| /cmsis-nn-latest/Tests/UnitTest/RefactoredTestGen/Lib/ |
| D | op_utils.py | 17 import numpy as np namespace
63 array = minval + (maxval - minval) * np.random.rand(*dims)
64 array = np.round(array, decimals=decimals)
94 return np.uint8
96 return np.uint16
98 return np.uint32
100 return np.uint64
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| D | op_lstm.py | 20 import numpy as np namespace
145 scales["input_scale"] = np.round(np.random.rand(1) * (maxval - minval) + minval, 6)[0]
146 scales["cell_scale"] = np.round(np.random.rand(1) * (maxval - minval) + maxval, 6)[0]
147 scales["output_scale"] = np.round(np.random.rand(1) * (maxval - minval) + minval, 6)[0]
158 scales[name + "_scale"] = np.round(np.random.rand(1) * (maxval - minval) + minval, 6)[0]
173 …tensors["input_gate_hidden_weights"] = np.random.randint(minval, maxval, size=shapes["hidden_weigh…
174 …tensors["forget_gate_hidden_weights"] = np.random.randint(minval, maxval, size=shapes["hidden_weig…
175 …tensors["cell_gate_hidden_weights"] = np.random.randint(minval, maxval, size=shapes["hidden_weight…
176 …tensors["output_gate_hidden_weights"] = np.random.randint(minval, maxval, size=shapes["hidden_weig…
177 …tensors["input_gate_input_weights"] = np.random.randint(minval, maxval, size=shapes["input_weights…
[all …]
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| D | op_fully_connected.py | 20 import numpy as np namespace
82 weights = np.random.randint(minval, maxval, size=shapes["weight_shape"])
86 weights = np.append(weights, 0)
88 temp = np.reshape(weights, (weights.size // 2, 2)).astype(np.uint8)
93 tensors["input_bias"] = np.random.randint(minval, maxval, size=shapes["bias_shape"])
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| D | op_conv.py | 23 import numpy as np namespace
213 tensors["output_multiplier"] = np.array(per_channel_multiplier)
214 tensors["output_shift"] = np.array(per_channel_shift)
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| D | test.py | 22 import numpy as np namespace
133 tensor = np.clip(tensor, params["out_activation_min"], params["out_activation_max"])
163 data = np.random.rand(*shape)
164 yield [data.astype(np.float32)]
242 values, counts = np.unique(data, return_counts=True)
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| /cmsis-nn-latest/Tests/UnitTest/ |
| D | test_settings.py | 27 import numpy as np namespace
74 randmin=np.iinfo(np.dtype('int8')).min,
75 randmax=np.iinfo(np.dtype('int8')).max,
82 bias_min=np.iinfo(np.dtype('int32')).min,
83 bias_max=np.iinfo(np.dtype('int32')).max,
91 …if self.INT8_MIN != np.iinfo(np.dtype('int8')).min or self.INT8_MAX != np.iinfo(np.dtype('int8')).…
92 …self.INT16_MIN != np.iinfo(np.dtype('int16')).min or self.INT16_MAX != np.iinfo(np.dtype('int16'))…
93 …self.INT32_MIN != np.iinfo(np.dtype('int32')).min or self.INT32_MAX != np.iinfo(np.dtype('int32'))…
212 np.savetxt(file, data.reshape(-1, data.shape[-1]), header=header, delimiter=',')
217 data = np.genfromtxt(f, delimiter=',').reshape(shape)
[all …]
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| D | conv_settings.py | 20 import numpy as np namespace
167 self.scaling_factors = np.random.uniform(0.001, 0.01, [self.output_ch]).tolist()
181 data_max = np.amax(data)
182 data_min = np.amin(data)
240 weights = np.append(weights, [0])
256 output_scale = np.random.uniform(0.02, 0.06)
259 scaling_factors = np.random.uniform(0.001, 0.01, [self.output_ch]).tolist()
300 temp = np.reshape(weights, (len(weights) // 2, 2)).astype(np.uint8)
441 … np.clip(output_data, self.out_activation_min, self.out_activation_max),
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| D | pooling_settings.py | 19 import numpy as np namespace
119 … np.clip(output_data, self.out_activation_min, self.out_activation_max),
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| D | fully_connected_settings.py | 20 import numpy as np namespace
212 temp = np.reshape(weights, (len(weights) // 2, 2)).astype(np.uint8)
294 … np.clip(output_data, self.out_activation_min, self.out_activation_max),
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| D | add_mul_settings.py | 20 import numpy as np namespace
133 … np.clip(output_data, self.out_activation_min, self.out_activation_max),
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| D | lstm_settings.py | 21 import numpy as np namespace
353 output = np.zeros((row, ), dtype=np.int32)
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| D | model_extractor.py | 25 import numpy as np namespace
299 …self.generate_c_array("output_ref", np.clip(output_data, self.out_activation_min, self.out_activat…
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