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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import unittest
import torch
import torch.nn as nn
from pytorchvideo.layers import Mlp, MultiScaleAttention, MultiScaleBlock
class TestMLP(unittest.TestCase):
def setUp(self):
super().setUp()
torch.set_rng_state(torch.manual_seed(42).get_state())
def test_MultiScaleAttention(self):
seq_len = 21
c_dim = 10
c_dim_out = 20
# Test MultiScaleAttention without dim expansion; i.e., no dim_out
multiscale_attention = MultiScaleAttention(c_dim, num_heads=2)
fake_input = torch.rand(8, seq_len, c_dim)
input_shape = (2, 2, 5)
output, output_shape = multiscale_attention(fake_input, input_shape)
self.assertTrue(output.shape, fake_input.shape)
# Test MultiScaleAttention with dim expansion
multiscale_attention = MultiScaleAttention(
c_dim, dim_out=c_dim_out, num_heads=2
)
fake_input = torch.rand(8, seq_len, c_dim)
input_shape = (2, 2, 5)
output, output_shape = multiscale_attention(fake_input, input_shape)
gt_shape_tensor = torch.rand(8, seq_len, c_dim_out)
self.assertTrue(output.shape, gt_shape_tensor.shape)
# Test pooling kernel without dim expansion.
multiscale_attention = MultiScaleAttention(
c_dim,
num_heads=2,
stride_q=(2, 2, 1),
)
output, output_shape = multiscale_attention(fake_input, input_shape)
gt_shape_tensor = torch.rand(8, 6, c_dim)
gt_output_shape = (1, 1, 5)
self.assertTrue(output.shape, gt_shape_tensor.shape)
self.assertTrue(output_shape, gt_output_shape)
# Test pooling kernel with dim expansion.
multiscale_attention = MultiScaleAttention(
c_dim,
dim_out=c_dim_out,
num_heads=2,
stride_q=(2, 2, 1),
)
output, output_shape = multiscale_attention(fake_input, input_shape)
gt_shape_tensor = torch.rand(8, 6, c_dim_out)
gt_output_shape = (1, 1, 5)
self.assertTrue(output.shape, gt_shape_tensor.shape)
self.assertTrue(output_shape, gt_output_shape)
# Test pooling kernel with no cls.
seq_len = 20
c_dim = 10
fake_input = torch.rand(8, seq_len, c_dim)
multiscale_attention = MultiScaleAttention(
c_dim, num_heads=2, stride_q=(2, 2, 1), has_cls_embed=False
)
output, output_shape = multiscale_attention(fake_input, input_shape)
gt_shape_tensor = torch.rand(8, int(seq_len / 2 / 2), c_dim)
gt_output_shape = [1, 1, 5]
self.assertEqual(output.shape, gt_shape_tensor.shape)
self.assertEqual(output_shape, gt_output_shape)
def test_MultiScaleBlock(self):
seq_len = 21
c_dim = 10
batch_dim = 8
fake_input = torch.rand(batch_dim, seq_len, c_dim)
input_shape = (2, 2, 5)
# Change of output dimension.
block = MultiScaleBlock(10, 20, 2)
output, output_shape = block(fake_input, input_shape)
gt_shape_tensor = torch.rand(8, seq_len, 20)
self.assertEqual(output.shape, gt_shape_tensor.shape)
self.assertEqual(output_shape, input_shape)
# Test dimension multiplication in attention
block = MultiScaleBlock(10, 20, 2, dim_mul_in_att=True)
output, output_shape = block(fake_input, input_shape)
gt_shape_tensor = torch.rand(8, seq_len, 20)
self.assertEqual(output.shape, gt_shape_tensor.shape)
self.assertEqual(output_shape, input_shape)
# Test pooling.
block = MultiScaleBlock(10, 20, 2, stride_q=(2, 2, 1))
output, output_shape = block(fake_input, input_shape)
gt_shape_tensor = torch.rand(8, int((seq_len - 1) / 2 / 2) + 1, 20)
gt_out_shape = [1, 1, 5]
self.assertEqual(output.shape, gt_shape_tensor.shape)
self.assertEqual(output_shape, gt_out_shape)
def test_Mlp(self):
fake_input = torch.rand((8, 64))
in_features = [10, 20, 30]
hidden_features = [10, 20, 20]
out_features = [10, 20, 30]
act_layers = [nn.GELU, nn.ReLU, nn.Sigmoid]
drop_rates = [0.0, 0.1, 0.5]
batch_size = 8
for in_feat, hidden_feat, out_feat, act_layer, drop_rate in itertools.product(
in_features, hidden_features, out_features, act_layers, drop_rates
):
mlp_block = Mlp(
in_features=in_feat,
hidden_features=hidden_feat,
out_features=out_feat,
act_layer=act_layer,
dropout_rate=drop_rate,
)
fake_input = torch.rand((batch_size, in_feat))
output = mlp_block(fake_input)
self.assertTrue(output.shape, torch.Size([batch_size, out_feat]))
def test_MultiScaleBlock_is_scriptable(self):
iter_qkv_bias = [True, False]
iter_separate_qkv = [True, False]
iter_dropout_rate = [0.0, 0.1]
iter_droppath_rate = [0.0, 0.1]
iter_norm_layer = [nn.LayerNorm]
iter_attn_norm_layer = [nn.LayerNorm]
iter_dim_mul_in_att = [True, False]
iter_pool_mode = ["conv", "avg", "max"]
iter_has_cls_embed = [True, False]
iter_pool_first = [True, False]
iter_residual_pool = [True, False]
iter_depthwise_conv = [True, False]
iter_bias_on = [True, False]
iter_separate_qkv = [True, False]
for (
qkv_bias,
dropout_rate,
droppath_rate,
norm_layer,
attn_norm_layer,
dim_mul_in_att,
pool_mode,
has_cls_embed,
pool_first,
residual_pool,
depthwise_conv,
bias_on,
separate_qkv,
) in itertools.product(
iter_qkv_bias,
iter_dropout_rate,
iter_droppath_rate,
iter_norm_layer,
iter_attn_norm_layer,
iter_dim_mul_in_att,
iter_pool_mode,
iter_has_cls_embed,
iter_pool_first,
iter_residual_pool,
iter_depthwise_conv,
iter_bias_on,
iter_separate_qkv,
):
msb = MultiScaleBlock(
dim=10,
dim_out=20,
num_heads=2,
stride_q=(2, 2, 1),
qkv_bias=qkv_bias,
dropout_rate=dropout_rate,
droppath_rate=droppath_rate,
norm_layer=norm_layer,
attn_norm_layer=attn_norm_layer,
dim_mul_in_att=dim_mul_in_att,
pool_mode=pool_mode,
has_cls_embed=has_cls_embed,
pool_first=pool_first,
residual_pool=residual_pool,
depthwise_conv=depthwise_conv,
bias_on=bias_on,
separate_qkv=separate_qkv,
)
torch.jit.script(msb)
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