python/relay/test_pass_convert_op_layout.py

38         x = relay.var("x", shape=(1, 64, 56, 56))
39         weight = relay.var("weight", shape=(64, 64, 3, 3))
40         y = relay.nn.conv2d(x, weight, channels=64, kernel_size=(3, 3), padding=(1, 1))
41         y = relay.nn.relu(y)
42         y = relay.Function([x, weight], y)
43         return y
57         x = relay.var("x", shape=(2, 2))
58         y = relay.var("y", shape=(1, 2))
60             [x, y],
63                 y,
84         x = relay.var("x", shape=(1, 56, 56, 64))
85         weight = relay.var("weight", shape=(3, 3, 64, 64))
86         y = relay.nn.conv2d(
95         y = relay.nn.relu(y)
96         y = relay.Function([x, weight], y)
97         return y
100         x = relay.var("x", shape=(1, 56, 56, 64))
101         weight = relay.var("weight", shape=(3, 3, 64, 64))
104         y = relay.nn.conv2d(x, weight, channels=64, kernel_size=(3, 3), padding=(1, 1))
105         y = relay.nn.relu(y)
106         y = relay.layout_transform(y, "NCHW", "NHWC")
107         y = relay.Function(relay.analysis.free_vars(y), y)
108         return y
119         x = relay.var("x", shape=(1, 64, 56, 56))
120         weight = relay.var("weight", shape=(64, 64, 3, 3))
121         y = relay.nn.conv2d(
130         y = relay.nn.relu(y)
131         y = relay.Function([x, weight], y)
132         return y
135         x = relay.var("x", shape=(1, 64, 56, 56))
136         weight = relay.var("weight", shape=(64, 64, 3, 3))
139         y = relay.nn.conv2d(
148         y = relay.nn.relu(y)
149         y = relay.layout_transform(y, "NHWC", "NCHW")
150         y = relay.Function(relay.analysis.free_vars(y), y)
151         return y
162         x = relay.var("x", shape=(1, 56, 56, 64))
163         weight = relay.var("weight", shape=(3, 3, 64, 64))
164         y = relay.nn.conv2d_transpose(
173         y = relay.nn.relu(y)
174         y = relay.Function([x, weight], y)
175         return y
178         x = relay.var("x", shape=(1, 56, 56, 64))
179         weight = relay.var("weight", shape=(3, 3, 64, 64))
182         y = relay.nn.conv2d_transpose(x, weight, channels=64, kernel_size=(3, 3), padding=(1, 1))
183         y = relay.nn.relu(y)
184         y = relay.layout_transform(y, "NCHW", "NHWC")
185         y = relay.Function(relay.analysis.free_vars(y), y)
186         return y
197         x = relay.var("x", shape=(1, 56, 56, 64))
198         bias = relay.var("bias", shape=(64,))
199         weight = relay.var("weight", shape=(3, 3, 64, 64))
200         y = relay.nn.conv2d(
209         y = relay.nn.bias_add(y, bias, axis=3)
211         y = relay.Tuple([y])[0]
212         y = relay.nn.relu(y)
213         y = relay.nn.max_pool2d(y, pool_size=(2, 2), layout="NHWC")
214         y = relay.cast(y, "int32")
215         y = relay.nn.batch_flatten(y)
216         y = relay.Function(analysis.free_vars(y), y)
217         return y
220         x = relay.var("x", shape=(1, 56, 56, 64))
221         bias = relay.var("bias", shape=(64,))
222         weight = relay.var("weight", shape=(3, 3, 64, 64))
225         y = relay.nn.conv2d(x, weight, channels=64, kernel_size=(3, 3), padding=(1, 1))
229         y = relay.add(y, bias)
231         y = relay.Tuple([y])[0]
232         y = relay.nn.relu(y)
233         y = relay.nn.max_pool2d(y, pool_size=(2, 2))
234         y = relay.cast(y, "int32")
235         y = relay.layout_transform(y, "NCHW", "NHWC")
236         y = relay.nn.batch_flatten(y)
237         y = relay.Function(analysis.free_vars(y), y)
238         return y
249         x = relay.var("x", shape=(1, 56, 56, 64))
250         weight1 = relay.var("weight1", shape=(3, 3, 64, 64))
251         weight2 = relay.var("weight2", shape=(3, 3, 64, 64))
252         y = relay.nn.conv2d(
262             y,
270         ret = relay.concatenate([y, y1], axis=3)
271         y = relay.Function(analysis.free_vars(ret), ret)
272         return y
275         x = relay.var("x", shape=(1, 56, 56, 64))
276         weight1 = relay.var("weight1", shape=(3, 3, 64, 64))
277         weight2 = relay.var("weight2", shape=(3, 3, 64, 64))
280         y = relay.layout_transform(x, "NHWC", "NCHW")
281         y = relay.nn.conv2d(y, weight1, channels=64, kernel_size=(3, 3), padding=(1, 1))
282         y1 = relay.nn.conv2d(y, weight2, channels=64, kernel_size=(3, 3), padding=(1, 1))
283         ret = relay.concatenate([y, y1], axis=1)
285         y = relay.Function(analysis.free_vars(ret), ret)
286         return y
297         x = relay.var("x", shape=(1, 56, 56, 64))
298         weight1 = relay.var("weight1", shape=(3, 3, 64, 32))
299         weight2 = relay.var("weight2", shape=(3, 3, 32, 32))
300         y = relay.nn.conv2d(
309         y = relay.nn.relu(y)
311             y,
320         y2 = relay.nn.batch_flatten(y)
322         y = relay.Function(analysis.free_vars(ret), ret)
323         return y
326         x = relay.var("x", shape=(1, 56, 56, 64))
327         weight1 = relay.var("weight1", shape=(3, 3, 64, 32))
328         weight2 = relay.var("weight2", shape=(3, 3, 32, 32))
331         y = relay.layout_transform(x, "NHWC", "NCHW")
332         y = relay.nn.conv2d(y, weight1, channels=32, kernel_size=(3, 3), padding=(1, 1))
333         y = relay.nn.relu(y)
334         y1 = relay.nn.conv2d(y, weight2, channels=32, kernel_size=(3, 3), padding=(1, 1))
337         y2 = relay.layout_transform(y, "NCHW", "NHWC")
340         y = relay.Function(analysis.free_vars(ret), ret)
341         return y
352         x = relay.var("x", shape=(1, 56, 56, 64))
353         weight1 = relay.var("weight1", shape=(3, 3, 64, 32))
354         y = relay.nn.conv2d(
363         gamma = relay.var("gamma")
364         beta = relay.var("beta")
365         mean = relay.var("mean")
366         variance = relay.var("variance")
367         y, _, _ = relay.nn.batch_norm(y, gamma, beta, mean, variance, axis=3)
368         return relay.Function(analysis.free_vars(y), y)
388         x = relay.var("x", shape=(1, 56, 56, 64))
389         weight1 = relay.var("weight1", shape=(3, 3, 64, 32))
390         weight2 = relay.var("weight2", shape=(1, 1, 64, 32))
391         y = relay.nn.conv2d(
400         y = relay.nn.relu(y)
405         y = y + y2
406         y = relay.nn.global_max_pool2d(y, layout="NHWC")
407         return relay.Function(analysis.free_vars(y), y)
410         x = relay.var("x", shape=(1, 56, 56, 64))
411         weight1 = relay.var("weight1", shape=(3, 3, 64, 32))
412         weight2 = relay.var("weight2", shape=(1, 1, 64, 32))
416         y = relay.nn.conv2d(x, weight1, channels=32, kernel_size=(3, 3), padding=(1, 1))
417         y = relay.nn.relu(y)
420         y = y + y2
421         y = relay.nn.global_max_pool2d(y)
422         y = relay.layout_transform(y, "NCHW", "NHWC")
423         return relay.Function(analysis.free_vars(y), y)
434         x = relay.var("x", shape=(1, 56, 56, 64))
435         weight = relay.var("weight", shape=(3, 3, 64, 64))
436         y = relay.nn.conv2d(
445         y = relay.add(y, relay.const(1, "float32"))
446         y = relay.Function(analysis.free_vars(y), y)
447         return y
450         x = relay.var("x", shape=(1, 56, 56, 64))
451         w = relay.var("weight", shape=(3, 3, 64, 64))
454         y = relay.nn.conv2d(x, w, channels=64, kernel_size=(3, 3), padding=(1, 1))
455         y = relay.add(y, relay.const(1.0, "float32"))
457         y = relay.layout_transform(y, "NCHW", "NHWC")
458         y = relay.Function(analysis.free_vars(y), y)
459         return y
472         x = relay.var("x", shape=(1, 56, 56, 64))
473         weight = relay.var("weight", shape=(3, 3, 64, 64))
474         y = relay.nn.conv2d(
485         beta = relay.var("beta", relay.TensorType((64,), dtype))
486         gamma = relay.var("gamma", relay.TensorType((64,), dtype))
487         moving_mean = relay.var("moving_mean", relay.TensorType((64,), dtype))
488         moving_var = relay.var("moving_var", relay.TensorType((64,), dtype))
490         y = relay.nn.batch_norm(y, gamma, beta, moving_mean, moving_var, axis=3)
491         y = relay.nn.relu(y[0])
492         y = relay.Function(analysis.free_vars(y), y)
493         return y
496         x = relay.var("x", shape=(1, 56, 56, 64))
497         w = relay.var("weight", shape=(3, 3, 64, 64))
500         y = relay.nn.conv2d(x, w, channels=64, kernel_size=(3, 3), padding=(1, 1))
503         beta = relay.var("beta", relay.TensorType((64,), dtype))
504         gamma = relay.var("gamma", relay.TensorType((64,), dtype))
505         moving_mean = relay.var("moving_mean", relay.TensorType((64,), dtype))
506         moving_var = relay.var("moving_var", relay.TensorType((64,), dtype))
508         y = relay.nn.batch_norm(y, gamma, beta, moving_mean, moving_var, axis=1)
509         y = relay.nn.relu(y[0])
510         y = relay.layout_transform(y, "NCHW", "NHWC")
511         y = relay.Function(analysis.free_vars(y), y)
512         return y
523         x = relay.var("x", shape=(1, 56, 56, 64), dtype="int8")
524         weight = relay.var("weight", shape=(3, 3, 64, 64), dtype="int8")
525         y = relay.qnn.op.conv2d(
538         y = relay.qnn.op.requantize(
539             y,
546         y = relay.nn.relu(y)
547         y = relay.Function([x, weight], y)
548         return y
551         x = relay.var("x", shape=(1, 56, 56, 64), dtype="int8")
552         weight = relay.var("weight", shape=(3, 3, 64, 64), dtype="int8")
555         y = relay.qnn.op.conv2d(
566         y = relay.qnn.op.requantize(
567             y,
575         y = relay.nn.relu(y)
576         y = relay.layout_transform(y, "NCHW", "NHWC")
577         y = relay.Function(relay.analysis.free_vars(y), y)
578         return y
589         x = relay.var("x", shape=(1, 56, 56, 64), dtype="int8")
590         weight1 = relay.var("weight1", shape=(3, 3, 64, 64), dtype="int8")
591         weight2 = relay.var("weight2", shape=(3, 3, 64, 64), dtype="int8")
592         y = relay.qnn.op.conv2d(
606             y,
618         y = relay.cast(y, "int8")
619         y1 = relay.cast(y, "int8")
621             [y, y1],
628         y = relay.Function(analysis.free_vars(ret), ret)
629         return y
632         x = relay.var("x", shape=(1, 56, 56, 64), dtype="int8")
633         weight1 = relay.var("weight1", shape=(3, 3, 64, 64), dtype="int8")
634         weight2 = relay.var("weight2", shape=(3, 3, 64, 64), dtype="int8")
637         y = relay.layout_transform(x, "NHWC", "NCHW")
638         y = relay.qnn.op.conv2d(
639             y,
650             y,
660         y = relay.cast(y, "int8")
661         y1 = relay.cast(y, "int8")
663             [y, y1],
671         y = relay.Function(analysis.free_vars(ret), ret)
672         return y
683         x = relay.var("x", shape=(1, 56, 56, 64), dtype="int8")
684         weight1 = relay.var("weight1", shape=(3, 3, 64, 64), dtype="int8")
685         weight2 = relay.var("weight2", shape=(3, 3, 64, 64), dtype="int8")
686         y = relay.qnn.op.conv2d(
700             y,
712         y = relay.cast(y, "int8")
713         y1 = relay.cast(y, "int8")
715             y,
724         y = relay.Function(analysis.free_vars(ret), ret)
725         return y
728         x = relay.var("x", shape=(1, 56, 56, 64), dtype="int8")
729         weight1 = relay.var("weight1", shape=(3, 3, 64, 64), dtype="int8")
730         weight2 = relay.var("weight2", shape=(3, 3, 64, 64), dtype="int8")
733         y = relay.layout_transform(x, "NHWC", "NCHW")
734         y = relay.qnn.op.conv2d(
735             y,
746             y,
756         y = relay.cast(y, "int8")
757         y1 = relay.cast(y, "int8")
759             y,
769         y = relay.Function(analysis.free_vars(ret), ret)
770         return y
781         x = relay.var("x", shape=(1, 64, 56, 56), dtype="int8")
782         weight = relay.var("weight", shape=(64, 64, 3, 3), dtype="int8")
783         y = relay.qnn.op.conv2d(
796         y = relay.nn.relu(y)
797         y = relay.Function([x, weight], y)
798         return y
801         x = relay.var("x", shape=(1, 64, 56, 56), dtype="int8")
802         weight = relay.var("weight", shape=(64, 64, 3, 3), dtype="int8")
805         y = relay.qnn.op.conv2d(
818         y = relay.nn.relu(y)
819         y = relay.layout_transform(y, "NHWC", "NCHW")
820         y = relay.Function(relay.analysis.free_vars(y), y)
821         return y
834         x = relay.var("x", shape=(1, 56, 56, 64))
835         weight = relay.var("weight", shape=(3, 3, 64, 64))
836         y = relay.nn.conv2d(
845         y = relay.Function(analysis.free_vars(y), y)
846         return y
849         x = relay.var("x", shape=(1, 56, 56, 64))
850         w = relay.var("weight", shape=(3, 3, 64, 64))
852         y = relay.nn.conv2d(
861         y = relay.Function(analysis.free_vars(y), y)
862         return y
873         x = relay.var("x", shape=(1, 64, 56, 56))
874         weight1 = relay.var("weight1", shape=(64, 64, 3, 3))
875         y = relay.nn.conv2d(
884         rois = relay.var("rois", shape=(32, 5))
885         y = relay.vision.roi_align(
886             y, rois, pooled_size=(14, 14), spatial_scale=0.0625, sample_ratio=2, layout="NCHW"
888         y = relay.Function(analysis.free_vars(y), y)
889         return y
892         x = relay.var("x", shape=(1, 64, 56, 56))
893         weight1 = relay.var("weight1", shape=(64, 64, 3, 3))
896         y = relay.nn.conv2d(
905         rois = relay.var("rois", shape=(32, 5))
906         y = relay.vision.roi_align(
907             y, rois, pooled_size=(14, 14), spatial_scale=0.0625, sample_ratio=2, layout="NHWC"
909         ret = relay.layout_transform(y, "NHWC", "NCHW")
910         y = relay.Function(analysis.free_vars(ret), ret)
911         return y
928         x = relay.var("x", shape=(1, 64, 56, 56))
929         weight = relay.var("weight", shape=(64, 3, 3, 64))
930         y = relay.nn.conv2d(
939         y = relay.Function(analysis.free_vars(y), y)
940         return y
943         x = relay.var("x", shape=(1, 64, 56, 56))
944         w = relay.var("weight", shape=(64, 3, 3, 64))
946         y = relay.nn.conv2d(
955         y = relay.Function(analysis.free_vars(y), y)
956         return y
971         x = relay.var("x", shape=(1, 64, 56, 56))
972         weight1 = relay.var("weight1", shape=(64, 3, 3, 64))
973         weight2 = relay.var("weight2", shape=(64, 3, 3, 64), dtype="int8")
974         weight3 = relay.var("weight3", shape=(64, 3, 3, 64))
1012         x = relay.var("x", shape=(1, 64, 56, 56))
1013         weight1 = relay.var("weight1", shape=(64, 3, 3, 64))
1014         weight2 = relay.var("weight2", shape=(64, 3, 3, 64), dtype="int8")
1015         weight3 = relay.var("weight3", shape=(64, 3, 3, 64))
1073         x = relay.var("x", shape=(1, 64, 56, 56))
1074         weight1 = relay.var("weight1", shape=(64, 64, 3, 3))
1075         y = relay.nn.conv2d(
1084         rois = relay.var("rois", shape=(32, 5))
1085         y = relay.vision.roi_align(
1086             y, rois, pooled_size=(14, 14), spatial_scale=0.0625, sample_ratio=2, layout="NCHW"
1088         y = relay.Function(analysis.free_vars(y), y)
1089         return y
1092         x = relay.var("x", shape=(1, 64, 56, 56))
1093         weight1 = relay.var("weight1", shape=(64, 64, 3, 3))
1096         y = relay.nn.conv2d(
1105         y = relay.layout_transform(y, "NHWC", "NCHW")
1106         rois = relay.var("rois", shape=(32, 5))
1107         y = relay.vision.roi_align(
1108             y, rois, pooled_size=(14, 14), spatial_scale=0.0625, sample_ratio=2, layout="NCHW"
1110         y = relay.Function(analysis.free_vars(y), y)
1111         return y