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EdgeTPU optimizations #6808

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merged 9 commits into from
Mar 12, 2022
Merged

EdgeTPU optimizations #6808

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3385498
removed transpose op for better edgetpu support
paradigmn Feb 28, 2022
48f9099
fix for training case
paradigmn Feb 28, 2022
e77f15c
Merge branch 'master' into edgetpu-optimizations
glenn-jocher Mar 4, 2022
8b31170
Merge branch 'master' of https://github.com/ultralytics/yolov5 into e…
paradigmn Mar 7, 2022
eb37fb9
enabled experimental new quantizer flag
paradigmn Mar 7, 2022
1c905a2
Merge branch 'edgetpu-optimizations' of github.com:paradigmn/yolov5 i…
paradigmn Mar 7, 2022
55f394b
precalculate add and mul ops at compile time
paradigmn Mar 7, 2022
40d4287
Merge branch 'master' into edgetpu-optimizations
glenn-jocher Mar 11, 2022
68fab69
Merge branch 'master' into edgetpu-optimizations
glenn-jocher Mar 12, 2022
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2 changes: 1 addition & 1 deletion export.py
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Original file line number Diff line number Diff line change
Expand Up @@ -331,7 +331,7 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
converter.target_spec.supported_types = []
converter.inference_input_type = tf.uint8 # or tf.int8
converter.inference_output_type = tf.uint8 # or tf.int8
converter.experimental_new_quantizer = False
converter.experimental_new_quantizer = True
f = str(file).replace('.pt', '-int8.tflite')

tflite_model = converter.convert()
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10 changes: 6 additions & 4 deletions models/tf.py
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Expand Up @@ -222,19 +222,21 @@ def call(self, inputs):
x.append(self.m[i](inputs[i]))
# x(bs,20,20,255) to x(bs,3,20,20,85)
ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
x[i] = tf.transpose(tf.reshape(x[i], [-1, ny * nx, self.na, self.no]), [0, 2, 1, 3])
x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no])

if not self.training: # inference
y = tf.sigmoid(x[i])
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5
anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4
xy = (y[..., 0:2] * 2 + grid) * self.stride[i] # xy
wh = y[..., 2:4] ** 2 * anchor_grid
# Normalize xywh to 0-1 to reduce calibration error
xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
y = tf.concat([xy, wh, y[..., 4:]], -1)
z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))

return x if self.training else (tf.concat(z, 1), x)
return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1), x)

@staticmethod
def _make_grid(nx=20, ny=20):
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