min: -2.87510347366333
- avg_max: 3.2264750003814697
- mean: -0.16020772010087964
- std: 0.4356245989366653
+ min: -6.828606605529785
+ max: 5.236318111419678
+ avg_min: -2.8953623533248902
+ avg_max: 3.21773624420166
+ mean: -0.15903166681528091
+ std: 0.4356199981478919
+ b: 0.2817686200141907
shape: (256, 64, 56, 56)
output:
- min: -5.676072597503662
- max: 2.838289737701416
- avg_min: -2.651957130432129
- avg_max: 1.2408627033233643
- mean: 0.018273601215332752
- std: 0.27017834978407007
+ min: -6.346645832061768
+ max: 2.856245517730713
+ avg_min: -2.6718324422836304
+ avg_max: 1.2453311443328858
+ mean: 0.019558261148631573
+ std: 0.271801838403236
+ b: 0.1766661003232002
shape: (256, 64, 56, 56)
layer1.0.relu1:
inputs:
0:
- min: -5.676072597503662
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shape: (256, 64, 56, 56)
output:
min: 0.0
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avg_min: 0.0
- avg_max: 1.2408627033233643
- mean: 0.09888825193047523
- std: 0.14426185012443266
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shape: (256, 64, 56, 56)
layer1.0.conv2:
inputs:
0:
min: 0.0
- max: 2.838289737701416
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avg_min: 0.0
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- mean: 0.09888825193047523
- std: 0.14426185012443266
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shape: (256, 64, 56, 56)
output:
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- avg_min: -1.5226194500923158
- avg_max: 1.462186813354492
- mean: 0.025130289234220983
- std: 0.24702317462922718
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shape: (256, 64, 56, 56)
layer1.0.bn2:
inputs:
0:
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- avg_max: 1.462186813354492
- mean: 0.025130289234220983
- std: 0.24702317462922718
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shape: (256, 64, 56, 56)
output:
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max: 2.4024081230163574
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shape: (256, 64, 56, 56)
layer1.0.relu2:
inputs:
0:
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max: 2.4024081230163574
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shape: (256, 64, 56, 56)
output:
min: 0.0
max: 2.4024081230163574
avg_min: 0.0
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- mean: 0.15067598968744278
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shape: (256, 64, 56, 56)
layer1.0.conv3:
inputs:
@@ -195,411 +216,457 @@ layer1.0.conv3:
min: 0.0
max: 2.4024081230163574
avg_min: 0.0
- avg_max: 1.685540783405304
- mean: 0.15067598968744278
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shape: (256, 64, 56, 56)
output:
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- std: 0.09642191440462164
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shape: (256, 256, 56, 56)
layer1.0.bn3:
inputs:
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shape: (256, 256, 56, 56)
output:
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shape: (256, 256, 56, 56)
layer1.0.relu3:
inputs:
0:
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shape: (256, 256, 56, 56)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 56, 56)
layer1.0.downsample.0:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 64, 56, 56)
output:
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shape: (256, 256, 56, 56)
layer1.0.downsample.1:
inputs:
0:
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shape: (256, 256, 56, 56)
output:
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shape: (256, 256, 56, 56)
layer1.0.add:
inputs:
0:
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shape: (256, 256, 56, 56)
1:
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shape: (256, 256, 56, 56)
output:
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shape: (256, 256, 56, 56)
layer1.1.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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- std: 0.15946307436766427
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shape: (256, 256, 56, 56)
output:
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- avg_max: 1.2206068515777586
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- std: 0.19780537441792345
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shape: (256, 64, 56, 56)
layer1.1.bn1:
inputs:
0:
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- std: 0.19780537441792345
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shape: (256, 64, 56, 56)
output:
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shape: (256, 64, 56, 56)
layer1.1.relu1:
inputs:
0:
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- std: 0.2574544790456561
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shape: (256, 64, 56, 56)
output:
min: 0.0
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avg_min: 0.0
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- mean: 0.07788005843758583
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shape: (256, 64, 56, 56)
layer1.1.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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- mean: 0.07788005843758583
- std: 0.12464164985578752
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shape: (256, 64, 56, 56)
output:
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- avg_max: 0.8191621780395507
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shape: (256, 64, 56, 56)
layer1.1.bn2:
inputs:
0:
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- std: 0.14252681331424472
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shape: (256, 64, 56, 56)
output:
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shape: (256, 64, 56, 56)
layer1.1.relu2:
inputs:
0:
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- avg_max: 1.8832740783691406
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shape: (256, 64, 56, 56)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 64, 56, 56)
layer1.1.conv3:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 64, 56, 56)
output:
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shape: (256, 256, 56, 56)
layer1.1.bn3:
inputs:
0:
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shape: (256, 256, 56, 56)
output:
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shape: (256, 256, 56, 56)
layer1.1.relu3:
inputs:
0:
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shape: (256, 256, 56, 56)
output:
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avg_min: 0.0
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shape: (256, 256, 56, 56)
layer1.1.add:
inputs:
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shape: (256, 256, 56, 56)
1:
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avg_min: 0.0
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shape: (256, 256, 56, 56)
output:
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shape: (256, 256, 56, 56)
layer1.2.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 56, 56)
output:
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- avg_max: 1.0224858760833737
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shape: (256, 64, 56, 56)
layer1.2.bn1:
inputs:
0:
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shape: (256, 64, 56, 56)
output:
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shape: (256, 64, 56, 56)
layer1.2.relu1:
inputs:
0:
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shape: (256, 64, 56, 56)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 64, 56, 56)
layer1.2.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 64, 56, 56)
output:
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shape: (256, 64, 56, 56)
layer1.2.bn2:
inputs:
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shape: (256, 64, 56, 56)
output:
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max: 3.6316139698028564
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shape: (256, 64, 56, 56)
layer1.2.relu2:
inputs:
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max: 3.6316139698028564
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- mean: -0.052389902994036674
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shape: (256, 64, 56, 56)
output:
min: 0.0
max: 3.6316139698028564
avg_min: 0.0
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- mean: 0.06840542927384377
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shape: (256, 64, 56, 56)
layer1.2.conv3:
inputs:
@@ -607,2665 +674,2963 @@ layer1.2.conv3:
min: 0.0
max: 3.6316139698028564
avg_min: 0.0
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shape: (256, 64, 56, 56)
output:
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shape: (256, 256, 56, 56)
layer1.2.bn3:
inputs:
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shape: (256, 256, 56, 56)
output:
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shape: (256, 256, 56, 56)
layer1.2.relu3:
inputs:
0:
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shape: (256, 256, 56, 56)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 56, 56)
layer1.2.add:
inputs:
0:
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- mean: -0.0139082214795053
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shape: (256, 256, 56, 56)
1:
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avg_min: 0.0
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shape: (256, 256, 56, 56)
output:
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shape: (256, 256, 56, 56)
layer2.0.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 56, 56)
output:
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- avg_max: 1.8101095080375673
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shape: (256, 128, 56, 56)
layer2.0.bn1:
inputs:
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shape: (256, 128, 56, 56)
output:
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shape: (256, 128, 56, 56)
layer2.0.relu1:
inputs:
0:
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shape: (256, 128, 56, 56)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 56, 56)
layer2.0.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 56, 56)
output:
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- avg_max: 1.1475389122962953
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shape: (256, 128, 28, 28)
layer2.0.bn2:
inputs:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.0.relu2:
inputs:
0:
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shape: (256, 128, 28, 28)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.0.conv3:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.0.bn3:
inputs:
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shape: (256, 512, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.0.relu3:
inputs:
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shape: (256, 512, 28, 28)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 28, 28)
layer2.0.downsample.0:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 56, 56)
output:
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shape: (256, 512, 28, 28)
layer2.0.downsample.1:
inputs:
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shape: (256, 512, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.0.add:
inputs:
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shape: (256, 512, 28, 28)
1:
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shape: (256, 512, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.1.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.1.bn1:
inputs:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.1.relu1:
inputs:
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shape: (256, 128, 28, 28)
output:
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.1.conv2:
inputs:
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min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.1.bn2:
inputs:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.1.relu2:
inputs:
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shape: (256, 128, 28, 28)
output:
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.1.conv3:
inputs:
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min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.1.bn3:
inputs:
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shape: (256, 512, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.1.relu3:
inputs:
0:
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shape: (256, 512, 28, 28)
output:
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avg_min: 0.0
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shape: (256, 512, 28, 28)
layer2.1.add:
inputs:
0:
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shape: (256, 512, 28, 28)
1:
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avg_min: 0.0
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shape: (256, 512, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.2.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.2.bn1:
inputs:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.2.relu1:
inputs:
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shape: (256, 128, 28, 28)
output:
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.2.conv2:
inputs:
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.2.bn2:
inputs:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.2.relu2:
inputs:
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shape: (256, 128, 28, 28)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.2.conv3:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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- avg_max: 0.41976919770240784
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shape: (256, 512, 28, 28)
layer2.2.bn3:
inputs:
0:
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shape: (256, 512, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.2.relu3:
inputs:
0:
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shape: (256, 512, 28, 28)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 28, 28)
layer2.2.add:
inputs:
0:
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shape: (256, 512, 28, 28)
1:
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avg_min: 0.0
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shape: (256, 512, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.3.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.3.bn1:
inputs:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.3.relu1:
inputs:
0:
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shape: (256, 128, 28, 28)
output:
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.3.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.3.bn2:
inputs:
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shape: (256, 128, 28, 28)
output:
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shape: (256, 128, 28, 28)
layer2.3.relu2:
inputs:
0:
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shape: (256, 128, 28, 28)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
layer2.3.conv3:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 128, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.3.bn3:
inputs:
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shape: (256, 512, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer2.3.relu3:
inputs:
0:
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shape: (256, 512, 28, 28)
output:
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avg_min: 0.0
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shape: (256, 512, 28, 28)
layer2.3.add:
inputs:
0:
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shape: (256, 512, 28, 28)
1:
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avg_min: 0.0
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shape: (256, 512, 28, 28)
output:
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shape: (256, 512, 28, 28)
layer3.0.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 28, 28)
output:
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shape: (256, 256, 28, 28)
layer3.0.bn1:
inputs:
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shape: (256, 256, 28, 28)
output:
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shape: (256, 256, 28, 28)
layer3.0.relu1:
inputs:
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shape: (256, 256, 28, 28)
output:
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avg_min: 0.0
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shape: (256, 256, 28, 28)
layer3.0.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 28, 28)
output:
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shape: (256, 256, 14, 14)
layer3.0.bn2:
inputs:
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.0.relu2:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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- mean: 0.09833981543779374
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shape: (256, 256, 14, 14)
layer3.0.conv3:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.0.bn3:
inputs:
0:
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.0.relu3:
inputs:
0:
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shape: (256, 1024, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
layer3.0.downsample.0:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 28, 28)
output:
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shape: (256, 1024, 14, 14)
layer3.0.downsample.1:
inputs:
0:
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.0.add:
inputs:
0:
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shape: (256, 1024, 14, 14)
1:
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.1.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.1.bn1:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.1.relu1:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.1.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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- avg_max: 1.0772642254829405
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- std: 0.20633945403652537
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shape: (256, 256, 14, 14)
layer3.1.bn2:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.1.relu2:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.1.conv3:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.1.bn3:
inputs:
0:
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.1.relu3:
inputs:
0:
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shape: (256, 1024, 14, 14)
output:
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
layer3.1.add:
inputs:
0:
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shape: (256, 1024, 14, 14)
1:
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.2.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.2.bn1:
inputs:
0:
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- avg_max: 0.897527688741684
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shape: (256, 256, 14, 14)
output:
- min: -1.6091384887695312
- max: 2.1613590717315674
- avg_min: -0.974888825416565
- avg_max: 1.1463863968849184
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shape: (256, 256, 14, 14)
layer3.2.relu1:
inputs:
0:
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- max: 2.1613590717315674
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- avg_max: 1.1463863968849184
- mean: -0.04899360761046409
- std: 0.17298899321911881
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
- avg_max: 1.1463863968849184
- mean: 0.04594111070036888
- std: 0.08596560170466168
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shape: (256, 256, 14, 14)
layer3.2.conv2:
inputs:
0:
min: 0.0
- max: 2.1613590717315674
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avg_min: 0.0
- avg_max: 1.1463863968849184
- mean: 0.04594111070036888
- std: 0.08596560170466168
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shape: (256, 256, 14, 14)
output:
- min: -1.7100857496261597
- max: 1.2296204566955566
- avg_min: -0.9203199565410615
- avg_max: 0.7516951560974121
- mean: -0.060491649061441416
- std: 0.14446424529132146
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shape: (256, 256, 14, 14)
layer3.2.bn2:
inputs:
0:
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- avg_max: 0.7516951560974121
- mean: -0.060491649061441416
- std: 0.14446424529132146
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shape: (256, 256, 14, 14)
output:
- min: -1.9307109117507935
- max: 1.9227837324142456
- avg_min: -1.1561131119728087
- avg_max: 0.9304027497768402
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- std: 0.20287761846310984
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shape: (256, 256, 14, 14)
layer3.2.relu2:
inputs:
0:
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- avg_max: 0.9304027497768402
- mean: -0.07359198182821274
- std: 0.20287761846310984
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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- mean: 0.046708225086331365
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shape: (256, 256, 14, 14)
layer3.2.conv3:
inputs:
0:
min: 0.0
- max: 1.9227837324142456
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avg_min: 0.0
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- mean: 0.046708225086331365
- std: 0.08785573478754449
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shape: (256, 256, 14, 14)
output:
- min: -0.5223075151443481
- max: 0.4661237299442291
- avg_min: -0.28980413973331454
- avg_max: 0.2777348279953003
- mean: -0.01015338534489274
- std: 0.03769439630679751
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shape: (256, 1024, 14, 14)
layer3.2.bn3:
inputs:
0:
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- mean: -0.01015338534489274
- std: 0.03769439630679751
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shape: (256, 1024, 14, 14)
output:
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- avg_max: 0.990718525648117
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shape: (256, 1024, 14, 14)
layer3.2.relu3:
inputs:
0:
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- avg_min: -0.9630495011806489
- avg_max: 2.45657742023468
- mean: 0.019510931707918643
- std: 0.17988520609270667
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shape: (256, 1024, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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- mean: 0.0736074797809124
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shape: (256, 1024, 14, 14)
layer3.2.add:
inputs:
0:
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- max: 2.3993289470672607
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- avg_max: 0.990718525648117
- mean: -0.05901466868817807
- std: 0.10956712860948606
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shape: (256, 1024, 14, 14)
1:
min: 0.0
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avg_min: 0.0
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- mean: 0.07852560207247734
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shape: (256, 1024, 14, 14)
output:
- min: -1.6363203525543213
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- avg_min: -0.9630495011806489
- avg_max: 2.45657742023468
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- std: 0.17988520609270667
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shape: (256, 1024, 14, 14)
layer3.3.conv1:
inputs:
0:
min: 0.0
- max: 5.327512264251709
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avg_min: 0.0
- avg_max: 2.45657742023468
- mean: 0.0736074797809124
- std: 0.135348297399026
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shape: (256, 1024, 14, 14)
output:
- min: -1.3141155242919922
- max: 1.7013614177703857
- avg_min: -0.8733409106731416
- avg_max: 0.9241234064102173
- mean: -0.06390224620699882
- std: 0.1473729016075356
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shape: (256, 256, 14, 14)
layer3.3.bn1:
inputs:
0:
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- max: 1.7013614177703857
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- avg_max: 0.9241234064102173
- mean: -0.06390224620699882
- std: 0.1473729016075356
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shape: (256, 256, 14, 14)
output:
- min: -1.820160984992981
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- avg_min: -1.074443531036377
- avg_max: 1.26715726852417
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- std: 0.1866364461272101
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shape: (256, 256, 14, 14)
layer3.3.relu1:
inputs:
0:
- min: -1.820160984992981
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- avg_max: 1.26715726852417
- mean: -0.08310422375798224
- std: 0.1866364461272101
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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- mean: 0.03824471198022366
- std: 0.08215332314659896
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shape: (256, 256, 14, 14)
layer3.3.conv2:
inputs:
0:
min: 0.0
- max: 3.5110983848571777
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avg_min: 0.0
- avg_max: 1.26715726852417
- mean: 0.03824471198022366
- std: 0.08215332314659896
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shape: (256, 256, 14, 14)
output:
- min: -1.9625822305679321
- max: 1.4796524047851562
- avg_min: -0.8453774690628051
- avg_max: 0.7829941272735595
- mean: -0.05455365739762783
- std: 0.12259960248373775
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shape: (256, 256, 14, 14)
layer3.3.bn2:
inputs:
0:
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- avg_min: -0.8453774690628051
- avg_max: 0.7829941272735595
- mean: -0.05455365739762783
- std: 0.12259960248373775
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shape: (256, 256, 14, 14)
output:
- min: -2.5048694610595703
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- avg_min: -1.197688341140747
- avg_max: 1.0186122715473174
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shape: (256, 256, 14, 14)
layer3.3.relu2:
inputs:
0:
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- avg_max: 1.0186122715473174
- mean: -0.07380441427230836
- std: 0.19102371682344027
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.3.conv3:
inputs:
0:
min: 0.0
- max: 2.1857171058654785
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avg_min: 0.0
- avg_max: 1.0186122715473174
- mean: 0.04190520346164703
- std: 0.08191135989157138
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shape: (256, 256, 14, 14)
output:
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- avg_max: 0.30033474564552304
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- std: 0.03284520891780353
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shape: (256, 1024, 14, 14)
layer3.3.bn3:
inputs:
0:
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- avg_max: 0.30033474564552304
- mean: -0.013642842136323451
- std: 0.03284520891780353
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shape: (256, 1024, 14, 14)
output:
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- avg_max: 1.3014169096946715
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shape: (256, 1024, 14, 14)
layer3.3.relu3:
inputs:
0:
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- avg_max: 2.773927688598633
- mean: 0.008767120074480772
- std: 0.18065239121137047
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shape: (256, 1024, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
layer3.3.add:
inputs:
0:
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- avg_max: 1.3014169096946715
- mean: -0.06484036147594452
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shape: (256, 1024, 14, 14)
1:
min: 0.0
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avg_min: 0.0
- avg_max: 2.45657742023468
- mean: 0.0736074797809124
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shape: (256, 1024, 14, 14)
output:
- min: -2.4268336296081543
- max: 6.842432498931885
- avg_min: -1.2578607439994811
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shape: (256, 1024, 14, 14)
layer3.4.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
output:
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- avg_max: 0.9027495622634889
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shape: (256, 256, 14, 14)
layer3.4.bn1:
inputs:
0:
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- mean: -0.07035565003752708
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shape: (256, 256, 14, 14)
output:
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- avg_max: 1.3030256152153015
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shape: (256, 256, 14, 14)
layer3.4.relu1:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.4.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.4.bn2:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
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- avg_max: 1.060443115234375
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shape: (256, 256, 14, 14)
layer3.4.relu2:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.4.conv3:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.4.bn3:
inputs:
0:
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.4.relu3:
inputs:
0:
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shape: (256, 1024, 14, 14)
output:
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
layer3.4.add:
inputs:
0:
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shape: (256, 1024, 14, 14)
1:
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.5.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.5.bn1:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.5.relu1:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.5.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.5.bn2:
inputs:
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shape: (256, 256, 14, 14)
output:
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shape: (256, 256, 14, 14)
layer3.5.relu2:
inputs:
0:
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shape: (256, 256, 14, 14)
output:
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avg_min: 0.0
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shape: (256, 256, 14, 14)
layer3.5.conv3:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 256, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.5.bn3:
inputs:
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer3.5.relu3:
inputs:
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shape: (256, 1024, 14, 14)
output:
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
layer3.5.add:
inputs:
0:
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shape: (256, 1024, 14, 14)
1:
min: 0.0
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 1024, 14, 14)
layer4.0.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
output:
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- avg_max: 1.1026112914085389
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shape: (256, 512, 14, 14)
layer4.0.bn1:
inputs:
0:
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shape: (256, 512, 14, 14)
output:
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- avg_max: 1.6812901854515077
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shape: (256, 512, 14, 14)
layer4.0.relu1:
inputs:
0:
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shape: (256, 512, 14, 14)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 14, 14)
layer4.0.conv2:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 14, 14)
output:
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shape: (256, 512, 7, 7)
layer4.0.bn2:
inputs:
0:
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shape: (256, 512, 7, 7)
output:
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shape: (256, 512, 7, 7)
layer4.0.relu2:
inputs:
0:
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shape: (256, 512, 7, 7)
output:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 7, 7)
layer4.0.conv3:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 512, 7, 7)
output:
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shape: (256, 2048, 7, 7)
layer4.0.bn3:
inputs:
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shape: (256, 2048, 7, 7)
output:
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shape: (256, 2048, 7, 7)
layer4.0.relu3:
inputs:
0:
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shape: (256, 2048, 7, 7)
output:
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avg_min: 0.0
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shape: (256, 2048, 7, 7)
layer4.0.downsample.0:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 1024, 14, 14)
output:
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shape: (256, 2048, 7, 7)
layer4.0.downsample.1:
inputs:
0:
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shape: (256, 2048, 7, 7)
output:
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shape: (256, 2048, 7, 7)
layer4.0.add:
inputs:
0:
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shape: (256, 2048, 7, 7)
1:
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shape: (256, 2048, 7, 7)
output:
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shape: (256, 2048, 7, 7)
layer4.1.conv1:
inputs:
0:
min: 0.0
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avg_min: 0.0
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shape: (256, 2048, 7, 7)
output:
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shape: (256, 512, 7, 7)
layer4.1.bn1:
inputs:
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shape: (256, 512, 7, 7)
output:
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shape: (256, 512, 7, 7)
layer4.1.relu1:
inputs:
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shape: (256, 512, 7, 7)
output:
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avg_min: 0.0
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shape: (256, 512, 7, 7)
layer4.1.conv2:
inputs:
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min: 0.0
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avg_min: 0.0
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shape: (256, 512, 7, 7)
output:
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shape: (256, 512, 7, 7)
layer4.1.bn2:
inputs:
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shape: (256, 512, 7, 7)
output:
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shape: (256, 512, 7, 7)
layer4.1.relu2:
inputs:
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shape: (256, 512, 7, 7)
output:
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avg_min: 0.0
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shape: (256, 512, 7, 7)
layer4.1.conv3:
inputs:
0:
min: 0.0
- max: 3.8549869060516357
+ max: 2.8101301193237305
avg_min: 0.0
- avg_max: 1.0324531495571136
- mean: 0.034236426651477816
- std: 0.08112417540127474
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+ mean: 0.0341867882758379
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shape: (256, 512, 7, 7)
output:
- min: -0.4920940697193146
- max: 0.9806932806968689
- avg_min: -0.3192786365747452
- avg_max: 0.3261650323867798
- mean: -0.0005754437879659235
- std: 0.03354625255049893
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+ max: 0.7271759510040283
+ avg_min: -0.3177788823843003
+ avg_max: 0.3269481897354126
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shape: (256, 2048, 7, 7)
layer4.1.bn3:
inputs:
0:
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- avg_max: 0.3261650323867798
- mean: -0.0005754437879659235
- std: 0.03354625255049893
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+ max: 0.7271759510040283
+ avg_min: -0.3177788823843003
+ avg_max: 0.3269481897354126
+ mean: -0.0006348539493046701
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shape: (256, 2048, 7, 7)
output:
- min: -5.395097732543945
- max: 14.336352348327637
- avg_min: -2.411039137840271
- avg_max: 3.4030594348907472
- mean: -0.055087806284427644
- std: 0.38007112753406547
+ min: -4.98611307144165
+ max: 10.873602867126465
+ avg_min: -2.399258136749268
+ avg_max: 3.3770268917083737
+ mean: -0.05591708905994892
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shape: (256, 2048, 7, 7)
layer4.1.relu3:
inputs:
0:
- min: -5.395097732543945
- max: 21.834545135498047
- avg_min: -2.382943248748779
- avg_max: 7.608690929412842
- mean: 0.09534295722842218
- std: 0.5429858198158471
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+ avg_min: -2.3728135108947757
+ avg_max: 7.612712144851685
+ mean: 0.09472521916031837
+ std: 0.5441690529259338
+ b: 0.38570883572101594
shape: (256, 2048, 7, 7)
output:
min: 0.0
- max: 21.834545135498047
+ max: 15.573689460754395
avg_min: 0.0
- avg_max: 7.608690929412842
- mean: 0.2376137301325798
- std: 0.42102969218495145
+ avg_max: 7.612712144851685
+ mean: 0.23763893991708757
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shape: (256, 2048, 7, 7)
layer4.1.add:
inputs:
0:
- min: -5.395097732543945
- max: 14.336352348327637
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- avg_max: 3.4030594348907472
- mean: -0.055087806284427644
- std: 0.38007112753406547
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+ avg_max: 3.3770268917083737
+ mean: -0.05591708905994892
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+ b: 0.28279241025447843
shape: (256, 2048, 7, 7)
1:
min: 0.0
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avg_min: 0.0
- avg_max: 6.008050060272216
- mean: 0.15043075829744337
- std: 0.30337482510307434
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shape: (256, 2048, 7, 7)
output:
- min: -5.395097732543945
- max: 21.834545135498047
- avg_min: -2.382943248748779
- avg_max: 7.608690929412842
- mean: 0.09534295722842218
- std: 0.5429858198158471
+ min: -4.98611307144165
+ max: 15.573689460754395
+ avg_min: -2.3728135108947757
+ avg_max: 7.612712144851685
+ mean: 0.09472521916031837
+ std: 0.5441690529259338
+ b: 0.38570883572101594
shape: (256, 2048, 7, 7)
layer4.2.conv1:
inputs:
0:
min: 0.0
- max: 21.834545135498047
+ max: 15.573689460754395
avg_min: 0.0
- avg_max: 7.608690929412842
- mean: 0.2376137301325798
- std: 0.42102969218495145
+ avg_max: 7.612712144851685
+ mean: 0.23763893991708757
+ std: 0.421922963559466
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shape: (256, 2048, 7, 7)
output:
- min: -8.234384536743164
- max: 10.143657684326172
- avg_min: -3.6996648788452147
- avg_max: 4.8178520679473875
- mean: -0.37534233629703523
- std: 0.5932820885697296
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+ avg_min: -3.72519166469574
+ avg_max: 4.8316075801849365
+ mean: -0.37615325152873996
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shape: (256, 512, 7, 7)
layer4.2.bn1:
inputs:
0:
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- avg_max: 4.8178520679473875
- mean: -0.37534233629703523
- std: 0.5932820885697296
+ min: -9.58436393737793
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+ avg_min: -3.72519166469574
+ avg_max: 4.8316075801849365
+ mean: -0.37615325152873996
+ std: 0.595941064560886
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shape: (256, 512, 7, 7)
output:
- min: -4.7313079833984375
- max: 3.271481513977051
- avg_min: -1.9235025644302368
- avg_max: 1.3267321467399598
- mean: -0.18585808724164962
- std: 0.23886620528361513
+ min: -4.725255012512207
+ max: 3.7600879669189453
+ avg_min: -1.9559222102165221
+ avg_max: 1.3456191301345826
+ mean: -0.18621423989534377
+ std: 0.23998140957227934
+ b: 0.18112000524997712
shape: (256, 512, 7, 7)
layer4.2.relu1:
inputs:
0:
- min: -4.7313079833984375
- max: 3.271481513977051
- avg_min: -1.9235025644302368
- avg_max: 1.3267321467399598
- mean: -0.18585808724164962
- std: 0.23886620528361513
+ min: -4.725255012512207
+ max: 3.7600879669189453
+ avg_min: -1.9559222102165221
+ avg_max: 1.3456191301345826
+ mean: -0.18621423989534377
+ std: 0.23998140957227934
+ b: 0.18112000524997712
shape: (256, 512, 7, 7)
output:
min: 0.0
- max: 3.271481513977051
+ max: 3.7600879669189453
avg_min: 0.0
- avg_max: 1.3267321467399598
- mean: 0.027702832594513893
- std: 0.08519395028712683
+ avg_max: 1.3456191301345826
+ mean: 0.027887877076864246
+ std: 0.08590010349519465
+ b: 0.04585573598742485
shape: (256, 512, 7, 7)
layer4.2.conv2:
inputs:
0:
min: 0.0
- max: 3.271481513977051
+ max: 3.7600879669189453
avg_min: 0.0
- avg_max: 1.3267321467399598
- mean: 0.027702832594513893
- std: 0.08519395028712683
+ avg_max: 1.3456191301345826
+ mean: 0.027887877076864246
+ std: 0.08590010349519465
+ b: 0.04585573598742485
shape: (256, 512, 7, 7)
output:
- min: -1.5278054475784302
+ min: -1.7627720832824707
max: 1.3895208835601807
- avg_min: -0.7480606913566589
- avg_max: 0.4983356356620789
- mean: -0.09653943628072739
- std: 0.11747523190693027
+ avg_min: -0.7464761197566987
+ avg_max: 0.49596920609474177
+ mean: -0.09737756326794625
+ std: 0.1179024859188269
+ b: 0.08659397140145303
shape: (256, 512, 7, 7)
layer4.2.bn2:
inputs:
0:
- min: -1.5278054475784302
+ min: -1.7627720832824707
max: 1.3895208835601807
- avg_min: -0.7480606913566589
- avg_max: 0.4983356356620789
- mean: -0.09653943628072739
- std: 0.11747523190693027
+ avg_min: -0.7464761197566987
+ avg_max: 0.49596920609474177
+ mean: -0.09737756326794625
+ std: 0.1179024859188269
+ b: 0.08659397140145303
shape: (256, 512, 7, 7)
output:
- min: -3.4718167781829834
- max: 3.0931997299194336
- avg_min: -1.5589052200317381
- avg_max: 1.1348651885986327
- mean: -0.10689140781760216
- std: 0.2426536058698193
+ min: -3.6767067909240723
+ max: 3.5860390663146973
+ avg_min: -1.5616470336914063
+ avg_max: 1.1276326537132264
+ mean: -0.10860036164522172
+ std: 0.24351960352418686
+ b: 0.17832687497138977
shape: (256, 512, 7, 7)
layer4.2.relu2:
inputs:
0:
- min: -3.4718167781829834
- max: 3.0931997299194336
- avg_min: -1.5589052200317381
- avg_max: 1.1348651885986327
- mean: -0.10689140781760216
- std: 0.2426536058698193
+ min: -3.6767067909240723
+ max: 3.5860390663146973
+ avg_min: -1.5616470336914063
+ avg_max: 1.1276326537132264
+ mean: -0.10860036164522172
+ std: 0.24351960352418686
+ b: 0.17832687497138977
shape: (256, 512, 7, 7)
output:
min: 0.0
- max: 3.0931997299194336
+ max: 3.5860390663146973
avg_min: 0.0
- avg_max: 1.1348651885986327
- mean: 0.04021419957280159
- std: 0.08940358768433947
+ avg_max: 1.1276326537132264
+ mean: 0.03995291851460933
+ std: 0.08932434644843026
+ b: 0.05705068595707417
shape: (256, 512, 7, 7)
layer4.2.conv3:
inputs:
0:
min: 0.0
- max: 3.0931997299194336
+ max: 3.5860390663146973
avg_min: 0.0
- avg_max: 1.1348651885986327
- mean: 0.04021419957280159
- std: 0.08940358768433947
+ avg_max: 1.1276326537132264
+ mean: 0.03995291851460933
+ std: 0.08932434644843026
+ b: 0.05705068595707417
shape: (256, 512, 7, 7)
output:
- min: -0.38114556670188904
- max: 0.9265015721321106
- avg_min: -0.17205106019973754
- avg_max: 0.33860428631305695
- mean: -0.004819883033633232
- std: 0.031674283868545705
+ min: -0.355619341135025
+ max: 0.9995235800743103
+ avg_min: -0.17218078225851058
+ avg_max: 0.33757891058921813
+ mean: -0.004912816500291228
+ std: 0.03159407352457316
+ b: 0.02231343388557434
shape: (256, 2048, 7, 7)
layer4.2.bn3:
inputs:
0:
- min: -0.38114556670188904
- max: 0.9265015721321106
- avg_min: -0.17205106019973754
- avg_max: 0.33860428631305695
- mean: -0.004819883033633232
- std: 0.031674283868545705
+ min: -0.355619341135025
+ max: 0.9995235800743103
+ avg_min: -0.17218078225851058
+ avg_max: 0.33757891058921813
+ mean: -0.004912816500291228
+ std: 0.03159407352457316
+ b: 0.02231343388557434
shape: (256, 2048, 7, 7)
output:
- min: -8.627450942993164
- max: 23.089508056640625
- avg_min: -3.8609111070632935
- avg_max: 8.419857215881347
- mean: 0.044161788746714586
- std: 0.7364083810472319
+ min: -8.60328197479248
+ max: 25.900434494018555
+ avg_min: -3.8589781522750854
+ avg_max: 8.387232303619385
+ mean: 0.0419552706182003
+ std: 0.7342189054779193
+ b: 0.5134885013103485
shape: (256, 2048, 7, 7)
layer4.2.relu3:
inputs:
0:
- min: -8.627450942993164
- max: 37.85447692871094
- avg_min: -3.8068380832672117
- avg_max: 12.922265243530273
- mean: 0.28177551329135897
- std: 0.9598800299108187
+ min: -8.60328197479248
+ max: 33.94049072265625
+ avg_min: -3.8049381256103514
+ avg_max: 12.866481399536132
+ mean: 0.27959421277046204
+ std: 0.9580428712984075
+ b: 0.6664626657962799
shape: (256, 2048, 7, 7)
output:
min: 0.0
- max: 37.85447692871094
+ max: 33.94049072265625
avg_min: 0.0
- avg_max: 12.922265243530273
- mean: 0.4688317000865936
- std: 0.7975003649671909
+ avg_max: 12.866481399536132
+ mean: 0.46672946214675903
+ std: 0.7957383819844129
+ b: 0.5334559381008148
shape: (256, 2048, 7, 7)
layer4.2.add:
inputs:
0:
- min: -8.627450942993164
- max: 23.089508056640625
- avg_min: -3.8609111070632935
- avg_max: 8.419857215881347
- mean: 0.044161788746714586
- std: 0.7364083810472319
+ min: -8.60328197479248
+ max: 25.900434494018555
+ avg_min: -3.8589781522750854
+ avg_max: 8.387232303619385
+ mean: 0.0419552706182003
+ std: 0.7342189054779193
+ b: 0.5134885013103485
shape: (256, 2048, 7, 7)
1:
min: 0.0
- max: 21.834545135498047
+ max: 15.573689460754395
avg_min: 0.0
- avg_max: 7.608690929412842
- mean: 0.2376137301325798
- std: 0.42102969218495145
+ avg_max: 7.612712144851685
+ mean: 0.23763893991708757
+ std: 0.421922963559466
+ b: 0.28288681507110597
shape: (256, 2048, 7, 7)
output:
- min: -8.627450942993164
- max: 37.85447692871094
- avg_min: -3.8068380832672117
- avg_max: 12.922265243530273
- mean: 0.28177551329135897
- std: 0.9598800299108187
+ min: -8.60328197479248
+ max: 33.94049072265625
+ avg_min: -3.8049381256103514
+ avg_max: 12.866481399536132
+ mean: 0.27959421277046204
+ std: 0.9580428712984075
+ b: 0.6664626657962799
shape: (256, 2048, 7, 7)
avgpool:
inputs:
0:
min: 0.0
- max: 37.85447692871094
+ max: 33.94049072265625
avg_min: 0.0
- avg_max: 12.922265243530273
- mean: 0.4688317000865936
- std: 0.7975003649671909
+ avg_max: 12.866481399536132
+ mean: 0.46672946214675903
+ std: 0.7957383819844129
+ b: 0.5334559381008148
shape: (256, 2048, 7, 7)
output:
min: 0.0
- max: 11.611199378967285
- avg_min: 0.00023435175244230778
- avg_max: 4.508990335464477
- mean: 0.46883170604705815
- std: 0.47031575008549364
+ max: 11.13111686706543
+ avg_min: 0.00022523058578372002
+ avg_max: 4.461482810974122
+ mean: 0.4667294651269912
+ std: 0.46829107389433205
+ b: 0.3259382039308548
shape: (256, 2048, 1, 1)
fc:
inputs:
0:
min: 0.0
- max: 11.611199378967285
- avg_min: 0.00023435175244230778
- avg_max: 4.508990335464477
- mean: 0.46883170604705815
- std: 0.47031575008549364
+ max: 11.13111686706543
+ avg_min: 0.00022523058578372002
+ avg_max: 4.461482810974122
+ mean: 0.4667294651269912
+ std: 0.46829107389433205
+ b: 0.3259382039308548
shape: (256, 2048)
output:
- min: -11.310169219970703
- max: 40.48712158203125
- avg_min: -5.810240793228149
- avg_max: 16.912589836120606
- mean: 8.312767749885096e-06
- std: 2.4802703537784003
+ min: -9.757831573486328
+ max: 36.7446174621582
+ avg_min: -5.790046501159668
+ avg_max: 16.712891578674316
+ mean: 8.333060395671055e-06
+ std: 2.483018996892336
+ b: 1.8292282938957214
shape: (256, 1000)
diff --git a/tests/test_quant_utils.py b/tests/test_quant_utils.py
index 4243438..de9afe3 100644
--- a/tests/test_quant_utils.py
+++ b/tests/test_quant_utils.py
@@ -241,3 +241,7 @@ def test_get_tensor_mean_n_stds_min_max():
t_min, t_max = qu.get_tensor_mean_n_stds_min_max(test_tensor, n_stds=2)
torch.testing.assert_allclose(t_min, torch.tensor(-95.))
torch.testing.assert_allclose(t_max, torch.tensor(87.))
+
+# TODO - Implement testing for ACIQ clipping
+# def test_aciq_clipping():
+# pass
--
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