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Fix regnetv/w tests, refactor regnet generator code a bit

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pull/1014/head
Ross Wightman 3 years ago
parent 4dec8c8087
commit 8a93ce6ee3
1 changed files with 13 additions and 14 deletions
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27
timm/models/regnet.py
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@ -143,8 +143,8 @@ default_cfgs = dict(
regnety_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'), regnety_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'),
regnety_040s_gn=_cfg(url=''), regnety_040s_gn=_cfg(url=''),
regnetv_040=_cfg(url=''), regnetv_040=_cfg(url='', first_conv='stem'),
regnetw_040=_cfg(url=''), regnetw_040=_cfg(url='', first_conv='stem', input_size=(3, 256, 256), pool_size=(8, 8)),
regnetz_005=_cfg(url=''), regnetz_005=_cfg(url=''),
regnetz_040=_cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)), regnetz_040=_cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
@ -165,16 +165,19 @@ def adjust_widths_groups_comp(widths, bottle_ratios, groups):
return widths, groups return widths, groups
def generate_regnet(width_slope, width_initial, width_mult, depth, q=8): def generate_regnet(width_slope, width_initial, width_mult, depth, group_size, q=8):
"""Generates per block widths from RegNet parameters.""" """Generates per block widths from RegNet parameters."""
assert width_slope >= 0 and width_initial > 0 and width_mult > 1 and width_initial % q == 0 assert width_slope >= 0 and width_initial > 0 and width_mult > 1 and width_initial % q == 0
# TODO dWr scaling?
# depth = int(depth * (scale ** 0.1))
# width_scale = scale ** 0.4 # dWr scale, exp 0.8 / 2, applied to both group and layer widths
widths_cont = np.arange(depth) * width_slope + width_initial widths_cont = np.arange(depth) * width_slope + width_initial
width_exps = np.round(np.log(widths_cont / width_initial) / np.log(width_mult)) width_exps = np.round(np.log(widths_cont / width_initial) / np.log(width_mult))
widths = width_initial * np.power(width_mult, width_exps) widths = width_initial * np.power(width_mult, width_exps)
widths = np.round(np.divide(widths, q)) * q widths = np.round(np.divide(widths, q)) * q
num_stages, max_stage = len(np.unique(widths)), width_exps.max() + 1 num_stages, max_stage = len(np.unique(widths)), width_exps.max() + 1
widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist() groups = np.array([group_size for _ in range(num_stages)])
return widths, num_stages, max_stage, widths_cont return widths.astype(int).tolist(), num_stages, groups.astype(int).tolist()
def downsample_conv(in_chs, out_chs, kernel_size=1, stride=1, dilation=1, norm_layer=None, preact=False): def downsample_conv(in_chs, out_chs, kernel_size=1, stride=1, dilation=1, norm_layer=None, preact=False):
@ -395,14 +398,11 @@ class RegNet(nn.Module):
def _get_stage_args(self, cfg: RegNetCfg, default_stride=2, output_stride=32, drop_path_rate=0.): def _get_stage_args(self, cfg: RegNetCfg, default_stride=2, output_stride=32, drop_path_rate=0.):
# Generate RegNet ws per block # Generate RegNet ws per block
widths, num_stages, _, _ = generate_regnet(cfg.wa, cfg.w0, cfg.wm, cfg.depth) widths, num_stages, stage_gs = generate_regnet(cfg.wa, cfg.w0, cfg.wm, cfg.depth, cfg.group_size)
# Convert to per stage format # Convert to per stage format
stage_widths, stage_depths = np.unique(widths, return_counts=True) stage_widths, stage_depths = np.unique(widths, return_counts=True)
stage_br = [cfg.bottle_ratio for _ in range(num_stages)]
# Use the same group width, bottleneck mult and stride for each stage
stage_groups = [cfg.group_size for _ in range(num_stages)]
stage_bottle_ratios = [cfg.bottle_ratio for _ in range(num_stages)]
stage_strides = [] stage_strides = []
stage_dilations = [] stage_dilations = []
net_stride = 2 net_stride = 2
@ -416,15 +416,14 @@ class RegNet(nn.Module):
net_stride *= stride net_stride *= stride
stage_strides.append(stride) stage_strides.append(stride)
stage_dilations.append(dilation) stage_dilations.append(dilation)
stage_dpr = np.split(np.linspace(0, drop_path_rate, cfg.depth), np.cumsum(stage_depths[:-1])) stage_dpr = np.split(np.linspace(0, drop_path_rate, sum(stage_depths)), np.cumsum(stage_depths[:-1]))
# Adjust the compatibility of ws and gws # Adjust the compatibility of ws and gws
stage_widths, stage_groups = adjust_widths_groups_comp(stage_widths, stage_bottle_ratios, stage_groups) stage_widths, stage_gs = adjust_widths_groups_comp(stage_widths, stage_br, stage_gs)
arg_names = ['out_chs', 'stride', 'dilation', 'depth', 'bottle_ratio', 'group_size', 'drop_path_rates'] arg_names = ['out_chs', 'stride', 'dilation', 'depth', 'bottle_ratio', 'group_size', 'drop_path_rates']
per_stage_args = [ per_stage_args = [
dict(zip(arg_names, params)) for params in dict(zip(arg_names, params)) for params in
zip(stage_widths, stage_strides, stage_dilations, stage_depths, stage_bottle_ratios, stage_groups, zip(stage_widths, stage_strides, stage_dilations, stage_depths, stage_br, stage_gs, stage_dpr)]
stage_dpr)]
common_args = dict( common_args = dict(
downsample=cfg.downsample, se_ratio=cfg.se_ratio, linear_out=cfg.linear_out, downsample=cfg.downsample, se_ratio=cfg.se_ratio, linear_out=cfg.linear_out,
act_layer=cfg.act_layer, norm_layer=cfg.norm_layer) act_layer=cfg.act_layer, norm_layer=cfg.norm_layer)

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