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How to use from_dtype method in hypothesis

Best Python code snippet using hypothesis

fixed_point_quantization.py

Source:fixed_point_quantization.py

1import numpy as np2import loss3import kl_divergence_quantization4# 1. Suppose that you have a layer with outputs in the range of [-a, a).5#    a is a real number could be float32 or float64.6# 2. Multiplying two 8-bit integers is a 16-bit integer7#    Multiplying two 32-bit float is a 64-bit float8# https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work-with-qat-networks9class FixedPointQuantizationStrategy():10    def __init__(self, from_dtype, to_dtype, verbose=0, scale_type='normal'):11        self.verbose = verbose12        self.from_dtype = from_dtype13        self.to_dtype = to_dtype14        self.scale_type = scale_type15        self.supported_to_dtype = ['int4', np.int8, np.uint8, np.int16, np.uint16, np.int32, np.uint32]16        if self.to_dtype not in self.supported_to_dtype:17            raise Exception("Conversion to dtype {} is not supported".format(self.to_dtype))18        self.quantization_dtype = ''19        self.scale = 120        self.values = None21        self.scale_factor = 022        self.zero_center = 023        self.values_quantized = None24        self.values_dequantized = None25        self.quantized = False26    def set_verbose(self, val):27        self.verbose = val28    def set_values(self, values):29        self.values = values30        # self.convert_zero_mean()31        max_tensor_value = np.max(abs(self.values))32        max_tensor_nearest_value = np.ceil(max_tensor_value)33        self.decide_scale()34        # This scale factor calculated bu analyzing the range of the real values35        # but this has to be done post training.36        # 1. Run the inference and find the best scale factor / range for real value37        # 2. Then try to use that scale factor instead of fixed real valued scale factor.38        if self.scale_type == 'kl':39            threshold, divergence = kl_divergence_quantization.kl_divergence_scale(self.values,40                                                           quantized_dtype='int8',41                                                           num_bins=8001,42                                                           eps=0.0001)43            self.scale_factor = self.scale / threshold44        else:45            self.scale_factor = self.scale / max_tensor_nearest_value46        print("Scale Factor :", self.scale_factor)47    def convert_zero_mean(self):48        self.zero_center = np.mean(self.values)49        self.values -= self.zero_center50    def decide_scale(self):51        if type(self.to_dtype) == str and self.to_dtype == 'int4':52            self.scale = 853            quantization_bits = 454            self.quantization_dtype = 'int4'55            self.to_dtype = 'int4'56        elif type(self.to_dtype) == str and self.to_dtype == 'uint4':57            self.scale = 1658            quantization_bits = 459            self.quantization_dtype = 'uint4'60            self.to_dtype = np.uint861        else:62            iinfo = np.iinfo(self.to_dtype)63            self.scale = max([abs(iinfo.min), abs(iinfo.max)])-164            quantization_bits = iinfo.bits65            self.quantization_dtype = iinfo.dtype66        finfo = np.finfo(self.from_dtype)67        if self.verbose:68            print("Quantization will be done as follows")69            print("============== From =================")70            print("Dtype: {} Bits: {}".format(finfo.dtype, finfo.bits))71            print("============== To =================")72            print("Dtype: {} Bits: {} Range: {}".format(self.quantization_dtype,73                                                        quantization_bits, self.scale))74            print("===================================")75    def quantize(self):76        if self.values is None:77            raise Exception("No value for quantization_research")78        if self.values.dtype != self.from_dtype:79            raise Exception("Dtype of from tensorflow value does not match to provided from dtype")80        scaled_values = self.values * self.scale_factor81        to_dtype = self.to_dtype82        if type(self.to_dtype) == str:83            to_dtype = np.int884        self.values_quantized = np.asarray(np.round(scaled_values), dtype=to_dtype)85        if self.verbose:86            print("Quantized value: ", self.values_quantized)87        self.quantized = True88        self.dequantize()89    def dequantize(self):90        if not self.quantized:91            raise Exception("Quantization is not performed")92        self.values_dequantized = self.values_quantized / self.scale_factor93    def information_loss(self):94        if not self.quantized:95            raise Exception("Quantization is not performed")96        loss.MAE(self.values, self.values_dequantized)97        loss.MSE(self.values, self.values_dequantized)98class Inference():99    def __init__(self, quant_weight_strategy: FixedPointQuantizationStrategy,100                 quant_activation_strategy: FixedPointQuantizationStrategy,101                 weights: np.ndarray,102                 activations: np.ndarray):103        self.quant_weight_strategy = quant_weight_strategy104        self.quant_activation_strategy = quant_activation_strategy105        self.quant_weight_strategy.set_values(weights)106        self.quant_activation_strategy.set_values(activations)107        dtypes_map = {108            'int4': np.int8,109            np.int8: np.int16,110            np.int16: np.int32,111            np.int32: np.int64112        }113        self.inference_quant_output_dtype = dtypes_map[self.quant_weight_strategy.to_dtype]114        iinfo = np.iinfo(self.inference_quant_output_dtype)115        self.descale_factor = max([abs(iinfo.min), abs(iinfo.max)]) // 2116        self.before_quantization_output = None117        self.after_quantization_output = None118        self.after_dequantization_output = None119    def forward_before_quantization(self):120        self.before_quantization_output = np.matmul(self.quant_weight_strategy.values,121                                                    self.quant_activation_strategy.values)122        print("Before quantization_research inference output \n", self.before_quantization_output)123    def quantize(self, verbose=0):124        self.quant_weight_strategy.set_verbose(verbose)125        self.quant_weight_strategy.quantize()126        self.quant_activation_strategy.set_verbose(verbose)127        self.quant_activation_strategy.quantize()128    def forward_after_quantization(self):129        if not (self.quant_weight_strategy.quantized and self.quant_activation_strategy.quantized):130            raise Exception("Quantization is not performed")131        self.after_quantization_output = np.matmul(self.quant_weight_strategy.values_quantized,132                                                   self.quant_activation_strategy.values_quantized,133                                                   dtype=self.inference_quant_output_dtype)134        dequantized_output = self.after_quantization_output / (self.quant_weight_strategy.scale_factor *135                                                               self.quant_activation_strategy.scale_factor)136        self.after_dequantization_output = dequantized_output137        print("After quantization_research inference output \n", self.after_dequantization_output)138    def information_loss(self, verbose=0):139        if self.before_quantization_output is None or \140                self.after_dequantization_output is None:141            raise Exception("Run the inference before and after quantization_research")142        if verbose:143            print("=========== Weight Quantization Information loss ============")144            self.quant_weight_strategy.information_loss()145            print("=========== Activation Quantization Information loss ============")146            self.quant_activation_strategy.information_loss()147        print("=========== Inference information loss ============")148        loss.MAE(self.before_quantization_output, self.after_dequantization_output)149        loss.MSE(self.before_quantization_output, self.after_dequantization_output)150def test(scale_type='kl'):151    from_dtype = np.float32152    # to_dtype = 'int4'153    to_dtype = np.int8154    quant_weight1_strategy = FixedPointQuantizationStrategy(from_dtype, to_dtype, scale_type=scale_type, verbose=1)155    quant_weight2_strategy = FixedPointQuantizationStrategy(from_dtype, to_dtype, scale_type=scale_type, verbose=1)156    weights1 = np.asarray([157        [-1.54, 0.22],158        [-0.26, 0.65]159    ], dtype=from_dtype)160    weights2 = np.asarray([161        [0.35],162        [-0.51]163    ], dtype=from_dtype)164    infer = Inference(quant_weight1_strategy,165                      quant_weight2_strategy,166                      weights1,167                      weights2)168    infer.forward_before_quantization()169    infer.quantize(0)170    infer.forward_after_quantization()171    infer.information_loss(1)172test('kl') # KL divergence to find the best threshold...

test_motion_plan.py

Source:test_motion_plan.py

...27@st.composite28def generate_coordinates(draw: st.DrawFn) -> Coordinates[str, np.float64]:29    """Create coordinates using Hypothesis."""30    coord = [31        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0, max_value=500)),32        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0, max_value=490)),33        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0, max_value=300)),34        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0, max_value=300)),35        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0, max_value=300)),36        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0, max_value=300)),37    ]38    formatted: Iterator[np.float64] = (np.float64(i) for i in coord)39    return dict(zip(SIXAXES, formatted))40@st.composite41def generate_close_coordinates(42    draw: st.DrawFn, prev_coord: Coordinates[str, np.float64]43) -> Coordinates[str, np.float64]:44    """Create coordinates using Hypothesis."""45    diff: List[np.typing.NDArray[np.float64]] = [46        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0.1, max_value=1.0)),47        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0.1, max_value=1.0)),48        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0.1, max_value=1.0)),49        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0.1, max_value=1.0)),50        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0.1, max_value=1.0)),51        draw(hynp.from_dtype(np.dtype(np.float64), min_value=0.1, max_value=1.0)),52    ]53    coord: np.typing.NDArray[np.float64] = vectorize(prev_coord) + diff54    return dict(zip(SIXAXES, (np.float64(i) for i in coord)))55def reject_close_coordinates(56    a: Coordinates[str, np.float64], b: Coordinates[str, np.float64]57) -> bool:58    """Reject example if the coordinates are too close.59    Consecutive coordinates must be at least 1mm apart in one of the axes.60    """61    return not np.any(np.isclose(vectorize(b), vectorize(a), atol=1.0))62@st.composite63def generate_target_list(64    draw: st.DrawFn,65    elements: st.SearchStrategy[Coordinates[str, np.float64]] = generate_coordinates(),...

compress_bin_data.py

Source:compress_bin_data.py

...12# buff_int_size = buff_size // oasis_int_size13areaperil_int_relative_size = areaperil_int.itemsize // oasis_int_size14oasis_float_relative_size = oasis_float.itemsize // oasis_int_size15results_relative_size = 2 * oasis_float_relative_size16EventIndexBin = nb.from_dtype(np.dtype([('event_id', np.int32),17                                        ('offset', np.int64),18                                        ('size', np.int64)19                                        ]))20Index_type = nb.from_dtype(np.dtype([('start', np.int64),21                                     ('end', np.int64)22                                     ]))23Event = nb.from_dtype(np.dtype([('areaperil_id', areaperil_int),24                                ('intensity_bin_id', np.int32),25                                ('probability', oasis_float)26                                ]))27event_size = Event.size28footprint_offset = 829damagebindictionary =  nb.from_dtype(np.dtype([('bin_index', np.int32),30                                               ('bin_from', oasis_float),31                                               ('bin_to', oasis_float),32                                               ('interpolation', oasis_float),33                                               ('interval_type', np.int32),34                                               ]))35damagebindictionaryCsv =  nb.from_dtype(np.dtype([('bin_index', np.int32),36                                                  ('bin_from', oasis_float),37                                                  ('bin_to', oasis_float),38                                                  ('interpolation', oasis_float)]))39EventCSV =  nb.from_dtype(np.dtype([('event_id', np.int32),40                                    ('areaperil_id', areaperil_int),41                                    ('intensity_bin_id', np.int32),42                                    ('probability', oasis_float)43                                    ]))44Item = nb.from_dtype(np.dtype([('id', np.int32),45                               ('coverage_id', np.int32),46                               ('areaperil_id', areaperil_int),47                               ('vulnerability_id', np.int32),48                               ('group_id', np.int32)49                               ]))50Vulnerability = nb.from_dtype(np.dtype([('vulnerability_id', np.int32),51                                        ('intensity_bin_id', np.int32),52                                        ('damage_bin_id', np.int32),53                                        ('probability', oasis_float)54                                        ]))55VulnerabilityIndex = nb.from_dtype(np.dtype([('vulnerability_id', np.int32),56                                             ('offset', np.int64),57                                             ('size', np.int64),58                                             ('original_size', np.int64)59                                             ]))60VulnerabilityRow = nb.from_dtype(np.dtype([('intensity_bin_id', np.int32),61                                           ('damage_bin_id', np.int64),62                                           ('probability', oasis_float)63                                           ]))64vuln_offset = 465def compress_bin_file_command(size: int) -> None:66    with open("./data_config.json") as file:67        config_data = json.load(file)68    intensity_bins: int = config_data["num_intensity_bins"]69    # compress the footprint data70    subprocess.call(f"footprinttocsv -b ./data/{size}/static/footprint.bin -x ./data/{size}/static/footprint.idx | "71                    f"footprinttobin -z -u -b ./data/{size}/static/footprint.bin.z -x ./data/{size}/static/footprint.idx.z "72                    f"-i {intensity_bins}",73                    shell=True)74    # compress the vulnerability data...

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